Science.gov

Sample records for abnormal event detection

  1. Detection of dominant flow and abnormal events in surveillance video

    NASA Astrophysics Data System (ADS)

    Kwak, Sooyeong; Byun, Hyeran

    2011-02-01

    We propose an algorithm for abnormal event detection in surveillance video. The proposed algorithm is based on a semi-unsupervised learning method, a kind of feature-based approach so that it does not detect the moving object individually. The proposed algorithm identifies dominant flow without individual object tracking using a latent Dirichlet allocation model in crowded environments. It can also automatically detect and localize an abnormally moving object in real-life video. The performance tests are taken with several real-life databases, and their results show that the proposed algorithm can efficiently detect abnormally moving objects in real time. The proposed algorithm can be applied to any situation in which abnormal directions or abnormal speeds are detected regardless of direction.

  2. Detection of Abnormal Events via Optical Flow Feature Analysis

    PubMed Central

    Wang, Tian; Snoussi, Hichem

    2015-01-01

    In this paper, a novel algorithm is proposed to detect abnormal events in video streams. The algorithm is based on the histogram of the optical flow orientation descriptor and the classification method. The details of the histogram of the optical flow orientation descriptor are illustrated for describing movement information of the global video frame or foreground frame. By combining one-class support vector machine and kernel principal component analysis methods, the abnormal events in the current frame can be detected after a learning period characterizing normal behaviors. The difference abnormal detection results are analyzed and explained. The proposed detection method is tested on benchmark datasets, then the experimental results show the effectiveness of the algorithm. PMID:25811227

  3. Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection

    PubMed Central

    Wang, Tian; Chen, Jie; Zhou, Yi; Snoussi, Hichem

    2013-01-01

    The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM), combined with its sparsified version (sparse online LS-OC-SVM). LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method. PMID:24351629

  4. Real-Time Microbiology Laboratory Surveillance System to Detect Abnormal Events and Emerging Infections, Marseille, France

    PubMed Central

    Abat, Cédric; Chaudet, Hervé; Colson, Philippe; Rolain, Jean-Marc

    2015-01-01

    Infectious diseases are a major threat to humanity, and accurate surveillance is essential. We describe how to implement a laboratory data–based surveillance system in a clinical microbiology laboratory. Two historical Microsoft Excel databases were implemented. The data were then sorted and used to execute the following 2 surveillance systems in Excel: the Bacterial real-time Laboratory-based Surveillance System (BALYSES) for monitoring the number of patients infected with bacterial species isolated at least once in our laboratory during the study periodl and the Marseille Antibiotic Resistance Surveillance System (MARSS), which surveys the primary β-lactam resistance phenotypes for 15 selected bacterial species. The first historical database contained 174,853 identifications of bacteria, and the second contained 12,062 results of antibiotic susceptibility testing. From May 21, 2013, through June 4, 2014, BALYSES and MARSS enabled the detection of 52 abnormal events for 24 bacterial species, leading to 19 official reports. This system is currently being refined and improved. PMID:26196165

  5. Abnormal behaviors detection using particle motion model

    NASA Astrophysics Data System (ADS)

    Chen, Yutao; Zhang, Hong; Cheng, Feiyang; Yuan, Ding; You, Yuhu

    2015-03-01

    Human abnormal behaviors detection is one of the most challenging tasks in the video surveillance for the public security control. Interaction Energy Potential model is an effective and competitive method published recently to detect abnormal behaviors, but their model of abnormal behaviors is not accurate enough, so it has some limitations. In order to solve this problem, we propose a novel Particle Motion model. Firstly, we extract the foreground to improve the accuracy of interest points detection since the complex background usually degrade the effectiveness of interest points detection largely. Secondly, we detect the interest points using the graphics features. Here, the movement of each human target can be represented by the movements of detected interest points of the target. Then, we track these interest points in videos to record their positions and velocities. In this way, the velocity angles, position angles and distance between each two points can be calculated. Finally, we proposed a Particle Motion model to calculate the eigenvalue of each frame. An adaptive threshold method is proposed to detect abnormal behaviors. Experimental results on the BEHAVE dataset and online videos show that our method could detect fight and robbery events effectively and has a promising performance.

  6. Varenicline and Abnormal Sleep Related Events

    PubMed Central

    Savage, Ruth L.; Zekarias, Alem; Caduff-Janosa, Pia

    2015-01-01

    Study Objectives: To assess adverse drug reaction reports of “abnormal sleep related events” associated with varenicline, a partial agonist to the α4β2 subtype of nicotinic acetylcholine receptors on neurones, indicated for smoking cessation. Design: Twenty-seven reports of “abnormal sleep related events” often associated with abnormal dreams, nightmares, or somnambulism, which are known to be associated with varenicline use, were identified in the World Health Organisation (WHO) Global Individual Case Safety Reports Database. Original anonymous reports were obtained from the four national pharmacovigilance centers that submitted these reports and assessed for reaction description and causality. Measurements and Results: These 27 reports include 10 of aggressive activity occurring during sleep and seven of other sleep related harmful or potentially harmful activities, such as apparently deliberate self-harm, moving a child or a car, or lighting a stove or a cigarette. Assessment of these 17 reports of aggression or other actual or potential harm showed that nine patients recovered or were recovering on varenicline withdrawal and there were no consistent alternative explanations. Thirteen patients experienced single events, and two had multiple events. Frequency was not stated for the remaining two patients. Conclusions: The descriptions of the reports of aggression during sleep with violent dreaming are similar to those of rapid eye movement sleep behavior disorder and also nonrapid eye movement (NREM) sleep parasomnias in some adults. Patients who experience somnambulism or dreams of a violent nature while taking varenicline should be advised to consult their health providers. Consideration should be given to clarifying the term sleep disorders in varenicline product information and including sleep related harmful and potentially harmful events. Citation: Savage RL, Zekarias A, Caduff-Janosa P. Varenicline and abnormal sleep related events. SLEEP 2015

  7. Detection of Structural Abnormalities Using Neural Nets

    NASA Technical Reports Server (NTRS)

    Zak, M.; Maccalla, A.; Daggumati, V.; Gulati, S.; Toomarian, N.

    1996-01-01

    This paper describes a feed-forward neural net approach for detection of abnormal system behavior based upon sensor data analyses. A new dynamical invariant representing structural parameters of the system is introduced in such a way that any structural abnormalities in the system behavior are detected from the corresponding changes to the invariant.

  8. Jiamusi Pulsar Observations: I. Abnormal emission events of PSR B0919+06

    NASA Astrophysics Data System (ADS)

    Han, Jun; Han, J. L.; Peng, Ling-Xiang; Tang, De-Yu; Wang, Jun; Li, Jun-Qiang; Wang, Chen; Yu, Ye-Zhao; Dong, Bin

    2016-03-01

    PSR B0919+06 generally radiates radio pulses in a normal phase range. It is known for its occasional perplexing abnormal emission events wherein individual pulses come to an earlier phase range for a few tens of periods and then it returns to its usual phase. Heretofore, only a few such events have been available for study. We observed PSR B0919+06 for about 30 h using the Jiamusi 66-m telescope at the Jiamusi Deep Space Station at the S band, and we detected 92 abnormal emission events. We identify four types of events based on the abrupted or gradual phase-shifting of individual pulses. The abnormal emission events are seen to occur randomly about every 1000-3000 periods, and they affect the leading edge of the mean profile by up to 2 per cent in amplitude. The abnormal emission events are probably related to gradual changes of emission processing in the pulsar magnetosphere.

  9. Unsupervised detection of abnormalities in medical images using salient features

    NASA Astrophysics Data System (ADS)

    Alpert, Sharon; Kisilev, Pavel

    2014-03-01

    In this paper we propose a new method for abnormality detection in medical images which is based on the notion of medical saliency. The proposed method is general and is suitable for a variety of tasks related to detection of: 1) lesions and microcalcifications (MCC) in mammographic images, 2) stenoses in angiographic images, 3) lesions found in magnetic resonance (MRI) images of brain. The main idea of our approach is that abnormalities manifest as rare events, that is, as salient areas compared to normal tissues. We define the notion of medical saliency by combining local patch information from the lightness channel with geometric shape local descriptors. We demonstrate the efficacy of the proposed method by applying it to various modalities, and to various abnormality detection problems. Promising results are demonstrated for detection of MCC and of masses in mammographic images, detection of stenoses in angiography images, and detection of lesions in brain MRI. We also demonstrate how the proposed automatic abnormality detection method can be combined with a system that performs supervised classification of mammogram images into benign or malignant/premalignant MCC's. We use a well known DDSM mammogram database for the experiment on MCC classification, and obtain 80% accuracy in classifying images containing premalignant MCC versus benign ones. In contrast to supervised detection methods, the proposed approach does not rely on ground truth markings, and, as such, is very attractive and applicable for big corpus image data processing.

  10. Detection of anomalous events

    DOEpatents

    Ferragut, Erik M.; Laska, Jason A.; Bridges, Robert A.

    2016-06-07

    A system is described for receiving a stream of events and scoring the events based on anomalousness and maliciousness (or other classification). The system can include a plurality of anomaly detectors that together implement an algorithm to identify low-probability events and detect atypical traffic patterns. The anomaly detector provides for comparability of disparate sources of data (e.g., network flow data and firewall logs.) Additionally, the anomaly detector allows for regulatability, meaning that the algorithm can be user configurable to adjust a number of false alerts. The anomaly detector can be used for a variety of probability density functions, including normal Gaussian distributions, irregular distributions, as well as functions associated with continuous or discrete variables.

  11. Detection of solar events

    SciTech Connect

    Fischbach, Ephraim; Jenkins, Jere

    2013-08-27

    A flux detection apparatus can include a radioactive sample having a decay rate capable of changing in response to interaction with a first particle or a field, and a detector associated with the radioactive sample. The detector is responsive to a second particle or radiation formed by decay of the radioactive sample. The rate of decay of the radioactive sample can be correlated to flux of the first particle or the field. Detection of the first particle or the field can provide an early warning for an impending solar event.

  12. First-Trimester Detection of Surface Abnormalities

    PubMed Central

    Rousian, Melek; Koning, Anton H. J.; Bonsel, Gouke J.; Eggink, Alex J.; Cornette, Jérôme M. J.; Schoonderwaldt, Ernst M.; Husen-Ebbinge, Margreet; Teunissen, Katinka K.; van der Spek, Peter J.; Steegers, Eric A. P.; Exalto, Niek

    2014-01-01

    The aim was to determine the diagnostic performance of 3-dimensional virtual reality ultrasound (3D_VR_US) and conventional 2- and 3-dimensional ultrasound (2D/3D_US) for first-trimester detection of structural abnormalities. Forty-eight first trimester cases (gold standard available, 22 normal, 26 abnormal) were evaluated offline using both techniques by 5 experienced, blinded sonographers. In each case, we analyzed whether each organ category was correctly indicated as normal or abnormal and whether the specific diagnosis was correctly made. Sensitivity in terms of normal or abnormal was comparable for both techniques (P = .24). The general sensitivity for specific diagnoses was 62.6% using 3D_VR_US and 52.2% using 2D/3D_US (P = .075). The 3D_VR_US more often correctly diagnosed skeleton/limb malformations (36.7% vs 10%; P = .013). Mean evaluation time in 3D_VR_US was 4:24 minutes and in 2D/3D_US 2:53 minutes (P < .001). General diagnostic performance of 3D_VR_US and 2D/3D_US apparently is comparable. Malformations of skeleton and limbs are more often detected using 3D_VR_US. Evaluation time is longer in 3D_VR_US. PMID:24440996

  13. Abnormal Activity Detection Using Pyroelectric Infrared Sensors

    PubMed Central

    Luo, Xiaomu; Tan, Huoyuan; Guan, Qiuju; Liu, Tong; Zhuo, Hankz Hankui; Shen, Baihua

    2016-01-01

    Healthy aging is one of the most important social issues. In this paper, we propose a method for abnormal activity detection without any manual labeling of the training samples. By leveraging the Field of View (FOV) modulation, the spatio-temporal characteristic of human activity is encoded into low-dimension data stream generated by the ceiling-mounted Pyroelectric Infrared (PIR) sensors. The similarity between normal training samples are measured based on Kullback-Leibler (KL) divergence of each pair of them. The natural clustering of normal activities is discovered through a self-tuning spectral clustering algorithm with unsupervised model selection on the eigenvectors of a modified similarity matrix. Hidden Markov Models (HMMs) are employed to model each cluster of normal activities and form feature vectors. One-Class Support Vector Machines (OSVMs) are used to profile the normal activities and detect abnormal activities. To validate the efficacy of our method, we conducted experiments in real indoor environments. The encouraging results show that our method is able to detect abnormal activities given only the normal training samples, which aims to avoid the laborious and inconsistent data labeling process. PMID:27271632

  14. Abnormal Activity Detection Using Pyroelectric Infrared Sensors.

    PubMed

    Luo, Xiaomu; Tan, Huoyuan; Guan, Qiuju; Liu, Tong; Zhuo, Hankz Hankui; Shen, Baihua

    2016-01-01

    Healthy aging is one of the most important social issues. In this paper, we propose a method for abnormal activity detection without any manual labeling of the training samples. By leveraging the Field of View (FOV) modulation, the spatio-temporal characteristic of human activity is encoded into low-dimension data stream generated by the ceiling-mounted Pyroelectric Infrared (PIR) sensors. The similarity between normal training samples are measured based on Kullback-Leibler (KL) divergence of each pair of them. The natural clustering of normal activities is discovered through a self-tuning spectral clustering algorithm with unsupervised model selection on the eigenvectors of a modified similarity matrix. Hidden Markov Models (HMMs) are employed to model each cluster of normal activities and form feature vectors. One-Class Support Vector Machines (OSVMs) are used to profile the normal activities and detect abnormal activities. To validate the efficacy of our method, we conducted experiments in real indoor environments. The encouraging results show that our method is able to detect abnormal activities given only the normal training samples, which aims to avoid the laborious and inconsistent data labeling process. PMID:27271632

  15. Hepatic perfusion abnormalities during CT angiography: Detection and interpretation

    SciTech Connect

    Freeny, P.C.; Marks, W.M.

    1986-06-01

    Twenty-seven perfusion abnormalities were detected in 17 of 50 patients who underwent computed tomographic angiography (CTA) of the liver. All but one of the perfusion abnormalities occurred in patients with primary or metastatic liver tumors. Perfusion abnormalities were lobar in nine cases, segmental in 11, and subsegmental in seven; 14 were hypoperfusion and 13 were hyperperfusion abnormalities. The causes for the abnormalities included nonperfusion of a replaced hepatic artery (n = 11), cirrhosis and nodular regeneration (n = 3), altered hepatic hemodynamics (e.g., siphoning, laminar flow) caused by tumor (n = 7), contrast media washout from a nonperfused vessel (n = 1), compression of adjacent hepatic parenchyma (n = 1), and unknown (n = 4). Differentiation of perfusion abnormalities from tumor usually can be made by comparing the morphology of the known tumor with the suspected perfusion abnormality, changes of each on delayed CTA scans, and review of initial angiograms and other imaging studies.

  16. Abnormal Early Cleavage Events Predict Early Embryo Demise: Sperm Oxidative Stress and Early Abnormal Cleavage

    PubMed Central

    Burruel, Victoria; Klooster, Katie; Barker, Christopher M.; Pera, Renee Reijo; Meyers, Stuart

    2014-01-01

    Human embryos resulting from abnormal early cleavage can result in aneuploidy and failure to develop normally to the blastocyst stage. The nature of paternal influence on early embryo development has not been directly demonstrated although many studies have suggested effects from spermatozoal chromatin packaging, DNA damage, centriolar and mitotic spindle integrity, and plasma membrane integrity. The goal of this study was to determine whether early developmental events were affected by oxidative damage to the fertilizing sperm. Survival analysis was used to compare patterns of blastocyst formation based on P2 duration. Kaplan-Meier survival curves demonstrate that relatively few embryos with short (<1 hr) P2 times reached blastocysts, and the two curves diverged beginning on day 4, with nearly all of the embryos with longer P2 times reaching blastocysts by day 6 (p < .01). We determined that duration of the 2nd to 3rd mitoses were sensitive periods in the presence of spermatozoal oxidative stress. Embryos that displayed either too long or too short cytokineses demonstrated an increased failure to reach blastocyst stage and therefore survive for further development. Although paternal-derived gene expression occurs later in development, this study suggests a specific role in early mitosis that is highly influenced by paternal factors. PMID:25307782

  17. Development of Abnormality Detection System for Bathers using Ultrasonic Sensors

    NASA Astrophysics Data System (ADS)

    Ohnishi, Yosuke; Abe, Takehiko; Nambo, Hidetaka; Kimura, Haruhiko; Ogoshi, Yasuhiro

    This paper proposes an abnormality detection system for bather sitting in bathtub. Increasing number of in-bathtub drowning accidents in Japan draws attention. Behind this large number of bathing accidents, Japan's unique social and cultural background come surface. For majority of people in Japan, bathing serves purpose in deep warming up of body, relax and enjoyable time. Therefore it is the custom for the Japanese to soak in bathtub. However overexposure to hot water may cause dizziness or fainting, which is possible to cause in-bathtub drowning. For drowning prevention, the system detects bather's abnormal state using an ultrasonic sensor array. The array, which has many ultrasonic sensors, is installed on the ceiling of bathroom above bathtub. The abnormality detection system uses the following two methods: posture detection and behavior detection. The function of posture detection is to estimate the risk of drowning by monitoring bather's posture. Meanwhile, the function of behavior detection is to estimate the risk of drowning by monitoring bather's behavior. By using these methods, the system detects bathers' different state from normal. As a result of experiment with a subject in the bathtub, the system was possible to detect abnormal state using subject's posture and behavior. Therefore the system is useful for monitoring bather to prevent drowning in bathtub.

  18. Event rates for WIMP detection

    SciTech Connect

    Vergados, J. D.; Moustakidis, Ch. C.; Oikonomou, V.

    2006-11-28

    The event rates for the direct detection of dark matter for various types of WIMPs are presented. In addition to the neutralino of SUSY models, we considered other candidates (exotic scalars as well as particles in Kaluza-Klein and technicolour theories) with masses in the TeV region. Then one finds reasonable branching ratios to excited states. Thus the detection of the WIMP can be made not only by recoil measurements, by measuring the de-excitation {gamma}-rays as well.

