Design of a Multi-Sensor Cooperation Travel Environment Perception System for Autonomous Vehicle

PubMed Central

Chen, Long; Li, Qingquan; Li, Ming; Zhang, Liang; Mao, Qingzhou

2012-01-01

This paper describes the environment perception system designed for intelligent vehicle SmartV-II, which won the 2010 Future Challenge. This system utilizes the cooperation of multiple lasers and cameras to realize several necessary functions of autonomous navigation: road curb detection, lane detection and traffic sign recognition. Multiple single scan lasers are integrated to detect the road curb based on Z-variance method. Vision based lane detection is realized by two scans method combining with image model. Haar-like feature based method is applied for traffic sign detection and SURF matching method is used for sign classification. The results of experiments validate the effectiveness of the proposed algorithms and the whole system.

Flexible Modeling of Survival Data with Covariates Subject to Detection Limits via Multiple Imputation.

PubMed

Bernhardt, Paul W; Wang, Huixia Judy; Zhang, Daowen

2014-01-01

Models for survival data generally assume that covariates are fully observed. However, in medical studies it is not uncommon for biomarkers to be censored at known detection limits. A computationally-efficient multiple imputation procedure for modeling survival data with covariates subject to detection limits is proposed. This procedure is developed in the context of an accelerated failure time model with a flexible seminonparametric error distribution. The consistency and asymptotic normality of the multiple imputation estimator are established and a consistent variance estimator is provided. An iterative version of the proposed multiple imputation algorithm that approximates the EM algorithm for maximum likelihood is also suggested. Simulation studies demonstrate that the proposed multiple imputation methods work well while alternative methods lead to estimates that are either biased or more variable. The proposed methods are applied to analyze the dataset from a recently-conducted GenIMS study.

Salient object detection method based on multiple semantic features

NASA Astrophysics Data System (ADS)

Wang, Chunyang; Yu, Chunyan; Song, Meiping; Wang, Yulei

2018-04-01

The existing salient object detection model can only detect the approximate location of salient object, or highlight the background, to resolve the above problem, a salient object detection method was proposed based on image semantic features. First of all, three novel salient features were presented in this paper, including object edge density feature (EF), object semantic feature based on the convex hull (CF) and object lightness contrast feature (LF). Secondly, the multiple salient features were trained with random detection windows. Thirdly, Naive Bayesian model was used for combine these features for salient detection. The results on public datasets showed that our method performed well, the location of salient object can be fixed and the salient object can be accurately detected and marked by the specific window.

Multiple targets detection method in detection of UWB through-wall radar

NASA Astrophysics Data System (ADS)

Yang, Xiuwei; Yang, Chuanfa; Zhao, Xingwen; Tian, Xianzhong

2017-11-01

In this paper, the problems and difficulties encountered in the detection of multiple moving targets by UWB radar are analyzed. The experimental environment and the penetrating radar system are established. An adaptive threshold method based on local area is proposed to effectively filter out clutter interference The objective of the moving target is analyzed, and the false target is further filtered out by extracting the target feature. Based on the correlation between the targets, the target matching algorithm is proposed to improve the detection accuracy. Finally, the effectiveness of the above method is verified by practical experiment.

Detection of admittivity anomaly on high-contrast heterogeneous backgrounds using frequency difference EIT.

PubMed

Jang, J; Seo, J K

2015-06-01

This paper describes a multiple background subtraction method in frequency difference electrical impedance tomography (fdEIT) to detect an admittivity anomaly from a high-contrast background conductivity distribution. The proposed method expands the use of the conventional weighted frequency difference EIT method, which has been used limitedly to detect admittivity anomalies in a roughly homogeneous background. The proposed method can be viewed as multiple weighted difference imaging in fdEIT. Although the spatial resolutions of the output images by fdEIT are very low due to the inherent ill-posedness, numerical simulations and phantom experiments of the proposed method demonstrate its feasibility to detect anomalies. It has potential application in stroke detection in a head model, which is highly heterogeneous due to the skull.

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

USGS Publications Warehouse

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

2012-01-01

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

[High-sensitive detection of multiple allergenic proteins in infant food with high-resolution mass spectrometry].

PubMed

Wu, Ci; Chen, Xi; Liu, Jianhui; Zhang, Xiaolin; Xue, Weifeng; Liang, Zhen; Liu, Mengyao; Cui, Yan; Huang, Daliang; Zhang, Lihua

2017-10-08

A novel method of the simultaneous detection of multiple kinds of allergenic proteins in infant food with parallel reaction monitoring (PRM) mode using liquid chromatography-tandem mass spectrometry (LC-MS/MS) was established. In this method, unique peptides with good stability and high sensibility were used to quantify the corresponding allergenic proteins. Furthermore, multiple kinds of allergenic proteins are inspected simultaneously with high sensitivity. In addition, such method was successfully used for the detection of multiple allergenic proteins in infant food. As for the sample preparation for infant food, compared with the traditional acetone precipitation strategy, the protein extraction efficiency and capacity of resisting disturbance are both higher with in-situ filter-aided sample pretreatment (i-FASP) method. All allergenic proteins gave a good linear response with the correlation coefficients ( R 2 ) ≥ 0.99, and the largest concentration range of the allergenic proteins could be four orders of magnitude, and the lowest detection limit was 0.028 mg/L, which was better than that reported in references. Finally, the method was conveniently used to detect the allergens from four imported infant food real samples. All the results demonstrate that this novel strategy is of great significance for providing a rapid and reliable analytical technique for allergen proteomics.

Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review.

PubMed

Uddin, M B; Chow, C M; Su, S W

2018-03-26

Sleep apnea (SA), a common sleep disorder, can significantly decrease the quality of life, and is closely associated with major health risks such as cardiovascular disease, sudden death, depression, and hypertension. The normal diagnostic process of SA using polysomnography is costly and time consuming. In addition, the accuracy of different classification methods to detect SA varies with the use of different physiological signals. If an effective, reliable, and accurate classification method is developed, then the diagnosis of SA and its associated treatment will be time-efficient and economical. This study aims to systematically review the literature and present an overview of classification methods to detect SA using respiratory and oximetry signals and address the automated detection approach. Sixty-two included studies revealed the application of single and multiple signals (respiratory and oximetry) for the diagnosis of SA. Both airflow and oxygen saturation signals alone were effective in detecting SA in the case of binary decision-making, whereas multiple signals were good for multi-class detection. In addition, some machine learning methods were superior to the other classification methods for SA detection using respiratory and oximetry signals. To deal with the respiratory and oximetry signals, a good choice of classification method as well as the consideration of associated factors would result in high accuracy in the detection of SA. An accurate classification method should provide a high detection rate with an automated (independent of human action) analysis of respiratory and oximetry signals. Future high-quality automated studies using large samples of data from multiple patient groups or record batches are recommended.

Online two-stage association method for robust multiple people tracking

NASA Astrophysics Data System (ADS)

Lv, Jingqin; Fang, Jiangxiong; Yang, Jie

2011-07-01

Robust multiple people tracking is very important for many applications. It is a challenging problem due to occlusion and interaction in crowded scenarios. This paper proposes an online two-stage association method for robust multiple people tracking. In the first stage, short tracklets generated by linking people detection responses grow longer by particle filter based tracking, with detection confidence embedded into the observation model. And, an examining scheme runs at each frame for the reliability of tracking. In the second stage, multiple people tracking is achieved by linking tracklets to generate trajectories. An online tracklet association method is proposed to solve the linking problem, which allows applications in time-critical scenarios. This method is evaluated on the popular CAVIAR dataset. The experimental results show that our two-stage method is robust.

Statistical Methods for Generalized Linear Models with Covariates Subject to Detection Limits.

PubMed

Bernhardt, Paul W; Wang, Huixia J; Zhang, Daowen

2015-05-01

Censored observations are a common occurrence in biomedical data sets. Although a large amount of research has been devoted to estimation and inference for data with censored responses, very little research has focused on proper statistical procedures when predictors are censored. In this paper, we consider statistical methods for dealing with multiple predictors subject to detection limits within the context of generalized linear models. We investigate and adapt several conventional methods and develop a new multiple imputation approach for analyzing data sets with predictors censored due to detection limits. We establish the consistency and asymptotic normality of the proposed multiple imputation estimator and suggest a computationally simple and consistent variance estimator. We also demonstrate that the conditional mean imputation method often leads to inconsistent estimates in generalized linear models, while several other methods are either computationally intensive or lead to parameter estimates that are biased or more variable compared to the proposed multiple imputation estimator. In an extensive simulation study, we assess the bias and variability of different approaches within the context of a logistic regression model and compare variance estimation methods for the proposed multiple imputation estimator. Lastly, we apply several methods to analyze the data set from a recently-conducted GenIMS study.

Detection of abrupt changes in dynamic systems

NASA Technical Reports Server (NTRS)

Willsky, A. S.

1984-01-01

Some of the basic ideas associated with the detection of abrupt changes in dynamic systems are presented. Multiple filter-based techniques and residual-based method and the multiple model and generalized likelihood ratio methods are considered. Issues such as the effect of unknown onset time on algorithm complexity and structure and robustness to model uncertainty are discussed.

Detecting multiple outliers in linear functional relationship model for circular variables using clustering technique

NASA Astrophysics Data System (ADS)

Mokhtar, Nurkhairany Amyra; Zubairi, Yong Zulina; Hussin, Abdul Ghapor

2017-05-01

Outlier detection has been used extensively in data analysis to detect anomalous observation in data and has important application in fraud detection and robust analysis. In this paper, we propose a method in detecting multiple outliers for circular variables in linear functional relationship model. Using the residual values of the Caires and Wyatt model, we applied the hierarchical clustering procedure. With the use of tree diagram, we illustrate the graphical approach of the detection of outlier. A simulation study is done to verify the accuracy of the proposed method. Also, an illustration to a real data set is given to show its practical applicability.

Differential Characteristics Based Iterative Multiuser Detection for Wireless Sensor Networks

PubMed Central

Chen, Xiaoguang; Jiang, Xu; Wu, Zhilu; Zhuang, Shufeng

2017-01-01

High throughput, low latency and reliable communication has always been a hot topic for wireless sensor networks (WSNs) in various applications. Multiuser detection is widely used to suppress the bad effect of multiple access interference in WSNs. In this paper, a novel multiuser detection method based on differential characteristics is proposed to suppress multiple access interference. The proposed iterative receive method consists of three stages. Firstly, a differential characteristics function is presented based on the optimal multiuser detection decision function; then on the basis of differential characteristics, a preliminary threshold detection is utilized to find the potential wrongly received bits; after that an error bit corrector is employed to correct the wrong bits. In order to further lower the bit error ratio (BER), the differential characteristics calculation, threshold detection and error bit correction process described above are iteratively executed. Simulation results show that after only a few iterations the proposed multiuser detection method can achieve satisfactory BER performance. Besides, BER and near far resistance performance are much better than traditional suboptimal multiuser detection methods. Furthermore, the proposed iterative multiuser detection method also has a large system capacity. PMID:28212328

Weak scratch detection and defect classification methods for a large-aperture optical element

NASA Astrophysics Data System (ADS)

Tao, Xian; Xu, De; Zhang, Zheng-Tao; Zhang, Feng; Liu, Xi-Long; Zhang, Da-Peng

2017-03-01

Surface defects on optics cause optic failure and heavy loss to the optical system. Therefore, surface defects on optics must be carefully inspected. This paper proposes a coarse-to-fine detection strategy of weak scratches in complicated dark-field images. First, all possible scratches are detected based on bionic vision. Then, each possible scratch is precisely positioned and connected to a complete scratch by the LSD and a priori knowledge. Finally, multiple scratches with various types can be detected in dark-field images. To classify defects and pollutants, a classification method based on GIST features is proposed. This paper uses many real dark-field images as experimental images. The results show that this method can detect multiple types of weak scratches in complex images and that the defects can be correctly distinguished with interference. This method satisfies the real-time and accurate detection requirements of surface defects.

Detection of high-risk mucosal human papillomavirus DNA in human specimens by a novel and sensitive multiplex PCR method combined with DNA microarray.

PubMed

Gheit, Tarik; Tommasino, Massimo

2011-01-01

Epidemiological and functional studies have clearly demonstrated that certain types of human papillomavirus (HPV) from the genus alpha of the HPV phylogenetic tree, referred to as high-risk (HR) types, are the etiological cause of cervical cancer. Several methods for HPV detection and typing have been developed, and their importance in clinical and epidemiological studies has been well demonstrated. However, comparative studies have shown that several assays have different sensitivities for the detection of specific HPV types, particularly in the case of multiple infections. In this chapter, we describe a novel one-shot method for the detection and typing of 19 mucosal HR HPV types (types 16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 70, 73, and 82). The assay combines the advantages of the multiplex PCR methods, i.e., high sensitivity and the possibility to perform multiple amplifications in a single reaction, with an array primer extension (APEX) assay. The latter method offers the benefits of Sanger dideoxy sequencing with the high-throughput potential of the microarray. Initial studies have revealed that the assay is very sensitive in detecting multiple HPV infections.

Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation.

PubMed

Szatkiewicz, Jin P; Wang, WeiBo; Sullivan, Patrick F; Wang, Wei; Sun, Wei

2013-02-01

Structural variation is an important class of genetic variation in mammals. High-throughput sequencing (HTS) technologies promise to revolutionize copy-number variation (CNV) detection but present substantial analytic challenges. Converging evidence suggests that multiple types of CNV-informative data (e.g. read-depth, read-pair, split-read) need be considered, and that sophisticated methods are needed for more accurate CNV detection. We observed that various sources of experimental biases in HTS confound read-depth estimation, and note that bias correction has not been adequately addressed by existing methods. We present a novel read-depth-based method, GENSENG, which uses a hidden Markov model and negative binomial regression framework to identify regions of discrete copy-number changes while simultaneously accounting for the effects of multiple confounders. Based on extensive calibration using multiple HTS data sets, we conclude that our method outperforms existing read-depth-based CNV detection algorithms. The concept of simultaneous bias correction and CNV detection can serve as a basis for combining read-depth with other types of information such as read-pair or split-read in a single analysis. A user-friendly and computationally efficient implementation of our method is freely available.

RNA-Based Methods Increase the Detection of Fecal Bacteria and Fecal Identifiers in Environmental Waters

EPA Science Inventory

We evaluated the use of qPCR RNA-based methods in the detection of fecal bacteria in environmental waters. We showed that RNA methods can increase the detection of fecal bacteria in multiple water matrices. The data suggest that this is a viable alternative for the detection of a...

Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system.

PubMed

Min, Jianliang; Wang, Ping; Hu, Jianfeng

2017-01-01

Driver fatigue is an important contributor to road accidents, and fatigue detection has major implications for transportation safety. The aim of this research is to analyze the multiple entropy fusion method and evaluate several channel regions to effectively detect a driver's fatigue state based on electroencephalogram (EEG) records. First, we fused multiple entropies, i.e., spectral entropy, approximate entropy, sample entropy and fuzzy entropy, as features compared with autoregressive (AR) modeling by four classifiers. Second, we captured four significant channel regions according to weight-based electrodes via a simplified channel selection method. Finally, the evaluation model for detecting driver fatigue was established with four classifiers based on the EEG data from four channel regions. Twelve healthy subjects performed continuous simulated driving for 1-2 hours with EEG monitoring on a static simulator. The leave-one-out cross-validation approach obtained an accuracy of 98.3%, a sensitivity of 98.3% and a specificity of 98.2%. The experimental results verified the effectiveness of the proposed method, indicating that the multiple entropy fusion features are significant factors for inferring the fatigue state of a driver.

Event Detection Challenges, Methods, and Applications in Natural and Artificial Systems

DTIC Science & Technology

2009-03-01

using the composite event detection method [Kerman, Jiang, Blumberg , and Buttrey, 2009]. Although the techniques and utility of the...aforementioned method have been clearly demonstrated, there is still much work and research to be conducted within the realm of event detection. This...detection methods . The paragraphs that follow summarize the discoveries of and lessons learned by multiple researchers and authors over many

An effective and robust method for tracking multiple fish in video image based on fish head detection.

PubMed

Qian, Zhi-Ming; Wang, Shuo Hong; Cheng, Xi En; Chen, Yan Qiu

2016-06-23

Fish tracking is an important step for video based analysis of fish behavior. Due to severe body deformation and mutual occlusion of multiple swimming fish, accurate and robust fish tracking from video image sequence is a highly challenging problem. The current tracking methods based on motion information are not accurate and robust enough to track the waving body and handle occlusion. In order to better overcome these problems, we propose a multiple fish tracking method based on fish head detection. The shape and gray scale characteristics of the fish image are employed to locate the fish head position. For each detected fish head, we utilize the gray distribution of the head region to estimate the fish head direction. Both the position and direction information from fish detection are then combined to build a cost function of fish swimming. Based on the cost function, global optimization method can be applied to associate the target between consecutive frames. Results show that our method can accurately detect the position and direction information of fish head, and has a good tracking performance for dozens of fish. The proposed method can successfully obtain the motion trajectories for dozens of fish so as to provide more precise data to accommodate systematic analysis of fish behavior.

Early detection of ecosystem regime shifts: a multiple method evaluation for management application.

PubMed

Lindegren, Martin; Dakos, Vasilis; Gröger, Joachim P; Gårdmark, Anna; Kornilovs, Georgs; Otto, Saskia A; Möllmann, Christian

2012-01-01

Critical transitions between alternative stable states have been shown to occur across an array of complex systems. While our ability to identify abrupt regime shifts in natural ecosystems has improved, detection of potential early-warning signals previous to such shifts is still very limited. Using real monitoring data of a key ecosystem component, we here apply multiple early-warning indicators in order to assess their ability to forewarn a major ecosystem regime shift in the Central Baltic Sea. We show that some indicators and methods can result in clear early-warning signals, while other methods may have limited utility in ecosystem-based management as they show no or weak potential for early-warning. We therefore propose a multiple method approach for early detection of ecosystem regime shifts in monitoring data that may be useful in informing timely management actions in the face of ecosystem change.

Early Detection of Ecosystem Regime Shifts: A Multiple Method Evaluation for Management Application

PubMed Central

Lindegren, Martin; Dakos, Vasilis; Gröger, Joachim P.; Gårdmark, Anna; Kornilovs, Georgs; Otto, Saskia A.; Möllmann, Christian

2012-01-01

Critical transitions between alternative stable states have been shown to occur across an array of complex systems. While our ability to identify abrupt regime shifts in natural ecosystems has improved, detection of potential early-warning signals previous to such shifts is still very limited. Using real monitoring data of a key ecosystem component, we here apply multiple early-warning indicators in order to assess their ability to forewarn a major ecosystem regime shift in the Central Baltic Sea. We show that some indicators and methods can result in clear early-warning signals, while other methods may have limited utility in ecosystem-based management as they show no or weak potential for early-warning. We therefore propose a multiple method approach for early detection of ecosystem regime shifts in monitoring data that may be useful in informing timely management actions in the face of ecosystem change. PMID:22808007

Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning

NASA Astrophysics Data System (ADS)

Fang, Leyuan; Yang, Liumao; Li, Shutao; Rabbani, Hossein; Liu, Zhimin; Peng, Qinghua; Chen, Xiangdong

2017-06-01

Detection and recognition of macular lesions in optical coherence tomography (OCT) are very important for retinal diseases diagnosis and treatment. As one kind of retinal disease (e.g., diabetic retinopathy) may contain multiple lesions (e.g., edema, exudates, and microaneurysms) and eye patients may suffer from multiple retinal diseases, multiple lesions often coexist within one retinal image. Therefore, one single-lesion-based detector may not support the diagnosis of clinical eye diseases. To address this issue, we propose a multi-instance multilabel-based lesions recognition (MIML-LR) method for the simultaneous detection and recognition of multiple lesions. The proposed MIML-LR method consists of the following steps: (1) segment the regions of interest (ROIs) for different lesions, (2) compute descriptive instances (features) for each lesion region, (3) construct multilabel detectors, and (4) recognize each ROI with the detectors. The proposed MIML-LR method was tested on 823 clinically labeled OCT images with normal macular and macular with three common lesions: epiretinal membrane, edema, and drusen. For each input OCT image, our MIML-LR method can automatically identify the number of lesions and assign the class labels, achieving the average accuracy of 88.72% for the cases with multiple lesions, which better assists macular disease diagnosis and treatment.

A Targeted LC-MS/MS Method for the Simultaneous Detection and Quantitation of Egg, Milk, and Peanut Allergens in Sugar Cookies.

PubMed

Boo, Chelsea C; Parker, Christine H; Jackson, Lauren S

2018-01-01

Food allergy is a growing public health concern, with many individuals reporting allergies to multiple food sources. Compliance with food labeling regulations and prevention of inadvertent cross-contact in manufacturing requires the use of reliable methods for the detection and quantitation of allergens in processed foods. In this work, a novel liquid chromatography-tandem mass spectrometry multiple-reaction monitoring method for multiallergen detection and quantitation of egg, milk, and peanut was developed and evaluated in an allergen-incurred baked sugar cookie matrix. A systematic evaluation of method parameters, including sample extraction, concentration, and digestion, were optimized for candidate allergen peptide markers. The optimized method enabled the reliable detection and quantitation of egg, milk, and peanut allergens in sugar cookies, with allergen concentrations as low as 5 ppm allergen-incurred ingredient.

Principal axis-based correspondence between multiple cameras for people tracking.

PubMed

Hu, Weiming; Hu, Min; Zhou, Xue; Tan, Tieniu; Lou, Jianguang; Maybank, Steve

2006-04-01

Visual surveillance using multiple cameras has attracted increasing interest in recent years. Correspondence between multiple cameras is one of the most important and basic problems which visual surveillance using multiple cameras brings. In this paper, we propose a simple and robust method, based on principal axes of people, to match people across multiple cameras. The correspondence likelihood reflecting the similarity of pairs of principal axes of people is constructed according to the relationship between "ground-points" of people detected in each camera view and the intersections of principal axes detected in different camera views and transformed to the same view. Our method has the following desirable properties: 1) Camera calibration is not needed. 2) Accurate motion detection and segmentation are less critical due to the robustness of the principal axis-based feature to noise. 3) Based on the fused data derived from correspondence results, positions of people in each camera view can be accurately located even when the people are partially occluded in all views. The experimental results on several real video sequences from outdoor environments have demonstrated the effectiveness, efficiency, and robustness of our method.

On Identifiability of Bias-Type Actuator-Sensor Faults in Multiple-Model-Based Fault Detection and Identification

NASA Technical Reports Server (NTRS)

Joshi, Suresh M.

2012-01-01

This paper explores a class of multiple-model-based fault detection and identification (FDI) methods for bias-type faults in actuators and sensors. These methods employ banks of Kalman-Bucy filters to detect the faults, determine the fault pattern, and estimate the fault values, wherein each Kalman-Bucy filter is tuned to a different failure pattern. Necessary and sufficient conditions are presented for identifiability of actuator faults, sensor faults, and simultaneous actuator and sensor faults. It is shown that FDI of simultaneous actuator and sensor faults is not possible using these methods when all sensors have biases.

Analysis of Urinary Metabolites of Nerve and Blister Chemical Warfare Agents

DTIC Science & Technology

2014-08-01

of CWAs. The analysis methods use UHPLC-MS/MS in Multiple Reaction Monitoring ( MRM ) mode to enhance the selectivity and sensitivity of the method...Chromatography Mass Spectrometry LOD Limit Of Detection LOQ Limit of Quantitation MRM Multiple Reaction Monitoring MSMS Tandem mass...urine [1]. Those analysis methods use UHPLC- MS/MS in Multiple Reaction Monitoring ( MRM ) mode to enhance the selectivity and sensitivity of the method

Comparison of Membrane Filtration and Multiple-Tube Fermentation by the Colilert and Enterolert Methods for Detection of Waterborne Coliform Bacteria, Escherichia coli, and Enterococci Used in Drinking and Bathing Water Quality Monitoring in Southern Sweden

PubMed Central

Eckner, Karl F.

1998-01-01

A total of 338 water samples, 261 drinking water samples and 77 bathing water samples, obtained for routine testing were analyzed in duplicate by Swedish standard methods using multiple-tube fermentation or membrane filtration and by the Colilert and/or Enterolert methods. Water samples came from a wide variety of sources in southern Sweden (Skåne). The Colilert method was found to be more sensitive than Swedish standard methods for detecting coliform bacteria and of equal sensitivity for detecting Escherichia coli when all drinking water samples were grouped together. Based on these results, Swedac, the Swedish laboratory accreditation body, approved for the first time in Sweden use of the Colilert method at this laboratory for the analysis of all water sources not falling under public water regulations (A-krav). The coliform detection study of bathing water yielded anomalous results due to confirmation difficulties. E. coli detection in bathing water was similar by both the Colilert and Swedish standard methods as was fecal streptococcus and enterococcus detection by both the Enterolert and Swedish standard methods. PMID:9687478

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.

Development and validation of a 48-target analytical method for high-throughput monitoring of genetically modified organisms.

PubMed

Li, Xiaofei; Wu, Yuhua; Li, Jun; Li, Yunjing; Long, Likun; Li, Feiwu; Wu, Gang

2015-01-05

The rapid increase in the number of genetically modified (GM) varieties has led to a demand for high-throughput methods to detect genetically modified organisms (GMOs). We describe a new dynamic array-based high throughput method to simultaneously detect 48 targets in 48 samples on a Fludigm system. The test targets included species-specific genes, common screening elements, most of the Chinese-approved GM events, and several unapproved events. The 48 TaqMan assays successfully amplified products from both single-event samples and complex samples with a GMO DNA amount of 0.05 ng, and displayed high specificity. To improve the sensitivity of detection, a preamplification step for 48 pooled targets was added to enrich the amount of template before performing dynamic chip assays. This dynamic chip-based method allowed the synchronous high-throughput detection of multiple targets in multiple samples. Thus, it represents an efficient, qualitative method for GMO multi-detection.

Development and Validation of A 48-Target Analytical Method for High-throughput Monitoring of Genetically Modified Organisms

PubMed Central

Li, Xiaofei; Wu, Yuhua; Li, Jun; Li, Yunjing; Long, Likun; Li, Feiwu; Wu, Gang

2015-01-01

The rapid increase in the number of genetically modified (GM) varieties has led to a demand for high-throughput methods to detect genetically modified organisms (GMOs). We describe a new dynamic array-based high throughput method to simultaneously detect 48 targets in 48 samples on a Fludigm system. The test targets included species-specific genes, common screening elements, most of the Chinese-approved GM events, and several unapproved events. The 48 TaqMan assays successfully amplified products from both single-event samples and complex samples with a GMO DNA amount of 0.05 ng, and displayed high specificity. To improve the sensitivity of detection, a preamplification step for 48 pooled targets was added to enrich the amount of template before performing dynamic chip assays. This dynamic chip-based method allowed the synchronous high-throughput detection of multiple targets in multiple samples. Thus, it represents an efficient, qualitative method for GMO multi-detection. PMID:25556930

Airplane detection based on fusion framework by combining saliency model with Deep Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Dou, Hao; Sun, Xiao; Li, Bin; Deng, Qianqian; Yang, Xubo; Liu, Di; Tian, Jinwen

2018-03-01

Aircraft detection from very high resolution remote sensing images, has gained more increasing interest in recent years due to the successful civil and military applications. However, several problems still exist: 1) how to extract the high-level features of aircraft; 2) locating objects within such a large image is difficult and time consuming; 3) A common problem of multiple resolutions of satellite images still exists. In this paper, inspirited by biological visual mechanism, the fusion detection framework is proposed, which fusing the top-down visual mechanism (deep CNN model) and bottom-up visual mechanism (GBVS) to detect aircraft. Besides, we use multi-scale training method for deep CNN model to solve the problem of multiple resolutions. Experimental results demonstrate that our method can achieve a better detection result than the other methods.

Novel Multistatic Adaptive Microwave Imaging Methods for Early Breast Cancer Detection

NASA Astrophysics Data System (ADS)

Xie, Yao; Guo, Bin; Li, Jian; Stoica, Petre

2006-12-01

Multistatic adaptive microwave imaging (MAMI) methods are presented and compared for early breast cancer detection. Due to the significant contrast between the dielectric properties of normal and malignant breast tissues, developing microwave imaging techniques for early breast cancer detection has attracted much interest lately. MAMI is one of the microwave imaging modalities and employs multiple antennas that take turns to transmit ultra-wideband (UWB) pulses while all antennas are used to receive the reflected signals. MAMI can be considered as a special case of the multi-input multi-output (MIMO) radar with the multiple transmitted waveforms being either UWB pulses or zeros. Since the UWB pulses transmitted by different antennas are displaced in time, the multiple transmitted waveforms are orthogonal to each other. The challenge to microwave imaging is to improve resolution and suppress strong interferences caused by the breast skin, nipple, and so forth. The MAMI methods we investigate herein utilize the data-adaptive robust Capon beamformer (RCB) to achieve high resolution and interference suppression. We will demonstrate the effectiveness of our proposed methods for breast cancer detection via numerical examples with data simulated using the finite-difference time-domain method based on a 3D realistic breast model.

Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system

PubMed Central

Min, Jianliang; Wang, Ping

2017-01-01

Driver fatigue is an important contributor to road accidents, and fatigue detection has major implications for transportation safety. The aim of this research is to analyze the multiple entropy fusion method and evaluate several channel regions to effectively detect a driver's fatigue state based on electroencephalogram (EEG) records. First, we fused multiple entropies, i.e., spectral entropy, approximate entropy, sample entropy and fuzzy entropy, as features compared with autoregressive (AR) modeling by four classifiers. Second, we captured four significant channel regions according to weight-based electrodes via a simplified channel selection method. Finally, the evaluation model for detecting driver fatigue was established with four classifiers based on the EEG data from four channel regions. Twelve healthy subjects performed continuous simulated driving for 1–2 hours with EEG monitoring on a static simulator. The leave-one-out cross-validation approach obtained an accuracy of 98.3%, a sensitivity of 98.3% and a specificity of 98.2%. The experimental results verified the effectiveness of the proposed method, indicating that the multiple entropy fusion features are significant factors for inferring the fatigue state of a driver. PMID:29220351

The Mediated MIMIC Model for Understanding the Underlying Mechanism of DIF.

PubMed

Cheng, Ying; Shao, Can; Lathrop, Quinn N

2016-02-01

Due to its flexibility, the multiple-indicator, multiple-causes (MIMIC) model has become an increasingly popular method for the detection of differential item functioning (DIF). In this article, we propose the mediated MIMIC model method to uncover the underlying mechanism of DIF. This method extends the usual MIMIC model by including one variable or multiple variables that may completely or partially mediate the DIF effect. If complete mediation effect is found, the DIF effect is fully accounted for. Through our simulation study, we find that the mediated MIMIC model is very successful in detecting the mediation effect that completely or partially accounts for DIF, while keeping the Type I error rate well controlled for both balanced and unbalanced sample sizes between focal and reference groups. Because it is successful in detecting such mediation effects, the mediated MIMIC model may help explain DIF and give guidance in the revision of a DIF item.

The Mediated MIMIC Model for Understanding the Underlying Mechanism of DIF

PubMed Central

Cheng, Ying; Shao, Can; Lathrop, Quinn N.

2015-01-01

Due to its flexibility, the multiple-indicator, multiple-causes (MIMIC) model has become an increasingly popular method for the detection of differential item functioning (DIF). In this article, we propose the mediated MIMIC model method to uncover the underlying mechanism of DIF. This method extends the usual MIMIC model by including one variable or multiple variables that may completely or partially mediate the DIF effect. If complete mediation effect is found, the DIF effect is fully accounted for. Through our simulation study, we find that the mediated MIMIC model is very successful in detecting the mediation effect that completely or partially accounts for DIF, while keeping the Type I error rate well controlled for both balanced and unbalanced sample sizes between focal and reference groups. Because it is successful in detecting such mediation effects, the mediated MIMIC model may help explain DIF and give guidance in the revision of a DIF item.

Nonstationary EO/IR Clutter Suppression and Dim Object Tracking

DTIC Science & Technology

2010-01-01

Brown, A., and Brown, J., Enhanced Algorithms for EO /IR Electronic Stabilization, Clutter Suppression, and Track - Before - Detect for Multiple Low...estimation-suppression and nonlinear filtering-based multiple-object track - before - detect . These algorithms are suitable for integration into...In such cases, it is imperative to develop efficient real or near-real time tracking before detection methods. This paper continues the work started

Detection and Identification of Multiple Stationary Human Targets Via Bio-Radar Based on the Cross-Correlation Method

PubMed Central

Zhang, Yang; Chen, Fuming; Xue, Huijun; Li, Zhao; An, Qiang; Wang, Jianqi; Zhang, Yang

2016-01-01

Ultra-wideband (UWB) radar has been widely used for detecting human physiological signals (respiration, movement, etc.) in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident, etc.), the presence, number, and location of the trapped victims to be detected and rescued are the key issues of concern. Ample research has been done on the first issue, whereas the identification and localization of multi-targets remains a challenge. False positive and negative identification results are two common problems associated with the detection of multiple stationary human targets. This is mainly because the energy of the signal reflected from the target close to the receiving antenna is considerably stronger than those of the targets at further range, often leading to missing or false recognition if the identification method is based on the energy of the respiratory signal. Therefore, a novel method based on cross-correlation is proposed in this paper that is based on the relativity and periodicity of the signals, rather than on the energy. The validity of this method is confirmed through experiments using different scenarios; the results indicate a discernible improvement in the detection precision and identification of the multiple stationary targets. PMID:27801795

Experimental Investigation on the Detection of Multiple Surface Cracks Using Vibrothermography with a Low-Power Piezoceramic Actuator.

PubMed

Xu, Changhang; Xie, Jing; Zhang, Wuyang; Kong, Qingzhao; Chen, Guoming; Song, Gangbing

2017-11-23

Vibrothermography often employs a high-power actuator to generate heat on a specimen to reveal damage, however, the high-power actuator brings inconvenience to the application and possibly introduces additional damage to the inspected objects. This study uses a low-power piezoceramic transducer as the actuator of vibrothermography and explores its ability to detect multiple surface cracks in a metal part. Experiments were conducted on a thin aluminum beam with three cracks in different orientations. Detailed analyses of both thermograms and temperature data are presented to validate the proposed vibrothermography method. To further investigate the performance of the proposed vibrothermography method, we experimentally studied the effects of several critical factors, including the amplitude of excitation signal, specimen constraints, relative position between the transducer and cracks (the transducer is mounted on the same or the opposite side with the cracks). The results demonstrate that all cracks can be detected conveniently and simultaneously by using the proposed low-power vibrothermography. We also found that the magnitude of excitation signal and the specimen constraints have a great influence on detection results. Combined with effective data processing methods, such as Fourier transformation employed in this study, the proposed method provides a promising potential to detect multiple cracks on a metal surface in a safe and effective manner.

Detection and Identification of Multiple Stationary Human Targets Via Bio-Radar Based on the Cross-Correlation Method.

PubMed

Zhang, Yang; Chen, Fuming; Xue, Huijun; Li, Zhao; An, Qiang; Wang, Jianqi; Zhang, Yang

2016-10-27

Ultra-wideband (UWB) radar has been widely used for detecting human physiological signals (respiration, movement, etc.) in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident, etc.), the presence, number, and location of the trapped victims to be detected and rescued are the key issues of concern. Ample research has been done on the first issue, whereas the identification and localization of multi-targets remains a challenge. False positive and negative identification results are two common problems associated with the detection of multiple stationary human targets. This is mainly because the energy of the signal reflected from the target close to the receiving antenna is considerably stronger than those of the targets at further range, often leading to missing or false recognition if the identification method is based on the energy of the respiratory signal. Therefore, a novel method based on cross-correlation is proposed in this paper that is based on the relativity and periodicity of the signals, rather than on the energy. The validity of this method is confirmed through experiments using different scenarios; the results indicate a discernible improvement in the detection precision and identification of the multiple stationary targets.

Rapid detection of multiple class pharmaceuticals in both municipal wastewater and sludge with ultra high performance liquid chromatography tandem mass spectrometry.

PubMed

Yuan, Xiangjuan; Qiang, Zhimin; Ben, Weiwei; Zhu, Bing; Liu, Junxin

2014-09-01

This work described the development, optimization and validation of an analytical method for rapid detection of multiple-class pharmaceuticals in both municipal wastewater and sludge samples based on ultrasonic solvent extraction, solid-phase extraction, and ultra high performance liquid chromatography-tandem mass spectrometry quantification. The results indicated that the developed method could effectively extract all the target pharmaceuticals (25) in a single process and analyze them within 24min. The recoveries of the target pharmaceuticals were in the range of 69%-131% for wastewater and 54%-130% for sludge at different spiked concentration levels. The method quantification limits in wastewater and sludge ranged from 0.02 to 0.73ng/L and from 0.02 to 1.00μg/kg, respectively. Subsequently, this method was validated and applied for residual pharmaceutical analysis in a wastewater treatment plant located in Beijing, China. All the target pharmaceuticals were detected in the influent samples with concentrations varying from 0.09ng/L (tiamulin) to 15.24μg/L (caffeine); meanwhile, up to 23 pharmaceuticals were detected in sludge samples with concentrations varying from 60ng/kg (sulfamethizole) to 8.55mg/kg (ofloxacin). The developed method demonstrated its selectivity, sensitivity, and reliability for detecting multiple-class pharmaceuticals in complex matrices such as municipal wastewater and sludge. Copyright © 2014. Published by Elsevier B.V.

Template reporter bacteriophage platform and multiple bacterial detection assays based thereon

NASA Technical Reports Server (NTRS)

Goodridge, Lawrence (Inventor)

2007-01-01

The invention is a method for the development of assays for the simultaneous detection of multiple bacteria. A bacteria of interest is selected. A host bacteria containing plasmid DNA from a T even bacteriophage that infects the bacteria of interest is infected with T4 reporter bacteriophage. After infection, the progeny bacteriophage are plating onto the bacteria of interest. The invention also includes single-tube, fast and sensitive assays which utilize the novel method.

Detection, localization and classification of multiple dipole-like magnetic sources using magnetic gradient tensor data

NASA Astrophysics Data System (ADS)

Gang, Yin; Yingtang, Zhang; Hongbo, Fan; Zhining, Li; Guoquan, Ren

2016-05-01

We have developed a method for automatic detection, localization and classification (DLC) of multiple dipole sources using magnetic gradient tensor data. First, we define modified tilt angles to estimate the approximate horizontal locations of the multiple dipole-like magnetic sources simultaneously and detect the number of magnetic sources using a fixed threshold. Secondly, based on the isotropy of the normalized source strength (NSS) response of a dipole, we obtain accurate horizontal locations of the dipoles. Then the vertical locations are calculated using magnitude magnetic transforms of magnetic gradient tensor data. Finally, we invert for the magnetic moments of the sources using the measured magnetic gradient tensor data and forward model. Synthetic and field data sets demonstrate effectiveness and practicality of the proposed method.

Elaborately designed diblock nanoprobes for simultaneous multicolor detection of microRNAs

NASA Astrophysics Data System (ADS)

Wang, Chenguang; Zhang, Huan; Zeng, Dongdong; Sun, Wenliang; Zhang, Honglu; Aldalbahi, Ali; Wang, Yunsheng; San, Lili; Fan, Chunhai; Zuo, Xiaolei; Mi, Xianqiang

2015-09-01

Simultaneous detection of multiple biomarkers has important prospects in the biomedical field. In this work, we demonstrated a novel strategy for the detection of multiple microRNAs (miRNAs) based on gold nanoparticles (Au NPs) and polyadenine (polyA) mediated nanoscale molecular beacon (MB) probes (denoted p-nanoMBs). Novel fluorescent labeled p-nanoMBs bearing consecutive adenines were designed, of which polyA served as an effective anchoring block binding to the surface of Au NPs, and the appended hairpin block formed an upright conformation that favored the hybridization with targets. Using the co-assembling method and the improved hybridization conformation of the hairpin probes, we achieved high selectivity for specifically distinguishing DNA targets from single-base mismatched DNA targets. We also realized multicolor detection of three different synthetic miRNAs in a wide dynamic range from 0.01 nM to 200 nM with a detection limit of 10 pM. What's more, we even detected miRNAs in a simulated serum environment, which indicated that our method could be used in complex media. Compared with the traditional method, our strategy provides a promising alternative method for the qualitative and quantitative detection of miRNAs.Simultaneous detection of multiple biomarkers has important prospects in the biomedical field. In this work, we demonstrated a novel strategy for the detection of multiple microRNAs (miRNAs) based on gold nanoparticles (Au NPs) and polyadenine (polyA) mediated nanoscale molecular beacon (MB) probes (denoted p-nanoMBs). Novel fluorescent labeled p-nanoMBs bearing consecutive adenines were designed, of which polyA served as an effective anchoring block binding to the surface of Au NPs, and the appended hairpin block formed an upright conformation that favored the hybridization with targets. Using the co-assembling method and the improved hybridization conformation of the hairpin probes, we achieved high selectivity for specifically distinguishing DNA targets from single-base mismatched DNA targets. We also realized multicolor detection of three different synthetic miRNAs in a wide dynamic range from 0.01 nM to 200 nM with a detection limit of 10 pM. What's more, we even detected miRNAs in a simulated serum environment, which indicated that our method could be used in complex media. Compared with the traditional method, our strategy provides a promising alternative method for the qualitative and quantitative detection of miRNAs. Electronic supplementary information (ESI) available: Sequences for oligonucleotides used for this work, dynamic light scattering (DLS) measurements, fluorescent signal intensity with different ratios between p-MBs and A5 oligonucleotides, quantification of the fluorescent p-MB, and UV-Vis spectra for naked AuNPs and the p-nanoMB. See DOI: 10.1039/c5nr04618a

Detecting multiple adulterants in dry milk using Raman chemical imaging

USDA-ARS?s Scientific Manuscript database

A Raman chemical imaging method was developed for detecting the presence of multiple chemical adulterants in dry milk powder. Four chemicals (ammonium sulfate, dicyandiamide, melamine, and urea) were added in equal concentrations, between 0.1% and 5.0%, to nonfat dry milk. An area of 25×25 mm2 for e...

Applying the Multiple Signal Classification Method to Silent Object Detection Using Ambient Noise

NASA Astrophysics Data System (ADS)

Mori, Kazuyoshi; Yokoyama, Tomoki; Hasegawa, Akio; Matsuda, Minoru

2004-05-01

The revolutionary concept of using ocean ambient noise positively to detect objects, called acoustic daylight imaging, has attracted much attention. The authors attempted the detection of a silent target object using ambient noise and a wide-band beam former consisting of an array of receivers. In experimental results obtained in air, using the wide-band beam former, we successfully applied the delay-sum array (DSA) method to detect a silent target object in an acoustic noise field generated by a large number of transducers. This paper reports some experimental results obtained by applying the multiple signal classification (MUSIC) method to a wide-band beam former to detect silent targets. The ocean ambient noise was simulated by transducers decentralized to many points in air. Both MUSIC and DSA detected a spherical target object in the noise field. The relative power levels near the target obtained with MUSIC were compared with those obtained by DSA. Then the effectiveness of the MUSIC method was evaluated according to the rate of increase in the maximum and minimum relative power levels.

Fault diagnosis of sensor networked structures with multiple faults using a virtual beam based approach

NASA Astrophysics Data System (ADS)

Wang, H.; Jing, X. J.

2017-07-01

This paper presents a virtual beam based approach suitable for conducting diagnosis of multiple faults in complex structures with limited prior knowledge of the faults involved. The "virtual beam", a recently-proposed concept for fault detection in complex structures, is applied, which consists of a chain of sensors representing a vibration energy transmission path embedded in the complex structure. Statistical tests and adaptive threshold are particularly adopted for fault detection due to limited prior knowledge of normal operational conditions and fault conditions. To isolate the multiple faults within a specific structure or substructure of a more complex one, a 'biased running' strategy is developed and embedded within the bacterial-based optimization method to construct effective virtual beams and thus to improve the accuracy of localization. The proposed method is easy and efficient to implement for multiple fault localization with limited prior knowledge of normal conditions and faults. With extensive experimental results, it is validated that the proposed method can localize both single fault and multiple faults more effectively than the classical trust index subtract on negative add on positive (TI-SNAP) method.

A spatial scan statistic for compound Poisson data.

PubMed

Rosychuk, Rhonda J; Chang, Hsing-Ming

2013-12-20

The topic of spatial cluster detection gained attention in statistics during the late 1980s and early 1990s. Effort has been devoted to the development of methods for detecting spatial clustering of cases and events in the biological sciences, astronomy and epidemiology. More recently, research has examined detecting clusters of correlated count data associated with health conditions of individuals. Such a method allows researchers to examine spatial relationships of disease-related events rather than just incident or prevalent cases. We introduce a spatial scan test that identifies clusters of events in a study region. Because an individual case may have multiple (repeated) events, we base the test on a compound Poisson model. We illustrate our method for cluster detection on emergency department visits, where individuals may make multiple disease-related visits. Copyright © 2013 John Wiley & Sons, Ltd.

A method based on multi-sensor data fusion for fault detection of planetary gearboxes.

PubMed

Lei, Yaguo; Lin, Jing; He, Zhengjia; Kong, Detong

2012-01-01

Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods that work well for fixed-axis gearboxes. It is a fact that for systems as complex as planetary gearboxes, multiple sensors mounted on different locations provide complementary information on the health condition of the systems. On this basis, a fault detection method based on multi-sensor data fusion is introduced in this paper. In this method, two features developed for planetary gearboxes are used to characterize the gear health conditions, and an adaptive neuro-fuzzy inference system (ANFIS) is utilized to fuse all features from different sensors. In order to demonstrate the effectiveness of the proposed method, experiments are carried out on a planetary gearbox test rig, on which multiple accelerometers are mounted for data collection. The comparisons between the proposed method and the methods based on individual sensors show that the former achieves much higher accuracies in detecting planetary gearbox faults.

The detection of local irreversibility in time series based on segmentation

NASA Astrophysics Data System (ADS)

Teng, Yue; Shang, Pengjian

2018-06-01

We propose a strategy for the detection of local irreversibility in stationary time series based on multiple scale. The detection is beneficial to evaluate the displacement of irreversibility toward local skewness. By means of this method, we can availably discuss the local irreversible fluctuations of time series as the scale changes. The method was applied to simulated nonlinear signals generated by the ARFIMA process and logistic map to show how the irreversibility functions react to the increasing of the multiple scale. The method was applied also to series of financial markets i.e., American, Chinese and European markets. The local irreversibility for different markets demonstrate distinct characteristics. Simulations and real data support the need of exploring local irreversibility.

Comparison of traditional culture and molecular qPCR for detection of simultaneous carriage of multiple pneumococcal serotypes in African children.

PubMed

Olwagen, Courtney P; Adrian, Peter V; Madhi, Shabir A

2017-07-05

S. pneumoniae is a common colonizer of the human nasopharynx in high income and low-middle income countries. Due to limitations of standard culture methods, the prevalence of concurrent colonization with multiple serotypes is unclear. We evaluated the use of multiplex quantitative PCR (qPCR) to detect multiple pneumococcal serotypes/group colonization in archived nasopharyngeal swabs of pneumococcal conjugate vaccine naive children who had previously been investigated by traditional culture methods. Overall the detection of pneumococcal colonization was higher by qPCR (82%) compared to standard culture (71%; p < 0.001), with a high concordance (kappa = 0.73) of serotypes/groups identified by culture also being identified by qPCR. Also, qPCR was more sensitive in detecting multiple serotype/groups among colonized cases (28.7%) compared to culture (4.5%; p < 0.001). Of the additional serotypes detected only by qPCR, the majority were of lower density (<10 4 CFU/ml) than the dominant colonizing serotype, with serotype/group 6A/B, 19B/F and 23F being the highest density colonizers, followed by serotype 5 and serogroup 9A/L/N/V being the most common second and third colonizers respectively. The ability of qPCR to detect multiple pneumococcal serotypes at a low carriage density might provide better insight into underlying mechanism for changes in serotype colonization in PCV vaccinated children.

A primitive study of voxel feature generation by multiple stacked denoising autoencoders for detecting cerebral aneurysms on MRA

NASA Astrophysics Data System (ADS)

Nemoto, Mitsutaka; Hayashi, Naoto; Hanaoka, Shouhei; Nomura, Yukihiro; Miki, Soichiro; Yoshikawa, Takeharu; Ohtomo, Kuni

2016-03-01

The purpose of this study is to evaluate the feasibility of a novel feature generation, which is based on multiple deep neural networks (DNNs) with boosting, for computer-assisted detection (CADe). It is hard and time-consuming to optimize the hyperparameters for DNNs such as stacked denoising autoencoder (SdA). The proposed method allows using SdA based features without the burden of the hyperparameter setting. The proposed method was evaluated by an application for detecting cerebral aneurysms on magnetic resonance angiogram (MRA). A baseline CADe process included four components; scaling, candidate area limitation, candidate detection, and candidate classification. Proposed feature generation method was applied to extract the optimal features for candidate classification. Proposed method only required setting range of the hyperparameters for SdA. The optimal feature set was selected from a large quantity of SdA based features by multiple SdAs, each of which was trained using different hyperparameter set. The feature selection was operated through ada-boost ensemble learning method. Training of the baseline CADe process and proposed feature generation were operated with 200 MRA cases, and the evaluation was performed with 100 MRA cases. Proposed method successfully provided SdA based features just setting the range of some hyperparameters for SdA. The CADe process by using both previous voxel features and SdA based features had the best performance with 0.838 of an area under ROC curve and 0.312 of ANODE score. The results showed that proposed method was effective in the application for detecting cerebral aneurysms on MRA.

A method for detecting IBD regions simultaneously in multiple individuals—with applications to disease genetics

PubMed Central

Moltke, Ida; Albrechtsen, Anders; Hansen, Thomas v.O.; Nielsen, Finn C.; Nielsen, Rasmus

2011-01-01

All individuals in a finite population are related if traced back long enough and will, therefore, share regions of their genomes identical by descent (IBD). Detection of such regions has several important applications—from answering questions about human evolution to locating regions in the human genome containing disease-causing variants. However, IBD regions can be difficult to detect, especially in the common case where no pedigree information is available. In particular, all existing non-pedigree based methods can only infer IBD sharing between two individuals. Here, we present a new Markov Chain Monte Carlo method for detection of IBD regions, which does not rely on any pedigree information. It is based on a probabilistic model applicable to unphased SNP data. It can take inbreeding, allele frequencies, genotyping errors, and genomic distances into account. And most importantly, it can simultaneously infer IBD sharing among multiple individuals. Through simulations, we show that the simultaneous modeling of multiple individuals makes the method more powerful and accurate than several other non-pedigree based methods. We illustrate the potential of the method by applying it to data from individuals with breast and/or ovarian cancer, and show that a known disease-causing mutation can be mapped to a 2.2-Mb region using SNP data from only five seemingly unrelated affected individuals. This would not be possible using classical linkage mapping or association mapping. PMID:21493780

Autonomous detection of crowd anomalies in multiple-camera surveillance feeds

NASA Astrophysics Data System (ADS)

Nordlöf, Jonas; Andersson, Maria

2016-10-01

A novel approach for autonomous detection of anomalies in crowded environments is presented in this paper. The proposed models uses a Gaussian mixture probability hypothesis density (GM-PHD) filter as feature extractor in conjunction with different Gaussian mixture hidden Markov models (GM-HMMs). Results, based on both simulated and recorded data, indicate that this method can track and detect anomalies on-line in individual crowds through multiple camera feeds in a crowded environment.

Microfluidic platform for multiplexed detection in single cells and methods thereof

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

Wu, Meiye; Singh, Anup K.

The present invention relates to a microfluidic device and platform configured to conduct multiplexed analysis within the device. In particular, the device allows multiple targets to be detected on a single-cell level. Also provided are methods of performing multiplexed analyses to detect one or more target nucleic acids, proteins, and post-translational modifications.

Multiple particle tracking in 3-D+t microscopy: method and application to the tracking of endocytosed quantum dots.

PubMed

Genovesio, Auguste; Liedl, Tim; Emiliani, Valentina; Parak, Wolfgang J; Coppey-Moisan, Maité; Olivo-Marin, Jean-Christophe

2006-05-01

We propose a method to detect and track multiple moving biological spot-like particles showing different kinds of dynamics in image sequences acquired through multidimensional fluorescence microscopy. It enables the extraction and analysis of information such as number, position, speed, movement, and diffusion phases of, e.g., endosomal particles. The method consists of several stages. After a detection stage performed by a three-dimensional (3-D) undecimated wavelet transform, we compute, for each detected spot, several predictions of its future state in the next frame. This is accomplished thanks to an interacting multiple model (IMM) algorithm which includes several models corresponding to different biologically realistic movement types. Tracks are constructed, thereafter, by a data association algorithm based on the maximization of the likelihood of each IMM. The last stage consists of updating the IMM filters in order to compute final estimations for the present image and to improve predictions for the next image. The performances of the method are validated on synthetic image data and used to characterize the 3-D movement of endocytic vesicles containing quantum dots.

The Mediated MIMIC Model for Understanding the Underlying Mechanism of DIF

ERIC Educational Resources Information Center

Cheng, Ying; Shao, Can; Lathrop, Quinn N.

2016-01-01

Due to its flexibility, the multiple-indicator, multiple-causes (MIMIC) model has become an increasingly popular method for the detection of differential item functioning (DIF). In this article, we propose the mediated MIMIC model method to uncover the underlying mechanism of DIF. This method extends the usual MIMIC model by including one variable…

Experimental Investigation on the Detection of Multiple Surface Cracks Using Vibrothermography with a Low-Power Piezoceramic Actuator

PubMed Central

Xu, Changhang; Xie, Jing; Zhang, Wuyang; Kong, Qingzhao; Chen, Guoming; Song, Gangbing

2017-01-01

Vibrothermography often employs a high-power actuator to generate heat on a specimen to reveal damage, however, the high-power actuator brings inconvenience to the application and possibly introduces additional damage to the inspected objects. This study uses a low-power piezoceramic transducer as the actuator of vibrothermography and explores its ability to detect multiple surface cracks in a metal part. Experiments were conducted on a thin aluminum beam with three cracks in different orientations. Detailed analyses of both thermograms and temperature data are presented to validate the proposed vibrothermography method. To further investigate the performance of the proposed vibrothermography method, we experimentally studied the effects of several critical factors, including the amplitude of excitation signal, specimen constraints, relative position between the transducer and cracks (the transducer is mounted on the same or the opposite side with the cracks). The results demonstrate that all cracks can be detected conveniently and simultaneously by using the proposed low-power vibrothermography. We also found that the magnitude of excitation signal and the specimen constraints have a great influence on detection results. Combined with effective data processing methods, such as Fourier transformation employed in this study, the proposed method provides a promising potential to detect multiple cracks on a metal surface in a safe and effective manner. PMID:29168759

Single Versus Multiple Events Error Potential Detection in a BCI-Controlled Car Game With Continuous and Discrete Feedback.

PubMed

Kreilinger, Alex; Hiebel, Hannah; Müller-Putz, Gernot R

2016-03-01

This work aimed to find and evaluate a new method for detecting errors in continuous brain-computer interface (BCI) applications. Instead of classifying errors on a single-trial basis, the new method was based on multiple events (MEs) analysis to increase the accuracy of error detection. In a BCI-driven car game, based on motor imagery (MI), discrete events were triggered whenever subjects collided with coins and/or barriers. Coins counted as correct events, whereas barriers were errors. This new method, termed ME method, combined and averaged the classification results of single events (SEs) and determined the correctness of MI trials, which consisted of event sequences instead of SEs. The benefit of this method was evaluated in an offline simulation. In an online experiment, the new method was used to detect erroneous MI trials. Such MI trials were discarded and could be repeated by the users. We found that, even with low SE error potential (ErrP) detection rates, feasible accuracies can be achieved when combining MEs to distinguish erroneous from correct MI trials. Online, all subjects reached higher scores with error detection than without, at the cost of longer times needed for completing the game. Findings suggest that ErrP detection may become a reliable tool for monitoring continuous states in BCI applications when combining MEs. This paper demonstrates a novel technique for detecting errors in online continuous BCI applications, which yields promising results even with low single-trial detection rates.

Multisignal detecting system of pile integrity testing

NASA Astrophysics Data System (ADS)

Liu, Zuting; Luo, Ying; Yu, Shihai

2002-05-01

The low strain reflection wave method plays a principal rule in the integrating detection of base piles. However, there are some deficiencies with this method. For example, there is a blind area of detection on top of the tested pile; it is difficult to recognize the defects at deep-seated parts of the pile; there is still the planar of 3D domino effect, etc. It is very difficult to solve these problems only with the single-transducer pile integrity testing system. A new multi-signal piles integrity testing system is proposed in this paper, which is able to impulse and collect signals on multiple points on top of the pile. By using the multiple superposition data processing method, the detecting system can effectively restrain the interference and elevate the precision and SNR of pile integrity testing. The system can also be applied to the evaluation of engineering structure health.

Simultaneous Determination of Oxysterols, Cholesterol and 25-Hydroxy-Vitamin D3 in Human Plasma by LC-UV-MS

PubMed Central

Narayanaswamy, Rohini; Iyer, Vignesh; Khare, Prachi; Bodziak, Mary Lou; Badgett, Darlene; Zivadinov, Robert; Weinstock-Guttman, Bianca; Rideout, Todd C.; Ramanathan, Murali; Browne, Richard W.

2015-01-01

Background Oxysterols are promising biomarkers of neurodegenerative diseases that are linked with cholesterol and vitamin D metabolism. There is an unmet need for methods capable of sensitive, and simultaneous quantitation of multiple oxysterols, vitamin D and cholesterol pathway biomarkers. Methods A method for simultaneous determination of 5 major oxysterols, 25-hydroxy vitamin D3 and cholesterol in human plasma was developed. Total oxysterols were prepared by room temperature saponification followed by solid phase extraction from plasma spiked with deuterated internal standards. Oxysterols were resolved by reverse phase HPLC using a methanol/water/0.1% formic acid gradient. Oxysterols and 25-hydroxy vitamin D3 were detected with atmospheric pressure chemical ionization mass spectrometry in positive ion mode; in-series photodiode array detection at 204nm was used for cholesterol. Method validation studies were performed. Oxysterol levels in 220 plasma samples from healthy control subjects, multiple sclerosis and other neurological disorders patients were quantitated. Results Our method quantitated 5 oxysterols, cholesterol and 25-hydroxy vitamin D3 from 200 μL plasma in 35 minutes. Recoveries were >85% for all analytes and internal standards. The limits of detection were 3-10 ng/mL for oxysterols and 25-hydroxy vitamin D3 and 1 μg/mL for simultaneous detection of cholesterol. Analytical imprecision was <10 %CV for 24(S)-, 25-, 27-, 7α-hydroxycholesterol (HC) and cholesterol and ≤15 % for 7-keto-cholesterol. Multiple Sclerosis and other neurological disorder patients had lower 27-hydroxycholesterol levels compared to controls whereas 7α-hydroxycholesterol was lower specifically in Multiple Sclerosis. Conclusion The method is suitable for measuring plasma oxysterols levels in human health and disease. Analysis of human plasma indicates that the oxysterol, bile acid precursors 7α-hydroxycholesterol and 27-hydroxycholesterol are lower in Multiple Sclerosis and may serve as potential biomarkers of disease. PMID:25875771

Unsupervised Anomaly Detection Based on Clustering and Multiple One-Class SVM

NASA Astrophysics Data System (ADS)

Song, Jungsuk; Takakura, Hiroki; Okabe, Yasuo; Kwon, Yongjin

Intrusion detection system (IDS) has played an important role as a device to defend our networks from cyber attacks. However, since it is unable to detect unknown attacks, i.e., 0-day attacks, the ultimate challenge in intrusion detection field is how we can exactly identify such an attack by an automated manner. Over the past few years, several studies on solving these problems have been made on anomaly detection using unsupervised learning techniques such as clustering, one-class support vector machine (SVM), etc. Although they enable one to construct intrusion detection models at low cost and effort, and have capability to detect unforeseen attacks, they still have mainly two problems in intrusion detection: a low detection rate and a high false positive rate. In this paper, we propose a new anomaly detection method based on clustering and multiple one-class SVM in order to improve the detection rate while maintaining a low false positive rate. We evaluated our method using KDD Cup 1999 data set. Evaluation results show that our approach outperforms the existing algorithms reported in the literature; especially in detection of unknown attacks.

Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods.

PubMed

Xu, Lina; Tetteh, Giles; Lipkova, Jana; Zhao, Yu; Li, Hongwei; Christ, Patrick; Piraud, Marie; Buck, Andreas; Shi, Kuangyu; Menze, Bjoern H

2018-01-01

The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). 68 Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner. Two convolutional neural networks (CNNs), V-Net and W-Net, were adopted to segment and detect the lesions. The feasibility of deep learning for lesion detection on 68 Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods. Then the proposed methods were evaluated on real 68 Ga-Pentixafor PET/CT scans of MM patients. The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, and W-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF), k -Nearest Neighbors ( k -NN), and support vector machine (SVM). The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study.

Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods

PubMed Central

Tetteh, Giles; Lipkova, Jana; Zhao, Yu; Li, Hongwei; Christ, Patrick; Buck, Andreas; Menze, Bjoern H.

2018-01-01

The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). 68Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner. Two convolutional neural networks (CNNs), V-Net and W-Net, were adopted to segment and detect the lesions. The feasibility of deep learning for lesion detection on 68Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods. Then the proposed methods were evaluated on real 68Ga-Pentixafor PET/CT scans of MM patients. The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, and W-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF), k-Nearest Neighbors (k-NN), and support vector machine (SVM). The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study. PMID:29531504

Multi-Objective Community Detection Based on Memetic Algorithm

PubMed Central

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels. PMID:25932646

Multi-objective community detection based on memetic algorithm.

PubMed

Wu, Peng; Pan, Li

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.

Microbial Monitoring of Common Opportunistic Pathogens by Comparing Multiple Real-time PCR Platforms for Potential Space Applications

NASA Technical Reports Server (NTRS)

Roman, Monserrate C.; Jones, Kathy U.; Oubre, Cherie M.; Castro, Victoria; Ott, Mark C.; Birmele, Michele; Venkateswaran, Kasthuri J.; Vaishampayan, Parag A.

2013-01-01

Current methods for microbial detection: a) Labor & time intensive cultivation-based approaches that can fail to detect or characterize all cells present. b) Requires collection of samples on orbit and transportation back to ground for analysis. Disadvantages to current detection methods: a) Unable to perform quick and reliable detection on orbit. b) Lengthy sampling intervals. c) No microbe identification.

A simplified implementation of edge detection in MATLAB is faster and more sensitive than fast fourier transform for actin fiber alignment quantification.

PubMed

Kemeny, Steven Frank; Clyne, Alisa Morss

2011-04-01

Fiber alignment plays a critical role in the structure and function of cells and tissues. While fiber alignment quantification is important to experimental analysis and several different methods for quantifying fiber alignment exist, many studies focus on qualitative rather than quantitative analysis perhaps due to the complexity of current fiber alignment methods. Speed and sensitivity were compared in edge detection and fast Fourier transform (FFT) for measuring actin fiber alignment in cells exposed to shear stress. While edge detection using matrix multiplication was consistently more sensitive than FFT, image processing time was significantly longer. However, when MATLAB functions were used to implement edge detection, MATLAB's efficient element-by-element calculations and fast filtering techniques reduced computation cost 100 times compared to the matrix multiplication edge detection method. The new computation time was comparable to the FFT method, and MATLAB edge detection produced well-distributed fiber angle distributions that statistically distinguished aligned and unaligned fibers in half as many sample images. When the FFT sensitivity was improved by dividing images into smaller subsections, processing time grew larger than the time required for MATLAB edge detection. Implementation of edge detection in MATLAB is simpler, faster, and more sensitive than FFT for fiber alignment quantification.

LEA Detection and Tracking Method for Color-Independent Visual-MIMO

PubMed Central

Kim, Jai-Eun; Kim, Ji-Won; Kim, Ki-Doo

2016-01-01

Communication performance in the color-independent visual-multiple input multiple output (visual-MIMO) technique is deteriorated by light emitting array (LEA) detection and tracking errors in the received image because the image sensor included in the camera must be used as the receiver in the visual-MIMO system. In this paper, in order to improve detection reliability, we first set up the color-space-based region of interest (ROI) in which an LEA is likely to be placed, and then use the Harris corner detection method. Next, we use Kalman filtering for robust tracking by predicting the most probable location of the LEA when the relative position between the camera and the LEA varies. In the last step of our proposed method, the perspective projection is used to correct the distorted image, which can improve the symbol decision accuracy. Finally, through numerical simulation, we show the possibility of robust detection and tracking of the LEA, which results in a symbol error rate (SER) performance improvement. PMID:27384563

LEA Detection and Tracking Method for Color-Independent Visual-MIMO.

PubMed

Kim, Jai-Eun; Kim, Ji-Won; Kim, Ki-Doo

2016-07-02

Communication performance in the color-independent visual-multiple input multiple output (visual-MIMO) technique is deteriorated by light emitting array (LEA) detection and tracking errors in the received image because the image sensor included in the camera must be used as the receiver in the visual-MIMO system. In this paper, in order to improve detection reliability, we first set up the color-space-based region of interest (ROI) in which an LEA is likely to be placed, and then use the Harris corner detection method. Next, we use Kalman filtering for robust tracking by predicting the most probable location of the LEA when the relative position between the camera and the LEA varies. In the last step of our proposed method, the perspective projection is used to correct the distorted image, which can improve the symbol decision accuracy. Finally, through numerical simulation, we show the possibility of robust detection and tracking of the LEA, which results in a symbol error rate (SER) performance improvement.

Accelerated SPECT Monte Carlo Simulation Using Multiple Projection Sampling and Convolution-Based Forced Detection

NASA Astrophysics Data System (ADS)

Liu, Shaoying; King, Michael A.; Brill, Aaron B.; Stabin, Michael G.; Farncombe, Troy H.