  19. Detecting Abnormal Machine Characteristics in Cloud Infrastructures

    NASA Technical Reports Server (NTRS)

    Bhaduri, Kanishka; Das, Kamalika; Matthews, Bryan L.

    2011-01-01

    In the cloud computing environment resources are accessed as services rather than as a product. Monitoring this system for performance is crucial because of typical pay-peruse packages bought by the users for their jobs. With the huge number of machines currently in the cloud system, it is often extremely difficult for system administrators to keep track of all machines using distributed monitoring programs such as Ganglia1 which lacks system health assessment and summarization capabilities. To overcome this problem, we propose a technique for automated anomaly detection using machine performance data in the cloud. Our algorithm is entirely distributed and runs locally on each computing machine on the cloud in order to rank the machines in order of their anomalous behavior for given jobs. There is no need to centralize any of the performance data for the analysis and at the end of the analysis, our algorithm generates error reports, thereby allowing the system administrators to take corrective actions. Experiments performed on real data sets collected for different jobs validate the fact that our algorithm has a low overhead for tracking anomalous machines in a cloud infrastructure.

  20. Using State Estimation Residuals to Detect Abnormal SCADA Data

    SciTech Connect

    Ma, Jian; Chen, Yousu; Huang, Zhenyu; Wong, Pak C.

    2010-04-30

    Detection of abnormal supervisory control and data acquisition (SCADA) data is critically important for safe and secure operation of modern power systems. In this paper, a methodology of abnormal SCADA data detection based on state estimation residuals is presented. Preceded with a brief overview of outlier detection methods and bad SCADA data detection for state estimation, the framework of the proposed methodology is described. Instead of using original SCADA measurements as the bad data sources, the residuals calculated based on the results of the state estimator are used as the input for the outlier detection algorithm. The BACON algorithm is applied to the outlier detection task. The IEEE 118-bus system is used as a test base to evaluate the effectiveness of the proposed methodology. The accuracy of the BACON method is compared with that of the 3-σ method for the simulated SCADA measurements and residuals.

  1. DETECTION AND ADJUSTMENT OF ABNORMAL TEST-DAY YIELDS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A method to detect and to adjust abnormally low or high milk, fat, and protein yields on test-day (TD) was developed. TD yields are compared to previous and subsequent yields and are restricted to be between a floor and ceiling based on predicted yield. Lactation yields are then calculated from the ...

  2. GPU Accelerated Event Detection Algorithm

    2011-05-25

    Smart grid external require new algorithmic approaches as well as parallel formulations. One of the critical components is the prediction of changes and detection of anomalies within the power grid. The state-of-the-art algorithms are not suited to handle the demands of streaming data analysis. (i) need for events detection algorithms that can scale with the size of data, (ii) need for algorithms that can not only handle multi dimensional nature of the data, but alsomore » model both spatial and temporal dependencies in the data, which, for the most part, are highly nonlinear, (iii) need for algorithms that can operate in an online fashion with streaming data. The GAEDA code is a new online anomaly detection techniques that take into account spatial, temporal, multi-dimensional aspects of the data set. The basic idea behind the proposed approach is to (a) to convert a multi-dimensional sequence into a univariate time series that captures the changes between successive windows extracted from the original sequence using singular value decomposition (SVD), and then (b) to apply known anomaly detection techniques for univariate time series. A key challenge for the proposed approach is to make the algorithm scalable to huge datasets by adopting techniques from perturbation theory, incremental SVD analysis. We used recent advances in tensor decomposition techniques which reduce computational complexity to monitor the change between successive windows and detect anomalies in the same manner as described above. Therefore we propose to develop the parallel solutions on many core systems such as GPUs, because these algorithms involve lot of numerical operations and are highly data-parallelizable.« less

  3. Proportionate Responses to Life Events Influence Clinicians' Judgments of Psychological Abnormality

    ERIC Educational Resources Information Center

    Kim, Nancy S.; Paulus, Daniel J.; Gonzalez, Jeffrey S.; Khalife, Danielle

    2012-01-01

    Psychological abnormality is a fundamental concept in the "Diagnostic and Statistical Manual of Mental Disorders" ("DSM-IV-TR"; American Psychiatric Association, 2000) and in all clinical evaluations. How do practicing clinical psychologists use the context of life events to judge the abnormality of a person's current behaviors? The appropriate…

  4. Detecting Adverse Events Using Information Technology

    PubMed Central

    Bates, David W.; Evans, R. Scott; Murff, Harvey; Stetson, Peter D.; Pizziferri, Lisa; Hripcsak, George

    2003-01-01

    Context: Although patient safety is a major problem, most health care organizations rely on spontaneous reporting, which detects only a small minority of adverse events. As a result, problems with safety have remained hidden. Chart review can detect adverse events in research settings, but it is too expensive for routine use. Information technology techniques can detect some adverse events in a timely and cost-effective way, in some cases early enough to prevent patient harm. Objective: To review methodologies of detecting adverse events using information technology, reports of studies that used these techniques to detect adverse events, and study results for specific types of adverse events. Design: Structured review. Methodology: English-language studies that reported using information technology to detect adverse events were identified using standard techniques. Only studies that contained original data were included. Main Outcome Measures: Adverse events, with specific focus on nosocomial infections, adverse drug events, and injurious falls. Results: Tools such as event monitoring and natural language processing can inexpensively detect certain types of adverse events in clinical databases. These approaches already work well for some types of adverse events, including adverse drug events and nosocomial infections, and are in routine use in a few hospitals. In addition, it appears likely that these techniques will be adaptable in ways that allow detection of a broad array of adverse events, especially as more medical information becomes computerized. Conclusion: Computerized detection of adverse events will soon be practical on a widespread basis. PMID:12595401

  5. Environmental impact assessment of abnormal events: a follow-up study

    SciTech Connect

    Hunsaker, D.B. Jr.; Lee, D.W.

    1985-01-01

    Impact analyses included in environmental assessments for a selected nuclear power plant, petroleum storage facility, crude oil pipeline, and geopressure well that have experienced operational, abnormal events are compared with the data quantifying the environmental impacts of the events. Comparisons of predicted vs actual impacts suggests that prediction of the types of events and associated impacts could be improved; in some instances, impacts have been underestimated. Analysis of abnormal events is especially important in environmental assessment documents addressing a technology that is novel or unique to a particular area. Incorporation of abnormal event impact analysis into project environmental monitoring and emergency response plans can help improve these plans and can help reduce the magnitude of environmental impacts resulting from said events.

  6. Guide to good practices for notifications and investigation of abnormal events

    SciTech Connect

    Not Available

    1993-06-01

    This Guide to Good Practices is written to enhance understanding of, and provide direction for, Notifications, Chapter VII, and Investigation of Abnormal Events, Chapter VI, of Department of Energy (DOE) Order 5480.19, ``Conduct of Operations Requirements for DOE Facilities.`` The practices in this guide should be considered when planning or reviewing programs for notifications and investigation of abnormal events. Contractors are advised to adopt procedures that meet the intent of DOE Order 5480.19. ``Notifications`` and ``Investigation of Abnormal Events`` are elements of an effective Conduct of Operations program. The complexity and array of activities performed in DOE facilities dictate the necessity for a coordinated notifications program and a consistent method for investigating abnormal events to promote safe and efficient operations.

  7. Guide to good practices for notifications and investigation of abnormal events

    SciTech Connect

    1998-12-01

    This Guide to Good Practices is written to enhance understanding of, and provide direction for, Notifications, Chapter VII, and Investigation of Abnormal Events, Chapter VI, of Department of Energy (DOE) Order 5480.19, Conduct of Operations Requirements for DOE Facilities. The practices in this guide should be considered when planning or reviewing programs for notifications and investigation of abnormal events. Contractors are advised to adopt procedures that meet the intent of DOE Order 5480.19. Notifications and Investigation of Abnormal Events are elements of an effective Conduct of Operations program. The complexity and array of activities performed in DOE facilities dictate the necessity for a coordinated notifications program and a consistent method for investigating abnormal events to promote safe and efficient operations.

  8. Methods and systems for detecting abnormal digital traffic

    DOEpatents

    Goranson, Craig A [Kennewick, WA; Burnette, John R [Kennewick, WA

    2011-03-22

    Aspects of the present invention encompass methods and systems for detecting abnormal digital traffic by assigning characterizations of network behaviors according to knowledge nodes and calculating a confidence value based on the characterizations from at least one knowledge node and on weighting factors associated with the knowledge nodes. The knowledge nodes include a characterization model based on prior network information. At least one of the knowledge nodes should not be based on fixed thresholds or signatures. The confidence value includes a quantification of the degree of confidence that the network behaviors constitute abnormal network traffic.

  9. Rare Event Detection Algorithm Of Water Quality

    NASA Astrophysics Data System (ADS)

    Ungs, M. J.

    2011-12-01

    A novel method is presented describing the development and implementation of an on-line water quality event detection algorithm. An algorithm was developed to distinguish between normal variation in water quality parameters and changes in these parameters triggered by the presence of contaminant spikes. Emphasis is placed on simultaneously limiting the number of false alarms (which are called false positives) that occur and the number of misses (called false negatives). The problem of excessive false alarms is common to existing change detection algorithms. EPA's standard measure of evaluation for event detection algorithms is to have a false alarm rate of less than 0.5 percent and a false positive rate less than 2 percent (EPA 817-R-07-002). A detailed description of the algorithm's development is presented. The algorithm is tested using historical water quality data collected by a public water supply agency at multiple locations and using spiking contaminants developed by the USEPA, Water Security Division. The water quality parameters of specific conductivity, chlorine residual, total organic carbon, pH, and oxidation reduction potential are considered. Abnormal data sets are generated by superimposing water quality changes on the historical or baseline data. Eddies-ET has defined reaction expressions which specify how the peak or spike concentration of a particular contaminant affects each water quality parameter. Nine default contaminants (Eddies-ET) were previously derived from pipe-loop tests performed at EPA's National Homeland Security Research Center (NHSRC) Test and Evaluation (T&E) Facility. A contaminant strength value of approximately 1.5 is considered to be a significant threat. The proposed algorithm has been able to achieve a combined false alarm rate of less than 0.03 percent for both false positives and for false negatives using contaminant spikes of strength 2 or more.

  10. On forecasting abnormal climatic events in the tropical Atlantic Ocean

    NASA Astrophysics Data System (ADS)

    Servain, Jacques; Arnault, Sabine

    1995-09-01

    Modelling and observational evidence indicate that interannual variabilities of dynamic height and sea surface temperature (SST) in the eastern part of the tropical Atlantic Ocean (Gulf of Guinea) are largely induced by preceding fluctuations in wind stress, mainly in the western equatorial basin. A wind-driven linear ocean model is used here to test the possibility of forecasting the abnormal dynamic heights. A control run of the model, forced by 1964-1993 wind stress monthly means, is first conducted. Yearly test runs (1964-1994) are subsequently performed from January to August by forcing the model with observed winds from January to May, and then by forcing with the May wind assumed to persist from June to August. During the last three decades the largest deviations of dynamic height simulated by the control run in the Gulf of Guinea in boreal summer would have been correctly forecast from wind data related only to conditions in May of each year. However, for weak climatic anomalies, the model may forecast overestimated values. For the most part (about 20 times during the last 30 years), the sign of the observed SST anomaly in the centre of the Gulf of Guinea during the boreal summer is identical to the sign of simulated anomalies of dynamic height deduced from both control and test runs. Along the eastern equatorial waveguide, the sea level forecasting skill slowly decreases from the first 2 weeks of June until the second 2 weeks of August, but remains high on both sides of the equator throughout boreal summer, as is expected from the adjustment in a linear ocean model. It is established that throughout the year in the Gulf of Guinea the accuracy of the 1-month forecast dynamic height anomaly provided by the simple linear method is greater than that of the 1-month forecast assuming persistence. Acknowledgements. The authors are grateful to Prof. A. K. Sen of the Institute of Radio Physics and Electronics, University of Calcutta for valuable discussions. One of

  11. Crowd Event Detection on Optical Flow Manifolds.

    PubMed

    Rao, Aravinda S; Gubbi, Jayavardhana; Marusic, Slaven; Palaniswami, Marimuthu

    2016-07-01

    Analyzing crowd events in a video is key to understanding the behavioral characteristics of people (humans). Detecting crowd events in videos is challenging because of articulated human movements and occlusions. The aim of this paper is to detect the events in a probabilistic framework for automatically interpreting the visual crowd behavior. In this paper, crowd event detection and classification in optical flow manifolds (OFMs) are addressed. A new algorithm to detect walking and running events has been proposed, which uses optical flow vector lengths in OFMs. Furthermore, a new algorithm to detect merging and splitting events has been proposed, which uses Riemannian connections in the optical flow bundle (OFB). The longest vector from the OFB provides a key feature for distinguishing walking and running events. Using a Riemannian connection, the optical flow vectors are parallel transported to localize the crowd groups. The geodesic lengths among the groups provide a criterion for merging and splitting events. Dispersion and evacuation events are jointly modeled from the walking/running and merging/splitting events. Our results show that the proposed approach delivers a comparable model to detect crowd events. Using the performance evaluation of tracking and surveillance 2009 dataset, the proposed method is shown to produce the best results in merging, splitting, and dispersion events, and comparable results in walking, running, and evacuation events when compared with other methods. PMID:26219100

  12. Event oriented dictionary learning for complex event detection.

    PubMed

    Yan, Yan; Yang, Yi; Meng, Deyu; Liu, Gaowen; Tong, Wei; Hauptmann, Alexander G; Sebe, Nicu

    2015-06-01

    Complex event detection is a retrieval task with the goal of finding videos of a particular event in a large-scale unconstrained Internet video archive, given example videos and text descriptions. Nowadays, different multimodal fusion schemes of low-level and high-level features are extensively investigated and evaluated for the complex event detection task. However, how to effectively select the high-level semantic meaningful concepts from a large pool to assist complex event detection is rarely studied in the literature. In this paper, we propose a novel strategy to automatically select semantic meaningful concepts for the event detection task based on both the events-kit text descriptions and the concepts high-level feature descriptions. Moreover, we introduce a novel event oriented dictionary representation based on the selected semantic concepts. Toward this goal, we leverage training images (frames) of selected concepts from the semantic indexing dataset with a pool of 346 concepts, into a novel supervised multitask lp -norm dictionary learning framework. Extensive experimental results on TRECVID multimedia event detection dataset demonstrate the efficacy of our proposed method. PMID:25794390

  13. Improving the performance of cardiac abnormality detection from PCG signal

    NASA Astrophysics Data System (ADS)

    Sujit, N. R.; Kumar, C. Santhosh; Rajesh, C. B.

    2016-03-01

    The Phonocardiogram (PCG) signal contains important information about the condition of heart. Using PCG signal analysis prior recognition of coronary illness can be done. In this work, we developed a biomedical system for the detection of abnormality in heart and methods to enhance the performance of the system using SMOTE and AdaBoost technique have been presented. Time and frequency domain features extracted from the PCG signal is input to the system. The back-end classifier to the system developed is Decision Tree using CART (Classification and Regression Tree), with an overall classification accuracy of 78.33% and sensitivity (alarm accuracy) of 40%. Here sensitivity implies the precision obtained from classifying the abnormal heart sound, which is an essential parameter for a system. We further improve the performance of baseline system using SMOTE and AdaBoost algorithm. The proposed approach outperforms the baseline system by an absolute improvement in overall accuracy of 5% and sensitivity of 44.92%.

  14. Automating identification of adverse events related to abnormal lab results using standard vocabularies.

    PubMed

    Brandt, C A; Lu, C C; Nadkarni, P M

    2005-01-01

    Laboratory data need to be imported automatically into central Clinical Study Data Management Systems (CSDMSs), and abnormal laboratory data need to be linked to clinically related adverse events. This import of laboratory data can be automated through mapping to standard vocabularies with HL7/LOINC mapping to the metadata within a CSDMS. We have designed a system that uses the UMLS metathesaurus as a common source to map or link abnormal laboratory values to adverse event CTCAE coded terms and grades in the metadata of TrialDB, a generic CSDMS. PMID:16779190

  15. A multiresolution analysis for detection of abnormal lung sounds

    PubMed Central

    Emmanouilidou, Dimitra; Patil, Kailash; West, James; Elhilali, Mounya

    2014-01-01

    Automated analysis and detection of abnormal lung sound patterns has great potential for improving access to standardized diagnosis of pulmonary diseases, especially in low-resource settings. In the current study, we develop signal processing tools for analysis of paediatric auscultations recorded under non-ideal noisy conditions. The proposed model is based on a biomimetic multi-resolution analysis of the spectro-temporal modulation details in lung sounds. The methodology provides a detailed description of joint spectral and temporal variations in the signal and proves to be more robust than frequency-based techniques in distinguishing crackles and wheezes from normal breathing sounds. PMID:23366591

  16. Proportionate Responses to Life Events Influence Clinicians’ Judgments Of Psychological Abnormality

    PubMed Central

    Kim, Nancy S.; Paulus, Daniel J.; Gonzalez, Jeffrey S.; Khalife, Danielle

    2012-01-01

    Psychological abnormality is a fundamental concept in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR; APA, 2000) and in all clinical evaluations. How do practicing clinical psychologists use the context of life events to judge the abnormality of a person’s current behaviors? The appropriate role of life-event context in assessment has long been the subject of intense debate and scrutiny among clinical theorists, yet relatively little is known about clinicians’ own judgments in practice. We propose a proportionate-response hypothesis, such that judgments of abnormality are influenced by whether the behaviors are a disproportionate response to past events, rendering them difficult to understand or explain. We presented licensed, practicing clinical psychologists (N=77) with vignettes describing hypothetical people’s behaviors (disordered, mildly distressed, or unaffected) that had been preceded by either traumatic or mildly distressing events. Experts’ judgments of abnormality were strongly and systematically influenced by the degree of mismatch between the past event and current behaviors in strength and valence, such that the greater the mismatch, the more abnormal the person seemed. A separate, additional group of clinical psychologists (N=20) further confirmed that the greater the degree of mismatch, the greater the perceived difficulty in understanding the patient. These findings held true across clinicians of different theoretical orientations and in disorders for which these patterns of judgments ran contrary to formal recommendations in the DSM-IV-TR (APA, 2000). The rationality of these effects and implications for clinical decision science are discussed. PMID:22142425

  17. Detection of cryptic chromosomal abnormalities in unexplained mental retardation: a general strategy using hypervariable subtelomeric DNA polymorphisms.