2008-02-01

Monte Carlo (MC) is a well-utilized tool for simulating photon transport in single photon emission computed tomography (SPECT) due to its ability to accurately model physical processes of photon transport. As a consequence of this accuracy, it suffers from a relatively low detection efficiency and long computation time. One technique used to improve the speed of MC modeling is the effective and well-established variance reduction technique (VRT) known as forced detection (FD). With this method, photons are followed as they traverse the object under study but are then forced to travel in the direction of the detector surface, whereby they are detected at a single detector location. Another method, called convolution-based forced detection (CFD), is based on the fundamental idea of FD with the exception that detected photons are detected at multiple detector locations and determined with a distance-dependent blurring kernel. In order to further increase the speed of MC, a method named multiple projection convolution-based forced detection (MP-CFD) is presented. Rather than forcing photons to hit a single detector, the MP-CFD method follows the photon transport through the object but then, at each scatter site, forces the photon to interact with a number of detectors at a variety of angles surrounding the object. This way, it is possible to simulate all the projection images of a SPECT simulation in parallel, rather than as independent projections. The result of this is vastly improved simulation time as much of the computation load of simulating photon transport through the object is done only once for all projection angles. The results of the proposed MP-CFD method agrees well with the experimental data in measurements of point spread function (PSF), producing a correlation coefficient (r2) of 0.99 compared to experimental data. The speed of MP-CFD is shown to be about 60 times faster than a regular forced detection MC program with similar results.

Multiple source associated particle imaging for simultaneous capture of multiple projections

DOEpatents

Bingham, Philip R; Hausladen, Paul A; McConchi, Seth M; Mihalczo, John T; Mullens, James A

2013-11-19

Disclosed herein are representative embodiments of methods, apparatus, and systems for performing neutron radiography. For example, in one exemplary method, an object is interrogated with a plurality of neutrons. The plurality of neutrons includes a first portion of neutrons generated from a first neutron source and a second portion of neutrons generated from a second neutron source. Further, at least some of the first portion and the second portion are generated during a same time period. In the exemplary method, one or more neutrons from the first portion and one or more neutrons from the second portion are detected, and an image of the object is generated based at least in part on the detected neutrons from the first portion and the detected neutrons from the second portion.

Concept of multiple-cell cavity for axion dark matter search

NASA Astrophysics Data System (ADS)

Jeong, Junu; Youn, SungWoo; Ahn, Saebyeok; Kim, Jihn E.; Semertzidis, Yannis K.

2018-02-01

In cavity-based axion dark matter search experiments exploring high mass regions, multiple-cavity design is under consideration as a method to increase the detection volume within a given magnet bore. We introduce a new idea, referred to as a multiple-cell cavity, which provides various benefits including a larger detection volume, simpler experimental setup, and easier phase-matching mechanism. We present the characteristics of this concept and demonstrate the experimental feasibility with an example of a double-cell cavity.

Spatial scan statistics for detection of multiple clusters with arbitrary shapes.

PubMed

Lin, Pei-Sheng; Kung, Yi-Hung; Clayton, Murray

2016-12-01

In applying scan statistics for public health research, it would be valuable to develop a detection method for multiple clusters that accommodates spatial correlation and covariate effects in an integrated model. In this article, we connect the concepts of the likelihood ratio (LR) scan statistic and the quasi-likelihood (QL) scan statistic to provide a series of detection procedures sufficiently flexible to apply to clusters of arbitrary shape. First, we use an independent scan model for detection of clusters and then a variogram tool to examine the existence of spatial correlation and regional variation based on residuals of the independent scan model. When the estimate of regional variation is significantly different from zero, a mixed QL estimating equation is developed to estimate coefficients of geographic clusters and covariates. We use the Benjamini-Hochberg procedure (1995) to find a threshold for p-values to address the multiple testing problem. A quasi-deviance criterion is used to regroup the estimated clusters to find geographic clusters with arbitrary shapes. We conduct simulations to compare the performance of the proposed method with other scan statistics. For illustration, the method is applied to enterovirus data from Taiwan. © 2016, The International Biometric Society.

Identifying reprioritization response shift in a stroke caregiver population: a comparison of missing data methods.

PubMed

Sajobi, Tolulope T; Lix, Lisa M; Singh, Gurbakhshash; Lowerison, Mark; Engbers, Jordan; Mayo, Nancy E

2015-03-01

Response shift (RS) is an important phenomenon that influences the assessment of longitudinal changes in health-related quality of life (HRQOL) studies. Given that RS effects are often small, missing data due to attrition or item non-response can contribute to failure to detect RS effects. Since missing data are often encountered in longitudinal HRQOL data, effective strategies to deal with missing data are important to consider. This study aims to compare different imputation methods on the detection of reprioritization RS in the HRQOL of caregivers of stroke survivors. Data were from a Canadian multi-center longitudinal study of caregivers of stroke survivors over a one-year period. The Stroke Impact Scale physical function score at baseline, with a cutoff of 75, was used to measure patient stroke severity for the reprioritization RS analysis. Mean imputation, likelihood-based expectation-maximization imputation, and multiple imputation methods were compared in test procedures based on changes in relative importance weights to detect RS in SF-36 domains over a 6-month period. Monte Carlo simulation methods were used to compare the statistical powers of relative importance test procedures for detecting RS in incomplete longitudinal data under different missing data mechanisms and imputation methods. Of the 409 caregivers, 15.9 and 31.3 % of them had missing data at baseline and 6 months, respectively. There were no statistically significant changes in relative importance weights on any of the domains when complete-case analysis was adopted. But statistical significant changes were detected on physical functioning and/or vitality domains when mean imputation or EM imputation was adopted. There were also statistically significant changes in relative importance weights for physical functioning, mental health, and vitality domains when multiple imputation method was adopted. Our simulations revealed that relative importance test procedures were least powerful under complete-case analysis method and most powerful when a mean imputation or multiple imputation method was adopted for missing data, regardless of the missing data mechanism and proportion of missing data. Test procedures based on relative importance measures are sensitive to the type and amount of missing data and imputation method. Relative importance test procedures based on mean imputation and multiple imputation are recommended for detecting RS in incomplete data.

Highly Sensitive Detection of Low-Abundance White Spot Syndrome Virus by a Pre-Amplification PCR Method.

PubMed

Pan, Xiaoming; Zhang, Yanfang; Sha, Xuejiao; Wang, Jing; Li, Jing; Dong, Ping; Liang, Xingguo

2017-03-28

White spot syndrome virus (WSSV) is a major threat to the shrimp farming industry and so far there is no effective therapy for it, and thus early diagnostic of WSSV is of great importance. However, at the early stage of infection, the extremely low-abundance of WSSV DNA challenges the detection sensitivity and accuracy of PCR. To effectively detect low-abundance WSSV, here we developed a pre-amplification PCR (pre-amp PCR) method to amplify trace amounts of WSSV DNA from massive background genomic DNA. Combining with normal specific PCR, 10 copies of target WSSV genes were detected from ~10 10 magnitude of backgrounds. In particular, multiple target genes were able to be balanced amplified with similar efficiency due to the usage of the universal primer. The efficiency of the pre-amp PCR was validated by nested-PCR and quantitative PCR, and pre-amp PCR showed higher efficiency than nested-PCR when multiple targets were detected. The developed method is particularly suitable for the super early diagnosis of WSSV, and has potential to be applied in other low-abundance sample detection cases.

Theory of chromatic noise masking applied to testing linearity of S-cone detection mechanisms.

PubMed

Giulianini, Franco; Eskew, Rhea T

2007-09-01

A method for testing the linearity of cone combination of chromatic detection mechanisms is applied to S-cone detection. This approach uses the concept of mechanism noise, the noise as seen by a postreceptoral neural mechanism, to represent the effects of superposing chromatic noise components in elevating thresholds and leads to a parameter-free prediction for a linear mechanism. The method also provides a test for the presence of multiple linear detectors and off-axis looking. No evidence for multiple linear mechanisms was found when using either S-cone increment or decrement tests. The results for both S-cone test polarities demonstrate that these mechanisms combine their cone inputs nonlinearly.

Double-observer line transect surveys with Markov-modulated Poisson process models for animal availability.

PubMed

Borchers, D L; Langrock, R

2015-12-01

We develop maximum likelihood methods for line transect surveys in which animals go undetected at distance zero, either because they are stochastically unavailable while within view or because they are missed when they are available. These incorporate a Markov-modulated Poisson process model for animal availability, allowing more clustered availability events than is possible with Poisson availability models. They include a mark-recapture component arising from the independent-observer survey, leading to more accurate estimation of detection probability given availability. We develop models for situations in which (a) multiple detections of the same individual are possible and (b) some or all of the availability process parameters are estimated from the line transect survey itself, rather than from independent data. We investigate estimator performance by simulation, and compare the multiple-detection estimators with estimators that use only initial detections of individuals, and with a single-observer estimator. Simultaneous estimation of detection function parameters and availability model parameters is shown to be feasible from the line transect survey alone with multiple detections and double-observer data but not with single-observer data. Recording multiple detections of individuals improves estimator precision substantially when estimating the availability model parameters from survey data, and we recommend that these data be gathered. We apply the methods to estimate detection probability from a double-observer survey of North Atlantic minke whales, and find that double-observer data greatly improve estimator precision here too. © 2015 The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.

Unsupervised Ensemble Anomaly Detection Using Time-Periodic Packet Sampling

NASA Astrophysics Data System (ADS)

Uchida, Masato; Nawata, Shuichi; Gu, Yu; Tsuru, Masato; Oie, Yuji

We propose an anomaly detection method for finding patterns in network traffic that do not conform to legitimate (i.e., normal) behavior. The proposed method trains a baseline model describing the normal behavior of network traffic without using manually labeled traffic data. The trained baseline model is used as the basis for comparison with the audit network traffic. This anomaly detection works in an unsupervised manner through the use of time-periodic packet sampling, which is used in a manner that differs from its intended purpose — the lossy nature of packet sampling is used to extract normal packets from the unlabeled original traffic data. Evaluation using actual traffic traces showed that the proposed method has false positive and false negative rates in the detection of anomalies regarding TCP SYN packets comparable to those of a conventional method that uses manually labeled traffic data to train the baseline model. Performance variation due to the probabilistic nature of sampled traffic data is mitigated by using ensemble anomaly detection that collectively exploits multiple baseline models in parallel. Alarm sensitivity is adjusted for the intended use by using maximum- and minimum-based anomaly detection that effectively take advantage of the performance variations among the multiple baseline models. Testing using actual traffic traces showed that the proposed anomaly detection method performs as well as one using manually labeled traffic data and better than one using randomly sampled (unlabeled) traffic data.

Multiple Detector Optimization for Hidden Radiation Source Detection

DTIC Science & Technology

2015-03-26

important in achieving operationally useful methods for optimizing detector emplacement, the 2-D attenuation model approach promises to speed up the...process of hidden source detection significantly. The model focused on detection of the full energy peak of a radiation source. Methods to optimize... radioisotope identification is possible without using a computationally intensive stochastic model such as the Monte Carlo n-Particle (MCNP) code

Detection of bifurcations in noisy coupled systems from multiple time series

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

Williamson, Mark S., E-mail: m.s.williamson@exeter.ac.uk; Lenton, Timothy M.

We generalize a method of detecting an approaching bifurcation in a time series of a noisy system from the special case of one dynamical variable to multiple dynamical variables. For a system described by a stochastic differential equation consisting of an autonomous deterministic part with one dynamical variable and an additive white noise term, small perturbations away from the system's fixed point will decay slower the closer the system is to a bifurcation. This phenomenon is known as critical slowing down and all such systems exhibit this decay-type behaviour. However, when the deterministic part has multiple coupled dynamical variables, themore » possible dynamics can be much richer, exhibiting oscillatory and chaotic behaviour. In our generalization to the multi-variable case, we find additional indicators to decay rate, such as frequency of oscillation. In the case of approaching a homoclinic bifurcation, there is no change in decay rate but there is a decrease in frequency of oscillations. The expanded method therefore adds extra tools to help detect and classify approaching bifurcations given multiple time series, where the underlying dynamics are not fully known. Our generalisation also allows bifurcation detection to be applied spatially if one treats each spatial location as a new dynamical variable. One may then determine the unstable spatial mode(s). This is also something that has not been possible with the single variable method. The method is applicable to any set of time series regardless of its origin, but may be particularly useful when anticipating abrupt changes in the multi-dimensional climate system.« less

Detection of bifurcations in noisy coupled systems from multiple time series

NASA Astrophysics Data System (ADS)

Williamson, Mark S.; Lenton, Timothy M.

2015-03-01

We generalize a method of detecting an approaching bifurcation in a time series of a noisy system from the special case of one dynamical variable to multiple dynamical variables. For a system described by a stochastic differential equation consisting of an autonomous deterministic part with one dynamical variable and an additive white noise term, small perturbations away from the system's fixed point will decay slower the closer the system is to a bifurcation. This phenomenon is known as critical slowing down and all such systems exhibit this decay-type behaviour. However, when the deterministic part has multiple coupled dynamical variables, the possible dynamics can be much richer, exhibiting oscillatory and chaotic behaviour. In our generalization to the multi-variable case, we find additional indicators to decay rate, such as frequency of oscillation. In the case of approaching a homoclinic bifurcation, there is no change in decay rate but there is a decrease in frequency of oscillations. The expanded method therefore adds extra tools to help detect and classify approaching bifurcations given multiple time series, where the underlying dynamics are not fully known. Our generalisation also allows bifurcation detection to be applied spatially if one treats each spatial location as a new dynamical variable. One may then determine the unstable spatial mode(s). This is also something that has not been possible with the single variable method. The method is applicable to any set of time series regardless of its origin, but may be particularly useful when anticipating abrupt changes in the multi-dimensional climate system.

Statistical sensor fusion analysis of near-IR polarimetric and thermal imagery for the detection of minelike targets

NASA Astrophysics Data System (ADS)

Weisenseel, Robert A.; Karl, William C.; Castanon, David A.; DiMarzio, Charles A.

1999-02-01

We present an analysis of statistical model based data-level fusion for near-IR polarimetric and thermal data, particularly for the detection of mines and mine-like targets. Typical detection-level data fusion methods, approaches that fuse detections from individual sensors rather than fusing at the level of the raw data, do not account rationally for the relative reliability of different sensors, nor the redundancy often inherent in multiple sensors. Representative examples of such detection-level techniques include logical AND/OR operations on detections from individual sensors and majority vote methods. In this work, we exploit a statistical data model for the detection of mines and mine-like targets to compare and fuse multiple sensor channels. Our purpose is to quantify the amount of knowledge that each polarimetric or thermal channel supplies to the detection process. With this information, we can make reasonable decisions about the usefulness of each channel. We can use this information to improve the detection process, or we can use it to reduce the number of required channels.

A new fault diagnosis algorithm for AUV cooperative localization system

NASA Astrophysics Data System (ADS)

Shi, Hongyang; Miao, Zhiyong; Zhang, Yi

2017-10-01

Multiple AUVs cooperative localization as a new kind of underwater positioning technology, not only can improve the positioning accuracy, but also has many advantages the single AUV does not have. It is necessary to detect and isolate the fault to increase the reliability and availability of the AUVs cooperative localization system. In this paper, the Extended Multiple Model Adaptive Cubature Kalmam Filter (EMMACKF) method is presented to detect the fault. The sensor failures are simulated based on the off-line experimental data. Experimental results have shown that the faulty apparatus can be diagnosed effectively using the proposed method. Compared with Multiple Model Adaptive Extended Kalman Filter and Multi-Model Adaptive Unscented Kalman Filter, both accuracy and timelines have been improved to some extent.

A method for release and multiple strand amplification of small quantities of DNA from endospores of the fastidious bacterium Pasteuria penetrans.

PubMed

Mauchline, T H; Mohan, S; Davies, K G; Schaff, J E; Opperman, C H; Kerry, B R; Hirsch, P R

2010-05-01

To establish a reliable protocol to extract DNA from Pasteuria penetrans endospores for use as template in multiple strand amplification, thus providing sufficient material for genetic analyses. To develop a highly sensitive PCR-based diagnostic tool for P. penetrans. An optimized method to decontaminate endospores, release and purify DNA enabled multiple strand amplification. DNA purity was assessed by cloning and sequencing gyrB and 16S rRNA gene fragments obtained from PCR using generic primers. Samples indicated to be 100%P. penetrans by the gyrB assay were estimated at 46% using the 16S rRNA gene. No bias was detected on cloning and sequencing 12 housekeeping and sporulation gene fragments from amplified DNA. The detection limit by PCR with Pasteuria-specific 16S rRNA gene primers following multiple strand amplification of DNA extracted using the method was a single endospore. Generation of large quantities DNA will facilitate genomic sequencing of P. penetrans. Apparent differences in sample purity are explained by variations in 16S rRNA gene copy number in Eubacteria leading to exaggerated estimations of sample contamination. Detection of single endospores will facilitate investigations of P. penetrans molecular ecology. These methods will advance studies on P. penetrans and facilitate research on other obligate and fastidious micro-organisms where it is currently impractical to obtain DNA in sufficient quantity and quality.

Beyond Multiple Regression: Using Commonality Analysis to Better Understand R[superscript 2] Results

ERIC Educational Resources Information Center

Warne, Russell T.

2011-01-01

Multiple regression is one of the most common statistical methods used in quantitative educational research. Despite the versatility and easy interpretability of multiple regression, it has some shortcomings in the detection of suppressor variables and for somewhat arbitrarily assigning values to the structure coefficients of correlated…

Detection of Biological Pathogens Using Multiple Wireless Magnetoelastic Biosensors

NASA Astrophysics Data System (ADS)

Shen, Wen

A number of recent, high-profile incidences of food-borne illness spreading through the food supply and the use of anthrax by terrorists after the September 11, 2001 attacks have demonstrated the need for new technologies that can rapidly detect the presence of biological pathogens. A bevy of biosensors show excellent detection sensitivity and specificity. However, false positive and false negative signals remain one of the primary reasons that many of these newly developed biosensors have not found application in the marketplace. The research described in this dissertation focuses on developing a free-standing magnetoelastic based bio-sensing system using a pulse method. This method allows fast detection, eliminates the bias magnetic field that is necessary in current methods, makes the system more simply and suitable for in-field detection. This system has two pairs of transformer coils, where a measurement sensor and a control sensor can be put in each pair of coils. The control sensor is used to compensate for environmental variables. The effect of pulse power on the performance of the magnetoelastic sensors in the pulse system is studied. The system is found to have excellent stability, good detection repeatability when used with multiple sensors. This research has investigated and demonstrated a multiple sensors approach. Because it will involve the simultaneous measurement of many sensors, it will significantly reduce problems encountered with false positive indications. The positioning and interference of sensors are investigated. By adding a multi-channel structure to the pulse detection system, the effect of sensor interference is minimized. The result of the repeatability test shows that the standard deviation when measuring three 1 mm magnetoelastic sensors is around 500 Hz, which is smaller than the minimum requirement for actual spores/bacteria detection. Magnetoelastic sensors immobilized with JRB7 phages and E2 phages have been used to specifically detect Bacillus anthracis spores and Salmonella typhimurium bacteria. The real-time monitoring of the detection of B. anthracis spores in a flowing system was performed using 2 mm sensors and 1 mm sensors. The detection of S. typhimurium in air has been performed using the pulse based system with both single and grouped sensors. Because grouped sensor detection involves the simultaneous measurement of many sensors, statistical evaluation shows that it can significantly reduce problems encountered with false positive indications. This method has been implemented in an investigation of a method that allows direct detection of S. typhimurium on cantaloupe surfaces. It has been demonstrated that multiple E2 phage based magnetoelastic sensors are able to detect Salmonella directly on fresh cantaloupe surfaces. Confirmation of the spore or bacteria binding to the sensor surfaces was achieved through SEM study of the sensor surfaces.

Occupancy Estimation and Modeling : Inferring Patterns and Dynamics of Species Occurrence

USGS Publications Warehouse

MacKenzie, D.I.; Nichols, J.D.; Royle, J. Andrew; Pollock, K.H.; Bailey, L.L.; Hines, J.E.

2006-01-01

This is the first book to examine the latest methods in analyzing presence/absence data surveys. Using four classes of models (single-species, single-season; single-species, multiple season; multiple-species, single-season; and multiple-species, multiple-season), the authors discuss the practical sampling situation, present a likelihood-based model enabling direct estimation of the occupancy-related parameters while allowing for imperfect detectability, and make recommendations for designing studies using these models. It provides authoritative insights into the latest in estimation modeling; discusses multiple models which lay the groundwork for future study designs; addresses critical issues of imperfect detectibility and its effects on estimation; and explores the role of probability in estimating in detail.

An Efficient Silent Data Corruption Detection Method with Error-Feedback Control and Even Sampling for HPC Applications

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

Di, Sheng; Berrocal, Eduardo; Cappello, Franck

The silent data corruption (SDC) problem is attracting more and more attentions because it is expected to have a great impact on exascale HPC applications. SDC faults are hazardous in that they pass unnoticed by hardware and can lead to wrong computation results. In this work, we formulate SDC detection as a runtime one-step-ahead prediction method, leveraging multiple linear prediction methods in order to improve the detection results. The contributions are twofold: (1) we propose an error feedback control model that can reduce the prediction errors for different linear prediction methods, and (2) we propose a spatial-data-based even-sampling method tomore » minimize the detection overheads (including memory and computation cost). We implement our algorithms in the fault tolerance interface, a fault tolerance library with multiple checkpoint levels, such that users can conveniently protect their HPC applications against both SDC errors and fail-stop errors. We evaluate our approach by using large-scale traces from well-known, large-scale HPC applications, as well as by running those HPC applications on a real cluster environment. Experiments show that our error feedback control model can improve detection sensitivity by 34-189% for bit-flip memory errors injected with the bit positions in the range [20,30], without any degradation on detection accuracy. Furthermore, memory size can be reduced by 33% with our spatial-data even-sampling method, with only a slight and graceful degradation in the detection sensitivity.« less

A novel statistical method for quantitative comparison of multiple ChIP-seq datasets.

PubMed

Chen, Li; Wang, Chi; Qin, Zhaohui S; Wu, Hao

2015-06-15

ChIP-seq is a powerful technology to measure the protein binding or histone modification strength in the whole genome scale. Although there are a number of methods available for single ChIP-seq data analysis (e.g. 'peak detection'), rigorous statistical method for quantitative comparison of multiple ChIP-seq datasets with the considerations of data from control experiment, signal to noise ratios, biological variations and multiple-factor experimental designs is under-developed. In this work, we develop a statistical method to perform quantitative comparison of multiple ChIP-seq datasets and detect genomic regions showing differential protein binding or histone modification. We first detect peaks from all datasets and then union them to form a single set of candidate regions. The read counts from IP experiment at the candidate regions are assumed to follow Poisson distribution. The underlying Poisson rates are modeled as an experiment-specific function of artifacts and biological signals. We then obtain the estimated biological signals and compare them through the hypothesis testing procedure in a linear model framework. Simulations and real data analyses demonstrate that the proposed method provides more accurate and robust results compared with existing ones. An R software package ChIPComp is freely available at http://web1.sph.emory.edu/users/hwu30/software/ChIPComp.html. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Multiple-Symbol Noncoherent Decoding of Uncoded and Convolutionally Codes Continous Phase Modulation

NASA Technical Reports Server (NTRS)

Divsalar, D.; Raphaeli, D.

2000-01-01

Recently, a method for combined noncoherent detection and decoding of trellis-codes (noncoherent coded modulation) has been proposed, which can practically approach the performance of coherent detection.

Data fusion of multi-scale representations for structural damage detection

NASA Astrophysics Data System (ADS)

Guo, Tian; Xu, Zili

2018-01-01

Despite extensive researches into structural health monitoring (SHM) in the past decades, there are few methods that can detect multiple slight damage in noisy environments. Here, we introduce a new hybrid method that utilizes multi-scale space theory and data fusion approach for multiple damage detection in beams and plates. A cascade filtering approach provides multi-scale space for noisy mode shapes and filters the fluctuations caused by measurement noise. In multi-scale space, a series of amplification and data fusion algorithms are utilized to search the damage features across all possible scales. We verify the effectiveness of the method by numerical simulation using damaged beams and plates with various types of boundary conditions. Monte Carlo simulations are conducted to illustrate the effectiveness and noise immunity of the proposed method. The applicability is further validated via laboratory cases studies focusing on different damage scenarios. Both results demonstrate that the proposed method has a superior noise tolerant ability, as well as damage sensitivity, without knowing material properties or boundary conditions.

A Chemoenzymatic Histology Method for O-GlcNAc Detection.

PubMed

Aguilar, Aime Lopez; Hou, Xiaomeng; Wen, Liuqing; Wang, Peng G; Wu, Peng

2017-12-14

Modification of nuclear and cytoplasmic proteins by the addition or removal of O-GlcNAc dynamically impacts multiple biological processes. Here, we present the development of a chemoenzymatic histology method for the detection of O-GlcNAc in tissue specimens. We applied this method to screen murine organs, uncovering specific O-GlcNAc distribution patterns in different tissue structures. We then utilized our histology method for O-GlcNAc detection in human brain specimens from healthy donors and donors with Alzheimer's disease and found higher levels of O-GlcNAc in specimens from healthy donors. We also performed an analysis using a multiple cancer tissue array, uncovering different O-GlcNAc levels between healthy and cancerous tissues, as well as different O-GlcNAc cellular distributions within certain tissue specimens. This chemoenzymatic histology method therefore holds great potential for revealing the biology of O-GlcNAc in physiopathological processes. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Fast Radio Burst Search Method for VLBI Observation

NASA Astrophysics Data System (ADS)

Liu, Lei; Tong, Fengxian; Zheng, Weimin; Zhang, Juan; Tong, Li

2018-02-01

We introduce the cross-spectrum-based fast radio burst (FRB) search method for Very Long Baseline Interferometer (VLBI) observation. This method optimizes the fringe fitting scheme in geodetic VLBI data post-processing, which fully utilizes the cross-spectrum fringe phase information and therefore maximizes the power of single-pulse signals. Working with cross-spectrum greatly reduces the effect of radio frequency interference compared with using auto-power spectrum. Single-pulse detection confidence increases by cross-identifying detections from multiple baselines. By combining the power of multiple baselines, we may improve the detection sensitivity. Our method is similar to that of coherent beam forming, but without the computational expense to form a great number of beams to cover the whole field of view of our telescopes. The data processing pipeline designed for this method is easy to implement and parallelize, which can be deployed in various kinds of VLBI observations. In particular, we point out that VGOS observations are very suitable for FRB search.

Research on polarization imaging information parsing method

NASA Astrophysics Data System (ADS)

Yuan, Hongwu; Zhou, Pucheng; Wang, Xiaolong

2016-11-01

Polarization information parsing plays an important role in polarization imaging detection. This paper focus on the polarization information parsing method: Firstly, the general process of polarization information parsing is given, mainly including polarization image preprocessing, multiple polarization parameters calculation, polarization image fusion and polarization image tracking, etc.; And then the research achievements of the polarization information parsing method are presented, in terms of polarization image preprocessing, the polarization image registration method based on the maximum mutual information is designed. The experiment shows that this method can improve the precision of registration and be satisfied the need of polarization information parsing; In terms of multiple polarization parameters calculation, based on the omnidirectional polarization inversion model is built, a variety of polarization parameter images are obtained and the precision of inversion is to be improve obviously; In terms of polarization image fusion , using fuzzy integral and sparse representation, the multiple polarization parameters adaptive optimal fusion method is given, and the targets detection in complex scene is completed by using the clustering image segmentation algorithm based on fractal characters; In polarization image tracking, the average displacement polarization image characteristics of auxiliary particle filtering fusion tracking algorithm is put forward to achieve the smooth tracking of moving targets. Finally, the polarization information parsing method is applied to the polarization imaging detection of typical targets such as the camouflage target, the fog and latent fingerprints.

Testing biological liquid samples using modified m-line spectroscopy method

NASA Astrophysics Data System (ADS)

Augusciuk, Elzbieta; Rybiński, Grzegorz

2005-09-01

Non-chemical method of detection of sugar concentration in biological (animal and plant source) liquids has been investigated. Simplified set was build to show the easy way of carrying out the survey and to make easy to gather multiple measurements for error detecting and statistics. Method is suggested as easy and cheap alternative for chemical methods of measuring sugar concentration, but needing a lot effort to be made precise.

Statistical analysis of water-quality data containing multiple detection limits: S-language software for regression on order statistics

USGS Publications Warehouse

Lee, L.; Helsel, D.

2005-01-01

Trace contaminants in water, including metals and organics, often are measured at sufficiently low concentrations to be reported only as values below the instrument detection limit. Interpretation of these "less thans" is complicated when multiple detection limits occur. Statistical methods for multiply censored, or multiple-detection limit, datasets have been developed for medical and industrial statistics, and can be employed to estimate summary statistics or model the distributions of trace-level environmental data. We describe S-language-based software tools that perform robust linear regression on order statistics (ROS). The ROS method has been evaluated as one of the most reliable procedures for developing summary statistics of multiply censored data. It is applicable to any dataset that has 0 to 80% of its values censored. These tools are a part of a software library, or add-on package, for the R environment for statistical computing. This library can be used to generate ROS models and associated summary statistics, plot modeled distributions, and predict exceedance probabilities of water-quality standards. ?? 2005 Elsevier Ltd. All rights reserved.

Stepwise Analysis of Differential Item Functioning Based on Multiple-Group Partial Credit Model.

ERIC Educational Resources Information Center

Muraki, Eiji

1999-01-01

Extended an Item Response Theory (IRT) method for detection of differential item functioning to the partial credit model and applied the method to simulated data using a stepwise procedure. Then applied the stepwise DIF analysis based on the multiple-group partial credit model to writing trend data from the National Assessment of Educational…

Multiple-Frame Detection of Subpixel Targets in Thermal Image Sequences

NASA Technical Reports Server (NTRS)

Thompson, David R.; Kremens, Robert

2013-01-01

The new technology in this approach combines the subpixel detection information from multiple frames of a sequence to achieve a more sensitive detection result, using only the information found in the images themselves. It is taken as a constraint that the method is automated, robust, and computationally feasible for field networks with constrained computation and data rates. This precludes simply downloading a video stream for pixel-wise co-registration on the ground. It is also important that this method not require precise knowledge of sensor position or direction, because such information is often not available. It is also assumed that the scene in question is approximately planar, which is appropriate for a high-altitude airborne or orbital view.

Method of remote powering and detecting multiple UWB passive tags in an RFID system

DOEpatents

Dowla, Farid U [Castro Valley, CA; Nekoogar, Faranak [San Ramon, CA; Benzel, David M [Livermore, CA; Dallum, Gregory E [Livermore, CA; Spiridon, Alex [Palo Alto, CA

2012-05-29

A new Radio Frequency Identification (RFID), tracking, powering apparatus/system and method using coded Ultra-wideband (UWB) signaling is introduced. The proposed hardware and techniques disclosed herein utilize a plurality of passive UWB transponders in a field of an RFID-radar system. The radar system itself enables multiple passive tags to be remotely powered (activated) at about the same time frame via predetermined frequency UWB pulsed formats. Once such tags are in an activated state, an UWB radar transmits specific "interrogating codes" to put predetermined tags in an awakened status. Such predetermined tags can then communicate by a unique "response code" so as to be detected by an UWB system using radar methods.

Engineered nanoconstructs for the multiplexed and sensitive detection of high-risk pathogens

NASA Astrophysics Data System (ADS)

Seo, Youngmin; Kim, Ji-Eun; Jeong, Yoon; Lee, Kwan Hong; Hwang, Jangsun; Hong, Jongwook; Park, Hansoo; Choi, Jonghoon

2016-01-01

Many countries categorize the causative agents of severe infectious diseases as high-risk pathogens. Given their extreme infectivity and potential to be used as biological weapons, a rapid and sensitive method for detection of high-risk pathogens (e.g., Bacillus anthracis, Francisella tularensis, Yersinia pestis, and Vaccinia virus) is highly desirable. Here, we report the construction of a novel detection platform comprising two units: (1) magnetic beads separately conjugated with multiple capturing antibodies against four different high-risk pathogens for simple and rapid isolation, and (2) genetically engineered apoferritin nanoparticles conjugated with multiple quantum dots and detection antibodies against four different high-risk pathogens for signal amplification. For each high-risk pathogen, we demonstrated at least 10-fold increase in sensitivity compared to traditional lateral flow devices that utilize enzyme-based detection methods. Multiplexed detection of high-risk pathogens in a sample was also successful by using the nanoconstructs harboring the dye molecules with fluorescence at different wavelengths. We ultimately envision the use of this novel nanoprobe detection platform in future applications that require highly sensitive on-site detection of high-risk pathogens.

SIMULTANEOUS CONCENTRATION AND REAL-TIME DETECTION OF MULTIPLE CLASSES OF MICROBIAL PATHOGENS FROM DRINKING WATER

EPA Science Inventory

Key CCL viruses will be rapidly detected at low levels in water samples concentrated by a rapid HFUF or a new thin-sheet (TSM) electropositive filter adsorption-elution method and compared with the approved EPA method (1MDS VIRADEL). A unified and rapid virus concentration, n...

COMPARISON OF MEMBRANE FILTER, MULTIPLE-FERMENTATION-TUBE, AND PRESENCE-ABSENCE TECHNIQUES FOR DETECTING TOTAL COLIFORMS IN SMALL COMMUNITY WATER SYSTEMS

EPA Science Inventory

Methods for detecting total coliform bacteria in drinking water were compared using 1483 different drinking water samples from 15 small community water systems in Vermont and New Hampshire. The methods included the membrane filter (MF) technique, a ten tube fermentation tube tech...

Maritime Search and Rescue via Multiple Coordinated UAS

DTIC Science & Technology

2017-06-12

performed by a set of UAS. Our investigation covers the detection of multiple mobile objects by a heterogeneous collection of UAS. Three methods (two...account for contingencies such as airspace deconfliction. Results are produced using simulation to verify the capability of the proposed method and to...compare the various par- titioning methods . Results from this simulation show that great gains in search efficiency can be made when the search space is

Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits.

PubMed

Fu, Liya; Wang, You-Gan

2011-02-15

Environmental data usually include measurements, such as water quality data, which fall below detection limits, because of limitations of the instruments or of certain analytical methods used. The fact that some responses are not detected needs to be properly taken into account in statistical analysis of such data. However, it is well-known that it is challenging to analyze a data set with detection limits, and we often have to rely on the traditional parametric methods or simple imputation methods. Distributional assumptions can lead to biased inference and justification of distributions is often not possible when the data are correlated and there is a large proportion of data below detection limits. The extent of bias is usually unknown. To draw valid conclusions and hence provide useful advice for environmental management authorities, it is essential to develop and apply an appropriate statistical methodology. This paper proposes rank-based procedures for analyzing non-normally distributed data collected at different sites over a period of time in the presence of multiple detection limits. To take account of temporal correlations within each site, we propose an optimal linear combination of estimating functions and apply the induced smoothing method to reduce the computational burden. Finally, we apply the proposed method to the water quality data collected at Susquehanna River Basin in United States of America, which clearly demonstrates the advantages of the rank regression models.

Detection of Multiple Waterborne Pathogens Using Microsequencing Arrays

EPA Science Inventory

Aims: A microarray was developed to simultaneously detect Cryptosporidium parvum, Cryptosporidium hominis, Enterococcus faecium, Bacillus anthracis and Francisella tularensis in water. Methods and Results: A DNA microarray was designed to contain probes that specifically dete...

Rapid multiple immunofluorescent staining for the simultaneous detection of cytokeratin and vimentin in the cytology of canine tumors.

PubMed

Sawa, Mariko; Yabuki, Akira; Kohyama, Moeko; Miyoshi, Noriaki; Yamato, Osamu

2018-06-01

Immunocytochemistry (ICC) is utilized as an advanced technique in veterinary cytology. In tumor diagnosis, cytokeratin and vimentin are markers used to distinguish the origin of tumor cells. Standard enzyme-based ICC has limitations in clinical use; and therefore, more convenient and reliable methods are needed. The purpose of this study was to develop a rapid multiple immunofluorescent (RMIF) detection method for dual cytokeratin and vimentin staining on cytology slides in dogs. Air-dried smear samples from solid tumors and sediments of pleural effusions were prepared from dogs (n = 14) that were admitted to the Veterinary Teaching Hospital, Kagoshima University, Japan. Mouse monoclonal anti-human cytokeratin (AE1/AE3) and rabbit monoclonal anti-human vimentin (SP20) antibodies were used as primary antibodies, followed by staining with Alexa Fluor-conjugated secondary antibodies. Staining using the RMIF method was compared with enzyme-based ICC staining. Rapid multiple immunofluorescent immunostaining was clear and specific in the evaluated smears, whereas the enzyme-based ICC showed nonspecific signals. By using the RMIF staining method, epithelial cells, mesenchymal cells, and mesothelial cells could be classified on a single smear of a pleural effusion. In smears of lymph nodes with epithelial tumor metastases, the RMIF method successfully detected metastatic epithelial tumor cells. The RMIF method might be a useful tool for diagnostic cytology in veterinary medicine. © 2018 American Society for Veterinary Clinical Pathology.

LITERATURE REVIEW OF MOLECULAR METHODS FOR SIMULTANEOUS DETECTION OF PATHOGENS IN WATER

EPA Science Inventory

This literature search is a review of molecular technologies (qPCR, microarray, microfluidics and lab-on-a-chip) for simultaneous detection of multiple waterborne pathogens in order to understand the state of the technology. The search content focuses on: pathogen detection witho...

Improved Statistical Methods Enable Greater Sensitivity in Rhythm Detection for Genome-Wide Data

PubMed Central

Hutchison, Alan L.; Maienschein-Cline, Mark; Chiang, Andrew H.; Tabei, S. M. Ali; Gudjonson, Herman; Bahroos, Neil; Allada, Ravi; Dinner, Aaron R.

2015-01-01

Robust methods for identifying patterns of expression in genome-wide data are important for generating hypotheses regarding gene function. To this end, several analytic methods have been developed for detecting periodic patterns. We improve one such method, JTK_CYCLE, by explicitly calculating the null distribution such that it accounts for multiple hypothesis testing and by including non-sinusoidal reference waveforms. We term this method empirical JTK_CYCLE with asymmetry search, and we compare its performance to JTK_CYCLE with Bonferroni and Benjamini-Hochberg multiple hypothesis testing correction, as well as to five other methods: cyclohedron test, address reduction, stable persistence, ANOVA, and F24. We find that ANOVA, F24, and JTK_CYCLE consistently outperform the other three methods when data are limited and noisy; empirical JTK_CYCLE with asymmetry search gives the greatest sensitivity while controlling for the false discovery rate. Our analysis also provides insight into experimental design and we find that, for a fixed number of samples, better sensitivity and specificity are achieved with higher numbers of replicates than with higher sampling density. Application of the methods to detecting circadian rhythms in a metadataset of microarrays that quantify time-dependent gene expression in whole heads of Drosophila melanogaster reveals annotations that are enriched among genes with highly asymmetric waveforms. These include a wide range of oxidation reduction and metabolic genes, as well as genes with transcripts that have multiple splice forms. PMID:25793520

Detecting and removing multiplicative spatial bias in high-throughput screening technologies.

PubMed

Caraus, Iurie; Mazoure, Bogdan; Nadon, Robert; Makarenkov, Vladimir

2017-10-15

Considerable attention has been paid recently to improve data quality in high-throughput screening (HTS) and high-content screening (HCS) technologies widely used in drug development and chemical toxicity research. However, several environmentally- and procedurally-induced spatial biases in experimental HTS and HCS screens decrease measurement accuracy, leading to increased numbers of false positives and false negatives in hit selection. Although effective bias correction methods and software have been developed over the past decades, almost all of these tools have been designed to reduce the effect of additive bias only. Here, we address the case of multiplicative spatial bias. We introduce three new statistical methods meant to reduce multiplicative spatial bias in screening technologies. We assess the performance of the methods with synthetic and real data affected by multiplicative spatial bias, including comparisons with current bias correction methods. We also describe a wider data correction protocol that integrates methods for removing both assay and plate-specific spatial biases, which can be either additive or multiplicative. The methods for removing multiplicative spatial bias and the data correction protocol are effective in detecting and cleaning experimental data generated by screening technologies. As our protocol is of a general nature, it can be used by researchers analyzing current or next-generation high-throughput screens. The AssayCorrector program, implemented in R, is available on CRAN. makarenkov.vladimir@uqam.ca. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Detecting a Weak Association by Testing its Multiple Perturbations: a Data Mining Approach

NASA Astrophysics Data System (ADS)

Lo, Min-Tzu; Lee, Wen-Chung

2014-05-01

Many risk factors/interventions in epidemiologic/biomedical studies are of minuscule effects. To detect such weak associations, one needs a study with a very large sample size (the number of subjects, n). The n of a study can be increased but unfortunately only to an extent. Here, we propose a novel method which hinges on increasing sample size in a different direction-the total number of variables (p). We construct a p-based `multiple perturbation test', and conduct power calculations and computer simulations to show that it can achieve a very high power to detect weak associations when p can be made very large. As a demonstration, we apply the method to analyze a genome-wide association study on age-related macular degeneration and identify two novel genetic variants that are significantly associated with the disease. The p-based method may set a stage for a new paradigm of statistical tests.

High-resolution melting (HRM) re-analysis of a polyposis patients cohort reveals previously undetected heterozygous and mosaic APC gene mutations.

PubMed

Out, Astrid A; van Minderhout, Ivonne J H M; van der Stoep, Nienke; van Bommel, Lysette S R; Kluijt, Irma; Aalfs, Cora; Voorendt, Marsha; Vossen, Rolf H A M; Nielsen, Maartje; Vasen, Hans F A; Morreau, Hans; Devilee, Peter; Tops, Carli M J; Hes, Frederik J

2015-06-01

Familial adenomatous polyposis is most frequently caused by pathogenic variants in either the APC gene or the MUTYH gene. The detection rate of pathogenic variants depends on the severity of the phenotype and sensitivity of the screening method, including sensitivity for mosaic variants. For 171 patients with multiple colorectal polyps without previously detectable pathogenic variant, APC was reanalyzed in leukocyte DNA by one uniform technique: high-resolution melting (HRM) analysis. Serial dilution of heterozygous DNA resulted in a lowest detectable allelic fraction of 6% for the majority of variants. HRM analysis and subsequent sequencing detected pathogenic fully heterozygous APC variants in 10 (6%) of the patients and pathogenic mosaic variants in 2 (1%). All these variants were previously missed by various conventional scanning methods. In parallel, HRM APC scanning was applied to DNA isolated from polyp tissue of two additional patients with apparently sporadic polyposis and without detectable pathogenic APC variant in leukocyte DNA. In both patients a pathogenic mosaic APC variant was present in multiple polyps. The detection of pathogenic APC variants in 7% of the patients, including mosaics, illustrates the usefulness of a complete APC gene reanalysis of previously tested patients, by a supplementary scanning method. HRM is a sensitive and fast pre-screening method for reliable detection of heterozygous and mosaic variants, which can be applied to leukocyte and polyp derived DNA.

Damage detection of an in-service condensation pipeline joint

NASA Astrophysics Data System (ADS)

Briand, Julie; Rezaei, Davood; Taheri, Farid

2010-04-01

The early detection of damage in structural or mechanical systems is of vital importance. With early detection, the damage may be repaired before the integrity of the system is jeopardized, resulting in monetary losses, loss of life or limb, and environmental impacts. Among the various types of structural health monitoring techniques, vibration-based methods are of significant interest since the damage location does not need to be known beforehand, making it a more versatile approach. The non-destructive damage detection method used for the experiments herein is a novel vibration-based method which uses an index called the EMD Energy Damage Index, developed with the aim of providing improved qualitative results compared to those methods currently available. As part of an effort to establish the integrity and limitation of this novel damage detection method, field testing was completed on a mechanical pipe joint on a condensation line, located in the physical plant of Dalhousie University. Piezoceramic sensors, placed at various locations around the joint were used to monitor the free vibration of the pipe imposed through the use of an impulse hammer. Multiple damage progression scenarios were completed, each having a healthy state and multiple damage cases. Subsequently, the recorded signals from the healthy and damaged joint were processed through the EMD Energy Damage Index developed in-house in an effort to detect the inflicted damage. The proposed methodology successfully detected the inflicted damages. In this paper, the effects of impact location, sensor location, frequency bandwidth, intrinsic mode functions, and boundary conditions are discussed.

A feasibility study for long-path multiple detection using a neural network

NASA Technical Reports Server (NTRS)

Feuerbacher, G. A.; Moebes, T. A.

1994-01-01

Least-squares inverse filters have found widespread use in the deconvolution of seismograms and the removal of multiples. The use of least-squares prediction filters with prediction distances greater than unity leads to the method of predictive deconvolution which can be used for the removal of long path multiples. The predictive technique allows one to control the length of the desired output wavelet by control of the predictive distance, and hence to specify the desired degree of resolution. Events which are periodic within given repetition ranges can be attenuated selectively. The method is thus effective in the suppression of rather complex reverberation patterns. A back propagation(BP) neural network is constructed to perform the detection of first arrivals of the multiples and therefore aid in the more accurate determination of the predictive distance of the multiples. The neural detector is applied to synthetic reflection coefficients and synthetic seismic traces. The processing results show that the neural detector is accurate and should lead to an automated fast method for determining predictive distances across vast amounts of data such as seismic field records. The neural network system used in this study was the NASA Software Technology Branch's NETS system.

Estimating weak ratiometric signals in imaging data. II. Meta-analysis with multiple, dual-channel datasets.

PubMed

Sornborger, Andrew; Broder, Josef; Majumder, Anirban; Srinivasamoorthy, Ganesh; Porter, Erika; Reagin, Sean S; Keith, Charles; Lauderdale, James D

2008-09-01

Ratiometric fluorescent indicators are used for making quantitative measurements of a variety of physiological variables. Their utility is often limited by noise. This is the second in a series of papers describing statistical methods for denoising ratiometric data with the aim of obtaining improved quantitative estimates of variables of interest. Here, we outline a statistical optimization method that is designed for the analysis of ratiometric imaging data in which multiple measurements have been taken of systems responding to the same stimulation protocol. This method takes advantage of correlated information across multiple datasets for objectively detecting and estimating ratiometric signals. We demonstrate our method by showing results of its application on multiple, ratiometric calcium imaging experiments.

Multiple sound source localization using gammatone auditory filtering and direct sound componence detection

NASA Astrophysics Data System (ADS)

Chen, Huaiyu; Cao, Li

2017-06-01

In order to research multiple sound source localization with room reverberation and background noise, we analyze the shortcomings of traditional broadband MUSIC and ordinary auditory filtering based broadband MUSIC method, then a new broadband MUSIC algorithm with gammatone auditory filtering of frequency component selection control and detection of ascending segment of direct sound componence is proposed. The proposed algorithm controls frequency component within the interested frequency band in multichannel bandpass filter stage. Detecting the direct sound componence of the sound source for suppressing room reverberation interference is also proposed, whose merits are fast calculation and avoiding using more complex de-reverberation processing algorithm. Besides, the pseudo-spectrum of different frequency channels is weighted by their maximum amplitude for every speech frame. Through the simulation and real room reverberation environment experiments, the proposed method has good performance. Dynamic multiple sound source localization experimental results indicate that the average absolute error of azimuth estimated by the proposed algorithm is less and the histogram result has higher angle resolution.

Optimization of OT-MACH Filter Generation for Target Recognition

NASA Technical Reports Server (NTRS)

Johnson, Oliver C.; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

2009-01-01

An automatic Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter generator for use in a gray-scale optical correlator (GOC) has been developed for improved target detection at JPL. While the OT-MACH filter has been shown to be an optimal filter for target detection, actually solving for the optimum is too computationally intensive for multiple targets. Instead, an adaptive step gradient descent method was tested to iteratively optimize the three OT-MACH parameters, alpha, beta, and gamma. The feedback for the gradient descent method was a composite of the performance measures, correlation peak height and peak to side lobe ratio. The automated method generated and tested multiple filters in order to approach the optimal filter quicker and more reliably than the current manual method. Initial usage and testing has shown preliminary success at finding an approximation of the optimal filter, in terms of alpha, beta, gamma values. This corresponded to a substantial improvement in detection performance where the true positive rate increased for the same average false positives per image.

Evaluating the Aging of Multiple Emulsions Using Resonance-Enhanced Multiphoton Ionization Time-of-Flight Mass Spectrometry.

PubMed

Tsuda, Yukihiro; Uchimura, Tomohiro

2016-01-01

Resonance-enhanced multiphoton ionization time-of-flight mass spectrometry was applied to measurements of multiple emulsions with no pretreatment; a method for the quantitative evaluation of aging was proposed. We prepared water-in-oil-in-water (W/O/W) multiple emulsions containing toluene and m-phenylenediamine. The samples were measured immediately following both preparation and after having been stirred for 24 h. Time profiles of the peak areas for each analyte species were obtained, and several intense spikes for toluene could be detected from each sample after stirring, which suggests that the concentration of toluene in the middle phase had increased during stirring. On the other hand, in the case of a W/O/W multiple emulsion containing phenol and m-phenylenediamine, spikes for m-phenylenediamine, rather than phenol, were detected after stirring. In the present study, the time-profile data were converted into a scatter plot in order to quantitatively evaluate the aging. As a result, the ratio of the plots where strong signal intensities of toluene were detected increased from 8.4% before stirring to 33.2% after stirring for 24 h. The present method could be a powerful tool for evaluating multiple emulsions, such as studies on the kinetics of the encapsulation and release of active ingredients.

Robust lane detection and tracking using multiple visual cues under stochastic lane shape conditions

NASA Astrophysics Data System (ADS)

Huang, Zhi; Fan, Baozheng; Song, Xiaolin

2018-03-01

As one of the essential components of environment perception techniques for an intelligent vehicle, lane detection is confronted with challenges including robustness against the complicated disturbance and illumination, also adaptability to stochastic lane shapes. To overcome these issues, we proposed a robust lane detection method named classification-generation-growth-based (CGG) operator to the detected lines, whereby the linear lane markings are identified by synergizing multiple visual cues with the a priori knowledge and spatial-temporal information. According to the quality of linear lane fitting, the linear and linear-parabolic models are dynamically switched to describe the actual lane. The Kalman filter with adaptive noise covariance and the region of interests (ROI) tracking are applied to improve the robustness and efficiency. Experiments were conducted with images covering various challenging scenarios. The experimental results evaluate the effectiveness of the presented method for complicated disturbances, illumination, and stochastic lane shapes.

A Point Kinetics Model for Estimating Neutron Multiplication of Bare Uranium Metal in Tagged Neutron Measurements

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

Tweardy, Matthew C.; McConchie, Seth; Hayward, Jason P.

An extension of the point kinetics model is developed in this paper to describe the neutron multiplicity response of a bare uranium object under interrogation by an associated particle imaging deuterium-tritium (D-T) measurement system. This extended model is used to estimate the total neutron multiplication of the uranium. Both MCNPX-PoliMi simulations and data from active interrogation measurements of highly enriched and depleted uranium geometries are used to evaluate the potential of this method and to identify the sources of systematic error. The detection efficiency correction for measured coincidence response is identified as a large source of systematic error. If themore » detection process is not considered, results suggest that the method can estimate total multiplication to within 13% of the simulated value. Values for multiplicity constants in the point kinetics equations are sensitive to enrichment due to (n, xn) interactions by D-T neutrons and can introduce another significant source of systematic bias. This can theoretically be corrected if isotopic composition is known a priori. Finally, the spatial dependence of multiplication is also suspected of introducing further systematic bias for high multiplication uranium objects.« less

A Point Kinetics Model for Estimating Neutron Multiplication of Bare Uranium Metal in Tagged Neutron Measurements

DOE PAGES

Tweardy, Matthew C.; McConchie, Seth; Hayward, Jason P.

2017-06-13

An extension of the point kinetics model is developed in this paper to describe the neutron multiplicity response of a bare uranium object under interrogation by an associated particle imaging deuterium-tritium (D-T) measurement system. This extended model is used to estimate the total neutron multiplication of the uranium. Both MCNPX-PoliMi simulations and data from active interrogation measurements of highly enriched and depleted uranium geometries are used to evaluate the potential of this method and to identify the sources of systematic error. The detection efficiency correction for measured coincidence response is identified as a large source of systematic error. If themore » detection process is not considered, results suggest that the method can estimate total multiplication to within 13% of the simulated value. Values for multiplicity constants in the point kinetics equations are sensitive to enrichment due to (n, xn) interactions by D-T neutrons and can introduce another significant source of systematic bias. This can theoretically be corrected if isotopic composition is known a priori. Finally, the spatial dependence of multiplication is also suspected of introducing further systematic bias for high multiplication uranium objects.« less

Immunochemical Detection Methods for Gluten in Food Products: Where Do We Go from Here?

PubMed

Slot, I D Bruins; van der Fels-Klerx, H J; Bremer, M G E G; Hamer, R J

2016-11-17

Accurate and reliable quantification methods for gluten in food are necessary to ensure proper product labeling and thus safeguard the gluten sensitive consumer against exposure. Immunochemical detection is the method of choice, as it is sensitive, rapid and relatively easy to use. Although a wide range of detection kits are commercially available, there are still many difficulties in gluten detection that have not yet been overcome. This review gives an overview of the currently commercially available immunochemical detection methods, and discusses the problems that still exist in gluten detection in food. The largest problems are encountered in the extraction of gluten from food matrices, the choice of epitopes targeted by the detection method, and the use of a standardized reference material. By comparing the available techniques with the unmet needs in gluten detection, the possible benefit of a new multiplex immunoassay is investigated. This detection method would allow for the detection and quantification of multiple harmful gluten peptides at once and would, therefore, be a logical advancement in gluten detection in food.

An improved EMD method for modal identification and a combined static-dynamic method for damage detection

NASA Astrophysics Data System (ADS)

Yang, Jinping; Li, Peizhen; Yang, Youfa; Xu, Dian

2018-04-01

Empirical mode decomposition (EMD) is a highly adaptable signal processing method. However, the EMD approach has certain drawbacks, including distortions from end effects and mode mixing. In the present study, these two problems are addressed using an end extension method based on the support vector regression machine (SVRM) and a modal decomposition method based on the characteristics of the Hilbert transform. The algorithm includes two steps: using the SVRM, the time series data are extended at both endpoints to reduce the end effects, and then, a modified EMD method using the characteristics of the Hilbert transform is performed on the resulting signal to reduce mode mixing. A new combined static-dynamic method for identifying structural damage is presented. This method combines the static and dynamic information in an equilibrium equation that can be solved using the Moore-Penrose generalized matrix inverse. The combination method uses the differences in displacements of the structure with and without damage and variations in the modal force vector. Tests on a four-story, steel-frame structure were conducted to obtain static and dynamic responses of the structure. The modal parameters are identified using data from the dynamic tests and improved EMD method. The new method is shown to be more accurate and effective than the traditional EMD method. Through tests with a shear-type test frame, the higher performance of the proposed static-dynamic damage detection approach, which can detect both single and multiple damage locations and the degree of the damage, is demonstrated. For structures with multiple damage, the combined approach is more effective than either the static or dynamic method. The proposed EMD method and static-dynamic damage detection method offer improved modal identification and damage detection, respectively, in structures.

A consensus embedding approach for segmentation of high resolution in vivo prostate magnetic resonance imagery

NASA Astrophysics Data System (ADS)

Viswanath, Satish; Rosen, Mark; Madabhushi, Anant

2008-03-01

Current techniques for localization of prostatic adenocarcinoma (CaP) via blinded trans-rectal ultrasound biopsy are associated with a high false negative detection rate. While high resolution endorectal in vivo Magnetic Resonance (MR) prostate imaging has been shown to have improved contrast and resolution for CaP detection over ultrasound, similarity in intensity characteristics between benign and cancerous regions on MR images contribute to a high false positive detection rate. In this paper, we present a novel unsupervised segmentation method that employs manifold learning via consensus schemes for detection of cancerous regions from high resolution 1.5 Tesla (T) endorectal in vivo prostate MRI. A significant contribution of this paper is a method to combine multiple weak, lower-dimensional representations of high dimensional feature data in a way analogous to classifier ensemble schemes, and hence create a stable and accurate reduced dimensional representation. After correcting for MR image intensity artifacts, such as bias field inhomogeneity and intensity non-standardness, our algorithm extracts over 350 3D texture features at every spatial location in the MR scene at multiple scales and orientations. Non-linear dimensionality reduction schemes such as Locally Linear Embedding (LLE) and Graph Embedding (GE) are employed to create multiple low dimensional data representations of this high dimensional texture feature space. Our novel consensus embedding method is used to average object adjacencies from within the multiple low dimensional projections so that class relationships are preserved. Unsupervised consensus clustering is then used to partition the objects in this consensus embedding space into distinct classes. Quantitative evaluation on 18 1.5 T prostate MR data against corresponding histology obtained from the multi-site ACRIN trials show a sensitivity of 92.65% and a specificity of 82.06%, which suggests that our method is successfully able to detect suspicious regions in the prostate.

Methods for radiation detection and characterization using a multiple detector probe

DOEpatents

Akers, Douglas William; Roybal, Lyle Gene

2014-11-04

Apparatuses, methods, and systems relating to radiological characterization of environments are disclosed. Multi-detector probes with a plurality of detectors in a common housing may be used to substantially concurrently detect a plurality of different radiation activities and types. Multiple multi-detector probes may be used in a down-hole environment to substantially concurrently detect radioactive activity and contents of a buried waste container. Software may process, analyze, and integrate the data from the different multi-detector probes and the different detector types therein to provide source location and integrated analysis as to the source types and activity in the measured environment. Further, the integrated data may be used to compensate for differential density effects and the effects of radiation shielding materials within the volume being measured.

Estimate of within population incremental selection through branch imbalance in lineage trees

PubMed Central

Liberman, Gilad; Benichou, Jennifer I.C.; Maman, Yaakov; Glanville, Jacob; Alter, Idan; Louzoun, Yoram

2016-01-01

Incremental selection within a population, defined as limited fitness changes following mutation, is an important aspect of many evolutionary processes. Strongly advantageous or deleterious mutations are detected using the synonymous to non-synonymous mutations ratio. However, there are currently no precise methods to estimate incremental selection. We here provide for the first time such a detailed method and show its precision in multiple cases of micro-evolution. The proposed method is a novel mixed lineage tree/sequence based method to detect within population selection as defined by the effect of mutations on the average number of offspring. Specifically, we propose to measure the log of the ratio between the number of leaves in lineage trees branches following synonymous and non-synonymous mutations. The method requires a high enough number of sequences, and a large enough number of independent mutations. It assumes that all mutations are independent events. It does not require of a baseline model and is practically not affected by sampling biases. We show the method's wide applicability by testing it on multiple cases of micro-evolution. We show that it can detect genes and inter-genic regions using the selection rate and detect selection pressures in viral proteins and in the immune response to pathogens. PMID:26586802

System and method for determination of the reflection wavelength of multiple low-reflectivity bragg gratings in a sensing optical fiber

NASA Technical Reports Server (NTRS)

Moore, Jason P. (Inventor)

2009-01-01

A system and method for determining a reflection wavelength of multiple Bragg gratings in a sensing optical fiber comprise: (1) a source laser; (2) an optical detector configured to detect a reflected signal from the sensing optical fiber; (3) a plurality of frequency generators configured to generate a signal having a frequency corresponding to an interferometer frequency of a different one of the plurality of Bragg gratings; (4) a plurality of demodulation elements, each demodulation element configured to combine the signal produced by a different one of the plurality of frequency generators with the detected signal from the sensing optical fiber; (5) a plurality of peak detectors, each peak detector configured to detect a peak of the combined signal from a different one of the demodulation elements; and (6) a laser wavenumber detection element configured to determine a wavenumber of the laser when any of the peak detectors detects a peak.

Methods for detection of GMOs in food and feed.

PubMed

Marmiroli, Nelson; Maestri, Elena; Gullì, Mariolina; Malcevschi, Alessio; Peano, Clelia; Bordoni, Roberta; De Bellis, Gianluca

2008-10-01

This paper reviews aspects relevant to detection and quantification of genetically modified (GM) material within the feed/food chain. The GM crop regulatory framework at the international level is evaluated with reference to traceability and labelling. Current analytical methods for the detection, identification, and quantification of transgenic DNA in food and feed are reviewed. These methods include quantitative real-time PCR, multiplex PCR, and multiplex real-time PCR. Particular attention is paid to methods able to identify multiple GM events in a single reaction and to the development of microdevices and microsensors, though they have not been fully validated for application.

Breast cancer evaluation by fluorescent dot detection using combined mathematical morphology and multifractal techniques

PubMed Central

2011-01-01

Background Fluorescence in situ hybridization (FISH) is very accurate method for measuring HER2 gene copies, as a sign of potential breast cancer. This method requires small tissue samples, and has a high sensitivity to detect abnormalities from a histological section. By using multiple colors, this method allows the detection of multiple targets simultaneously. The target parts in the cells become visible as colored dots. The HER-2 probes are visible as orange stained spots under a fluorescent microscope while probes for centromere 17 (CEP-17), the chromosome on which the gene HER-2/neu is located, are visible as green spots. Methods The conventional analysis involves the scoring of the ratio of HER-2/neu over CEP 17 dots within each cell nucleus and then averaging the scores for a number of 60 cells. A ratio of 2.0 of HER-2/neu to CEP 17 copy number denotes amplification. Several methods have been proposed for the detection and automated evaluation (dot counting) of FISH signals. In this paper the combined method based on the mathematical morphology (MM) and inverse multifractal (IMF) analysis is suggested. Similar method was applied recently in detection of microcalcifications in digital mammograms, and was very successful. Results The combined MM using top-hat and bottom-hat filters, and the IMF method was applied to FISH images from Molecular Biology Lab, Department of Pathology, Wielkoposka Cancer Center, Poznan. Initial results indicate that this method can be applied to FISH images for the evaluation of HER2/neu status. Conclusions Mathematical morphology and multifractal approach are used for colored dot detection and counting in FISH images. Initial results derived on clinical cases are promising. Note that the overlapping of colored dots, particularly red/orange dots, needs additional improvements in post-processing. PMID:21489192

Comparative quantification of human intestinal bacteria based on cPCR and LDR/LCR

PubMed Central

Tang, Zhou-Rui; Li, Kai; Zhou, Yu-Xun; Xiao, Zhen-Xian; Xiao, Jun-Hua; Huang, Rui; Gu, Guo-Hao

2012-01-01

AIM: To establish a multiple detection method based on comparative polymerase chain reaction (cPCR) and ligase detection reaction (LDR)/ligase chain reaction (LCR) to quantify the intestinal bacterial components. METHODS: Comparative quantification of 16S rDNAs from different intestinal bacterial components was used to quantify multiple intestinal bacteria. The 16S rDNAs of different bacteria were amplified simultaneously by cPCR. The LDR/LCR was examined to actualize the genotyping and quantification. Two beneficial (Bifidobacterium, Lactobacillus) and three conditionally pathogenic bacteria (Enterococcus, Enterobacterium and Eubacterium) were used in this detection. With cloned standard bacterial 16S rDNAs, standard curves were prepared to validate the quantitative relations between the ratio of original concentrations of two templates and the ratio of the fluorescence signals of their final ligation products. The internal controls were added to monitor the whole detection flow. The quantity ratio between two bacteria was tested. RESULTS: cPCR and LDR revealed obvious linear correlations with standard DNAs, but cPCR and LCR did not. In the sample test, the distributions of the quantity ratio between each two bacterial species were obtained. There were significant differences among these distributions in the total samples. But these distributions of quantity ratio of each two bacteria remained stable among groups divided by age or sex. CONCLUSION: The detection method in this study can be used to conduct multiple intestinal bacteria genotyping and quantification, and to monitor the human intestinal health status as well. PMID:22294830

Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking.

PubMed

Bae, Seung-Hwan; Yoon, Kuk-Jin

2018-03-01

Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-the-arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking.

Automatic detection of multiple UXO-like targets using magnetic anomaly inversion and self-adaptive fuzzy c-means clustering

NASA Astrophysics Data System (ADS)

Yin, Gang; Zhang, Yingtang; Fan, Hongbo; Ren, Guoquan; Li, Zhining

2017-12-01

We have developed a method for automatically detecting UXO-like targets based on magnetic anomaly inversion and self-adaptive fuzzy c-means clustering. Magnetic anomaly inversion methods are used to estimate the initial locations of multiple UXO-like sources. Although these initial locations have some errors with respect to the real positions, they form dense clouds around the actual positions of the magnetic sources. Then we use the self-adaptive fuzzy c-means clustering algorithm to cluster these initial locations. The estimated number of cluster centroids represents the number of targets and the cluster centroids are regarded as the locations of magnetic targets. Effectiveness of the method has been demonstrated using synthetic datasets. Computational results show that the proposed method can be applied to the case of several UXO-like targets that are randomly scattered within in a confined, shallow subsurface, volume. A field test was carried out to test the validity of the proposed method and the experimental results show that the prearranged magnets can be detected unambiguously and located precisely.

Large-scale monitoring of shorebird populations using count data and N-mixture models: Black Oystercatcher (Haematopus bachmani) surveys by land and sea

USGS Publications Warehouse

Lyons, James E.; Andrew, Royle J.; Thomas, Susan M.; Elliott-Smith, Elise; Evenson, Joseph R.; Kelly, Elizabeth G.; Milner, Ruth L.; Nysewander, David R.; Andres, Brad A.