    PubMed Central

    Wilkie, A O

    1993-01-01

    Given the availability of DNA from both parents, unusual segregation of hypervariable DNA polymorphisms (HVPs) in the offspring may be attributable to deletion, unbalanced chromosomal translocation, or uniparental disomy. The telomeric regions of chromosomes are rich in both genes and hypervariable minisatellite sequences and may also be particularly prone to cryptic breakage events. Here I describe and analyze a general approach to the detection of subtelomeric abnormalities and uniparental disomy in patients with unexplained mental retardation. With 29 available polymorphic systems, approximately 50%-70% of these abnormalities could currently be detected. Development of subtelomeric HVPs physically localized with respect to their telomeres should provide a valuable resource in routine diagnostics. PMID:8352277

  18. Abnormal Policy Detection and Correction Using Overlapping Transition

    NASA Astrophysics Data System (ADS)

    Kim, Sunghyun; Lee, Heejo

    Policy in security devices such as firewalls and Network Intrusion Prevention Systems (NIPS) is usually implemented as a sequence of rules. This allows network packets to proceed or to be discarded based on rule's decision. Since attack methods are increasing rapidly, a huge number of security rules are generated and maintained in security devices. Under attack or during heavy traffic, the policy configured wrong creates security holes and prevents the system from deciding quickly whether to allow or deny a packet. Anomalies between the rules occur when there is overlap among the rules. In this paper, we propose a new method to detect anomalies among rules and generate new rules without configuration error in multiple security devices as well as in a single security device. The proposed method cuts the overlap regions among rules into minimum overlap regions and finds the abnormal domain regions of rules' predicates. Classifying rules by the network traffic flow, the proposed method not only reduces computation overhead but blocks unnecessary traffic among distributed devices.

  19. Novel instrumentation of multispectral imaging technology for detecting tissue abnormity

    NASA Astrophysics Data System (ADS)

    Yi, Dingrong; Kong, Linghua

    2012-10-01

    Multispectral imaging is becoming a powerful tool in a wide range of biological and clinical studies by adding spectral, spatial and temporal dimensions to visualize tissue abnormity and the underlying biological processes. A conventional spectral imaging system includes two physically separated major components: a band-passing selection device (such as liquid crystal tunable filter and diffraction grating) and a scientific-grade monochromatic camera, and is expensive and bulky. Recently micro-arrayed narrow-band optical mosaic filter was invented and successfully fabricated to reduce the size and cost of multispectral imaging devices in order to meet the clinical requirement for medical diagnostic imaging applications. However the challenging issue of how to integrate and place the micro filter mosaic chip to the targeting focal plane, i.e., the imaging sensor, of an off-shelf CMOS/CCD camera is not reported anywhere. This paper presents the methods and results of integrating such a miniaturized filter with off-shelf CMOS imaging sensors to produce handheld real-time multispectral imaging devices for the application of early stage pressure ulcer (ESPU) detection. Unlike conventional multispectral imaging devices which are bulky and expensive, the resulting handheld real-time multispectral ESPU detector can produce multiple images at different center wavelengths with a single shot, therefore eliminates the image registration procedure required by traditional multispectral imaging technologies.

  20. Abnormal/Emergency Situations. Impact of Unmanned Aircraft Systems Emergency and Abnormal Events on the National Airspace System

    NASA Technical Reports Server (NTRS)

    2006-01-01

    Access 5 analyzed the differences between UAS and manned aircraft operations under five categories of abnormal or emergency situations: Link Failure, Lost Communications, Onboard System Failures, Control Station Failures and Abnormal Weather. These analyses were made from the vantage point of the impact that these operations have on the US air traffic control system, with recommendations for new policies and procedures included where appropriate.

  1. Methodology to automatically detect abnormal values of vital parameters in anesthesia time-series: Proposal for an adaptable algorithm.

    PubMed

    Lamer, Antoine; Jeanne, Mathieu; Marcilly, Romaric; Kipnis, Eric; Schiro, Jessica; Logier, Régis; Tavernier, Benoît

    2016-06-01

    Abnormal values of vital parameters such as hypotension or tachycardia may occur during anesthesia and may be detected by analyzing time-series data collected during the procedure by the Anesthesia Information Management System. When crossed with other data from the Hospital Information System, abnormal values of vital parameters have been linked with postoperative morbidity and mortality. However, methods for the automatic detection of these events are poorly documented in the literature and differ between studies, making it difficult to reproduce results. In this paper, we propose a methodology for the automatic detection of abnormal values of vital parameters. This methodology uses an algorithm allowing the configuration of threshold values for any vital parameters as well as the management of missing data. Four examples illustrate the application of the algorithm, after which it is applied to three vital signs (heart rate, SpO2, and mean arterial pressure) to all 2014 anesthetic records at our institution. PMID:26817405

  2. Causality-weighted active learning for abnormal event identification based on the topic model

    NASA Astrophysics Data System (ADS)

    Fan, Yawen; Zheng, Shibao; Yang, Hua; Zhang, Chongyang; Su, Hang

    2012-07-01

    Abnormal event identification in crowded scenes is a fundamental task for video surveillance. However, it is still challenging for most current approaches because of the general insufficiency of labeled data for training, particularly for abnormal data. We propose a novel active-supervised joint topic model for learning activity and training sample collection. First, a multi-class topic model is constructed based on the initial training data. Then the remaining unlabeled data stream is surveyed. The system actively decides whether it can label a new sample by itself or if it has to ask a human annotator. After each query, the current model is incrementally updated. To alleviate class imbalance, causality-weighted method is applied to both likelihood and uncertainty sampling for active learning. Furthermore, a combination of a new measure termed query entropy and the overall classification accuracy is used for assessing the model performance. Experimental results on two real-world traffic videos for abnormal event identification tasks demonstrate the effectiveness of the proposed method.

  3. Automated Detection of Events of Scientific Interest

    NASA Technical Reports Server (NTRS)

    James, Mark

    2007-01-01

    A report presents a slightly different perspective of the subject matter of Fusing Symbolic and Numerical Diagnostic Computations (NPO-42512), which appears elsewhere in this issue of NASA Tech Briefs. Briefly, the subject matter is the X-2000 Anomaly Detection Language, which is a developmental computing language for fusing two diagnostic computer programs one implementing a numerical analysis method, the other implementing a symbolic analysis method into a unified event-based decision analysis software system for real-time detection of events. In the case of the cited companion NASA Tech Briefs article, the contemplated events that one seeks to detect would be primarily failures or other changes that could adversely affect the safety or success of a spacecraft mission. In the case of the instant report, the events to be detected could also include natural phenomena that could be of scientific interest. Hence, the use of X- 2000 Anomaly Detection Language could contribute to a capability for automated, coordinated use of multiple sensors and sensor-output-data-processing hardware and software to effect opportunistic collection and analysis of scientific data.

  4. Detection and diagnosis of abnormal transients in nuclear power plants

    SciTech Connect

    Lee, J.C.; Rank, P.J.; Hawkes, E.; Wehe, D.K. . Dept. of Nuclear Engineering); Reifman, J. )

    1991-01-01

    This document describes a simulation-based algorithm that combines fuzzy logic with macroscopic conservation equations to diagnose multiple-failure events subject to uncertainties in transient data. Clusters of single-failure data points of similar characteristics are obtained through a pattern recognition algorithm and the cluster centers are combined in the space of macroscopic inventory derivatives to generate multiple-failure cluster centers. A fuzzy membership function is used to represent the likelihood of a data point belonging to a cluster, and failure estimates are obtained through solution of a fuzzy matrix equation. The algorithm has been successful in detecting simulated malfunctions in the pressurizer of a pressurized water reactor. 11 refs., 9 figs., 1 tab.

  5. On event-based optical flow detection

    PubMed Central

    Brosch, Tobias; Tschechne, Stephan; Neumann, Heiko

    2015-01-01

    Event-based sensing, i.e., the asynchronous detection of luminance changes, promises low-energy, high dynamic range, and sparse sensing. This stands in contrast to whole image frame-wise acquisition by standard cameras. Here, we systematically investigate the implications of event-based sensing in the context of visual motion, or flow, estimation. Starting from a common theoretical foundation, we discuss different principal approaches for optical flow detection ranging from gradient-based methods over plane-fitting to filter based methods and identify strengths and weaknesses of each class. Gradient-based methods for local motion integration are shown to suffer from the sparse encoding in address-event representations (AER). Approaches exploiting the local plane like structure of the event cloud, on the other hand, are shown to be well suited. Within this class, filter based approaches are shown to define a proper detection scheme which can also deal with the problem of representing multiple motions at a single location (motion transparency). A novel biologically inspired efficient motion detector is proposed, analyzed and experimentally validated. Furthermore, a stage of surround normalization is incorporated. Together with the filtering this defines a canonical circuit for motion feature detection. The theoretical analysis shows that such an integrated circuit reduces motion ambiguity in addition to decorrelating the representation of motion related activations. PMID:25941470

  6. Phenological Event Detection from Multitemporal Image Data

    SciTech Connect

    Vatsavai, Raju

    2009-01-01

    Monitoring biomass over large geographic regions for seasonal changes in vegetation and crop phenology is important for many applications. In this paper we a present a novel clustering based change detection method using MODIS NDVI time series data. We used well known EM technique to find GMM parameters and Bayesian Information Criteria (BIC) for determining the number of clusters. KL Divergence measure is then used to establish the cluster correspondence across two years (2001 and 2006) to determine changes between these two years. The changes identied were further analyzed for understanding phenological events. This preliminary study shows interesting relationships between key phenological events such as onset, length, end of growing seasons.

  7. Detection and recognition of indoor smoking events

    NASA Astrophysics Data System (ADS)

    Bien, Tse-Lun; Lin, Chang Hong

    2013-03-01

    Smoking in public indoor spaces has become prohibited in many countries since it not only affects the health of the people around you, but also increases the risk of fire outbreaks. This paper proposes a novel scheme to automatically detect and recognize smoking events by using exsiting surveillance cameras. The main idea of our proposed method is to detect human smoking events by recognizing their actions. In this scheme, the human pose estimation is introduced to analyze human actions from their poses. The human pose estimation method segments head and both hands from human body parts by using a skin color detection method. However, the skin color methods may fail in insufficient light conditions. Therefore, the lighting compensation is applied to help the skin color detection method become more accurate. Due to the human body parts may be covered by shadows, which may cause the human pose estimation to fail, the Kalman filter is applied to track the missed body parts. After that, we evaluate the probability features of hands approaching the head. The support vector machine (SVM) is applied to learn and recognize the smoking events by the probability features. To analysis the performance of proposed method, the datasets established in the survillance camera view under indoor enviroment are tested. The experimental results show the effectiveness of our proposed method with accuracy rate of 83.33%.

  8. Real-time detection of traffic events using smart cameras

    NASA Astrophysics Data System (ADS)

    Macesic, M.; Jelaca, V.; Niño-Castaneda, J. O.; Prodanovic, N.; Panic, M.; Pizurica, A.; Crnojevic, V.; Philips, W.

    2012-01-01

    With rapid increase of number of vehicles on roads it is necessary to maintain close monitoring of traffic. For this purpose many surveillance cameras are placed along roads and on crossroads, creating a huge communication load between the cameras and the monitoring center. Therefore, the data needs to be processed on site and transferred to the monitoring centers in form of metadata or as a set of selected images. For this purpose it is necessary to detect events of interest already on the camera side, which implies using smart cameras as visual sensors. In this paper we propose a method for tracking of vehicles and analysis of vehicle trajectories to detect different traffic events. Kalman filtering is used for tracking, combining foreground and optical flow measurements. Obtained vehicle trajectories are used to detect different traffic events. Every new trajectory is compared with collection of normal routes and clustered accordingly. If the observed trajectory differs from all normal routes more than a predefined threshold, it is marked as abnormal and the alarm is raised. The system was developed and tested on Texas Instruments OMAP platform. Testing was done on four different locations, two locations in the city and two locations on the open road.

  9. Phase-Space Detection of Cyber Events

    SciTech Connect

    Hernandez Jimenez, Jarilyn M; Ferber, Aaron E; Prowell, Stacy J; Hively, Lee M

    2015-01-01

    Energy Delivery Systems (EDS) are a network of processes that produce, transfer and distribute energy. EDS are increasingly dependent on networked computing assets, as are many Industrial Control Systems. Consequently, cyber-attacks pose a real and pertinent threat, as evidenced by Stuxnet, Shamoon and Dragonfly. Hence, there is a critical need for novel methods to detect, prevent, and mitigate effects of such attacks. To detect cyber-attacks in EDS, we developed a framework for gathering and analyzing timing data that involves establishing a baseline execution profile and then capturing the effect of perturbations in the state from injecting various malware. The data analysis was based on nonlinear dynamics and graph theory to improve detection of anomalous events in cyber applications. The goal was the extraction of changing dynamics or anomalous activity in the underlying computer system. Takens' theorem in nonlinear dynamics allows reconstruction of topologically invariant, time-delay-embedding states from the computer data in a sufficiently high-dimensional space. The resultant dynamical states were nodes, and the state-to-state transitions were links in a mathematical graph. Alternatively, sequential tabulation of executing instructions provides the nodes with corresponding instruction-to-instruction links. Graph theorems guarantee graph-invariant measures to quantify the dynamical changes in the running applications. Results showed a successful detection of cyber events.

  10. Autonomous detection of heart sound abnormalities using an auscultation jacket.

    PubMed

    Visagie, C; Scheffer, C; Lubbe, W W; Doubell, A F

    2009-12-01

    This paper presents a study using an auscultation jacket with embedded electronic stethoscopes, and a software classification system capable of differentiating between normal and certain auscultatory abnormalities. The aim of the study is to demonstrate the potential of such a system for semi-automated diagnosis for underserved locations, for instance in rural areas or in developing countries where patients far outnumber the available medical personnel. Using an "auscultation jacket", synchronous data was recorded at multiple chest locations on 31 healthy volunteers and 21 patients with heart pathologies. Electrocardiograms (ECGs) were also recorded simultaneously with phonocardiographic data. Features related to heart pathologies were extracted from the signals and used as input to a feed-forward artificial neural network. The system is able to classify between normal and certain abnormal heart sounds with a sensitivity of 84% and a specificity of 86%. Though the number of training and testing samples presented are limited, the system performed well in differentiating between normal and abnormal heart sounds in the given database of available recordings. The results of this study demonstrate the potential of such a system to be used as a fast and cost-effective screening tool for heart pathologies. PMID:20169844

  11. LAN attack detection using Discrete Event Systems.

    PubMed

    Hubballi, Neminath; Biswas, Santosh; Roopa, S; Ratti, Ritesh; Nandi, Sukumar

    2011-01-01

    Address Resolution Protocol (ARP) is used for determining the link layer or Medium Access Control (MAC) address of a network host, given its Internet Layer (IP) or Network Layer address. ARP is a stateless protocol and any IP-MAC pairing sent by a host is accepted without verification. This weakness in the ARP may be exploited by malicious hosts in a Local Area Network (LAN) by spoofing IP-MAC pairs. Several schemes have been proposed in the literature to circumvent these attacks; however, these techniques either make IP-MAC pairing static, modify the existing ARP, patch operating systems of all the hosts etc. In this paper we propose a Discrete Event System (DES) approach for Intrusion Detection System (IDS) for LAN specific attacks which do not require any extra constraint like static IP-MAC, changing the ARP etc. A DES model is built for the LAN under both a normal and compromised (i.e., spoofed request/response) situation based on the sequences of ARP related packets. Sequences of ARP events in normal and spoofed scenarios are similar thereby rendering the same DES models for both the cases. To create different ARP events under normal and spoofed conditions the proposed technique uses active ARP probing. However, this probing adds extra ARP traffic in the LAN. Following that a DES detector is built to determine from observed ARP related events, whether the LAN is operating under a normal or compromised situation. The scheme also minimizes extra ARP traffic by probing the source IP-MAC pair of only those ARP packets which are yet to be determined as genuine/spoofed by the detector. Also, spoofed IP-MAC pairs determined by the detector are stored in tables to detect other LAN attacks triggered by spoofing namely, man-in-the-middle (MiTM), denial of service etc. The scheme is successfully validated in a test bed. PMID:20804980

  12. WCEDS: A waveform correlation event detection system

    SciTech Connect

    Young, C.J.; Beiriger, J.I.; Trujillo, J.R.; Withers, M.M.; Aster, R.C.; Astiz, L.; Shearer, P.M.

    1995-08-01

    We have developed a working prototype of a grid-based global event detection system based on waveform correlation. The algorithm comes from a long-period detector but we have recast it in a full matrix formulation which can reduce the number of multiplications needed by better than two orders of magnitude for realistic monitoring scenarios. The reduction is made possible by eliminating redundant multiplications in the original formulation. All unique correlations for a given origin time are stored in a correlation matrix (C) which is formed by a full matrix product of a Master Image matrix (M) and a data matrix (D). The detector value at each grid point is calculated by following a different summation path through the correlation matrix. Master Images can be derived either empirically or synthetically. Our testing has used synthetic Master Images because their influence on the detector is easier to understand. We tested the system using the matrix formulation with continuous data from the IRIS (Incorporate Research Institutes for Seismology) broadband global network to monitor a 2 degree evenly spaced surface grid with a time discretization of 1 sps; we successfully detected the largest event in a two hour segment from October 1993. The output at the correct gridpoint was at least 33% larger than at adjacent grid points, and the output at the correct gridpoint at the correct origin time was more than 500% larger than the output at the same gridpoint immediately before or after. Analysis of the C matrix for the origin time of the event demonstrates that there are many significant ``false`` correlations of observed phases with incorrect predicted phases. These false correlations dull the sensitivity of the detector and so must be dealt with if our system is to attain detection thresholds consistent with a Comprehensive Test Ban Treaty (CTBT).

  13. Detection of fetal structural abnormalities with US during early pregnancy.

    PubMed

    Fong, Katherine W; Toi, Ants; Salem, Shia; Hornberger, Lisa K; Chitayat, David; Keating, Sarah J; McAuliffe, Fionnuala; Johnson, Jo-Ann

    2004-01-01

    Ultrasonography (US) is performed during early pregnancy for dating, determination of the number of fetuses, assessment of early complications, and increasingly for evaluation of the fetus, including measurement of the thickness of the nuchal translucency (NT). Measurement of NT thickness between 11 and 14 weeks gestation, combined with maternal age and maternal serum biochemistry, can be an effective method of screening for trisomy 21 and other chromosomal abnormalities. Furthermore, an increased NT thickness in the presence of a normal karyotype is associated with an increased frequency of structural defects and genetic syndromes. Therefore, this finding is an indication for a more detailed anatomic survey of the fetus. Besides nuchal abnormalities, a wide range of other congenital anomalies can be diagnosed with US at 11-14 weeks gestation, including defects of the central nervous system, heart, anterior abdominal wall, urinary tract, and skeleton. The anatomic survey can be performed with a standardized protocol by using transabdominal US and, when necessary, transvaginal US. A thorough knowledge of the US features of normal fetal development is necessary to avoid potential diagnostic pitfalls. PMID:14730044

  14. Pickless event detection and location: The waveform correlation event detection system (WCEDS) revisited

    DOE PAGESBeta

    Arrowsmith, Stephen John; Young, Christopher J.; Ballard, Sanford; Slinkard, Megan Elizabeth

    2016-01-01

    The standard paradigm for seismic event monitoring breaks the event detection problem down into a series of processing stages that can be categorized at the highest level into station-level processing and network-level processing algorithms (e.g., Le Bras and Wuster (2002)). At the station-level, waveforms are typically processed to detect signals and identify phases, which may subsequently be updated based on network processing. At the network-level, phase picks are associated to form events, which are subsequently located. Furthermore, waveforms are typically directly exploited only at the station-level, while network-level operations rely on earth models to associate and locate the events thatmore » generated the phase picks.« less

  15. Pickless event detection and location: The waveform correlation event detection system (WCEDS) revisited

    SciTech Connect

    Arrowsmith, Stephen John; Young, Christopher J.; Ballard, Sanford; Slinkard, Megan Elizabeth

    2016-01-01

    The standard paradigm for seismic event monitoring breaks the event detection problem down into a series of processing stages that can be categorized at the highest level into station-level processing and network-level processing algorithms (e.g., Le Bras and Wuster (2002)). At the station-level, waveforms are typically processed to detect signals and identify phases, which may subsequently be updated based on network processing. At the network-level, phase picks are associated to form events, which are subsequently located. Furthermore, waveforms are typically directly exploited only at the station-level, while network-level operations rely on earth models to associate and locate the events that generated the phase picks.

  16. Using State Estimation Residuals to Detect Abnormal SCADA Data

    SciTech Connect

    Ma, Jian; Chen, Yousu; Huang, Zhenyu; Wong, Pak C.

    2010-06-14

    Detection of manipulated supervisory control and data acquisition (SCADA) data is critically important for the safe and secure operation of modern power systems. In this paper, a methodology of detecting manipulated SCADA data based on state estimation residuals is presented. A framework of the proposed methodology is described. Instead of using original SCADA measurements as the bad data sources, the residuals calculated based on the results of the state estimator are used as the input for the outlier detection process. The BACON algorithm is applied to detect outliers in the state estimation residuals. The IEEE 118-bus system is used as a test case to evaluate the effectiveness of the proposed methodology. The accuracy of the BACON method is compared with that of the 3-σ method for the simulated SCADA measurements and residuals.

  17. Detection of cryptic chromosomal abnormalities in unexplained mental retardation: A general strategy using hypervariable subtelomeric DNA polymorphisms

    SciTech Connect

    Wilkie, A.O.M.

    1993-09-01

    Given the availability of DNA from both parents, unusual segregation of hypervariable DNA polymorphisms (HVPs) in the offspring may be attributable to deletion, unbalanced chromosomal translocation, or uniparental disomy. The telomeric regions of chromosomes are rich in both genes and hypervariable minisatellite sequences and may also be particularly prone to cryptic breakage events. Here the author describes and analyzes a general approach to the detection of subtelomeric abnormalities and uniparental disomy in patients with unexplained mental retardation. With 29 available polymorphic systems, [approximately]50%-70% of these abnormalities could currently be detected. Development of subtelomeric HVPs physically localized with respect to their telomers should provide a valuable resource in routine diagnostics. 73 refs., 4 figs., 4 tabs.

  18. A novel scheme for abnormal cell detection in Pap smear images

    NASA Astrophysics Data System (ADS)

    Zhao, Tong; Wachman, Elliot S.; Farkas, Daniel L.

    2004-07-01

    Finding malignant cells in Pap smear images is a "needle in a haystack"-type problem, tedious, labor-intensive and error-prone. It is therefore desirable to have an automatic screening tool in order that human experts can concentrate on the evaluation of the more difficult cases. Most research on automatic cervical screening tries to extract morphometric and texture features at the cell level, in accordance with the NIH "The Bethesda System" rules. Due to variances in image quality and features, such as brightness, magnification and focus, morphometric and texture analysis is insufficient to provide robust cervical cancer detection. Using a microscopic spectral imaging system, we have produced a set of multispectral Pap smear images with wavelengths from 400 nm to 690 nm, containing both spectral signatures and spatial attributes. We describe a novel scheme that combines spatial information (including texture and morphometric features) with spectral information to significantly improve abnormal cell detection. Three kinds of wavelet features, orthogonal, bi-orthogonal and non-orthogonal, are carefully chosen to optimize recognition performance. Multispectral feature sets are then extracted in the wavelet domain. Using a Back-Propagation Neural Network classifier that greatly decreases the influence of spurious events, we obtain a classification error rate of 5%. Cell morphometric features, such as area and shape, are then used to eliminate most remaining small artifacts. We report initial results from 149 cells from 40 separate image sets, in which only one abnormal cell was missed (TPR = 97.6%) and one normal cell was falsely classified as cancerous (FPR = 1%).

  19. Method of detecting genetic translocations identified with chromosomal abnormalities

    DOEpatents

    Gray, Joe W.; Pinkel, Daniel; Tkachuk, Douglas

    2001-01-01

    Methods and compositions for staining based upon nucleic acid sequence that employ nucleic acid probes are provided. Said methods produce staining patterns that can be tailored for specific cytogenetic analyses. Said probes are appropriate for in situ hybridization and stain both interphase and metaphase chromosomal material with reliable signals. The nucleic acid probes are typically of a complexity greater than 50 kb, the complexity depending upon the cytogenetic application. Methods and reagents are provided for the detection of genetic rearrangements. Probes and test kits are provided for use in detecting genetic rearrangements, particularly for use in tumor cytogenetics, in the detection of disease related loci, specifically cancer, such as chronic myelogenous leukemia (CML) and for biological dosimetry. Methods and reagents are described for cytogenetic research, for the differentiation of cytogenetically similar but genetically different diseases, and for many prognostic and diagnostic applications.

  20. Method of detecting genetic deletions identified with chromosomal abnormalities

    DOEpatents

    Gray, Joe W; Pinkel, Daniel; Tkachuk, Douglas

    2013-11-26

    Methods and compositions for staining based upon nucleic acid sequence that employ nucleic acid probes are provided. Said methods produce staining patterns that can be tailored for specific cytogenetic analyzes. Said probes are appropriate for in situ hybridization and stain both interphase and metaphase chromosomal material with reliable signals. The nucleic acids probes are typically of a complexity greater tha 50 kb, the complexity depending upon the cytogenetic application. Methods and reagents are provided for the detection of genetic rearrangements. Probes and test kits are provided for use in detecting genetic rearrangements, particlularly for use in tumor cytogenetics, in the detection of disease related loci, specifically cancer, such as chronic myelogenous leukemia (CML) and for biological dosimetry. Methods and reagents are described for cytogenetic research, for the differentiation of cytogenetically similar ut genetically different diseases, and for many prognostic and diagnostic applications.

  1. Noninvasive detection of fetal subchromosomal abnormalities by semiconductor sequencing of maternal plasma DNA.

    PubMed

    Yin, Ai-hua; Peng, Chun-fang; Zhao, Xin; Caughey, Bennett A; Yang, Jie-xia; Liu, Jian; Huang, Wei-wei; Liu, Chang; Luo, Dong-hong; Liu, Hai-liang; Chen, Yang-yi; Wu, Jing; Hou, Rui; Zhang, Mindy; Ai, Michael; Zheng, Lianghong; Xue, Rachel Q; Mai, Ming-qin; Guo, Fang-fang; Qi, Yi-ming; Wang, Dong-mei; Krawczyk, Michal; Zhang, Daniel; Wang, Yu-nan; Huang, Quan-fei; Karin, Michael; Zhang, Kang

    2015-11-24

    Noninvasive prenatal testing (NIPT) using sequencing of fetal cell-free DNA from maternal plasma has enabled accurate prenatal diagnosis of aneuploidy and become increasingly accepted in clinical practice. We investigated whether NIPT using semiconductor sequencing platform (SSP) could reliably detect subchromosomal deletions/duplications in women carrying high-risk fetuses. We first showed that increasing concentration of abnormal DNA and sequencing depth improved detection. Subsequently, we analyzed plasma from 1,456 pregnant women to develop a method for estimating fetal DNA concentration based on the size distribution of DNA fragments. Finally, we collected plasma from 1,476 pregnant women with fetal structural abnormalities detected on ultrasound who also underwent an invasive diagnostic procedure. We used SSP of maternal plasma DNA to detect subchromosomal abnormalities and validated our results with array comparative genomic hybridization (aCGH). With 3.5 million reads, SSP detected 56 of 78 (71.8%) subchromosomal abnormalities detected by aCGH. With increased sequencing depth up to 10 million reads and restriction of the size of abnormalities to more than 1 Mb, sensitivity improved to 69 of 73 (94.5%). Of 55 false-positive samples, 35 were caused by deletions/duplications present in maternal DNA, indicating the necessity of a validation test to exclude maternal karyotype abnormalities. This study shows that detection of fetal subchromosomal abnormalities is a viable extension of NIPT based on SSP. Although we focused on the application of cell-free DNA sequencing for NIPT, we believe that this method has broader applications for genetic diagnosis, such as analysis of circulating tumor DNA for detection of cancer. PMID:26554006

  2. Noninvasive detection of fetal subchromosomal abnormalities by semiconductor sequencing of maternal plasma DNA

    PubMed Central

    Yin, Ai-hua; Peng, Chun-fang; Zhao, Xin; Caughey, Bennett A.; Yang, Jie-xia; Liu, Jian; Huang, Wei-wei; Liu, Chang; Luo, Dong-hong; Liu, Hai-liang; Chen, Yang-yi; Wu, Jing; Hou, Rui; Zhang, Mindy; Ai, Michael; Zheng, Lianghong; Xue, Rachel Q.; Mai, Ming-qin; Guo, Fang-fang; Qi, Yi-ming; Wang, Dong-mei; Krawczyk, Michal; Zhang, Daniel; Wang, Yu-nan; Huang, Quan-fei; Karin, Michael; Zhang, Kang

    2015-01-01

    Noninvasive prenatal testing (NIPT) using sequencing of fetal cell-free DNA from maternal plasma has enabled accurate prenatal diagnosis of aneuploidy and become increasingly accepted in clinical practice. We investigated whether NIPT using semiconductor sequencing platform (SSP) could reliably detect subchromosomal deletions/duplications in women carrying high-risk fetuses. We first showed that increasing concentration of abnormal DNA and sequencing depth improved detection. Subsequently, we analyzed plasma from 1,456 pregnant women to develop a method for estimating fetal DNA concentration based on the size distribution of DNA fragments. Finally, we collected plasma from 1,476 pregnant women with fetal structural abnormalities detected on ultrasound who also underwent an invasive diagnostic procedure. We used SSP of maternal plasma DNA to detect subchromosomal abnormalities and validated our results with array comparative genomic hybridization (aCGH). With 3.5 million reads, SSP detected 56 of 78 (71.8%) subchromosomal abnormalities detected by aCGH. With increased sequencing depth up to 10 million reads and restriction of the size of abnormalities to more than 1 Mb, sensitivity improved to 69 of 73 (94.5%). Of 55 false-positive samples, 35 were caused by deletions/duplications present in maternal DNA, indicating the necessity of a validation test to exclude maternal karyotype abnormalities. This study shows that detection of fetal subchromosomal abnormalities is a viable extension of NIPT based on SSP. Although we focused on the application of cell-free DNA sequencing for NIPT, we believe that this method has broader applications for genetic diagnosis, such as analysis of circulating tumor DNA for detection of cancer. PMID:26554006

  3. Zone-based analysis for automated detection of abnormalities in chest radiographs

    SciTech Connect

    Kao, E-Fong; Kuo, Yu-Ting; Hsu, Jui-Sheng; Chou, Ming-Chung; Liu, Gin-Chung

    2011-07-15

    Purpose: The aim of this study was to develop an automated method for detection of local texture-based and density-based abnormalities in chest radiographs. Methods: The method was based on profile analysis to detect abnormalities in chest radiographs. In the method, one density-based feature, Density Symmetry Index, and two texture-based features, Roughness Maximum Index and Roughness Symmetry Index, were used to detect abnormalities in the lung fields. In each chest radiograph, the lung fields were divided into four zones initially and then the method was applied to each zone separately. For each zone, Density Symmetry Index was obtained from the projection profile of each zone, and Roughness Maximum Index and Roughness Symmetry Index were obtained by measuring the roughness of the horizontal profiles via moving average technique. Linear discriminant analysis was used to classify normal and abnormal cases based on the three indices. The discriminant performance of the method was evaluated using ROC analysis. Results: The method was evaluated on a database of 250 normal and 250 abnormal chest images. In the optimized conditions, the zone-based performance Az of the method for zones 1, 2, 3, and 4 were 0.917, 0.897, 0.892, and 0.814, respectively, and the case-based performance Az of the method was 0.842. Our previous method for detection of gross abnormalities was also evaluated on the same database. The case-based performance of our previous method was 0.689. Conclusions: In comparing the previous method and the new method proposed in this study, there was a great improvement by the new method for detection of local texture-based and density-based abnormalities. The new method combined with the previous one has potential for screening abnormalities in chest radiographs.

  4. Object-Oriented Query Language For Events Detection From Images Sequences

    NASA Astrophysics Data System (ADS)

    Ganea, Ion Eugen

    2015-09-01

    In this paper is presented a method to represent the events extracted from images sequences and the query language used for events detection. Using an object oriented model the spatial and temporal relationships between salient objects and also between events are stored and queried. This works aims to unify the storing and querying phases for video events processing. The object oriented language syntax used for events processing allow the instantiation of the indexes classes in order to improve the accuracy of the query results. The experiments were performed on images sequences provided from sport domain and it shows the reliability and the robustness of the proposed language. To extend the language will be added a specific syntax for constructing the templates for abnormal events and for detection of the incidents as the final goal of the research.

  5. Aircraft Abnormal Conditions Detection, Identification, and Evaluation Using Innate and Adaptive Immune Systems Interaction

    NASA Astrophysics Data System (ADS)

    Al Azzawi, Dia

    Abnormal flight conditions play a major role in aircraft accidents frequently causing loss of control. To ensure aircraft operation safety in all situations, intelligent system monitoring and adaptation must rely on accurately detecting the presence of abnormal conditions as soon as they take place, identifying their root cause(s), estimating their nature and severity, and predicting their impact on the flight envelope. Due to the complexity and multidimensionality of the aircraft system under abnormal conditions, these requirements are extremely difficult to satisfy using existing analytical and/or statistical approaches. Moreover, current methodologies have addressed only isolated classes of abnormal conditions and a reduced number of aircraft dynamic parameters within a limited region of the flight envelope. This research effort aims at developing an integrated and comprehensive framework for the aircraft abnormal conditions detection, identification, and evaluation based on the artificial immune systems paradigm, which has the capability to address the complexity and multidimensionality issues related to aircraft systems. Within the proposed framework, a novel algorithm was developed for the abnormal conditions detection problem and extended to the abnormal conditions identification and evaluation. The algorithm and its extensions were inspired from the functionality of the biological dendritic cells (an important part of the innate immune system) and their interaction with the different components of the adaptive immune system. Immunity-based methodologies for re-assessing the flight envelope at post-failure and predicting the impact of the abnormal conditions on the performance and handling qualities are also proposed and investigated in this study. The generality of the approach makes it applicable to any system. Data for artificial immune system development were collected from flight tests of a supersonic research aircraft within a motion-based flight

  6. GABAergic influences on ORX receptor-dependent abnormal motor behaviors and neurodegenerative events in fish

    SciTech Connect

    Facciolo, Rosa Maria; Crudo, Michele; Giusi, Giuseppina; Canonaco, Marcello

    2010-02-15

    At date the major neuroreceptors i.e. gamma-aminobutyric acid{sub A} (GABA{sub A}R) and orexin (ORXR) systems are beginning to be linked to homeostasis, neuroendocrine and emotional states. In this study, intraperitoneal treatment of the marine teleost Thalassoma pavo with the highly selective GABA{sub A}R agonist (muscimol, MUS; 0,1 mug/g body weight) and/or its antagonist bicuculline (BIC; 1 mug/g body weight) have corroborated a GABA{sub A}ergic role on motor behaviors. In particular, MUS induced moderate (p < 0.05) and great (p < 0.01) increases of swimming towards food sources and resting states after 24 (1 dose) and 96 (4 doses) h treatment sessions, respectively, when compared to controls. Conversely, BIC caused a very strong (p < 0.001) reduction of the former behavior and in some cases convulsive swimming. From the correlation of BIC-dependent behavioral changes to neuronal morphological and ORXR transcriptional variations, it appeared that the disinhibitory action of GABA{sub A}R was very likely responsible for very strong and strong ORXR mRNA reductions in cerebellum valvula and torus longitudinalis, respectively. Moreover these effects were linked to evident ultra-structural changes such as shrunken cell membranes and loss of cytoplasmic architecture. In contrast, MUS supplied a very low, if any, argyrophilic reaction in hypothalamic and mesencephalic regions plus a scarce level of ultra-structural damages. Interestingly, combined administrations of MUS + BIC were not related to consistent damages, aside mild neuronal alterations in motor-related areas such as optic tectum. Overall it is tempting to suggest, for the first time, a neuroprotective role of GABA{sub A}R inhibitory actions against the overexcitatory ORXR-dependent neurodegeneration and consequently abnormal swimming events in fish.