2012-01-01

Large-scale monitoring of bird populations is often based on count data collected across spatial scales that may include multiple physiographic regions and habitat types. Monitoring at large spatial scales may require multiple survey platforms (e.g., from boats and land when monitoring coastal species) and multiple survey methods. It becomes especially important to explicitly account for detection probability when analyzing count data that have been collected using multiple survey platforms or methods. We evaluated a new analytical framework, N-mixture models, to estimate actual abundance while accounting for multiple detection biases. During May 2006, we made repeated counts of Black Oystercatchers (Haematopus bachmani) from boats in the Puget Sound area of Washington (n = 55 sites) and from land along the coast of Oregon (n = 56 sites). We used a Bayesian analysis of N-mixture models to (1) assess detection probability as a function of environmental and survey covariates and (2) estimate total Black Oystercatcher abundance during the breeding season in the two regions. Probability of detecting individuals during boat-based surveys was 0.75 (95% credible interval: 0.42–0.91) and was not influenced by tidal stage. Detection probability from surveys conducted on foot was 0.68 (0.39–0.90); the latter was not influenced by fog, wind, or number of observers but was ~35% lower during rain. The estimated population size was 321 birds (262–511) in Washington and 311 (276–382) in Oregon. N-mixture models provide a flexible framework for modeling count data and covariates in large-scale bird monitoring programs designed to understand population change.

Automatic Selection of Multiple Images in the Frontier Field Clusters

NASA Astrophysics Data System (ADS)

Mahler, Guillaume; Richard, Johan; Patricio, Vera; Clément, Benjamin; Lagattuta, David

2015-08-01

Probing the central mass distribution of massive galaxy clusters is an important step towards mapping the overall distribution of their dark matter content. Thanks to gravitational lensing and the appearance of multiple images, we can constrain the inner region of galaxy clusters with a high precision. The Frontier Fields (FF) provide us with the deepest HST data ever in such clusters. Currently, most multiple-image systems are found by eye, yet in the FF, we expect hundreds to exist.Thus, In order to deal with such huge amounts of data, we need to method develop an automated detection method.I present a new tool to perform this task, MISE (Multiple Images SEarcher), a program which identifies multiple images by combining their specific properties. In particular, multiple images must: a) have similar colors, b) have similar surface brightnesses, and c) appear in locations predicted by a specific lensing configuration.I will describe the tuning and performances of MISE on both the FF clusters and the simulated clusters HERA and ARES. MISE allows us to not confirm multiple images identified visually, but also detect new multiple-image candidates in MACS0416 and A2744, giving us additional constraints on the mass distribution in these clusters. A spectroscopic follow-up of these candidates is currently underway with MUSE.

Iterative nonlinear joint transform correlation for the detection of objects in cluttered scenes

NASA Astrophysics Data System (ADS)

Haist, Tobias; Tiziani, Hans J.

1999-03-01

An iterative correlation technique with digital image processing in the feedback loop for the detection of small objects in cluttered scenes is proposed. A scanning aperture is combined with the method in order to improve the immunity against noise and clutter. Multiple reference objects or different views of one object are processed in parallel. We demonstrate the method by detecting a noisy and distorted face in a crowd with a nonlinear joint transform correlator.

A rapid method for the detection of foodborne pathogens by extraction of a trace amount of DNA from raw milk based on amino-modified silica-coated magnetic nanoparticles and polymerase chain reaction.

PubMed

Bai, Yalong; Song, Minghui; Cui, Yan; Shi, Chunlei; Wang, Dapeng; Paoli, George C; Shi, Xianming

2013-07-17

A method based on amino-modified silica-coated magnetic nanoparticles (ASMNPs) and polymerase chain reaction (PCR) was developed to rapidly and sensitively detect foodborne pathogens in raw milk. After optimizing parameters such as pH, temperature, and time, a trace amount of genomic DNA of pathogens could be extracted directly from complex matrices such as raw milk using ASMNPs. The magnetically separated complexes of genomic DNA and ASMNPs were directly subjected to single PCR (S-PCR) or multiplex PCR (M-PCR) to detect single or multiple pathogens from raw milk samples. Salmonella Enteritidis (Gram-negative) and Listeria monocytogenes (Gram-positive) were used as model organisms to artificially contaminate raw milk samples. After magnetic separation and S-PCR, the detection sensitivities were 8 CFU mL(-1) and 13 CFU mL(-1) respectively for these two types of pathogens. Furthermore, this method was successfully used to detect multiple pathogens (S. Enteritidis and L. monocytogenes) from artificially contaminated raw milk using M-PCR at sensitivities of 15 CFU mL(-1) and 25 CFU mL(-1), respectively. This method has great potential to rapidly and sensitively detect pathogens in raw milk or other complex food matrices. Copyright © 2013 Elsevier B.V. All rights reserved.

System and method for detecting components of a mixture including tooth elements for alignment

DOEpatents

Sommer, Gregory Jon; Schaff, Ulrich Y.

2016-11-22

Examples are described including assay platforms having tooth elements. An impinging element may sequentially engage tooth elements on the assay platform to sequentially align corresponding detection regions with a detection unit. In this manner, multiple measurements may be made of detection regions on the assay platform without necessarily requiring the starting and stopping of a motor.

Deep Whole-Genome Sequencing to Detect Mixed Infection of Mycobacterium tuberculosis

PubMed Central

Gan, Mingyu; Liu, Qingyun; Yang, Chongguang; Gao, Qian; Luo, Tao

2016-01-01

Mixed infection by multiple Mycobacterium tuberculosis (MTB) strains is associated with poor treatment outcome of tuberculosis (TB). Traditional genotyping methods have been used to detect mixed infections of MTB, however, their sensitivity and resolution are limited. Deep whole-genome sequencing (WGS) has been proved highly sensitive and discriminative for studying population heterogeneity of MTB. Here, we developed a phylogenetic-based method to detect MTB mixed infections using WGS data. We collected published WGS data of 782 global MTB strains from public database. We called homogeneous and heterogeneous single nucleotide variations (SNVs) of individual strains by mapping short reads to the ancestral MTB reference genome. We constructed a phylogenomic database based on 68,639 homogeneous SNVs of 652 MTB strains. Mixed infections were determined if multiple evolutionary paths were identified by mapping the SNVs of individual samples to the phylogenomic database. By simulation, our method could specifically detect mixed infections when the sequencing depth of minor strains was as low as 1× coverage, and when the genomic distance of two mixed strains was as small as 16 SNVs. By applying our methods to all 782 samples, we detected 47 mixed infections and 45 of them were caused by locally endemic strains. The results indicate that our method is highly sensitive and discriminative for identifying mixed infections from deep WGS data of MTB isolates. PMID:27391214

Methods to Detect Nitric Oxide and its Metabolites in Biological Samples

PubMed Central

Bryan, Nathan S.; Grisham, Matthew B.

2007-01-01

Nitric oxide (NO) methodology is a complex and often confusing science and the focus of many debates and discussion concerning NO biochemistry. NO is involved in many physiological processes including regulation of blood pressure, immune response and neural communication. Therefore its accurate detection and quantification is critical to understanding health and disease. Due to the extremely short physiological half life of this gaseous free radical, alternative strategies for the detection of reaction products of NO biochemistry have been developed. The quantification of NO metabolites in biological samples provides valuable information with regards to in vivo NO production, bioavailability and metabolism. Simply sampling a single compartment such as blood or plasma may not always provide an accurate assessment of whole body NO status, particularly in tissues. Therefore, extrapolation of plasma or blood NO status to specific tissues of interest is no longer a valid approach. As a result, methods continue to be developed and validated which allow the detection and quantification of NO and NO-related products/metabolites in multiple compartments of experimental animals in vivo. The methods described in this review is not an exhaustive or comprehensive discussion of all methods available for the detection of NO but rather a description of the most commonly used and practical methods which allow accurate and sensitive quantification of NO products/metabolites in multiple biological matrices under normal physiological conditions. PMID:17664129

Method and apparatus for simultaneous detection and measurement of charged particles at one or more levels of particle flux for analysis of same

DOEpatents

Denton, M Bonner [Tucson, AZ; Sperline, Roger , Koppenaal, David W. , Barinaga, Charles J. , Hieftje, Gary , Barnes, IV, James H.; Atlas, Eugene [Irvine, CA

2009-03-03

A charged particle detector and method are disclosed providing for simultaneous detection and measurement of charged particles at one or more levels of particle flux in a measurement cycle. The detector provides multiple and independently selectable levels of integration and/or gain in a fully addressable readout manner.

A real-time method for autonomous passive acoustic detection-classification of humpback whales.

PubMed

Abbot, Ted A; Premus, Vincent E; Abbot, Philip A

2010-05-01

This paper describes a method for real-time, autonomous, joint detection-classification of humpback whale vocalizations. The approach adapts the spectrogram correlation method used by Mellinger and Clark [J. Acoust. Soc. Am. 107, 3518-3529 (2000)] for bowhead whale endnote detection to the humpback whale problem. The objective is the implementation of a system to determine the presence or absence of humpback whales with passive acoustic methods and to perform this classification with low false alarm rate in real time. Multiple correlation kernels are used due to the diversity of humpback song. The approach also takes advantage of the fact that humpbacks tend to vocalize repeatedly for extended periods of time, and identification is declared only when multiple song units are detected within a fixed time interval. Humpback whale vocalizations from Alaska, Hawaii, and Stellwagen Bank were used to train the algorithm. It was then tested on independent data obtained off Kaena Point, Hawaii in February and March of 2009. Results show that the algorithm successfully classified humpback whales autonomously in real time, with a measured probability of correct classification in excess of 74% and a measured probability of false alarm below 1%.

Median Filtering Methods for Non-volcanic Tremor Detection

NASA Astrophysics Data System (ADS)

Damiao, L. G.; Nadeau, R. M.; Dreger, D. S.; Luna, B.; Zhang, H.

2016-12-01

Various properties of median filtering over time and space are used to address challenges posed by the Non-volcanic tremor detection problem. As part of a "Big-Data" effort to characterize the spatial and temporal distribution of ambient tremor throughout the Northern San Andreas Fault system, continuous seismic data from multiple seismic networks with contrasting operational characteristics and distributed over a variety of regions are being used. Automated median filtering methods that are flexible enough to work consistently with these data are required. Tremor is characterized by a low-amplitude, long-duration signal-train whose shape is coherent at multiple stations distributed over a large area. There are no consistent phase arrivals or mechanisms in a given tremor's signal and even the durations and shapes among different tremors vary considerably. A myriad of masquerading noise, anthropogenic and natural-event signals must also be discriminated in order to obtain accurate tremor detections. We present here results of the median methods applied to data from four regions of the San Andreas Fault system in northern California (Geysers Geothermal Field, Napa, Bitterwater and Parkfield) to illustrate the ability of the methods to detect tremor under diverse conditions.

Crack Damage Detection Method via Multiple Visual Features and Efficient Multi-Task Learning Model.

PubMed

Wang, Baoxian; Zhao, Weigang; Gao, Po; Zhang, Yufeng; Wang, Zhe

2018-06-02

This paper proposes an effective and efficient model for concrete crack detection. The presented work consists of two modules: multi-view image feature extraction and multi-task crack region detection. Specifically, multiple visual features (such as texture, edge, etc.) of image regions are calculated, which can suppress various background noises (such as illumination, pockmark, stripe, blurring, etc.). With the computed multiple visual features, a novel crack region detector is advocated using a multi-task learning framework, which involves restraining the variability for different crack region features and emphasizing the separability between crack region features and complex background ones. Furthermore, the extreme learning machine is utilized to construct this multi-task learning model, thereby leading to high computing efficiency and good generalization. Experimental results of the practical concrete images demonstrate that the developed algorithm can achieve favorable crack detection performance compared with traditional crack detectors.

Effect of processing on recovery and variability associated with immunochemical analytical methods for multiple allergens in a single matrix: sugar cookies.

PubMed

Khuda, Sefat; Slate, Andrew; Pereira, Marion; Al-Taher, Fadwa; Jackson, Lauren; Diaz-Amigo, Carmen; Bigley, Elmer C; Whitaker, Thomas; Williams, Kristina M

2012-05-02

Among the major food allergies, peanut, egg, and milk are the most common. The immunochemical detection of food allergens depends on various factors, such as the food matrix and processing method, which can affect allergen conformation and extractability. This study aimed to (1) develop matrix-specific incurred reference materials for allergen testing, (2) determine whether multiple allergens in the same model food can be simultaneously detected, and (3) establish the effect of processing on reference material stability and allergen detection. Defatted peanut flour, whole egg powder, and spray-dried milk were added to cookie dough at seven incurred levels before baking. Allergens were measured using five commercial enzyme-linked immunosorbent assay (ELISA) kits. All kits showed decreased recovery of all allergens after baking. Analytical coefficients of variation for most kits increased with baking time, but decreased with incurred allergen level. Thus, food processing negatively affects the recovery and variability of peanut, egg, and milk detection in a sugar cookie matrix when using immunochemical methods.

Detection of ingested nitromethane and reliable creatinine assessment using multiple common analytical methods.

PubMed

Murphy, Christine M; Devlin, John J; Beuhler, Michael C; Cheifetz, Paul; Maynard, Susan; Schwartz, Michael D; Kacinko, Sherri

2018-04-01

Nitromethane, found in fuels used for short distance racing, model cars, and model airplanes, produces a falsely elevated serum creatinine with standard creatinine analysis via the Jaffé method. Erroneous creatinine elevation often triggers extensive testing, leads to inaccurate diagnoses, and delayed or inappropriate medical interventions. Multiple reports in the literature identify "enzymatic assays" as an alternative method to detect the true value of creatinine, but this ambiguity does not help providers translate what type of enzymatic assay testing can be done in real time to determine if there is indeed false elevation. We report seven cases of ingested nitromethane where creatinine was determined via Beckman Coulter ® analyser using the Jaffé method, Vitros ® analyser, or i-Stat ® point-of-care testing. Nitromethane was detected and semi-quantified using a common clinical toxic alcohol analysis method, and quantified by headspace-gas chromatography-mass spectrometry. When creatinine was determined using i-Stat ® point-of-care testing or a Vitros ® analyser, levels were within the normal range. Comparatively, all initial creatinine levels obtained via the Jaffé method were elevated. Nitromethane concentrations ranged from 42 to 310 μg/mL. These cases demonstrate reliable assessment of creatinine through other enzymatic methods using a Vitros ® analyser or i-STAT ® . Additionally, nitromethane is detectable and quantifiable using routine alcohols gas chromatography analysis and by headspace-gas chromatography-mass spectrometry.

Development of a Flow Cytometry-Based Method for Rapid Detection of Escherichia coli and Shigella Spp. Using an Oligonucleotide Probe

PubMed Central

Xue, Yong; Wilkes, Jon G.; Moskal, Ted J.; Williams, Anna J.; Cooper, Willie M.; Nayak, Rajesh; Rafii, Fatemeh; Buzatu, Dan A.

2016-01-01

Standard methods to detect Escherichia coli contamination in food use the polymerase chain reaction (PCR) and agar culture plates. These methods require multiple incubation steps and take a long time to results. An improved rapid flow-cytometry based detection method was developed, using a fluorescence-labeled oligonucleotide probe specifically binding a16S rRNA sequence. The method positively detected 51 E. coli isolates as well as 4 Shigella species. All 27 non-E. coli strains tested gave negative results. Comparison of the new genetic assay with a total plate count (TPC) assay and agar plate counting indicated similar sensitivity, agreement between cytometry cell and colony counts. This method can detect a small number of E.coli cells in the presence of large numbers of other bacteria. This method can be used for rapid, economical, and stable detection of E. coli and Shigella contamination in the food industry and other contexts. PMID:26913737

Development of a Flow Cytometry-Based Method for Rapid Detection of Escherichia coli and Shigella Spp. Using an Oligonucleotide Probe.

PubMed

Xue, Yong; Wilkes, Jon G; Moskal, Ted J; Williams, Anna J; Cooper, Willie M; Nayak, Rajesh; Rafii, Fatemeh; Buzatu, Dan A

2016-01-01

Standard methods to detect Escherichia coli contamination in food use the polymerase chain reaction (PCR) and agar culture plates. These methods require multiple incubation steps and take a long time to results. An improved rapid flow-cytometry based detection method was developed, using a fluorescence-labeled oligonucleotide probe specifically binding a16S rRNA sequence. The method positively detected 51 E. coli isolates as well as 4 Shigella species. All 27 non-E. coli strains tested gave negative results. Comparison of the new genetic assay with a total plate count (TPC) assay and agar plate counting indicated similar sensitivity, agreement between cytometry cell and colony counts. This method can detect a small number of E.coli cells in the presence of large numbers of other bacteria. This method can be used for rapid, economical, and stable detection of E. coli and Shigella contamination in the food industry and other contexts.

Statistical methods for detecting and comparing periodic data and their application to the nycthemeral rhythm of bodily harm: A population based study

PubMed Central

2010-01-01

Background Animals, including humans, exhibit a variety of biological rhythms. This article describes a method for the detection and simultaneous comparison of multiple nycthemeral rhythms. Methods A statistical method for detecting periodic patterns in time-related data via harmonic regression is described. The method is particularly capable of detecting nycthemeral rhythms in medical data. Additionally a method for simultaneously comparing two or more periodic patterns is described, which derives from the analysis of variance (ANOVA). This method statistically confirms or rejects equality of periodic patterns. Mathematical descriptions of the detecting method and the comparing method are displayed. Results Nycthemeral rhythms of incidents of bodily harm in Middle Franconia are analyzed in order to demonstrate both methods. Every day of the week showed a significant nycthemeral rhythm of bodily harm. These seven patterns of the week were compared to each other revealing only two different nycthemeral rhythms, one for Friday and Saturday and one for the other weekdays. PMID:21059197

Privacy protection in surveillance systems based on JPEG DCT baseline compression and spectral domain watermarking

NASA Astrophysics Data System (ADS)

Sablik, Thomas; Velten, Jörg; Kummert, Anton

2015-03-01

An novel system for automatic privacy protection in digital media based on spectral domain watermarking and JPEG compression is described in the present paper. In a first step private areas are detected. Therefore a detection method is presented. The implemented method uses Haar cascades to detects faces. Integral images are used to speed up calculations and the detection. Multiple detections of one face are combined. Succeeding steps comprise embedding the data into the image as part of JPEG compression using spectral domain methods and protecting the area of privacy. The embedding process is integrated into and adapted to JPEG compression. A Spread Spectrum Watermarking method is used to embed the size and position of the private areas into the cover image. Different methods for embedding regarding their robustness are compared. Moreover the performance of the method concerning tampered images is presented.

VIRUS ISOLATION AND MOLECULAR DETECTION OF BLUETONGUE AND EPIZOOTIC HEMORRHAGIC DISEASE VIRUSES FROM NATURALLY INFECTED WHITE-TAILED DEER (ODOCOILEUS VIRGINIANUS).

PubMed

Kienzle, Clara; Poulson, Rebecca L; Ruder, Mark G; Stallknecht, David E

2017-10-01

Hemorrhagic disease in North America is caused by multiple serotypes of epizootic hemorrhagic disease virus (EHDV) and bluetongue virus (BTV). Diagnostic tests for detection of EHDV and BTV include virus isolation (VI), reverse transcriptase (RT)-PCR, and real-time RT-PCR (rRT-PCR). Our objective was to compare the diagnostic capabilities of three rRT-PCR protocols for detection of EHDV and BTV from naturally infected white-tailed deer (Odocoileus virginianus). We compared the effectiveness of these assays to traditional viral detection methods (e.g., VI) for historic and current clinical cases. Because of the variable nature of tissue collection and storage before diagnostic testing, an evaluation of viral persistence on multiple freeze-thaw events was also conducted. Two of the rRT-PCR assays provided for reliable detection of EHDV and BTV from 100% of clinically affected and VI-confirmed infected animals. Additionally, no significant change in viral titer was observed on multiple freeze-thaw events.

Liquid chromatography-electrospray ionization tandem mass spectrometry and dynamic multiple reaction monitoring method for determining multiple pesticide residues in tomato.

PubMed

Andrade, G C R M; Monteiro, S H; Francisco, J G; Figueiredo, L A; Botelho, R G; Tornisielo, V L

2015-05-15

A quick and sensitive liquid chromatography-electrospray ionization tandem mass spectrometry method, using dynamic multiple reaction monitoring and a 1.8-μm particle size analytical column, was developed to determine 57 pesticides in tomato in a 13-min run. QuEChERS (quick, easy, cheap, effective, rugged, and safe) method for samples preparations and validations was carried out in compliance with EU SANCO guidelines. The method was applied to 58 tomato samples. More than 84% of the compounds investigated showed limits of detection equal to or lower than 5 mg kg(-1). A mild (<20%), medium (20-50%), and strong (>50%) matrix effect was observed for 72%, 25%, and 3% of the pesticides studied, respectively. Eighty-one percent of the pesticides showed recoveries ranging between 70% and 120%. Twelve pesticides were detected in 35 samples, all below the maximum residue levels permitted in the Brazilian legislation; 15 samples exceeded the maximum residue levels established by the EU legislation for methamidophos; and 10 exceeded limits for acephate and four for bromuconazole. Copyright © 2014 Elsevier Ltd. All rights reserved.

Contact-free heart rate measurement using multiple video data

NASA Astrophysics Data System (ADS)

Hung, Pang-Chan; Lee, Kual-Zheng; Tsai, Luo-Wei

2013-10-01

In this paper, we propose a contact-free heart rate measurement method by analyzing sequential images of multiple video data. In the proposed method, skin-like pixels are firstly detected from multiple video data for extracting the color features. These color features are synchronized and analyzed by independent component analysis. A representative component is finally selected among these independent component candidates to measure the HR, which achieves under 2% deviation on average compared with a pulse oximeter in the controllable environment. The advantages of the proposed method include: 1) it uses low cost and high accessibility camera device; 2) it eases users' discomfort by utilizing contact-free measurement; and 3) it achieves the low error rate and the high stability by integrating multiple video data.

Electronic nose for detecting multiple targets

NASA Astrophysics Data System (ADS)

Chakraborty, Anirban; Parthasarathi, Ganga; Poddar, Rakesh; Zhao, Weiqiang; Luo, Cheng

2006-05-01

The discovery of high conductivity in doped polyacetylene in 1977 (garnering the 2000 Nobel Prize in Chemistry for the three discovering scientists) has attracted considerable interest in the application of polymers as the semiconducting and conducting materials due to their promising potential to replace silicon and metals in building devices. Previous and current efforts in developing conducting polymer microsystems mainly focus on generating a device of a single function. When multiple micropatterns made of different conducting polymers are produced on the same substrate, many microsystems of multiple functions can be envisioned. For example, analogous to the mammalian olfactory system which includes over 1,000 receptor genes in detecting various odors (e.g., beer, soda etc.), a sensor consisting of multiple distinct conducting polymer sensing elements will be capable of detecting a number of analytes simultaneously. However, existing techniques present significant technical challenges of degradation, low throughput, low resolution, depth of field, and/or residual layer in producing conducting polymer microstructures. To circumvent these challenges, an intermediate-layer lithography method developed in our group is used to generate multiple micropatterns made of different, commonly used conducting polymers, Polypyrrole (PPy), Poly(3,4-ethylenedioxy)thiophene (PEDOT) and Polyaniline (PANI). The generated multiple micropatterns are further used in an "electronic nose" to detect water vapor, glucose, toluene and acetone.

Fault detection and isolation for complex system

NASA Astrophysics Data System (ADS)

Jing, Chan Shi; Bayuaji, Luhur; Samad, R.; Mustafa, M.; Abdullah, N. R. H.; Zain, Z. M.; Pebrianti, Dwi

2017-07-01

Fault Detection and Isolation (FDI) is a method to monitor, identify, and pinpoint the type and location of system fault in a complex multiple input multiple output (MIMO) non-linear system. A two wheel robot is used as a complex system in this study. The aim of the research is to construct and design a Fault Detection and Isolation algorithm. The proposed method for the fault identification is using hybrid technique that combines Kalman filter and Artificial Neural Network (ANN). The Kalman filter is able to recognize the data from the sensors of the system and indicate the fault of the system in the sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. Additionally, Artificial Neural Network (ANN) is another algorithm used to determine the type of fault and isolate the fault in the system.

Edge detection, cosmic strings and the south pole telescope

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

Stewart, Andrew; Brandenberger, Robert, E-mail: stewarta@physics.mcgill.ca, E-mail: rhb@physics.mcgill.ca

2009-02-15

We develop a method of constraining the cosmic string tension G{mu} which uses the Canny edge detection algorithm as a means of searching CMB temperature maps for the signature of the Kaiser-Stebbins effect. We test the potential of this method using high resolution, simulated CMB temperature maps. By modeling the future output from the South Pole Telescope project (including anticipated instrumental noise), we find that cosmic strings with G{mu} > 5.5 Multiplication-Sign 10{sup -8} could be detected.

A Comparison of the Sensitivity and Fecal Egg Counts of the McMaster Egg Counting and Kato-Katz Thick Smear Methods for Soil-Transmitted Helminths

PubMed Central

Levecke, Bruno; Behnke, Jerzy M.; Ajjampur, Sitara S. R.; Albonico, Marco; Ame, Shaali M.; Charlier, Johannes; Geiger, Stefan M.; Hoa, Nguyen T. V.; Kamwa Ngassam, Romuald I.; Kotze, Andrew C.; McCarthy, James S.; Montresor, Antonio; Periago, Maria V.; Roy, Sheela; Tchuem Tchuenté, Louis-Albert; Thach, D. T. C.; Vercruysse, Jozef

2011-01-01

Background The Kato-Katz thick smear (Kato-Katz) is the diagnostic method recommended for monitoring large-scale treatment programs implemented for the control of soil-transmitted helminths (STH) in public health, yet it is difficult to standardize. A promising alternative is the McMaster egg counting method (McMaster), commonly used in veterinary parasitology, but rarely so for the detection of STH in human stool. Methodology/Principal Findings The Kato-Katz and McMaster methods were compared for the detection of STH in 1,543 subjects resident in five countries across Africa, Asia and South America. The consistency of the performance of both methods in different trials, the validity of the fixed multiplication factor employed in the Kato-Katz method and the accuracy of these methods for estimating ‘true’ drug efficacies were assessed. The Kato-Katz method detected significantly more Ascaris lumbricoides infections (88.1% vs. 75.6%, p<0.001), whereas the difference in sensitivity between the two methods was non-significant for hookworm (78.3% vs. 72.4%) and Trichuris trichiura (82.6% vs. 80.3%). The sensitivity of the methods varied significantly across trials and magnitude of fecal egg counts (FEC). Quantitative comparison revealed a significant correlation (Rs >0.32) in FEC between both methods, and indicated no significant difference in FEC, except for A. lumbricoides, where the Kato-Katz resulted in significantly higher FEC (14,197 eggs per gram of stool (EPG) vs. 5,982 EPG). For the Kato-Katz, the fixed multiplication factor resulted in significantly higher FEC than the multiplication factor adjusted for mass of feces examined for A. lumbricoides (16,538 EPG vs. 15,396 EPG) and T. trichiura (1,490 EPG vs. 1,363 EPG), but not for hookworm. The McMaster provided more accurate efficacy results (absolute difference to ‘true’ drug efficacy: 1.7% vs. 4.5%). Conclusions/Significance The McMaster is an alternative method for monitoring large-scale treatment programs. It is a robust (accurate multiplication factor) and accurate (reliable efficacy results) method, which can be easily standardized. PMID:21695104

Three-dimensional, position-sensitive radiation detection

DOEpatents

He, Zhong; Zhang, Feng

2010-04-06

Disclosed herein is a method of determining a characteristic of radiation detected by a radiation detector via a multiple-pixel event having a plurality of radiation interactions. The method includes determining a cathode-to-anode signal ratio for a selected interaction of the plurality of radiation interactions based on electron drift time data for the selected interaction, and determining the radiation characteristic for the multiple-pixel event based on both the cathode-to-anode signal ratio and the electron drift time data. In some embodiments, the method further includes determining a correction factor for the radiation characteristic based on an interaction depth of the plurality of radiation interactions, a lateral distance between the selected interaction and a further interaction of the plurality of radiation interactions, and the lateral positioning of the plurality of radiation interactions.

Develop guidelines for new vehicle detectors at high-speed signalized intersections : project summary.

DOT National Transportation Integrated Search

2017-01-01

The traditional vehicle detection method that has been used by the Texas Department of Transportation (TxDOT) on high-speed signalized intersection approaches for many years involved multiple detection points, with inductive loops being the early fav...

Development of In Vivo Biomarkers for Progressive Tau Pathology after Traumatic Brain Injury

DTIC Science & Technology

2015-02-01

distribution unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Athletes in contact sports who have sustained multiple concussive traumatic brain...who have sustained multiple concussive traumatic brain injuries 15-17 may also be at risk for this condition. Currently, there are no methods to...repetitive concussive TBI in mice has been optimal. Ongoing efforts include development of more sensitive methods to detect tau, and combinations of

Adaptive Automation for Human Supervision of Multiple Uninhabited Vehicles: Effects on Change Detection, Situation Awareness, and Mental Workload

DTIC Science & Technology

2009-01-01

transient was present. BASELINE EXPERIMENT Methods Participants Sixteen young adults (9 women , 7 men ) aged 18–26 years (mean = 20.5) partic- ipated...Sixteen young adults (8 women , 8 men ) aged 18–28 years (mean = 21.9) partici- pated. The experiment lasted approximately 2 hours and participants were...based on the operator’s change detection performance. Mis- sion scenarios involved supervision of multiple UVs and required multitasking . Effects of

Joint detection and localization of multiple anatomical landmarks through learning

NASA Astrophysics Data System (ADS)

Dikmen, Mert; Zhan, Yiqiang; Zhou, Xiang Sean

2008-03-01

Reliable landmark detection in medical images provides the essential groundwork for successful automation of various open problems such as localization, segmentation, and registration of anatomical structures. In this paper, we present a learning-based system to jointly detect (is it there?) and localize (where?) multiple anatomical landmarks in medical images. The contributions of this work exist in two aspects. First, this method takes the advantage from the learning scenario that is able to automatically extract the most distinctive features for multi-landmark detection. Therefore, it is easily adaptable to detect arbitrary landmarks in various kinds of imaging modalities, e.g., CT, MRI and PET. Second, the use of multi-class/cascaded classifier architecture in different phases of the detection stage combined with robust features that are highly efficient in terms of computation time enables a seemingly real time performance, with very high localization accuracy. This method is validated on CT scans of different body sections, e.g., whole body scans, chest scans and abdominal scans. Aside from improved robustness (due to the exploitation of spatial correlations), it gains a run time efficiency in landmark detection. It also shows good scalability performance under increasing number of landmarks.

On-line bolt-loosening detection method of key components of running trains using binocular vision

NASA Astrophysics Data System (ADS)

Xie, Yanxia; Sun, Junhua

2017-11-01

Bolt loosening, as one of hidden faults, affects the running quality of trains and even causes serious safety accidents. However, the developed fault detection approaches based on two-dimensional images cannot detect bolt-loosening due to lack of depth information. Therefore, we propose a novel online bolt-loosening detection method using binocular vision. Firstly, the target detection model based on convolutional neural network (CNN) is used to locate the target regions. And then, stereo matching and three-dimensional reconstruction are performed to detect bolt-loosening faults. The experimental results show that the looseness of multiple bolts can be characterized by the method simultaneously. The measurement repeatability and precision are less than 0.03mm, 0.09mm respectively, and its relative error is controlled within 1.09%.

Use of ultrasonic array method for positioning multiple partial discharge sources in transformer oil.

PubMed

Xie, Qing; Tao, Junhan; Wang, Yongqiang; Geng, Jianghai; Cheng, Shuyi; Lü, Fangcheng

2014-08-01

Fast and accurate positioning of partial discharge (PD) sources in transformer oil is very important for the safe, stable operation of power systems because it allows timely elimination of insulation faults. There is usually more than one PD source once an insulation fault occurs in the transformer oil. This study, which has both theoretical and practical significance, proposes a method of identifying multiple PD sources in the transformer oil. The method combines the two-sided correlation transformation algorithm in the broadband signal focusing and the modified Gerschgorin disk estimator. The method of classification of multiple signals is used to determine the directions of arrival of signals from multiple PD sources. The ultrasonic array positioning method is based on the multi-platform direction finding and the global optimization searching. Both the 4 × 4 square planar ultrasonic sensor array and the ultrasonic array detection platform are built to test the method of identifying and positioning multiple PD sources. The obtained results verify the validity and the engineering practicability of this method.

Method for registration of solar cosmic rays by detecting neutrons

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

Andreev, A. V.; Mordovskoy, M. V., E-mail: mvmordovsk@mail.ru; Skorkin, V. M.

2016-12-15

We consider a method of detecting the ionizing component of solar cosmic rays (SCRs) with energy from tens of MeV to tens of GeV by measuring the energy loss of SCR protons and light nuclei in scintillators and the multiplicity of the local neutron generation in a converter. Scintillation detectors based on stilbene, lithium glass, and solid-state photomultiplier tubes are capable of detecting fast neutrons with a temporal resolution of 10 ns and rejecting the gamma-ray background in the measuring system. The method will allow investigating the nucleon components of primary SCRs in circumterrestrial space.

Viral Diagnostics in Plants Using Next Generation Sequencing: Computational Analysis in Practice.

PubMed

Jones, Susan; Baizan-Edge, Amanda; MacFarlane, Stuart; Torrance, Lesley

2017-01-01

Viruses cause significant yield and quality losses in a wide variety of cultivated crops. Hence, the detection and identification of viruses is a crucial facet of successful crop production and of great significance in terms of world food security. Whilst the adoption of molecular techniques such as RT-PCR has increased the speed and accuracy of viral diagnostics, such techniques only allow the detection of known viruses, i.e., each test is specific to one or a small number of related viruses. Therefore, unknown viruses can be missed and testing can be slow and expensive if molecular tests are unavailable. Methods for simultaneous detection of multiple viruses have been developed, and (NGS) is now a principal focus of this area, as it enables unbiased and hypothesis-free testing of plant samples. The development of NGS protocols capable of detecting multiple known and emergent viruses present in infected material is proving to be a major advance for crops, nuclear stocks or imported plants and germplasm, in which disease symptoms are absent, unspecific or only triggered by multiple viruses. Researchers want to answer the question "how many different viruses are present in this crop plant?" without knowing what they are looking for: RNA-sequencing (RNA-seq) of plant material allows this question to be addressed. As well as needing efficient nucleic acid extraction and enrichment protocols, virus detection using RNA-seq requires fast and robust bioinformatics methods to enable host sequence removal and virus classification. In this review recent studies that use RNA-seq for virus detection in a variety of crop plants are discussed with specific emphasis on the computational methods implemented. The main features of a number of specific bioinformatics workflows developed for virus detection from NGS data are also outlined and possible reasons why these have not yet been widely adopted are discussed. The review concludes by discussing the future directions of this field, including the use of bioinformatics tools for virus detection deployed in analytical environments using cloud computing.

Development and evaluation of modified envelope correlation method for deep tectonic tremor

NASA Astrophysics Data System (ADS)

Mizuno, N.; Ide, S.

2017-12-01

We develop a new location method for deep tectonic tremors, as an improvement of widely used envelope correlation method, and applied it to construct a tremor catalog in western Japan. Using the cross-correlation functions as objective functions and weighting components of data by the inverse of error variances, the envelope cross-correlation method is redefined as a maximum likelihood method. This method is also capable of multiple source detection, because when several events occur almost simultaneously, they appear as local maxima of likelihood.The average of weighted cross-correlation functions, defined as ACC, is a nonlinear function whose variable is a position of deep tectonic tremor. The optimization method has two steps. First, we fix the source depth to 30 km and use a grid search with 0.2 degree intervals to find the maxima of ACC, which are candidate event locations. Then, using each of the candidate locations as initial values, we apply a gradient method to determine horizontal and vertical components of a hypocenter. Sometimes, several source locations are determined in a time window of 5 minutes. We estimate the resolution, which is defined as a distance of sources to be detected separately by the location method, is about 100 km. The validity of this estimation is confirmed by a numerical test using synthetic waveforms. Applying to continuous seismograms in western Japan for over 10 years, the new method detected 27% more tremors than a previous method, owing to the multiple detection and improvement of accuracy by appropriate weighting scheme.

Approach to explosive hazard detection using sensor fusion and multiple kernel learning with downward-looking GPR and EMI sensor data

NASA Astrophysics Data System (ADS)

Pinar, Anthony; Masarik, Matthew; Havens, Timothy C.; Burns, Joseph; Thelen, Brian; Becker, John

2015-05-01

This paper explores the effectiveness of an anomaly detection algorithm for downward-looking ground penetrating radar (GPR) and electromagnetic inductance (EMI) data. Threat detection with GPR is challenged by high responses to non-target/clutter objects, leading to a large number of false alarms (FAs), and since the responses of target and clutter signatures are so similar, classifier design is not trivial. We suggest a method based on a Run Packing (RP) algorithm to fuse GPR and EMI data into a composite confidence map to improve detection as measured by the area-under-ROC (NAUC) metric. We examine the value of a multiple kernel learning (MKL) support vector machine (SVM) classifier using image features such as histogram of oriented gradients (HOG), local binary patterns (LBP), and local statistics. Experimental results on government furnished data show that use of our proposed fusion and classification methods improves the NAUC when compared with the results from individual sensors and a single kernel SVM classifier.

A phantom design for assessment of detectability in PET imaging

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

Wollenweber, Scott D., E-mail: scott.wollenweber@g

2016-09-15

Purpose: The primary clinical role of positron emission tomography (PET) imaging is the detection of anomalous regions of {sup 18}F-FDG uptake, which are often indicative of malignant lesions. The goal of this work was to create a task-configurable fillable phantom for realistic measurements of detectability in PET imaging. Design goals included simplicity, adjustable feature size, realistic size and contrast levels, and inclusion of a lumpy (i.e., heterogeneous) background. Methods: The detection targets were hollow 3D-printed dodecahedral nylon features. The exostructure sphere-like features created voids in a background of small, solid non-porous plastic (acrylic) spheres inside a fillable tank. The featuresmore » filled at full concentration while the background concentration was reduced due to filling only between the solid spheres. Results: Multiple iterations of feature size and phantom construction were used to determine a configuration at the limit of detectability for a PET/CT system. A full-scale design used a 20 cm uniform cylinder (head-size) filled with a fixed pattern of features at a contrast of approximately 3:1. Known signal-present and signal-absent PET sub-images were extracted from multiple scans of the same phantom and with detectability in a challenging (i.e., useful) range. These images enabled calculation and comparison of the quantitative observer detectability metrics between scanner designs and image reconstruction methods. The phantom design has several advantages including filling simplicity, wall-less contrast features, the control of the detectability range via feature size, and a clinically realistic lumpy background. Conclusions: This phantom provides a practical method for testing and comparison of lesion detectability as a function of imaging system, acquisition parameters, and image reconstruction methods and parameters.« less

Phased Array Beamforming and Imaging in Composite Laminates Using Guided Waves

NASA Technical Reports Server (NTRS)

Tian, Zhenhua; Leckey, Cara A. C.; Yu, Lingyu

2016-01-01

This paper presents the phased array beamforming and imaging using guided waves in anisotropic composite laminates. A generic phased array beamforming formula is presented, based on the classic delay-and-sum principle. The generic formula considers direction-dependent guided wave properties induced by the anisotropic material properties of composites. Moreover, the array beamforming and imaging are performed in frequency domain where the guided wave dispersion effect has been considered. The presented phased array method is implemented with a non-contact scanning laser Doppler vibrometer (SLDV) to detect multiple defects at different locations in an anisotropic composite plate. The array is constructed of scan points in a small area rapidly scanned by the SLDV. Using the phased array method, multiple defects at different locations are successfully detected. Our study shows that the guided wave phased array method is a potential effective method for rapid inspection of large composite structures.

Automated detection of age-related macular degeneration in OCT images using multiple instance learning

NASA Astrophysics Data System (ADS)

Sun, Weiwei; Liu, Xiaoming; Yang, Zhou

2017-07-01

Age-related Macular Degeneration (AMD) is a kind of macular disease which mostly occurs in old people，and it may cause decreased vision or even lead to permanent blindness. Drusen is an important clinical indicator for AMD which can help doctor diagnose disease and decide the strategy of treatment. Optical Coherence Tomography (OCT) is widely used in the diagnosis of ophthalmic diseases, include AMD. In this paper, we propose a classification method based on Multiple Instance Learning (MIL) to detect AMD. Drusen can exist in a few slices of OCT images, and MIL is utilized in our method. We divided the method into two phases: training phase and testing phase. We train the initial features and clustered to create a codebook, and employ the trained classifier in the test set. Experiment results show that our method achieved high accuracy and effectiveness.

Detection of multiple thin surface cracks using vibrothermography with low-power piezoceramic-based ultrasonic actuator—a numerical study with experimental verification

NASA Astrophysics Data System (ADS)

Parvasi, Seyed Mohammad; Xu, Changhang; Kong, Qingzhao; Song, Gangbing

2016-05-01

Ultrasonic vibrations in cracked structures generate heat at the location of defects mainly due to frictional rubbing and viscoelastic losses at the defects. Vibrothermography is an effective nondestructive evaluation method which uses infrared imaging (IR) techniques to locate defects such as cracks and delaminations by detecting the heat generated at the defects. In this paper a coupled thermo-electro-mechanical analysis with the use of implicit finite element method was used to simulate a low power (10 W) piezoceramic-based ultrasonic actuator and the corresponding heat generation in a metallic plate with multiple surface cracks. Numerical results show that the finite element software Abaqus can be used to simultaneously model the electrical properties of the actuator, the ultrasonic waves propagating within the plate, as well as the thermal properties of the plate. Obtained numerical results demonstrate the ability of these low power transducers in detecting multiple cracks in the simulated aluminum plate. The validity of the numerical simulations was verified through experimental studies on a physical aluminum plate with multiple surface cracks while the same low power piezoceramic stack actuator was used to excite the plate and generate heat at the cracks. An excellent qualitative agreement exists between the experimental results and the numerical simulation’s results.

Methods of analysis by the U.S. Geological Survey National Water Quality Laboratory; determination of 86 volatile organic compounds in water by gas chromatography/mass spectrometry, including detections less than reporting limits

USGS Publications Warehouse

Connor, Brooke F.; Rose, Donna L.; Noriega, Mary C.; Murtaugh, Lucinda K.; Abney, Sonja R.

1998-01-01

This report presents precision and accuracy data for volatile organic compounds (VOCs) in the nanogram-per-liter range, including aromatic hydrocarbons, reformulated fuel components, and halogenated hydrocarbons using purge and trap capillary-column gas chromatography/mass spectrometry. One-hundred-four VOCs were initially tested. Of these, 86 are suitable for determination by this method. Selected data are provided for the 18 VOCs that were not included. This method also allows for the reporting of semiquantitative results for tentatively identified VOCs not included in the list of method compounds. Method detection limits, method performance data, preservation study results, and blank results are presented. The authors describe a procedure for reporting low-concentration detections at less than the reporting limit. The nondetection value (NDV) is introduced as a statistically defined reporting limit designed to limit false positives and false negatives to less than 1 percent. Nondetections of method compounds are reported as ?less than NDV.? Positive detections measured at less than NDV are reported as estimated concentrations to alert the data user to decreased confidence in accurate quantitation. Instructions are provided for analysts to report data at less than the reporting limits. This method can support the use of either method reporting limits that censor detections at lower concentrations or the use of NDVs as reporting limits. The data-reporting strategy for providing analytical results at less than the reporting limit is a result of the increased need to identify the presence or absence of environmental contaminants in water samples at increasingly lower concentrations. Long-term method detection limits (LTMDLs) for 86 selected compounds range from 0.013 to 2.452 micrograms per liter (?g/L) and differ from standard method detection limits (MDLs) in that the LTMDLs include the long-term variance of multiple instruments, multiple operators, and multiple calibrations over a longer time. For these reasons, LTMDLs are expected to be slightly higher than standard MDLs. Recoveries for all of the VOCs tested ranged from 36 (tert-butyl formate) to 155 percent (pentachlorobenzene). The majority of the compounds ranged from 85 to 115 percent recovery and had less than 5 percent relative standard deviation for concentrations spiked between 1 to 500 ?g/L in volatile blank-, surface-, and ground-water samples. Recoveries of 60 set spikes at low concentrations ranged from 70 to 114 percent (1,2,3- trimethylbenzene and acetone). Recovery data were collected over 6 months with multiple instruments, operators, and calibrations. In this method, volatile organic compounds are extracted from a water sample by actively purging with helium. The VOCs are collected onto a sorbent trap, thermally desorbed, separated by a Megabore gas chromatographic capillary column, and finally determined by a full-scan quadrupole mass spectrometer. Compound identification is confirmed by the gas chromatographic retention time and by the resultant mass spectrum, typically identified by three unique ions. An unknown compound detected in a sample can be tentatively identified by comparing the unknown mass spectrum to reference spectra in the mass-spectra computer-data system library compiled by the National Institute of Standards and Technology.

Detecting and characterizing coal mine related seismicity in the Western U.S. using subspace methods

NASA Astrophysics Data System (ADS)

Chambers, Derrick J. A.; Koper, Keith D.; Pankow, Kristine L.; McCarter, Michael K.

2015-11-01

We present an approach for subspace detection of small seismic events that includes methods for estimating magnitudes and associating detections from multiple stations into unique events. The process is used to identify mining related seismicity from a surface coal mine and an underground coal mining district, both located in the Western U.S. Using a blasting log and a locally derived seismic catalogue as ground truth, we assess detector performance in terms of verified detections, false positives and failed detections. We are able to correctly identify over 95 per cent of the surface coal mine blasts and about 33 per cent of the events from the underground mining district, while keeping the number of potential false positives relatively low by requiring all detections to occur on two stations. We find that most of the potential false detections for the underground coal district are genuine events missed by the local seismic network, demonstrating the usefulness of regional subspace detectors in augmenting local catalogues. We note a trade-off in detection performance between stations at smaller source-receiver distances, which have increased signal-to-noise ratio, and stations at larger distances, which have greater waveform similarity. We also explore the increased detection capabilities of a single higher dimension subspace detector, compared to multiple lower dimension detectors, in identifying events that can be described as linear combinations of training events. We find, in our data set, that such an advantage can be significant, justifying the use of a subspace detection scheme over conventional correlation methods.

Association analysis of multiple traits by an approach of combining P values.

PubMed

Chen, Lili; Wang, Yong; Zhou, Yajing

2018-03-01

Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region, we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutral variants and the different directions of effects of causal variants.

Detection of infarct lesions from single MRI modality using inconsistency between voxel intensity and spatial location--a 3-D automatic approach.

PubMed

Shen, Shan; Szameitat, André J; Sterr, Annette

2008-07-01

Detection of infarct lesions using traditional segmentation methods is always problematic due to intensity similarity between lesions and normal tissues, so that multispectral MRI modalities were often employed for this purpose. However, the high costs of MRI scan and the severity of patient conditions restrict the collection of multiple images. Therefore, in this paper, a new 3-D automatic lesion detection approach was proposed, which required only a single type of anatomical MRI scan. It was developed on a theory that, when lesions were present, the voxel-intensity-based segmentation and the spatial-location-based tissue distribution should be inconsistent in the regions of lesions. The degree of this inconsistency was calculated, which indicated the likelihood of tissue abnormality. Lesions were identified when the inconsistency exceeded a defined threshold. In this approach, the intensity-based segmentation was implemented by the conventional fuzzy c-mean (FCM) algorithm, while the spatial location of tissues was provided by prior tissue probability maps. The use of simulated MRI lesions allowed us to quantitatively evaluate the performance of the proposed method, as the size and location of lesions were prespecified. The results showed that our method effectively detected lesions with 40-80% signal reduction compared to normal tissues (similarity index > 0.7). The capability of the proposed method in practice was also demonstrated on real infarct lesions from 15 stroke patients, where the lesions detected were in broad agreement with true lesions. Furthermore, a comparison to a statistical segmentation approach presented in the literature suggested that our 3-D lesion detection approach was more reliable. Future work will focus on adapting the current method to multiple sclerosis lesion detection.

Quantitative Detection of Nucleoside Analogues by Multi-enzyme Biosensors using Time-Resolved Kinetic Measurements.

PubMed

Muthu, Pravin; Lutz, Stefan

2016-04-05

Fast, simple and cost-effective methods for detecting and quantifying pharmaceutical agents in patients are highly sought after to replace equipment and labor-intensive analytical procedures. The development of new diagnostic technology including portable detection devices also enables point-of-care by non-specialists in resource-limited environments. We have focused on the detection and dose monitoring of nucleoside analogues used in viral and cancer therapies. Using deoxyribonucleoside kinases (dNKs) as biosensors, our chemometric model compares observed time-resolved kinetics of unknown analytes to known substrate interactions across multiple enzymes. The resulting dataset can simultaneously identify and quantify multiple nucleosides and nucleoside analogues in complex sample mixtures. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Three-dimensional fluorescent microscopy via simultaneous illumination and detection at multiple planes.

PubMed

Ma, Qian; Khademhosseinieh, Bahar; Huang, Eric; Qian, Haoliang; Bakowski, Malina A; Troemel, Emily R; Liu, Zhaowei

2016-08-16

The conventional optical microscope is an inherently two-dimensional (2D) imaging tool. The objective lens, eyepiece and image sensor are all designed to capture light emitted from a 2D 'object plane'. Existing technologies, such as confocal or light sheet fluorescence microscopy have to utilize mechanical scanning, a time-multiplexing process, to capture a 3D image. In this paper, we present a 3D optical microscopy method based upon simultaneously illuminating and detecting multiple focal planes. This is implemented by adding two diffractive optical elements to modify the illumination and detection optics. We demonstrate that the image quality of this technique is comparable to conventional light sheet fluorescent microscopy with the advantage of the simultaneous imaging of multiple axial planes and reduced number of scans required to image the whole sample volume.

Linking landscape characteristics to local grizzly bear abundance using multiple detection methods in a hierarchical model

USGS Publications Warehouse

Graves, T.A.; Kendall, Katherine C.; Royle, J. Andrew; Stetz, J.B.; Macleod, A.C.

2011-01-01

Few studies link habitat to grizzly bear Ursus arctos abundance and these have not accounted for the variation in detection or spatial autocorrelation. We collected and genotyped bear hair in and around Glacier National Park in northwestern Montana during the summer of 2000. We developed a hierarchical Markov chain Monte Carlo model that extends the existing occupancy and count models by accounting for (1) spatially explicit variables that we hypothesized might influence abundance; (2) separate sub-models of detection probability for two distinct sampling methods (hair traps and rub trees) targeting different segments of the population; (3) covariates to explain variation in each sub-model of detection; (4) a conditional autoregressive term to account for spatial autocorrelation; (5) weights to identify most important variables. Road density and per cent mesic habitat best explained variation in female grizzly bear abundance; spatial autocorrelation was not supported. More female bears were predicted in places with lower road density and with more mesic habitat. Detection rates of females increased with rub tree sampling effort. Road density best explained variation in male grizzly bear abundance and spatial autocorrelation was supported. More male bears were predicted in areas of low road density. Detection rates of males increased with rub tree and hair trap sampling effort and decreased over the sampling period. We provide a new method to (1) incorporate multiple detection methods into hierarchical models of abundance; (2) determine whether spatial autocorrelation should be included in final models. Our results suggest that the influence of landscape variables is consistent between habitat selection and abundance in this system.

A microfluidic device integrating dual CMOS polysilicon nanowire sensors for on-chip whole blood processing and simultaneous detection of multiple analytes.

PubMed

Kuan, Da-Han; Wang, I-Shun; Lin, Jiun-Rue; Yang, Chao-Han; Huang, Chi-Hsien; Lin, Yen-Hung; Lin, Chih-Ting; Huang, Nien-Tsu

2016-08-02

The hemoglobin-A1c test, measuring the ratio of glycated hemoglobin (HbA1c) to hemoglobin (Hb) levels, has been a standard assay in diabetes diagnosis that removes the day-to-day glucose level variation. Currently, the HbA1c test is restricted to hospitals and central laboratories due to the laborious, time-consuming whole blood processing and bulky instruments. In this paper, we have developed a microfluidic device integrating dual CMOS polysilicon nanowire sensors (MINS) for on-chip whole blood processing and simultaneous detection of multiple analytes. The micromachined polymethylmethacrylate (PMMA) microfluidic device consisted of a serpentine microchannel with multiple dam structures designed for non-lysed cells or debris trapping, uniform plasma/buffer mixing and dilution. The CMOS-fabricated polysilicon nanowire sensors integrated with the microfluidic device were designed for the simultaneous, label-free electrical detection of multiple analytes. Our study first measured the Hb and HbA1c levels in 11 clinical samples via these nanowire sensors. The results were compared with those of standard Hb and HbA1c measurement methods (Hb: the sodium lauryl sulfate hemoglobin detection method; HbA1c: cation-exchange high-performance liquid chromatography) and showed comparable outcomes. Finally, we successfully demonstrated the efficacy of the MINS device's on-chip whole blood processing followed by simultaneous Hb and HbA1c measurement in a clinical sample. Compared to current Hb and HbA1c sensing instruments, the MINS platform is compact and can simultaneously detect two analytes with only 5 μL of whole blood, which corresponds to a 300-fold blood volume reduction. The total assay time, including the in situ sample processing and analyte detection, was just 30 minutes. Based on its on-chip whole blood processing and simultaneous multiple analyte detection functionalities with a lower sample volume requirement and shorter process time, the MINS device can be effectively applied to real-time diabetes diagnostics and monitoring in point-of-care settings.

A sensitive mass spectrometric method for hypothesis-driven detection of peptide post-translational modifications: multiple reaction monitoring-initiated detection and sequencing (MIDAS).

PubMed

Unwin, Richard D; Griffiths, John R; Whetton, Anthony D

2009-01-01

The application of a targeted mass spectrometric workflow to the sensitive identification of post-translational modifications is described. This protocol employs multiple reaction monitoring (MRM) to search for all putative peptides specifically modified in a target protein. Positive MRMs trigger an MS/MS experiment to confirm the nature and site of the modification. This approach, termed MIDAS (MRM-initiated detection and sequencing), is more sensitive than approaches using neutral loss scanning or precursor ion scanning methodologies, due to a more efficient use of duty cycle along with a decreased background signal associated with MRM. We describe the use of MIDAS for the identification of phosphorylation, with a typical experiment taking just a couple of hours from obtaining a peptide sample. With minor modifications, the MIDAS method can be applied to other protein modifications or unmodified peptides can be used as a MIDAS target.

In house validation of a high resolution mass spectrometry Orbitrap-based method for multiple allergen detection in a processed model food.

PubMed

Pilolli, Rosa; De Angelis, Elisabetta; Monaci, Linda

2018-02-13

In recent years, mass spectrometry (MS) has been establishing its role in the development of analytical methods for multiple allergen detection, but most analyses are being carried out on low-resolution mass spectrometers such as triple quadrupole or ion traps. In this investigation, performance provided by a high resolution (HR) hybrid quadrupole-Orbitrap™ MS platform for the multiple allergens detection in processed food matrix is presented. In particular, three different acquisition modes were compared: full-MS, targeted-selected ion monitoring with data-dependent fragmentation (t-SIM/dd2), and parallel reaction monitoring. In order to challenge the HR-MS platform, the sample preparation was kept as simple as possible, limited to a 30-min ultrasound-aided protein extraction followed by clean-up with disposable size exclusion cartridges. Selected peptide markers tracing for five allergenic ingredients namely skim milk, whole egg, soy flour, ground hazelnut, and ground peanut were monitored in home-made cookies chosen as model processed matrix. Timed t-SIM/dd2 was found the best choice as a good compromise between sensitivity and accuracy, accomplishing the detection of 17 peptides originating from the five allergens in the same run. The optimized method was validated in-house through the evaluation of matrix and processing effects, recoveries, and precision. The selected quantitative markers for each allergenic ingredient provided quantification of 60-100 μg ingred /g allergenic ingredient/matrix in incurred cookies.

Circulating Mycobacterium bovis peptides and host response proteins as biomarkers for unambiguous detection of subclinical infection

USDA-ARS?s Scientific Manuscript database

Background: Bovine tuberculosis remains one of the most damaging zoonotic diseases. A critical need exists for rapid and inexpensive diagnostics capable of detecting and differentiating M. bovis infection from other pathogenic and environmental mycobacteria at multiple surveillance levels. Method...

Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering.

PubMed

Guo, Xuan; Meng, Yu; Yu, Ning; Pan, Yi

2014-04-10

Taking the advantage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger. In this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions. Experimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS.

Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering

PubMed Central

2014-01-01

Backgroud Taking the advan tage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger. Results In this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions. Conclusions Experimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS. PMID:24717145

Multiple Approaches to the Investigation of Cell Assembly in Memory Research-Present and Future.

PubMed

Sakurai, Yoshio; Osako, Yuma; Tanisumi, Yuta; Ishihara, Eriko; Hirokawa, Junya; Manabe, Hiroyuki

2018-01-01

In this review article we focus on research methodologies for detecting the actual activity of cell assemblies, which are populations of functionally connected neurons that encode information in the brain. We introduce and discuss traditional and novel experimental methods and those currently in development and briefly discuss their advantages and disadvantages for the detection of cell-assembly activity. First, we introduce the electrophysiological method, i.e., multineuronal recording, and review former and recent examples of studies showing models of dynamic coding by cell assemblies in behaving rodents and monkeys. We also discuss how the firing correlation of two neurons reflects the firing synchrony among the numerous surrounding neurons that constitute cell assemblies. Second, we review the recent outstanding studies that used the novel method of optogenetics to show causal relationships between cell-assembly activity and behavioral change. Third, we review the most recently developed method of live-cell imaging, which facilitates the simultaneous observation of firings of a large number of neurons in behaving rodents. Currently, all these available methods have both advantages and disadvantages, and no single measurement method can directly and precisely detect the actual activity of cell assemblies. The best strategy is to combine the available methods and utilize each of their advantages with the technique of operant conditioning of multiple-task behaviors in animals and, if necessary, with brain-machine interface technology to verify the accuracy of neural information detected as cell-assembly activity.

Improved assessment of multiple sclerosis lesion segmentation agreement via detection and outline error estimates

PubMed Central

2012-01-01

Background Presented is the method “Detection and Outline Error Estimates” (DOEE) for assessing rater agreement in the delineation of multiple sclerosis (MS) lesions. The DOEE method divides operator or rater assessment into two parts: 1) Detection Error (DE) -- rater agreement in detecting the same regions to mark, and 2) Outline Error (OE) -- agreement of the raters in outlining of the same lesion. Methods DE, OE and Similarity Index (SI) values were calculated for two raters tested on a set of 17 fluid-attenuated inversion-recovery (FLAIR) images of patients with MS. DE, OE, and SI values were tested for dependence with mean total area (MTA) of the raters' Region of Interests (ROIs). Results When correlated with MTA, neither DE (ρ = .056, p=.83) nor the ratio of OE to MTA (ρ = .23, p=.37), referred to as Outline Error Rate (OER), exhibited significant correlation. In contrast, SI is found to be strongly correlated with MTA (ρ = .75, p < .001). Furthermore, DE and OER values can be used to model the variation in SI with MTA. Conclusions The DE and OER indices are proposed as a better method than SI for comparing rater agreement of ROIs, which also provide specific information for raters to improve their agreement. PMID:22812697

The Gaia-ESO Survey: double-, triple-, and quadruple-line spectroscopic binary candidates

NASA Astrophysics Data System (ADS)

Merle, T.; Van Eck, S.; Jorissen, A.; Van der Swaelmen, M.; Masseron, T.; Zwitter, T.; Hatzidimitriou, D.; Klutsch, A.; Pourbaix, D.; Blomme, R.; Worley, C. C.; Sacco, G.; Lewis, J.; Abia, C.; Traven, G.; Sordo, R.; Bragaglia, A.; Smiljanic, R.; Pancino, E.; Damiani, F.; Hourihane, A.; Gilmore, G.; Randich, S.; Koposov, S.; Casey, A.; Morbidelli, L.; Franciosini, E.; Magrini, L.; Jofre, P.; Costado, M. T.; Jeffries, R. D.; Bergemann, M.; Lanzafame, A. C.; Bayo, A.; Carraro, G.; Flaccomio, E.; Monaco, L.; Zaggia, S.

2017-12-01

Context. The Gaia-ESO Survey (GES) is a large spectroscopic survey that provides a unique opportunity to study the distribution of spectroscopic multiple systems among different populations of the Galaxy. Aims: Our aim is to detect binarity/multiplicity for stars targeted by the GES from the analysis of the cross-correlation functions (CCFs) of the GES spectra with spectral templates. Methods: We developed a method based on the computation of the CCF successive derivatives to detect multiple peaks and determine their radial velocities, even when the peaks are strongly blended. The parameters of the detection of extrema (DOE) code have been optimized for each GES GIRAFFE and UVES setup to maximize detection. The DOE code therefore allows to automatically detect multiple line spectroscopic binaries (SBn, n ≥ 2). Results: We apply this method on the fourth GES internal data release and detect 354 SBn candidates (342 SB2, 11 SB3, and even one SB4), including only nine SBs known in the literature. This implies that about 98% of these SBn candidates are new because of their faint visual magnitude that can reach V = 19. Visual inspection of the SBn candidate spectra reveals that the most probable candidates have indeed a composite spectrum. Among the SB2 candidates, an orbital solution could be computed for two previously unknown binaries: CNAME 06404608+0949173 (known as V642 Mon) in NGC 2264 and CNAME 19013257-0027338 in Berkeley 81 (Be 81). A detailed analysis of the unique SB4 (four peaks in the CCF) reveals that CNAME 08414659-5303449 (HD 74438) in the open cluster IC 2391 is a physically bound stellar quadruple system. The SB candidates belonging to stellar clusters are reviewed in detail to discard false detections. We suggest that atmospheric parameters should not be used for these system components; SB-specific pipelines should be used instead. Conclusions: Our implementation of an automatic detection of spectroscopic binaries within the GES has allowed the efficient discovery of many new multiple systems. With the detection of the SB1 candidates that will be the subject of a forthcoming paper, the study of the statistical and physical properties of the spectroscopic multiple systems will soon be possible for the entire GES sample. Based on data products from observations made with ESO Telescopes at the La Silla Paranal Observatory under programme ID 188.B-3002. These data products have been processed by the Cambridge Astronomy Survey Unit (CASU) at the Institute of Astronomy, University of Cambridge, and by the FLAMES/UVES reduction team at INAF/Osservatorio Astrofisico di Arcetri. These data have been obtained from the Gaia-ESO Survey Data Archive, prepared and hosted by the Wide Field Astronomy Unit, Institute for Astronomy, University of Edinburgh, which is funded by the UK Science and Technology Facilities Council.

Local multiplicity adjustment for the spatial scan statistic using the Gumbel distribution.

PubMed

Gangnon, Ronald E

2012-03-01

The spatial scan statistic is an important and widely used tool for cluster detection. It is based on the simultaneous evaluation of the statistical significance of the maximum likelihood ratio test statistic over a large collection of potential clusters. In most cluster detection problems, there is variation in the extent of local multiplicity across the study region. For example, using a fixed maximum geographic radius for clusters, urban areas typically have many overlapping potential clusters, whereas rural areas have relatively few. The spatial scan statistic does not account for local multiplicity variation. We describe a previously proposed local multiplicity adjustment based on a nested Bonferroni correction and propose a novel adjustment based on a Gumbel distribution approximation to the distribution of a local scan statistic. We compare the performance of all three statistics in terms of power and a novel unbiased cluster detection criterion. These methods are then applied to the well-known New York leukemia dataset and a Wisconsin breast cancer incidence dataset. © 2011, The International Biometric Society.

Local multiplicity adjustment for the spatial scan statistic using the Gumbel distribution

PubMed Central

Gangnon, Ronald E.

2011-01-01

Summary The spatial scan statistic is an important and widely used tool for cluster detection. It is based on the simultaneous evaluation of the statistical significance of the maximum likelihood ratio test statistic over a large collection of potential clusters. In most cluster detection problems, there is variation in the extent of local multiplicity across the study region. For example, using a fixed maximum geographic radius for clusters, urban areas typically have many overlapping potential clusters, while rural areas have relatively few. The spatial scan statistic does not account for local multiplicity variation. We describe a previously proposed local multiplicity adjustment based on a nested Bonferroni correction and propose a novel adjustment based on a Gumbel distribution approximation to the distribution of a local scan statistic. We compare the performance of all three statistics in terms of power and a novel unbiased cluster detection criterion. These methods are then applied to the well-known New York leukemia dataset and a Wisconsin breast cancer incidence dataset. PMID:21762118

IAEA Sampling Plan

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

Geist, William H.

2017-09-15

The objectives for this presentation are to describe the method that the IAEA uses to determine a sampling plan for nuclear material measurements; describe the terms detection probability and significant quantity; list the three nuclear materials measurement types; describe the sampling method applied to an item facility; and describe multiple method sampling.

Infrared target tracking via weighted correlation filter

NASA Astrophysics Data System (ADS)

He, Yu-Jie; Li, Min; Zhang, JinLi; Yao, Jun-Ping

2015-11-01

Design of an effective target tracker is an important and challenging task for many applications due to multiple factors which can cause disturbance in infrared video sequences. In this paper, an infrared target tracking method under tracking by detection framework based on a weighted correlation filter is presented. This method consists of two parts: detection and filtering. For the detection stage, we propose a sequential detection method for the infrared target based on low-rank representation. For the filtering stage, a new multi-feature weighted function which fuses different target features is proposed, which takes the importance of the different regions into consideration. The weighted function is then incorporated into a correlation filter to compute a confidence map more accurately, in order to indicate the best target location based on the detection results obtained from the first stage. Extensive experimental results on different video sequences demonstrate that the proposed method performs favorably for detection and tracking compared with baseline methods in terms of efficiency and accuracy.