  7. Optical and electrical observations of an abnormal triggered lightning event with two upward propagations

    NASA Astrophysics Data System (ADS)

    Zheng, Dong; Zhang, Yijun; Lu, Weitao; Zhang, Yang; Dong, Wansheng; Chen, Shaodong; Dan, Jianru

    2012-08-01

    This study investigates an abnormal artificially triggered lightning event that produced two positive upward propagations: one during the initial stage (i.e., the upward leader (UL)) and the other after a negative downward aborted leader (DAL). The triggered lightning was induced in a weak thunderstorm over the experiment site and did not produce a return stroke. All of the intra-cloud lightning around the experiment site produced positive changes in the electric field. The initial stage was a weak discharge process. A downward dart leader propagated along the channel produced by the first UL, ending at a height of approximately 453 m and forming a DAL. Under the influence of the DAL, the electric field at a point located 78 m from the rod experienced a steady reduction of about 6.8 kV m-1 over 5.24 ms prior to the initiation of a new upward channel (i.e., the second upward propagation (UP)). The second UP, which started approximately 4.1 ms after the termination of the DAL and propagated along the original channel, was triggered by the DAL and sustained for approximately 2.95 ms. Two distinct current pulses were superimposed on the current of the second UP. The first pulse, which was related to the sudden initiation of the second UP, was characterized by a more rapid increase and decrease and a larger peak value than the second pulse, which was related to the development of the second UP into the area affected by the DAL. The second UP contained both a similar-to-leader process and a following neutralization process. This study introduces a new type of triggering leader, in which a new upward discharge is triggered in an established channel by an aborted leader propagating along the same channel with opposite polarity and propagation direction.

  8. Preliminary research on abnormal brain detection by wavelet-energy and quantum- behaved PSO.

    PubMed

    Zhang, Yudong; Ji, Genlin; Yang, Jiquan; Wang, Shuihua; Dong, Zhengchao; Phillips, Preetha; Sun, Ping

    2016-04-29

    It is important to detect abnormal brains accurately and early. The wavelet-energy (WE) was a successful feature descriptor that achieved excellent performance in various applications; hence, we proposed a WE based new approach for automated abnormal detection, and reported its preliminary results in this study. The kernel support vector machine (KSVM) was used as the classifier, and quantum-behaved particle swarm optimization (QPSO) was introduced to optimize the weights of the SVM. The results based on a 5 × 5-fold cross validation showed the performance of the proposed WE + QPSO-KSVM was superior to ``DWT + PCA + BP-NN'', ``DWT + PCA + RBF-NN'', ``DWT + PCA + PSO-KSVM'', ``WE + BPNN'', ``WE +$ KSVM'', and ``DWT $+$ PCA $+$ GA-KSVM'' w.r.t. sensitivity, specificity, and accuracy. The work provides a novel means to detect abnormal brains with excellent performance. PMID:27163327

  9. Optimizing Detection Rate and Characterization of Subtle Paroxysmal Neonatal Abnormal Facial Movements with Multi-Camera Video-Electroencephalogram Recordings.

    PubMed

    Pisani, Francesco; Pavlidis, Elena; Cattani, Luca; Ferrari, Gianluigi; Raheli, Riccardo; Spagnoli, Carlotta

    2016-06-01

    Objectives We retrospectively analyze the diagnostic accuracy for paroxysmal abnormal facial movements, comparing one camera versus multi-camera approach. Background Polygraphic video-electroencephalogram (vEEG) recording is the current gold standard for brain monitoring in high-risk newborns, especially when neonatal seizures are suspected. One camera synchronized with the EEG is commonly used. Methods Since mid-June 2012, we have started using multiple cameras, one of which point toward newborns' faces. We evaluated vEEGs recorded in newborns in the study period between mid-June 2012 and the end of September 2014 and compared, for each recording, the diagnostic accuracies obtained with one-camera and multi-camera approaches. Results We recorded 147 vEEGs from 87 newborns and found 73 episodes of paroxysmal facial abnormal movements in 18 vEEGs of 11 newborns with the multi-camera approach. By using the single-camera approach, only 28.8% of these events were identified (21/73). Ten positive vEEGs with multicamera with 52 paroxysmal facial abnormal movements (52/73, 71.2%) would have been considered as negative with the single-camera approach. Conclusions The use of one additional facial camera can significantly increase the diagnostic accuracy of vEEGs in the detection of paroxysmal abnormal facial movements in the newborns. PMID:27111027

  10. Abnormal Circulation Changes in the Winter Stratosphere, Detected Through Variations of D Region Ionospheric Absorption

    NASA Technical Reports Server (NTRS)

    Delamorena, B. A.

    1984-01-01

    A method to detect stratospheric warmings using ionospheric absorption records obtained by an Absorption Meter (method A3) is introduced. The activity of the stratospheric circulation and the D region ionospheric absorption as well as other atmospheric parameters during the winter anomaly experience an abnormal variation. A simultaneity was found in the beginning of abnormal variation in the mentioned parameters, using the absorption records for detecting the initiation of the stratospheric warming. Results of this scientific experience of forecasting in the El Arenosillo Range, are presented.

  11. Detecting seismic events using Benford's Law

    NASA Astrophysics Data System (ADS)

    Diaz, Jordi; Gallart, Josep; Ruiz, Mario

    2015-04-01

    The Benford's Law (BL) states that the distribution of first significant digits is not uniform but follows a logarithmic frequency distribution. Even if a remarkable wide range of natural and socioeconomical data sets, from stock market values to quantum phase transitions, fit this peculiar law, the conformity to it has deserved few scientific applications, being used mainly as a test to pinpoint anomalous or fraudulent data. We developed a procedure to detect the arrival of seismic waves based on the degree of conformity of the amplitude values in the raw seismic trace to the BL. The signal is divided in time windows of appropriate length and the fitting of the first digits distribution to BL is checked in each time window using a conformity estimator. We document that both teleseismic and local earthquakes can be clearly identified in this procedure and we compare its performance with respect to the classical STA/LTA approach. Moreover, we show that the conformity of the seismic record to the BL does not depend on the amplitude of the incoming series, as the occurrence of events with very different amplitudes result in quite similar degree of BL fitting. On the other hand, we show that natural or man-made quasi-monochromatic seismic signals, surface wave trains or engine-generated vibrations can be identified through their very low BL estimator values, when appropriate interval lengths are used. Therefore, we conclude that the degree of conformity of a seismic signal with the BL is primarily dependent on the frequency content of that signal.

  12. AESOP: Adaptive Event detection SOftware using Programming by example

    NASA Astrophysics Data System (ADS)

    Thangali, Ashwin; Prasad, Harsha; Kethamakka, Sai; Demirdjian, David; Checka, Neal

    2015-05-01

    This paper presents AESOP, a software tool for automatic event detection in video. AESOP employs a super- vised learning approach for constructing event models, given training examples from different event classes. A trajectory-based formulation is used for modeling events with an aim towards incorporating invariance to changes in the camera location and orientation parameters. The proposed formulation is designed to accommodate events that involve interactions between two or more entities over an extended period of time. AESOP's event models are formulated as HMMs to improve the event detection algorithm's robustness to noise in input data and to achieve computationally efficient algorithms for event model training and event detection. AESOP's performance is demonstrated on a wide range of different scenarios, including stationary camera surveillance and aerial video footage captured in land and maritime environments.

  13. Value of human papillomavirus typing for detection of anal cytological abnormalities

    PubMed Central

    Maia, Livia Bravo; Marinho, Larissa Cardoso; Barbosa, Tânia Wanderley Paes; Velasco, Lara Franciele Ribeiro; Costa, Patrícia Godoy Garcia; Carneiro, Fabiana Pirani; de Oliveira, Paulo Gonçalves

    2013-01-01

    Purpose: The objective of this study was to evaluate anal cytology and human papillomavirus (HPV) typing in patients with human immunodeficiency virus infection. Materials and Methods: Anal samples were collected from 61 patients (44 men and 17 women) and analyzed by PapilloCheck test and conventional cytology. Results: Of all anal samples, 37.7% had cytological abnormalities, 47.54% were negative and 14.75% were unsatisfactory. High-risk HPV, multiple high-risk HPV and HPV 16 infection was detected in 91.13%, 78.26% and 47.82% of the samples with cytological abnormalities and in 47.54%, 6.89% and 3.44% of the negative samples, respectively. High-risk HPV infection was significantly more frequent in anal samples with cytological abnormalities than in negative samples (P = 0.0005, Fisher's test), particularly multiple high-risk HPV infection (P < 0.0001) and HPV 16 infection (P = 0.0002). Conclusions: High-risk HPV, multiple high-risk HPV and HPV 16 infections are significantly associated with anal cytological abnormalities. Furthermore, the frequency of HPV infection in anal cytological samples suggests that high-risk HPV detection has high sensitivity, but low specificity for detection of anal cytological abnormalities, but multiple high-risk HPV typing and HPV 16 typing have a lower sensitivity and high specificity. Results suggest that HPV typing may be useful as an adjunct to cytology to screen patients for high-resolution anoscopy and biopsy. PMID:24339460

  14. Real-time surveillance for abnormal events: the case of influenza outbreaks.

    PubMed

    Rao, Yao; McCabe, Brendan

    2016-06-15

    This paper introduces a method of surveillance using deviations from probabilistic forecasts. Realised observations are compared with probabilistic forecasts, and the "deviation" metric is based on low probability events. If an alert is declared, the algorithm continues to monitor until an all-clear is announced. Specifically, this article addresses the problem of syndromic surveillance for influenza (flu) with the intention of detecting outbreaks, due to new strains of viruses, over and above the normal seasonal pattern. The syndrome is hospital admissions for flu-like illness, and hence, the data are low counts. In accordance with the count properties of the observations, an integer-valued autoregressive process is used to model flu occurrences. Monte Carlo evidence suggests the method works well in stylised but somewhat realistic situations. An application to real flu data indicates that the ideas may have promise. The model estimated on a short run of training data did not declare false alarms when used with new observations deemed in control, ex post. The model easily detected the 2009 H1N1 outbreak. Copyright © 2016 John Wiley & Sons, Ltd. PMID:26782751

  15. Laplacian eigenmap with temporal constraints for local abnormality detection in crowded scenes.

    PubMed

    Thida, Myo; Eng, How-Lung; Remagnino, Paolo

    2013-12-01

    This paper addresses the problem of detecting and localizing abnormal activities in crowded scenes. A spatiotemporal Laplacian eigenmap method is proposed to extract different crowd activities from videos. This is achieved by learning the spatial and temporal variations of local motions in an embedded space. We employ representatives of different activities to construct the model which characterizes the regular behavior of a crowd. This model of regular crowd behavior allows the detection of abnormal crowd activities both in local and global contexts and the localization of regions which show abnormal behavior. Experiments on the recently published data sets show that the proposed method achieves comparable results with the state-of-the-art methods without sacrificing computational simplicity. PMID:23757524

  16. Abnormal Image Detection in Endoscopy Videos Using a Filter Bank and Local Binary Patterns

    PubMed Central

    Nawarathna, Ruwan; Oh, JungHwan; Muthukudage, Jayantha; Tavanapong, Wallapak; Wong, Johnny; de Groen, Piet C.; Tang, Shou Jiang

    2014-01-01

    Finding mucosal abnormalities (e.g., erythema, blood, ulcer, erosion, and polyp) is one of the most essential tasks during endoscopy video review. Since these abnormalities typically appear in a small number of frames (around 5% of the total frame number), automated detection of frames with an abnormality can save physician’s time significantly. In this paper, we propose a new multi-texture analysis method that effectively discerns images showing mucosal abnormalities from the ones without any abnormality since most abnormalities in endoscopy images have textures that are clearly distinguishable from normal textures using an advanced image texture analysis method. The method uses a “texton histogram” of an image block as features. The histogram captures the distribution of different “textons” representing various textures in an endoscopy image. The textons are representative response vectors of an application of a combination of Leung and Malik (LM) filter bank (i.e., a set of image filters) and a set of Local Binary Patterns on the image. Our experimental results indicate that the proposed method achieves 92% recall and 91.8% specificity on wireless capsule endoscopy (WCE) images and 91% recall and 90.8% specificity on colonoscopy images. PMID:25132723

  17. Abnormal Image Detection in Endoscopy Videos Using a Filter Bank and Local Binary Patterns.

    PubMed

    Nawarathna, Ruwan; Oh, JungHwan; Muthukudage, Jayantha; Tavanapong, Wallapak; Wong, Johnny; de Groen, Piet C; Tang, Shou Jiang

    2014-11-20

    Finding mucosal abnormalities (e.g., erythema, blood, ulcer, erosion, and polyp) is one of the most essential tasks during endoscopy video review. Since these abnormalities typically appear in a small number of frames (around 5% of the total frame number), automated detection of frames with an abnormality can save physician's time significantly. In this paper, we propose a new multi-texture analysis method that effectively discerns images showing mucosal abnormalities from the ones without any abnormality since most abnormalities in endoscopy images have textures that are clearly distinguishable from normal textures using an advanced image texture analysis method. The method uses a "texton histogram" of an image block as features. The histogram captures the distribution of different "textons" representing various textures in an endoscopy image. The textons are representative response vectors of an application of a combination of Leung and Malik (LM) filter bank (i.e., a set of image filters) and a set of Local Binary Patterns on the image. Our experimental results indicate that the proposed method achieves 92% recall and 91.8% specificity on wireless capsule endoscopy (WCE) images and 91% recall and 90.8% specificity on colonoscopy images. PMID:25132723

  18. Sensitive and specific detection of mosaic chromosomal abnormalities using the Parent-of-Origin-based Detection (POD) method

    PubMed Central

    2013-01-01

    Background Mosaic somatic alterations are present in all multi-cellular organisms, but the physiological effects of low-level mosaicism are largely unknown. Most mosaic alterations remain undetectable with current analytical approaches, although the presence of such alterations is increasingly implicated as causative for disease. Results Here, we present the Parent-of-Origin-based Detection (POD) method for chromosomal abnormality detection in trio-based SNP microarray data. Our software implementation, triPOD, was benchmarked using a simulated dataset, outperformed comparable software for sensitivity of abnormality detection, and displayed substantial improvement in the detection of low-level mosaicism while maintaining comparable specificity. Examples of low-level mosaic abnormalities from a large autism dataset demonstrate the benefits of the increased sensitivity provided by triPOD. The triPOD analyses showed robustness across multiple types of Illumina microarray chips. Two large, clinically-relevant datasets were characterized and compared. Conclusions Our method and software provide a significant advancement in the ability to detect low-level mosaic abnormalities, thereby opening new avenues for research into the implications of mosaicism in pathogenic and non-pathogenic processes. PMID:23724825

  19. MRI-based methods to detect placental and fetal brain abnormalities in utero.

    PubMed

    Girardi, Guillermina

    2016-04-01

    There are very few methods for screening women for pregnancy complications. Identification of pregnancies at risk would be of enormous clinical significance as would influence decisions made about pregnancy management and delivery. Adverse pregnancy outcomes such as obstetric antiphospholipid syndrome (APS) and preterm birth (PTB), characterized by placental insufficiency and abnormal fetal brain development, in mice and humans have been associated with activation of inflammatory pathways, in particular the complement cascade. Recently, antibodies against C3 activation products conjugated with contrast agent ultrasmall superparamagnetic iron oxide (USPIO) nanoparticles were used to detect non-invasively sites of inflammation within the placenta and the fetal brain in mouse models of APS and PTB. In utero, magnetic resonance imaging (MRI)-based detection of C3 deposition in the placenta in the APS model was associated with signs of placental insufficiency and intrauterine growth restriction. In both models, fetal brain C3 deposition was associated with cortical axonal cytoarchitecture disruption and increased neurodegeneration. Proton magnetic resonance spectroscopy ((1)H MRS), another non invasive method, is used to identify metabolic abnormalities to predict fetal brain abnormalities. This review describes the recent development of preclinical MRI-based methods for the detection of inflammatory markers of placental insufficiency and abnormal fetal brain development and metabolism to predict pregnancy outcomes. PMID:26187242

  20. Effectiveness of routine ultrasonography in detecting fetal structural abnormalities in a low risk population.

    PubMed Central

    Chitty, L S; Hunt, G H; Moore, J; Lobb, M O

    1991-01-01

    OBJECTIVE--To review the efficacy of routine prenatal ultrasonography for detecting fetal structural abnormalities. DESIGN--Retrospective study of the ultrasonographic findings and outcome of all pregnancies in women scanned in 1988-9. SETTING--Maternity ultrasonography department of a district general hospital. SUBJECTS--8785 fetuses. MAIN OUTCOME MEASURES--Correlation of prenatal ultrasonographic findings with outcome in the neonate. RESULTS--8733 babies were born during 1988-9, and 52 pregnancies were terminated after a fetal malformation was identified. 8432 (95%) of the fetuses were examined by ultrasonography in the second trimester. 130 fetuses (1.5%) were found to have an abnormality at birth or after termination of pregnancy, 125 of which had been examined in the second trimester. In 93 cases the abnormality was detected before 24 weeks (sensitivity 74.4%, 95% confidence interval to 66.7% to 82.1%. Two false positive diagnoses occurred, in both cases the pregnancies were not terminated and apparently normal infants were born. This gives a specificity of 99.98% (99.9% to 99.99%). The positive predictive value of ultrasonography in the second trimester was 97.9% (92.6% to 99.7%). Of the 125 abnormalities, 87 were lethal or severely disabling; 72 of the 87 were detected by the routine screening programme (sensitivity 82.8%, 73.2% to 90.0%). CONCLUSION--Routine fetal examination by ultrasonography in a low risk population detects many fetal structural abnormalities but can present several dilemmas in counselling. PMID:1747613

  1. Frequency and patterns of abnormality detected by iodine-123 amine emission CT after cerebral infarction

    SciTech Connect

    Brott, T.G.; Gelfand, M.J.; Williams, C.C.; Spilker, J.A.; Hertzberg, V.S.