Understanding environmental DNA detection probabilities: A case study using a stream-dwelling char Salvelinus fontinalis

Treesearch

Taylor M. Wilcox; Kevin S. McKelvey; Michael K. Young; Adam J. Sepulveda; Bradley B. Shepard; Stephen F. Jane; Andrew R. Whiteley; Winsor H. Lowe; Michael K. Schwartz

2016-01-01

Environmental DNA sampling (eDNA) has emerged as a powerful tool for detecting aquatic animals. Previous research suggests that eDNA methods are substantially more sensitive than traditional sampling. However, the factors influencing eDNA detection and the resulting sampling costs are still not well understood. Here we use multiple experiments to derive...

ICPD-a new peak detection algorithm for LC/MS.

PubMed

Zhang, Jianqiu; Haskins, William

2010-12-01

The identification and quantification of proteins using label-free Liquid Chromatography/Mass Spectrometry (LC/MS) play crucial roles in biological and biomedical research. Increasing evidence has shown that biomarkers are often low abundance proteins. However, LC/MS systems are subject to considerable noise and sample variability, whose statistical characteristics are still elusive, making computational identification of low abundance proteins extremely challenging. As a result, the inability of identifying low abundance proteins in a proteomic study is the main bottleneck in protein biomarker discovery. In this paper, we propose a new peak detection method called Information Combining Peak Detection (ICPD ) for high resolution LC/MS. In LC/MS, peptides elute during a certain time period and as a result, peptide isotope patterns are registered in multiple MS scans. The key feature of the new algorithm is that the observed isotope patterns registered in multiple scans are combined together for estimating the likelihood of the peptide existence. An isotope pattern matching score based on the likelihood probability is provided and utilized for peak detection. The performance of the new algorithm is evaluated based on protein standards with 48 known proteins. The evaluation shows better peak detection accuracy for low abundance proteins than other LC/MS peak detection methods.

Atmospheric turbulence profiling with SLODAR using multiple adaptive optics wavefront sensors.

PubMed

Wang, Lianqi; Schöck, Matthias; Chanan, Gary

2008-04-10

The slope detection and ranging (SLODAR) method recovers atmospheric turbulence profiles from time averaged spatial cross correlations of wavefront slopes measured by Shack-Hartmann wavefront sensors. The Palomar multiple guide star unit (MGSU) was set up to test tomographic multiple guide star adaptive optics and provided an ideal test bed for SLODAR turbulence altitude profiling. We present the data reduction methods and SLODAR results from MGSU observations made in 2006. Wind profiling is also performed using delayed wavefront cross correlations along with SLODAR analysis. The wind profiling analysis is shown to improve the height resolution of the SLODAR method and in addition gives the wind velocities of the turbulent layers.

Analysis of the Multiple-Solution Response of a Flexible Rotor Supported on Non-Linear Squeeze Film Dampers

NASA Astrophysics Data System (ADS)

ZHU, C. S.; ROBB, D. A.; EWINS, D. J.

2002-05-01

The multiple-solution response of rotors supported on squeeze film dampers is a typical non-linear phenomenon. The behaviour of the multiple-solution response in a flexible rotor supported on two identical squeeze film dampers with centralizing springs is studied by three methods: synchronous circular centred-orbit motion solution, numerical integration method and slow acceleration method using the assumption of a short bearing and cavitated oil film; the differences of computational results obtained by the three different methods are compared in this paper. It is shown that there are three basic forms for the multiple-solution response in the flexible rotor system supported on the squeeze film dampers, which are the resonant, isolated bifurcation and swallowtail bifurcation multiple solutions. In the multiple-solution speed regions, the rotor motion may be subsynchronous, super-subsynchronous, almost-periodic and even chaotic, besides synchronous circular centred, even if the gravity effect is not considered. The assumption of synchronous circular centred-orbit motion for the journal and rotor around the static deflection line can be used only in some special cases; the steady state numerical integration method is very useful, but time consuming. Using the slow acceleration method, not only can the multiple-solution speed regions be detected, but also the non-synchronous response regions.

Multiple feature extraction by using simultaneous wavelet transforms

NASA Astrophysics Data System (ADS)

Mazzaferri, Javier; Ledesma, Silvia; Iemmi, Claudio

2003-07-01

We propose here a method to optically perform multiple feature extraction by using wavelet transforms. The method is based on obtaining the optical correlation by means of a Vander Lugt architecture, where the scene and the filter are displayed on spatial light modulators (SLMs). Multiple phase filters containing the information about the features that we are interested in extracting are designed and then displayed on an SLM working in phase mostly mode. We have designed filters to simultaneously detect edges and corners or different characteristic frequencies contained in the input scene. Simulated and experimental results are shown.

Round-robin comparison of methods for the detection of human enteric viruses in lettuce.

PubMed

Le Guyader, Françoise S; Schultz, Anna-Charlotte; Haugarreau, Larissa; Croci, Luciana; Maunula, Leena; Duizer, Erwin; Lodder-Verschoor, Froukje; von Bonsdorff, Carl-Henrik; Suffredini, Elizabetha; van der Poel, Wim M M; Reymundo, Rosanna; Koopmans, Marion

2004-10-01

Five methods that detect human enteric virus contamination in lettuce were compared. To mimic multiple contaminations as observed after sewage contamination, artificial contamination was with human calicivirus and poliovirus and animal calicivirus strains at different concentrations. Nucleic acid extractions were done at the same time in the same laboratory to reduce assay-to-assay variability. Results showed that the two critical steps are the washing step and removal of inhibitors. The more reliable methods (sensitivity, simplicity, low cost) included an elution/concentration step and a commercial kit. Such development of sensitive methods for viral detection in foods other than shellfish is important to improve food safety.

Use of Multiscale Entropy to Facilitate Artifact Detection in Electroencephalographic Signals

PubMed Central

Mariani, Sara; Borges, Ana F. T.; Henriques, Teresa; Goldberger, Ary L.; Costa, Madalena D.

2016-01-01

Electroencephalographic (EEG) signals present a myriad of challenges to analysis, beginning with the detection of artifacts. Prior approaches to noise detection have utilized multiple techniques, including visual methods, independent component analysis and wavelets. However, no single method is broadly accepted, inviting alternative ways to address this problem. Here, we introduce a novel approach based on a statistical physics method, multiscale entropy (MSE) analysis, which quantifies the complexity of a signal. We postulate that noise corrupted EEG signals have lower information content, and, therefore, reduced complexity compared with their noise free counterparts. We test the new method on an open-access database of EEG signals with and without added artifacts due to electrode motion. PMID:26738116

Detecting Signage and Doors for Blind Navigation and Wayfinding

PubMed Central

Wang, Shuihua; Yang, Xiaodong; Tian, Yingli

2013-01-01

Signage plays a very important role to find destinations in applications of navigation and wayfinding. In this paper, we propose a novel framework to detect doors and signage to help blind people accessing unfamiliar indoor environments. In order to eliminate the interference information and improve the accuracy of signage detection, we first extract the attended areas by using a saliency map. Then the signage is detected in the attended areas by using a bipartite graph matching. The proposed method can handle multiple signage detection. Furthermore, in order to provide more information for blind users to access the area associated with the detected signage, we develop a robust method to detect doors based on a geometric door frame model which is independent to door appearances. Experimental results on our collected datasets of indoor signage and doors demonstrate the effectiveness and efficiency of our proposed method. PMID:23914345

Detecting Signage and Doors for Blind Navigation and Wayfinding.

PubMed

Wang, Shuihua; Yang, Xiaodong; Tian, Yingli

2013-07-01

Signage plays a very important role to find destinations in applications of navigation and wayfinding. In this paper, we propose a novel framework to detect doors and signage to help blind people accessing unfamiliar indoor environments. In order to eliminate the interference information and improve the accuracy of signage detection, we first extract the attended areas by using a saliency map. Then the signage is detected in the attended areas by using a bipartite graph matching. The proposed method can handle multiple signage detection. Furthermore, in order to provide more information for blind users to access the area associated with the detected signage, we develop a robust method to detect doors based on a geometric door frame model which is independent to door appearances. Experimental results on our collected datasets of indoor signage and doors demonstrate the effectiveness and efficiency of our proposed method.

Real-Time Data Filtering and Compression in Wide Area Simulation Networks

DTIC Science & Technology

1992-10-02

Area Simulation Networks Achieving the real-time linkage among multiple , geographically-distant, local area networks that support distributed...November 1989, pp. 52-61. [IEEE85] IEEE/ANSI Standard 8802/3 "Carrier sense multiple access with collision detection (CSMA/CD) access method and...decoding/encoding of multiple bits. The hardware is programmable, easily adaptable and yields a high compression rate. A prototype 2-micron VLSI chip

Joint detection and tracking of size-varying infrared targets based on block-wise sparse decomposition

NASA Astrophysics Data System (ADS)

Li, Miao; Lin, Zaiping; Long, Yunli; An, Wei; Zhou, Yiyu

2016-05-01

The high variability of target size makes small target detection in Infrared Search and Track (IRST) a challenging task. A joint detection and tracking method based on block-wise sparse decomposition is proposed to address this problem. For detection, the infrared image is divided into overlapped blocks, and each block is weighted on the local image complexity and target existence probabilities. Target-background decomposition is solved by block-wise inexact augmented Lagrange multipliers. For tracking, label multi-Bernoulli (LMB) tracker tracks multiple targets taking the result of single-frame detection as input, and provides corresponding target existence probabilities for detection. Unlike fixed-size methods, the proposed method can accommodate size-varying targets, due to no special assumption for the size and shape of small targets. Because of exact decomposition, classical target measurements are extended and additional direction information is provided to improve tracking performance. The experimental results show that the proposed method can effectively suppress background clutters, detect and track size-varying targets in infrared images.

Multiple layer identification label using stacked identification symbols

NASA Technical Reports Server (NTRS)

Schramm, Harry F. (Inventor)

2005-01-01

An automatic identification system and method are provided which employ a machine readable multiple layer label. The label has a plurality of machine readable marking layers stacked one upon another. Each of the marking layers encodes an identification symbol detectable using one or more sensing technologies. The various marking layers may comprise the same marking material or each marking layer may comprise a different medium having characteristics detectable by a different sensing technology. These sensing technologies include x-ray, radar, capacitance, thermal, magnetic and ultrasonic. A complete symbol may be encoded within each marking layer or a symbol may be segmented into fragments which are then divided within a single marking layer or encoded across multiple marking layers.

A multiple hollow fibre liquid-phase microextraction method for the determination of halogenated solvent residues in olive oil.

PubMed

Manso, J; García-Barrera, T; Gómez-Ariza, J L; González, A G

2014-02-01

The present paper describes a method based on the extraction of analytes by multiple hollow fibre liquid-phase microextraction and detection by ion-trap mass spectrometry and electron capture detectors after gas chromatographic separation. The limits of detection are in the range of 0.13-0.67 μg kg(-1), five orders of magnitude lower than those reached with the European Commission Official method of analysis, with three orders of magnitude of linear range (from the quantification limits to 400 μg kg(-1) for all the analytes) and recoveries in fortified olive oils in the range of 78-104 %. The main advantages of the analytical method are the absence of sample carryover (due to the disposable nature of the membranes), high enrichment factors in the range of 79-488, high throughput and low cost. The repeatability of the analytical method ranged from 8 to 15 % for all the analytes, showing a good performance.

Development of a Raman chemical imaging detection method for authenticating skim milk powder

USDA-ARS?s Scientific Manuscript database

This research demonstrated that Raman chemical imaging coupled with a simple image classification algorithm can be used to detect multiple chemical adulterants in skim milk powder. Ammonium sulfate, dicyandiamide, melamine, and urea were mixed into the milk powder as chemical adulterants in the conc...

Multiplex surface plasmon resonance imaging platform for label-free detection of foodborne pathogens

USDA-ARS?s Scientific Manuscript database

Salmonellae are among the leading causes of foodborne outbreaks in the United States, and more rapid and efficient detection methods are needed. Surface plasmon resonance imaging (SPRi) is an emerging optical technique, which allows for rapid and label-free screening of multiple targets simultaneous...

Detection of Myelination Using a Novel Histological Probe

PubMed Central

Xiang, Zhongmin; Nesterov, Evgueni E.; Skoch, Jesse; Lin, Tong; Hyman, Bradley T.; Swager, Timothy M.; Bacskai, Brian J.; Reeves, Steven A.

2005-01-01

Current methods for myelin staining in tissue sections include both histological and immunohistochemical techniques. Fluorescence immunohistochemistry, which uses antibodies against myelin components such as myelin basic protein, is often used because of the convenience for multiple labeling. To facilitate studies on myelin, this paper describes a quick and easy method for direct myelin staining in rodent and human tissues using novel near-infrared myelin (NIM) dyes that are comparable to other well-characterized histochemical reagents. The near-infrared fluorescence spectra of these probes allow fluorescent staining of tissue sections in multiple channels using visible light fluorophores commonly used in immunocytochemistry. These dyes have been used successfully to detect normal myelin structure and myelin loss in a mouse model of demyelination disease. PMID:16046669

Seeing is believing: video classification for computed tomographic colonography using multiple-instance learning.

PubMed

Wang, Shijun; McKenna, Matthew T; Nguyen, Tan B; Burns, Joseph E; Petrick, Nicholas; Sahiner, Berkman; Summers, Ronald M

2012-05-01

In this paper, we present development and testing results for a novel colonic polyp classification method for use as part of a computed tomographic colonography (CTC) computer-aided detection (CAD) system. Inspired by the interpretative methodology of radiologists using 3-D fly-through mode in CTC reading, we have developed an algorithm which utilizes sequences of images (referred to here as videos) for classification of CAD marks. For each CAD mark, we created a video composed of a series of intraluminal, volume-rendered images visualizing the detection from multiple viewpoints. We then framed the video classification question as a multiple-instance learning (MIL) problem. Since a positive (negative) bag may contain negative (positive) instances, which in our case depends on the viewing angles and camera distance to the target, we developed a novel MIL paradigm to accommodate this class of problems. We solved the new MIL problem by maximizing a L2-norm soft margin using semidefinite programming, which can optimize relevant parameters automatically. We tested our method by analyzing a CTC data set obtained from 50 patients from three medical centers. Our proposed method showed significantly better performance compared with several traditional MIL methods.

Seeing is Believing: Video Classification for Computed Tomographic Colonography Using Multiple-Instance Learning

PubMed Central

Wang, Shijun; McKenna, Matthew T.; Nguyen, Tan B.; Burns, Joseph E.; Petrick, Nicholas; Sahiner, Berkman

2012-01-01

In this paper we present development and testing results for a novel colonic polyp classification method for use as part of a computed tomographic colonography (CTC) computer-aided detection (CAD) system. Inspired by the interpretative methodology of radiologists using 3D fly-through mode in CTC reading, we have developed an algorithm which utilizes sequences of images (referred to here as videos) for classification of CAD marks. For each CAD mark, we created a video composed of a series of intraluminal, volume-rendered images visualizing the detection from multiple viewpoints. We then framed the video classification question as a multiple-instance learning (MIL) problem. Since a positive (negative) bag may contain negative (positive) instances, which in our case depends on the viewing angles and camera distance to the target, we developed a novel MIL paradigm to accommodate this class of problems. We solved the new MIL problem by maximizing a L2-norm soft margin using semidefinite programming, which can optimize relevant parameters automatically. We tested our method by analyzing a CTC data set obtained from 50 patients from three medical centers. Our proposed method showed significantly better performance compared with several traditional MIL methods. PMID:22552333

Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis.

PubMed

Wang, Longfei; Lee, Sungyoung; Gim, Jungsoo; Qiao, Dandi; Cho, Michael; Elston, Robert C; Silverman, Edwin K; Won, Sungho

2016-09-01

Family-based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family-based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family-based designs. In this report, we describe one such implementation: the multivariate family-based rare variant association tool (mFARVAT). mFARVAT is a quasi-likelihood-based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows. © 2016 WILEY PERIODICALS, INC.

A multiplex microplatform for the detection of multiple DNA methylation events using gold-DNA affinity.

PubMed

Sina, Abu Ali Ibn; Foster, Matthew Thomas; Korbie, Darren; Carrascosa, Laura G; Shiddiky, Muhammad J A; Gao, Jing; Dey, Shuvashis; Trau, Matt

2017-10-07

We report a new multiplexed strategy for the electrochemical detection of regional DNA methylation across multiple regions. Using the sequence dependent affinity of bisulfite treated DNA towards gold surfaces, the method integrates the high sensitivity of a micro-fabricated multiplex device comprising a microarray of gold electrodes, with the powerful multiplexing capability of multiplex-PCR. The synergy of this combination enables the monitoring of the methylation changes across several genomic regions simultaneously from as low as 500 pg μl -1 of DNA with no sequencing requirement.

Detection of electromagnetic radiation using micromechanical multiple quantum wells structures

DOEpatents

Datskos, Panagiotis G [Knoxville, TN; Rajic, Slobodan [Knoxville, TN; Datskou, Irene [Knoxville, TN

2007-07-17

An apparatus and method for detecting electromagnetic radiation employs a deflectable micromechanical apparatus incorporating multiple quantum wells structures. When photons strike the quantum-well structure, physical stresses are created within the sensor, similar to a "bimetallic effect." The stresses cause the sensor to bend. The extent of deflection of the sensor can be measured through any of a variety of conventional means to provide a measurement of the photons striking the sensor. A large number of such sensors can be arranged in a two-dimensional array to provide imaging capability.

A Simple Method to Simultaneously Detect and Identify Spikes from Raw Extracellular Recordings.

PubMed

Petrantonakis, Panagiotis C; Poirazi, Panayiota

2015-01-01

The ability to track when and which neurons fire in the vicinity of an electrode, in an efficient and reliable manner can revolutionize the neuroscience field. The current bottleneck lies in spike sorting algorithms; existing methods for detecting and discriminating the activity of multiple neurons rely on inefficient, multi-step processing of extracellular recordings. In this work, we show that a single-step processing of raw (unfiltered) extracellular signals is sufficient for both the detection and identification of active neurons, thus greatly simplifying and optimizing the spike sorting approach. The efficiency and reliability of our method is demonstrated in both real and simulated data.

Nucleic acid detection system and method for detecting influenza

DOEpatents

Cai, Hong; Song, Jian

2015-03-17

The invention provides a rapid, sensitive and specific nucleic acid detection system which utilizes isothermal nucleic acid amplification in combination with a lateral flow chromatographic device, or DNA dipstick, for DNA-hybridization detection. The system of the invention requires no complex instrumentation or electronic hardware, and provides a low cost nucleic acid detection system suitable for highly sensitive pathogen detection. Hybridization to single-stranded DNA amplification products using the system of the invention provides a sensitive and specific means by which assays can be multiplexed for the detection of multiple target sequences.

Multiple Strategy Bio-Detection Sensor Platforms Made From Carbon and Polymer Materials

DTIC Science & Technology

2006-08-10

binding of antigens. Further investigations were conducted on the antibody-antigen detection scheme using known test antigens (i.e. bacteria ). While...this method worked in preliminary studies, the use of antibodies was determined not to be feasible for detecting many different types of bacteria in...solution. Also, as the number of bacteria in a solution increased, it became necessary to wash the detection surface more extensively to favor specific

Highly sensitive detection of multiple tumor markers for lung cancer using gold nanoparticle probes and microarrays.

PubMed

Gao, Wanlei; Wang, Wentao; Yao, Shihua; Wu, Shan; Zhang, Honglian; Zhang, Jishen; Jing, Fengxiang; Mao, Hongju; Jin, Qinghui; Cong, Hui; Jia, Chunping; Zhang, Guojun; Zhao, Jianlong

2017-03-15

Assay of multiple serum tumor markers such as carcinoembryonic antigen (CEA), cytokeratin 19 fragment antigen (CYFRA21-1), and neuron specific enolase (NSE), is important for the early diagnosis of lung cancer. Dickkopf-1 (DKK1), a novel serological and histochemical biomarker, was recently reported to be preferentially expressed in lung cancer. Four target proteins were sandwiched by capture antibodies attached to microarrays and detection antibodies carried on modified gold nanoparticles. Optical signals generated by the sandwich structures were amplified by gold deposition with HAuCl 4 and H 2 O 2 , and were observable by microscopy or the naked eye. The four tumor markers were subsequently measured in 106 lung cancer patients and 42 healthy persons. The assay was capable of detecting multiple biomarkers in serum sample at concentration of <1 ng mL -1 in 1 h. Combined detection of the four tumor markers highly improved the sensitivity (to 87.74%) for diagnosis of lung cancer compared with sensitivity of single markers. A rapid, highly sensitive co-detection method for multiple biomarkers based on gold nanoparticles and microarrays was developed. In clinical use, it would be expected to improve the early diagnosis of lung cancer. Copyright © 2016 Elsevier B.V. All rights reserved.

Detecting Structural Failures Via Acoustic Impulse Responses

NASA Technical Reports Server (NTRS)

Bayard, David S.; Joshi, Sanjay S.

1995-01-01

Advanced method of acoustic pulse reflectivity testing developed for use in determining sizes and locations of failures within structures. Used to detect breaks in electrical transmission lines, detect faults in optical fibers, and determine mechanical properties of materials. In method, structure vibrationally excited with acoustic pulse (a "ping") at one location and acoustic response measured at same or different location. Measured acoustic response digitized, then processed by finite-impulse-response (FIR) filtering algorithm unique to method and based on acoustic-wave-propagation and -reflection properties of structure. Offers several advantages: does not require training, does not require prior knowledge of mathematical model of acoustic response of structure, enables detection and localization of multiple failures, and yields data on extent of damage at each location.

Systematic assessment of the performance of whole-genome amplification for SNP/CNV detection and β-thalassemia genotyping.

PubMed

He, Fei; Zhou, Wanjun; Cai, Ren; Yan, Tizhen; Xu, Xiangmin

2018-04-01

In this study, we aimed to assess the performance of two whole-genome amplification methods, multiple displacement amplification (MDA), and multiple annealing and looping-based amplification cycle (MALBAC), for β-thalassemia genotyping and single-nucleotide polymorphism (SNP)/copy-number variant (CNV) detection using two DNA sequencing assays. We collected peripheral blood, cell lines, and discarded embryos, and carried out MALBAC and MDA on single-cell and five-cell samples. We detected and statistically analyzed differences in the amplification efficiency, positive predictive value, sensitivity, allele dropout (ADO) rate, SNPs, and CV values between the two methods. Through Sanger sequencing at the single-cell and five-cell levels, we showed that both the amplification rate and ADO rate of MDA were better than those using MALBAC, and the sensitivity and positive predictive value obtained from MDA were higher than those from MALBAC for β-thalassemia genotyping. Using next-generation sequencing (NGS) at the single-cell level, we confirmed that MDA has better properties than MALBAC for SNP detection. However, MALBAC was more stable and homogeneous than MDA using low-depth NGS at the single-cell level for CNV detection. We conclude that MALBAC is the better option for CNV detection, while MDA is better suited for SNV detection.

Clustering Multiple Sclerosis Subgroups with Multifractal Methods and Self-Organizing Map Algorithm

NASA Astrophysics Data System (ADS)

Karaca, Yeliz; Cattani, Carlo

Magnetic resonance imaging (MRI) is the most sensitive method to detect chronic nervous system diseases such as multiple sclerosis (MS). In this paper, Brownian motion Hölder regularity functions (polynomial, periodic (sine), exponential) for 2D image, such as multifractal methods were applied to MR brain images, aiming to easily identify distressed regions, in MS patients. With these regions, we have proposed an MS classification based on the multifractal method by using the Self-Organizing Map (SOM) algorithm. Thus, we obtained a cluster analysis by identifying pixels from distressed regions in MR images through multifractal methods and by diagnosing subgroups of MS patients through artificial neural networks.

A New Cell Block Method for Multiple Immunohistochemical Analysis of Circulating Tumor Cells in Patients with Liver Cancer.

PubMed

Nam, Soo Jeong; Yeo, Hyun Yang; Chang, Hee Jin; Kim, Bo Hyun; Hong, Eun Kyung; Park, Joong-Won

2016-10-01

We developed a new method of detecting circulating tumor cells (CTCs) in liver cancer patients by constructing cell blocks from peripheral blood cells, including CTCs, followed by multiple immunohistochemical analysis. Cell blockswere constructed from the nucleated cell pellets of peripheral blood afterremoval of red blood cells. The blood cell blocks were obtained from 29 patients with liver cancer, and from healthy donor blood spikedwith seven cell lines. The cell blocks and corresponding tumor tissues were immunostained with antibodies to seven markers: cytokeratin (CK), epithelial cell adhesion molecule (EpCAM), epithelial membrane antigen (EMA), CK18, α-fetoprotein (AFP), Glypican 3, and HepPar1. The average recovery rate of spiked SW620 cells from blood cell blocks was 91%. CTCs were detected in 14 out of 29 patients (48.3%); 11/23 hepatocellular carcinomas (HCC), 1/2 cholangiocarcinomas (CC), 1/1 combined HCC-CC, and 1/3 metastatic cancers. CTCs from 14 patients were positive for EpCAM (57.1%), EMA (42.9%), AFP (21.4%), CK18 (14.3%), Gypican3 and CK (7.1%, each), and HepPar1 (0%). Patients with HCC expressed EpCAM, EMA, CK18, and AFP in tissue and/or CTCs, whereas CK, HepPar1, and Glypican3 were expressed only in tissue. Only EMA was significantly associated with the expressions in CTC and tissue. CTC detection was associated with higher T stage and portal vein invasion in HCC patients. This cell block method allows cytologic detection and multiple immunohistochemical analysis of CTCs. Our results show that tissue biomarkers of HCC may not be useful for the detection of CTC. EpCAM could be a candidate marker for CTCs in patients with HCC.

Detection of ESBL among ampc producing enterobacteriaceae using inhibitor-based method

PubMed Central

Bakthavatchalu, Sasirekha; Shakthivel, Uma; Mishra, Tannu

2013-01-01

Introduction The occurrence of multiple β-lactamases among bacteria only limits the therapeutic options but also poses a challenge. A study using boronic acid (BA), an AmpC enzyme inhibitor, was designed to detect the combined expression of AmpC β-lactamases and extended-spectrum β-lactamases (ESBLs) in bacterial isolates further different phenotypic methods are compared to detect ESBL and AmpC. Methods A total of 259 clinical isolates of Enterobacteriaceae were isolated and screened for ESBL production by (i) CLSI double-disk diffusion method (ii) cefepime- clavulanic acid method (iii) boronic disk potentiation method. AmpC production was detected using cefoxitin alone and in combination with boronic acid and confirmation was done by three dimensional disk methods. Isolates were also subjected to detailed antibiotic susceptibility test. Results Among 259 isolates, 20.46% were coproducers of ESBL and AmpC, 26.45% were ESBL and 5.40% were AmpC. All of the 53 AmpC and ESBL coproducers were accurately detected by boronic acid disk potentiation method. Conclusion The BA disk test using Clinical and Laboratory Standards Institute methodology is simple and very efficient method that accurately detects the isolates that harbor both AmpCs and ESBLs. PMID:23504148

Role of data aggregation in biosurveillance detection strategies with applications from ESSENCE.

PubMed

Burkom, Howard S; Elbert, Y; Feldman, A; Lin, J

2004-09-24

Syndromic surveillance systems are used to monitor daily electronic data streams for anomalous counts of features of varying specificity. The monitored quantities might be counts of clinical diagnoses, sales of over-the-counter influenza remedies, school absenteeism among a given age group, and so forth. Basic data-aggregation decisions for these systems include determining which records to count and how to group them in space and time. This paper discusses the application of spatial and temporal data-aggregation strategies for multiple data streams to alerting algorithms appropriate to the surveillance region and public health threat of interest. Such a strategy was applied and evaluated for a complex, authentic, multisource, multiregion environment, including >2 years of data records from a system-evaluation exercise for the Defense Advanced Research Project Agency (DARPA). Multivariate and multiple univariate statistical process control methods were adapted and applied to the DARPA data collection. Comparative parametric analyses based on temporal aggregation were used to optimize the performance of these algorithms for timely detection of a set of outbreaks identified in the data by a team of epidemiologists. The sensitivity and timeliness of the most promising detection methods were tested at empirically calculated thresholds corresponding to multiple practical false-alert rates. Even at the strictest false-alert rate, all but one of the outbreaks were detected by the best method, and the best methods achieved a 1-day median time before alert over the set of test outbreaks. These results indicate that a biosurveillance system can provide a substantial alerting-timeliness advantage over traditional public health monitoring for certain outbreaks. Comparative analyses of individual algorithm results indicate further achievable improvement in sensitivity and specificity.

Comparison of different detection methods for persistent multiple hypothesis tracking in wide area motion imagery

NASA Astrophysics Data System (ADS)

Hartung, Christine; Spraul, Raphael; Schuchert, Tobias

2017-10-01

Wide area motion imagery (WAMI) acquired by an airborne multicamera sensor enables continuous monitoring of large urban areas. Each image can cover regions of several square kilometers and contain thousands of vehicles. Reliable vehicle tracking in this imagery is an important prerequisite for surveillance tasks, but remains challenging due to low frame rate and small object size. Most WAMI tracking approaches rely on moving object detections generated by frame differencing or background subtraction. These detection methods fail when objects slow down or stop. Recent approaches for persistent tracking compensate for missing motion detections by combining a detection-based tracker with a second tracker based on appearance or local context. In order to avoid the additional complexity introduced by combining two trackers, we employ an alternative single tracker framework that is based on multiple hypothesis tracking and recovers missing motion detections with a classifierbased detector. We integrate an appearance-based similarity measure, merge handling, vehicle-collision tests, and clutter handling to adapt the approach to the specific context of WAMI tracking. We apply the tracking framework on a region of interest of the publicly available WPAFB 2009 dataset for quantitative evaluation; a comparison to other persistent WAMI trackers demonstrates state of the art performance of the proposed approach. Furthermore, we analyze in detail the impact of different object detection methods and detector settings on the quality of the output tracking results. For this purpose, we choose four different motion-based detection methods that vary in detection performance and computation time to generate the input detections. As detector parameters can be adjusted to achieve different precision and recall performance, we combine each detection method with different detector settings that yield (1) high precision and low recall, (2) high recall and low precision, and (3) best f-score. Comparing the tracking performance achieved with all generated sets of input detections allows us to quantify the sensitivity of the tracker to different types of detector errors and to derive recommendations for detector and parameter choice.

The Genetic-Algorithm-Based Normal Boundary Intersection (GANBI) Method; An Efficient Approach to Pareto Multiobjective Optimization for Engineering Design

DTIC Science & Technology

2006-05-15

alarm performance in a cost-effective manner is the use of track - before - detect strategies, in which multiple sensor detections must occur within the...corresponding to the traditional sensor coverage problem. Also, in the track - before - detect context, reference is made to the field-level functions of...detection and false alarm as successful search and false search, respectively, because the track - before - detect process serves as a searching function

Video Analytics Evaluation: Survey of Datasets, Performance Metrics and Approaches

DTIC Science & Technology

2014-09-01

training phase and a fusion of the detector outputs. 6.3.1 Training Techniques 1. Bagging: The basic idea of Bagging is to train multiple classifiers...can reduce more noise interesting points. Person detection and background subtraction methods were used to create hot regions. The hot regions were...detection algorithms are incorporated with MHT to construct one integrated detector /tracker. 6.8 IRDS-CASIA team IRDS-CASIA proposed a method to solve a

Arc Fault Detection & Localization by Electromagnetic-Acoustic Remote Sensing

NASA Astrophysics Data System (ADS)

Vasile, C.; Ioana, C.

2017-05-01

Electrical arc faults that occur in photovoltaic systems represent a danger due to their economic impact on production and distribution. In this paper we propose a complete system, with focus on the methodology, that enables the detection and localization of the arc fault, by the use of an electromagnetic-acoustic sensing system. By exploiting the multiple emissions of the arc fault, in conjunction with a real-time detection signal processing method, we ensure accurate detection and localization. In its final form, this present work will present in greater detail the complete system, the methods employed, results and performance, alongside further works that will be carried on.

Toward multimodal signal detection of adverse drug reactions.

PubMed

Harpaz, Rave; DuMouchel, William; Schuemie, Martijn; Bodenreider, Olivier; Friedman, Carol; Horvitz, Eric; Ripple, Anna; Sorbello, Alfred; White, Ryen W; Winnenburg, Rainer; Shah, Nigam H

2017-12-01

Improving mechanisms to detect adverse drug reactions (ADRs) is key to strengthening post-marketing drug safety surveillance. Signal detection is presently unimodal, relying on a single information source. Multimodal signal detection is based on jointly analyzing multiple information sources. Building on, and expanding the work done in prior studies, the aim of the article is to further research on multimodal signal detection, explore its potential benefits, and propose methods for its construction and evaluation. Four data sources are investigated; FDA's adverse event reporting system, insurance claims, the MEDLINE citation database, and the logs of major Web search engines. Published methods are used to generate and combine signals from each data source. Two distinct reference benchmarks corresponding to well-established and recently labeled ADRs respectively are used to evaluate the performance of multimodal signal detection in terms of area under the ROC curve (AUC) and lead-time-to-detection, with the latter relative to labeling revision dates. Limited to our reference benchmarks, multimodal signal detection provides AUC improvements ranging from 0.04 to 0.09 based on a widely used evaluation benchmark, and a comparative added lead-time of 7-22 months relative to labeling revision dates from a time-indexed benchmark. The results support the notion that utilizing and jointly analyzing multiple data sources may lead to improved signal detection. Given certain data and benchmark limitations, the early stage of development, and the complexity of ADRs, it is currently not possible to make definitive statements about the ultimate utility of the concept. Continued development of multimodal signal detection requires a deeper understanding the data sources used, additional benchmarks, and further research on methods to generate and synthesize signals. Copyright © 2017 Elsevier Inc. All rights reserved.

Breast sentinel lymph node navigation with three-dimensional computed tomography-lymphography: a 12-year study.

PubMed

Yamamoto, Shigeru; Suga, Kazuyoshi; Maeda, Kazunari; Maeda, Noriko; Yoshimura, Kiyoshi; Oka, Masaaki

2016-05-01

To evaluate the utility of three-dimensional (3D) computed tomography (CT)-lymphography (LG) breast sentinel lymph node navigation in our institute. Between 2002 and 2013, we preoperatively identified sentinel lymph nodes (SLNs) in 576 clinically node-negative breast cancer patients with T1 and T2 breast cancer using 3D CT-LG method. SLN biopsy (SLNB) was performed in 557 of 576 patients using both the images of 3D CT-LG for guidance and the blue dye method. Using 3D CT-LG, SLNs were visualized in 569 (99%) of 576 patients. Of 569 patients, both lymphatic draining ducts and SLNs from the peritumoral and periareolar areas were visualized in 549 (96%) patients. Only SLNs without lymphatic draining ducts were visualized in 20 patients. Drainage lymphatic pathways visualized with 3D CT-LG (549 cases) were classified into four patterns: single route/single SLN (355 cases, 65%), multiple routes/single SLN (59 cases, 11%) single route/multiple SLNs (62 cases, 11%) and multiple routes/multiple SLNs (73 cases, 13%). SLNs were detected in 556 (99.8%) of 557 patients during SLNB. CT-LG is useful for preoperative visualization of SLNs and breast lymphatic draining routes. This preoperative method should contribute greatly to the easy detection of SLNs during SLNB.

Estimating occupancy and abundance using aerial images with imperfect detection

USGS Publications Warehouse

Williams, Perry J.; Hooten, Mevin B.; Womble, Jamie N.; Bower, Michael R.

2017-01-01

Species distribution and abundance are critical population characteristics for efficient management, conservation, and ecological insight. Point process models are a powerful tool for modelling distribution and abundance, and can incorporate many data types, including count data, presence-absence data, and presence-only data. Aerial photographic images are a natural tool for collecting data to fit point process models, but aerial images do not always capture all animals that are present at a site. Methods for estimating detection probability for aerial surveys usually include collecting auxiliary data to estimate the proportion of time animals are available to be detected.We developed an approach for fitting point process models using an N-mixture model framework to estimate detection probability for aerial occupancy and abundance surveys. Our method uses multiple aerial images taken of animals at the same spatial location to provide temporal replication of sample sites. The intersection of the images provide multiple counts of individuals at different times. We examined this approach using both simulated and real data of sea otters (Enhydra lutris kenyoni) in Glacier Bay National Park, southeastern Alaska.Using our proposed methods, we estimated detection probability of sea otters to be 0.76, the same as visual aerial surveys that have been used in the past. Further, simulations demonstrated that our approach is a promising tool for estimating occupancy, abundance, and detection probability from aerial photographic surveys.Our methods can be readily extended to data collected using unmanned aerial vehicles, as technology and regulations permit. The generality of our methods for other aerial surveys depends on how well surveys can be designed to meet the assumptions of N-mixture models.

Evaluation of Targeted Next-Generation Sequencing for Detection of Bovine Pathogens in Clinical Samples.

PubMed

Anis, Eman; Hawkins, Ian K; Ilha, Marcia R S; Woldemeskel, Moges W; Saliki, Jeremiah T; Wilkes, Rebecca P

2018-07-01

The laboratory diagnosis of infectious diseases, especially those caused by mixed infections, is challenging. Routinely, it requires submission of multiple samples to separate laboratories. Advances in next-generation sequencing (NGS) have provided the opportunity for development of a comprehensive method to identify infectious agents. This study describes the use of target-specific primers for PCR-mediated amplification with the NGS technology in which pathogen genomic regions of interest are enriched and selectively sequenced from clinical samples. In the study, 198 primers were designed to target 43 common bovine and small-ruminant bacterial, fungal, viral, and parasitic pathogens, and a bioinformatics tool was specifically constructed for the detection of targeted pathogens. The primers were confirmed to detect the intended pathogens by testing reference strains and isolates. The method was then validated using 60 clinical samples (including tissues, feces, and milk) that were also tested with other routine diagnostic techniques. The detection limits of the targeted NGS method were evaluated using 10 representative pathogens that were also tested by quantitative PCR (qPCR), and the NGS method was able to detect the organisms from samples with qPCR threshold cycle ( C T ) values in the 30s. The method was successful for the detection of multiple pathogens in the clinical samples, including some additional pathogens missed by the routine techniques because the specific tests needed for the particular organisms were not performed. The results demonstrate the feasibility of the approach and indicate that it is possible to incorporate NGS as a diagnostic tool in a cost-effective manner into a veterinary diagnostic laboratory. Copyright © 2018 Anis et al.

Towards establishing a human fecal contamination index in microbial source tracking

EPA Science Inventory

There have been significant advances in development of PCR-based methods to detect source associated DNA sequences (markers), but method evaluation has focused on performance with individual challenge samples. Little attention has been given to integration of multiple samples fro...

Detecting bacteria and Determining Their Susceptibility to Antibiotics by Stochastic Confinement in Nanoliter Droplets using Plug-Based Microfluidics

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

Boedicker, J.; Li, L; Kline, T

2008-01-01

This article describes plug-based microfluidic technology that enables rapid detection and drug susceptibility screening of bacteria in samples, including complex biological matrices, without pre-incubation. Unlike conventional bacterial culture and detection methods, which rely on incubation of a sample to increase the concentration of bacteria to detectable levels, this method confines individual bacteria into droplets nanoliters in volume. When single cells are confined into plugs of small volume such that the loading is less than one bacterium per plug, the detection time is proportional to plug volume. Confinement increases cell density and allows released molecules to accumulate around the cell, eliminatingmore » the pre-incubation step and reducing the time required to detect the bacteria. We refer to this approach as stochastic confinement. Using the microfluidic hybrid method, this technology was used to determine the antibiogram - or chart of antibiotic sensitivity - of methicillin-resistant Staphylococcus aureus (MRSA) to many antibiotics in a single experiment and to measure the minimal inhibitory concentration (MIC) of the drug cefoxitin (CFX) against this strain. In addition, this technology was used to distinguish between sensitive and resistant strains of S. aureus in samples of human blood plasma. High-throughput microfluidic techniques combined with single-cell measurements also enable multiple tests to be performed simultaneously on a single sample containing bacteria. This technology may provide a method of rapid and effective patient-specific treatment of bacterial infections and could be extended to a variety of applications that require multiple functional tests of bacterial samples on reduced timescales.« less

Detection of Salmonella spp. in veterinary samples by combining selective enrichment and real-time PCR.

PubMed

Goodman, Laura B; McDonough, Patrick L; Anderson, Renee R; Franklin-Guild, Rebecca J; Ryan, James R; Perkins, Gillian A; Thachil, Anil J; Glaser, Amy L; Thompson, Belinda S

2017-11-01

Rapid screening for enteric bacterial pathogens in clinical environments is essential for biosecurity. Salmonella found in veterinary hospitals, particularly Salmonella enterica serovar Dublin, can pose unique challenges for culture and testing because of its poor growth. Multiple Salmonella serovars including Dublin are emerging threats to public health given increasing prevalence and antimicrobial resistance. We adapted an automated food testing method to veterinary samples and evaluated the performance of the method in a variety of matrices including environmental samples ( n = 81), tissues ( n = 52), feces ( n = 148), and feed ( n = 29). A commercial kit was chosen as the basis for this approach in view of extensive performance characterizations published by multiple independent organizations. A workflow was established for efficiently and accurately testing veterinary matrices and environmental samples by use of real-time PCR after selective enrichment in Rappaport-Vassiliadis soya (RVS) medium. Using this method, the detection limit for S. Dublin improved by 100-fold over subculture on selective agars (eosin-methylene blue, brilliant green, and xylose-lysine-deoxycholate). Overall, the procedure was effective in detecting Salmonella spp. and provided next-day results.

Evaluation of a Pair-Wise Conflict Detection and Resolution Algorithm in a Multiple Aircraft Scenario

NASA Technical Reports Server (NTRS)

Carreno, Victor A.

2002-01-01

The KB3D algorithm is a pairwise conflict detection and resolution (CD&R) algorithm. It detects and generates trajectory vectoring for an aircraft which has been predicted to be in an airspace minima violation within a given look-ahead time. It has been proven, using mechanized theorem proving techniques, that for a pair of aircraft, KB3D produces at least one vectoring solution and that all solutions produced are correct. Although solutions produced by the algorithm are mathematically correct, they might not be physically executable by an aircraft or might not solve multiple aircraft conflicts. This paper describes a simple solution selection method which assesses all solutions generated by KB3D and determines the solution to be executed. The solution selection method and KB3D are evaluated using a simulation in which N aircraft fly in a free-flight environment and each aircraft in the simulation uses KB3D to maintain separation. Specifically, the solution selection method filters KB3D solutions which are procedurally undesirable or physically not executable and uses a predetermined criteria for selection.

Stepwise and stagewise approaches for spatial cluster detection

PubMed Central

Xu, Jiale

2016-01-01

Spatial cluster detection is an important tool in many areas such as sociology, botany and public health. Previous work has mostly taken either hypothesis testing framework or Bayesian framework. In this paper, we propose a few approaches under a frequentist variable selection framework for spatial cluster detection. The forward stepwise methods search for multiple clusters by iteratively adding currently most likely cluster while adjusting for the effects of previously identified clusters. The stagewise methods also consist of a series of steps, but with tiny step size in each iteration. We study the features and performances of our proposed methods using simulations on idealized grids or real geographic area. From the simulations, we compare the performance of the proposed methods in terms of estimation accuracy and power of detections. These methods are applied to the the well-known New York leukemia data as well as Indiana poverty data. PMID:27246273

Novel multiplex qualitative detection using universal primer-multiplex-PCR combined with pyrosequencing.

PubMed

Shang, Ying; Xu, Wentao; Wang, Yong; Xu, Yuancong; Huang, Kunlun

2017-12-15

This study described a novel multiplex qualitative detection method using pyrosequencing. Based on the principle of the universal primer-multiplex-PCR, only one sequencing primer was employed to realize the detection of the multiple targets. Samples containing three genetically modified (GM) crops in different proportions were used to validate the method. The dNTP dispensing order was designed based on the product sequences. Only 12 rounds (ATCTGATCGACT) of dNTPs addition and, often, as few as three rounds (CAT) under ideal conditions, were required to detect the GM events qualitatively, and sensitivity was as low as 1% of a mixture. However, when considering a mixture, calculating signal values allowed the proportion of each GM to be estimated. Based on these results, we concluded that our novel method not only realized detection but also allowed semi-quantitative detection of individual events. Copyright © 2017. Published by Elsevier Ltd.

Comparative quantification of human intestinal bacteria based on cPCR and LDR/LCR.

PubMed

Tang, Zhou-Rui; Li, Kai; Zhou, Yu-Xun; Xiao, Zhen-Xian; Xiao, Jun-Hua; Huang, Rui; Gu, Guo-Hao

2012-01-21

To establish a multiple detection method based on comparative polymerase chain reaction (cPCR) and ligase detection reaction (LDR)/ligase chain reaction (LCR) to quantify the intestinal bacterial components. Comparative quantification of 16S rDNAs from different intestinal bacterial components was used to quantify multiple intestinal bacteria. The 16S rDNAs of different bacteria were amplified simultaneously by cPCR. The LDR/LCR was examined to actualize the genotyping and quantification. Two beneficial (Bifidobacterium, Lactobacillus) and three conditionally pathogenic bacteria (Enterococcus, Enterobacterium and Eubacterium) were used in this detection. With cloned standard bacterial 16S rDNAs, standard curves were prepared to validate the quantitative relations between the ratio of original concentrations of two templates and the ratio of the fluorescence signals of their final ligation products. The internal controls were added to monitor the whole detection flow. The quantity ratio between two bacteria was tested. cPCR and LDR revealed obvious linear correlations with standard DNAs, but cPCR and LCR did not. In the sample test, the distributions of the quantity ratio between each two bacterial species were obtained. There were significant differences among these distributions in the total samples. But these distributions of quantity ratio of each two bacteria remained stable among groups divided by age or sex. The detection method in this study can be used to conduct multiple intestinal bacteria genotyping and quantification, and to monitor the human intestinal health status as well.

Effect of the sequence data deluge on the performance of methods for detecting protein functional residues.

PubMed

Garrido-Martín, Diego; Pazos, Florencio

2018-02-27

The exponential accumulation of new sequences in public databases is expected to improve the performance of all the approaches for predicting protein structural and functional features. Nevertheless, this was never assessed or quantified for some widely used methodologies, such as those aimed at detecting functional sites and functional subfamilies in protein multiple sequence alignments. Using raw protein sequences as only input, these approaches can detect fully conserved positions, as well as those with a family-dependent conservation pattern. Both types of residues are routinely used as predictors of functional sites and, consequently, understanding how the sequence content of the databases affects them is relevant and timely. In this work we evaluate how the growth and change with time in the content of sequence databases affect five sequence-based approaches for detecting functional sites and subfamilies. We do that by recreating historical versions of the multiple sequence alignments that would have been obtained in the past based on the database contents at different time points, covering a period of 20 years. Applying the methods to these historical alignments allows quantifying the temporal variation in their performance. Our results show that the number of families to which these methods can be applied sharply increases with time, while their ability to detect potentially functional residues remains almost constant. These results are informative for the methods' developers and final users, and may have implications in the design of new sequencing initiatives.

Screening for Multiple Genes Influencing Dyslexia.

ERIC Educational Resources Information Center

Smith, Shelley D.; And Others

1991-01-01

Examines the "sib pair" method of linkage analysis designed to locate genes influencing dyslexia, which has several advantages over the "LOD" score method. Notes that the sib pair analysis was able to detect the same linkages as the LOD method, plus a possible third region. Confirms that the sib pair method is an effective means of screening. (RS)

Addressing the Analytic Challenges of Cross-Sectional Pediatric Pneumonia Etiology Data.

PubMed

Hammitt, Laura L; Feikin, Daniel R; Scott, J Anthony G; Zeger, Scott L; Murdoch, David R; O'Brien, Katherine L; Deloria Knoll, Maria

2017-06-15

Despite tremendous advances in diagnostic laboratory technology, identifying the pathogen(s) causing pneumonia remains challenging because the infected lung tissue cannot usually be sampled for testing. Consequently, to obtain information about pneumonia etiology, clinicians and researchers test specimens distant to the site of infection. These tests may lack sensitivity (eg, blood culture, which is only positive in a small proportion of children with pneumonia) and/or specificity (eg, detection of pathogens in upper respiratory tract specimens, which may indicate asymptomatic carriage or a less severe syndrome, such as upper respiratory infection). While highly sensitive nucleic acid detection methods and testing of multiple specimens improve sensitivity, multiple pathogens are often detected and this adds complexity to the interpretation as the etiologic significance of results may be unclear (ie, the pneumonia may be caused by none, one, some, or all of the pathogens detected). Some of these challenges can be addressed by adjusting positivity rates to account for poor sensitivity or incorporating test results from controls without pneumonia to account for poor specificity. However, no classical analytic methods can account for measurement error (ie, sensitivity and specificity) for multiple specimen types and integrate the results of measurements for multiple pathogens to produce an accurate understanding of etiology. We describe the major analytic challenges in determining pneumonia etiology and review how the common analytical approaches (eg, descriptive, case-control, attributable fraction, latent class analysis) address some but not all challenges. We demonstrate how these limitations necessitate a new, integrated analytical approach to pneumonia etiology data. © The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America.

Addressing the Analytic Challenges of Cross-Sectional Pediatric Pneumonia Etiology Data

PubMed Central

Feikin, Daniel R.; Scott, J. Anthony G.; Zeger, Scott L.; Murdoch, David R.; O’Brien, Katherine L.; Deloria Knoll, Maria

2017-01-01

Abstract Despite tremendous advances in diagnostic laboratory technology, identifying the pathogen(s) causing pneumonia remains challenging because the infected lung tissue cannot usually be sampled for testing. Consequently, to obtain information about pneumonia etiology, clinicians and researchers test specimens distant to the site of infection. These tests may lack sensitivity (eg, blood culture, which is only positive in a small proportion of children with pneumonia) and/or specificity (eg, detection of pathogens in upper respiratory tract specimens, which may indicate asymptomatic carriage or a less severe syndrome, such as upper respiratory infection). While highly sensitive nucleic acid detection methods and testing of multiple specimens improve sensitivity, multiple pathogens are often detected and this adds complexity to the interpretation as the etiologic significance of results may be unclear (ie, the pneumonia may be caused by none, one, some, or all of the pathogens detected). Some of these challenges can be addressed by adjusting positivity rates to account for poor sensitivity or incorporating test results from controls without pneumonia to account for poor specificity. However, no classical analytic methods can account for measurement error (ie, sensitivity and specificity) for multiple specimen types and integrate the results of measurements for multiple pathogens to produce an accurate understanding of etiology. We describe the major analytic challenges in determining pneumonia etiology and review how the common analytical approaches (eg, descriptive, case-control, attributable fraction, latent class analysis) address some but not all challenges. We demonstrate how these limitations necessitate a new, integrated analytical approach to pneumonia etiology data. PMID:28575372

An Algorithm Based Wavelet Entropy for Shadowing Effect of Human Detection Using Ultra-Wideband Bio-Radar

PubMed Central

Liu, Miao; Zhang, Yang; Liang, Fulai; Qi, Fugui; Lv, Hao; Wang, Jianqi; Zhang, Yang

2017-01-01

Ultra-wide band (UWB) radar for short-range human target detection is widely used to find and locate survivors in some rescue missions after a disaster. The results of the application of bistatic UWB radar for detecting multi-stationary human targets have shown that human targets close to the radar antennas are very often visible, while those farther from radar antennas are detected with less reliability. In this paper, on account of the significant difference of frequency content between the echo signal of the human target and that of noise in the shadowing region, an algorithm based on wavelet entropy is proposed to detect multiple targets. Our findings indicate that the entropy value of human targets was much lower than that of noise. Compared with the method of adaptive filtering and the energy spectrum, wavelet entropy can accurately detect the person farther from the radar antennas, and it can be employed as a useful tool in detecting multiple targets by bistatic UWB radar. PMID:28973988

An Algorithm Based Wavelet Entropy for Shadowing Effect of Human Detection Using Ultra-Wideband Bio-Radar.

PubMed

Xue, Huijun; Liu, Miao; Zhang, Yang; Liang, Fulai; Qi, Fugui; Chen, Fuming; Lv, Hao; Wang, Jianqi; Zhang, Yang

2017-09-30

Ultra-wide band (UWB) radar for short-range human target detection is widely used to find and locate survivors in some rescue missions after a disaster. The results of the application of bistatic UWB radar for detecting multi-stationary human targets have shown that human targets close to the radar antennas are very often visible, while those farther from radar antennas are detected with less reliability. In this paper, on account of the significant difference of frequency content between the echo signal of the human target and that of noise in the shadowing region, an algorithm based on wavelet entropy is proposed to detect multiple targets. Our findings indicate that the entropy value of human targets was much lower than that of noise. Compared with the method of adaptive filtering and the energy spectrum, wavelet entropy can accurately detect the person farther from the radar antennas, and it can be employed as a useful tool in detecting multiple targets by bistatic UWB radar.

Information fusion for diabetic retinopathy CAD in digital color fundus photographs.

PubMed

Niemeijer, Meindert; Abramoff, Michael D; van Ginneken, Bram

2009-05-01

The purpose of computer-aided detection or diagnosis (CAD) technology has so far been to serve as a second reader. If, however, all relevant lesions in an image can be detected by CAD algorithms, use of CAD for automatic reading or prescreening may become feasible. This work addresses the question how to fuse information from multiple CAD algorithms, operating on multiple images that comprise an exam, to determine a likelihood that the exam is normal and would not require further inspection by human operators. We focus on retinal image screening for diabetic retinopathy, a common complication of diabetes. Current CAD systems are not designed to automatically evaluate complete exams consisting of multiple images for which several detection algorithm output sets are available. Information fusion will potentially play a crucial role in enabling the application of CAD technology to the automatic screening problem. Several different fusion methods are proposed and their effect on the performance of a complete comprehensive automatic diabetic retinopathy screening system is evaluated. Experiments show that the choice of fusion method can have a large impact on system performance. The complete system was evaluated on a set of 15,000 exams (60,000 images). The best performing fusion method obtained an area under the receiver operator characteristic curve of 0.881. This indicates that automated prescreening could be applied in diabetic retinopathy screening programs.

Characterization of Fissile Assemblies Using Low-Efficiency Detection Systems

DOE PAGES

Chapline, George F.; Verbeke, Jerome M.

2017-02-02

Here, we have investigated the possibility that the amount, chemical form, multiplication, and shape of the fissile material in an assembly can be passively assayed using scintillator detection systems by only measuring the fast neutron pulse height distribution and distribution of time intervals Δt between fast neutrons. We have previously demonstrated that the alpha-ratio can be obtained from the observed pulse height distribution for fast neutrons. In this paper we report that we report that when the distribution of time intervals is plotted as a function of logΔt, the position of the correlated neutron peak is nearly independent of detectormore » efficiency and determines the internal relaxation rate for fast neutrons. If this information is combined with knowledge of the alpha-ratio, then the position of the minimum between the correlated and uncorrelated peaks can be used to rapidly estimate the mass, multiplication, and shape of fissile material. This method does not require a priori knowledge of either the efficiency for neutron detection or the alpha-ratio. Although our method neglects 3-neutron correlations, we have used previously obtained experimental data for metallic and oxide forms of Pu to demonstrate that our method yields good estimates for multiplications as large as 2, and that the only constraint on detector efficiency/observation time is that a peak in the interval time distribution due to correlated neutrons is visible.« less

Integration of single-molecule detection with magnetic separation for multiplexed detection of DNA glycosylases.

PubMed

Li, Chen-Chen; Zhang, Yan; Tang, Bo; Zhang, Chun-Yang

2018-06-05

We combine single-molecule detection with magnetic separation for simultaneous measurement of human 8-oxoG DNA glycosylase 1 (hOGG1) and uracil DNA glycosylase (UDG) based on excision repair-initiated endonuclease IV (Endo IV)-assisted signal amplification. This method can sensitively detect multiple DNA glycosylases, and it can be further applied for the simultaneous measurement of enzyme kinetic parameters and screening of both hOGG1 and UDG inhibitors.

The PneuCarriage Project: A Multi-Centre Comparative Study to Identify the Best Serotyping Methods for Examining Pneumococcal Carriage in Vaccine Evaluation Studies

PubMed Central

Satzke, Catherine; Dunne, Eileen M.; Porter, Barbara D.; Klugman, Keith P.; Mulholland, E. Kim

2015-01-01

Background The pneumococcus is a diverse pathogen whose primary niche is the nasopharynx. Over 90 different serotypes exist, and nasopharyngeal carriage of multiple serotypes is common. Understanding pneumococcal carriage is essential for evaluating the impact of pneumococcal vaccines. Traditional serotyping methods are cumbersome and insufficient for detecting multiple serotype carriage, and there are few data comparing the new methods that have been developed over the past decade. We established the PneuCarriage project, a large, international multi-centre study dedicated to the identification of the best pneumococcal serotyping methods for carriage studies. Methods and Findings Reference sample sets were distributed to 15 research groups for blinded testing. Twenty pneumococcal serotyping methods were used to test 81 laboratory-prepared (spiked) samples. The five top-performing methods were used to test 260 nasopharyngeal (field) samples collected from children in six high-burden countries. Sensitivity and positive predictive value (PPV) were determined for the test methods and the reference method (traditional serotyping of >100 colonies from each sample). For the alternate serotyping methods, the overall sensitivity ranged from 1% to 99% (reference method 98%), and PPV from 8% to 100% (reference method 100%), when testing the spiked samples. Fifteen methods had ≥70% sensitivity to detect the dominant (major) serotype, whilst only eight methods had ≥70% sensitivity to detect minor serotypes. For the field samples, the overall sensitivity ranged from 74.2% to 95.8% (reference method 93.8%), and PPV from 82.2% to 96.4% (reference method 99.6%). The microarray had the highest sensitivity (95.8%) and high PPV (93.7%). The major limitation of this study is that not all of the available alternative serotyping methods were included. Conclusions Most methods were able to detect the dominant serotype in a sample, but many performed poorly in detecting the minor serotype populations. Microarray with a culture amplification step was the top-performing method. Results from this comprehensive evaluation will inform future vaccine evaluation and impact studies, particularly in low-income settings, where pneumococcal disease burden remains high. PMID:26575033

A Method of Detections' Fusion for GNSS Anti-Spoofing.

PubMed

Tao, Huiqi; Li, Hong; Lu, Mingquan

2016-12-19

The spoofing attack is one of the security threats of systems depending on the Global Navigation Satellite System (GNSS). There have been many GNSS spoofing detection methods, and each of them focuses on a characteristic of the GNSS signal or a measurement that the receiver has obtained. The method based on a single detector is insufficient against spoofing attacks in some scenarios. How to fuse multiple detections together is a problem that concerns the performance of GNSS anti-spoofing. Scholars have put forward a model to fuse different detection results based on the Dempster-Shafer theory (DST) of evidence combination. However, there are some problems in the application. The main challenge is the valuation of the belief function, which is a key issue in DST. This paper proposes a practical method of detections' fusion based on an approach to assign the belief function for spoofing detections. The frame of discernment is simplified, and the hard decision of hypothesis testing is replaced by the soft decision; then, the belief functions for some detections can be evaluated. The method is discussed in detail, and a performance evaluation is provided, as well. Detections' fusion reduces false alarms of detection and makes the result more reliable. Experimental results based on public test datasets demonstrate the performance of the proposed method.

Development of a Raman chemical image detection algorithm for authenticating dry milk

USDA-ARS?s Scientific Manuscript database

This research developed a Raman chemical imaging method for detecting multiple adulterants in skim milk powder. Ammonium sulfate, dicyandiamide, melamine, and urea were mixed into the milk powder as chemical adulterants in the concentration range of 0.1–5.0%. A Raman imaging system using a 785-nm la...

Mouse epileptic seizure detection with multiple EEG features and simple thresholding technique

NASA Astrophysics Data System (ADS)

Tieng, Quang M.; Anbazhagan, Ashwin; Chen, Min; Reutens, David C.

2017-12-01

Objective. Epilepsy is a common neurological disorder characterized by recurrent, unprovoked seizures. The search for new treatments for seizures and epilepsy relies upon studies in animal models of epilepsy. To capture data on seizures, many applications require prolonged electroencephalography (EEG) with recordings that generate voluminous data. The desire for efficient evaluation of these recordings motivates the development of automated seizure detection algorithms. Approach. A new seizure detection method is proposed, based on multiple features and a simple thresholding technique. The features are derived from chaos theory, information theory and the power spectrum of EEG recordings and optimally exploit both linear and nonlinear characteristics of EEG data. Main result. The proposed method was tested with real EEG data from an experimental mouse model of epilepsy and distinguished seizures from other patterns with high sensitivity and specificity. Significance. The proposed approach introduces two new features: negative logarithm of adaptive correlation integral and power spectral coherence ratio. The combination of these new features with two previously described features, entropy and phase coherence, improved seizure detection accuracy significantly. Negative logarithm of adaptive correlation integral can also be used to compute the duration of automatically detected seizures.

RS-Forest: A Rapid Density Estimator for Streaming Anomaly Detection.

PubMed

Wu, Ke; Zhang, Kun; Fan, Wei; Edwards, Andrea; Yu, Philip S

Anomaly detection in streaming data is of high interest in numerous application domains. In this paper, we propose a novel one-class semi-supervised algorithm to detect anomalies in streaming data. Underlying the algorithm is a fast and accurate density estimator implemented by multiple fully randomized space trees (RS-Trees), named RS-Forest. The piecewise constant density estimate of each RS-tree is defined on the tree node into which an instance falls. Each incoming instance in a data stream is scored by the density estimates averaged over all trees in the forest. Two strategies, statistical attribute range estimation of high probability guarantee and dual node profiles for rapid model update, are seamlessly integrated into RS-Forest to systematically address the ever-evolving nature of data streams. We derive the theoretical upper bound for the proposed algorithm and analyze its asymptotic properties via bias-variance decomposition. Empirical comparisons to the state-of-the-art methods on multiple benchmark datasets demonstrate that the proposed method features high detection rate, fast response, and insensitivity to most of the parameter settings. Algorithm implementations and datasets are available upon request.

RS-Forest: A Rapid Density Estimator for Streaming Anomaly Detection

PubMed Central

Wu, Ke; Zhang, Kun; Fan, Wei; Edwards, Andrea; Yu, Philip S.

2015-01-01

Anomaly detection in streaming data is of high interest in numerous application domains. In this paper, we propose a novel one-class semi-supervised algorithm to detect anomalies in streaming data. Underlying the algorithm is a fast and accurate density estimator implemented by multiple fully randomized space trees (RS-Trees), named RS-Forest. The piecewise constant density estimate of each RS-tree is defined on the tree node into which an instance falls. Each incoming instance in a data stream is scored by the density estimates averaged over all trees in the forest. Two strategies, statistical attribute range estimation of high probability guarantee and dual node profiles for rapid model update, are seamlessly integrated into RS-Forest to systematically address the ever-evolving nature of data streams. We derive the theoretical upper bound for the proposed algorithm and analyze its asymptotic properties via bias-variance decomposition. Empirical comparisons to the state-of-the-art methods on multiple benchmark datasets demonstrate that the proposed method features high detection rate, fast response, and insensitivity to most of the parameter settings. Algorithm implementations and datasets are available upon request. PMID:25685112

Thin Cloud Detection Method by Linear Combination Model of Cloud Image

NASA Astrophysics Data System (ADS)

Liu, L.; Li, J.; Wang, Y.; Xiao, Y.; Zhang, W.; Zhang, S.

2018-04-01

The existing cloud detection methods in photogrammetry often extract the image features from remote sensing images directly, and then use them to classify images into cloud or other things. But when the cloud is thin and small, these methods will be inaccurate. In this paper, a linear combination model of cloud images is proposed, by using this model, the underlying surface information of remote sensing images can be removed. So the cloud detection result can become more accurate. Firstly, the automatic cloud detection program in this paper uses the linear combination model to split the cloud information and surface information in the transparent cloud images, then uses different image features to recognize the cloud parts. In consideration of the computational efficiency, AdaBoost Classifier was introduced to combine the different features to establish a cloud classifier. AdaBoost Classifier can select the most effective features from many normal features, so the calculation time is largely reduced. Finally, we selected a cloud detection method based on tree structure and a multiple feature detection method using SVM classifier to compare with the proposed method, the experimental data shows that the proposed cloud detection program in this paper has high accuracy and fast calculation speed.

Detection of Multiple Pathogens in Serum Using Silica-Encapsulated Nanotags in a Surface-Enhanced Raman Scattering-Based Immunoassay.

PubMed

Neng, Jing; Li, Yina; Driscoll, Ashley J; Wilson, William C; Johnson, Patrick A

2018-06-06

A robust immunoassay based on surface-enhanced Raman scattering (SERS) has been developed to simultaneously detect trace quantities of multiple pathogenic antigens from West Nile virus, Rift Valley fever virus, and Yersinia pestis in fetal bovine serum. Antigens were detected by capture with silica-encapsulated nanotags and magnetic nanoparticles conjugated with polyclonal antibodies. The magnetic pull-down resulted in aggregation of the immune complexes, and the silica-encapsulated nanotags provided distinct spectra corresponding to each antigen captured. The limit of detection was ∼10 pg/mL in 20% fetal bovine serum, a significant improvement over previous studies in terms of sensitivity, level of multiplexing, and medium complexity. This highly sensitive multiplex immunoassay platform provides a promising method to detect various antigens directly in crude serum samples without the tedious process of sample preparation, which is desirable for on-site diagnostic testing and real-time disease monitoring.

Indicator saturation: a novel approach to detect multiple breaks in geodetic time series.

NASA Astrophysics Data System (ADS)

Jackson, L. P.; Pretis, F.; Williams, S. D. P.

2016-12-01

Geodetic time series can record long term trends, quasi-periodic signals at a variety of time scales from days to decades, and sudden breaks due to natural or anthropogenic causes. The causes of breaks range from instrument replacement to earthquakes to unknown (i.e. no attributable cause). Furthermore, breaks can be permanent or short-lived and range at least two orders of magnitude in size (mm to 100's mm). To account for this range of possible signal-characteristics requires a flexible time series method that can distinguish between true and false breaks, outliers and time-varying trends. One such method, Indicator Saturation (IS) comes from the field of econometrics where analysing stochastic signals in these terms is a common problem. The IS approach differs from alternative break detection methods by considering every point in the time series as a break until it is demonstrated statistically that it is not. A linear model is constructed with a break function at every point in time, and all but statistically significant breaks are removed through a general-to-specific model selection algorithm for more variables than observations. The IS method is flexible because it allows multiple breaks of different forms (e.g. impulses, shifts in the mean, and changing trends) to be detected, while simultaneously modelling any underlying variation driven by additional covariates. We apply the IS method to identify breaks in a suite of synthetic GPS time series used for the Detection of Offsets in GPS Experiments (DOGEX). We optimise the method to maximise the ratio of true-positive to false-positive detections, which improves estimates of errors in the long term rates of land motion currently required by the GPS community.

Using a two-phase evolutionary framework to select multiple network spreaders based on community structure

NASA Astrophysics Data System (ADS)

Fu, Yu-Hsiang; Huang, Chung-Yuan; Sun, Chuen-Tsai

2016-11-01

Using network community structures to identify multiple influential spreaders is an appropriate method for analyzing the dissemination of information, ideas and infectious diseases. For example, data on spreaders selected from groups of customers who make similar purchases may be used to advertise products and to optimize limited resource allocation. Other examples include community detection approaches aimed at identifying structures and groups in social or complex networks. However, determining the number of communities in a network remains a challenge. In this paper we describe our proposal for a two-phase evolutionary framework (TPEF) for determining community numbers and maximizing community modularity. Lancichinetti-Fortunato-Radicchi benchmark networks were used to test our proposed method and to analyze execution time, community structure quality, convergence, and the network spreading effect. Results indicate that our proposed TPEF generates satisfactory levels of community quality and convergence. They also suggest a need for an index, mechanism or sampling technique to determine whether a community detection approach should be used for selecting multiple network spreaders.

Multiple-Beam Detection of Fast Transient Radio Sources

NASA Technical Reports Server (NTRS)

Thompson, David R.; Wagstaff, Kiri L.; Majid, Walid A.

2011-01-01

A method has been designed for using multiple independent stations to discriminate fast transient radio sources from local anomalies, such as antenna noise or radio frequency interference (RFI). This can improve the sensitivity of incoherent detection for geographically separated stations such as the very long baseline array (VLBA), the future square kilometer array (SKA), or any other coincident observations by multiple separated receivers. The transients are short, broadband pulses of radio energy, often just a few milliseconds long, emitted by a variety of exotic astronomical phenomena. They generally represent rare, high-energy events making them of great scientific value. For RFI-robust adaptive detection of transients, using multiple stations, a family of algorithms has been developed. The technique exploits the fact that the separated stations constitute statistically independent samples of the target. This can be used to adaptively ignore RFI events for superior sensitivity. If the antenna signals are independent and identically distributed (IID), then RFI events are simply outlier data points that can be removed through robust estimation such as a trimmed or Winsorized estimator. The alternative "trimmed" estimator is considered, which excises the strongest n signals from the list of short-beamed intensities. Because local RFI is independent at each antenna, this interference is unlikely to occur at many antennas on the same step. Trimming the strongest signals provides robustness to RFI that can theoretically outperform even the detection performance of the same number of antennas at a single site. This algorithm requires sorting the signals at each time step and dispersion measure, an operation that is computationally tractable for existing array sizes. An alternative uses the various stations to form an ensemble estimate of the conditional density function (CDF) evaluated at each time step. Both methods outperform standard detection strategies on a test sequence of VLBA data, and both are efficient enough for deployment in real-time, online transient detection applications.

The PneuCarriage Project: A Multi-Centre Comparative Study to Identify the Best Serotyping Methods for Examining Pneumococcal Carriage in Vaccine Evaluation Studies.

PubMed

Satzke, Catherine; Dunne, Eileen M; Porter, Barbara D; Klugman, Keith P; Mulholland, E Kim

2015-11-01

The pneumococcus is a diverse pathogen whose primary niche is the nasopharynx. Over 90 different serotypes exist, and nasopharyngeal carriage of multiple serotypes is common. Understanding pneumococcal carriage is essential for evaluating the impact of pneumococcal vaccines. Traditional serotyping methods are cumbersome and insufficient for detecting multiple serotype carriage, and there are few data comparing the new methods that have been developed over the past decade. We established the PneuCarriage project, a large, international multi-centre study dedicated to the identification of the best pneumococcal serotyping methods for carriage studies. Reference sample sets were distributed to 15 research groups for blinded testing. Twenty pneumococcal serotyping methods were used to test 81 laboratory-prepared (spiked) samples. The five top-performing methods were used to test 260 nasopharyngeal (field) samples collected from children in six high-burden countries. Sensitivity and positive predictive value (PPV) were determined for the test methods and the reference method (traditional serotyping of >100 colonies from each sample). For the alternate serotyping methods, the overall sensitivity ranged from 1% to 99% (reference method 98%), and PPV from 8% to 100% (reference method 100%), when testing the spiked samples. Fifteen methods had ≥70% sensitivity to detect the dominant (major) serotype, whilst only eight methods had ≥70% sensitivity to detect minor serotypes. For the field samples, the overall sensitivity ranged from 74.2% to 95.8% (reference method 93.8%), and PPV from 82.2% to 96.4% (reference method 99.6%). The microarray had the highest sensitivity (95.8%) and high PPV (93.7%). The major limitation of this study is that not all of the available alternative serotyping methods were included. Most methods were able to detect the dominant serotype in a sample, but many performed poorly in detecting the minor serotype populations. Microarray with a culture amplification step was the top-performing method. Results from this comprehensive evaluation will inform future vaccine evaluation and impact studies, particularly in low-income settings, where pneumococcal disease burden remains high.

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.

Field calibration of blowfly-derived DNA against traditional methods for assessing mammal diversity in tropical forests.

PubMed

Lee, Ping-Shin; Gan, Han Ming; Clements, Gopalasamy Reuben; Wilson, John-James

2016-11-01

Mammal diversity assessments based on DNA derived from invertebrates have been suggested as alternatives to assessments based on traditional methods; however, no study has field-tested both approaches simultaneously. In Peninsular Malaysia, we calibrated the performance of mammal DNA derived from blowflies (Diptera: Calliphoridae) against traditional methods used to detect species. We first compared five methods (cage trapping, mist netting, hair trapping, scat collection, and blowfly-derived DNA) in a forest reserve with no recent reports of megafauna. Blowfly-derived DNA and mist netting detected the joint highest number of species (n = 6). Only one species was detected by multiple methods. Compared to the other methods, blowfly-derived DNA detected both volant and non-volant species. In another forest reserve, rich in megafauna, we calibrated blowfly-derived DNA against camera traps. Blowfly-derived DNA detected more species (n = 11) than camera traps (n = 9), with only one species detected by both methods. The rarefaction curve indicated that blowfly-derived DNA would continue to detect more species with greater sampling effort. With further calibration, blowfly-derived DNA may join the list of traditional field methods. Areas for further investigation include blowfly feeding and dispersal biology, primer biases, and the assembly of a comprehensive and taxonomically-consistent DNA barcode reference library.

Allelic-based gene-gene interaction associated with quantitative traits.

PubMed

Jung, Jeesun; Sun, Bin; Kwon, Deukwoo; Koller, Daniel L; Foroud, Tatiana M

2009-05-01

Recent studies have shown that quantitative phenotypes may be influenced not only by multiple single nucleotide polymorphisms (SNPs) within a gene but also by the interaction between SNPs at unlinked genes. We propose a new statistical approach that can detect gene-gene interactions at the allelic level which contribute to the phenotypic variation in a quantitative trait. By testing for the association of allelic combinations at multiple unlinked loci with a quantitative trait, we can detect the SNP allelic interaction whether or not it can be detected as a main effect. Our proposed method assigns a score to unrelated subjects according to their allelic combination inferred from observed genotypes at two or more unlinked SNPs, and then tests for the association of the allelic score with a quantitative trait. To investigate the statistical properties of the proposed method, we performed a simulation study to estimate type I error rates and power and demonstrated that this allelic approach achieves greater power than the more commonly used genotypic approach to test for gene-gene interaction. As an example, the proposed method was applied to data obtained as part of a candidate gene study of sodium retention by the kidney. We found that this method detects an interaction between the calcium-sensing receptor gene (CaSR), the chloride channel gene (CLCNKB) and the Na, K, 2Cl cotransporter gene (CLC12A1) that contributes to variation in diastolic blood pressure.

Multiple-clone infections of Plasmodium vivax: definition of a panel of markers for molecular epidemiology.

PubMed

de Souza, Aracele M; de Araújo, Flávia C F; Fontes, Cor J F; Carvalho, Luzia H; de Brito, Cristiana F A; de Sousa, Taís N

2015-08-25

Plasmodium vivax infections commonly contain multiple genetically distinct parasite clones. The detection of multiple-clone infections depends on several factors, such as the accuracy of the genotyping method, and the type and number of the molecular markers analysed. Characterizing the multiplicity of infection has broad implications that range from population genetic studies of the parasite to malaria treatment and control. This study compared and evaluated the efficiency of neutral and non-neutral markers that are widely used in studies of molecular epidemiology to detect the multiplicity of P. vivax infection. The performance of six markers was evaluated using 11 mixtures of DNA with well-defined proportions of two different parasite genotypes for each marker. These mixtures were generated by mixing cloned PCR products or patient-derived genomic DNA. In addition, 51 samples of natural infections from the Brazil were genotyped for all markers. The PCR-capillary electrophoresis-based method was used to permit direct comparisons among the markers. The criteria for differentiating minor peaks from artifacts were also evaluated. The analysis of DNA mixtures showed that the tandem repeat MN21 and the polymorphic blocks 2 (msp1B2) and 10 (msp1B10) of merozoite surface protein-1 allowed for the estimation of the expected ratio of both alleles in the majority of preparations. Nevertheless, msp1B2 was not able to detect the majority of multiple-clone infections in field samples; it identified only 6 % of these infections. The merozoite surface protein-3 alpha and microsatellites (PvMS6 and PvMS7) did not accurately estimate the relative clonal proportions in artificial mixtures, but the microsatellites performed well in detecting natural multiple-clone infections. Notably, the use of a less stringent criterion to score rare alleles significantly increased the sensitivity of the detection of multi-clonal infections. Depending on the type of marker used, a considerable amplification bias was observed, which may have serious implications for the characterization of the complexity of a P. vivax infection. Based on the performance of markers in artificial mixtures of DNA and natural infections, a minimum panel of four genetic markers (PvMS6, PvMS7, MN21, and msp1B10) was defined, and these markers are highly informative regarding the genetic variability of P. vivax populations.

Detection of Road Surface States from Tire Noise Using Neural Network Analysis

NASA Astrophysics Data System (ADS)

Kongrattanaprasert, Wuttiwat; Nomura, Hideyuki; Kamakura, Tomoo; Ueda, Koji

This report proposes a new processing method for automatically detecting the states of road surfaces from tire noises of passing vehicles. In addition to multiple indicators of the signal features in the frequency domain, we propose a few feature indicators in the time domain to successfully classify the road states into four categories: snowy, slushy, wet, and dry states. The method is based on artificial neural networks. The proposed classification is carried out in multiple neural networks using learning vector quantization. The outcomes of the networks are then integrated by the voting decision-making scheme. Experimental results obtained from recorded signals for ten days in the snowy season demonstrated that an accuracy of approximately 90% can be attained for predicting road surface states using only tire noise data.

A Novel Technique to Detect Code for SAC-OCDMA System

NASA Astrophysics Data System (ADS)

Bharti, Manisha; Kumar, Manoj; Sharma, Ajay K.

2018-04-01

The main task of optical code division multiple access (OCDMA) system is the detection of code used by a user in presence of multiple access interference (MAI). In this paper, new method of detection known as XOR subtraction detection for spectral amplitude coding OCDMA (SAC-OCDMA) based on double weight codes has been proposed and presented. As MAI is the main source of performance deterioration in OCDMA system, therefore, SAC technique is used in this paper to eliminate the effect of MAI up to a large extent. A comparative analysis is then made between the proposed scheme and other conventional detection schemes used like complimentary subtraction detection, AND subtraction detection and NAND subtraction detection. The system performance is characterized by Q-factor, BER and received optical power (ROP) with respect to input laser power and fiber length. The theoretical and simulation investigations reveal that the proposed detection technique provides better quality factor, security and received power in comparison to other conventional techniques. The wide opening of eye in case of proposed technique also proves its robustness.

ICPD-A New Peak Detection Algorithm for LC/MS

PubMed Central

2010-01-01

Background The identification and quantification of proteins using label-free Liquid Chromatography/Mass Spectrometry (LC/MS) play crucial roles in biological and biomedical research. Increasing evidence has shown that biomarkers are often low abundance proteins. However, LC/MS systems are subject to considerable noise and sample variability, whose statistical characteristics are still elusive, making computational identification of low abundance proteins extremely challenging. As a result, the inability of identifying low abundance proteins in a proteomic study is the main bottleneck in protein biomarker discovery. Results In this paper, we propose a new peak detection method called Information Combining Peak Detection (ICPD ) for high resolution LC/MS. In LC/MS, peptides elute during a certain time period and as a result, peptide isotope patterns are registered in multiple MS scans. The key feature of the new algorithm is that the observed isotope patterns registered in multiple scans are combined together for estimating the likelihood of the peptide existence. An isotope pattern matching score based on the likelihood probability is provided and utilized for peak detection. Conclusions The performance of the new algorithm is evaluated based on protein standards with 48 known proteins. The evaluation shows better peak detection accuracy for low abundance proteins than other LC/MS peak detection methods. PMID:21143790

Viral Diagnostics in Plants Using Next Generation Sequencing: Computational Analysis in Practice

PubMed Central

Jones, Susan; Baizan-Edge, Amanda; MacFarlane, Stuart; Torrance, Lesley

2017-01-01

Viruses cause significant yield and quality losses in a wide variety of cultivated crops. Hence, the detection and identification of viruses is a crucial facet of successful crop production and of great significance in terms of world food security. Whilst the adoption of molecular techniques such as RT-PCR has increased the speed and accuracy of viral diagnostics, such techniques only allow the detection of known viruses, i.e., each test is specific to one or a small number of related viruses. Therefore, unknown viruses can be missed and testing can be slow and expensive if molecular tests are unavailable. Methods for simultaneous detection of multiple viruses have been developed, and (NGS) is now a principal focus of this area, as it enables unbiased and hypothesis-free testing of plant samples. The development of NGS protocols capable of detecting multiple known and emergent viruses present in infected material is proving to be a major advance for crops, nuclear stocks or imported plants and germplasm, in which disease symptoms are absent, unspecific or only triggered by multiple viruses. Researchers want to answer the question “how many different viruses are present in this crop plant?” without knowing what they are looking for: RNA-sequencing (RNA-seq) of plant material allows this question to be addressed. As well as needing efficient nucleic acid extraction and enrichment protocols, virus detection using RNA-seq requires fast and robust bioinformatics methods to enable host sequence removal and virus classification. In this review recent studies that use RNA-seq for virus detection in a variety of crop plants are discussed with specific emphasis on the computational methods implemented. The main features of a number of specific bioinformatics workflows developed for virus detection from NGS data are also outlined and possible reasons why these have not yet been widely adopted are discussed. The review concludes by discussing the future directions of this field, including the use of bioinformatics tools for virus detection deployed in analytical environments using cloud computing. PMID:29123534

Microbleed detection using automated segmentation (MIDAS): a new method applicable to standard clinical MR images.

PubMed

Seghier, Mohamed L; Kolanko, Magdalena A; Leff, Alexander P; Jäger, Hans R; Gregoire, Simone M; Werring, David J

2011-03-23

Cerebral microbleeds, visible on gradient-recalled echo (GRE) T2* MRI, have generated increasing interest as an imaging marker of small vessel diseases, with relevance for intracerebral bleeding risk or brain dysfunction. Manual rating methods have limited reliability and are time-consuming. We developed a new method for microbleed detection using automated segmentation (MIDAS) and compared it with a validated visual rating system. In thirty consecutive stroke service patients, standard GRE T2* images were acquired and manually rated for microbleeds by a trained observer. After spatially normalizing each patient's GRE T2* images into a standard stereotaxic space, the automated microbleed detection algorithm (MIDAS) identified cerebral microbleeds by explicitly incorporating an "extra" tissue class for abnormal voxels within a unified segmentation-normalization model. The agreement between manual and automated methods was assessed using the intraclass correlation coefficient (ICC) and Kappa statistic. We found that MIDAS had generally moderate to good agreement with the manual reference method for the presence of lobar microbleeds (Kappa = 0.43, improved to 0.65 after manual exclusion of obvious artefacts). Agreement for the number of microbleeds was very good for lobar regions: (ICC = 0.71, improved to ICC = 0.87). MIDAS successfully detected all patients with multiple (≥2) lobar microbleeds. MIDAS can identify microbleeds on standard MR datasets, and with an additional rapid editing step shows good agreement with a validated visual rating system. MIDAS may be useful in screening for multiple lobar microbleeds.

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.

Orphan spin operators enable the acquisition of multiple 2D and 3D magic angle spinning solid-state NMR spectra

NASA Astrophysics Data System (ADS)

Gopinath, T.; Veglia, Gianluigi

2013-05-01

We propose a general method that enables the acquisition of multiple 2D and 3D solid-state NMR spectra for U-13C, 15N-labeled proteins. This method, called MEIOSIS (Multiple ExperIments via Orphan SpIn operatorS), makes it possible to detect four coherence transfer pathways simultaneously, utilizing orphan (i.e., neglected) spin operators of nuclear spin polarization generated during 15N-13C cross polarization (CP). In the MEIOSIS experiments, two phase-encoded free-induction decays are decoded into independent nuclear polarization pathways using Hadamard transformations. As a proof of principle, we show the acquisition of multiple 2D and 3D spectra of U-13C, 15N-labeled microcrystalline ubiquitin. Hadamard decoding of CP coherences into multiple independent spin operators is a new concept in solid-state NMR and is extendable to many other multidimensional experiments. The MEIOSIS method will increase the throughput of solid-state NMR techniques for microcrystalline proteins, membrane proteins, and protein fibrils.

Kinetics of T-cell receptor-dependent antigen recognition determined in vivo by multi-spectral normalized epifluorescence laser scanning

NASA Astrophysics Data System (ADS)

Favicchio, Rosy; Zacharakis, Giannis; Oikonomaki, Katerina; Zacharopoulos, Athanasios; Mamalaki, Clio; Ripoll, Jorge

2012-07-01

Detection of multiple fluorophores in conditions of low signal represents a limiting factor for the application of in vivo optical imaging techniques in immunology where fluorescent labels report for different functional characteristics. A noninvasive in vivo Multi-Spectral Normalized Epifluorescence Laser scanning (M-SNELS) method was developed for the simultaneous and quantitative detection of multiple fluorophores in low signal to noise ratios and used to follow T-cell activation and clonal expansion. Colocalized DsRed- and GFP-labeled T cells were followed in tandem during the mounting of an immune response. Spectral unmixing was used to distinguish the overlapping fluorescent emissions representative of the two distinct cell populations and longitudinal data reported the discrete pattern of antigen-driven proliferation. Retrieved values were validated both in vitro and in vivo with flow cytometry and significant correlation between all methodologies was achieved. Noninvasive M-SNELS successfully quantified two colocalized fluorescent populations and provides a valid alternative imaging approach to traditional invasive methods for detecting T cell dynamics.

Damage detection of structures identified with deterministic-stochastic models using seismic data.

PubMed

Huang, Ming-Chih; Wang, Yen-Po; Chang, Ming-Lian

2014-01-01

A deterministic-stochastic subspace identification method is adopted and experimentally verified in this study to identify the equivalent single-input-multiple-output system parameters of the discrete-time state equation. The method of damage locating vector (DLV) is then considered for damage detection. A series of shaking table tests using a five-storey steel frame has been conducted. Both single and multiple damage conditions at various locations have been considered. In the system identification analysis, either full or partial observation conditions have been taken into account. It has been shown that the damaged stories can be identified from global responses of the structure to earthquakes if sufficiently observed. In addition to detecting damage(s) with respect to the intact structure, identification of new or extended damages of the as-damaged counterpart has also been studied. This study gives further insights into the scheme in terms of effectiveness, robustness, and limitation for damage localization of frame systems.

Apparatus and method for detecting flaws in conductive material

DOEpatents

Hockey, Ronald L.; Riechers, Douglas M.

1999-01-01

The present invention is an improved sensing unit for detecting flaws in conductive material wherein the sensing coil is positioned away from a datum of either the datum point, the datum orientation, or a combination thereof. Position of the sensing coil away from a datum increases sensitivity for detecting flaws having a characteristic volume less than about 1 mm.sup.3, and further permits detection of subsurface flaws. Use of multiple sensing coils permits quantification of flaw area or volume.

Microbial and Bioconversion Production of D-xylitol and Its Detection and Application

PubMed Central

Chen, Xi; Jiang, Zi-Hua; Chen, Sanfeng; Qin, Wensheng

2010-01-01

D-Xylitol is found in low content as a natural constituent of many fruits and vegetables. It is a five-carbon sugar polyol and has been used as a food additive and sweetening agent to replace sucrose, especially for non-insulin dependent diabetics. It has multiple beneficial health effects, such as the prevention of dental caries, and acute otitis media. In industry, it has been produced by chemical reduction of D-xylose mainly from photosynthetic biomass hydrolysates. As an alternative method of chemical reduction, biosynthesis of D-xylitol has been focused on the metabolically engineered Saccharomyces cerevisiae and Candida strains. In order to detect D-xylitol in the production processes, several detection methods have been established, such as gas chromatography (GC)-based methods, high performance liquid chromatography (HPLC)-based methods, LC-MS methods, and capillary electrophoresis methods (CE). The advantages and disadvantages of these methods are compared in this review. PMID:21179590

A new method for registration of heterogeneous sensors in a dimensional measurement system

NASA Astrophysics Data System (ADS)

Zhao, Yan; Wang, Zhong; Fu, Luhua; Qu, Xinghua; Zhang, Heng; Liu, Changjie

2017-10-01

Registration of multiple sensors is a basic step in multi-sensor dimensional or coordinate measuring systems before any measurement. In most cases, a common standard is used to be measured by all sensors, and this may work well for general registration of multiple homogeneous sensors. However, when inhomogeneous sensors detect a common standard, it is usually very difficult to obtain the same information, because of the different working principles of the sensors. In this paper, a new method called multiple steps registration is proposed to register two sensors: a video camera sensor (VCS) and a tactile probe sensor (TPS). In this method, the two sensors measure two separated standards: a chrome circle on a reticle and a reference sphere with a constant distance between them, fixed on a steel plate. The VCS captures only the circle and the TPS touches only the sphere. Both simulations and real experiments demonstrate that the proposed method is robust and accurate in the registration of multiple inhomogeneous sensors in a dimensional measurement system.

Normal uniform mixture differential gene expression detection for cDNA microarrays

PubMed Central

Dean, Nema; Raftery, Adrian E

2005-01-01

Background One of the primary tasks in analysing gene expression data is finding genes that are differentially expressed in different samples. Multiple testing issues due to the thousands of tests run make some of the more popular methods for doing this problematic. Results We propose a simple method, Normal Uniform Differential Gene Expression (NUDGE) detection for finding differentially expressed genes in cDNA microarrays. The method uses a simple univariate normal-uniform mixture model, in combination with new normalization methods for spread as well as mean that extend the lowess normalization of Dudoit, Yang, Callow and Speed (2002) [1]. It takes account of multiple testing, and gives probabilities of differential expression as part of its output. It can be applied to either single-slide or replicated experiments, and it is very fast. Three datasets are analyzed using NUDGE, and the results are compared to those given by other popular methods: unadjusted and Bonferroni-adjusted t tests, Significance Analysis of Microarrays (SAM), and Empirical Bayes for microarrays (EBarrays) with both Gamma-Gamma and Lognormal-Normal models. Conclusion The method gives a high probability of differential expression to genes known/suspected a priori to be differentially expressed and a low probability to the others. In terms of known false positives and false negatives, the method outperforms all multiple-replicate methods except for the Gamma-Gamma EBarrays method to which it offers comparable results with the added advantages of greater simplicity, speed, fewer assumptions and applicability to the single replicate case. An R package called nudge to implement the methods in this paper will be made available soon at . PMID:16011807

Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth.

PubMed

Zhang, Zhaoyang; Fang, Hua; Wang, Honggang

2016-06-01

Web-delivered trials are an important component in eHealth services. These trials, mostly behavior-based, generate big heterogeneous data that are longitudinal, high dimensional with missing values. Unsupervised learning methods have been widely applied in this area, however, validating the optimal number of clusters has been challenging. Built upon our multiple imputation (MI) based fuzzy clustering, MIfuzzy, we proposed a new multiple imputation based validation (MIV) framework and corresponding MIV algorithms for clustering big longitudinal eHealth data with missing values, more generally for fuzzy-logic based clustering methods. Specifically, we detect the optimal number of clusters by auto-searching and -synthesizing a suite of MI-based validation methods and indices, including conventional (bootstrap or cross-validation based) and emerging (modularity-based) validation indices for general clustering methods as well as the specific one (Xie and Beni) for fuzzy clustering. The MIV performance was demonstrated on a big longitudinal dataset from a real web-delivered trial and using simulation. The results indicate MI-based Xie and Beni index for fuzzy-clustering are more appropriate for detecting the optimal number of clusters for such complex data. The MIV concept and algorithms could be easily adapted to different types of clustering that could process big incomplete longitudinal trial data in eHealth services.

Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth

PubMed Central

Zhang, Zhaoyang; Wang, Honggang

2016-01-01

Web-delivered trials are an important component in eHealth services. These trials, mostly behavior-based, generate big heterogeneous data that are longitudinal, high dimensional with missing values. Unsupervised learning methods have been widely applied in this area, however, validating the optimal number of clusters has been challenging. Built upon our multiple imputation (MI) based fuzzy clustering, MIfuzzy, we proposed a new multiple imputation based validation (MIV) framework and corresponding MIV algorithms for clustering big longitudinal eHealth data with missing values, more generally for fuzzy-logic based clustering methods. Specifically, we detect the optimal number of clusters by auto-searching and -synthesizing a suite of MI-based validation methods and indices, including conventional (bootstrap or cross-validation based) and emerging (modularity-based) validation indices for general clustering methods as well as the specific one (Xie and Beni) for fuzzy clustering. The MIV performance was demonstrated on a big longitudinal dataset from a real web-delivered trial and using simulation. The results indicate MI-based Xie and Beni index for fuzzy-clustering is more appropriate for detecting the optimal number of clusters for such complex data. The MIV concept and algorithms could be easily adapted to different types of clustering that could process big incomplete longitudinal trial data in eHealth services. PMID:27126063

Detection of nucleic acids by multiple sequential invasive cleavages

DOEpatents

Hall, Jeff G.; Lyamichev, Victor I.; Mast, Andrea L.; Brow, Mary Ann D.

1999-01-01

The present invention relates to means for the detection and characterization of nucleic acid sequences, as well as variations in nucleic acid sequences. The present invention also relates to methods for forming a nucleic acid cleavage structure on a target sequence and cleaving the nucleic acid cleavage structure in a site-specific manner. The structure-specific nuclease activity of a variety of enzymes is used to cleave the target-dependent cleavage structure, thereby indicating the presence of specific nucleic acid sequences or specific variations thereof. The present invention further relates to methods and devices for the separation of nucleic acid molecules based on charge. The present invention also provides methods for the detection of non-target cleavage products via the formation of a complete and activated protein binding region. The invention further provides sensitive and specific methods for the detection of human cytomegalovirus nucleic acid in a sample.

Detection of nucleic acids by multiple sequential invasive cleavages 02

DOEpatents

Hall, Jeff G.; Lyamichev, Victor I.; Mast, Andrea L.; Brow, Mary Ann D.

2002-01-01

The present invention relates to means for the detection and characterization of nucleic acid sequences, as well as variations in nucleic acid sequences. The present invention also relates to methods for forming a nucleic acid cleavage structure on a target sequence and cleaving the nucleic acid cleavage structure in a site-specific manner. The structure-specific nuclease activity of a variety of enzymes is used to cleave the target-dependent cleavage structure, thereby indicating the presence of specific nucleic acid sequences or specific variations thereof. The present invention further relates to methods and devices for the separation of nucleic acid molecules based on charge. The present invention also provides methods for the detection of non-target cleavage products via the formation of a complete and activated protein binding region. The invention further provides sensitive and specific methods for the detection of human cytomegalovirus nucleic acid in a sample.

Detection of nucleic acids by multiple sequential invasive cleavages

DOEpatents

Hall, Jeff G; Lyamichev, Victor I; Mast, Andrea L; Brow, Mary Ann D

2012-10-16

The present invention relates to means for the detection and characterization of nucleic acid sequences, as well as variations in nucleic acid sequences. The present invention also relates to methods for forming a nucleic acid cleavage structure on a target sequence and cleaving the nucleic acid cleavage structure in a site-specific manner. The structure-specific nuclease activity of a variety of enzymes is used to cleave the target-dependent cleavage structure, thereby indicating the presence of specific nucleic acid sequences or specific variations thereof. The present invention further relates to methods and devices for the separation of nucleic acid molecules based on charge. The present invention also provides methods for the detection of non-target cleavage products via the formation of a complete and activated protein binding region. The invention further provides sensitive and specific methods for the detection of human cytomegalovirus nucleic acid in a sample.

The method of pulsed x-ray detection with a diode laser.

PubMed

Liu, Jun; Ouyang, Xiaoping; Zhang, Zhongbing; Sheng, Liang; Chen, Liang; Tan, Xinjian; Weng, Xiufeng

2016-12-01

A new class of pulsed X-ray detection methods by sensing carrier changes in a diode laser cavity has been presented and demonstrated. The proof-of-principle experiments on detecting pulsed X-ray temporal profile have been done through the diode laser with a multiple quantum well active layer. The result shows that our method can achieve the aim of detecting the temporal profile of a pulsed X-ray source. We predict that there is a minimum value for the pre-bias current of the diode laser by analyzing the carrier rate equation, which exists near the threshold current of the diode laser chip in experiments. This behaviour generally agrees with the characterizations of theoretical analysis. The relative sensitivity is estimated at about 3.3 × 10 -17 C ⋅ cm 2 . We have analyzed the time scale of about 10 ps response with both rate equation and Monte Carlo methods.

Seamless image stitching by homography refinement and structure deformation using optimal seam pair detection

NASA Astrophysics Data System (ADS)

Lee, Daeho; Lee, Seohyung

2017-11-01

We propose an image stitching method that can remove ghost effects and realign the structure misalignments that occur in common image stitching methods. To reduce the artifacts caused by different parallaxes, an optimal seam pair is selected by comparing the cross correlations from multiple seams detected by variable cost weights. Along the optimal seam pair, a histogram of oriented gradients is calculated, and feature points for matching are detected. The homography is refined using the matching points, and the remaining misalignment is eliminated using the propagation of deformation vectors calculated from matching points. In multiband blending, the overlapping regions are determined from a distance between the matching points to remove overlapping artifacts. The experimental results show that the proposed method more robustly eliminates misalignments and overlapping artifacts than the existing method that uses single seam detection and gradient features.

Improved MIMO radar GMTI via cyclic-shift transmission of orthogonal frequency division signals

NASA Astrophysics Data System (ADS)

Li, Fuyou; He, Feng; Dong, Zhen; Wu, Manqing

2018-05-01

Minimum detectable velocity (MDV) and maximum detectable velocity are both important in ground moving target indication (GMTI) systems. Smaller MDV can be achieved by longer baseline via multiple-input multiple-output (MIMO) radar. Maximum detectable velocity is decided by blind velocities associated with carrier frequencies, and blind velocities can be mitigated by orthogonal frequency division signals. However, the scattering echoes from different carrier frequencies are independent, which is not good for improving MDV performance. An improved cyclic-shift transmission is applied in MIMO GMTI system in this paper. MDV performance is improved due to the longer baseline, and maximum detectable velocity performance is improved due to the mitigation of blind velocities via multiple carrier frequencies. The signal model for this mode is established, the principle of mitigating blind velocities with orthogonal frequency division signals is presented; the performance of different MIMO GMTI waveforms is analysed; and the performance of different array configurations is analysed. Simulation results by space-time-frequency adaptive processing proves that our proposed method is a valid way to improve GMTI performance.

[Comparison of acetonitrile, ethanol and chromatographic column to eliminate high-abundance proteins in human serum].

PubMed

Li, Yin; Liao, Ming; He, Xiao; Zhou, Yi; Luo, Rong; Li, Hongtao; Wang, Yun; He, Min

2012-11-01

To compare the effects of acetonitrile precipitation, ethanol precipitation and multiple affinity chromatography column Human 14 removal to eliminate high-abundance proteins in human serum. Elimination of serum high-abundance proteins performed with acetonitrile precipitation, ethanol precipitation and multiple affinity chromatography column Human 14 removal. Bis-Tris Mini Gels electrophoresis and two-dimensional gel electrophoresis to detect the effect. Grey value analysis from 1-DE figure showed that after serum processed by acetonitrile method, multiple affinity chromatography column Human 14 removal method and ethanol method, the grey value of albumin changed into 157.2, 40.8 and 8.2 respectively from the original value of 19. 2-DE analysis results indicated that using multiple affinity chromatography column Human 14 method, the protein points noticeable increased by 137 compared to the original serum. After processed by acetonitrile method and ethanol method, the protein point reduced, but the low abundance protein point emerged. The acetonitrile precipitation could eliminate the vast majority of high abundance proteins in serum and gain more proteins of low molecular weight, ethanol precipitation could eliminate part of high abundance proteins in serum, but low abundance proteins less harvested, and multiple affinity chromatography column Human 14 method could effectively removed the high abundance proteins, and keep a large number of low abundance proteins.

Detection of the Dinozoans Pfiesteria piscicida and P. shumwayae: a review of detection methods and geographic distribution.

PubMed

Rublee, Parke A; Remington, David L; Schaefer, Eric F; Marshall, Michael M

2005-01-01

Molecular methods, including conventional PCR, real-time PCR, denaturing gradient gel electrophoresis, fluorescent fragment detection PCR, and fluorescent in situ hybridization, have all been developed for use in identifying and studying the distribution of the toxic dinoflagellates Pfiesteria piscicida and P. shumwayae. Application of the methods has demonstrated a worldwide distribution of both species and provided insight into their environmental tolerance range and temporal changes in distribution. Genetic variability among geographic locations generally appears low in rDNA genes, and detection of the organisms in ballast water is consistent with rapid dispersal or high gene flow among populations, but additional sequence data are needed to verify this hypothesis. The rapid development and application of these tools serves as a model for study of other microbial taxa and provides a basis for future development of tools that can simultaneously detect multiple targets.

Delamination Detection Using Guided Wave Phased Arrays

NASA Technical Reports Server (NTRS)

Tian, Zhenhua; Yu, Lingyu; Leckey, Cara

2016-01-01

This paper presents a method for detecting multiple delaminations in composite laminates using non-contact phased arrays. The phased arrays are implemented with a non-contact scanning laser Doppler vibrometer (SLDV). The array imaging algorithm is performed in the frequency domain where both the guided wave dispersion effect and direction dependent wave properties are considered. By using the non-contact SLDV array with a frequency domain imaging algorithm, an intensity image of the composite plate can be generated for delamination detection. For the proof of concept, a laboratory test is performed using a non-contact phased array to detect two delaminations (created through quasi-static impact test) at different locations in a composite plate. Using the non-contact phased array and frequency domain imaging, the two impact-induced delaminations are successfully detected. This study shows that the non-contact phased array method is a potentially effective method for rapid delamination inspection in large composite structures.

Dynamic sequence analysis of a decision making task of multielement target tracking and its usage as a learning method

NASA Astrophysics Data System (ADS)

Kang, Ziho

This dissertation is divided into four parts: 1) Development of effective methods for comparing visual scanning paths (or scanpaths) for a dynamic task of multiple moving targets, 2) application of the methods to compare the scanpaths of experts and novices for a conflict detection task of multiple aircraft on radar screen, 3) a post-hoc analysis of other eye movement characteristics of experts and novices, and 4) finding out whether the scanpaths of experts can be used to teach the novices. In order to compare experts' and novices' scanpaths, two methods are developed. The first proposed method is the matrix comparisons using the Mantel test. The second proposed method is the maximum transition-based agglomerative hierarchical clustering (MTAHC) where comparisons of multi-level visual groupings are held out. The matrix comparison method was useful for a small number of targets during the preliminary experiment, but turned out to be inapplicable to a realistic case when tens of aircraft were presented on screen; however, MTAHC was effective with large number of aircraft on screen. The experiments with experts and novices on the aircraft conflict detection task showed that their scanpaths are different. The MTAHC result was able to explicitly show how experts visually grouped multiple aircraft based on similar altitudes while novices tended to group them based on convergence. Also, the MTAHC results showed that novices paid much attention to the converging aircraft groups even if they are safely separated by altitude; therefore, less attention was given to the actual conflicting pairs resulting in low correct conflict detection rates. Since the analysis showed the scanpath differences, experts' scanpaths were shown to novices in order to find out its effectiveness. The scanpath treatment group showed indications that they changed their visual movements from trajectory-based to altitude-based movements. Between the treatment and the non-treatment group, there were no significant differences in terms of number of correct detections; however, the treatment group made significantly fewer false alarms.

System and Method for Detecting Unauthorized Device Access by Comparing Multiple Independent Spatial-Time Data Sets from Other Devices

NASA Technical Reports Server (NTRS)

Westmeyer, Paul A. (Inventor); Wertenberg, Russell F. (Inventor); Krage, Frederick J. (Inventor); Riegel, Jack F. (Inventor)

2017-01-01

An authentication procedure utilizes multiple independent sources of data to determine whether usage of a device, such as a desktop computer, is authorized. When a comparison indicates an anomaly from the base-line usage data, the system, provides a notice that access of the first device is not authorized.

Mapping Health Data: Improved Privacy Protection With Donut Method Geomasking

PubMed Central

Hampton, Kristen H.; Fitch, Molly K.; Allshouse, William B.; Doherty, Irene A.; Gesink, Dionne C.; Leone, Peter A.; Serre, Marc L.; Miller, William C.

2010-01-01

A major challenge in mapping health data is protecting patient privacy while maintaining the spatial resolution necessary for spatial surveillance and outbreak identification. A new adaptive geomasking technique, referred to as the donut method, extends current methods of random displacement by ensuring a user-defined minimum level of geoprivacy. In donut method geomasking, each geocoded address is relocated in a random direction by at least a minimum distance, but less than a maximum distance. The authors compared the donut method with current methods of random perturbation and aggregation regarding measures of privacy protection and cluster detection performance by masking multiple disease field simulations under a range of parameters. Both the donut method and random perturbation performed better than aggregation in cluster detection measures. The performance of the donut method in geoprivacy measures was at least 42.7% higher and in cluster detection measures was less than 4.8% lower than that of random perturbation. Results show that the donut method provides a consistently higher level of privacy protection with a minimal decrease in cluster detection performance, especially in areas where the risk to individual geoprivacy is greatest. PMID:20817785

Mapping health data: improved privacy protection with donut method geomasking.

PubMed

Hampton, Kristen H; Fitch, Molly K; Allshouse, William B; Doherty, Irene A; Gesink, Dionne C; Leone, Peter A; Serre, Marc L; Miller, William C

2010-11-01

A major challenge in mapping health data is protecting patient privacy while maintaining the spatial resolution necessary for spatial surveillance and outbreak identification. A new adaptive geomasking technique, referred to as the donut method, extends current methods of random displacement by ensuring a user-defined minimum level of geoprivacy. In donut method geomasking, each geocoded address is relocated in a random direction by at least a minimum distance, but less than a maximum distance. The authors compared the donut method with current methods of random perturbation and aggregation regarding measures of privacy protection and cluster detection performance by masking multiple disease field simulations under a range of parameters. Both the donut method and random perturbation performed better than aggregation in cluster detection measures. The performance of the donut method in geoprivacy measures was at least 42.7% higher and in cluster detection measures was less than 4.8% lower than that of random perturbation. Results show that the donut method provides a consistently higher level of privacy protection with a minimal decrease in cluster detection performance, especially in areas where the risk to individual geoprivacy is greatest.

Eye Tracking and Early Detection of Confusion in Digital Learning Environments: Proof of Concept

ERIC Educational Resources Information Center

Pachman, Mariya; Arguel, Amaël; Lockyer, Lori; Kennedy, Gregor; Lodge, Jason M.

2016-01-01

Research on incidence of and changes in confusion during complex learning and problem-solving calls for advanced methods of confusion detection in digital learning environments (DLEs). In this study we attempt to address this issue by investigating the use of multiple measures, including psychophysiological indicators and self-ratings, to detect…

Predicting carnivore occurrence with noninvasive surveys and occupancy modeling

Treesearch

Robert Long; Therese Donovan; Paula MacKay; William Zielinski; Jeffrey Buzas

2011-01-01

Terrestrial carnivores typically have large home ranges and exist at low population densities, thus presenting challenges to wildlife researchers. We employed multiple, noninvasive survey methods—scat detection dogs, remote cameras, and hair snares—to collect detection–nondetection data for elusive American black bears (Ursus americanus), fishers...

Statistical detection of EEG synchrony using empirical bayesian inference.

PubMed

Singh, Archana K; Asoh, Hideki; Takeda, Yuji; Phillips, Steven

2015-01-01

There is growing interest in understanding how the brain utilizes synchronized oscillatory activity to integrate information across functionally connected regions. Computing phase-locking values (PLV) between EEG signals is a popular method for quantifying such synchronizations and elucidating their role in cognitive tasks. However, high-dimensionality in PLV data incurs a serious multiple testing problem. Standard multiple testing methods in neuroimaging research (e.g., false discovery rate, FDR) suffer severe loss of power, because they fail to exploit complex dependence structure between hypotheses that vary in spectral, temporal and spatial dimension. Previously, we showed that a hierarchical FDR and optimal discovery procedures could be effectively applied for PLV analysis to provide better power than FDR. In this article, we revisit the multiple comparison problem from a new Empirical Bayes perspective and propose the application of the local FDR method (locFDR; Efron, 2001) for PLV synchrony analysis to compute FDR as a posterior probability that an observed statistic belongs to a null hypothesis. We demonstrate the application of Efron's Empirical Bayes approach for PLV synchrony analysis for the first time. We use simulations to validate the specificity and sensitivity of locFDR and a real EEG dataset from a visual search study for experimental validation. We also compare locFDR with hierarchical FDR and optimal discovery procedures in both simulation and experimental analyses. Our simulation results showed that the locFDR can effectively control false positives without compromising on the power of PLV synchrony inference. Our results from the application locFDR on experiment data detected more significant discoveries than our previously proposed methods whereas the standard FDR method failed to detect any significant discoveries.

ASPIC: a novel method to predict the exon-intron structure of a gene that is optimally compatible to a set of transcript sequences.

PubMed

Bonizzoni, Paola; Rizzi, Raffaella; Pesole, Graziano

2005-10-05

Currently available methods to predict splice sites are mainly based on the independent and progressive alignment of transcript data (mostly ESTs) to the genomic sequence. Apart from often being computationally expensive, this approach is vulnerable to several problems--hence the need to develop novel strategies. We propose a method, based on a novel multiple genome-EST alignment algorithm, for the detection of splice sites. To avoid limitations of splice sites prediction (mainly, over-predictions) due to independent single EST alignments to the genomic sequence our approach performs a multiple alignment of transcript data to the genomic sequence based on the combined analysis of all available data. We recast the problem of predicting constitutive and alternative splicing as an optimization problem, where the optimal multiple transcript alignment minimizes the number of exons and hence of splice site observations. We have implemented a splice site predictor based on this algorithm in the software tool ASPIC (Alternative Splicing PredICtion). It is distinguished from other methods based on BLAST-like tools by the incorporation of entirely new ad hoc procedures for accurate and computationally efficient transcript alignment and adopts dynamic programming for the refinement of intron boundaries. ASPIC also provides the minimal set of non-mergeable transcript isoforms compatible with the detected splicing events. The ASPIC web resource is dynamically interconnected with the Ensembl and Unigene databases and also implements an upload facility. Extensive bench marking shows that ASPIC outperforms other existing methods in the detection of novel splicing isoforms and in the minimization of over-predictions. ASPIC also requires a lower computation time for processing a single gene and an EST cluster. The ASPIC web resource is available at http://aspic.algo.disco.unimib.it/aspic-devel/.

Constructing Benchmark Databases and Protocols for Medical Image Analysis: Diabetic Retinopathy

PubMed Central

Kauppi, Tomi; Kämäräinen, Joni-Kristian; Kalesnykiene, Valentina; Sorri, Iiris; Uusitalo, Hannu; Kälviäinen, Heikki

2013-01-01

We address the performance evaluation practices for developing medical image analysis methods, in particular, how to establish and share databases of medical images with verified ground truth and solid evaluation protocols. Such databases support the development of better algorithms, execution of profound method comparisons, and, consequently, technology transfer from research laboratories to clinical practice. For this purpose, we propose a framework consisting of reusable methods and tools for the laborious task of constructing a benchmark database. We provide a software tool for medical image annotation helping to collect class label, spatial span, and expert's confidence on lesions and a method to appropriately combine the manual segmentations from multiple experts. The tool and all necessary functionality for method evaluation are provided as public software packages. As a case study, we utilized the framework and tools to establish the DiaRetDB1 V2.1 database for benchmarking diabetic retinopathy detection algorithms. The database contains a set of retinal images, ground truth based on information from multiple experts, and a baseline algorithm for the detection of retinopathy lesions. PMID:23956787

Application of Scan Statistics to Detect Suicide Clusters in Australia

PubMed Central

Cheung, Yee Tak Derek; Spittal, Matthew J.; Williamson, Michelle Kate; Tung, Sui Jay; Pirkis, Jane

2013-01-01

Background Suicide clustering occurs when multiple suicide incidents take place in a small area or/and within a short period of time. In spite of the multi-national research attention and particular efforts in preparing guidelines for tackling suicide clusters, the broader picture of epidemiology of suicide clustering remains unclear. This study aimed to develop techniques in using scan statistics to detect clusters, with the detection of suicide clusters in Australia as example. Methods and Findings Scan statistics was applied to detect clusters among suicides occurring between 2004 and 2008. Manipulation of parameter settings and change of area for scan statistics were performed to remedy shortcomings in existing methods. In total, 243 suicides out of 10,176 (2.4%) were identified as belonging to 15 suicide clusters. These clusters were mainly located in the Northern Territory, the northern part of Western Australia, and the northern part of Queensland. Among the 15 clusters, 4 (26.7%) were detected by both national and state cluster detections, 8 (53.3%) were only detected by the state cluster detection, and 3 (20%) were only detected by the national cluster detection. Conclusions These findings illustrate that the majority of spatial-temporal clusters of suicide were located in the inland northern areas, with socio-economic deprivation and higher proportions of indigenous people. Discrepancies between national and state/territory cluster detection by scan statistics were due to the contrast of the underlying suicide rates across states/territories. Performing both small-area and large-area analyses, and applying multiple parameter settings may yield the maximum benefits for exploring clusters. PMID:23342098

Method and apparatus for dynamic focusing of ultrasound energy

DOEpatents

Candy, James V.

2002-01-01

Method and system disclosed herein include noninvasively detecting, separating and destroying multiple masses (tumors, cysts, etc.) through a plurality of iterations from tissue (e.g., breast tissue). The method and system may open new frontiers with the implication of noninvasive treatment of masses in the biomedical area along with the expanding technology of acoustic surgery.

Attribution of hydrological change using the Method of Multiple Working Hypotheses

NASA Astrophysics Data System (ADS)

Harrigan, Shaun

2017-04-01

The methods we have developed for managing our long-term water supply and protection from extreme hydrological events such as droughts and floods have been founded on the assumption that the hydrological cycle operates under natural conditions. However, it increasingly recognised that humans have the potential to induce significant change in almost every component of the hydrological cycle, for example, climate change, land-use change, and river engineering. Statistical detection of change in streamflow, outside that of natural variability, is an important scientific endeavour, but it does not tell us anything about the drivers of change. Attribution is the process of establishing the most likely cause(s) of a detected change - the why. Attribution is complex due to the integrated nature of streamflow and the proliferation of multiple possible drivers. It is perhaps this complexity, combined with few proven theoretical approaches to this problem in hydrology that has led to others to call for "more efforts and scientific rigour" (Merz et al., 2012). It is easier to limit the cause of a detected change to a single driver, or use simple correlation analysis alone as evidence of causation. It is convenient when the direction of a change in streamflow is consistent with what is expected from a well-known driver such as climate change. Over a century ago, Thomas Chamberlin argued these types of issues were common in many disciplines given how the scientific method is approached in general. His 1890 article introduces the Method of Multiple Working Hypotheses (MMWH) in an attempt to limit our confirmation bias and strives for increased objectivity. This presentation will argue that the MMWH offers an attractive theoretical approach to the attribution of hydrological change in modern hydrology as demonstrated through a case study of a well-documented change point in streamflow within the Boyne Catchment in Ireland. Further Reading Chamberlin, T. C.: The Method of Multiple Working Hypotheses, Science (old series), 15(366), 92-96, doi:10.1126/science.ns-15.366.92, 1890. Harrigan, S., Murphy, C., Hall, J., Wilby, R. L. and Sweeney, J.: Attribution of detected changes in streamflow using multiple working hypotheses, Hydrol. Earth Syst. Sci., 18(5), 1935-1952, doi:10.5194/hess-18-1935-2014, 2014. Merz, B., Vorogushyn, S., Uhlemann, S., Delgado, J. and Hundecha, Y.: HESS Opinions "More efforts and scientific rigour are needed to attribute trends in flood time series," Hydrol. Earth Syst. Sci., 16(5), 1379-1387, doi:10.5194/hess-16-1379-2012, 2012.

Incident Detection of High-Risk Human Papillomavirus Infections in a Cohort of High-Risk Women Aged 25–65 Years

PubMed Central

Winer, Rachel L.; Hughes, James P.; Feng, Qinghua; Stern, Joshua E.; Xi, Long Fu; Koutsky, Laura A.

2016-01-01

Background. The risk of incident high-risk human papillomavirus (HR-HPV) infection associated with recent sexual behaviors is undefined in mid-adult women (defined as women aged 25–65 years). Methods. Triannually, 420 female online daters aged 25–65 years submitted vaginal specimens for HPV testing and completed health and sexual behavior questionnaires. The cumulative incidence of and risk factors for incident HR-HPV detection were estimated by Kaplan–Meier and Cox proportional hazards methods. Results. The 12-month cumulative incidence of HR-HPV detection was 25.4% (95% confidence interval [CI], 21.3%–30.1%). Current hormonal contraceptive use was positively associated with incident HR-HPV detection. Lifetime number of male sex partners was also positively associated but only among women not recently sexually active with male partners. In analysis that adjusted for hormonal contraceptive use and marital status, women reporting multiple male partners or male partners who were new, casual, or had ≥1 concurrent partnership had a hazard of incident HR-HPV detection that was 2.81 times (95% CI, 1.38–5.69 times) that for women who reported no male sex partners in the past 6 months. Thus, among women with multiple male partners or male partners who were new, casual, or had ≥1 concurrent partnership, approximately 64% of incident HR-HPV infections were attributable to one of those partners. Conclusions. Among high-risk mid-adult women with recent new male partners, multiple male partners, or male partners who were casual or had ≥1 concurrent partnership, about two thirds of incident HR-HPV detections are likely new acquisitions, whereas about one third of cases are likely redetections of prior infections. PMID:27009602

Bayesian adaptive survey protocols for resource management

USGS Publications Warehouse

Halstead, Brian J.; Wylie, Glenn D.; Coates, Peter S.; Casazza, Michael L.

2011-01-01

Transparency in resource management decisions requires a proper accounting of uncertainty at multiple stages of the decision-making process. As information becomes available, periodic review and updating of resource management protocols reduces uncertainty and improves management decisions. One of the most basic steps to mitigating anthropogenic effects on populations is determining if a population of a species occurs in an area that will be affected by human activity. Species are rarely detected with certainty, however, and falsely declaring a species absent can cause improper conservation decisions or even extirpation of populations. We propose a method to design survey protocols for imperfectly detected species that accounts for multiple sources of uncertainty in the detection process, is capable of quantitatively incorporating expert opinion into the decision-making process, allows periodic updates to the protocol, and permits resource managers to weigh the severity of consequences if the species is falsely declared absent. We developed our method using the giant gartersnake (Thamnophis gigas), a threatened species precinctive to the Central Valley of California, as a case study. Survey date was negatively related to the probability of detecting the giant gartersnake, and water temperature was positively related to the probability of detecting the giant gartersnake at a sampled location. Reporting sampling effort, timing and duration of surveys, and water temperatures would allow resource managers to evaluate the probability that the giant gartersnake occurs at sampled sites where it is not detected. This information would also allow periodic updates and quantitative evaluation of changes to the giant gartersnake survey protocol. Because it naturally allows multiple sources of information and is predicated upon the idea of updating information, Bayesian analysis is well-suited to solving the problem of developing efficient sampling protocols for species of conservation concern.

Optimization of Multiple Pathogen Detection Using the TaqMan Array Card: Application for a Population-Based Study of Neonatal Infection

PubMed Central

Diaz, Maureen H.; Waller, Jessica L.; Napoliello, Rebecca A.; Islam, Md. Shahidul; Wolff, Bernard J.; Burken, Daniel J.; Holden, Rhiannon L.; Srinivasan, Velusamy; Arvay, Melissa; McGee, Lesley; Oberste, M. Steven; Whitney, Cynthia G.; Schrag, Stephanie J.; Winchell, Jonas M.; Saha, Samir K.

2013-01-01

Identification of etiology remains a significant challenge in the diagnosis of infectious diseases, particularly in resource-poor settings. Viral, bacterial, and fungal pathogens, as well as parasites, play a role for many syndromes, and optimizing a single diagnostic system to detect a range of pathogens is challenging. The TaqMan Array Card (TAC) is a multiple-pathogen detection method that has previously been identified as a valuable technique for determining etiology of infections and holds promise for expanded use in clinical microbiology laboratories and surveillance studies. We selected TAC for use in the Aetiology of Neonatal Infection in South Asia (ANISA) study for identifying etiologies of severe disease in neonates in Bangladesh, India, and Pakistan. Here we report optimization of TAC to improve pathogen detection and overcome technical challenges associated with use of this technology in a large-scale surveillance study. Specifically, we increased the number of assay replicates, implemented a more robust RT-qPCR enzyme formulation, and adopted a more efficient method for extraction of total nucleic acid from blood specimens. We also report the development and analytical validation of ten new assays for use in the ANISA study. Based on these data, we revised the study-specific TACs for detection of 22 pathogens in NP/OP swabs and 12 pathogens in blood specimens as well as two control reactions (internal positive control and human nucleic acid control) for each specimen type. The cumulative improvements realized through these optimization studies will benefit ANISA and perhaps other studies utilizing multiple-pathogen detection approaches. These lessons may also contribute to the expansion of TAC technology to the clinical setting. PMID:23805203

Critical analysis of dual-chamber implantable cardioverter-defibrillator arrhythmia detection : results and technical considerations.

PubMed

Wilkoff, B L; Kühlkamp, V; Volosin, K; Ellenbogen, K; Waldecker, B; Kacet, S; Gillberg, J M; DeSouza, C M

2001-01-23

One of the perceived benefits of dual-chamber implantable cardioverter-defibrillators (ICDs) is the reduction in inappropriate therapy due to new detection algorithms. It was the purpose of the present investigation to propose methods to minimize bias during such comparisons and to report the arrhythmia detection clinical results of the PR Logic dual-chamber detection algorithm in the GEM DR ICD in the context of these methods. Between November 1997 and October 1998, 933 patients received the GEM DR ICD in this prospective multicenter study. A total of 4856 sustained arrhythmia episodes (n=311) with stored electrogram and marker channel were classified by the investigators; 3488 episodes (n=232) were ventricular tachycardia (VT)/ventricular fibrillation (VF), and 1368 episodes (n=149) were supraventricular tachycardia (SVT). The overall detection results were corrected for multiple episodes within a patient with the generalized estimating equations (GEE) method with an exchangeable correlation structure between episodes. The relative sensitivity for detection of sustained VT and/or VF was 100.0% (3488 of 3488, n=232; 95% CI 98.3% to 100%), the VT/VF positive predictivity was 88.4% uncorrected (3488 of 3945, n=278) and 78.1% corrected (95% CI 73.3% to 82.3%) with the GEE method, and the SVT positive predictivity was 100.0% (911 of 911, n=101; 95% CI 96% to 100%). A structured approach to analysis limits the bias inherent in the evaluation of tachycardia discrimination algorithms through the use of relative VT/VF sensitivity, VT/VF positive predictivity, and SVT positive predictivity along with corrections for multiple tachycardia episodes in a single patient.

Detection and measurement of surface contamination by multiple antineoplastic drugs using multiplex bead assay

PubMed Central

Smith, Jerome P; Sammons, Deborah L; Robertson, Shirley A; Pretty, Jack; Debord, D Gayle; Connor, Thomas H; Snawder, John

2015-01-01

Objectives Contamination of workplace surfaces by antineoplastic drugs presents an exposure risk for healthcare workers. Traditional instrumental methods to detect contamination such as liquid chromatography-tandem mass spectrometry (LC-MS/MS) are sensitive and accurate but expensive. Since immunochemical methods may be cheaper and faster than instrumental methods, we wanted to explore their use for routine drug residue detection for preventing worker exposure. Methods In this study we examined the feasibility of using fluorescence covalent microbead immunosorbent assay (FCMIA) for simultaneous detection and semi-quantitative measurement of three antineoplastic drugs (5-fluorouracil, paclitaxel, and doxorubicin). The concentration ranges for the assay were 0–1000 ng/ml for 5-fluorouracil, 0–100 ng/ml for paclitaxel, and 0–2 ng/ml for doxorubicin. The surface sampling technique involved wiping a loaded surface with a swab wetted with wash buffer, extracting the swab in storage/blocking buffer, and measuring drugs in the extract using FCMIA. Results There was no significant cross reactivity between these drugs at the ranges studied indicated by a lack of response in the assay to cross analytes. The limit of detection (LOD) for 5-fluorouracil on the surface studied was 0.93 ng/cm2 with a limit of quantitation (LOQ) of 2.8 ng/cm2, the LOD for paclitaxel was 0.57 ng/cm2 with an LOQ of 2.06 ng/cm2, and the LOD for doxorubicin was 0.0036 ng/cm2 with an LOQ of 0.013 ng/cm2. Conclusion The use of FCMIA with a simple sampling technique has potential for low cost simultaneous detection and semi-quantitative measurement of surface contamination from multiple antineoplastic drugs. PMID:25293722

A rapid low-cost high-density DNA-based multi-detection test for routine inspection of meat species.

PubMed

Lin, Chun Chi; Fung, Lai Ling; Chan, Po Kwok; Lee, Cheuk Man; Chow, Kwok Fai; Cheng, Shuk Han

2014-02-01

The increasing occurrence of food frauds suggests that species identification should be part of food authentication. Current molecular-based species identification methods have their own limitations or drawbacks, such as relatively time-consuming experimental steps, expensive equipment and, in particular, these methods cannot identify mixed species in a single experiment. This project proposes an improved method involving PCR amplification of the COI gene and detection of species-specific sequences by hybridisation. Major innovative breakthrough lies in the detection of multiple species, including pork, beef, lamb, horse, cat, dog and mouse, from a mixed sample within a single experiment. The probes used are species-specific either in sole or mixed species samples. As little as 5 pg of DNA template in the PCR is detectable in the proposed method. By designing species-specific probes and adopting reverse dot blot hybridisation and flow-through hybridisation, a low-cost high-density DNA-based multi-detection test suitable for routine inspection of meat species was developed. © 2013.

ROKU: a novel method for identification of tissue-specific genes.

PubMed

Kadota, Koji; Ye, Jiazhen; Nakai, Yuji; Terada, Tohru; Shimizu, Kentaro

2006-06-12

One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes. We describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues. ROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes.

Portable and sensitive quantitative detection of DNA based on personal glucose meters and isothermal circular strand-displacement polymerization reaction.

PubMed

Xu, Xue-tao; Liang, Kai-yi; Zeng, Jia-ying

2015-02-15

A portable and sensitive quantitative DNA detection method based on personal glucose meters and isothermal circular strand-displacement polymerization reaction was developed. The target DNA triggered target recycling process, which opened capture DNA. The released target then found another capture DNA to trigger another polymerization cycle, which was repeated for many rounds, resulting in the multiplication of the DNA-invertase conjugation on the surface of Streptavidin-MNBs. The DNA-invertase was used to catalyze the hydrolysis of sucrose into glucose for PGM readout. There was a liner relationship between the signal of PGM and the concentration of target DNA in the range of 5.0 to 1000 fM, which is lower than some DNA detection method. In addition, the method exhibited excellent sequence selectivity and there was almost no effect of biological complex to the detection performance, which suggested our method can be successfully applied to DNA detection in real biological samples. Copyright © 2014 Elsevier B.V. All rights reserved.

Comparative analysis of whole mount processing and systematic sampling of radical prostatectomy specimens: pathological outcomes and risk of biochemical recurrence.

PubMed

Salem, Shady; Chang, Sam S; Clark, Peter E; Davis, Rodney; Herrell, S Duke; Kordan, Yakup; Wills, Marcia L; Shappell, Scott B; Baumgartner, Roxelyn; Phillips, Sharon; Smith, Joseph A; Cookson, Michael S; Barocas, Daniel A

2010-10-01

Whole mount processing is more resource intensive than routine systematic sampling of radical retropubic prostatectomy specimens. We compared whole mount and systematic sampling for detecting pathological outcomes, and compared the prognostic value of pathological findings across pathological methods. We included men (608 whole mount and 525 systematic sampling samples) with no prior treatment who underwent radical retropubic prostatectomy at Vanderbilt University Medical Center between January 2000 and June 2008. We used univariate and multivariate analysis to compare the pathological outcome detection rate between pathological methods. Kaplan-Meier curves and the log rank test were used to compare the prognostic value of pathological findings across pathological methods. There were no significant differences between the whole mount and the systematic sampling groups in detecting extraprostatic extension (25% vs 30%), positive surgical margins (31% vs 31%), pathological Gleason score less than 7 (49% vs 43%), 7 (39% vs 43%) or greater than 7 (12% vs 13%), seminal vesicle invasion (8% vs 10%) or lymph node involvement (3% vs 5%). Tumor volume was higher in the systematic sampling group and whole mount detected more multiple surgical margins (each p <0.01). There were no significant differences in the likelihood of biochemical recurrence between the pathological methods when patients were stratified by pathological outcome. Except for estimated tumor volume and multiple margins whole mount and systematic sampling yield similar pathological information. Each method stratifies patients into comparable risk groups for biochemical recurrence. Thus, while whole mount is more resource intensive, it does not appear to result in improved detection of clinically important pathological outcomes or prognostication. Copyright © 2010 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

New Method for Analysis of Multiple Anthelmintic Residues in Animal Tissue

USDA-ARS?s Scientific Manuscript database

For the first time, 39 of the major anthelmintics can be detected in one rapid and sensitive LC-MS/MS method, including the flukicides, which have been generally overlooked in surveillance programs. Utilizing the QuEChERS approach, residues were extracted from liver and milk using acetonitrile, sod...

Multiplicity counting from fission detector signals with time delay effects

NASA Astrophysics Data System (ADS)

Nagy, L.; Pázsit, I.; Pál, L.

2018-03-01

In recent work, we have developed the theory of using the first three auto- and joint central moments of the currents of up to three fission chambers to extract the singles, doubles and triples count rates of traditional multiplicity counting (Pázsit and Pál, 2016; Pázsit et al., 2016). The objective is to elaborate a method for determining the fissile mass, neutron multiplication, and (α, n) neutron emission rate of an unknown assembly of fissile material from the statistics of the fission chamber signals, analogous to the traditional multiplicity counting methods with detectors in the pulse mode. Such a method would be an alternative to He-3 detector systems, which would be free from the dead time problems that would be encountered in high counting rate applications, for example the assay of spent nuclear fuel. A significant restriction of our previous work was that all neutrons born in a source event (spontaneous fission) were assumed to be detected simultaneously, which is not fulfilled in reality. In the present work, this restriction is eliminated, by assuming an independent, identically distributed random time delay for all neutrons arising from one source event. Expressions are derived for the same auto- and joint central moments of the detector current(s) as in the previous case, expressed with the singles, doubles, and triples (S, D and T) count rates. It is shown that if the time-dispersion of neutron detections is of the same order of magnitude as the detector pulse width, as they typically are in measurements of fast neutrons, the multiplicity rates can still be extracted from the moments of the detector current, although with more involved calibration factors. The presented formulae, and hence also the performance of the proposed method, are tested by both analytical models of the time delay as well as with numerical simulations. Methods are suggested also for the modification of the method for large time delay effects (for thermalised neutrons).

Automated matching of multiple terrestrial laser scans for stem mapping without the use of artificial references

NASA Astrophysics Data System (ADS)

Liu, Jingbin; Liang, Xinlian; Hyyppä, Juha; Yu, Xiaowei; Lehtomäki, Matti; Pyörälä, Jiri; Zhu, Lingli; Wang, Yunsheng; Chen, Ruizhi

2017-04-01

Terrestrial laser scanning has been widely used to analyze the 3D structure of a forest in detail and to generate data at the level of a reference plot for forest inventories without destructive measurements. Multi-scan terrestrial laser scanning is more commonly applied to collect plot-level data so that all of the stems can be detected and analyzed. However, it is necessary to match the point clouds of multiple scans to yield a point cloud with automated processing. Mismatches between datasets will lead to errors during the processing of multi-scan data. Classic registration methods based on flat surfaces cannot be directly applied in forest environments; therefore, artificial reference objects have conventionally been used to assist with scan matching. The use of artificial references requires additional labor and expertise, as well as greatly increasing the cost. In this study, we present an automated processing method for plot-level stem mapping that matches multiple scans without artificial references. In contrast to previous studies, the registration method developed in this study exploits the natural geometric characteristics among a set of tree stems in a plot and combines the point clouds of multiple scans into a unified coordinate system. Integrating multiple scans improves the overall performance of stem mapping in terms of the correctness of tree detection, as well as the bias and the root-mean-square errors of forest attributes such as diameter at breast height and tree height. In addition, the automated processing method makes stem mapping more reliable and consistent among plots, reduces the costs associated with plot-based stem mapping, and enhances the efficiency.