    1986-03-01

    Single photon emission computed tomography (SPECT) was performed in 31 patients with cerebral infarction and 13 who had had transient ischemic attacks, using iodine-123-labeled N,N,N'-trimethyl-N'-(2-hydroxyl-3-methyl-5-iodobenzyl)-1,3-propanediamin e (I-123-HIPDM) as the radiopharmaceutical. SPECT scans were compared with computed tomographic (CT) scans. SPECT was as sensitive as CT in detecting cerebral infarction (94% vs. 84%). The abnormalities were larger on the SPECT scans than on the CT scans in 19 cases, equal in seven, and smaller in five (SPECT abnormalities greater than or equal to CT abnormalities in 86% of cases). Fifteen of 30 patients with hemispheric infarction had decreased perfusion (decreased uptake of I-123-HIPDM) to the cerebellar hemisphere contralateral to the cerebral hemisphere involved by the infarction (crossed cerebellar diaschisis). Nine of these 15 patients had major motor deficits, while only one of the 15 without crossed cerebellar diaschisis had a major motor deficit.

  2. Abnormality Detection via Iterative Deformable Registration and Basis-Pursuit Decomposition.

    PubMed

    Zeng, Ke; Erus, Guray; Sotiras, Aristeidis; Shinohara, Russell T; Davatzikos, Christos

    2016-08-01

    We present a generic method for automatic detection of abnormal regions in medical images as deviations from a normative data base. The algorithm decomposes an image, or more broadly a function defined on the image grid, into the superposition of a normal part and a residual term. A statistical model is constructed with regional sparse learning to represent normative anatomical variations among a reference population (e.g., healthy controls), in conjunction with a Markov random field regularization that ensures mutual consistency of the regional learning among partially overlapping image blocks. The decomposition is performed in a principled way so that the normal part fits well with the learned normative model, while the residual term absorbs pathological patterns, which may then be detected through a statistical significance test. The decomposition is applied to multiple image features from an individual scan, detecting abnormalities using both intensity and shape information. We form an iterative scheme that interleaves abnormality detection with deformable registration, gradually improving robustness of the spatial normalization and precision of the detection. The algorithm is evaluated with simulated images and clinical data of brain lesions, and is shown to achieve robust deformable registration and localize pathological regions simultaneously. The algorithm is also applied on images from Alzheimer's disease patients to demonstrate the generality of the method. PMID:27046847

  3. Method of detecting abnormality in a reference crank angle position detection system of an internal combustion engine

    SciTech Connect

    Suzuki, Y.

    1987-05-12

    A method is described of detecting abnormality in a reference crank angle position detection system of a control system for controlling an internal combustion engine. The method comprises a crankshaft, the control system using at least reference pulses generated, respectively, at predetermined crank angles of the crankshaft and detected by the reference crank angle position detection system. Crank angle pulses are generated, respectively, at other predetermined angles of the crankshaft and with a pulse repetition period shorter than that of the reference pulses, for controlling the engine.

  4. Asynchronous event-based corner detection and matching.

    PubMed

    Clady, Xavier; Ieng, Sio-Hoi; Benosman, Ryad

    2015-06-01

    This paper introduces an event-based luminance-free method to detect and match corner events from the output of asynchronous event-based neuromorphic retinas. The method relies on the use of space-time properties of moving edges. Asynchronous event-based neuromorphic retinas are composed of autonomous pixels, each of them asynchronously generating "spiking" events that encode relative changes in pixels' illumination at high temporal resolutions. Corner events are defined as the spatiotemporal locations where the aperture problem can be solved using the intersection of several geometric constraints in events' spatiotemporal spaces. A regularization process provides the required constraints, i.e. the motion attributes of the edges with respect to their spatiotemporal locations using local geometric properties of visual events. Experimental results are presented on several real scenes showing the stability and robustness of the detection and matching. PMID:25828960

  5. System for detection of hazardous events

    DOEpatents

    Kulesz, James J.; Worley, Brian A.

    2006-05-23

    A system for detecting the occurrence of anomalies, includes a plurality of spaced apart nodes, with each node having adjacent nodes, each of the nodes having one or more sensors associated with the node and capable of detecting anomalies, and each of the nodes having a controller connected to the sensors associated with the node. The system also includes communication links between adjacent nodes, whereby the nodes form a network. Each controller is programmed to query its adjacent nodes to assess the status of the adjacent nodes and the communication links.

  6. System For Detection Of Hazardous Events

    DOEpatents

    Kulesz, James J [Oak Ridge, TN; Worley, Brian A [Knoxville, TN

    2005-08-16

    A system for detecting the occurrence of anomalies, includes a plurality of spaced apart nodes, with each node having adjacent nodes, each of the nodes having one or more sensors associated with the node and capable of detecting anomalies, and each of the nodes having a controller connected to the sensors associated with the node. The system also includes communication links between adjacent nodes, whereby the nodes form a network. Each controller is programmed to query its adjacent nodes to assess the status of the adjacent nodes and the communication links.

  7. Subsurface event detection and classification using Wireless Signal Networks.

    PubMed

    Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T

    2012-01-01

    Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events. PMID:23202191

  8. Subsurface Event Detection and Classification Using Wireless Signal Networks

    PubMed Central

    Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T.

    2012-01-01

    Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events. PMID:23202191

  9. Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features.

    PubMed

    Tripathy, R K; Dandapat, S

    2016-06-01

    The cardiac activities such as the depolarization and the relaxation of atria and ventricles are observed in electrocardiogram (ECG). The changes in the morphological features of ECG are the symptoms of particular heart pathology. It is a cumbersome task for medical experts to visually identify any subtle changes in the morphological features during 24 hours of ECG recording. Therefore, the automated analysis of ECG signal is a need for accurate detection of cardiac abnormalities. In this paper, a novel method for automated detection of cardiac abnormalities from multilead ECG is proposed. The method uses multiscale phase alternation (PA) features of multilead ECG and two classifiers, k-nearest neighbor (KNN) and fuzzy KNN for classification of bundle branch block (BBB), myocardial infarction (MI), heart muscle defect (HMD) and healthy control (HC). The dual tree complex wavelet transform (DTCWT) is used to decompose the ECG signal of each lead into complex wavelet coefficients at different scales. The phase of the complex wavelet coefficients is computed and the PA values at each wavelet scale are used as features for detection and classification of cardiac abnormalities. A publicly available multilead ECG database (PTB database) is used for testing of the proposed method. The experimental results show that, the proposed multiscale PA features and the fuzzy KNN classifier have better performance for detection of cardiac abnormalities with sensitivity values of 78.12 %, 80.90 % and 94.31 % for BBB, HMD and MI classes. The sensitivity value of proposed method for MI class is compared with the state-of-art techniques from multilead ECG. PMID:27118009

  10. Contralateral subtraction technique for detection of asymmetric abnormalities on whole-body bone scintigrams

    NASA Astrophysics Data System (ADS)

    Shiraishi, Junji; Li, Qiang; Appelbaum, Daniel; Pu, Yonglin; Doi, Kunio

    2007-03-01

    We developed a computer-aided diagnostic (CAD) scheme for assisting radiologists in the detection of asymmetric abnormalities on a single whole-body bone scintigram by applying a contralateral subtraction (CS) technique. Twenty whole-body bone scans including 107 abnormal lesions in anterior and/or posterior images (the number of lesions per case ranged from 1 to 16, mean 5.4) were used in this study. In our scheme, the original bone scan image was flipped horizontally to provide a mirror image. The mirror image was first rotated and shifted globally to match the original image approximately, and then was nonlinearly warped by use of an elastic matching technique in order to match the original image accurately. We applied a nonlinear lookup table to convert the difference in pixel values between the original and the warped images to new pixel values for a CS image, in order to enhance dark shadows at the locations of abnormal lesions where uptake of radioisotope was asymmetrically high, and to suppress light shadows of the lesions on the contralateral side. In addition, we applied a CAD scheme for the detection of asymmetric abnormalities by use of rule-based tests and sequential application of artificial neural networks with 25 image features extracted from the original and CS images. The performance of the CAD scheme, which was evaluated by a leave-one-case-out method, indicated an average sensitivity of 80.4 % with 3.8 false positives per case. This CAD scheme with the contralateral subtraction technique has the potential to improve radiologists' diagnostic accuracy and could be used for computerized identification of asymmetric abnormalities on whole-body bone scans.

  11. Comparison of Efficacy in Abnormal Cervical Cell Detection between Liquid-based Cytology and Conventional Cytology.

    PubMed

    Tanabodee, Jitraporn; Thepsuwan, Kitisak; Karalak, Anant; Laoaree, Orawan; Krachang, Anong; Manmatt, Kittipong; Anontwatanawong, Nualpan

    2015-01-01

    This study was conducted to 1206 women who had cervical cancer screening at Chonburi Cancer Hospital. The spilt-sample study aimed to compare the efficacy of abnormal cervical cells detection between liquid-based cytology (LBC) and conventional cytology (CC). The collection of cervical cells was performed by broom and directly smeared on a glass slide for CC then the rest of specimen was prepared for LBC. All slides were evaluated and classified by The Bethesda System. The results of the two cytological tests were compared to the gold standard. The LBC smear significantly decreased inflammatory cell and thick smear on slides. These two techniques were not difference in detection rate of abnormal cytology and had high cytological diagnostic agreement of 95.7%. The histologic diagnosis of cervical tissue was used as the gold standard in 103 cases. Sensitivity, specificity, positive predictive value, negative predictive value, false positive, false negative and accuracy of LBC at ASC-US cut off were 81.4, 75.0, 70.0, 84.9, 25.0, 18.6 and 77.7%, respectively. CC had higher false positive and false negative than LBC. LBC had shown higher sensitivity, specificity, PPV, NPV and accuracy than CC but no statistical significance. In conclusion, LBC method can improve specimen quality, more sensitive, specific and accurate at ASC-US cut off and as effective as CC in detecting cervical epithelial cell abnormalities. PMID:26514540

  12. Automated contralateral subtraction of dental panoramic radiographs for detecting abnormalities in paranasal sinus

    NASA Astrophysics Data System (ADS)

    Hara, Takeshi; Mori, Shintaro; Kaneda, Takashi; Hayashi, Tatsuro; Katsumata, Akitoshi; Fujita, Hiroshi

    2011-03-01

    Inflammation in the paranasal sinus is often observed in seasonal allergic rhinitis or with colds, but is also an indication for odontogenic tumors, carcinoma of the maxillary sinus or a maxillary cyst. The detection of those findings in dental panoramic radiographs is not difficult for radiologists, but general dentists may miss the findings since they focus on treatments of teeth. The purpose of this work is to develop a contralateral subtraction method for detecting the odontogenic sinusitis region on dental panoramic radiographs. We developed a contralateral subtraction technique in paranasal sinus region, consisting of 1) image filtering of the smoothing and sobel operation for noise reduction and edge extraction, 2) image registration of mirrored image by using mutual information, and 3) image display method of subtracted pixel data. We employed 56 cases (24 normal and 32 abnormal). The abnormal regions and the normal cases were verified by a board-certified radiologist using CT scans. Observer studies with and without subtraction images were performed for 9 readers. The true-positive rate at a 50% confidence level in 7 out of 9 readers was improved, but there was no statistical significance in the difference of area-under-curve (AUC) in each radiologist. In conclusion, the contralateral subtraction images of dental panoramic radiographs may improve the detection rate of abnormal regions in paranasal sinus.

  13. Global Seismic Event Detection Using Surface Waves: 15 Possible Antarctic Glacial Sliding Events

    NASA Astrophysics Data System (ADS)

    Chen, X.; Shearer, P. M.; Walker, K. T.; Fricker, H. A.

    2008-12-01

    To identify overlooked or anomalous seismic events not listed in standard catalogs, we have developed an algorithm to detect and locate global seismic events using intermediate-period (35-70s) surface waves. We apply our method to continuous vertical-component seismograms from the global seismic networks as archived in the IRIS UV FARM database from 1997 to 2007. We first bandpass filter the seismograms, apply automatic gain control, and compute envelope functions. We then examine 1654 target event locations defined at 5 degree intervals and stack the seismogram envelopes along the predicted Rayleigh-wave travel times. The resulting function has spatial and temporal peaks that indicate possible seismic events. We visually check these peaks using a graphical user interface to eliminate artifacts and assign an overall reliability grade (A, B or C) to the new events. We detect 78% of events in the Global Centroid Moment Tensor (CMT) catalog. However, we also find 840 new events not listed in the PDE, ISC and REB catalogs. Many of these new events were previously identified by Ekstrom (2006) using a different Rayleigh-wave detection scheme. Most of these new events are located along oceanic ridges and transform faults. Some new events can be associated with volcanic eruptions such as the 2000 Miyakejima sequence near Japan and others with apparent glacial sliding events in Greenland (Ekstrom et al., 2003). We focus our attention on 15 events detected from near the Antarctic coastline and relocate them using a cross-correlation approach. The events occur in 3 groups which are well-separated from areas of cataloged earthquake activity. We speculate that these are iceberg calving and/or glacial sliding events, and hope to test this by inverting for their source mechanisms and examining remote sensing data from their source regions.

  14. Event Detection using Twitter: A Spatio-Temporal Approach

    PubMed Central

    Cheng, Tao; Wicks, Thomas

    2014-01-01

    Background Every day, around 400 million tweets are sent worldwide, which has become a rich source for detecting, monitoring and analysing news stories and special (disaster) events. Existing research within this field follows key words attributed to an event, monitoring temporal changes in word usage. However, this method requires prior knowledge of the event in order to know which words to follow, and does not guarantee that the words chosen will be the most appropriate to monitor. Methods This paper suggests an alternative methodology for event detection using space-time scan statistics (STSS). This technique looks for clusters within the dataset across both space and time, regardless of tweet content. It is expected that clusters of tweets will emerge during spatio-temporally relevant events, as people will tweet more than expected in order to describe the event and spread information. The special event used as a case study is the 2013 London helicopter crash. Results and Conclusion A spatio-temporally significant cluster is found relating to the London helicopter crash. Although the cluster only remains significant for a relatively short time, it is rich in information, such as important key words and photographs. The method also detects other special events such as football matches, as well as train and flight delays from Twitter data. These findings demonstrate that STSS is an effective approach to analysing Twitter data for event detection. PMID:24893168

  15. Detecting abnormality in optic nerve head images using a feature extraction analysis

    PubMed Central

    Zhu, Haogang; Poostchi, Ali; Vernon, Stephen A; Crabb, David P

    2014-01-01

    Imaging and evaluation of the optic nerve head (ONH) plays an essential part in the detection and clinical management of glaucoma. The morphological characteristics of ONHs vary greatly from person to person and this variability means it is difficult to quantify them in a standardized way. We developed and evaluated a feature extraction approach using shift-invariant wavelet packet and kernel principal component analysis to quantify the shape features in ONH images acquired by scanning laser ophthalmoscopy (Heidelberg Retina Tomograph [HRT]). The methods were developed and tested on 1996 eyes from three different clinical centers. A shape abnormality score (SAS) was developed from extracted features using a Gaussian process to identify glaucomatous abnormality. SAS can be used as a diagnostic index to quantify the overall likelihood of ONH abnormality. Maps showing areas of likely abnormality within the ONH were also derived. Diagnostic performance of the technique, as estimated by ROC analysis, was significantly better than the classification tools currently used in the HRT software – the technique offers the additional advantage of working with all images and is fully automated. PMID:25071960

  16. Video Event Detection Framework on Large-Scale Video Data

    ERIC Educational Resources Information Center

    Park, Dong-Jun

    2011-01-01

    Detection of events and actions in video entails substantial processing of very large, even open-ended, video streams. Video data present a unique challenge for the information retrieval community because properly representing video events is challenging. We propose a novel approach to analyze temporal aspects of video data. We consider video data…

  17. Automatic detection of iceberg calving events using seismic observations

    NASA Astrophysics Data System (ADS)

    Andersen, M. L.; Larsen, T.; Hamilton, G. S.; Nettles, M.

    2014-12-01

    Iceberg calving at large, marine-terminating glaciers has been shown to be seismogenic. Seismic energy from these events is released slowly, resulting in characteristic low-frequency signals. The events therefore typically escape detection by traditional systematic methods. Here we show the results of a detection algorithm applied to data observed at two stations, both ~100 km from Helheim Glacier, South East Greenland, in 2007 and 2008 for the purpose of detecting calving-related seismic signals. The detector entails sliding a 150 s wide window over the observed vertical displacement seismograms at steps of one second. Relative power in the 1.1-3.3 s band is monitored, and the detector is activated when a pre-defined threshold is exceeded. We determine the threshold by calibrating the detector with a record of known events observed by time lapse cameras at Helheim Glacier and automatic detections of glacial earthquakes from the GSN (Global Seismic Network) stations. The resulting list of detections is then filtered for events overlapping with tectonic events, both local and global. We observe a clear periodicity in the detections, with most events occurring during the late summer and early fall, roughly coinciding with the end of the melt season. This apparent offset from peak melt intensity leads us to speculate that the pattern in calving is the result of a combination of the seasonal development of multiple physical properties of the glacier, i.e., surface crevassing, subglacial melt and crevassing, and the subglacial drainage system.

  18. An Organic Model for Detecting Cyber Events

    SciTech Connect

    Oehmen, Christopher S.; Peterson, Elena S.; Dowson, Scott T.

    2010-04-21

    Cyber entities in many ways mimic the behavior of organic systems. Individuals or groups compete for limited resources using a variety of strategies and effective strategies are re-used and refined in later ‘generations’. Traditionally this drift has made detection of malicious entities very difficult because 1) recognition systems are often built on exact matching to a pattern that can only be ‘learned’ after a malicious entity reveals itself and 2) the enormous volume and variation in benign entities is an overwhelming source of previously unseen entities that often confound detectors. To turn the tables of complexity on the would-be attackers, we have developed a method for mapping the sequence of behaviors in which cyber entities engage to strings of text and analyze these strings using modified bioinformatics algorithms. Bioinformatics algorithms optimize the alignment between text strings even in the presence of mismatches, insertions or deletions and do not require an a priori definition of the patterns one is seeking. Nor does it require any type of exact matching. This allows the data itself to suggest meaningful patterns that are conserved between cyber entities. We demonstrate this method on data generated from network traffic. The impact of this approach is that it can rapidly calculate similarity measures of previously unseen cyber entities in terms of well-characterized entities. These measures may also be used to organize large collections of data into families, making it possible to identify motifs indicative of each family.