Adapting sensory data for multiple robots performing spill cleanup

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

Storjohann, K.; Saltzen, E.

1990-09-01

This paper describes a possible method of converting a single performing robot algorithm into a multiple performing robot algorithm without the need to modify previously written codes. The algorithm to be converted involves spill detection and clean up by the HERMIES-III mobile robot. In order to achieve the goal of multiple performing robots with this algorithm, two steps are taken. First, the task is formally divided into two sub-tasks, spill detection and spill clean-up, the former of which is allocated to the added performing robot, HERMIES-IIB. Second, a inverse perspective mapping, is applied to the data acquired by the newmore » performing robot (HERMIES-IIB), allowing the data to be processed by the previously written algorithm without re-writing the code. 6 refs., 4 figs.« less

Multiple crack detection in 3D using a stable XFEM and global optimization

NASA Astrophysics Data System (ADS)

Agathos, Konstantinos; Chatzi, Eleni; Bordas, Stéphane P. A.

2018-02-01

A numerical scheme is proposed for the detection of multiple cracks in three dimensional (3D) structures. The scheme is based on a variant of the extended finite element method (XFEM) and a hybrid optimizer solution. The proposed XFEM variant is particularly well-suited for the simulation of 3D fracture problems, and as such serves as an efficient solution to the so-called forward problem. A set of heuristic optimization algorithms are recombined into a multiscale optimization scheme. The introduced approach proves effective in tackling the complex inverse problem involved, where identification of multiple flaws is sought on the basis of sparse measurements collected near the structural boundary. The potential of the scheme is demonstrated through a set of numerical case studies of varying complexity.

Theoretical and experimental investigation of multispectral photoacoustic osteoporosis detection method

NASA Astrophysics Data System (ADS)

Steinberg, Idan; Hershkovich, Hadas Sara; Gannot, Israel; Eyal, Avishay

2014-03-01

Osteoporosis is a widespread disorder, which has a catastrophic impact on patients lives and overwhelming related to healthcare costs. Recently, we proposed a multispectral photoacoustic technique for early detection of osteoporosis. Such technique has great advantages over pure ultrasonic or optical methods as it allows the deduction of both bone functionality from the bone absorption spectrum and bone resistance to fracture from the characteristics of the ultrasound propagation. We demonstrated the propagation of multiple acoustic modes in animal bones in-vitro. To further investigate the effects of multiple wavelength excitations and of induced osteoporosis on the PA signal a multispectral photoacoustic system is presented. The experimental investigation is based on measuring the interference of multiple acoustic modes. The performance of the system is evaluated and a simple two mode theoretical model is fitted to the measured phase signals. The results show that such PA technique is accurate and repeatable. Then a multiple wavelength excitation is tested. It is shown that the PA response due to different excitation wavelengths revels that absorption by the different bone constitutes has a profound effect on the mode generation. The PA response is measured in single wavelength before and after induced osteoporosis. Results show that induced osteoporosis alters the measured amplitude and phase in a consistent manner which allows the detection of the onset of osteoporosis. These results suggest that a complete characterization of the bone over a region of both acoustic and optical frequencies might be used as a powerful tool for in-vivo bone evaluation.

Statistical Algorithms Accounting for Background Density in the Detection of UXO Target Areas at DoD Munitions Sites

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

Matzke, Brett D.; Wilson, John E.; Hathaway, J.

2008-02-12

Statistically defensible methods are presented for developing geophysical detector sampling plans and analyzing data for munitions response sites where unexploded ordnance (UXO) may exist. Detection methods for identifying areas of elevated anomaly density from background density are shown. Additionally, methods are described which aid in the choice of transect pattern and spacing to assure with degree of confidence that a target area (TA) of specific size, shape, and anomaly density will be identified using the detection methods. Methods for evaluating the sensitivity of designs to variation in certain parameters are also discussed. Methods presented have been incorporated into the Visualmore » Sample Plan (VSP) software (free at http://dqo.pnl.gov/vsp) and demonstrated at multiple sites in the United States. Application examples from actual transect designs and surveys from the previous two years are demonstrated.« less

Proteomics goes forensic: Detection and mapping of blood signatures in fingermarks.

PubMed

Deininger, Lisa; Patel, Ekta; Clench, Malcolm R; Sears, Vaughn; Sammon, Chris; Francese, Simona

2016-06-01

A bottom up in situ proteomic method has been developed enabling the mapping of multiple blood signatures on the intact ridges of blood fingermarks by Matrix Assisted Laser Desorption Mass Spectrometry Imaging (MALDI-MSI). This method, at a proof of concept stage, builds upon recently published work demonstrating the opportunity to profile and identify multiple blood signatures in bloodstains via a bottom up proteomic approach. The present protocol addresses the limitation of the previously developed profiling method with respect to destructivity; destructivity should be avoided for evidence such as blood fingermarks, where the ridge detail must be preserved in order to provide the associative link between the biometric information and the events of bloodshed. Using a blood mark reference model, trypsin concentration and spraying conditions have been optimised within the technical constraints of the depositor eventually employed; the application of MALDI-MSI and Ion Mobility MS have enabled the detection, confirmation and visualisation of blood signatures directly onto the ridge pattern. These results are to be considered a first insight into a method eventually informing investigations (and judicial debates) of violent crimes in which the reliable and non-destructive detection and mapping of blood in fingermarks is paramount to reconstruct the events of bloodshed. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Sequential Monte Carlo tracking of the marginal artery by multiple cue fusion and random forest regression.

PubMed

Cherry, Kevin M; Peplinski, Brandon; Kim, Lauren; Wang, Shijun; Lu, Le; Zhang, Weidong; Liu, Jianfei; Wei, Zhuoshi; Summers, Ronald M

2015-01-01

Given the potential importance of marginal artery localization in automated registration in computed tomography colonography (CTC), we have devised a semi-automated method of marginal vessel detection employing sequential Monte Carlo tracking (also known as particle filtering tracking) by multiple cue fusion based on intensity, vesselness, organ detection, and minimum spanning tree information for poorly enhanced vessel segments. We then employed a random forest algorithm for intelligent cue fusion and decision making which achieved high sensitivity and robustness. After applying a vessel pruning procedure to the tracking results, we achieved statistically significantly improved precision compared to a baseline Hessian detection method (2.7% versus 75.2%, p<0.001). This method also showed statistically significantly improved recall rate compared to a 2-cue baseline method using fewer vessel cues (30.7% versus 67.7%, p<0.001). These results demonstrate that marginal artery localization on CTC is feasible by combining a discriminative classifier (i.e., random forest) with a sequential Monte Carlo tracking mechanism. In so doing, we present the effective application of an anatomical probability map to vessel pruning as well as a supplementary spatial coordinate system for colonic segmentation and registration when this task has been confounded by colon lumen collapse. Published by Elsevier B.V.

Seismic data fusion anomaly detection

NASA Astrophysics Data System (ADS)

Harrity, Kyle; Blasch, Erik; Alford, Mark; Ezekiel, Soundararajan; Ferris, David

2014-06-01

Detecting anomalies in non-stationary signals has valuable applications in many fields including medicine and meteorology. These include uses such as identifying possible heart conditions from an Electrocardiography (ECG) signals or predicting earthquakes via seismographic data. Over the many choices of anomaly detection algorithms, it is important to compare possible methods. In this paper, we examine and compare two approaches to anomaly detection and see how data fusion methods may improve performance. The first approach involves using an artificial neural network (ANN) to detect anomalies in a wavelet de-noised signal. The other method uses a perspective neural network (PNN) to analyze an arbitrary number of "perspectives" or transformations of the observed signal for anomalies. Possible perspectives may include wavelet de-noising, Fourier transform, peak-filtering, etc.. In order to evaluate these techniques via signal fusion metrics, we must apply signal preprocessing techniques such as de-noising methods to the original signal and then use a neural network to find anomalies in the generated signal. From this secondary result it is possible to use data fusion techniques that can be evaluated via existing data fusion metrics for single and multiple perspectives. The result will show which anomaly detection method, according to the metrics, is better suited overall for anomaly detection applications. The method used in this study could be applied to compare other signal processing algorithms.

Evaluation of fluorescence in situ hybridization techniques to study long non-coding RNA expression in cultured cells

PubMed Central

Soares, Ricardo J; Maglieri, Giulia; Gutschner, Tony; Lund, Anders H; Nielsen, Boye S

2018-01-01

Abstract Deciphering the functions of long non-coding RNAs (lncRNAs) is facilitated by visualization of their subcellular localization using in situ hybridization (ISH) techniques. We evaluated four different ISH methods for detection of MALAT1 and CYTOR in cultured cells: a multiple probe detection approach with or without enzymatic signal amplification, a branched-DNA (bDNA) probe and an LNA-modified probe with enzymatic signal amplification. All four methods adequately stained MALAT1 in the nucleus in all of three cell lines investigated, HeLa, NHDF and T47D, and three of the methods detected the less expressed CYTOR. The sensitivity of the four ISH methods was evaluated by image analysis. In all three cell lines, the two methods involving enzymatic amplification gave the most intense MALAT1 signal, but the signal-to-background ratios were not different. CYTOR was best detected using the bDNA method. All four ISH methods showed significantly reduced MALAT1 signal in knock-out cells, and siRNA-induced knock-down of CYTOR resulted in significantly reduced CYTOR ISH signal, indicating good specificity of the probe designs and detection systems. Our data suggest that the ISH methods allow detection of both abundant and less abundantly expressed lncRNAs, although the latter required the use of the most specific and sensitive probe detection system. PMID:29059327

Monte Carlo based statistical power analysis for mediation models: methods and software.

PubMed

Zhang, Zhiyong

2014-12-01

The existing literature on statistical power analysis for mediation models often assumes data normality and is based on a less powerful Sobel test instead of the more powerful bootstrap test. This study proposes to estimate statistical power to detect mediation effects on the basis of the bootstrap method through Monte Carlo simulation. Nonnormal data with excessive skewness and kurtosis are allowed in the proposed method. A free R package called bmem is developed to conduct the power analysis discussed in this study. Four examples, including a simple mediation model, a multiple-mediator model with a latent mediator, a multiple-group mediation model, and a longitudinal mediation model, are provided to illustrate the proposed method.

Automatically Detect and Track Multiple Fish Swimming in Shallow Water with Frequent Occlusion

PubMed Central

Qian, Zhi-Ming; Cheng, Xi En; Chen, Yan Qiu

2014-01-01

Due to its universality, swarm behavior in nature attracts much attention of scientists from many fields. Fish schools are examples of biological communities that demonstrate swarm behavior. The detection and tracking of fish in a school are of important significance for the quantitative research on swarm behavior. However, different from other biological communities, there are three problems in the detection and tracking of fish school, that is, variable appearances, complex motion and frequent occlusion. To solve these problems, we propose an effective method of fish detection and tracking. In this method, first, the fish head region is positioned through extremum detection and ellipse fitting; second, The Kalman filtering and feature matching are used to track the target in complex motion; finally, according to the feature information obtained by the detection and tracking, the tracking problems caused by frequent occlusion are processed through trajectory linking. We apply this method to track swimming fish school of different densities. The experimental results show that the proposed method is both accurate and reliable. PMID:25207811

Detection of Coronal Mass Ejections Using Multiple Features and Space-Time Continuity

NASA Astrophysics Data System (ADS)

Zhang, Ling; Yin, Jian-qin; Lin, Jia-ben; Feng, Zhi-quan; Zhou, Jin

2017-07-01

Coronal Mass Ejections (CMEs) release tremendous amounts of energy in the solar system, which has an impact on satellites, power facilities and wireless transmission. To effectively detect a CME in Large Angle Spectrometric Coronagraph (LASCO) C2 images, we propose a novel algorithm to locate the suspected CME regions, using the Extreme Learning Machine (ELM) method and taking into account the features of the grayscale and the texture. Furthermore, space-time continuity is used in the detection algorithm to exclude the false CME regions. The algorithm includes three steps: i) define the feature vector which contains textural and grayscale features of a running difference image; ii) design the detection algorithm based on the ELM method according to the feature vector; iii) improve the detection accuracy rate by using the decision rule of the space-time continuum. Experimental results show the efficiency and the superiority of the proposed algorithm in the detection of CMEs compared with other traditional methods. In addition, our algorithm is insensitive to most noise.

Comparison of real-time PCR methods for the detection of Naegleria fowleri in surface water and sediment.

PubMed

Streby, Ashleigh; Mull, Bonnie J; Levy, Karen; Hill, Vincent R

2015-05-01

Naegleria fowleri is a thermophilic free-living ameba found in freshwater environments worldwide. It is the cause of a rare but potentially fatal disease in humans known as primary amebic meningoencephalitis. Established N. fowleri detection methods rely on conventional culture techniques and morphological examination followed by molecular testing. Multiple alternative real-time PCR assays have been published for rapid detection of Naegleria spp. and N. fowleri. Foursuch assays were evaluated for the detection of N. fowleri from surface water and sediment. The assays were compared for thermodynamic stability, analytical sensitivity and specificity, detection limits, humic acid inhibition effects, and performance with seeded environmental matrices. Twenty-one ameba isolates were included in the DNA panel used for analytical sensitivity and specificity analyses. N. fowleri genotypes I and III were used for method performance testing. Two of the real-time PCR assays were determined to yield similar performance data for specificity and sensitivity for detecting N. fowleri in environmental matrices.

Comparison of real-time PCR methods for the detection of Naegleria fowleri in surface water and sediment

PubMed Central

Streby, Ashleigh; Mull, Bonnie J.; Levy, Karen

2015-01-01

Naegleria fowleri is a thermophilic free-living ameba found in freshwater environments worldwide. It is the cause of a rare but potentially fatal disease in humans known as primary amebic meningoencephalitis. Established N. fowleri detection methods rely on conventional culture techniques and morphological examination followed by molecular testing. Multiple alternative real-time PCR assays have been published for rapid detection of Naegleria spp. and N. fowleri. Four such assays were evaluated for the detection of N. fowleri from surface water and sediment. The assays were compared for thermodynamic stability, analytical sensitivity and specificity, detection limits, humic acid inhibition effects, and performance with seeded environmental matrices. Twenty-one ameba isolates were included in the DNA panel used for analytical sensitivity and specificity analyses. N. fowleri genotypes I and III were used for method performance testing. Two of the real-time PCR assays were determined to yield similar performance data for specificity and sensitivity for detecting N. fowleri in environmental matrices. PMID:25855343

A fast button surface defects detection method based on convolutional neural network

NASA Astrophysics Data System (ADS)

Liu, Lizhe; Cao, Danhua; Wu, Songlin; Wu, Yubin; Wei, Taoran

2018-01-01

Considering the complexity of the button surface texture and the variety of buttons and defects, we propose a fast visual method for button surface defect detection, based on convolutional neural network (CNN). CNN has the ability to extract the essential features by training, avoiding designing complex feature operators adapted to different kinds of buttons, textures and defects. Firstly, we obtain the normalized button region and then use HOG-SVM method to identify the front and back side of the button. Finally, a convolutional neural network is developed to recognize the defects. Aiming at detecting the subtle defects, we propose a network structure with multiple feature channels input. To deal with the defects of different scales, we take a strategy of multi-scale image block detection. The experimental results show that our method is valid for a variety of buttons and able to recognize all kinds of defects that have occurred, including dent, crack, stain, hole, wrong paint and uneven. The detection rate exceeds 96%, which is much better than traditional methods based on SVM and methods based on template match. Our method can reach the speed of 5 fps on DSP based smart camera with 600 MHz frequency.

Noninvasive deep Raman detection with 2D correlation analysis

NASA Astrophysics Data System (ADS)

Kim, Hyung Min; Park, Hyo Sun; Cho, Youngho; Jin, Seung Min; Lee, Kang Taek; Jung, Young Mee; Suh, Yung Doug

2014-07-01

The detection of poisonous chemicals enclosed in daily necessaries is prerequisite essential for homeland security with the increasing threat of terrorism. For the detection of toxic chemicals, we combined a sensitive deep Raman spectroscopic method with 2D correlation analysis. We obtained the Raman spectra from concealed chemicals employing spatially offset Raman spectroscopy in which incident line-shaped light experiences multiple scatterings before being delivered to inner component and yielding deep Raman signal. Furthermore, we restored the pure Raman spectrum of each component using 2D correlation spectroscopic analysis with chemical inspection. Using this method, we could elucidate subsurface component under thick powder and packed contents in a bottle.

Unsupervised Approaches for Post-Processing in Computationally Efficient Waveform-Similarity-Based Earthquake Detection

NASA Astrophysics Data System (ADS)

Bergen, K.; Yoon, C. E.; OReilly, O. J.; Beroza, G. C.

2015-12-01

Recent improvements in computational efficiency for waveform correlation-based detections achieved by new methods such as Fingerprint and Similarity Thresholding (FAST) promise to allow large-scale blind search for similar waveforms in long-duration continuous seismic data. Waveform similarity search applied to datasets of months to years of continuous seismic data will identify significantly more events than traditional detection methods. With the anticipated increase in number of detections and associated increase in false positives, manual inspection of the detection results will become infeasible. This motivates the need for new approaches to process the output of similarity-based detection. We explore data mining techniques for improved detection post-processing. We approach this by considering similarity-detector output as a sparse similarity graph with candidate events as vertices and similarities as weighted edges. Image processing techniques are leveraged to define candidate events and combine results individually processed at multiple stations. Clustering and graph analysis methods are used to identify groups of similar waveforms and assign a confidence score to candidate detections. Anomaly detection and classification are applied to waveform data for additional false detection removal. A comparison of methods will be presented and their performance will be demonstrated on a suspected induced and non-induced earthquake sequence.

Detection of mitochondrial COII DNA sequences in ant guts as a method for assessing termite predation by ants.

PubMed

Fayle, Tom M; Scholtz, Olivia; Dumbrell, Alex J; Russell, Stephen; Segar, Simon T; Eggleton, Paul

2015-01-01

Termites and ants contribute more to animal biomass in tropical rain forests than any other single group and perform vital ecosystem functions. Although ants prey on termites, at the community level the linkage between these groups is poorly understood. Thus, assessing the distribution and specificity of ant termitophagy is of considerable interest. We describe an approach for quantifying ant-termite food webs by sequencing termite DNA (cytochrome c oxidase subunit II, COII) from ant guts and apply this to a soil-dwelling ant community from tropical rain forest in Gabon. We extracted DNA from 215 ants from 15 species. Of these, 17.2 % of individuals had termite DNA in their guts, with BLAST analysis confirming the identity of 34.1 % of these termites to family level or better. Although ant species varied in detection of termite DNA, ranging from 63 % (5/7; Camponotus sp. 1) to 0 % (0/7; Ponera sp. 1), there was no evidence (with small sample sizes) for heterogeneity in termite consumption across ant taxa, and no evidence for species-specific ant-termite predation. In all three ant species with identifiable termite DNA in multiple individuals, multiple termite species were represented. Furthermore, the two termite species that were detected on multiple occasions in ant guts were in both cases found in multiple ant species, suggesting that ant-termite food webs are not strongly compartmentalised. However, two ant species were found to consume only Anoplotermes-group termites, indicating possible predatory specialisation at a higher taxonomic level. Using a laboratory feeding test, we were able to detect termite COII sequences in ant guts up to 2 h after feeding, indicating that our method only detects recent feeding events. Our data provide tentative support for the hypothesis that unspecialised termite predation by ants is widespread and highlight the use of molecular approaches for future studies of ant-termite food webs.

Simple, accurate formula for the average bit error probability of multiple-input multiple-output free-space optical links over negative exponential turbulence channels.

PubMed

Peppas, Kostas P; Lazarakis, Fotis; Alexandridis, Antonis; Dangakis, Kostas

2012-08-01

In this Letter we investigate the error performance of multiple-input multiple-output free-space optical communication systems employing intensity modulation/direct detection and operating over strong atmospheric turbulence channels. Atmospheric-induced strong turbulence fading is modeled using the negative exponential distribution. For the considered system, an approximate yet accurate analytical expression for the average bit error probability is derived and an efficient method for its numerical evaluation is proposed. Numerically evaluated and computer simulation results are further provided to demonstrate the validity of the proposed mathematical analysis.

A novel interacting multiple model based network intrusion detection scheme

NASA Astrophysics Data System (ADS)

Xin, Ruichi; Venkatasubramanian, Vijay; Leung, Henry

2006-04-01

In today's information age, information and network security are of primary importance to any organization. Network intrusion is a serious threat to security of computers and data networks. In internet protocol (IP) based network, intrusions originate in different kinds of packets/messages contained in the open system interconnection (OSI) layer 3 or higher layers. Network intrusion detection and prevention systems observe the layer 3 packets (or layer 4 to 7 messages) to screen for intrusions and security threats. Signature based methods use a pre-existing database that document intrusion patterns as perceived in the layer 3 to 7 protocol traffics and match the incoming traffic for potential intrusion attacks. Alternately, network traffic data can be modeled and any huge anomaly from the established traffic pattern can be detected as network intrusion. The latter method, also known as anomaly based detection is gaining popularity for its versatility in learning new patterns and discovering new attacks. It is apparent that for a reliable performance, an accurate model of the network data needs to be established. In this paper, we illustrate using collected data that network traffic is seldom stationary. We propose the use of multiple models to accurately represent the traffic data. The improvement in reliability of the proposed model is verified by measuring the detection and false alarm rates on several datasets.

Target-triggering multiple-cycle signal amplification strategy for ultrasensitive detection of DNA based on QCM and SPR.

PubMed

Song, Weiling; Yin, Wenshuo; Sun, Wenbo; Guo, Xiaoyan; He, Peng; Yang, Xiaoyan; Zhang, Xiaoru

2018-04-24

Detection of ultralow concentrations of nucleic acid sequences is a central challenge in the early diagnosis of genetic diseases. Herein, we developed a target-triggering cascade multiple cycle amplification for ultrasensitive DNA detection using quartz crystal microbalance (QCM) and surface plasmon resonance (SPR). It was based on the exonuclease Ⅲ (Exo Ⅲ)-assisted signal amplification and the hybridization chain reaction (HCR). The streptavidin-coated Au-NPs (Au-NPs-SA) were assembled on the HCR products as recognition element. Upon sensing of target DNA, the duplex DNA probe triggered the Exo Ⅲ cleavage process, accompanied by generating a new secondary target DNA and releasing target DNA. The released target DNA and the secondary target DNA were recycled. Simultaneously, numerous single strands were liberated and acted as the trigger of HCR to generate further signal amplification, resulting in the immobilization of abundant Au-NPs-SA on the gold substrate. The QCM sensor results were found to be comparable to that achieved using a SPR sensor platform. This method exhibited a high sensitivity toward target DNA with a detection limit of 0.70 fM. The high sensitivity and specificity make this method a great potential for detecting DNA with trace amounts in bioanalysis and clinical biomedicine. Copyright © 2018 Elsevier Inc. All rights reserved.

Multiple Hotspot Mutations Scanning by Single Droplet Digital PCR.

PubMed

Decraene, Charles; Silveira, Amanda B; Bidard, François-Clément; Vallée, Audrey; Michel, Marc; Melaabi, Samia; Vincent-Salomon, Anne; Saliou, Adrien; Houy, Alexandre; Milder, Maud; Lantz, Olivier; Ychou, Marc; Denis, Marc G; Pierga, Jean-Yves; Stern, Marc-Henri; Proudhon, Charlotte

2018-02-01

Progress in the liquid biopsy field, combined with the development of droplet digital PCR (ddPCR), has enabled noninvasive monitoring of mutations with high detection accuracy. However, current assays detect a restricted number of mutations per reaction. ddPCR is a recognized method for detecting alterations previously characterized in tumor tissues, but its use as a discovery tool when the mutation is unknown a priori remains limited. We established 2 ddPCR assays detecting all genomic alterations within KRAS exon 2 and EGFR exon 19 mutation hotspots, which are of clinical importance in colorectal and lung cancer, with use of a unique pair of TaqMan ® oligoprobes. The KRAS assay scanned for the 7 most common mutations in codons 12/13 but also all other mutations found in that region. The EGFR assay screened for all in-frame deletions of exon 19, which are frequent EGFR-activating events. The KRAS and EGFR assays were highly specific and both reached a limit of detection of <0.1% in mutant allele frequency. We further validated their performance on multiple plasma and formalin-fixed and paraffin-embedded tumor samples harboring a panel of different KRAS or EGFR mutations. This method presents the advantage of detecting a higher number of mutations with single-reaction ddPCRs while consuming a minimum of patient sample. This is particularly useful in the context of liquid biopsy because the amount of circulating tumor DNA is often low. This method should be useful as a discovery tool when the tumor tissue is unavailable or to monitor disease during therapy. © 2017 American Association for Clinical Chemistry.

Damage diagnosis algorithm using a sequential change point detection method with an unknown distribution for damage

NASA Astrophysics Data System (ADS)

Noh, Hae Young; Rajagopal, Ram; Kiremidjian, Anne S.

2012-04-01

This paper introduces a damage diagnosis algorithm for civil structures that uses a sequential change point detection method for the cases where the post-damage feature distribution is unknown a priori. This algorithm extracts features from structural vibration data using time-series analysis and then declares damage using the change point detection method. The change point detection method asymptotically minimizes detection delay for a given false alarm rate. The conventional method uses the known pre- and post-damage feature distributions to perform a sequential hypothesis test. In practice, however, the post-damage distribution is unlikely to be known a priori. Therefore, our algorithm estimates and updates this distribution as data are collected using the maximum likelihood and the Bayesian methods. We also applied an approximate method to reduce the computation load and memory requirement associated with the estimation. The algorithm is validated using multiple sets of simulated data and a set of experimental data collected from a four-story steel special moment-resisting frame. Our algorithm was able to estimate the post-damage distribution consistently and resulted in detection delays only a few seconds longer than the delays from the conventional method that assumes we know the post-damage feature distribution. We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.

A Nanocoaxial-Based Electrochemical Sensor for the Detection of Cholera Toxin

NASA Astrophysics Data System (ADS)

Archibald, Michelle M.; Rizal, Binod; Connolly, Timothy; Burns, Michael J.; Naughton, Michael J.; Chiles, Thomas C.

2015-03-01

Sensitive, real-time detection of biomarkers is of critical importance for rapid and accurate diagnosis of disease for point of care (POC) technologies. Current methods do not allow for POC applications due to several limitations, including sophisticated instrumentation, high reagent consumption, limited multiplexing capability, and cost. Here, we report a nanocoaxial-based electrochemical sensor for the detection of bacterial toxins using an electrochemical enzyme-linked immunosorbent assay (ELISA) and differential pulse voltammetry (DPV). Proof-of-concept was demonstrated for the detection of cholera toxin (CT). The linear dynamic range of detection was 10 ng/ml - 1 μg/ml, and the limit of detection (LOD) was found to be 2 ng/ml. This level of sensitivity is comparable to the standard optical ELISA used widely in clinical applications. In addition to matching the detection profile of the standard ELISA, the nanocoaxial array provides a simple electrochemical readout and a miniaturized platform with multiplexing capabilities for the simultaneous detection of multiple biomarkers, giving the nanocoax a desirable advantage over the standard method towards POC applications. Sensitive, real-time detection of biomarkers is of critical importance for rapid and accurate diagnosis of disease for point of care (POC) technologies. Current methods do not allow for POC applications due to several limitations, including sophisticated instrumentation, high reagent consumption, limited multiplexing capability, and cost. Here, we report a nanocoaxial-based electrochemical sensor for the detection of bacterial toxins using an electrochemical enzyme-linked immunosorbent assay (ELISA) and differential pulse voltammetry (DPV). Proof-of-concept was demonstrated for the detection of cholera toxin (CT). The linear dynamic range of detection was 10 ng/ml - 1 μg/ml, and the limit of detection (LOD) was found to be 2 ng/ml. This level of sensitivity is comparable to the standard optical ELISA used widely in clinical applications. In addition to matching the detection profile of the standard ELISA, the nanocoaxial array provides a simple electrochemical readout and a miniaturized platform with multiplexing capabilities for the simultaneous detection of multiple biomarkers, giving the nanocoax a desirable advantage over the standard method towards POC applications. This work was supported by the National Institutes of Health (National Cancer Institute award No. CA137681 and National Institute of Allergy and Infectious Diseases Award No. AI100216).

Fully automatic detection of deep white matter T1 hypointense lesions in multiple sclerosis

NASA Astrophysics Data System (ADS)

Spies, Lothar; Tewes, Anja; Suppa, Per; Opfer, Roland; Buchert, Ralph; Winkler, Gerhard; Raji, Alaleh

2013-12-01

A novel method is presented for fully automatic detection of candidate white matter (WM) T1 hypointense lesions in three-dimensional high-resolution T1-weighted magnetic resonance (MR) images. By definition, T1 hypointense lesions have similar intensity as gray matter (GM) and thus appear darker than surrounding normal WM in T1-weighted images. The novel method uses a standard classification algorithm to partition T1-weighted images into GM, WM and cerebrospinal fluid (CSF). As a consequence, T1 hypointense lesions are assigned an increased GM probability by the standard classification algorithm. The GM component image of a patient is then tested voxel-by-voxel against GM component images of a normative database of healthy individuals. Clusters (≥0.1 ml) of significantly increased GM density within a predefined mask of deep WM are defined as lesions. The performance of the algorithm was assessed on voxel level by a simulation study. A maximum dice similarity coefficient of 60% was found for a typical T1 lesion pattern with contrasts ranging from WM to cortical GM, indicating substantial agreement between ground truth and automatic detection. Retrospective application to 10 patients with multiple sclerosis demonstrated that 93 out of 96 T1 hypointense lesions were detected. On average 3.6 false positive T1 hypointense lesions per patient were found. The novel method is promising to support the detection of hypointense lesions in T1-weighted images which warrants further evaluation in larger patient samples.

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model.

PubMed

Mei, Shuang; Wang, Yudan; Wen, Guojun

2018-04-02

Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.

Power-efficient method for IM-DD optical transmission of multiple OFDM signals.

PubMed

Effenberger, Frank; Liu, Xiang

2015-05-18

We propose a power-efficient method for transmitting multiple frequency-division multiplexed (FDM) orthogonal frequency-division multiplexing (OFDM) signals in intensity-modulation direct-detection (IM-DD) optical systems. This method is based on quadratic soft clipping in combination with odd-only channel mapping. We show, both analytically and experimentally, that the proposed approach is capable of improving the power efficiency by about 3 dB as compared to conventional FDM OFDM signals under practical bias conditions, making it a viable solution in applications such as optical fiber-wireless integrated systems where both IM-DD optical transmission and OFDM signaling are important.

Simultaneous Detection of Genetically Modified Organisms in a Mixture by Multiplex PCR-Chip Capillary Electrophoresis.

PubMed

Patwardhan, Supriya; Dasari, Srikanth; Bhagavatula, Krishna; Mueller, Steffen; Deepak, Saligrama Adavigowda; Ghosh, Sudip; Basak, Sanjay

2015-01-01

An efficient PCR-based method to trace genetically modified food and feed products is in demand due to regulatory requirements and contaminant issues in India. However, post-PCR detection with conventional methods has limited sensitivity in amplicon separation that is crucial in multiplexing. The study aimed to develop a sensitive post-PCR detection method by using PCR-chip capillary electrophoresis (PCR-CCE) to detect and identify specific genetically modified organisms in their genomic DNA mixture by targeting event-specific nucleotide sequences. Using the PCR-CCE approach, novel multiplex methods were developed to detect MON531 cotton, EH 92-527-1 potato, Bt176 maize, GT73 canola, or GA21 maize simultaneously when their genomic DNAs in mixtures were amplified using their primer mixture. The repeatability RSD (RSDr) of the peak migration time was 0.06 and 3.88% for the MON531 and Bt176, respectively. The RSD (RSDR) of the Cry1Ac peak ranged from 0.12 to 0.40% in multiplex methods. The method was sensitive in resolving amplicon of size difference up to 4 bp. The PCR-CCE method is suitable to detect multiple genetically modified events in a composite DNA sample by tagging their event specific sequences.

Microbleed Detection Using Automated Segmentation (MIDAS): A New Method Applicable to Standard Clinical MR Images

PubMed Central

Seghier, Mohamed L.; Kolanko, Magdalena A.; Leff, Alexander P.; Jäger, Hans R.; Gregoire, Simone M.; Werring, David J.

2011-01-01

Background Cerebral microbleeds, visible on gradient-recalled echo (GRE) T2* MRI, have generated increasing interest as an imaging marker of small vessel diseases, with relevance for intracerebral bleeding risk or brain dysfunction. Methodology/Principal Findings Manual rating methods have limited reliability and are time-consuming. We developed a new method for microbleed detection using automated segmentation (MIDAS) and compared it with a validated visual rating system. In thirty consecutive stroke service patients, standard GRE T2* images were acquired and manually rated for microbleeds by a trained observer. After spatially normalizing each patient's GRE T2* images into a standard stereotaxic space, the automated microbleed detection algorithm (MIDAS) identified cerebral microbleeds by explicitly incorporating an “extra” tissue class for abnormal voxels within a unified segmentation-normalization model. The agreement between manual and automated methods was assessed using the intraclass correlation coefficient (ICC) and Kappa statistic. We found that MIDAS had generally moderate to good agreement with the manual reference method for the presence of lobar microbleeds (Kappa = 0.43, improved to 0.65 after manual exclusion of obvious artefacts). Agreement for the number of microbleeds was very good for lobar regions: (ICC = 0.71, improved to ICC = 0.87). MIDAS successfully detected all patients with multiple (≥2) lobar microbleeds. Conclusions/Significance MIDAS can identify microbleeds on standard MR datasets, and with an additional rapid editing step shows good agreement with a validated visual rating system. MIDAS may be useful in screening for multiple lobar microbleeds. PMID:21448456

GMHDIF: A Computer Program for Detecting DIF in Dichotomous and Polytomous Items Using Generalized Mantel-Haenszel Statistics

ERIC Educational Resources Information Center

Fidalgo, Angel M.

2011-01-01

Mantel-Haenszel (MH) methods constitute one of the most popular nonparametric differential item functioning (DIF) detection procedures. GMHDIF has been developed to provide an easy-to-use program for conducting DIF analyses. Some of the advantages of this program are that (a) it performs two-stage DIF analyses in multiple groups simultaneously;…

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

ERIC Educational Resources Information Center

Verde, Pablo E.; Ohmann, Christian

2015-01-01

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

Multiple tag labeling method for DNA sequencing

DOEpatents

Mathies, Richard A.; Huang, Xiaohua C.; Quesada, Mark A.

1995-01-01

A DNA sequencing method described which uses single lane or channel electrophoresis. Sequencing fragments are separated in said lane and detected using a laser-excited, confocal fluorescence scanner. Each set of DNA sequencing fragments is separated in the same lane and then distinguished using a binary coding scheme employing only two different fluorescent labels. Also described is a method of using radio-isotope labels.

Laser-based pedestrian tracking in outdoor environments by multiple mobile robots.

PubMed

Ozaki, Masataka; Kakimuma, Kei; Hashimoto, Masafumi; Takahashi, Kazuhiko

2012-10-29

This paper presents an outdoors laser-based pedestrian tracking system using a group of mobile robots located near each other. Each robot detects pedestrians from its own laser scan image using an occupancy-grid-based method, and the robot tracks the detected pedestrians via Kalman filtering and global-nearest-neighbor (GNN)-based data association. The tracking data is broadcast to multiple robots through intercommunication and is combined using the covariance intersection (CI) method. For pedestrian tracking, each robot identifies its own posture using real-time-kinematic GPS (RTK-GPS) and laser scan matching. Using our cooperative tracking method, all the robots share the tracking data with each other; hence, individual robots can always recognize pedestrians that are invisible to any other robot. The simulation and experimental results show that cooperating tracking provides the tracking performance better than conventional individual tracking does. Our tracking system functions in a decentralized manner without any central server, and therefore, this provides a degree of scalability and robustness that cannot be achieved by conventional centralized architectures.

Sensor And Method For Detecting A Superstrate

NASA Technical Reports Server (NTRS)

Arndt, G. Dickey (Inventor); Cari, James R. (Inventor); Ngo, Phong H. (Inventor); Fink, Patrick W. (Inventor); Siekierski, James D. (Inventor)

2006-01-01

Method and apparatus are provided for determining a superstrate on or near a sensor, e.g., for detecting the presence of an ice superstrate on an airplane wing or a road. In one preferred embodiment, multiple measurement cells are disposed along a transmission line. While the present invention is operable with different types of transmission lines, construction details for a presently preferred coplanar waveguide and a microstrip waveguide are disclosed. A computer simulation is provided as part of the invention for predicting results of a simulated superstrate detector system. The measurement cells may be physically partitioned, nonphysically partitioned with software or firmware, or include a combination of different types of partitions. In one embodiment, a plurality of transmission lines are utilized wherein each transmission line includes a plurality of measurement cells. The plurality of transmission lines may be multiplexed with the signal from each transmission line being applied to the same phase detector. In one embodiment, an inverse problem method is applied to determine the superstrate dielectric for a transmission line with multiple measurement cells.

A method of multiplex PCR for detection of field released Beauveria bassiana, a fungal entomopathogen applied for pest management in jute (Corchorus olitorius).

PubMed

Biswas, Chinmay; Dey, Piyali; Gotyal, B S; Satpathy, Subrata

2015-04-01

The fungal entomopathogen Beauveria bassiana is a promising biocontrol agent for many pests. Some B. bassiana strains have been found effective against jute pests. To monitor the survival of field released B. bassiana a rapid and efficient detection technique is essential. Conventional methods such as plating method or direct culture method which are based on cultivation on selective media followed by microscopy are time consuming and not so sensitive. PCR based methods are rapid, sensitive and reliable. A single primer PCR may fail to amplify some of the strains. However, multiplex PCR increases the possibility of detection as it uses multiple primers. Therefore, in the present investigation a multiplex PCR protocol was developed by multiplexing three primers SCA 14, SCA 15 and SCB 9 to detect field released B. bassiana strains from soil as well as foliage of jute field. Using our multiplex PCR protocol all the five B. bassiana strains could be detected from soil and three strains viz., ITCC 6063, ITCC 4563 and ITCC 4796 could be detected even from the crop foliage after 45 days of spray.

[Detection of factor VIII intron 1 inversion in severe haemophilia A].

PubMed

Liang, Yan; Yan, Zhen-yu; Yan, Mei; Hua, Bao-lai; Xiao, Bai; Zhao, Yong-qiang; Liu, Jing-zhong

2009-06-01

Screening the intron 1 inversion of factor VIII (FVIII) in the population of severe haemophilia A(HA) in China and performing carrier detection and prenatal diagnosis. Using LD-PCR to detect intron 22 inversions and multiple-PCR within two tubes to intron 1 inversions in severe HA patients. Carrier detection and prenatal diagnosis were performed in affected families. Linkage analysis and DNA sequencing were used to verify these tests. One hundred and eighteen patients were seven diagnosed as intron 22 inversions and 7 were intron 1 inversions out of 247 severe HA patients. The prevalence of the intron 1 inversion in Chinese severe haemophilia A patients was 2.8% (7/247). Six women from family A and 2 from family B were diagnosed as carriers. One fetus from family A was affected fetus. Intron 1 inversion could be detected directly by multiple-PCR within two tubes. This method made the strategy more perfective in carrier and prenatal diagnosis of haemophilia A.

Effect of processing on recovery and variability associated with immunochemical analytical methods for multiple allergens in a single matrix: dark chocolate.

PubMed

Khuda, Sefat; Slate, Andrew; Pereira, Marion; Al-Taher, Fadwa; Jackson, Lauren; Diaz-Amigo, Carmen; Bigley, Elmer C; Whitaker, Thomas; Williams, Kristina

2012-05-02

Immunodetection of allergens in dark chocolate is complicated by interference from the chocolate components. The objectives of this study were to establish reference materials for detecting multiple allergens in dark chocolate and to determine the accuracy and precision of allergen detection by enzyme-linked immunosorbent assay (ELISA) before and after chocolate processing. Defatted peanut flour, whole egg powder, and spray-dried milk were added to melted chocolate at seven incurred levels and tempered for 4 h. Allergen concentrations were measured using commercial ELISA kits. Tempering decreased the detection of casein and β-lactoglobulin (BLG), but had no significant effect on the detection of peanut and egg. Total coefficients of variation were higher in tempered than untempered chocolate for casein and BLG, but total and analytical CVs were comparable for peanut and egg. These findings indicate that processing has a greater effect on recovery and variability of casein and BLG than peanut and egg detection in a dark chocolate matrix.

A Real-Time High Performance Computation Architecture for Multiple Moving Target Tracking Based on Wide-Area Motion Imagery via Cloud and Graphic Processing Units

PubMed Central

Liu, Kui; Wei, Sixiao; Chen, Zhijiang; Jia, Bin; Chen, Genshe; Ling, Haibin; Sheaff, Carolyn; Blasch, Erik

2017-01-01

This paper presents the first attempt at combining Cloud with Graphic Processing Units (GPUs) in a complementary manner within the framework of a real-time high performance computation architecture for the application of detecting and tracking multiple moving targets based on Wide Area Motion Imagery (WAMI). More specifically, the GPU and Cloud Moving Target Tracking (GC-MTT) system applied a front-end web based server to perform the interaction with Hadoop and highly parallelized computation functions based on the Compute Unified Device Architecture (CUDA©). The introduced multiple moving target detection and tracking method can be extended to other applications such as pedestrian tracking, group tracking, and Patterns of Life (PoL) analysis. The cloud and GPUs based computing provides an efficient real-time target recognition and tracking approach as compared to methods when the work flow is applied using only central processing units (CPUs). The simultaneous tracking and recognition results demonstrate that a GC-MTT based approach provides drastically improved tracking with low frame rates over realistic conditions. PMID:28208684

A Real-Time High Performance Computation Architecture for Multiple Moving Target Tracking Based on Wide-Area Motion Imagery via Cloud and Graphic Processing Units.

PubMed

Liu, Kui; Wei, Sixiao; Chen, Zhijiang; Jia, Bin; Chen, Genshe; Ling, Haibin; Sheaff, Carolyn; Blasch, Erik

2017-02-12

This paper presents the first attempt at combining Cloud with Graphic Processing Units (GPUs) in a complementary manner within the framework of a real-time high performance computation architecture for the application of detecting and tracking multiple moving targets based on Wide Area Motion Imagery (WAMI). More specifically, the GPU and Cloud Moving Target Tracking (GC-MTT) system applied a front-end web based server to perform the interaction with Hadoop and highly parallelized computation functions based on the Compute Unified Device Architecture (CUDA©). The introduced multiple moving target detection and tracking method can be extended to other applications such as pedestrian tracking, group tracking, and Patterns of Life (PoL) analysis. The cloud and GPUs based computing provides an efficient real-time target recognition and tracking approach as compared to methods when the work flow is applied using only central processing units (CPUs). The simultaneous tracking and recognition results demonstrate that a GC-MTT based approach provides drastically improved tracking with low frame rates over realistic conditions.

Design and synthesis of multiple antigenic peptides and their application for dengue diagnosis.

PubMed

Rai, Reeta; Dubey, Sameer; Santosh, K V; Biswas, Ashutosh; Mehrotra, Vinit; Rao, D N

2017-09-01

Major difficulty in development of dengue diagnostics is availability of suitable antigens. To overcome this, we made an attempt to develop a peptide based diagnosis which offers significant advantage over other methods. With the help of in silico methods, two epitopes were selected from envelope protein and three from NS1 protein of dengue virus. These were synthesized in combination as three multiple antigenic peptides (MAPs). We have tested 157 dengue positive sera confirmed for NS1 antigen. MAP1 showed 96.81% sera positive for IgM and 68.15% positive for IgG. MAP2 detected 94.90% IgM and 59.23% IgG positive sera. MAP3 also detected 96.17% IgM and 59.87% IgG positive sera. To the best of our knowledge this is the first study describing the use of synthetic multiple antigenic peptides for the diagnosis of dengue infection. This study describes MAPs as a promising tool for the use in serodiagnosis of dengue. Copyright © 2017 International Alliance for Biological Standardization. Published by Elsevier Ltd. All rights reserved.

Surface plasmon resonance spectroscopy sensor and methods for using same

DOEpatents

Anderson, Brian Benjamin; Nave, Stanley Eugene

2002-01-01

A surface plasmon resonance ("SPR") probe with a detachable sensor head and system and methods for using the same in various applications is described. The SPR probe couples fiber optic cables directly to an SPR substrate that has a generally planar input surface and a generally curved reflecting surface, such as a substrate formed as a hemisphere. Forming the SPR probe in this manner allows the probe to be miniaturized and operate without the need for high precision, expensive and bulky collimating or focusing optics. Additionally, the curved reflecting surface of the substrate can be coated with one or multiple patches of sensing medium to allow the probe to detect for multiple analytes of interest or to provide multiple readings for comparison and higher precision. Specific applications for the probe are disclosed, including extremely high sensitive relative humidity and dewpoint detection for, e.g., moisture-sensitive environment such as volatile chemical reactions. The SPR probe disclosed operates with a large dynamic range and provides extremely high quality spectra despite being robust enough for field deployment and readily manufacturable.

Robust sensor fault detection and isolation of gas turbine engines subjected to time-varying parameter uncertainties

NASA Astrophysics Data System (ADS)

Pourbabaee, Bahareh; Meskin, Nader; Khorasani, Khashayar

2016-08-01

In this paper, a novel robust sensor fault detection and isolation (FDI) strategy using the multiple model-based (MM) approach is proposed that remains robust with respect to both time-varying parameter uncertainties and process and measurement noise in all the channels. The scheme is composed of robust Kalman filters (RKF) that are constructed for multiple piecewise linear (PWL) models that are constructed at various operating points of an uncertain nonlinear system. The parameter uncertainty is modeled by using a time-varying norm bounded admissible structure that affects all the PWL state space matrices. The robust Kalman filter gain matrices are designed by solving two algebraic Riccati equations (AREs) that are expressed as two linear matrix inequality (LMI) feasibility conditions. The proposed multiple RKF-based FDI scheme is simulated for a single spool gas turbine engine to diagnose various sensor faults despite the presence of parameter uncertainties, process and measurement noise. Our comparative studies confirm the superiority of our proposed FDI method when compared to the methods that are available in the literature.

Rapid gas chromatography with flame photometric detection of multiple organophosphorus pesticides in Salvia miltiorrhizae after ultrasonication assisted one-step extraction.

PubMed

Zhang, Shanshan; Liu, Xiaofei; Qin, Jia'an; Yang, Meihua; Zhao, Hongzheng; Wang, Yong; Guo, Weiying; Ma, Zhijie; Kong, Weijun

2017-11-15

A simple and rapid gas chromatography-flame photometric detection (GC-FPD) method was developed for the determination of 12 organophosphorus pesticides (OPPs) in Salvia miltiorrhizae by using ultrasonication assisted one-step extraction (USAE) without any clean-up steps. Some crucial parameters such as type of extraction solvent were optimized to improve the method performance for trace analysis. Any clean-up steps were negligent as no interferences were detected in the GC-FPD chromatograms for sensitive detection. Under the optimized conditions, limits of detection (LODs) and quantitation (LOQs) for all pesticides were in the range of 0.001-0.002mg/kg and 0.002-0.01mg/kg and 0.002-0.01mg/kg, respectively, which were all below the regulatory maximum residue limits suggested. RSDs for method precision (intra- and inter-day variations) were lower than 6.8% in approval with international regulations. Average recovery rates for all pesticides at three fortification levels (0.5, 1.0 and 5.0mg/kg) were in the range of 71.2-101.0% with relative standard deviations (RSDs) <13%. The developed method was evaluated for its feasibility in the simultaneous pre-concentration and determination of 12 OPPs in 32 batches of real S. miltiorrhizae samples. Only one pesticide (dimethoate) out of the 12 targets was simultaneously detected in four samples at concentrations of 0.016-0.02mg/kg. Dichlorvos and omethoate were found in the same sample from Sichuan province at 0.004 and 0.027mg/kg, respectively. Malathion and monocrotophos were determined in the other two samples at 0.014 and 0.028mg/kg, respectively. All the positive samples were confirmed by LC-MS/MS. The simple, reliable and rapid USAE-GC-FPD method with many advantages over traditional techniques would be preferred for trace analysis of multiple pesticides in more complex matrices. Copyright © 2017 Elsevier B.V. All rights reserved.

Electrochemical and Infrared Absorption Spectroscopy Detection of SF₆ Decomposition Products.

PubMed

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

2017-11-15

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

Applied Graph-Mining Algorithms to Study Biomolecular Interaction Networks

PubMed Central

2014-01-01

Protein-protein interaction (PPI) networks carry vital information on the organization of molecular interactions in cellular systems. The identification of functionally relevant modules in PPI networks is one of the most important applications of biological network analysis. Computational analysis is becoming an indispensable tool to understand large-scale biomolecular interaction networks. Several types of computational methods have been developed and employed for the analysis of PPI networks. Of these computational methods, graph comparison and module detection are the two most commonly used strategies. This review summarizes current literature on graph kernel and graph alignment methods for graph comparison strategies, as well as module detection approaches including seed-and-extend, hierarchical clustering, optimization-based, probabilistic, and frequent subgraph methods. Herein, we provide a comprehensive review of the major algorithms employed under each theme, including our recently published frequent subgraph method, for detecting functional modules commonly shared across multiple cancer PPI networks. PMID:24800226

Stepwise and stagewise approaches for spatial cluster detection.

PubMed

Xu, Jiale; Gangnon, Ronald E

2016-05-01

Spatial cluster detection is an important tool in many areas such as sociology, botany and public health. Previous work has mostly taken either a hypothesis testing framework or a Bayesian framework. In this paper, we propose a few approaches under a frequentist variable selection framework for spatial cluster detection. The forward stepwise methods search for multiple clusters by iteratively adding currently most likely cluster while adjusting for the effects of previously identified clusters. The stagewise methods also consist of a series of steps, but with a tiny step size in each iteration. We study the features and performances of our proposed methods using simulations on idealized grids or real geographic areas. From the simulations, we compare the performance of the proposed methods in terms of estimation accuracy and power. These methods are applied to the the well-known New York leukemia data as well as Indiana poverty data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Quantitative ultrasonic evaluation of concrete structures using one-sided access

NASA Astrophysics Data System (ADS)

Khazanovich, Lev; Hoegh, Kyle

2016-02-01

Nondestructive diagnostics of concrete structures is an important and challenging problem. A recent introduction of array ultrasonic dry point contact transducer systems offers opportunities for quantitative assessment of the subsurface condition of concrete structures, including detection of defects and inclusions. The methods described in this paper are developed for signal interpretation of shear wave impulse response time histories from multiple fixed distance transducer pairs in a self-contained ultrasonic linear array. This included generalizing Kirchoff migration-based synthetic aperture focusing technique (SAFT) reconstruction methods to handle the spatially diverse transducer pair locations, creating expanded virtual arrays with associated reconstruction methods, and creating automated reconstruction interpretation methods for reinforcement detection and stochastic flaw detection. Interpretation of the reconstruction techniques developed in this study were validated using the results of laboratory and field forensic studies. Applicability of the developed methods for solving practical engineering problems was demonstrated.

Development of a general method for detection and quantification of the P35S promoter based on assessment of existing methods

PubMed Central

Wu, Yuhua; Wang, Yulei; Li, Jun; Li, Wei; Zhang, Li; Li, Yunjing; Li, Xiaofei; Li, Jun; Zhu, Li; Wu, Gang

2014-01-01

The Cauliflower mosaic virus (CaMV) 35S promoter (P35S) is a commonly used target for detection of genetically modified organisms (GMOs). There are currently 24 reported detection methods, targeting different regions of the P35S promoter. Initial assessment revealed that due to the absence of primer binding sites in the P35S sequence, 19 of the 24 reported methods failed to detect P35S in MON88913 cotton, and the other two methods could only be applied to certain GMOs. The rest three reported methods were not suitable for measurement of P35S in some testing events, because SNPs in binding sites of the primer/probe would result in abnormal amplification plots and poor linear regression parameters. In this study, we discovered a conserved region in the P35S sequence through sequencing of P35S promoters from multiple transgenic events, and developed new qualitative and quantitative detection systems targeting this conserved region. The qualitative PCR could detect the P35S promoter in 23 unique GMO events with high specificity and sensitivity. The quantitative method was suitable for measurement of P35S promoter, exhibiting good agreement between the amount of template and Ct values for each testing event. This study provides a general P35S screening method, with greater coverage than existing methods. PMID:25483893

Development and applications of optical interferometric micrometrology in the Angstrom and subangstrom range

NASA Technical Reports Server (NTRS)

Lauer, James L.; Abel, Phillip B.

1988-01-01

The characteristics of the scanning tunneling microscope and atomic force microscope (AFM) are briefly reviewed, and optical methods, mainly interferometry, of sufficient resolution to measure AFM deflections are discussed. The methods include optical resonators, laser interferometry, multiple-beam interferometry, and evanescent wave detection. Experimental results using AFM are reviewed.

Detection of Inter-chromosomal Stable Aberrations by Multiple Fluorescence In Situ Hybridization (mFISH) and Spectral Karyotyping (SKY) in Irradiated Mice

PubMed Central

Pathak, Rupak; Koturbash, Igor; Hauer-Jensen, Martin

2017-01-01

Ionizing radiation (IR) induces numerous stable and unstable chromosomal aberrations. Unstable aberrations, where chromosome morphology is substantially compromised, can easily be identified by conventional chromosome staining techniques. However, detection of stable aberrations, which involve exchange or translocation of genetic materials without considerable modification in the chromosome morphology, requires sophisticated chromosome painting techniques that rely on in situ hybridization of fluorescently labeled DNA probes, a chromosome painting technique popularly known as fluorescence in situ hybridization (FISH). FISH probes can be specific for whole chromosome/s or precise sub-region on chromosome/s. The method not only allows visualization of stable aberrations, but it can also allow detection of the chromosome/s or specific DNA sequence/s involved in a particular aberration formation. A variety of chromosome painting techniques are available in cytogenetics; here two highly sensitive methods, multiple fluorescence in situ hybridization (mFISH) and spectral karyotyping (SKY), are discussed to identify inter-chromosomal stable aberrations that form in the bone marrow cells of mice after exposure to total body irradiation. Although both techniques rely on fluorescent labeled DNA probes, the method of detection and the process of image acquisition of the fluorescent signals are different. These two techniques have been used in various research areas, such as radiation biology, cancer cytogenetics, retrospective radiation biodosimetry, clinical cytogenetics, evolutionary cytogenetics, and comparative cytogenetics. PMID:28117817

Adaptive testing for multiple traits in a proportional odds model with applications to detect SNP-brain network associations.

PubMed

Kim, Junghi; Pan, Wei

2017-04-01

There has been increasing interest in developing more powerful and flexible statistical tests to detect genetic associations with multiple traits, as arising from neuroimaging genetic studies. Most of existing methods treat a single trait or multiple traits as response while treating an SNP as a predictor coded under an additive inheritance mode. In this paper, we follow an earlier approach in treating an SNP as an ordinal response while treating traits as predictors in a proportional odds model (POM). In this way, it is not only easier to handle mixed types of traits, e.g., some quantitative and some binary, but it is also potentially more robust to the commonly adopted additive inheritance mode. More importantly, we develop an adaptive test in a POM so that it can maintain high power across many possible situations. Compared to the existing methods treating multiple traits as responses, e.g., in a generalized estimating equation (GEE) approach, the proposed method can be applied to a high dimensional setting where the number of phenotypes (p) can be larger than the sample size (n), in addition to a usual small P setting. The promising performance of the proposed method was demonstrated with applications to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data, in which either structural MRI driven phenotypes or resting-state functional MRI (rs-fMRI) derived brain functional connectivity measures were used as phenotypes. The applications led to the identification of several top SNPs of biological interest. Furthermore, simulation studies showed competitive performance of the new method, especially for p>n. © 2017 WILEY PERIODICALS, INC.

Modular method of detection, localization, and counting of multiple-taxon pollen apertures using bag-of-words

NASA Astrophysics Data System (ADS)

Lozano-Vega, Gildardo; Benezeth, Yannick; Marzani, Franck; Boochs, Frank

2014-09-01

Accurate recognition of airborne pollen taxa is crucial for understanding and treating allergic diseases which affect an important proportion of the world population. Modern computer vision techniques enable the detection of discriminant characteristics. Apertures are among the important characteristics which have not been adequately explored until now. A flexible method of detection, localization, and counting of apertures of different pollen taxa with varying appearances is proposed. Aperture description is based on primitive images following the bag-of-words strategy. A confidence map is estimated based on the classification of sampled regions. The method is designed to be extended modularly to new aperture types employing the same algorithm by building individual classifiers. The method was evaluated on the top five allergenic pollen taxa in Germany, and its robustness to unseen particles was verified.

A novel method of multiple nucleic acid detection: Real-time RT-PCR coupled with probe-melting curve analysis.

PubMed

Han, Yang; Hou, Shao-Yang; Ji, Shang-Zhi; Cheng, Juan; Zhang, Meng-Yue; He, Li-Juan; Ye, Xiang-Zhong; Li, Yi-Min; Zhang, Yi-Xuan

2017-11-15

A novel method, real-time reverse transcription PCR (real-time RT-PCR) coupled with probe-melting curve analysis, has been established to detect two kinds of samples within one fluorescence channel. Besides a conventional TaqMan probe, this method employs another specially designed melting-probe with a 5' terminus modification which meets the same label with the same fluorescent group. By using an asymmetric PCR method, the melting-probe is able to detect an extra sample in the melting stage effectively while it almost has little influence on the amplification detection. Thus, this method allows the availability of united employment of both amplification stage and melting stage for detecting samples in one reaction. The further demonstration by simultaneous detection of human immunodeficiency virus (HIV) and hepatitis C virus (HCV) in one channel as a model system is presented in this essay. The sensitivity of detection by real-time RT-PCR coupled with probe-melting analysis was proved to be equal to that detected by conventional real-time RT-PCR. Because real-time RT-PCR coupled with probe-melting analysis can double the detection throughputs within one fluorescence channel, it is expected to be a good solution for the problem of low-throughput in current real-time PCR. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

Multilevel depth and image fusion for human activity detection.

PubMed

Ni, Bingbing; Pei, Yong; Moulin, Pierre; Yan, Shuicheng

2013-10-01

Recognizing complex human activities usually requires the detection and modeling of individual visual features and the interactions between them. Current methods only rely on the visual features extracted from 2-D images, and therefore often lead to unreliable salient visual feature detection and inaccurate modeling of the interaction context between individual features. In this paper, we show that these problems can be addressed by combining data from a conventional camera and a depth sensor (e.g., Microsoft Kinect). We propose a novel complex activity recognition and localization framework that effectively fuses information from both grayscale and depth image channels at multiple levels of the video processing pipeline. In the individual visual feature detection level, depth-based filters are applied to the detected human/object rectangles to remove false detections. In the next level of interaction modeling, 3-D spatial and temporal contexts among human subjects or objects are extracted by integrating information from both grayscale and depth images. Depth information is also utilized to distinguish different types of indoor scenes. Finally, a latent structural model is developed to integrate the information from multiple levels of video processing for an activity detection. Extensive experiments on two activity recognition benchmarks (one with depth information) and a challenging grayscale + depth human activity database that contains complex interactions between human-human, human-object, and human-surroundings demonstrate the effectiveness of the proposed multilevel grayscale + depth fusion scheme. Higher recognition and localization accuracies are obtained relative to the previous methods.

Analysis of the accuracy and precision of the McMaster method in detection of the eggs of Toxocara and Trichuris species (Nematoda) in dog faeces.

PubMed

Kochanowski, Maciej; Dabrowska, Joanna; Karamon, Jacek; Cencek, Tomasz; Osiński, Zbigniew

2013-07-01

The aim of this study was to determine the accuracy and precision of McMaster method with Raynaud's modification in the detection of the eggs of the nematodes Toxocara canis (Werner, 1782) and Trichuris ovis (Abildgaard, 1795) in faeces of dogs. Four variants of McMaster method were used for counting: in one grid, two grids, the whole McMaster chamber and flotation in the tube. One hundred sixty samples were prepared from dog faeces (20 repetitions for each egg quantity) containing 15, 25, 50, 100, 150, 200, 250 and 300 eggs of T. canis and T. ovis in 1 g of faeces. To compare the influence of kind of faeces on the results, samples of dog faeces were enriched at the same levels with the eggs of another nematode, Ascaris suum Goeze, 1782. In addition, 160 samples of pig faeces were prepared and enriched only with A. suum eggs in the same way. The highest limit of detection (the lowest level of eggs that were detected in at least 50% of repetitions) in all McMaster chamber variants were obtained for T. canis eggs (25-250 eggs/g faeces). In the variant with flotation in the tube, the highest limit of detection was obtained for T. ovis eggs (100 eggs/g). The best results of the limit of detection, sensitivity and the lowest coefficients of variation were obtained with the use of the whole McMaster chamber variant. There was no significant impact of properties of faeces on the obtained results. Multiplication factors for the whole chamber were calculated on the basis of the transformed equation of the regression line, illustrating the relationship between the number of detected eggs and that of the eggs added to the'sample. Multiplication factors calculated for T. canis and T. ovis eggs were higher than those expected using McMaster method with Raynaud modification.

Culture-dependent enumeration methods failed to simultaneously detect disinfectant-injured and genetically modified Escherichia coli in drinking water.

PubMed

Li, Jing; Liu, Lu; Yang, Dong; Liu, Wei-Li; Shen, Zhi-Qiang; Qu, Hong-Mei; Qiu, Zhi-Gang; Hou, Ai-Ming; Wang, Da-Ning; Ding, Chen-Shi; Li, Jun-Wen; Guo, Jian-Hua; Jin, Min

2017-05-24

Underestimation of Escherichia coli in drinking water, an indicator microorganism of sanitary risk, may result in potential risks of waterborne diseases. However, the detection of disinfectant-injured or genetically modified (GM) E. coli has been largely overlooked so far. To evaluate the accuracy of culture-dependent enumeration with regard to disinfectant-injured and GM E. coli, chlorine- or ozone-injured wild-type (WT) and GM E. coli were prepared and characterized. Then, water samples contaminated with these E. coli strains were assayed by four widely used methods, including lactose tryptose broth-based multiple-tube fermentation (MTF), m-endo-based membrane filtration method (MFM), an enzyme substrate test (EST) known as Colilert, and Petrifilm-based testing slip method (TSM). It was found that MTF was the most effective method to detect disinfectant-injured WT E. coli (with 76.9% trials detecting all these bacteria), while this method could not effectively detect GM E. coli (with uninjured bacteria undetectable and a maximal detection rate of 21.5% for the injured). The EST was the only method which enabled considerable enumeration of uninjured GM E. coli, with a detection rate of over 93%. However, the detection rate declined to lower than 45.4% once the GM E. coli was injured by disinfectants. The MFM was invalid for both disinfectant-injured and GM E. coli. This is the first study to report the failure of these commonly used enumeration methods to simultaneously detect disinfectant-injured and GM E. coli. Thus, it highlights the urgent requirement for the development of a more accurate and versatile enumeration method which allows the detection of disinfectant-injured and GM E. coli on the assessment of microbial quality of drinking water.

Use of diaminofluoresceins to detect and measure nitric oxide in low level generating human immune cells.

PubMed

Tiscornia, Adriana; Cairoli, Ernesto; Marquez, Maria; Denicola, Ana; Pritsch, Otto; Cayota, Alfonso

2009-03-15

Nitric oxide ((*)NO) has been implicated in multiple physiological and pathological immune processes. Different methods have been developed to detect and quantify (*)NO, where one of the principal difficulties are the accurately detection in cellular system with low levels of (*)NO production. The choice of the (*)NO detection method to be used depends on the characteristics of the experimental system and the levels of (*)NO production which depend on either the organism source of samples or the experimental conditions. Recently, high sensitive methods to detect and image (*)NO have been reported using 4,5-diaminofluorescein-based fluorescent probes (DAF) and its derivate 4,5-diaminofluorescein diacetate (DAF-2 DA). This work was aimed to adapt and optimize the use of DAF probes to detect and quantify the (*)NO production in systems of high, moderate and low out-put production, especially in human PBMC and their subpopulations. Here, we report an original experimental design which is useful to detect and estimate (*)NO fluxes in human PBMC and their subpopulations with high specificity and sensitivity.

An islanding detection methodology combining decision trees and Sandia frequency shift for inverter-based distributed generations

DOE PAGES

Azim, Riyasat; Li, Fangxing; Xue, Yaosuo; ...

2017-07-14

Distributed generations (DGs) for grid-connected applications require an accurate and reliable islanding detection methodology (IDM) for secure system operation. This paper presents an IDM for grid-connected inverter-based DGs. The proposed method is a combination of passive and active islanding detection techniques for aggregation of their advantages and elimination/minimisation of the drawbacks. In the proposed IDM, the passive method utilises critical system attributes extracted from local voltage measurements at target DG locations as well as employs decision tree-based classifiers for characterisation and detection of islanding events. The active method is based on Sandia frequency shift technique and is initiated only whenmore » the passive method is unable to differentiate islanding events from other system events. Thus, the power quality degradation introduced into the system by active islanding detection techniques can be minimised. Furthermore, a combination of active and passive techniques allows detection of islanding events under low power mismatch scenarios eliminating the disadvantage associated with the use of passive techniques alone. Finally, detailed case study results demonstrate the effectiveness of the proposed method in detection of islanding events under various power mismatch scenarios, load quality factors and in the presence of single or multiple grid-connected inverter-based DG units.« less

An islanding detection methodology combining decision trees and Sandia frequency shift for inverter-based distributed generations

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

Azim, Riyasat; Li, Fangxing; Xue, Yaosuo

Distributed generations (DGs) for grid-connected applications require an accurate and reliable islanding detection methodology (IDM) for secure system operation. This paper presents an IDM for grid-connected inverter-based DGs. The proposed method is a combination of passive and active islanding detection techniques for aggregation of their advantages and elimination/minimisation of the drawbacks. In the proposed IDM, the passive method utilises critical system attributes extracted from local voltage measurements at target DG locations as well as employs decision tree-based classifiers for characterisation and detection of islanding events. The active method is based on Sandia frequency shift technique and is initiated only whenmore » the passive method is unable to differentiate islanding events from other system events. Thus, the power quality degradation introduced into the system by active islanding detection techniques can be minimised. Furthermore, a combination of active and passive techniques allows detection of islanding events under low power mismatch scenarios eliminating the disadvantage associated with the use of passive techniques alone. Finally, detailed case study results demonstrate the effectiveness of the proposed method in detection of islanding events under various power mismatch scenarios, load quality factors and in the presence of single or multiple grid-connected inverter-based DG units.« less

Multiple Biopsies and Detection of Cervical Cancer Precursors at Colposcopy

PubMed Central

Wentzensen, Nicolas; Walker, Joan L.; Gold, Michael A.; Smith, Katie M.; Zuna, Rosemary E.; Mathews, Cara; Dunn, S. Terence; Zhang, Roy; Moxley, Katherine; Bishop, Erin; Tenney, Meaghan; Nugent, Elizabeth; Graubard, Barry I.; Wacholder, Sholom; Schiffman, Mark

2015-01-01

Purpose Women with abnormal cervical cancer screening results are referred to colposcopy and biopsy for diagnosis of cervical cancer precursors (high-grade squamous intraepithelial lesions [HSILs]). Colposcopy with a single biopsy can miss identification of HSILs. No systematic study has quantified the improved detection of HSIL by taking multiple lesion-directed biopsies. Methods The Biopsy Study was an observational study of 690 women referred to colposcopy after abnormal cervical cancer screening results. Up to four directed biopsies were taken from distinct acetowhite lesions and ranked by colposcopic impression. A nondirected biopsy of a normal-appearing area was added if fewer than four directed biopsies were taken. HSIL identified by any biopsy was the reference standard of disease used to evaluate the incremental yield and sensitivity of multiple biopsies. Results In the overall population, sensitivities for detecting HSIL increased from 60.6% (95% CI, 54.8% to 66.6%) from a single biopsy to 85.6% (95% CI, 80.3% to 90.2%) after two biopsies and to 95.6% (95% CI, 91.3% to 99.2%) after three biopsies. A significant increase in sensitivity of multiple biopsies was observed in all subgroups. The highest increase in yield of HSIL was observed for women with a high-grade colposcopic impression, HSIL cytology, and human papillomavirus (HPV) type 16 positivity. Only 2% of all HSILs diagnosed in the participants were detected by biopsies of normal-appearing transformation zone. Conclusion Collection of additional lesion-directed biopsies during colposcopy increased detection of histologic HSIL, regardless of patient characteristics. Taking additional biopsies when multiple lesions are present should become the standard practice of colposcopic biopsy. PMID:25422481

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Vibration based algorithm for crack detection in cantilever beam containing two different types of cracks

NASA Astrophysics Data System (ADS)

Behzad, Mehdi; Ghadami, Amin; Maghsoodi, Ameneh; Michael Hale, Jack

2013-11-01

In this paper, a simple method for detection of multiple edge cracks in Euler-Bernoulli beams having two different types of cracks is presented based on energy equations. Each crack is modeled as a massless rotational spring using Linear Elastic Fracture Mechanics (LEFM) theory, and a relationship among natural frequencies, crack locations and stiffness of equivalent springs is demonstrated. In the procedure, for detection of m cracks in a beam, 3m equations and natural frequencies of healthy and cracked beam in two different directions are needed as input to the algorithm. The main accomplishment of the presented algorithm is the capability to detect the location, severity and type of each crack in a multi-cracked beam. Concise and simple calculations along with accuracy are other advantages of this method. A number of numerical examples for cantilever beams including one and two cracks are presented to validate the method.

Ion sensing method

DOEpatents

Smith, Richard Harding; Martin, Glenn Brian

2004-05-18

The present invention allows the determination of trace levels of ionic substances in a sample solution (ions, metal ions, and other electrically charged molecules) by coupling a separation method, such as liquid chromatography, with ion selective electrodes (ISE) prepared so as to allow detection at activities below 10.sup.-6 M. The separation method distributes constituent molecules into fractions due to unique chemical and physical properties, such as charge, hydrophobicity, specific binding interactions, or movement in an electrical field. The separated fractions are detected by means of the ISE(s). These ISEs can be used singly or in an array. Accordingly, modifications in the ISEs are used to permit detection of low activities, specifically, below 10.sup.-6 M, by using low activities of the primary analyte (the molecular species which is specifically detected) in the inner filling solution of the ISE. Arrays constructed in various ways allow flow-through sensing for multiple ions.

Estimation of descriptive statistics for multiply censored water quality data

USGS Publications Warehouse

Helsel, Dennis R.; Cohn, Timothy A.

1988-01-01

This paper extends the work of Gilliom and Helsel (1986) on procedures for estimating descriptive statistics of water quality data that contain “less than” observations. Previously, procedures were evaluated when only one detection limit was present. Here we investigate the performance of estimators for data that have multiple detection limits. Probability plotting and maximum likelihood methods perform substantially better than simple substitution procedures now commonly in use. Therefore simple substitution procedures (e.g., substitution of the detection limit) should be avoided. Probability plotting methods are more robust than maximum likelihood methods to misspecification of the parent distribution and their use should be encouraged in the typical situation where the parent distribution is unknown. When utilized correctly, less than values frequently contain nearly as much information for estimating population moments and quantiles as would the same observations had the detection limit been below them.

Automated Microfluidic Filtration and Immunocytochemistry Detection System for Capture and Enumeration of Circulating Tumor Cells and Other Rare Cell Populations in Blood.

PubMed

Pugia, Michael; Magbanua, Mark Jesus M; Park, John W

2017-01-01

Isolation by size using a filter membrane offers an antigen-independent method for capturing rare cells present in blood of cancer patients. Multiple cell types, including circulating tumor cells (CTCs), captured on the filter membrane can be simultaneously identified via immunocytochemistry (ICC) analysis of specific cellular biomarkers. Here, we describe an automated microfluidic filtration method combined with a liquid handling system for sequential ICC assays to detect and enumerate non-hematologic rare cells in blood.

Military applications and examples of near-surface seismic surface wave methods (Invited)

NASA Astrophysics Data System (ADS)

sloan, S.; Stevens, R.

2013-12-01

Although not always widely known or publicized, the military uses a variety of geophysical methods for a wide range of applications--some that are already common practice in the industry while others are truly novel. Some of those applications include unexploded ordnance detection, general site characterization, anomaly detection, countering improvised explosive devices (IEDs), and security monitoring, to name a few. Techniques used may include, but are not limited to, ground penetrating radar, seismic, electrical, gravity, and electromagnetic methods. Seismic methods employed include surface wave analysis, refraction tomography, and high-resolution reflection methods. Although the military employs geophysical methods, that does not necessarily mean that those methods enable or support combat operations--often times they are being used for humanitarian applications within the military's area of operations to support local populations. The work presented here will focus on the applied use of seismic surface wave methods, including multichannel analysis of surface waves (MASW) and backscattered surface waves, often in conjunction with other methods such as refraction tomography or body-wave diffraction analysis. Multiple field examples will be shown, including explosives testing, tunnel detection, pre-construction site characterization, and cavity detection.

Development and Validation of a Multiplexed Protein Quantitation Assay for the Determination of Three Recombinant Proteins in Soybean Tissues by Liquid Chromatography with Tandem Mass Spectrometry.

PubMed

Hill, Ryan C; Oman, Trent J; Shan, Guomin; Schafer, Barry; Eble, Julie; Chen, Cynthia

2015-08-26

Currently, traditional immunochemistry technologies such as enzyme-linked immunosorbent assays (ELISA) are the predominant analytical tool used to measure levels of recombinant proteins expressed in genetically engineered (GE) plants. Recent advances in agricultural biotechnology have created a need to develop methods capable of selectively detecting and quantifying multiple proteins in complex matrices because of increasing numbers of transgenic proteins being coexpressed or "stacked" to achieve tolerance to multiple herbicides or to provide multiple modes of action for insect control. A multiplexing analytical method utilizing liquid chromatography with tandem mass spectrometry (LC-MS/MS) has been developed and validated to quantify three herbicide-tolerant proteins in soybean tissues: aryloxyalkanoate dioxygenase (AAD-12), 5-enol-pyruvylshikimate-3-phosphate synthase (2mEPSPS), and phosphinothricin acetyltransferase (PAT). Results from the validation showed high recovery and precision over multiple analysts and laboratories. Results from this method were comparable to those obtained with ELISA with respect to protein quantitation, and the described method was demonstrated to be suitable for multiplex quantitation of transgenic proteins in GE crops.

CT scan range estimation using multiple body parts detection: let PACS learn the CT image content.

PubMed

Wang, Chunliang; Lundström, Claes

2016-02-01

The aim of this study was to develop an efficient CT scan range estimation method that is based on the analysis of image data itself instead of metadata analysis. This makes it possible to quantitatively compare the scan range of two studies. In our study, 3D stacks are first projected to 2D coronal images via a ray casting-like process. Trained 2D body part classifiers are then used to recognize different body parts in the projected image. The detected candidate regions go into a structure grouping process to eliminate false-positive detections. Finally, the scale and position of the patient relative to the projected figure are estimated based on the detected body parts via a structural voting. The start and end lines of the CT scan are projected to a standard human figure. The position readout is normalized so that the bottom of the feet represents 0.0, and the top of the head is 1.0. Classifiers for 18 body parts were trained using 184 CT scans. The final application was tested on 136 randomly selected heterogeneous CT scans. Ground truth was generated by asking two human observers to mark the start and end positions of each scan on the standard human figure. When compared with the human observers, the mean absolute error of the proposed method is 1.2% (max: 3.5%) and 1.6% (max: 5.4%) for the start and end positions, respectively. We proposed a scan range estimation method using multiple body parts detection and relative structure position analysis. In our preliminary tests, the proposed method delivered promising results.

Detection method for dissociation of multiple-charged ions

DOEpatents

Smith, Richard D.; Udseth, Harold R.; Rockwood, Alan L.

1991-01-01

Dissociations of multiple-charged ions are detected and analyzed by charge-separation tandem mass spectrometry. Analyte molecules are ionized to form multiple-charged parent ions. A particular charge parent ion state is selected in a first-stage mass spectrometer and its mass-to-charge ratio (M/Z) is detected to determine its mass and charge. The selected parent ions are then dissociated, each into a plurality of fragments including a set of daughter ions each having a mass of at least one molecular weight and a charge of at least one. Sets of daughter ions resulting from the dissociation of one parent ion (sibling ions) vary in number but typically include two to four ions, one or more multiply-charged. A second stage mass spectrometer detects mass-to-charge ratio (m/z) of the daughter ions and a temporal or temporo-spatial relationship among them. This relationship is used to correlate the daughter ions to determine which (m/z) ratios belong to a set of sibling ions. Values of mass and charge of each of the sibling ions are determined simultaneously from their respective (m/z) ratios such that the sibling ion charges are integers and sum to the parent ion charge.

Gaussian process surrogates for failure detection: A Bayesian experimental design approach

NASA Astrophysics Data System (ADS)

Wang, Hongqiao; Lin, Guang; Li, Jinglai

2016-05-01

An important task of uncertainty quantification is to identify the probability of undesired events, in particular, system failures, caused by various sources of uncertainties. In this work we consider the construction of Gaussian process surrogates for failure detection and failure probability estimation. In particular, we consider the situation that the underlying computer models are extremely expensive, and in this setting, determining the sampling points in the state space is of essential importance. We formulate the problem as an optimal experimental design for Bayesian inferences of the limit state (i.e., the failure boundary) and propose an efficient numerical scheme to solve the resulting optimization problem. In particular, the proposed limit-state inference method is capable of determining multiple sampling points at a time, and thus it is well suited for problems where multiple computer simulations can be performed in parallel. The accuracy and performance of the proposed method is demonstrated by both academic and practical examples.

Protein mass analysis of histones.

PubMed

Galasinski, Scott C; Resing, Katheryn A; Ahn, Natalie G

2003-09-01

Posttranslational modification of chromatin-associated proteins, including histones and high-mobility-group (HMG) proteins, provides an important mechanism to control gene expression, genome integrity, and epigenetic inheritance. Protein mass analysis provides a rapid and unbiased approach to monitor multiple chemical modifications on individual molecules. This review describes methods for acid extraction of histones and HMG proteins, followed by separation by reverse-phase chromatography coupled to electrospray ionization mass spectrometry (LC/ESI-MS). Posttranslational modifications are detected by analysis of full-length protein masses. Confirmation of protein identity and modification state is obtained through enzymatic digestion and peptide sequencing by MS/MS. For differentially modified forms of each protein, the measured intensities are semiquantitative and allow determination of relative abundance and stoichiometry. The method simultaneously detects covalent modifications on multiple proteins and provides a facile assay for comparing chromatin modification states between different cell types and/or cellular responses.

Research on driver fatigue detection

NASA Astrophysics Data System (ADS)

Zhang, Ting; Chen, Zhong; Ouyang, Chao

2018-03-01

Driver fatigue is one of the main causes of frequent traffic accidents. In this case, driver fatigue detection system has very important significance in avoiding traffic accidents. This paper presents a real-time method based on fusion of multiple facial features, including eye closure, yawn and head movement. The eye state is classified as being open or closed by a linear SVM classifier trained using HOG features of the detected eye. The mouth state is determined according to the width-height ratio of the mouth. The head movement is detected by head pitch angle calculated by facial landmark. The driver's fatigue state can be reasoned by the model trained by above features. According to experimental results, drive fatigue detection obtains an excellent performance. It indicates that the developed method is valuable for the application of avoiding traffic accidents caused by driver's fatigue.

[Analysis of H2S/PH3/NH3/AsH3/Cl2 by Full-Spectral Flame Photometric Detector].

PubMed

Ding, Zhi-jun; Wang, Pu-hong; Li, Zhi-jun; Du, Bin; Guo, Lei; Yu, Jian-hua

2015-07-01

Flame photometric analysis technology has been proven to be a rapid and sensitive method for sulfur and phosphorus detection. It has been widely used in environmental inspections, pesticide detection, industrial and agricultural production. By improving the design of the traditional flame photometric detector, using grating and CCD sensor array as a photoelectric conversion device, the types of compounds that can be detected were expanded. Instead of a single point of characteristic spectral lines, full spectral information has been used for qualitative and quantitative analysis of H2S, PH3, NH3, AsH3 and Cl2. Combined with chemometric method, flame photometric analysis technology is expected to become an alternative fast, real-time on-site detection technology to simultaneously detect multiple toxic and harmful gases.

Single-Side Two-Location Spotlight Imaging for Building Based on MIMO Through-Wall-Radar.

PubMed

Jia, Yong; Zhong, Xiaoling; Liu, Jiangang; Guo, Yong

2016-09-07

Through-wall-radar imaging is of interest for mapping the wall layout of buildings and for the detection of stationary targets within buildings. In this paper, we present an easy single-side two-location spotlight imaging method for both wall layout mapping and stationary target detection by utilizing multiple-input multiple-output (MIMO) through-wall-radar. Rather than imaging for building walls directly, the images of all building corners are generated to speculate wall layout indirectly by successively deploying the MIMO through-wall-radar at two appropriate locations on only one side of the building and then carrying out spotlight imaging with two different squint-views. In addition to the ease of implementation, the single-side two-location squint-view detection also has two other advantages for stationary target imaging. The first one is the fewer multi-path ghosts, and the second one is the smaller region of side-lobe interferences from the corner images in comparison to the wall images. Based on Computer Simulation Technology (CST) electromagnetic simulation software, we provide multiple sets of validation results where multiple binary panorama images with clear images of all corners and stationary targets are obtained by combining two single-location images with the use of incoherent additive fusion and two-dimensional cell-averaging constant-false-alarm-rate (2D CA-CFAR) detection.

Correlation between skin-prick testing, individual specific IgE tests, and a multiallergen IgE assay for allergy detection in patients with chronic rhinitis.

PubMed

Cho, Jae Hoon; Suh, Jeffrey D; Kim, Jin Kook; Hong, Seok-Chan; Park, Il-Ho; Lee, Heung-Man

2014-01-01

Allergy test results can differ based on the method used. The most common tests include skin-prick testing (SPT) and in vitro tests to detect allergen-specific IgE. This study was designed to assess allergy test results using SPT, individual specific IgE tests, and a multiallergen IgE assay (multiple allergen simultaneous test) in patients with chronic rhinitis and controls. One hundred forty total patients were prospectively enrolled in the study, including 100 patients with chronic rhinitis and 40 control patients without atopy. All eligible patients underwent SPT, serum analysis using individual specific IgE test, and multiple allergen simultaneous test against 10 common allergens. Allergy test results were then compared to identify correlation and interest agreement. There was an 81-97% agreement between SPT and individual specific IgE test in allergen detection and an 80-98% agreement between SPT and multiple allergen simultaneous test. Individual specific IgE test and multiple allergen simultaneous test allergy detection prevalence was generally similar to SPT in patients with chronic rhinitis. All control patients had negative SPT (0/40), but low positive results were found with both individual specific IgE test (5-12.5%) and multiple allergen simultaneous test (2.5-7.5%) to some allergens, especially cockroach, Dermatophagoides farina, and ragweed. Agreement and correlation between individual specific IgE test and multiple allergen simultaneous test were good to excellent for a majority of tested allergens. This study shows good agreement and correlation between SPT with individual specific IgE test and multiple allergen simultaneous test on a majority of the tested allergens for patients with chronic rhinitis. Comparing the two in vitro tests, individual specific IgE test agrees with SPT better than multiple allergen simultaneous test.

A high-throughput method for the detection of homoeologous gene deletions in hexaploid wheat

PubMed Central

2010-01-01

Background Mutational inactivation of plant genes is an essential tool in gene function studies. Plants with inactivated or deleted genes may also be exploited for crop improvement if such mutations/deletions produce a desirable agronomical and/or quality phenotype. However, the use of mutational gene inactivation/deletion has been impeded in polyploid plant species by genetic redundancy, as polyploids contain multiple copies of the same genes (homoeologous genes) encoded by each of the ancestral genomes. Similar to many other crop plants, bread wheat (Triticum aestivum L.) is polyploid; specifically allohexaploid possessing three progenitor genomes designated as 'A', 'B', and 'D'. Recently modified TILLING protocols have been developed specifically for mutation detection in wheat. Whilst extremely powerful in detecting single nucleotide changes and small deletions, these methods are not suitable for detecting whole gene deletions. Therefore, high-throughput methods for screening of candidate homoeologous gene deletions are needed for application to wheat populations generated by the use of certain mutagenic agents (e.g. heavy ion irradiation) that frequently generate whole-gene deletions. Results To facilitate the screening for specific homoeologous gene deletions in hexaploid wheat, we have developed a TaqMan qPCR-based method that allows high-throughput detection of deletions in homoeologous copies of any gene of interest, provided that sufficient polymorphism (as little as a single nucleotide difference) amongst homoeologues exists for specific probe design. We used this method to identify deletions of individual TaPFT1 homoeologues, a wheat orthologue of the disease susceptibility and flowering regulatory gene PFT1 in Arabidopsis. This method was applied to wheat nullisomic-tetrasomic lines as well as other chromosomal deletion lines to locate the TaPFT1 gene to the long arm of chromosome 5. By screening of individual DNA samples from 4500 M2 mutant wheat lines generated by heavy ion irradiation, we detected multiple mutants with deletions of each TaPFT1 homoeologue, and confirmed these deletions using a CAPS method. We have subsequently designed, optimized, and applied this method for the screening of homoeologous deletions of three additional wheat genes putatively involved in plant disease resistance. Conclusions We have developed a method for automated, high-throughput screening to identify deletions of individual homoeologues of a wheat gene. This method is also potentially applicable to other polyploidy plants. PMID:21114819

Design of an Evolutionary Approach for Intrusion Detection

PubMed Central

2013-01-01

A novel evolutionary approach is proposed for effective intrusion detection based on benchmark datasets. The proposed approach can generate a pool of noninferior individual solutions and ensemble solutions thereof. The generated ensembles can be used to detect the intrusions accurately. For intrusion detection problem, the proposed approach could consider conflicting objectives simultaneously like detection rate of each attack class, error rate, accuracy, diversity, and so forth. The proposed approach can generate a pool of noninferior solutions and ensembles thereof having optimized trade-offs values of multiple conflicting objectives. In this paper, a three-phase, approach is proposed to generate solutions to a simple chromosome design in the first phase. In the first phase, a Pareto front of noninferior individual solutions is approximated. In the second phase of the proposed approach, the entire solution set is further refined to determine effective ensemble solutions considering solution interaction. In this phase, another improved Pareto front of ensemble solutions over that of individual solutions is approximated. The ensemble solutions in improved Pareto front reported improved detection results based on benchmark datasets for intrusion detection. In the third phase, a combination method like majority voting method is used to fuse the predictions of individual solutions for determining prediction of ensemble solution. Benchmark datasets, namely, KDD cup 1999 and ISCX 2012 dataset, are used to demonstrate and validate the performance of the proposed approach for intrusion detection. The proposed approach can discover individual solutions and ensemble solutions thereof with a good support and a detection rate from benchmark datasets (in comparison with well-known ensemble methods like bagging and boosting). In addition, the proposed approach is a generalized classification approach that is applicable to the problem of any field having multiple conflicting objectives, and a dataset can be represented in the form of labelled instances in terms of its features. PMID:24376390

Multiple Strategy Bio-Detection Sensor Platforms Made From Carbon and Polymer Materials

DTIC Science & Technology

2006-01-31

strands for detection purposes using the cyclic voltammetry (impedance) method. 6. Design of an actual set (Au patttern) to best detect the DNA binding. 7...chronoamperometry and cyclic voltammetry are used for electropolymerization. When chronoamperometry is used, the applied potential was kept at 0.8V, and the...others remained constant. When cyclic voltammetry is used, the scan rate is kept at 1OOmV/s with a scan range from -0.4V tol.OV. The thickness or the

Targeted Quantification of Isoforms of a Thylakoid-Bound Protein: MRM Method Development.

PubMed

Bru-Martínez, Roque; Martínez-Márquez, Ascensión; Morante-Carriel, Jaime; Sellés-Marchart, Susana; Martínez-Esteso, María José; Pineda-Lucas, José Luis; Luque, Ignacio

2018-01-01

Targeted mass spectrometric methods such as selected/multiple reaction monitoring (SRM/MRM) have found intense application in protein detection and quantification which competes with classical immunoaffinity techniques. It provides a universal procedure to develop a fast, highly specific, sensitive, accurate, and cheap methodology for targeted detection and quantification of proteins based on the direct analysis of their surrogate peptides typically generated by tryptic digestion. This methodology can be advantageously applied in the field of plant proteomics and particularly for non-model species since immunoreagents are scarcely available. Here, we describe the issues to take into consideration in order to develop a MRM method to detect and quantify isoforms of the thylakoid-bound protein polyphenol oxidase from the non-model and database underrepresented species Eriobotrya japonica Lindl.

Case, Teacher and School Characteristics Influencing Teachers' Detection and Reporting of Child Physical Abuse and Neglect: Results from an Australian Survey

ERIC Educational Resources Information Center

Walsh, Kerryann; Bridgstock, Ruth; Farrell, Ann; Rassafiani, Mehdi; Schweitzer, Robert

2008-01-01

Objective: To identify the influence of multiple case, teacher and school characteristics on Australian primary school teachers' propensity to detect and report child physical abuse and neglect using vignettes as short hypothetical cases. Methods: A sample of 254 teachers completed a self-report questionnaire. They responded to a series of 32…

iASeq: integrative analysis of allele-specificity of protein-DNA interactions in multiple ChIP-seq datasets

PubMed Central

2012-01-01

Background ChIP-seq provides new opportunities to study allele-specific protein-DNA binding (ASB). However, detecting allelic imbalance from a single ChIP-seq dataset often has low statistical power since only sequence reads mapped to heterozygote SNPs are informative for discriminating two alleles. Results We develop a new method iASeq to address this issue by jointly analyzing multiple ChIP-seq datasets. iASeq uses a Bayesian hierarchical mixture model to learn correlation patterns of allele-specificity among multiple proteins. Using the discovered correlation patterns, the model allows one to borrow information across datasets to improve detection of allelic imbalance. Application of iASeq to 77 ChIP-seq samples from 40 ENCODE datasets and 1 genomic DNA sample in GM12878 cells reveals that allele-specificity of multiple proteins are highly correlated, and demonstrates the ability of iASeq to improve allelic inference compared to analyzing each individual dataset separately. Conclusions iASeq illustrates the value of integrating multiple datasets in the allele-specificity inference and offers a new tool to better analyze ASB. PMID:23194258

Multiple tag labeling method for DNA sequencing

DOEpatents

Mathies, R.A.; Huang, X.C.; Quesada, M.A.

1995-07-25

A DNA sequencing method is described which uses single lane or channel electrophoresis. Sequencing fragments are separated in the lane and detected using a laser-excited, confocal fluorescence scanner. Each set of DNA sequencing fragments is separated in the same lane and then distinguished using a binary coding scheme employing only two different fluorescent labels. Also described is a method of using radioisotope labels. 5 figs.

Detecting breast microcalcifications using super-resolution ultrasound imaging: a clinical study

NASA Astrophysics Data System (ADS)

Huang, Lianjie; Labyed, Yassin; Hanson, Kenneth; Sandoval, Daniel; Pohl, Jennifer; Williamson, Michael

2013-03-01

Imaging breast microcalcifications is crucial for early detection and diagnosis of breast cancer. It is challenging for current clinical ultrasound to image breast microcalcifications. However, new imaging techniques using data acquired with a synthetic-aperture ultrasound system have the potential to significantly improve ultrasound imaging. We recently developed a super-resolution ultrasound imaging method termed the phase-coherent multiple-signal classification (PC-MUSIC). This signal subspace method accounts for the phase response of transducer elements to improve image resolution. In this paper, we investigate the clinical feasibility of our super-resolution ultrasound imaging method for detecting breast microcalcifications. We use our custom-built, real-time synthetic-aperture ultrasound system to acquire breast ultrasound data for 40 patients whose mammograms show the presence of breast microcalcifications. We apply our super-resolution ultrasound imaging method to the patient data, and produce clear images of breast calcifications. Our super-resolution ultrasound PC-MUSIC imaging with synthetic-aperture ultrasound data can provide a new imaging modality for detecting breast microcalcifications in clinic without using ionizing radiation.

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.

Fresh Fuel Measurements With the Differential Die-Away Self-Interrogation Instrument

NASA Astrophysics Data System (ADS)

Trahan, Alexis C.; Belian, Anthony P.; Swinhoe, Martyn T.; Menlove, Howard O.; Flaska, Marek; Pozzi, Sara A.

2017-07-01

The purpose of the Next Generation Safeguards Initiative (NGSI)-Spent Fuel (SF) Project is to strengthen the technical toolkit of safeguards inspectors and/or other interested parties. The NGSI-SF team is working to achieve the following technical goals more easily and efficiently than in the past using nondestructive assay measurements of spent fuel assemblies: 1) verify the initial enrichment, burnup, and cooling time of facility declaration; 2) detect the diversion or replacement of pins; 3) estimate the plutonium mass; 4) estimate decay heat; and 5) determine the reactivity of spent fuel assemblies. The differential die-away self-interrogation (DDSI) instrument is one instrument that was assessed for years regarding its feasibility for robust, timely verification of spent fuel assemblies. The instrument was recently built and was tested using fresh fuel assemblies in a variety of configurations, including varying enrichment, neutron absorber content, and symmetry. The early die-away method, a multiplication determination method developed in simulation space, was successfully tested on the fresh fuel assembly data and determined multiplication with a root-mean-square (RMS) error of 2.9%. The experimental results were compared with MCNP simulations of the instrument as well. Low multiplication assemblies had agreement with an average RMS error of 0.2% in the singles count rate (i.e., total neutrons detected per second) and 3.4% in the doubles count rates (i.e., neutrons detected in coincidence per second). High-multiplication assemblies had agreement with an average RMS error of 4.1% in the singles and 13.3% in the doubles count rates.

Fresh Fuel Measurements With the Differential Die-Away Self-Interrogation Instrument

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

Trahan, Alexis C.; Belian, Anthony P.; Swinhoe, Martyn T.

The purpose of the Next Generation Safeguards Initiative (NGSI)-Spent Fuel (SF) Project is to strengthen the technical toolkit of safeguards inspectors and/or other interested parties. Thus the NGSI-SF team is working to achieve the following technical goals more easily and efficiently than in the past using nondestructive assay measurements of spent fuel assemblies: 1) verify the initial enrichment, burnup, and cooling time of facility declaration; 2) detect the diversion or replacement of pins; 3) estimate the plutonium mass; 4) estimate decay heat; and 5) determine the reactivity of spent fuel assemblies. The differential die-away self-interrogation (DDSI) instrument is one instrumentmore » that was assessed for years regarding its feasibility for robust, timely verification of spent fuel assemblies. The instrument was recently built and was tested using fresh fuel assemblies in a variety of configurations, including varying enrichment, neutron absorber content, and symmetry. The early die-away method, a multiplication determination method developed in simulation space, was successfully tested on the fresh fuel assembly data and determined multiplication with a root-mean-square (RMS) error of 2.9%. The experimental results were compared with MCNP simulations of the instrument as well. Low multiplication assemblies had agreement with an average RMS error of 0.2% in the singles count rate (i.e., total neutrons detected per second) and 3.4% in the doubles count rates (i.e., neutrons detected in coincidence per second). High-multiplication assemblies had agreement with an average RMS error of 4.1% in the singles and 13.3% in the doubles count rates.« less

SIRE: a MIMO radar for landmine/IED detection

NASA Astrophysics Data System (ADS)

Ojowu, Ode; Wu, Yue; Li, Jian; Nguyen, Lam

2013-05-01

Multiple-input multiple-output (MIMO) radar systems have been shown to have significant performance improvements over their single-input multiple-output (SIMO) counterparts. For transmit and receive elements that are collocated, the waveform diversity afforded by this radar is exploited for performance improvements. These improvements include but are not limited to improved target detection, improved parameter identifiability and better resolvability. In this paper, we present the Synchronous Impulse Reconstruction Radar (SIRE) Ultra-wideband (UWB) radar designed by the Army Research Lab (ARL) for landmine and improvised explosive device (IED) detection as a 2 by 16 MIMO radar (with collocated antennas). Its improvement over its SIMO counterpart in terms of beampattern/cross range resolution are discussed and demonstrated using simulated data herein. The limitations of this radar for Radio Frequency Interference (RFI) suppression are also discussed in this paper. A relaxation method (RELAX) combined with averaging of multiple realizations of the measured data is presented for RFI suppression; results show no noticeable target signature distortion after suppression. In this paper, the back-projection (delay and sum) data independent method is used for generating SAR images. A side-lobe minimization technique called recursive side-lobe minimization (RSM) is also discussed for reducing side-lobes in this data independent approach. We introduce a data-dependent sparsity based spectral estimation technique called Sparse Learning via Iterative Minimization (SLIM) as well as a data-dependent CLEAN approach for generating SAR images for the SIRE radar. These data-adaptive techniques show improvement in side-lobe reduction and resolution for simulated data for the SIRE radar.

Fresh Fuel Measurements With the Differential Die-Away Self-Interrogation Instrument

DOE PAGES

Trahan, Alexis C.; Belian, Anthony P.; Swinhoe, Martyn T.; ...

2017-01-05

The purpose of the Next Generation Safeguards Initiative (NGSI)-Spent Fuel (SF) Project is to strengthen the technical toolkit of safeguards inspectors and/or other interested parties. Thus the NGSI-SF team is working to achieve the following technical goals more easily and efficiently than in the past using nondestructive assay measurements of spent fuel assemblies: 1) verify the initial enrichment, burnup, and cooling time of facility declaration; 2) detect the diversion or replacement of pins; 3) estimate the plutonium mass; 4) estimate decay heat; and 5) determine the reactivity of spent fuel assemblies. The differential die-away self-interrogation (DDSI) instrument is one instrumentmore » that was assessed for years regarding its feasibility for robust, timely verification of spent fuel assemblies. The instrument was recently built and was tested using fresh fuel assemblies in a variety of configurations, including varying enrichment, neutron absorber content, and symmetry. The early die-away method, a multiplication determination method developed in simulation space, was successfully tested on the fresh fuel assembly data and determined multiplication with a root-mean-square (RMS) error of 2.9%. The experimental results were compared with MCNP simulations of the instrument as well. Low multiplication assemblies had agreement with an average RMS error of 0.2% in the singles count rate (i.e., total neutrons detected per second) and 3.4% in the doubles count rates (i.e., neutrons detected in coincidence per second). High-multiplication assemblies had agreement with an average RMS error of 4.1% in the singles and 13.3% in the doubles count rates.« less

Edge-directed inference for microaneurysms detection in digital fundus images

NASA Astrophysics Data System (ADS)

Huang, Ke; Yan, Michelle; Aviyente, Selin

2007-03-01

Microaneurysms (MAs) detection is a critical step in diabetic retinopathy screening, since MAs are the earliest visible warning of potential future problems. A variety of algorithms have been proposed for MAs detection in mass screening. Different methods have been proposed for MAs detection. The core technology for most of existing methods is based on a directional mathematical morphological operation called "Top-Hat" filter that requires multiple filtering operations at each pixel. Background structure, uneven illumination and noise often cause confusion between MAs and some non-MA structures and limits the applicability of the filter. In this paper, a novel detection framework based on edge directed inference is proposed for MAs detection. The candidate MA regions are first delineated from the edge map of a fundus image. Features measuring shape, brightness and contrast are extracted for each candidate MA region to better exclude false detection from true MAs. Algorithmic analysis and empirical evaluation reveal that the proposed edge directed inference outperforms the "Top-Hat" based algorithm in both detection accuracy and computational speed.

Multiple sensor fault diagnosis for dynamic processes.

PubMed

Li, Cheng-Chih; Jeng, Jyh-Cheng

2010-10-01

Modern industrial plants are usually large scaled and contain a great amount of sensors. Sensor fault diagnosis is crucial and necessary to process safety and optimal operation. This paper proposes a systematic approach to detect, isolate and identify multiple sensor faults for multivariate dynamic systems. The current work first defines deviation vectors for sensor observations, and further defines and derives the basic sensor fault matrix (BSFM), consisting of the normalized basic fault vectors, by several different methods. By projecting a process deviation vector to the space spanned by BSFM, this research uses a vector with the resulted weights on each direction for multiple sensor fault diagnosis. This study also proposes a novel monitoring index and derives corresponding sensor fault detectability. The study also utilizes that vector to isolate and identify multiple sensor faults, and discusses the isolatability and identifiability. Simulation examples and comparison with two conventional PCA-based contribution plots are presented to demonstrate the effectiveness of the proposed methodology. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Sensitive SERS detection of DNA methyltransferase by target triggering primer generation-based multiple signal amplification strategy.

PubMed

Li, Ying; Yu, Chuanfeng; Han, Huixia; Zhao, Caisheng; Zhang, Xiaoru

2016-07-15

A novel and sensitive surface-enhanced Raman scattering (SERS) method is proposed for the assay of DNA methyltransferase (MTase) activity and evaluation of inhibitors by developing a target triggering primer generation-based multiple signal amplification strategy. By using of a duplex substrate for Dam MTase, two hairpin templates and a Raman probe, multiple signal amplification mode is achieved. Once recognized by Dam MTase, the duplex substrate can be cleaved by Dpn I endonuclease and two primers are released for triggering the multiple signal amplification reaction. Consequently, a wide dynamic range and remarkably high sensitivity are obtained under isothermal conditions. The detection limit is 2.57×10(-4)UmL(-1). This assay exhibits an excellent selectivity and is successfully applied in the screening of inhibitors for Dam MTase. In addition, this novel sensing system is potentially universal as the recognition element can be conveniently designed for other target analytes by changing the substrate of DNA MTase. Copyright © 2016 Elsevier B.V. All rights reserved.

Improved detection of multiple environmental antibiotics through an optimized sample extraction strategy in liquid chromatography-mass spectrometry analysis.

PubMed

Yi, Xinzhu; Bayen, Stéphane; Kelly, Barry C; Li, Xu; Zhou, Zhi

2015-12-01

A solid-phase extraction/liquid chromatography/electrospray ionization/multi-stage mass spectrometry (SPE-LC-ESI-MS/MS) method was optimized in this study for sensitive and simultaneous detection of multiple antibiotics in urban surface waters and soils. Among the seven classes of tested antibiotics, extraction efficiencies of macrolides, lincosamide, chloramphenicol, and polyether antibiotics were significantly improved under optimized sample extraction pH. Instead of only using acidic extraction in many existing studies, the results indicated that antibiotics with low pK a values (<7) were extracted more efficiently under acidic conditions and antibiotics with high pK a values (>7) were extracted more efficiently under neutral conditions. The effects of pH were more obvious on polar compounds than those on non-polar compounds. Optimization of extraction pH resulted in significantly improved sample recovery and better detection limits. Compared with reported values in the literature, the average reduction of minimal detection limits obtained in this study was 87.6% in surface waters (0.06-2.28 ng/L) and 67.1% in soils (0.01-18.16 ng/g dry wt). This method was subsequently applied to detect antibiotics in environmental samples in a heavily populated urban city, and macrolides, sulfonamides, and lincomycin were frequently detected. Antibiotics with highest detected concentrations were sulfamethazine (82.5 ng/L) in surface waters and erythromycin (6.6 ng/g dry wt) in soils. The optimized sample extraction strategy can be used to improve the detection of a variety of antibiotics in environmental surface waters and soils.

ROKU: a novel method for identification of tissue-specific genes

PubMed Central

Kadota, Koji; Ye, Jiazhen; Nakai, Yuji; Terada, Tohru; Shimizu, Kentaro

2006-01-01

Background One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes. Results We describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues. Conclusion ROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes. PMID:16764735

Detection of Toxoplasma gondii oocysts in water: proposition of a strategy and evaluation in Champagne-Ardenne Region, France.

PubMed

Aubert, D; Villena, I

2009-03-01

Water is a vehicle for disseminating human and veterinary toxoplasmosis due to oocyst contamination. Several outbreaks of toxoplasmosis throughout the world have been related to contaminated drinking water. We have developed a method for the detection of Toxoplasma gondii oocysts in water and we propose a strategy for the detection of multiple waterborne parasites, including Cryptosporidium spp. and Giardia. Water samples were filtered to recover Toxoplasma oocysts and, after the detection of Cryptosporidium oocysts and Giardia cysts by immunofluorescence, as recommended by French norm procedure NF T 90-455, the samples were purified on a sucrose density gradient. Detection of Toxoplasma was based on PCR amplification and mouse inoculation to determine the presence and infectivity of recovered oocysts. After experimental seeding assays, we determined that the PCR assay was more sensitive than the bioassay. This strategy was then applied to 482 environmental water samples collected since 2001. We detected Toxoplasma DNA in 37 environmental samples (7.7%), including public drinking water; however, none of them were positive by bioassay. This strategy efficiently detects Toxoplasma oocysts in water and may be suitable as a public health sentinel method. Alternative methods can be used in conjunction with this one to determine the infectivity of parasites that were detected by molecular methods.

Apparatus and method for measuring fluorescence intensities at a plurality of wavelengths and lifetimes

DOEpatents

Buican, T.N.

1993-05-04

Apparatus and method is described for measuring intensities at a plurality of wavelengths and lifetimes. A source of multiple-wavelength electromagnetic radiation is passed through a first interferometer modulated at a first frequency, the output thereof being directed into a sample to be investigated. The light emitted from the sample as a result of the interaction thereof with the excitation radiation is directed into a second interferometer modulated at a second frequency, and the output detected and analyzed. In this manner excitation, emission, and lifetime information may be obtained for a multiplicity of fluorochromes in the sample.

Rapid detection and identification of N-acetyl-L-cysteine thioethers using constant neutral loss and theoretical multiple reaction monitoring combined with enhanced product-ion scans on a linear ion trap mass spectrometer.

PubMed

Scholz, Karoline; Dekant, Wolfgang; Völkel, Wolfgang; Pähler, Axel

2005-12-01

A sensitive and specific liquid chromatography-mass spectrometry (LC-MS) method based on the combination of constant neutral loss scans (CNL) with product ion scans was developed on a linear ion trap. The method is applicable for the detection and identification of analytes with identical chemical substructures (such as conjugates of xenobiotics formed in biological systems) which give common CNLs. A specific CNL was observed for thioethers of N-acetyl-L-cysteine (mercapturic acids, MA) by LC-MS/MS. MS and HPLC parameters were optimized with 16 MAs available as reference compounds. All of these provided a CNL of 129 Da in the negative-ion mode. To assess sensitivity, a multiple reaction monitoring (MRM) mode with 251 theoretical transitions using the CNL of 129 Da combined with a product ion scan (IDA thMRM) was compared with CNL combined with a product ion scan (IDA CNL). An information-dependent acquisition (IDA) uses a survey scan such as MRM (multiple reaction monitoring) to generate "informations" and starting a second acquisition experiment such as a product ion scan using these "informations." Th-MRM means calculated transitions and not transitions generated from an available standard in the tuning mode. The product ion spectra provide additional information on the chemical structure of the unknown analytes. All MA standards were spiked in low concentrations to rat urines and were detected with both methods with LODs ranging from 60 pmol/mL to 1.63 nmol/mL with IDA thMRM. The expected product ion spectra were observed in urine. Application of this screening method to biological samples indicated the presence of a number of MAs in urine of unexposed rats, and resulted in the identification of 1,4-dihydroxynonene mercapturic acid as one of these MAs by negative and positive product ion spectra. These results show that the developed methods have a high potential to serve as both a prescreen to detect unknown MAs and to identify these analytes in complex matrix.

Quaternion-valued single-phase model for three-phase power system

NASA Astrophysics Data System (ADS)

Gou, Xiaoming; Liu, Zhiwen; Liu, Wei; Xu, Yougen; Wang, Jiabin

2018-03-01

In this work, a quaternion-valued model is proposed in lieu of the Clarke's α, β transformation to convert three-phase quantities to a hypercomplex single-phase signal. The concatenated signal can be used for harmonic distortion detection in three-phase power systems. In particular, the proposed model maps all the harmonic frequencies into frequencies in the quaternion domain, while the Clarke's transformation-based methods will fail to detect the zero sequence voltages. Based on the quaternion-valued model, the Fourier transform, the minimum variance distortionless response (MVDR) algorithm and the multiple signal classification (MUSIC) algorithm are presented as examples to detect harmonic distortion. Simulations are provided to demonstrate the potentials of this new modeling method.

Detection of Several Classes of Pesticides and Metabolites in Meconium by Gas Chromatography-Mass Spectrometry.

PubMed

Bielawski, D; Ostrea, E; Posecion, N; Corrion, M; Seagraves, J

2005-01-01

A solid phase extraction method was developed to isolate multiple classes of parent pesticides from meconium. A methanolic/hydrochloric acid methyl ester derivatization with liquid-liquid extraction technique was also developed for the analysis of metabolites. Identification and quantitation was by electron impact gas chromatography-mass spectrometry. For the parent compounds and metabolites, recoveries in spiked meconium ranged between 72-109%, with coefficients of variation ranging from 1.55-16.92% and limits of detection between 0.01-4.15 μg g(-1). Meconium samples obtained from infants in the Philippines were assayed using these methods, and propoxur, cypermethrin, pretilachlor, malathion, 4,4'-dichlorodiphenyltrichloroethylene, bioallethrin, and cyfluthrin were detected.

Current Technical Approaches for the Early Detection of Foodborne Pathogens: Challenges and Opportunities.

PubMed

Cho, Il-Hoon; Ku, Seockmo

2017-09-30

The development of novel and high-tech solutions for rapid, accurate, and non-laborious microbial detection methods is imperative to improve the global food supply. Such solutions have begun to address the need for microbial detection that is faster and more sensitive than existing methodologies (e.g., classic culture enrichment methods). Multiple reviews report the technical functions and structures of conventional microbial detection tools. These tools, used to detect pathogens in food and food homogenates, were designed via qualitative analysis methods. The inherent disadvantage of these analytical methods is the necessity for specimen preparation, which is a time-consuming process. While some literature describes the challenges and opportunities to overcome the technical issues related to food industry legal guidelines, there is a lack of reviews of the current trials to overcome technological limitations related to sample preparation and microbial detection via nano and micro technologies. In this review, we primarily explore current analytical technologies, including metallic and magnetic nanomaterials, optics, electrochemistry, and spectroscopy. These techniques rely on the early detection of pathogens via enhanced analytical sensitivity and specificity. In order to introduce the potential combination and comparative analysis of various advanced methods, we also reference a novel sample preparation protocol that uses microbial concentration and recovery technologies. This technology has the potential to expedite the pre-enrichment step that precedes the detection process.

Alignment and integration of complex networks by hypergraph-based spectral clustering

NASA Astrophysics Data System (ADS)

Michoel, Tom; Nachtergaele, Bruno

2012-11-01

Complex networks possess a rich, multiscale structure reflecting the dynamical and functional organization of the systems they model. Often there is a need to analyze multiple networks simultaneously, to model a system by more than one type of interaction, or to go beyond simple pairwise interactions, but currently there is a lack of theoretical and computational methods to address these problems. Here we introduce a framework for clustering and community detection in such systems using hypergraph representations. Our main result is a generalization of the Perron-Frobenius theorem from which we derive spectral clustering algorithms for directed and undirected hypergraphs. We illustrate our approach with applications for local and global alignment of protein-protein interaction networks between multiple species, for tripartite community detection in folksonomies, and for detecting clusters of overlapping regulatory pathways in directed networks.

Alignment and integration of complex networks by hypergraph-based spectral clustering.

PubMed

Michoel, Tom; Nachtergaele, Bruno

2012-11-01

Complex networks possess a rich, multiscale structure reflecting the dynamical and functional organization of the systems they model. Often there is a need to analyze multiple networks simultaneously, to model a system by more than one type of interaction, or to go beyond simple pairwise interactions, but currently there is a lack of theoretical and computational methods to address these problems. Here we introduce a framework for clustering and community detection in such systems using hypergraph representations. Our main result is a generalization of the Perron-Frobenius theorem from which we derive spectral clustering algorithms for directed and undirected hypergraphs. We illustrate our approach with applications for local and global alignment of protein-protein interaction networks between multiple species, for tripartite community detection in folksonomies, and for detecting clusters of overlapping regulatory pathways in directed networks.

Comparison of single cell sequencing data between two whole genome amplification methods on two sequencing platforms.

PubMed

Chen, DaYang; Zhen, HeFu; Qiu, Yong; Liu, Ping; Zeng, Peng; Xia, Jun; Shi, QianYu; Xie, Lin; Zhu, Zhu; Gao, Ya; Huang, GuoDong; Wang, Jian; Yang, HuanMing; Chen, Fang

2018-03-21

Research based on a strategy of single-cell low-coverage whole genome sequencing (SLWGS) has enabled better reproducibility and accuracy for detection of copy number variations (CNVs). The whole genome amplification (WGA) method and sequencing platform are critical factors for successful SLWGS (<0.1 × coverage). In this study, we compared single cell and multiple cells sequencing data produced by the HiSeq2000 and Ion Proton platforms using two WGA kits and then comprehensively evaluated the GC-bias, reproducibility, uniformity and CNV detection among different experimental combinations. Our analysis demonstrated that the PicoPLEX WGA Kit resulted in higher reproducibility, lower sequencing error frequency but more GC-bias than the GenomePlex Single Cell WGA Kit (WGA4 kit) independent of the cell number on the HiSeq2000 platform. While on the Ion Proton platform, the WGA4 kit (both single cell and multiple cells) had higher uniformity and less GC-bias but lower reproducibility than those of the PicoPLEX WGA Kit. Moreover, on these two sequencing platforms, depending on cell number, the performance of the two WGA kits was different for both sensitivity and specificity on CNV detection. The results can help researchers who plan to use SLWGS on single or multiple cells to select appropriate experimental conditions for their applications.

Computerized multiple image analysis on mammograms: performance improvement of nipple identification for registration of multiple views using texture convergence analyses

NASA Astrophysics Data System (ADS)

Zhou, Chuan; Chan, Heang-Ping; Sahiner, Berkman; Hadjiiski, Lubomir M.; Paramagul, Chintana

2004-05-01

Automated registration of multiple mammograms for CAD depends on accurate nipple identification. We developed two new image analysis techniques based on geometric and texture convergence analyses to improve the performance of our previously developed nipple identification method. A gradient-based algorithm is used to automatically track the breast boundary. The nipple search region along the boundary is then defined by geometric convergence analysis of the breast shape. Three nipple candidates are identified by detecting the changes along the gray level profiles inside and outside the boundary and the changes in the boundary direction. A texture orientation-field analysis method is developed to estimate the fourth nipple candidate based on the convergence of the tissue texture pattern towards the nipple. The final nipple location is determined from the four nipple candidates by a confidence analysis. Our training and test data sets consisted of 419 and 368 randomly selected mammograms, respectively. The nipple location identified on each image by an experienced radiologist was used as the ground truth. For 118 of the training and 70 of the test images, the radiologist could not positively identify the nipple, but provided an estimate of its location. These were referred to as invisible nipple images. In the training data set, 89.37% (269/301) of the visible nipples and 81.36% (96/118) of the invisible nipples could be detected within 1 cm of the truth. In the test data set, 92.28% (275/298) of the visible nipples and 67.14% (47/70) of the invisible nipples were identified within 1 cm of the truth. In comparison, our previous nipple identification method without using the two convergence analysis techniques detected 82.39% (248/301), 77.12% (91/118), 89.93% (268/298) and 54.29% (38/70) of the nipples within 1 cm of the truth for the visible and invisible nipples in the training and test sets, respectively. The results indicate that the nipple on mammograms can be detected accurately. This will be an important step towards automatic multiple image analysis for CAD techniques.

Spinal focal lesion detection in multiple myeloma using multimodal image features

NASA Astrophysics Data System (ADS)

Fränzle, Andrea; Hillengass, Jens; Bendl, Rolf

2015-03-01

Multiple myeloma is a tumor disease in the bone marrow that affects the skeleton systemically, i.e. multiple lesions can occur in different sites in the skeleton. To quantify overall tumor mass for determining degree of disease and for analysis of therapy response, volumetry of all lesions is needed. Since the large amount of lesions in one patient impedes manual segmentation of all lesions, quantification of overall tumor volume is not possible until now. Therefore development of automatic lesion detection and segmentation methods is necessary. Since focal tumors in multiple myeloma show different characteristics in different modalities (changes in bone structure in CT images, hypointensity in T1 weighted MR images and hyperintensity in T2 weighted MR images), multimodal image analysis is necessary for the detection of focal tumors. In this paper a pattern recognition approach is presented that identifies focal lesions in lumbar vertebrae based on features from T1 and T2 weighted MR images. Image voxels within bone are classified using random forests based on plain intensities and intensity value derived features (maximum, minimum, mean, median) in a 5 x 5 neighborhood around a voxel from both T1 and T2 weighted MR images. A test data sample of lesions in 8 lumbar vertebrae from 4 multiple myeloma patients can be classified at an accuracy of 95% (using a leave-one-patient-out test). The approach provides a reasonable delineation of the example lesions. This is an important step towards automatic tumor volume quantification in multiple myeloma.

Robust Scale Transformation Methods in IRT True Score Equating under Common-Item Nonequivalent Groups Design

ERIC Educational Resources Information Center

He, Yong

2013-01-01

Common test items play an important role in equating multiple test forms under the common-item nonequivalent groups design. Inconsistent item parameter estimates among common items can lead to large bias in equated scores for IRT true score equating. Current methods extensively focus on detection and elimination of outlying common items, which…

[MLPA technique--principles and use in practice].

PubMed

Rusu, Cristina; Sireteanu, Adriana; Puiu, Maria; Skrypnyk, Cristina; Tomescu, E; Csep, Katalin; Creţ, Victoria; Barbarii, Ligia

2007-01-01

MLPA (Multiplex Ligation-dependent Probe Amplification) is a recently introduced method, based on PCR principle, useful for the detection of different genetic abnormalities (aneuploidies, gene deletions/duplications, subtelomeric rearrangements, methylation status etc). The technique is simple, reliable and cheap. We present this method to discuss its importance for a modern genetic service and to underline its multiple advantages.

Surface Enhanced Raman Spectroscopy (SERS) for the Multiplex Detection of Braf, Kras, and Pik3ca Mutations in Plasma of Colorectal Cancer Patients

PubMed Central

Li, Xiaozhou; Yang, Tianyue; Li, Caesar Siqi; Song, Youtao; Lou, Hong; Guan, Dagang; Jin, Lili

2018-01-01

In this paper, we discuss the use of a procedure based on polymerase chain reaction (PCR) and surface enhanced Raman spectroscopy (SERS) (PCR-SERS) to detect DNA mutations. Methods: This method was implemented by first amplifying DNA-containing target mutations, then by annealing probes, and finally by applying SERS detection. The obtained SERS spectra were from a mixture of fluorescence tags labeled to complementary sequences on the mutant DNA. Then, the SERS spectra of multiple tags were decomposed to component tag spectra by multiple linear regression (MLR). Results: The detection limit was 10-11 M with a coefficient of determination (R2) of 0.88. To demonstrate the applicability of this process on real samples, the PCR-SERS method was applied on blood plasma taken from 49 colorectal cancer patients to detect six mutations located at the BRAF, KRAS, and PIK3CA genes. The mutation rates obtained by the PCR-SERS method were in concordance with previous research. Fisher's exact test showed that only two detected mutations at BRAF (V600E) and PIK3CA (E542K) were significantly positively correlated with right-sided colon cancer. No other clinical feature such as gender, age, cancer stage, or differentiation was correlated with mutation (V600E at BRAF, G12C, G12D, G12V, G13D at KRAS, and E542K at PIK3CA). Visually, a dendrogram drawn through hierarchical clustering analysis (HCA) supported the results of Fisher's exact test. The clusters drawn by all six mutations did not conform to the distributions of cancer stages, differentiation or cancer positions. However, the cluster drawn by the two mutations of V600E and E542K showed that all samples with those mutations belonged to the right-sided colon cancer group. Conclusion: The suggested PCR-SERS method is multiplexed, flexible in probe design, easy to incorporate into existing PCR conditions, and was sensitive enough to detect mutations in blood plasma. PMID:29556349

Animal Models for Candidiasis

PubMed Central

Conti, Heather R.; Huppler, Anna R.; Whibley, Natasha; Gaffen, Sarah L.

2014-01-01

Multiple forms of candidiasis are clinically important in humans. Established murine models of disseminated, oropharyngeal, vaginal, and cutaneous candidiasis caused by Candida albicans are described in this unit. Detailed materials and methods for C. albicans growth and detection are also described. PMID:24700323

Simultaneous and multiplexed detection of exosome microRNAs using molecular beacons.

PubMed

Lee, Ji Hye; Kim, Jeong Ah; Jeong, Seunga; Rhee, Won Jong

2016-12-15

Simultaneous and multiplexed detection of microRNAs (miRNAs) in a whole exosome is developed, which can be utilized as a PCR-free efficient diagnosis method for various diseases. Exosomes are small extracellular vesicles that contain biomarker miRNAs from parental cells. Because they circulate throughout bodily fluids, exosomal biomarkers offer great advantages for diagnosis in many aspects. In general, PCR-based methods can be used for exosomal miRNA detection but they are laborious, expensive, and time-consuming, which make them unsuitable for high-throughput diagnosis of diseases. Previously, we reported that single miRNA in the exosomes can be detected specifically using an oligonucleotide probe or molecular beacon. Herein, we demonstrate for the first time that multiple miRNAs can be detected simultaneously in exosomes using miRNA-targeting molecular beacons. Exosomes from a breast cancer cell line, MCF-7, were used for the production of exosomes because MCF-7 has a high level of miR-21, miR-375, and miR-27a as target miRNAs. Molecular beacons successfully hybridized with multiple miRNAs in the cancer cell-derived exosomes even in the presence of high human serum concentration. In addition, it is noteworthy that the choice of fluorophores for multiplexing biomarkers in an exosome is crucial because of its small size. The proposed method described in this article is beneficial to high-throughput analysis for disease diagnosis, prognosis, and response to treatment because it is a time-, labor-, and cost-saving technique. Copyright © 2016 Elsevier B.V. All rights reserved.

Wavelet-based identification of DNA focal genomic aberrations from single nucleotide polymorphism arrays

PubMed Central

2011-01-01

Background Copy number aberrations (CNAs) are an important molecular signature in cancer initiation, development, and progression. However, these aberrations span a wide range of chromosomes, making it hard to distinguish cancer related genes from other genes that are not closely related to cancer but are located in broadly aberrant regions. With the current availability of high-resolution data sets such as single nucleotide polymorphism (SNP) microarrays, it has become an important issue to develop a computational method to detect driving genes related to cancer development located in the focal regions of CNAs. Results In this study, we introduce a novel method referred to as the wavelet-based identification of focal genomic aberrations (WIFA). The use of the wavelet analysis, because it is a multi-resolution approach, makes it possible to effectively identify focal genomic aberrations in broadly aberrant regions. The proposed method integrates multiple cancer samples so that it enables the detection of the consistent aberrations across multiple samples. We then apply this method to glioblastoma multiforme and lung cancer data sets from the SNP microarray platform. Through this process, we confirm the ability to detect previously known cancer related genes from both cancer types with high accuracy. Also, the application of this approach to a lung cancer data set identifies focal amplification regions that contain known oncogenes, though these regions are not reported using a recent CNAs detecting algorithm GISTIC: SMAD7 (chr18q21.1) and FGF10 (chr5p12). Conclusions Our results suggest that WIFA can be used to reveal cancer related genes in various cancer data sets. PMID:21569311

Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index

PubMed Central

Tsai, Yu-Shuen; Aguan, Kripamoy; Pal, Nikhil R.; Chung, I-Fang

2011-01-01

Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing drug on other diseases as well as designing a single drug for multiple diseases. PMID:21909426

Comparison of Four Human Papillomavirus Genotyping Methods: Next-generation Sequencing, INNO-LiPA, Electrochemical DNA Chip, and Nested-PCR.

PubMed

Nilyanimit, Pornjarim; Chansaenroj, Jira; Poomipak, Witthaya; Praianantathavorn, Kesmanee; Payungporn, Sunchai; Poovorawan, Yong

2018-03-01

Human papillomavirus (HPV) infection causes cervical cancer, thus necessitating early detection by screening. Rapid and accurate HPV genotyping is crucial both for the assessment of patients with HPV infection and for surveillance studies. Fifty-eight cervicovaginal samples were tested for HPV genotypes using four methods in parallel: nested-PCR followed by conventional sequencing, INNO-LiPA, electrochemical DNA chip, and next-generation sequencing (NGS). Seven HPV genotypes (16, 18, 31, 33, 45, 56, and 58) were identified by all four methods. Nineteen HPV genotypes were detected by NGS, but not by nested-PCR, INNO-LiPA, or electrochemical DNA chip. Although NGS is relatively expensive and complex, it may serve as a sensitive HPV genotyping method. Because of its highly sensitive detection of multiple HPV genotypes, NGS may serve as an alternative for diagnostic HPV genotyping in certain situations. © The Korean Society for Laboratory Medicine

Parallel heuristics for scalable community detection

DOE PAGES

Lu, Hao; Halappanavar, Mahantesh; Kalyanaraman, Ananth

2015-08-14

Community detection has become a fundamental operation in numerous graph-theoretic applications. Despite its potential for application, there is only limited support for community detection on large-scale parallel computers, largely owing to the irregular and inherently sequential nature of the underlying heuristics. In this paper, we present parallelization heuristics for fast community detection using the Louvain method as the serial template. The Louvain method is an iterative heuristic for modularity optimization. Originally developed in 2008, the method has become increasingly popular owing to its ability to detect high modularity community partitions in a fast and memory-efficient manner. However, the method ismore » also inherently sequential, thereby limiting its scalability. Here, we observe certain key properties of this method that present challenges for its parallelization, and consequently propose heuristics that are designed to break the sequential barrier. For evaluation purposes, we implemented our heuristics using OpenMP multithreading, and tested them over real world graphs derived from multiple application domains. Compared to the serial Louvain implementation, our parallel implementation is able to produce community outputs with a higher modularity for most of the inputs tested, in comparable number or fewer iterations, while providing real speedups of up to 16x using 32 threads.« less

A novel amino acid analysis method using derivatization of multiple functional groups followed by liquid chromatography/tandem mass spectrometry.

PubMed

Sakaguchi, Yohei; Kinumi, Tomoya; Yamazaki, Taichi; Takatsu, Akiko

2015-03-21

We have developed a novel amino acid analysis method using derivatization of multiple functional groups (amino, carboxyl, and phenolic hydroxyl groups). The amino, carboxyl, and phenolic hydroxyl groups of the amino acids were derivatized with 1-bromobutane so that the hydrophobicities and basicities of the amino acids were improved. The derivatized amino acids, including amino group-modified amino acids, could be detected with high sensitivity using liquid chromatography/tandem mass spectrometry (LC-MS/MS). In this study, 17 amino acids obtained by hydrolyzing proteins and 4 amino group-modified amino acids found in the human body (N,N-dimethylglycine, N-formyl-L-methionine, L-pyroglutamic acid, and sarcosine) were selected as target compounds. The 21 derivatized amino acids could be separated using an octadecyl-silylated silica column within 20 min and simultaneously detected. The detection limits for the 21 amino acids were 5.4-91 fmol, and the calibration curves were linear over the range of 10-100 nmol L(-1) (r(2) > 0.9984) with good repeatability. A confirmatory experiment showed that our proposed method could be applied to the determination of a protein certified reference material using the analysis of 12 amino acids combined with isotope dilution mass spectrometry. Furthermore, the proposed method was successfully applied to a stable isotope-coded derivatization method using 1-bromobutane and 1-bromobutane-4,4,4-d3 for comparative analysis of amino acids in human serum.

Detection and analysis of a transient energy burst with beamforming of multiple teleseismic phases

NASA Astrophysics Data System (ADS)

Retailleau, Lise; Landès, Matthieu; Gualtieri, Lucia; Shapiro, Nikolai M.; Campillo, Michel; Roux, Philippe; Guilbert, Jocelyn

2018-01-01

Seismological detection methods are traditionally based on picking techniques. These methods cannot be used to analyse emergent signals where the arrivals cannot be picked. Here, we detect and locate seismic events by applying a beamforming method that combines multiple body-wave phases to USArray data. This method explores the consistency and characteristic behaviour of teleseismic body waves that are recorded by a large-scale, still dense, seismic network. We perform time-slowness analysis of the signals and correlate this with the time-slowness equivalent of the different body-wave phases predicted by a global traveltime calculator, to determine the occurrence of an event with no a priori information about it. We apply this method continuously to one year of data to analyse the different events that generate signals reaching the USArray network. In particular, we analyse in detail a low-frequency secondary microseismic event that occurred on 2010 February 1. This event, that lasted 1 d, has a narrow frequency band around 0.1 Hz, and it occurred at a distance of 150° to the USArray network, South of Australia. We show that the most energetic phase observed is the PKPab phase. Direct amplitude analysis of regional seismograms confirms the occurrence of this event. We compare the seismic observations with models of the spectral density of the pressure field generated by the interferences between oceanic waves. We attribute the observed signals to a storm-generated microseismic event that occurred along the South East Indian Ridge.

Estimating the dose response relationship for occupational radiation exposure measured with minimum detection level.

PubMed

Xue, Xiaonan; Shore, Roy E; Ye, Xiangyang; Kim, Mimi Y

2004-10-01

Occupational exposures are often recorded as zero when the exposure is below the minimum detection level (BMDL). This can lead to an underestimation of the doses received by individuals and can lead to biased estimates of risk in occupational epidemiologic studies. The extent of the exposure underestimation is increased with the magnitude of the minimum detection level (MDL) and the frequency of monitoring. This paper uses multiple imputation methods to impute values for the missing doses due to BMDL. A Gibbs sampling algorithm is developed to implement the method, which is applied to two distinct scenarios: when dose information is available for each measurement (but BMDL is recorded as zero or some other arbitrary value), or when the dose information available represents the summation of a series of measurements (e.g., only yearly cumulative exposure is available but based on, say, weekly measurements). Then the average of the multiple imputed exposure realizations for each individual is used to obtain an unbiased estimate of the relative risk associated with exposure. Simulation studies are used to evaluate the performance of the estimators. As an illustration, the method is applied to a sample of historical occupational radiation exposure data from the Oak Ridge National Laboratory.

Temporally consistent probabilistic detection of new multiple sclerosis lesions in brain MRI.