  19. Genomic Characterization of Prenatally Detected Chromosomal Structural Abnormalities Using Oligonucleotide Array Comparative Genomic Hybridization

    PubMed Central

    Li, Peining; Pomianowski, Pawel; DiMaio, Miriam S.; Florio, Joanne R.; Rossi, Michael R.; Xiang, Bixia; Xu, Fang; Yang, Hui; Geng, Qian; Xie, Jiansheng; Mahoney, Maurice J.

    2013-01-01

    Detection of chromosomal structural abnormalities using conventional cytogenetic methods poses a challenge for prenatal genetic counseling due to unpredictable clinical outcomes and risk of recurrence. Of the 1,726 prenatal cases in a 3-year period, we performed oligonucleotide array comparative genomic hybridization (aCGH) analysis on 11 cases detected with various structural chromosomal abnormalities. In nine cases, genomic aberrations and gene contents involving a 3p distal deletion, a marker chromosome from chromosome 4, a derivative chromosome 5 from a 5p/7q translocation, a de novo distal 6q deletion, a recombinant chromosome 8 comprised of an 8p duplication and an 8q deletion, an extra derivative chromosome 9 from an 8p/9q translocation, mosaicism for chromosome 12q with added material of initially unknown origin, an unbalanced 13q/15q rearrangement, and a distal 18q duplication and deletion were delineated. An absence of pathogenic copy number changes was noted in one case with a de novo 11q/14q translocation and in another with a familial insertion of 21q into a 19q. Genomic characterization of the structural abnormalities aided in the prediction of clinical outcomes. These results demonstrated the value of aCGH analysis in prenatal cases with subtle or complex chromosomal rearrangements. Furthermore, a retrospective analysis of clinical indications of our prenatal cases showed that approximately 20% of them had abnormal ultrasound findings and should be considered as high risk pregnancies for a combined chromosome and aCGH analysis. PMID:21671377

  20. Urinary Screening for Detection of Renal Abnormalities in Asymptomatic School Children

    PubMed Central

    Parakh, Prince; Bhatta, Nisha K; Mishra, Om P; Shrestha, Pramod; Budhathoki, Sunil; Majhi, Shankar; Sinha, Arvind; Dhungel, Kanchan; Prabhakar, Rahul; Haldhar, Niladri

    2012-01-01

    Background Urinary screening tests for early detection of renal diseases in asymptomatic school children and adolescents are important in the detection of silent renal diseases. Objectives The purpose of the study was to determine the prevalence of occult renal diseases by dipstick test (reagent strips) in asymptomatic Nepalese children. Patients and Methods A total of 2,243 school children, aged 5–15 years, were screened for urinary abnormalities using dipstick test screening. The children who tested positive in the first screening were re-tested after 2–4 weeks. Results In the first screening, 123 children (5.5%) tested positive for isolated hematuria and proteinuria and for combined hematuria and proteinuria. Of these children, 16 (0.71%) cases tested positive in a second screening. Subsequently, 1 child from the secondary screening group was lost to follow up, 5 tested normal and 10 revealed abnormalities. Glomerulonephritis was the most commonly detected disorder (50%). Conclusions Urinary screening was found to be useful in identifying occult renal diseases in asymptomatic children. Urinary screening would therefore not only help in early detection but also in the prevention of the deterioration of renal function later in life. PMID:23573484

  1. Detection of flood events in hydrological discharge time series

    NASA Astrophysics Data System (ADS)

    Seibert, S. P.; Ehret, U.

    2012-04-01

    The shortcomings of mean-squared-error (MSE) based distance metrics are well known (Beran 1999, Schaeffli & Gupta 2007) and the development of novel distance metrics (Pappenberger & Beven 2004, Ehret & Zehe 2011) and multi-criteria-approaches enjoy increasing popularity (Reusser 2009, Gupta et al. 2009). Nevertheless, the hydrological community still lacks metrics which identify and thus, allow signature based evaluations of hydrological discharge time series. Signature based information/evaluations are required wherever specific time series features, such as flood events, are of special concern. Calculation of event based runoff coefficients or precise knowledge on flood event characteristics (like onset or duration of rising limp or the volume of falling limp, etc.) are possible applications. The same applies for flood forecasting/simulation models. Directly comparing simulated and observed flood event features may reveal thorough insights into model dynamics. Compared to continuous space-and-time-aggregated distance metrics, event based evaluations may provide answers like the distributions of event characteristics or the percentage of the events which were actually reproduced by a hydrological model. It also may help to provide information on the simulation accuracy of small, medium and/or large events in terms of timing and magnitude. However, the number of approaches which expose time series features is small and their usage is limited to very specific questions (Merz & Blöschl 2009, Norbiato et al. 2009). We believe this is due to the following reasons: i) a generally accepted definition of the signature of interest is missing or difficult to obtain (in our case: what makes a flood event a flood event?) and/or ii) it is difficult to translate such a definition into a equation or (graphical) procedure which exposes the feature of interest in the discharge time series. We reviewed approaches which detect event starts and/or ends in hydrological discharge time

  2. Detection of interstitial lung abnormalities on picture archive and communication system video monitors.

    PubMed

    Washowich, T L; Williams, S C; Richardson, L A; Simmons, G E; Dao, N V; Allen, T W; Hammet, G C; Morris, M J

    1997-02-01

    The purpose of this study was to compare the detection of interstitial lung abnormalities on video display workstation monitors between radiologists experienced with video image interpretation and radiologists who lack this experience. Twenty-four patients with interstitial lung abnormalities documented by high-resolution computed tomography (HRCT) and lung biopsy, and 26 control patients with no history of pulmonary disease or a normal HRCT and normal chest radiographs were studied. Images were acquired using storage phosphor digital radiography and displayed on 1,640 x 2,048 pixel resolution video monitors. Five board-certified radiologists evaluated the images in a blinded and randomized manner by using a six-point presence of abnormality grading scale. Three radiologists were from 1 to 4 years out of residency and considered to be experienced workstation monitor readers with between 1 to 3 years of video monitor image interpretation. For the inexperienced readers, one radiologist had no prior experience with reading images from a video monitor and was direct out of residency, and the other radiologist had less than 4 months of intermittent exposure and was 1 year out of residency. Sensitivity and specificity were determined for individual readers. Positive predictive values, negative predictive values, accuracy, and receiver-operating curves were also generated. A comparison was made between experienced and inexperienced readers. For readers experienced with video monitor image interpretation, the sensitivity ranged from 87.5% to 92%, specificity from 69% to 92%, positive predictive value (PPV) from 73% to 87.5%, negative predictive value (NPV) from 87% to 90%, and accuracy from 80% to 88%. For inexperienced readers, these values were sensitivity 58%, specificity 50% to 65%, PPV 52% to 61%, NPV 56.5% to 63%, and accuracy 54% to 62%. Comparing image interpretation between experienced and inexperienced readers, there were statistically significant differences for

  3. Stable algorithm for event detection in event-driven particle dynamics: logical states

    NASA Astrophysics Data System (ADS)

    Strobl, Severin; Bannerman, Marcus N.; Pöschel, Thorsten

    2016-07-01

    Following the recent development of a stable event-detection algorithm for hard-sphere systems, the implications of more complex interaction models are examined. The relative location of particles leads to ambiguity when it is used to determine the interaction state of a particle in stepped potentials, such as the square-well model. To correctly predict the next event in these systems, the concept of an additional state that is tracked separately from the particle position is introduced and integrated into the stable algorithm for event detection.

  4. Structuring an event ontology for disease outbreak detection

    PubMed Central

    Kawazoe, Ai; Chanlekha, Hutchatai; Shigematsu, Mika; Collier, Nigel

    2008-01-01

    Background This paper describes the design of an event ontology being developed for application in the machine understanding of infectious disease-related events reported in natural language text. This event ontology is designed to support timely detection of disease outbreaks and rapid judgment of their alerting status by 1) bridging a gap between layman's language used in disease outbreak reports and public health experts' deep knowledge, and 2) making multi-lingual information available. Construction and content This event ontology integrates a model of experts' knowledge for disease surveillance, and at the same time sets of linguistic expressions which denote disease-related events, and formal definitions of events. In this ontology, rather general event classes, which are suitable for application to language-oriented tasks such as recognition of event expressions, are placed on the upper-level, and more specific events of the experts' interest are in the lower level. Each class is related to other classes which represent participants of events, and linked with multi-lingual synonym sets and axioms. Conclusions We consider that the design of the event ontology and the methodology introduced in this paper are applicable to other domains which require integration of natural language information and machine support for experts to assess them. The first version of the ontology, with about 40 concepts, will be available in March 2008. PMID:18426553

  5. Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks.

    PubMed

    Zhou, Zhangbing; Xing, Riliang; Duan, Yucong; Zhu, Yueqin; Xiang, Jianming

    2015-01-01

    With the advent of the Internet of Underwater Things, smart things are deployed in the ocean space and establish underwater wireless sensor networks for the monitoring of vast and dynamic underwater environments. When events are found to have possibly occurred, accurate event coverage should be detected, and potential event sources should be determined for the enactment of prompt and proper responses. To address this challenge, a technique that detects event coverage and determines event sources is developed in this article. Specifically, the occurrence of possible events corresponds to a set of neighboring sensor nodes whose sensory data may deviate from a normal sensing range in a collective fashion. An appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s). When sensory data are collected at sink node(s), the event coverage is detected and represented as a weighted graph, where the vertices in this graph correspond to sensor nodes and the weight specified upon the edges reflects the extent of sensory data deviating from a normal sensing range. Event sources are determined, which correspond to the barycenters in this graph. The results of the experiments show that our technique is more energy efficient, especially when the network topology is relatively steady. PMID:26694394

  6. Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks

    PubMed Central

    Zhou, Zhangbing; Xing, Riliang; Duan, Yucong; Zhu, Yueqin; Xiang, Jianming

    2015-01-01

    With the advent of the Internet of Underwater Things, smart things are deployed in the ocean space and establish underwater wireless sensor networks for the monitoring of vast and dynamic underwater environments. When events are found to have possibly occurred, accurate event coverage should be detected, and potential event sources should be determined for the enactment of prompt and proper responses. To address this challenge, a technique that detects event coverage and determines event sources is developed in this article. Specifically, the occurrence of possible events corresponds to a set of neighboring sensor nodes whose sensory data may deviate from a normal sensing range in a collective fashion. An appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s). When sensory data are collected at sink node(s), the event coverage is detected and represented as a weighted graph, where the vertices in this graph correspond to sensor nodes and the weight specified upon the edges reflects the extent of sensory data deviating from a normal sensing range. Event sources are determined, which correspond to the barycenters in this graph. The results of the experiments show that our technique is more energy efficient, especially when the network topology is relatively steady. PMID:26694394

  7. Spectral Cytopathology of Cervical Samples: Detecting Cellular Abnormalities in Cytologically Normal Cells

    PubMed Central

    Schubert, Jennifer M.; Bird, Benjamin; Papamarkakis, Kostas; Miljković, Miloš; Bedrossian, Kristi; Laver, Nora; Diem, Max

    2010-01-01

    Aim Spectral Cytopathology (SCP) is a novel spectroscopic method for objective and unsupervised classification of individual exfoliated cells. The limitations of conventional cytopathology are well-recognized within the pathology community. In SCP, cellular differentiation is made by observing molecular changes in the nucleus and the cytoplasm, which may or may not produce morphological changes detectable by conventional cytopathology. This proof of concept study demonstrates SCP’s potential as an enhancing tool for cytopathologists by aiding in the accurate and reproducible diagnosis of cells in all states of disease. Method Infrared spectra are collected from cervical cells deposited onto reflectively coated glass slides. Each cell has a corresponding infrared spectrum that describes its unique biochemical composition. Spectral data are processed and analyzed by an unsupervised chemometric algorithm, Principal Component Analysis (PCA). Results In this blind study, cervical samples are classified by analyzing the spectra of morphologically normal looking squamous cells from normal samples and samples diagnosed by conventional cytopathology with low grade squamous intraepithelial lesions (LSIL). SCP discriminated cytopathological diagnoses amongst twelve different cervical samples with a high degree of specificity and sensitivity. SCP also correlated two samples with abnormal spectral changes: these samples had a normal cytopathological diagnosis but had a history of abnormal cervical cytology. The spectral changes observed in the morphologically normal looking cells are most likely due to an infection with human papillomavirus, HPV. HPV DNA testing was conducted on five additional samples, and SCP accurately differentiated these samples by their HPV status. Conclusions SCP tracks biochemical variations in cells that are consistent with the onset of disease. HPV has been implicated as the cause of these changes detected spectroscopically. SCP does not depend on

  8. Abnormality detection in retinal images using ant colony optimization and artificial neural networks - biomed 2010.

    PubMed

    Kavitha, Ganesan; Ramakrishnan, Swaminathan

    2010-01-01

    Optic disc and retinal vasculature are important anatomical structures in the retina of the eye and any changes observed in these structures provide vital information on severity of various diseases. Digital retinal images are shown to provide a meaningful way of documenting and assessing some of the key elements inside the eye including the optic nerve and the tiny retinal blood vessels. In this work, an attempt has been made to detect and differentiate abnormalities of the retina using Digital image processing together with Optimization based segmentation and Artificial Neural Network methods. The retinal fundus images were recorded using standard protocols. Ant Colony Optimization is employed to extract the most significant objects namely the optic disc and blood vessel. The features related to these objects are obtained and corresponding indices are also derived. Further, these features are subjected to classification using Radial Basis Function Neural Networks and compared with conventional training algorithms. Results show that the Ant Colony Optimization is efficient in extracting useful information from retinal images. The features derived are effective for classification of normal and abnormal images using Radial basis function networks compared to other methods. As Optic disc and blood vessels are significant markers of abnormality in retinal images, the method proposed appears to be useful for mass screening. In this paper, the objectives of the study, methodology and significant observations are presented. PMID:20467104

  9. Diagnostic Accuracy of Transvaginal Sonography in the Detection of Uterine Abnormalities in Infertile Women

    PubMed Central

    Niknejadi, Maryam; Haghighi, Hadieh; Ahmadi, Firoozeh; Niknejad, Fatemeh; Chehrazi, Mohammad; Vosough, Ahmad; Moenian, Deena

    2012-01-01

    Background Accurate diagnosis of uterine abnormalities has become a core part of the fertility work-up. A variety of modalities can be used for the diagnosis of uterine abnormalities. Objectives This study was designed to assess the diagnostic accuracy of transvaginal ultrasonography (TVS) in uterine pathologies of infertile patients using hysteroscopy as the gold standard. Patients and Methods This was a cross-sectional study carried out in the Department of Reproductive Imaging at Royan Institute from October 2007 to October 2008. In this study, the medical documents of 719 infertile women who were investigated with transvaginal ultrasound (TVS) and then hysteroscopy were reviewed. All women underwent hysteroscopy in the same cycle time after TVS. Seventy-six out of 719 patients were excluded from the study and 643 patients were studied. TVS was performed in the follicular phase after cessation of bleeding. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated for TVS. Hysteroscopy served as the gold standard. Results The overall sensitivity, specificity, positive and negative predictive values for TVS in the diagnosis of uterine abnormality was 79%, 82%, 84% and 71%, respectively. The sensitivity and PPV of TVS in detection of polyp were 88.3% and 81.6%, respectively. These indices were 89.2% and 92.5%, respectively for fibroma, 67% and 98.3%, respectively for subseptated uterus and 90.9% and 100%, respectively for septated uterus. Adhesion and unicornuated uterus have the lowest sensitivity with a sensitivity of 35% and PPV of 57.1%. Conclusion TVS is a cost-effective and non-invasive method for diagnosis of intrauterine lesions such as polyps, submucosal fibroids and septum. It is a valuable adjunctive to hysteroscopy with high accuracy for identification and characterization of intrauterine abnormalities. This may lead to a more precise surgery plan and performance. PMID:23329979

  10. White Matter Abnormalities in Post-traumatic Stress Disorder Following a Specific Traumatic Event.

    PubMed

    Li, Lei; Lei, Du; Li, Lingjiang; Huang, Xiaoqi; Suo, Xueling; Xiao, Fenglai; Kuang, Weihong; Li, Jin; Bi, Feng; Lui, Su; Kemp, Graham J; Sweeney, John A; Gong, Qiyong

    2016-02-01

    Studies of posttraumatic stress disorder (PTSD) are complicated by wide variability in the intensity and duration of prior stressors in patient participants, secondary effects of chronic psychiatric illness, and a variable history of treatment with psychiatric medications. In magnetic resonance imaging (MRI) studies, patient samples have often been small, and they were not often compared to similarly stressed patients without PTSD in order to control for general stress effects. Findings from these studies have been inconsistent. The present study investigated whole-brain microstructural alterations of white matter in a large drug-naive population who survived a specific, severe traumatic event (a major 8.0-magnitude earthquake). Using diffusion tensor imaging (DTI), we explored group differences between 88 PTSD patients and 91 matched traumatized non-PTSD controls in fractional anisotropy (FA), as well as its component elements axial diffusivity (AD) and radial diffusivity (RD), and examined these findings in relation to findings from deterministic DTI tractography. Relations between white matter alterations and psychiatric symptom severity were examined. PTSD patients, relative to similarly stressed controls, showed an FA increase as well as AD and RD changes in the white matter beneath left dorsolateral prefrontal cortex and forceps major. The observation of increased FA in the PTSD group suggests that the pathophysiology of PTSD after a specific acute traumatic event is distinct from what has been reported in patients with several years duration of illness. Alterations in dorsolateral prefrontal cortex may be an important aspect of illness pathophysiology, possibly via the region's established role in fear extinction circuitry. Use-dependent myelination or other secondary compensatory changes in response to heightened demands for threat appraisal and emotion regulation may be involved. PMID:26981581

  11. White Matter Abnormalities in Post-traumatic Stress Disorder Following a Specific Traumatic Event

    PubMed Central

    Li, Lei; Lei, Du; Li, Lingjiang; Huang, Xiaoqi; Suo, Xueling; Xiao, Fenglai; Kuang, Weihong; Li, Jin; Bi, Feng; Lui, Su; Kemp, Graham J.; Sweeney, John A.; Gong, Qiyong

    2016-01-01

    Studies of posttraumatic stress disorder (PTSD) are complicated by wide variability in the intensity and duration of prior stressors in patient participants, secondary effects of chronic psychiatric illness, and a variable history of treatment with psychiatric medications. In magnetic resonance imaging (MRI) studies, patient samples have often been small, and they were not often compared to similarly stressed patients without PTSD in order to control for general stress effects. Findings from these studies have been inconsistent. The present study investigated whole-brain microstructural alterations of white matter in a large drug-naive population who survived a specific, severe traumatic event (a major 8.0-magnitude earthquake). Using diffusion tensor imaging (DTI), we explored group differences between 88 PTSD patients and 91 matched traumatized non-PTSD controls in fractional anisotropy (FA), as well as its component elements axial diffusivity (AD) and radial diffusivity (RD), and examined these findings in relation to findings from deterministic DTI tractography. Relations between white matter alterations and psychiatric symptom severity were examined. PTSD patients, relative to similarly stressed controls, showed an FA increase as well as AD and RD changes in the white matter beneath left dorsolateral prefrontal cortex and forceps major. The observation of increased FA in the PTSD group suggests that the pathophysiology of PTSD after a specific acute traumatic event is distinct from what has been reported in patients with several years duration of illness. Alterations in dorsolateral prefrontal cortex may be an important aspect of illness pathophysiology, possibly via the region's established role in fear extinction circuitry. Use-dependent myelination or other secondary compensatory changes in response to heightened demands for threat appraisal and emotion regulation may be involved. PMID:26981581

  12. Mathematical impairment associated with high-contrast abnormalities in change detection and magnocellular visual evoked response.