PubMed

Elliott, Colm; Arnold, Douglas L; Collins, D Louis; Arbel, Tal

2013-08-01

Detection of new Multiple Sclerosis (MS) lesions on magnetic resonance imaging (MRI) is important as a marker of disease activity and as a potential surrogate for relapses. We propose an approach where sequential scans are jointly segmented, to provide a temporally consistent tissue segmentation while remaining sensitive to newly appearing lesions. The method uses a two-stage classification process: 1) a Bayesian classifier provides a probabilistic brain tissue classification at each voxel of reference and follow-up scans, and 2) a random-forest based lesion-level classification provides a final identification of new lesions. Generative models are learned based on 364 scans from 95 subjects from a multi-center clinical trial. The method is evaluated on sequential brain MRI of 160 subjects from a separate multi-center clinical trial, and is compared to 1) semi-automatically generated ground truth segmentations and 2) fully manual identification of new lesions generated independently by nine expert raters on a subset of 60 subjects. For new lesions greater than 0.15 cc in size, the classifier has near perfect performance (99% sensitivity, 2% false detection rate), as compared to ground truth. The proposed method was also shown to exceed the performance of any one of the nine expert manual identifications.

Real-time heart rate measurement for multi-people using compressive tracking

NASA Astrophysics Data System (ADS)

Liu, Lingling; Zhao, Yuejin; Liu, Ming; Kong, Lingqin; Dong, Liquan; Ma, Feilong; Pang, Zongguang; Cai, Zhi; Zhang, Yachu; Hua, Peng; Yuan, Ruifeng

2017-09-01

The rise of aging population has created a demand for inexpensive, unobtrusive, automated health care solutions. Image PhotoPlethysmoGraphy(IPPG) aids in the development of these solutions by allowing for the extraction of physiological signals from video data. However, the main deficiencies of the recent IPPG methods are non-automated, non-real-time and susceptible to motion artifacts(MA). In this paper, a real-time heart rate(HR) detection method for multiple subjects simultaneously was proposed and realized using the open computer vision(openCV) library, which consists of getting multiple subjects' facial video automatically through a Webcam, detecting the region of interest (ROI) in the video, reducing the false detection rate by our improved Adaboost algorithm, reducing the MA by our improved compress tracking(CT) algorithm, wavelet noise-suppression algorithm for denoising and multi-threads for higher detection speed. For comparison, HR was measured simultaneously using a medical pulse oximetry device for every subject during all sessions. Experimental results on a data set of 30 subjects show that the max average absolute error of heart rate estimation is less than 8 beats per minute (BPM), and the processing speed of every frame has almost reached real-time: the experiments with video recordings of ten subjects under the condition of the pixel resolution of 600× 800 pixels show that the average HR detection time of 10 subjects was about 17 frames per second (fps).

The Novel Multiple Inner Primers-Loop-Mediated Isothermal Amplification (MIP-LAMP) for Rapid Detection and Differentiation of Listeria monocytogenes.

PubMed

Wang, Yi; Wang, Yan; Ma, Aijing; Li, Dongxun; Luo, Lijuan; Liu, Dongxin; Hu, Shoukui; Jin, Dong; Liu, Kai; Ye, Changyun

2015-12-03

Here, a novel model of loop-mediated isothermal amplification (LAMP), termed multiple inner primers-LAMP (MIP-LAMP), was devised and successfully applied to detect Listeria monocytogenes. A set of 10 specific MIP-LAMP primers, which recognized 14 different regions of target gene, was designed to target a sequence in the hlyA gene. The MIP-LAMP assay efficiently amplified the target element within 35 min at 63 °C and was evaluated for sensitivity and specificity. The templates were specially amplified in the presence of the genomic DNA from L. monocytogenes. The limit of detection (LoD) of MIP-LAMP assay was 62.5 fg/reaction using purified L. monocytogenes DNA. The LoD for DNA isolated from serial dilutions of L. monocytogenes cells in buffer and in milk corresponded to 2.4 CFU and 24 CFU, respectively. The amplified products were analyzed by real-time monitoring of changes in turbidity, and visualized by adding Loop Fluorescent Detection Reagent (FD), or as a ladder-like banding pattern on gel electrophoresis. A total of 48 pork samples were investigated for L. monocytogenes by the novel MIP-LAMP method, and the diagnostic accuracy was shown to be 100% when compared to the culture-biotechnical method. In conclusion, the MIP-LAMP methodology was demonstrated to be a reliable, sensitive and specific tool for rapid detection of L. monocytogenes strains.

Automated multiple target detection and tracking in UAV videos

NASA Astrophysics Data System (ADS)

Mao, Hongwei; Yang, Chenhui; Abousleman, Glen P.; Si, Jennie

2010-04-01

In this paper, a novel system is presented to detect and track multiple targets in Unmanned Air Vehicles (UAV) video sequences. Since the output of the system is based on target motion, we first segment foreground moving areas from the background in each video frame using background subtraction. To stabilize the video, a multi-point-descriptor-based image registration method is performed where a projective model is employed to describe the global transformation between frames. For each detected foreground blob, an object model is used to describe its appearance and motion information. Rather than immediately classifying the detected objects as targets, we track them for a certain period of time and only those with qualified motion patterns are labeled as targets. In the subsequent tracking process, a Kalman filter is assigned to each tracked target to dynamically estimate its position in each frame. Blobs detected at a later time are used as observations to update the state of the tracked targets to which they are associated. The proposed overlap-rate-based data association method considers the splitting and merging of the observations, and therefore is able to maintain tracks more consistently. Experimental results demonstrate that the system performs well on real-world UAV video sequences. Moreover, careful consideration given to each component in the system has made the proposed system feasible for real-time applications.

Category-Specific Comparison of Univariate Alerting Methods for Biosurveillance Decision Support

PubMed Central

Elbert, Yevgeniy; Hung, Vivian; Burkom, Howard

2013-01-01

Objective For a multi-source decision support application, we sought to match univariate alerting algorithms to surveillance data types to optimize detection performance. Introduction Temporal alerting algorithms commonly used in syndromic surveillance systems are often adjusted for data features such as cyclic behavior but are subject to overfitting or misspecification errors when applied indiscriminately. In a project for the Armed Forces Health Surveillance Center to enable multivariate decision support, we obtained 4.5 years of out-patient, prescription and laboratory test records from all US military treatment facilities. A proof-of-concept project phase produced 16 events with multiple evidence corroboration for comparison of alerting algorithms for detection performance. We used the representative streams from each data source to compare sensitivity of 6 algorithms to injected spikes, and we used all data streams from 16 known events to compare them for detection timeliness. Methods The six methods compared were: Holt-Winters generalized exponential smoothing method (1)automated choice between daily methods, regression and an exponential weighted moving average (2)adaptive daily Shewhart-type chartadaptive one-sided daily CUSUMEWMA applied to 7-day means with a trend correction; and7-day temporal scan statistic Sensitivity testing: We conducted comparative sensitivity testing for categories of time series with similar scales and seasonal behavior. We added multiples of the standard deviation of each time series as single-day injects in separate algorithm runs. For each candidate method, we then used as a sensitivity measure the proportion of these runs for which the output of each algorithm was below alerting thresholds estimated empirically for each algorithm using simulated data streams. We identified the algorithm(s) whose sensitivity was most consistently high for each data category. For each syndromic query applied to each data source (outpatient, lab test orders, and prescriptions), 502 authentic time series were derived, one for each reporting treatment facility. Data categories were selected in order to group time series with similar expected algorithm performance: Median > 100 < Median ≤ 10Median = 0Lag 7 Autocorrelation Coefficient ≥ 0.2Lag 7 Autocorrelation Coefficient < 0.2 Timeliness testing: For the timeliness testing, we avoided artificiality of simulated signals by measuring alerting detection delays in the 16 corroborated outbreaks. The multiple time series from these events gave a total of 141 time series with outbreak intervals for timeliness testing. The following measures were computed to quantify timeliness of detection: Median Detection Delay – median number of days to detect the outbreak.Penalized Mean Detection Delay –mean number of days to detect the outbreak with outbreak misses penalized as 1 day plus the maximum detection time. Results Based on the injection results, the Holt-Winters algorithm was most sensitive among time series with positive medians. The adaptive CUSUM and the Shewhart methods were most sensitive for data streams with median zero. Table 1 provides timeliness results using the 141 outbreak-associated streams on sparse (Median=0) and non-sparse data categories. [Insert table #1 here] Data median Detection Delay, days Holt-winters Regression EWMA Adaptive Shewhart Adaptive CUSUM 7-day Trend-adj. EWMA 7-day Temporal Scan Median 0 Median 3 2 4 2 4.5 2 Penalized Mean 7.2 7 6.6 6.2 7.3 7.6 Median >0 Median 2 2 2.5 2 6 4 Penalized Mean 6.1 7 7.2 7.1 7.7 6.6 The gray shading in the table 1 indicates methods with shortest detection delays for sparse and non-sparse data streams. The Holt-Winters method was again superior for non-sparse data. For data with median=0, the adaptive CUSUM was superior for a daily false alarm probability of 0.01, but the Shewhart method was timelier for more liberal thresholds. Conclusions Both kinds of detection performance analysis showed the method based on Holt-Winters exponential smoothing superior on non-sparse time series with day-of-week effects. The adaptive CUSUM and She-whart methods proved optimal on sparse data and data without weekly patterns.

Vision Sensor-Based Road Detection for Field Robot Navigation

PubMed Central

Lu, Keyu; Li, Jian; An, Xiangjing; He, Hangen

2015-01-01

Road detection is an essential component of field robot navigation systems. Vision sensors play an important role in road detection for their great potential in environmental perception. In this paper, we propose a hierarchical vision sensor-based method for robust road detection in challenging road scenes. More specifically, for a given road image captured by an on-board vision sensor, we introduce a multiple population genetic algorithm (MPGA)-based approach for efficient road vanishing point detection. Superpixel-level seeds are then selected in an unsupervised way using a clustering strategy. Then, according to the GrowCut framework, the seeds proliferate and iteratively try to occupy their neighbors. After convergence, the initial road segment is obtained. Finally, in order to achieve a globally-consistent road segment, the initial road segment is refined using the conditional random field (CRF) framework, which integrates high-level information into road detection. We perform several experiments to evaluate the common performance, scale sensitivity and noise sensitivity of the proposed method. The experimental results demonstrate that the proposed method exhibits high robustness compared to the state of the art. PMID:26610514

Rare Variant Association Test with Multiple Phenotypes

PubMed Central

Lee, Selyeong; Won, Sungho; Kim, Young Jin; Kim, Yongkang; Kim, Bong-Jo; Park, Taesung

2016-01-01

Although genome-wide association studies (GWAS) have now discovered thousands of genetic variants associated with common traits, such variants cannot explain the large degree of “missing heritability,” likely due to rare variants. The advent of next generation sequencing technology has allowed rare variant detection and association with common traits, often by investigating specific genomic regions for rare variant effects on a trait. Although multiply correlated phenotypes are often concurrently observed in GWAS, most studies analyze only single phenotypes, which may lessen statistical power. To increase power, multivariate analyses, which consider correlations between multiple phenotypes, can be used. However, few existing multi-variant analyses can identify rare variants for assessing multiple phenotypes. Here, we propose Multivariate Association Analysis using Score Statistics (MAAUSS), to identify rare variants associated with multiple phenotypes, based on the widely used Sequence Kernel Association Test (SKAT) for a single phenotype. We applied MAAUSS to Whole Exome Sequencing (WES) data from a Korean population of 1,058 subjects, to discover genes associated with multiple traits of liver function. We then assessed validation of those genes by a replication study, using an independent dataset of 3,445 individuals. Notably, we detected the gene ZNF620 among five significant genes. We then performed a simulation study to compare MAAUSS's performance with existing methods. Overall, MAAUSS successfully conserved type 1 error rates and in many cases, had a higher power than the existing methods. This study illustrates a feasible and straightforward approach for identifying rare variants correlated with multiple phenotypes, with likely relevance to missing heritability. PMID:28039885

A universal DNA-based protein detection system.

PubMed

Tran, Thua N N; Cui, Jinhui; Hartman, Mark R; Peng, Songming; Funabashi, Hisakage; Duan, Faping; Yang, Dayong; March, John C; Lis, John T; Cui, Haixin; Luo, Dan

2013-09-25

Protein immune detection requires secondary antibodies which must be carefully selected in order to avoid interspecies cross-reactivity, and is therefore restricted by the limited availability of primary/secondary antibody pairs. Here we present a versatile DNA-based protein detection system using a universal adapter to interface between IgG antibodies and DNA-modified reporter molecules. As a demonstration of this capability, we successfully used DNA nano-barcodes, quantum dots, and horseradish peroxidase enzyme to detect multiple proteins using our DNA-based labeling system. Our system not only eliminates secondary antibodies but also serves as a novel method platform for protein detection with modularity, high capacity, and multiplexed capability.

A Universal DNA-Based Protein Detection System

PubMed Central

Tran, Thua N. N.; Cui, Jinhui; Hartman, Mark R.; Peng, Songming; Funabashi, Hisakage; Duan, Faping; Yang, Dayong; March, John C.; Lis, John T.; Cui, Haixin; Luo, Dan

2014-01-01

Protein immune detection requires secondary antibodies which must be carefully selected in order to avoid interspecies cross-reactivity, and is therefore restricted by the limited availability of primary/secondary antibody pairs. Here we present a versatile DNA-based protein detection system using a universal adapter to interface between IgG antibodies and DNA-modified reporter molecules. As a demonstration of this capability, we successfully used DNA nano-barcodes, quantum dots, and horseradish peroxidase enzyme to detect multiple proteins using our DNA-based labeling system. Our system not only eliminates secondary antibodies but also serves as a novel method platform for protein detection with modularity, high capacity, and multiplexed capability. PMID:23978265

Addressing the targeting range of the ABILHAND-56 in relapsing-remitting multiple sclerosis: A mixed methods psychometric study.

PubMed

Cleanthous, Sophie; Strzok, Sara; Pompilus, Farrah; Cano, Stefan; Marquis, Patrick; Cohan, Stanley; Goldman, Myla D; Kresa-Reahl, Kiren; Petrillo, Jennifer; Castrillo-Viguera, Carmen; Cadavid, Diego; Chen, Shih-Yin

2018-01-01

ABILHAND, a manual ability patient-reported outcome instrument originally developed for stroke patients, has been used in multiple sclerosis clinical trials; however, psychometric analyses indicated the measure's limited measurement range and precision in higher-functioning multiple sclerosis patients. The purpose of this study was to identify candidate items to expand the measurement range of the ABILHAND-56, thus improving its ability to detect differences in manual ability in higher-functioning multiple sclerosis patients. A step-wise mixed methods design strategy was used, comprising two waves of patient interviews, a combination of qualitative (concept elicitation and cognitive debriefing) and quantitative (Rasch measurement theory) analytic techniques, and consultation interviews with three clinical neurologists specializing in multiple sclerosis. Original ABILHAND was well understood in this context of use. Eighty-two new manual ability concepts were identified. Draft supplementary items were generated and refined with patient and neurologist input. Rasch measurement theory psychometric analysis indicated supplementary items improved targeting to higher-functioning multiple sclerosis patients and measurement precision. The final pool of Early Multiple Sclerosis Manual Ability items comprises 20 items. The synthesis of qualitative and quantitative methods used in this study improves the ABILHAND content validity to more effectively identify manual ability changes in early multiple sclerosis and potentially help determine treatment effect in higher-functioning patients in clinical trials.

A comparison of two follow-up analyses after multiple analysis of variance, analysis of variance, and descriptive discriminant analysis: A case study of the program effects on education-abroad programs

Treesearch

Alvin H. Yu; Garry Chick

2010-01-01

This study compared the utility of two different post-hoc tests after detecting significant differences within factors on multiple dependent variables using multivariate analysis of variance (MANOVA). We compared the univariate F test (the Scheffé method) to descriptive discriminant analysis (DDA) using an educational-tour survey of university study-...

Multiple biomarkers in molecular oncology. I. Molecular diagnostics applications in cervical cancer detection.

PubMed

Malinowski, Douglas P

2007-03-01

The screening for cervical carcinoma and its malignant precursors (cervical neoplasia) currently employs morphology-based detection methods (Papanicolaou [Pap] smear) in addition to the detection of high-risk human papillomavirus. The combination of the Pap smear with human papillomavirus testing has achieved significant improvements in sensitivity for the detection of cervical disease. Diagnosis of cervical neoplasia is dependent upon histology assessment of cervical biopsy specimens. Attempts to improve the specificity of cervical disease screening have focused on the investigation of molecular biomarkers for adjunctive use in combination with the Pap smear. Active research into the genomic and proteomic alterations that occur during human papillomavirus-induced neoplastic transformation have begun to characterize some of the basic mechanisms inherent to the disease process of cervical cancer development. This research continues to demonstrate the complexity of multiple genomic and proteomic alterations that accumulate during the tumorigenesis process. Despite this diversity, basic patterns of uncontrolled signal transduction, cell cycle deregulation, activation of DNA replication and altered extracellular matrix interactions are beginning to emerge as common features inherent to cervical cancer development. Some of these gene or protein expression alterations have been investigated as potential biomarkers for screening and diagnostics applications. The contribution of multiple gene alterations in the development of cervical cancer suggests that the application of multiple biomarker panels has the potential to develop clinically useful molecular diagnostics. In this review, the application of biomarkers for the improvement of sensitivity and specificity of the detection of cervical neoplasia within cytology specimens will be discussed.

[Design and experiment of micro biochemical detector based on micro spectrometer].

PubMed

Yu, Qing-hua; Wen, Zhi-yu; Chen, Gang; Dai, Wei-wei; Liu, Nian-ci; Wu, Xin

2012-03-01

According to the requirements of rapid detection of important life parameters for the sick and wounded, a new micro bio-chemical detection configuration was proposed utilizing continuous spectroscopy analysis, which was founded on MOEMS and embedded technology. The configuration was developed as so much research work was carried out on the detecting objects and methods. Important parameters such as stray light, absorbance linearity, absorbance ratability, stability and temperature accuracy of the instrument were tested, which are all in good agreement with the design requirements. Clinic tests show that it can detect multiple life parameters quickly (Na+, GLU, Hb eg.).

Sequence harmony: detecting functional specificity from alignments

PubMed Central

Feenstra, K. Anton; Pirovano, Walter; Krab, Klaas; Heringa, Jaap

2007-01-01

Multiple sequence alignments are often used for the identification of key specificity-determining residues within protein families. We present a web server implementation of the Sequence Harmony (SH) method previously introduced. SH accurately detects subfamily specific positions from a multiple alignment by scoring compositional differences between subfamilies, without imposing conservation. The SH web server allows a quick selection of subtype specific sites from a multiple alignment given a subfamily grouping. In addition, it allows the predicted sites to be directly mapped onto a protein structure and displayed. We demonstrate the use of the SH server using the family of plant mitochondrial alternative oxidases (AOX). In addition, we illustrate the usefulness of combining sequence and structural information by showing that the predicted sites are clustered into a few distinct regions in an AOX homology model. The SH web server can be accessed at www.ibi.vu.nl/programs/seqharmwww. PMID:17584793

Average Likelihood Methods for Code Division Multiple Access (CDMA)

DTIC Science & Technology

2014-05-01

lengths in the range of 22 to 213 and possibly higher. Keywords: DS / CDMA signals, classification, balanced CDMA load, synchronous CDMA , decision...likelihood ratio test (ALRT). We begin this classification problem by finding the size of the spreading matrix that generated the DS - CDMA signal. As...Theoretical Background The classification of DS / CDMA signals should not be confused with the problem of multiuser detection. The multiuser detection deals

Cross-reactivity by botanicals used in dietary supplements and spices using the multiplex xMAP food allergen detection assay (xMAP FADA).

PubMed

Pedersen, Ronnie O; Nowatzke, William L; Cho, Chung Y; Oliver, Kerry G; Garber, Eric A E

2018-06-18

Food allergies affect some 15 million Americans. The only treatment for food allergies is a strict avoidance diet. To help ensure the reliability of food labels, analytical methods are employed; the most common being enzyme-linked immunosorbent assays (ELISAs). However, the commonly employed ELISAs are single analyte-specific and cannot distinguish between false positives due to cross-reactive homologous proteins; making the method of questionable utility for regulatory purposes when analyzing for unknown or multiple food allergens. Also, should the need arise to detect additional analytes, extensive research must be undertaken to develop new ELISAs. To address these and other limitations, a multiplex immunoassay, the xMAP® food allergen detection assay (xMAP FADA), was developed using 30 different antibodies against 14 different food allergens plus gluten. Besides incorporating two antibodies for the detection of most analytes, the xMAP FADA also relies on two different extraction protocols; providing multiple confirmatory end-points. Using the xMAP FADA, the cross-reactivities of 45 botanicals used in dietary supplements and spices commercially sold in the USA were assessed. Only a few displayed cross-reactivities with the antibodies in the xMAP FADA at levels exceeding 0.0001%. The utility of the xMAP FADA was exemplified by its ability to detect and distinguish between betel nut, saw palmetto, and acai which are in the same family as coconut. Other botanicals examined included allspice, amchur, anise seed, black pepper, caraway seed, cardamom, cayenne red pepper, sesame seed, poppy seed, white pepper, and wheat grass. The combination of direct antibody detection, multi-antibody profiling, high sensitivity, and a modular design made it possible for the xMAP FADA to distinguish between homologous antigens, provide multiple levels of built-in confirmatory analysis, and optimize the bead set cocktail to address specific needs.

Electrochemical and Infrared Absorption Spectroscopy Detection of SF6 Decomposition Products

PubMed Central

Dong, Ming; Ren, Ming; Ye, Rixin

2017-01-01

Sulfur hexafluoride (SF6) gas-insulated electrical equipment is widely used in high-voltage (HV) and extra-high-voltage (EHV) power systems. Partial discharge (PD) and local heating can occur in the electrical equipment because of insulation faults, which results in SF6 decomposition and ultimately generates several types of decomposition products. These SF6 decomposition products can be qualitatively and quantitatively detected with relevant detection methods, and such detection contributes to diagnosing the internal faults and evaluating the security risks of the equipment. At present, multiple detection methods exist for analyzing the SF6 decomposition products, and electrochemical sensing (ES) and infrared (IR) spectroscopy are well suited for application in online detection. In this study, the combination of ES with IR spectroscopy is used to detect SF6 gas decomposition. First, the characteristics of these two detection methods are studied, and the data analysis matrix is established. Then, a qualitative and quantitative analysis ES-IR model is established by adopting a two-step approach. A SF6 decomposition detector is designed and manufactured by combining an electrochemical sensor and IR spectroscopy technology. The detector is used to detect SF6 gas decomposition and is verified to reliably and accurately detect the gas components and concentrations. PMID:29140268

Rate of Detection of Multiple Organisms and Clostridium difficile with Stool Multiplex PCR Detection Test in Pediatrics

PubMed Central

Mangla, Saisho; Villalobos, Tibisay

2017-01-01

Abstract Background New multiplex molecular assays have been developed to determine the etiology of infectious gastroenteritis. Unfortunately, these assays can detect multiple organisms simultaneously along with Clostridium difficile (C.diff), making it difficult to differentiate true pathogen vs. colonization. In January 2015, our institution switched from traditional testing methods to a multiplex polymerase chain reaction (PCR) detection test (FilmArrayTM Gastrointestinal Panel. BioFireDX, Salt Lake City, Utah). The objective of our study was to determine the number of FilmArrayTMpanels that detected C.diff and/or multiple organisms. Methods We conducted a retrospective data review of FilmArray™ panels in pediatric patients 18 years and younger from January 2015 to December 2016. Stool samples were received from both inpatient and outpatient setting. Results In 2016, 495 FilmArray™ panels were reviewed and 300 (61%) isolated at least one organism. Among the positives panels, 206 (69%) detected one organism, 73 (24%) detected 2 organisms and 21 (7.0%) detected 3 or more organisms. No more than 4 organisms were detected in a single panel. C.diff was most commonly isolated, found 105 times (25%), and 34 (31%) of these were in children younger than 2 years. Amongst the 105 C.diff isolates, 64% were alone and 35% with another organism. Amongst children younger than 2, C.diff was isolated alone in 13 (38%) samples and with another organism in 21 (62%) samples. In 2015, 353 panels were reviewed with a detection rate of 60.3%. C.Diff was isolated 70 times (24% of total isolates) and 22 (31%) were in children younger than 2 years. Amongst those C.diff isolates, 49% were alone and 51% with another organism. Amongst children younger than 2, C.diff was isolated alone in 8 (38%) samples and with another organism in 14 (62%) samples. Conclusion Although the FilmArray™ Gastrointestinal Panel is a useful single modality for determining the etiology of infectious gastroenteritis, more than one organism is frequently detected. C.diff has become the most common organism isolated among children at our institution. Caution should be used when interpreting the isolation of C.diff in younger children and when isolated with other organisms. Disclosures All authors: No reported disclosures.

Possible Synergistic Interactions Among Multiple HPV Genotypes in Women Suffering from Genital Neoplasia

PubMed

Hajia, Massoud; Sohrabi, Amir

2018-03-27

Objective: Persistence of HPV infection is the true cause of cervical disorders. It is reported that competition may exist among HPV genotypes for colonization. This survey was designed to establish the multiple HPV genotype status in our community and the probability of multiple HPV infections involvement. Methods: All multiple HPV infections were selected for investigation in women suffering from genital infections referred to private laboratories in Tehran, Iran. A total of 160 multi HPV positive specimens from cervical scraping were identified by the HPV genotyping methods, "INNO-LiPA and Geno Array". Result: In present study, HPV 6 (LR), 16 (HR), 53 (pHR), 31 (HR) and 11 (LR) were included in 48.8% of detected infections as the most five dominant genotypes. HPV 16 was detected at the highest rate with genotypes 53, 31 and 52, while HPV 53 appeared linked with HPV 16, 51 and 56 in concurrent infections. It appears that HPV 16 and 53 may have significant tendencies to associate with each other rather than with other genotypes. Analysis of the data revealed there may be some synergistic interactions with a few particular genotypes such as "HPV 53". Conclusion: Multiple HPV genotypes appear more likely to be linked with development of cervical abnormalities especially in patients with genital infections. Since, there are various patterns of dominant HPV genotypes in different regions of world, more investigations of this type should be performed for careHPV programs in individual countries. Creative Commons Attribution License

Antibody-Mediated Small Molecule Detection Using Programmable DNA-Switches.

PubMed

Rossetti, Marianna; Ippodrino, Rudy; Marini, Bruna; Palleschi, Giuseppe; Porchetta, Alessandro

2018-06-13

The development of rapid, cost-effective, and single-step methods for the detection of small molecules is crucial for improving the quality and efficiency of many applications ranging from life science to environmental analysis. Unfortunately, current methodologies still require multiple complex, time-consuming washing and incubation steps, which limit their applicability. In this work we present a competitive DNA-based platform that makes use of both programmable DNA-switches and antibodies to detect small target molecules. The strategy exploits both the advantages of proximity-based methods and structure-switching DNA-probes. The platform is modular and versatile and it can potentially be applied for the detection of any small target molecule that can be conjugated to a nucleic acid sequence. Here the rational design of programmable DNA-switches is discussed, and the sensitive, rapid, and single-step detection of different environmentally relevant small target molecules is demonstrated.

Penalty dynamic programming algorithm for dim targets detection in sensor systems.

PubMed

Huang, Dayu; Xue, Anke; Guo, Yunfei

2012-01-01

In order to detect and track multiple maneuvering dim targets in sensor systems, an improved dynamic programming track-before-detect algorithm (DP-TBD) called penalty DP-TBD (PDP-TBD) is proposed. The performances of tracking techniques are used as a feedback to the detection part. The feedback is constructed by a penalty term in the merit function, and the penalty term is a function of the possible target state estimation, which can be obtained by the tracking methods. With this feedback, the algorithm combines traditional tracking techniques with DP-TBD and it can be applied to simultaneously detect and track maneuvering dim targets. Meanwhile, a reasonable constraint that a sensor measurement can originate from one target or clutter is proposed to minimize track separation. Thus, the algorithm can be used in the multi-target situation with unknown target numbers. The efficiency and advantages of PDP-TBD compared with two existing methods are demonstrated by several simulations.

Detection and quantification of beef and pork materials in meat products by duplex droplet digital PCR.

PubMed

Cai, Yicun; He, Yuping; Lv, Rong; Chen, Hongchao; Wang, Qiang; Pan, Liangwen

2017-01-01

Meat products often consist of meat from multiple animal species, and inaccurate food product adulteration and mislabeling can negatively affect consumers. Therefore, a cost-effective and reliable method for identification and quantification of animal species in meat products is required. In this study, we developed a duplex droplet digital PCR (dddPCR) detection and quantification system to simultaneously identify and quantify the source of meat in samples containing a mixture of beef (Bos taurus) and pork (Sus scrofa) in a single digital PCR reaction tube. Mixed meat samples of known composition were used to test the accuracy and applicability of this method. The limit of detection (LOD) and the limit of quantification (LOQ) of this detection and quantification system were also identified. We conclude that our dddPCR detection and quantification system is suitable for quality control and routine analyses of meat products.

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 effective in each case. PMID:23091608

The effect of ion-exchange purification on the determination of plutonium at the New Brunswick Laboratory

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

Mitchell, W.G.; Spaletto, M.I.; Lewis, K.

The method of plutonium (Pu) determination at the Brunswick Laboratory (NBL) consists of a combination of ion-exchange purification followed by controlled-potential coulometric analysis (IE/CPC). The present report's purpose is to quantify any detectable Pu loss occurring in the ion-exchange (IE) purification step which would cause a negative bias in the NBL method for Pu analysis. The magnitude of any such loss would be contained within the reproducibility (0.05%) of the IE/CPC method which utilizes a state-of-the-art autocoulometer developed at NBL. When the NBL IE/CPC method is used for Pu analysis, any loss in ion-exchange purification (<0.05%) is confounded with themore » repeatability of the ion-exchange and the precision of the CPC analysis technique (<0.05%). Consequently, to detect a bias in the IE/CPC method due to the IE alone using the IE/CPC method itself requires that many randomized analyses on a single material be performed over time and that statistical analysis of the data be performed. The initial approach described in this report to quantify any IE loss was an independent method, Isotope Dilution Mass Spectrometry; however, the number of analyses performed was insufficient to assign a statistically significant value to the IE loss (<0.02% of 10 mg samples of Pu). The second method used for quantifying any IE loss of Pu was multiple ion exchanges of the same Pu aliquant; the small number of analyses possible per individual IE together with the column-to-column variability over multiple ion exchanges prevented statistical detection of any loss of <0.05%. 12 refs.« less

Comparison of imaging characteristics of multiple-beam equalization and storage phosphor direct digitizer radiographic systems

NASA Astrophysics Data System (ADS)

Sankaran, A.; Chuang, Keh-Shih; Yonekawa, Hisashi; Huang, H. K.

1992-06-01

The imaging characteristics of two chest radiographic equipment, Advanced Multiple Beam Equalization Radiography (AMBER) and Konica Direct Digitizer [using a storage phosphor (SP) plate] systems have been compared. The variables affecting image quality and the computer display/reading systems used are detailed. Utilizing specially designed wedge, geometric, and anthropomorphic phantoms, studies were conducted on: exposure and energy response of detectors; nodule detectability; different exposure techniques; various look- up tables (LUTs), gray scale displays and laser printers. Methods for scatter estimation and reduction were investigated. It is concluded that AMBER with screen-film and equalization techniques provides better nodule detectability than SP plates. However, SP plates have other advantages such as flexibility in the selection of exposure techniques, image processing features, and excellent sensitivity when combined with optimum reader operating modes. The equalization feature of AMBER provides better nodule detectability under the denser regions of the chest. Results of diagnostic accuracy are demonstrated with nodule detectability plots and analysis of images obtained with phantoms.

Seroprevalence study of Equine rhinitis B virus (ERBV) in Australian weanling horses using serotype-specific ERBV enzyme-linked immunosorbent assays.

PubMed

Horsington, Jacquelyn; Hartley, Carol A; Gilkerson, James R

2013-09-01

Respiratory infections are a major burden in the performance horse industry. Equine rhinitis B virus (ERBV) has been isolated from horses displaying clinical respiratory disease, and ERBV-neutralizing antibodies have been detected in 50-80% of horses in reported surveys. Current ERBV isolation and detection methods may underestimate the number of ERBV-positive animals and do not identify multiple serotype infections. The aim of the current study was to develop a serotyping ERBV antibody-detection enzyme-linked immunosorbent assay (ELISA) and examine the seroprevalence of ERBV in a group of Australian weanling horses. ELISAs with high sensitivity and specificity were developed. The seroprevalence of ERBV in the weanling horses was high (74-86%); ERBV-3 antibodies were most prevalent (58-62%) and ERBV-2 antibodies were least prevalent (10-16%). Many horses were seropositive to 2 or more serotypes. All 3 serotypes of ERBV were detected, and concurrent positivity to multiple serotypes was common.

Advances in quantum cascade lasers for security and crime-fighting

NASA Astrophysics Data System (ADS)

Normand, Erwan L.; Stokes, Robert J.; Hay, Kenneth; Foulger, Brian; Lewis, Colin

2010-10-01

Advances in the application of Quantum Cascade Lasers (QCL) to trace gas detection will be presented. The solution is real time (~1 μsec per scan), is insensitive to turbulence and vibration, and performs multiple measurements in one sweep. The QCL provides a large dynamic range, which is a linear response from ppt to % level. The concentration can be derived with excellent immunity from cross interference. Point sensing sensors developed by Cascade for home made and commercial explosives operate by monitoring key constituents in real time and matching this to a spatial event (i.e. sniffer device placed close to an object or person walking through portal (overt or covert). Programmable signature detection capability allows for detection of multiple chemical compounds along the most likely array of explosive chemical formulation. The advantages of configuration as "point sensing" or "stand off" will be discussed. In addition to explosives this method is highly applicable to the detection of mobile drugs labs through volatile chemical release.

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.

Sensitivity and accuracy of high-throughput metabarcoding methods for early detection of invasive fish species

NASA Astrophysics Data System (ADS)

Hatzenbuhler, Chelsea; Kelly, John R.; Martinson, John; Okum, Sara; Pilgrim, Erik

2017-04-01

High-throughput DNA metabarcoding has gained recognition as a potentially powerful tool for biomonitoring, including early detection of aquatic invasive species (AIS). DNA based techniques are advancing, but our understanding of the limits to detection for metabarcoding complex samples is inadequate. For detecting AIS at an early stage of invasion when the species is rare, accuracy at low detection limits is key. To evaluate the utility of metabarcoding in future fish community monitoring programs, we conducted several experiments to determine the sensitivity and accuracy of routine metabarcoding methods. Experimental mixes used larval fish tissue from multiple “common” species spiked with varying proportions of tissue from an additional “rare” species. Pyrosequencing of genetic marker, COI (cytochrome c oxidase subunit I) and subsequent sequence data analysis provided experimental evidence of low-level detection of the target “rare” species at biomass percentages as low as 0.02% of total sample biomass. Limits to detection varied interspecifically and were susceptible to amplification bias. Moreover, results showed some data processing methods can skew sequence-based biodiversity measurements from corresponding relative biomass abundances and increase false absences. We suggest caution in interpreting presence/absence and relative abundance in larval fish assemblages until metabarcoding methods are optimized for accuracy and precision.

Automatic Detection and Evaluation of Solar Cell Micro-Cracks in Electroluminescence Images Using Matched Filters

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

Spataru, Sergiu; Hacke, Peter; Sera, Dezso

A method for detecting micro-cracks in solar cells using two dimensional matched filters was developed, derived from the electroluminescence intensity profile of typical micro-cracks. We describe the image processing steps to obtain a binary map with the location of the micro-cracks. Finally, we show how to automatically estimate the total length of each micro-crack from these maps, and propose a method to identify severe types of micro-cracks, such as parallel, dendritic, and cracks with multiple orientations. With an optimized threshold parameter, the technique detects over 90 % of cracks larger than 3 cm in length. The method shows great potentialmore » for quantifying micro-crack damage after manufacturing or module transportation for the determination of a module quality criterion for cell cracking in photovoltaic modules.« less

A novel GMO biosensor for rapid ultrasensitive and simultaneous detection of multiple DNA components in GMO products.

PubMed

Huang, Lin; Zheng, Lei; Chen, Yinji; Xue, Feng; Cheng, Lin; Adeloju, Samuel B; Chen, Wei

2015-04-15

Since the introduction of genetically modified organisms (GMOs), there has been on-going and continuous concern and debates on the commercialization of products derived from GMOs. There is an urgent need for development of highly efficient analytical methods for rapid and high throughput screening of GMOs components, as required for appropriate labeling of GMO-derived foods, as well as for on-site inspection and import/export quarantine. In this study, we describe, for the first time, a multi-labeling based electrochemical biosensor for simultaneous detection of multiple DNA components of GMO products on the same sensing interface. Two-round signal amplification was applied by using both an exonuclease enzyme catalytic reaction and gold nanoparticle-based bio-barcode related strategies, respectively. Simultaneous multiple detections of different DNA components of GMOs were successfully achieved with satisfied sensitivity using this electrochemical biosensor. Furthermore, the robustness and effectiveness of the proposed approach was successfully demonstrated by application to various GMO products, including locally obtained and confirmed commercial GMO seeds and transgenetic plants. The proposed electrochemical biosensor demonstrated unique merits that promise to gain more interest in its use for rapid and on-site simultaneous multiple screening of different components of GMO products. Copyright © 2014 Elsevier B.V. All rights reserved.

Improved Detection of Vowel Envelope Frequency Following Responses Using Hotelling's T2 Analysis.

PubMed

Vanheusden, Frederique J; Bell, Steven L; Chesnaye, Michael A; Simpson, David M

2018-05-11

Objective detection of brainstem responses to natural speech stimuli is an important tool for the evaluation of hearing aid fitting, especially in people who may not be able to respond reliably in behavioral tests. Of particular interest is the envelope frequency following response (eFFR), which refers to the EEG response at the stimulus' fundamental frequency (and its harmonics), and here in particular to the response to natural spoken vowel sounds. This article introduces the frequency-domain Hotelling's T (HT2) method for eFFR detection. This method was compared, in terms of sensitivity in detecting eFFRs at the fundamental frequency (HT2_F0), to two different single-channel frequency domain methods (F test on Fourier analyzer (FA) amplitude spectra [FA-F-Test] and magnitude-squared coherence [MSC]) in detecting envelope following responses to natural vowel stimuli in simulated data and EEG data from normal-hearing subjects. Sensitivity was assessed based on the number of detections and the time needed to detect a response for a false-positive rate of 5%. The study also explored whether a single-channel, multifrequency HT2 (HT2_3F) and a multichannel, multifrequency HT2 (HT2_MC) could further improve response detection. Four repeated words were presented sequentially at 70 dB SPL LAeq through ER-2 insert earphones. The stimuli consisted of a prolonged vowel in a /hVd/ structure (where V represents different vowel sounds). Each stimulus was presented over 440 sweeps (220 condensation and 220 rarefaction). EEG data were collected from 12 normal-hearing adult participants. After preprocessing and artifact removal, eFFR detection was compared between the algorithms. For the simulation study, simulated EEG signals were generated by adding random noise at multiple signal to noise ratios (SNRs; 0 to -60dB) to the auditory stimuli as well as to a single sinusoid at the fluctuating and flattened fundamental frequency (f0). For each SNR, 1000 sets of 440 simulated epochs were generated. Performance of the algorithms was assessed based on the number of sets for which a response could be detected at each SNR. In simulation studies, HT2_3F significantly outperformed the other algorithms when detecting a vowel stimulus in noise. For simulations containing responses only at a single frequency, HT2_3F performs worse compared with other approaches applied in this study as the additional frequencies included do not contain additional information. For recorded EEG data, HT2_MC showed a significantly higher response detection rate compared with MSC and FA-F-Test. Both HT2_MC and HT2_F0 also showed a significant reduction in detection time compared with the FA-F-Test algorithm. Comparisons between different electrode locations confirmed a higher number of detections for electrodes close to Cz compared to more peripheral locations. The HT2 method is more sensitive than FA-F-Test and MSC in detecting responses to complex stimuli because it allows detection of multiple frequencies (HT2_F3) and multiple EEG channels (HT2_MC) simultaneously. This effect was shown in simulation studies for HT2_3F and in EEG data for the HT2_MC algorithm. The spread in detection time across subjects is also lower for the HT2 algorithm, with decision on the presence of an eFFR possible within 5 min.

Challenges for Detecting Valproic Acid in a Nontargeted Urine Drug Screening Method.

PubMed

Pope, Jeffrey D; Black, Marion J; Drummer, Olaf H; Schneider, Hans G

2017-08-01

Valproic acid (VPA) is a widely prescribed medicine, and acute toxicity is possible. As such, it should be included in any nontargeted urine drug screening method. In many published liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS/MS) methods, VPA is usually measured using a pseudo-multiple reaction monitoring (MRM) transition. We investigate a simple ultra-high-performance liquid chromatography-quadrupole time-of-flight (QTof) approach to detect the presence of VPA with more confidence. Three commercially sourced VPA metabolites were characterized and added to a nontargeted high-resolution MS urine drug screening method. All analyses were performed on a Waters Xevo G2-XS LC-QTof in negative electrospray ionization mode. The mass detector was operated in MS mode, and data were processed with UNIFI software. Sixty-eight patient urine samples, which were previously identified by a well-established gas chromatography-MS method as containing VPA, were analyzed on the Waters Xevo G2-XS LC-QTof, to validate this approach. VPA metabolite standards were characterized, and their detection data were added to the broad drug screening library. VPA metabolites were readily detectable in the urine of patients taking VPA. The inclusion of characterized VPA metabolites provides a simple and reliable method enabling the detection of VPA in nontargeted urine drug screening.

Improved Sensor Fault Detection, Isolation, and Mitigation Using Multiple Observers Approach

PubMed Central

Wang, Zheng; Anand, D. M.; Moyne, J.; Tilbury, D. M.

2017-01-01

Traditional Fault Detection and Isolation (FDI) methods analyze a residual signal to detect and isolate sensor faults. The residual signal is the difference between the sensor measurements and the estimated outputs of the system based on an observer. The traditional residual-based FDI methods, however, have some limitations. First, they require that the observer has reached its steady state. In addition, residual-based methods may not detect some sensor faults, such as faults on critical sensors that result in an unobservable system. Furthermore, the system may be in jeopardy if actions required for mitigating the impact of the faulty sensors are not taken before the faulty sensors are identified. The contribution of this paper is to propose three new methods to address these limitations. Faults that occur during the observers' transient state can be detected by analyzing the convergence rate of the estimation error. Open-loop observers, which do not rely on sensor information, are used to detect faults on critical sensors. By switching among different observers, we can potentially mitigate the impact of the faulty sensor during the FDI process. These three methods are systematically integrated with a previously developed residual-based method to provide an improved FDI and mitigation capability framework. The overall approach is validated mathematically, and the effectiveness of the overall approach is demonstrated through simulation on a 5-state suspension system. PMID:28924303

Polarization-multiplexing ghost imaging

NASA Astrophysics Data System (ADS)

Dongfeng, Shi; Jiamin, Zhang; Jian, Huang; Yingjian, Wang; Kee, Yuan; Kaifa, Cao; Chenbo, Xie; Dong, Liu; Wenyue, Zhu

2018-03-01

A novel technique for polarization-multiplexing ghost imaging is proposed to simultaneously obtain multiple polarimetric information by a single detector. Here, polarization-division multiplexing speckles are employed for object illumination. The light reflected from the objects is detected by a single-pixel detector. An iterative reconstruction method is used to restore the fused image containing the different polarimetric information by using the weighted sum of the multiplexed speckles based on the correlation coefficients obtained from the detected intensities. Next, clear images of the different polarimetric information are recovered by demultiplexing the fused image. The results clearly demonstrate that the proposed method is effective.

Background controlled QTL mapping in pure-line genetic populations derived from four-way crosses

PubMed Central

Zhang, S; Meng, L; Wang, J; Zhang, L

2017-01-01

Pure lines derived from multiple parents are becoming more important because of the increased genetic diversity, the possibility to conduct replicated phenotyping trials in multiple environments and potentially high mapping resolution of quantitative trait loci (QTL). In this study, we proposed a new mapping method for QTL detection in pure-line populations derived from four-way crosses, which is able to control the background genetic variation through a two-stage mapping strategy. First, orthogonal variables were created for each marker and used in an inclusive linear model, so as to completely absorb the genetic variation in the mapping population. Second, inclusive composite interval mapping approach was implemented for one-dimensional scanning, during which the inclusive linear model was employed to control the background variation. Simulation studies using different genetic models demonstrated that the new method is efficient when considering high detection power, low false discovery rate and high accuracy in estimating quantitative trait loci locations and effects. For illustration, the proposed method was applied in a reported wheat four-way recombinant inbred line population. PMID:28722705

Background controlled QTL mapping in pure-line genetic populations derived from four-way crosses.

PubMed

Zhang, S; Meng, L; Wang, J; Zhang, L

2017-10-01

Pure lines derived from multiple parents are becoming more important because of the increased genetic diversity, the possibility to conduct replicated phenotyping trials in multiple environments and potentially high mapping resolution of quantitative trait loci (QTL). In this study, we proposed a new mapping method for QTL detection in pure-line populations derived from four-way crosses, which is able to control the background genetic variation through a two-stage mapping strategy. First, orthogonal variables were created for each marker and used in an inclusive linear model, so as to completely absorb the genetic variation in the mapping population. Second, inclusive composite interval mapping approach was implemented for one-dimensional scanning, during which the inclusive linear model was employed to control the background variation. Simulation studies using different genetic models demonstrated that the new method is efficient when considering high detection power, low false discovery rate and high accuracy in estimating quantitative trait loci locations and effects. For illustration, the proposed method was applied in a reported wheat four-way recombinant inbred line population.

Laser-Based Pedestrian Tracking in Outdoor Environments by Multiple Mobile Robots

PubMed Central

Ozaki, Masataka; Kakimuma, Kei; Hashimoto, Masafumi; Takahashi, Kazuhiko

2012-01-01

This paper presents an outdoors laser-based pedestrian tracking system using a group of mobile robots located near each other. Each robot detects pedestrians from its own laser scan image using an occupancy-grid-based method, and the robot tracks the detected pedestrians via Kalman filtering and global-nearest-neighbor (GNN)-based data association. The tracking data is broadcast to multiple robots through intercommunication and is combined using the covariance intersection (CI) method. For pedestrian tracking, each robot identifies its own posture using real-time-kinematic GPS (RTK-GPS) and laser scan matching. Using our cooperative tracking method, all the robots share the tracking data with each other; hence, individual robots can always recognize pedestrians that are invisible to any other robot. The simulation and experimental results show that cooperating tracking provides the tracking performance better than conventional individual tracking does. Our tracking system functions in a decentralized manner without any central server, and therefore, this provides a degree of scalability and robustness that cannot be achieved by conventional centralized architectures. PMID:23202171

Integrated analysis of error detection and recovery

NASA Technical Reports Server (NTRS)

Shin, K. G.; Lee, Y. H.

1985-01-01

An integrated modeling and analysis of error detection and recovery is presented. When fault latency and/or error latency exist, the system may suffer from multiple faults or error propagations which seriously deteriorate the fault-tolerant capability. Several detection models that enable analysis of the effect of detection mechanisms on the subsequent error handling operations and the overall system reliability were developed. Following detection of the faulty unit and reconfiguration of the system, the contaminated processes or tasks have to be recovered. The strategies of error recovery employed depend on the detection mechanisms and the available redundancy. Several recovery methods including the rollback recovery are considered. The recovery overhead is evaluated as an index of the capabilities of the detection and reconfiguration mechanisms.

The Use of Multiple Imputation for Missing Data in Uniform DIF Analysis: Power and Type I Error Rates

ERIC Educational Resources Information Center

Finch, Holmes

2011-01-01

Methods of uniform differential item functioning (DIF) detection have been extensively studied in the complete data case. However, less work has been done examining the performance of these methods when missing item responses are present. Research that has been done in this regard appears to indicate that treating missing item responses as…

A multiplex method for detection of glucose-6-phosphate dehydrogenase (G6PD) gene mutations.

PubMed

Zhang, L; Yang, Y; Liu, R; Li, Q; Yang, F; Ma, L; Liu, H; Chen, X; Yang, Z; Cui, L; He, Y

2015-12-01

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is the most common human enzyme defect caused by G6PD gene mutations. This study aimed to develop a cost-effective, multiplex, genotyping method for detecting common mutations in the G6PD gene. We used a SNaPshot approach to genotype multiple G6PD mutations that are common to human populations in South-East Asia. This assay is based on multiplex PCR coupled with primer extension reactions. Different G6PD gene mutations were determined by peak retention time and colors of the primer extension products. We designed PCR primers for multiplex amplification of the G6PD gene fragments and for primer extension reactions to genotype 11 G6PD mutations. DNA samples from a total of 120 unrelated G6PD-deficient individuals from the China-Myanmar border area were used to establish and validate this method. Direct sequencing of the PCR products demonstrated 100% concordance between the SNaPshot and the sequencing results. The SNaPshot method offers a specific and sensitive alternative for simultaneously interrogating multiple G6PD mutations. © 2015 John Wiley & Sons Ltd.

Plant-RRBS, a bisulfite and next-generation sequencing-based methylome profiling method enriching for coverage of cytosine positions.

PubMed

Schmidt, Martin; Van Bel, Michiel; Woloszynska, Magdalena; Slabbinck, Bram; Martens, Cindy; De Block, Marc; Coppens, Frederik; Van Lijsebettens, Mieke

2017-07-06

Cytosine methylation in plant genomes is important for the regulation of gene transcription and transposon activity. Genome-wide methylomes are studied upon mutation of the DNA methyltransferases, adaptation to environmental stresses or during development. However, from basic biology to breeding programs, there is a need to monitor multiple samples to determine transgenerational methylation inheritance or differential cytosine methylation. Methylome data obtained by sodium hydrogen sulfite (bisulfite)-conversion and next-generation sequencing (NGS) provide genome-wide information on cytosine methylation. However, a profiling method that detects cytosine methylation state dispersed over the genome would allow high-throughput analysis of multiple plant samples with distinct epigenetic signatures. We use specific restriction endonucleases to enrich for cytosine coverage in a bisulfite and NGS-based profiling method, which was compared to whole-genome bisulfite sequencing of the same plant material. We established an effective methylome profiling method in plants, termed plant-reduced representation bisulfite sequencing (plant-RRBS), using optimized double restriction endonuclease digestion, fragment end repair, adapter ligation, followed by bisulfite conversion, PCR amplification and NGS. We report a performant laboratory protocol and a straightforward bioinformatics data analysis pipeline for plant-RRBS, applicable for any reference-sequenced plant species. As a proof of concept, methylome profiling was performed using an Oryza sativa ssp. indica pure breeding line and a derived epigenetically altered line (epiline). Plant-RRBS detects methylation levels at tens of millions of cytosine positions deduced from bisulfite conversion in multiple samples. To evaluate the method, the coverage of cytosine positions, the intra-line similarity and the differential cytosine methylation levels between the pure breeding line and the epiline were determined. Plant-RRBS reproducibly covers commonly up to one fourth of the cytosine positions in the rice genome when using MspI-DpnII within a group of five biological replicates of a line. The method predominantly detects cytosine methylation in putative promoter regions and not-annotated regions in rice. Plant-RRBS offers high-throughput and broad, genome-dispersed methylation detection by effective read number generation obtained from reproducibly covered genome fractions using optimized endonuclease combinations, facilitating comparative analyses of multi-sample studies for cytosine methylation and transgenerational stability in experimental material and plant breeding populations.

Methods for meta-analysis of multiple traits using GWAS summary statistics.

PubMed

Ray, Debashree; Boehnke, Michael

2018-03-01

Genome-wide association studies (GWAS) for complex diseases have focused primarily on single-trait analyses for disease status and disease-related quantitative traits. For example, GWAS on risk factors for coronary artery disease analyze genetic associations of plasma lipids such as total cholesterol, LDL-cholesterol, HDL-cholesterol, and triglycerides (TGs) separately. However, traits are often correlated and a joint analysis may yield increased statistical power for association over multiple univariate analyses. Recently several multivariate methods have been proposed that require individual-level data. Here, we develop metaUSAT (where USAT is unified score-based association test), a novel unified association test of a single genetic variant with multiple traits that uses only summary statistics from existing GWAS. Although the existing methods either perform well when most correlated traits are affected by the genetic variant in the same direction or are powerful when only a few of the correlated traits are associated, metaUSAT is designed to be robust to the association structure of correlated traits. metaUSAT does not require individual-level data and can test genetic associations of categorical and/or continuous traits. One can also use metaUSAT to analyze a single trait over multiple studies, appropriately accounting for overlapping samples, if any. metaUSAT provides an approximate asymptotic P-value for association and is computationally efficient for implementation at a genome-wide level. Simulation experiments show that metaUSAT maintains proper type-I error at low error levels. It has similar and sometimes greater power to detect association across a wide array of scenarios compared to existing methods, which are usually powerful for some specific association scenarios only. When applied to plasma lipids summary data from the METSIM and the T2D-GENES studies, metaUSAT detected genome-wide significant loci beyond the ones identified by univariate analyses. Evidence from larger studies suggest that the variants additionally detected by our test are, indeed, associated with lipid levels in humans. In summary, metaUSAT can provide novel insights into the genetic architecture of a common disease or traits. © 2017 WILEY PERIODICALS, INC.

Swarm intelligence in bioinformatics: methods and implementations for discovering patterns of multiple sequences.

PubMed

Cui, Zhihua; Zhang, Yi

2014-02-01

As a promising and innovative research field, bioinformatics has attracted increasing attention recently. Beneath the enormous number of open problems in this field, one fundamental issue is about the accurate and efficient computational methodology that can deal with tremendous amounts of data. In this paper, we survey some applications of swarm intelligence to discover patterns of multiple sequences. To provide a deep insight, ant colony optimization, particle swarm optimization, artificial bee colony and artificial fish swarm algorithm are selected, and their applications to multiple sequence alignment and motif detecting problem are discussed.

Real-Time Continuous Identification of Greenhouse Plant Pathogens Based on Recyclable Microfluidic Bioassay System.

PubMed

Qu, Xiangmeng; Li, Min; Zhang, Hongbo; Lin, Chenglie; Wang, Fei; Xiao, Mingshu; Zhou, Yi; Shi, Jiye; Aldalbahi, Ali; Pei, Hao; Chen, Hong; Li, Li

2017-09-20

The development of a real-time continuous analytical platform for the pathogen detection is of great scientific importance for achieving better disease control and prevention. In this work, we report a rapid and recyclable microfluidic bioassay system constructed from oligonucleotide arrays for selective and sensitive continuous identification of DNA targets of fungal pathogens. We employ the thermal denaturation method to effectively regenerate the oligonucleotide arrays for multiple sample detection, which could considerably reduce the screening effort and costs. The combination of thermal denaturation and laser-induced fluorescence detection technique enables real-time continuous identification of multiple samples (<10 min per sample). As a proof of concept, we have demonstrated that two DNA targets of fungal pathogens (Botrytis cinerea and Didymella bryoniae) can be sequentially analyzed using our rapid microfluidic bioassay system, which provides a new paradigm in the design of microfluidic bioassay system and will be valuable for chemical and biomedical analysis.

Simultaneous multicolor imaging of wide-field epi-fluorescence microscopy with four-bucket detection

PubMed Central

Park, Kwan Seob; Kim, Dong Uk; Lee, Jooran; Kim, Geon Hee; Chang, Ki Soo

2016-01-01

We demonstrate simultaneous imaging of multiple fluorophores using wide-field epi-fluorescence microscopy with a monochrome camera. The intensities of the three lasers are modulated by a sinusoidal waveform in order to excite each fluorophore with the same modulation frequency and a different time-delay. Then, the modulated fluorescence emissions are simultaneously detected by a camera operating at four times the excitation frequency. We show that two different fluorescence beads having crosstalk can be clearly separated using digital processing based on the phase information. In addition, multiple organelles within multi-stained single cells are shown with the phase mapping method, demonstrating an improved dynamic range and contrast compared to the conventional fluorescence image. These findings suggest that wide-field epi-fluorescence microscopy with four-bucket detection could be utilized for high-contrast multicolor imaging applications such as drug delivery and fluorescence in situ hybridization. PMID:27375944

Meat Authentication via Multiple Reaction Monitoring Mass Spectrometry of Myoglobin Peptides.

PubMed

Watson, Andrew D; Gunning, Yvonne; Rigby, Neil M; Philo, Mark; Kemsley, E Kate

2015-10-20

A rapid multiple reaction monitoring (MRM) mass spectrometric method for the detection and relative quantitation of the adulteration of meat with that of an undeclared species is presented. Our approach uses corresponding proteins from the different species under investigation and corresponding peptides from those proteins, or CPCP. Selected peptide markers can be used for species detection. The use of ratios of MRM transition peak areas for corresponding peptides is proposed for relative quantitation. The approach is introduced by use of myoglobin from four meats: beef, pork, horse and lamb. Focusing in the present work on species identification, by use of predictive tools, we determine peptide markers that allow the identification of all four meats and detection of one meat added to another at levels of 1% (w/w). Candidate corresponding peptide pairs to be used for the relative quantification of one meat added to another have been observed. Preliminary quantitation data presented here are encouraging.

Detection of time delays and directional interactions based on time series from complex dynamical systems

NASA Astrophysics Data System (ADS)

Ma, Huanfei; Leng, Siyang; Tao, Chenyang; Ying, Xiong; Kurths, Jürgen; Lai, Ying-Cheng; Lin, Wei

2017-07-01

Data-based and model-free accurate identification of intrinsic time delays and directional interactions is an extremely challenging problem in complex dynamical systems and their networks reconstruction. A model-free method with new scores is proposed to be generally capable of detecting single, multiple, and distributed time delays. The method is applicable not only to mutually interacting dynamical variables but also to self-interacting variables in a time-delayed feedback loop. Validation of the method is carried out using physical, biological, and ecological models and real data sets. Especially, applying the method to air pollution data and hospital admission records of cardiovascular diseases in Hong Kong reveals the major air pollutants as a cause of the diseases and, more importantly, it uncovers a hidden time delay (about 30-40 days) in the causal influence that previous studies failed to detect. The proposed method is expected to be universally applicable to ascertaining and quantifying subtle interactions (e.g., causation) in complex systems arising from a broad range of disciplines.

High-Performance Anti-Retransmission Deception Jamming Utilizing Range Direction Multiple Input and Multiple Output (MIMO) Synthetic Aperture Radar (SAR).

PubMed

Wang, Ruijia; Chen, Jie; Wang, Xing; Sun, Bing

2017-01-09

Retransmission deception jamming seriously degrades the Synthetic Aperture Radar (SAR) detection efficiency and can mislead SAR image interpretation by forming false targets. In order to suppress retransmission deception jamming, this paper proposes a novel multiple input and multiple output (MIMO) SAR structure range direction MIMO SAR, whose multiple channel antennas are vertical to the azimuth. First, based on the multiple channels of range direction MIMO SAR, the orthogonal frequency division multiplexing (OFDM) linear frequency modulation (LFM) signal was adopted as the transmission signal of each channel, which is defined as a sub-band signal. This sub-band signal corresponds to the transmission channel. Then, all of the sub-band signals are modulated with random initial phases and concurrently transmitted. The signal form is more complex and difficult to intercept. Next, the echoes of the sub-band signal are utilized to synthesize a wide band signal after preprocessing. The proposed method will increase the signal to interference ratio and peak amplitude ratio of the signal to resist retransmission deception jamming. Finally, well-focused SAR imagery is obtained using a conventional imaging method where the retransmission deception jamming strength is degraded and defocused. Simulations demonstrated the effectiveness of the proposed method.

High-Performance Anti-Retransmission Deception Jamming Utilizing Range Direction Multiple Input and Multiple Output (MIMO) Synthetic Aperture Radar (SAR)

PubMed Central

Wang, Ruijia; Chen, Jie; Wang, Xing; Sun, Bing

2017-01-01

Retransmission deception jamming seriously degrades the Synthetic Aperture Radar (SAR) detection efficiency and can mislead SAR image interpretation by forming false targets. In order to suppress retransmission deception jamming, this paper proposes a novel multiple input and multiple output (MIMO) SAR structure range direction MIMO SAR, whose multiple channel antennas are vertical to the azimuth. First, based on the multiple channels of range direction MIMO SAR, the orthogonal frequency division multiplexing (OFDM) linear frequency modulation (LFM) signal was adopted as the transmission signal of each channel, which is defined as a sub-band signal. This sub-band signal corresponds to the transmission channel. Then, all of the sub-band signals are modulated with random initial phases and concurrently transmitted. The signal form is more complex and difficult to intercept. Next, the echoes of the sub-band signal are utilized to synthesize a wide band signal after preprocessing. The proposed method will increase the signal to interference ratio and peak amplitude ratio of the signal to resist retransmission deception jamming. Finally, well-focused SAR imagery is obtained using a conventional imaging method where the retransmission deception jamming strength is degraded and defocused. Simulations demonstrated the effectiveness of the proposed method. PMID:28075367

Development and Comparison of Two Assay Formats for Parallel Detection of Four Biothreat Pathogens by Using Suspension Microarrays

PubMed Central

Janse, Ingmar; Bok, Jasper M.; Hamidjaja, Raditijo A.; Hodemaekers, Hennie M.; van Rotterdam, Bart J.

2012-01-01

Microarrays provide a powerful analytical tool for the simultaneous detection of multiple pathogens. We developed diagnostic suspension microarrays for sensitive and specific detection of the biothreat pathogens Bacillus anthracis, Yersinia pestis, Francisella tularensis and Coxiella burnetii. Two assay chemistries for amplification and labeling were developed, one method using direct hybridization and the other using target-specific primer extension, combined with hybridization to universal arrays. Asymmetric PCR products for both assay chemistries were produced by using a multiplex asymmetric PCR amplifying 16 DNA signatures (16-plex). The performances of both assay chemistries were compared and their advantages and disadvantages are discussed. The developed microarrays detected multiple signature sequences and an internal control which made it possible to confidently identify the targeted pathogens and assess their virulence potential. The microarrays were highly specific and detected various strains of the targeted pathogens. Detection limits for the different pathogen signatures were similar or slightly higher compared to real-time PCR. Probit analysis showed that even a few genomic copies could be detected with 95% confidence. The microarrays detected DNA from different pathogens mixed in different ratios and from spiked or naturally contaminated samples. The assays that were developed have a potential for application in surveillance and diagnostics. PMID:22355407

Development and comparison of two assay formats for parallel detection of four biothreat pathogens by using suspension microarrays.

PubMed

Janse, Ingmar; Bok, Jasper M; Hamidjaja, Raditijo A; Hodemaekers, Hennie M; van Rotterdam, Bart J

2012-01-01

Microarrays provide a powerful analytical tool for the simultaneous detection of multiple pathogens. We developed diagnostic suspension microarrays for sensitive and specific detection of the biothreat pathogens Bacillus anthracis, Yersinia pestis, Francisella tularensis and Coxiella burnetii. Two assay chemistries for amplification and labeling were developed, one method using direct hybridization and the other using target-specific primer extension, combined with hybridization to universal arrays. Asymmetric PCR products for both assay chemistries were produced by using a multiplex asymmetric PCR amplifying 16 DNA signatures (16-plex). The performances of both assay chemistries were compared and their advantages and disadvantages are discussed. The developed microarrays detected multiple signature sequences and an internal control which made it possible to confidently identify the targeted pathogens and assess their virulence potential. The microarrays were highly specific and detected various strains of the targeted pathogens. Detection limits for the different pathogen signatures were similar or slightly higher compared to real-time PCR. Probit analysis showed that even a few genomic copies could be detected with 95% confidence. The microarrays detected DNA from different pathogens mixed in different ratios and from spiked or naturally contaminated samples. The assays that were developed have a potential for application in surveillance and diagnostics.

Space and time resolved representation of a vacuum arc light emission

NASA Astrophysics Data System (ADS)

Georgescu, N.; Sandolache, G.; Zoita, V.

1999-04-01

An optoelectronic multichannel detection system for the study of the visible light emission of a vacuum circuit breaker arc is described. The system consists of two multiple slit collimator assemblies coupled directly to the arc discharge chamber and an electronic detection part. The light emitted by the arc is collected by the two collimator assemblies and is transmitted through optical fibres to the electronic detection part. By using a new, simple computational method two-dimensional plots of the vacuum arc light emission at different times are obtained.

A simple, rapid, cost-effective and sensitive method for detection of Salmonella in environmental and pecan samples.

PubMed

Dobhal, S; Zhang, G; Rohla, C; Smith, M W; Ma, L M

2014-10-01

PCR is widely used in the routine detection of foodborne human pathogens; however, challenges remain in overcoming PCR inhibitors present in some sample matrices. The objective of this study was to develop a simple, sensitive, cost-effective and rapid method for processing large numbers of environmental and pecan samples for Salmonella detection. This study was also aimed at validation of a new protocol for the detection of Salmonella from in-shell pecans. Different DNA template preparation methods, including direct boiling, prespin, multiple washing and commercial DNA extraction kits, were evaluated with pure cultures of Salmonella Typhimurium and with enriched soil, cattle feces and in-shell pecan each spiked individually with Salmonella Typhimurium. PCR detection of Salmonella was conducted using invA and 16S rRNA gene (internal amplification control) specific primers. The effect of amplification facilitators, including bovine serum albumin (BSA), polyvinylpyrrolidone (PVP), polyethylene glycol (PEG) and gelatin on PCR sensitivity, was also evaluated. Conducting a prespin of sample matrices in combination with the addition of 0·4% (w/v) BSA and 1% (w/v) PVP in PCR mix was the simplest, most rapid, cost-effective and sensitive method for PCR detection of Salmonella, with up to 40 CFU Salmonella per reaction detectable in the presence of over 10(9 ) CFU ml(-1) of background micro-organisms from enriched feces soil or pecan samples. The developed method is rapid, cost-effective and sensitive for detection of Salmonella from different matrices. This study provides a method with broad applicability for PCR detection of Salmonella in complex sample matrices. This method has a potential for its application in different research arenas and diagnostic laboratories. © 2014 The Society for Applied Microbiology.