    PubMed

    Jastrzebski, Nicola R; Crewther, Sheila G; Crewther, David P

    2015-10-01

    The cause of developmental dyscalculia, a specific deficit in acquisition of arithmetic skills, particularly of enumeration, has never been investigated with respect to the patency of the visual magnocellular system. Here, the question of dysfunction of the afferent magnocellular cortical input and its dorsal stream projections was tested directly using nonlinear analysis of the visual evoked potential (VEP) and through the psychophysical ability to rapidly detect visual change. A group of young adults with self-reported deficiencies of arithmetical ability, showed marked impairment in magnitude estimation and enumeration performance-though not in lexical decision reaction times when compared with an arithmetically capable group controlled for age and handedness. Multifocal nonlinear VEPs were recorded at low (24 %) and high (96 %) contrast. First- and second-order VEP kernels were comparable between groups at low contrast, but not at high contrast. The mathematically impaired group showed an abnormal lack of contrast saturation in the shortest latency first-order peak (N60) and a delayed P100 positivity in the first slice of the second-order kernel. Both features have previously been argued to be physiological markers of magnocellular function. Mathematically impaired participants also performed worse on a gap paradigm change detection for digit task showing increased reaction times for high-contrast stimuli but not for low-contrast stimuli compared with controls. The VEP results give direct evidence of abnormality in the occipital processing of magnocellular information in those with mathematical impairment. The anomalous high visual contrast physiological and psychophysical performance suggests an abnormality in the inhibitory processes that normally result in saturation of contrast gain in the magnocellular system. PMID:26195163

  13. Adaptive noise estimation and suppression for improving microseismic event detection

    NASA Astrophysics Data System (ADS)

    Mousavi, S. Mostafa; Langston, Charles A.

    2016-09-01

    Microseismic data recorded by surface arrays are often strongly contaminated by unwanted noise. This background noise makes the detection of small magnitude events difficult. A noise level estimation and noise reduction algorithm is presented for microseismic data analysis based upon minimally controlled recursive averaging and neighborhood shrinkage estimators. The method might not be compared with more sophisticated and computationally expensive denoising algorithm in terms of preserving detailed features of seismic signal. However, it is fast and data-driven and can be applied in real-time processing of continuous data for event detection purposes. Results from application of this algorithm to synthetic and real seismic data show that it holds a great promise for improving microseismic event detection.

  14. Automatic detection system for cough sounds as a symptom of abnormal health condition.

    PubMed

    Shin, Sung-Hwan; Hashimoto, Takeo; Hatano, Shigeko

    2009-07-01

    The problem of attending to the health of the aged who live alone has became an important issue in developed countries. One way of solving the problem is to check their health condition by a remote-monitoring technique and support them with well-timed treatment. The purpose of this study is to develop an automatic system that can monitor a health condition in real time using acoustical information and detect an abnormal symptom. In this study, cough sound was chosen as a representative acoustical symptom of abnormal health conditions. For the development of the system distinguishing a cough sound from other environmental sounds, a hybrid model was proposed that consists of an artificial neural network (ANN) model and a hidden Markov model (HMM). The ANN model used energy cepstral coefficients obtained by filter banks based on human auditory characteristics as input parameters representing a spectral feature of a sound signal. Subsequently, an output of this ANN model and a filtered envelope of the signal were used for making an input sequence for the HMM that deals with the temporal variation of the sound signal. Compared with the conventional HMM using Mel-frequency cepstral coefficients, the proposed hybrid model improved recognition rates on low SNR from 5 dB down to -10 dB. Finally, a preliminary prototype of the automatic detection system was simply illustrated. PMID:19273017

  15. Detection of abnormal living patterns for elderly living alone using support vector data description.

    PubMed

    Shin, Jae Hyuk; Lee, Boreom; Park, Kwang Suk

    2011-05-01

    In this study, we developed an automated behavior analysis system using infrared (IR) motion sensors to assist the independent living of the elderly who live alone and to improve the efficiency of their healthcare. An IR motion-sensor-based activity-monitoring system was installed in the houses of the elderly subjects to collect motion signals and three different feature values, activity level, mobility level, and nonresponse interval (NRI). These factors were calculated from the measured motion signals. The support vector data description (SVDD) method was used to classify normal behavior patterns and to detect abnormal behavioral patterns based on the aforementioned three feature values. The simulation data and real data were used to verify the proposed method in the individual analysis. A robust scheme is presented in this paper for optimally selecting the values of different parameters especially that of the scale parameter of the Gaussian kernel function involving in the training of the SVDD window length, T of the circadian rhythmic approach with the aim of applying the SVDD to the daily behavior patterns calculated over 24 h. Accuracies by positive predictive value (PPV) were 95.8% and 90.5% for the simulation and real data, respectively. The results suggest that the monitoring system utilizing the IR motion sensors and abnormal-behavior-pattern detection with SVDD are effective methods for home healthcare of elderly people living alone. PMID:21317086

  16. Automated Detection of Vessel Abnormalities on Fluorescein Angiogram in Malarial Retinopathy

    PubMed Central

    Zhao, Yitian; MacCormick, Ian J. C.; Parry, David G.; Beare, Nicholas A. V.; Harding, Simon P.; Zheng, Yalin

    2015-01-01

    The detection and assessment of intravascular filling defects is important, because they may represent a process central to cerebral malaria pathogenesis: neurovascular sequestration. We have developed and validated a framework that can automatically detect intravascular filling defects in fluorescein angiogram images. It first employs a state-of-the-art segmentation approach to extract the vessels from images and then divide them into individual segments by geometrical analysis. A feature vector based on the intensity and shape of saliency maps is generated to represent the level of abnormality of each vessel segment. An AdaBoost classifier with weighted cost coefficient is trained to classify the vessel segments into normal and abnormal categories. To demonstrate its effectiveness, we apply this framework to 6,358 vessel segments in images from 10 patients with malarial retinopathy. The test sensitivity, specificity, accuracy, and area under curve (AUC) are 74.7%, 73.5%, 74.1% and 74.2% respectively when compared to the reference standard of human expert manual annotations. This performance is comparable to the agreement that we find between human observers of intravascular filling defects. Our method will be a powerful new tool for studying malarial retinopathy. PMID:26053690

  17. Data mining for signal detection of adverse event safety data.

    PubMed

    Chen, Hung-Chia; Tsong, Yi; Chen, James J

    2013-01-01

    The Adverse Event Reporting System (AERS) is the primary database designed to support the Food and Drug Administration (FDA) postmarketing safety surveillance program for all approved drugs and therapeutic biologic products. Most current disproportionality analysis focuses on the detection of potential adverse events (AE) involving a single drug and a single AE only. In this paper, we present a data mining biclustering technique based on the singular value decomposition to extract local regions of association for a safety study. The analysis consists of collection of biclusters, each representing an association between a set of drugs with the corresponding set of adverse events. Significance of each bicluster can be tested using disproportionality analysis. Individual drug-event combination can be further tested. A safety data set consisting of 193 drugs with 8453 adverse events is analyzed as an illustration. PMID:23331228

  18. Method for early detection of cooling-loss events

    SciTech Connect

    Bermudez, Sergio A.; Hamann, Hendrik F.; Marianno, Fernando J.

    2015-12-22

    A method of detecting cooling-loss event early is provided. The method includes defining a relative humidity limit and change threshold for a given space, measuring relative humidity in the given space, determining, with a processing unit, whether the measured relative humidity is within the defined relative humidity limit, generating a warning in an event the measured relative humidity is outside the defined relative humidity limit and determining whether a change in the measured relative humidity is less than the defined change threshold for the given space and generating an alarm in an event the change is greater than the defined change threshold.

  19. Method for early detection of cooling-loss events

    SciTech Connect

    Bermudez, Sergio A.; Hamann, Hendrik; Marianno, Fernando J.

    2015-06-30

    A method of detecting cooling-loss event early is provided. The method includes defining a relative humidity limit and change threshold for a given space, measuring relative humidity in the given space, determining, with a processing unit, whether the measured relative humidity is within the defined relative humidity limit, generating a warning in an event the measured relative humidity is outside the defined relative humidity limit and determining whether a change in the measured relative humidity is less than the defined change threshold for the given space and generating an alarm in an event the change is greater than the defined change threshold.

  20. Automatic computer aided detection of abnormalities in multi-parametric prostate MRI

    NASA Astrophysics Data System (ADS)

    Litjens, G. J. S.; Vos, P. C.; Barentsz, J. O.; Karssemeijer, N.; Huisman, H. J.

    2011-03-01

    Development of CAD systems for detection of prostate cancer has been a recent topic of research. A multi-stage computer aided detection scheme is proposed to help reduce perception and oversight errors in multi-parametric prostate cancer screening MRI. In addition, important features for development of computer aided detection systems for prostate cancer screening MRI are identified. A fast, robust prostate segmentation routine is used to segment the prostate, based on coupled appearance and anatomy models. Subsequently a voxel classification is performed using a support vector machine to compute an abnormality likelihood map of the prostate. This classification step is based on quantitative voxel features like the apparent diffusion coefficient (ADC) and pharmacokinetic parameters. Local maxima in the likelihood map are found using a local maxima detector, after which regions around the local maxima are segmented. Region features are computed to represent statistical properties of the voxel features within the regions. Region classification is performed using these features, which results in a likelihood of abnormality per region. Performance was validated using a 188 patient dataset in a leave-one-patient-out manner. Ground truth was annotated by two expert radiologists. The results were evaluated using FROC analysis. The FROC curves show that inclusion of ADC and pharmacokinetic parameter features increases the performance of an automatic detection system. In addition it shows the potential of such an automated system in aiding radiologists diagnosing prostate MR, obtaining a sensitivity of respectively 74.7% and 83.4% at 7 and 9 false positives per patient.

  1. Detection of abnormal item based on time intervals for recommender systems.

    PubMed

    Gao, Min; Yuan, Quan; Ling, Bin; Xiong, Qingyu

    2014-01-01

    With the rapid development of e-business, personalized recommendation has become core competence for enterprises to gain profits and improve customer satisfaction. Although collaborative filtering is the most successful approach for building a recommender system, it suffers from "shilling" attacks. In recent years, the research on shilling attacks has been greatly improved. However, the approaches suffer from serious problem in attack model dependency and high computational cost. To solve the problem, an approach for the detection of abnormal item is proposed in this paper. In the paper, two common features of all attack models are analyzed at first. A revised bottom-up discretized approach is then proposed based on time intervals and the features for the detection. The distributions of ratings in different time intervals are compared to detect anomaly based on the calculation of chi square distribution (χ(2)). We evaluated our approach on four types of items which are defined according to the life cycles of these items. The experimental results show that the proposed approach achieves a high detection rate with low computational cost when the number of attack profiles is more than 15. It improves the efficiency in shilling attacks detection by narrowing down the suspicious users. PMID:24693248

  2. Detection of Abnormal Item Based on Time Intervals for Recommender Systems

    PubMed Central

    Yuan, Quan; Ling, Bin; Xiong, Qingyu

    2014-01-01

    With the rapid development of e-business, personalized recommendation has become core competence for enterprises to gain profits and improve customer satisfaction. Although collaborative filtering is the most successful approach for building a recommender system, it suffers from “shilling” attacks. In recent years, the research on shilling attacks has been greatly improved. However, the approaches suffer from serious problem in attack model dependency and high computational cost. To solve the problem, an approach for the detection of abnormal item is proposed in this paper. In the paper, two common features of all attack models are analyzed at first. A revised bottom-up discretized approach is then proposed based on time intervals and the features for the detection. The distributions of ratings in different time intervals are compared to detect anomaly based on the calculation of chi square distribution (χ2). We evaluated our approach on four types of items which are defined according to the life cycles of these items. The experimental results show that the proposed approach achieves a high detection rate with low computational cost when the number of attack profiles is more than 15. It improves the efficiency in shilling attacks detection by narrowing down the suspicious users. PMID:24693248

  3. Development of the IDC Infrasound Event Detection Pipeline

    NASA Astrophysics Data System (ADS)

    Mialle, P.; Bittner, P.; Brown, D.; Given, J. W.

    2012-12-01

    The first atmospheric event built only from infrasound arrivals was reported in the Reviewed Event Bulletin (REB) of the International Data Centre (IDC) of the Comprehensive Nuclear Test Ban Treaty Organization (CTBTO) in 2003. In the subsequent decade, 45 infrasound stations from the International Monitoring System (IMS) have been installed and are transmitting continuous data to the IDC. The growing amount of infrasound data and detections produced by the automatic system challenges the station and network processing at the IDC and requires the Organization to improve the infrasound data processing. In 2010, the IDC began full-time operational automatic processing of infrasound data followed by interactive analysis. The detected and located events are systematically included in the analyst-reviewed Late Event Bulletin (LEB) and REB. Approximately 16% of SEL3 (Selected Event List 3, automatically produced 6 hours after real-time) events with associated infrasound signals pass interactive analysis and make it to the IDC bulletins. 41% of those SEL3 events rejected after review have only 2 associated infrasound phases (and possibly other seismic and hydro-acoustic detections). Therefore, the process whereby infrasound detections are associated with events needs to be investigated further. The objective of this study is to reduce the number of associated infrasound arrivals that are falsely associated during the creation of the SEL3. There are two parts to the study. First, the detection accuracy at the infrasound arrays is improved by improving the infrasound signal detector, which is based on the PMCC (Progressive Multi-Channel Correlation) algorithm. The second part focuses on improving the reliability of the association algorithm. The association algorithm is enhanced to include better characterization of the variable atmospheric phenomena, which profoundly affect the detection patterns of the infrasound signals. The algorithm is then further tuned to reduce the

  4. The effects of anatomical information and observer expertise on abnormality detection task

    NASA Astrophysics Data System (ADS)

    Zhang, L.; Cavaro-Ménard, C.; Le Callet, P.; Cooper, L. H. K.; Hunault, G.; Tanguy, J.-Y.

    2011-03-01

    This paper presents a novel study investigating the influences of Magnetic Resonance (MR) image anatomical information and observer expertise on an abnormality detection task. MRI is exquisitely sensitive for detecting brain abnormalities, particularly in the evaluation of white matter diseases, e.g. multiple sclerosis (MS). For this reason, MS lesions are simulated as the target stimuli for detection in the present study. Two different image backgrounds are used in the following experiments: a) homogeneous region of white matter tissue, and b) one slice of a healthy brain MR image. One expert radiologist (more than 10 years' experience), three radiologists (less than 5 years' experience) and eight naïve observers (without any prior medical knowledge) have performed these experiments, during which they have been asked different questions dependent upon level of experience; the three radiologists and eight naïve observers were asked if they were aware of any hyper-signal, likely to represent an MS lesion, while the most experienced consultant was asked if a clinically significant sign was present. With the percentages of response "yes" displayed on the y-axis and the lesion intensity contrasts on the x-axis, psychometric function is generated from the observer' responses. Results of psychometric functions and calculated thresholds indicate that radiologists have better hyper-signal detection ability than naïve observers, which is intuitively shown by the lower simple visibility thresholds of radiologists. However, when radiologists perform a task with clinical implications, e.g. to detect a clinically significant sign, their detection thresholds are elevated. Moreover, the study indicates that for the radiologists, the simple visibility thresholds remain the same with and without the anatomical information, which reduces the threshold for the clinically significant sign detection task. Findings provide further insight into human visual system processing for this

  5. Signal Detection of Adverse Drug Reaction of Amoxicillin Using the Korea Adverse Event Reporting System Database.

    PubMed

    Soukavong, Mick; Kim, Jungmee; Park, Kyounghoon; Yang, Bo Ram; Lee, Joongyub; Jin, Xue Mei; Park, Byung Joo

    2016-09-01

    We conducted pharmacovigilance data mining for a β-lactam antibiotics, amoxicillin, and compare the adverse events (AEs) with the drug labels of 9 countries including Korea, USA, UK, Japan, Germany, Swiss, Italy, France, and Laos. We used the Korea Adverse Event Reporting System (KAERS) database, a nationwide database of AE reports, between December 1988 and June 2014. Frequentist and Bayesian methods were used to calculate disproportionality distribution of drug-AE pairs. The AE which was detected by all the three indices of proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC) was defined as a signal. The KAERS database contained a total of 807,582 AE reports, among which 1,722 reports were attributed to amoxicillin. Among the 192,510 antibiotics-AE pairs, the number of amoxicillin-AE pairs was 2,913. Among 241 AEs, 52 adverse events were detected as amoxicillin signals. Comparing the drug labels of 9 countries, 12 adverse events including ineffective medicine, bronchitis, rhinitis, sinusitis, dry mouth, gastroesophageal reflux, hypercholesterolemia, gastric carcinoma, abnormal crying, induration, pulmonary carcinoma, and influenza-like symptoms were not listed on any of the labels of nine countries. In conclusion, we detected 12 new signals of amoxicillin which were not listed on the labels of 9 countries. Therefore, it should be followed by signal evaluation including causal association, clinical significance, and preventability. PMID:27510377

  6. Signal Detection of Adverse Drug Reaction of Amoxicillin Using the Korea Adverse Event Reporting System Database

    PubMed Central

    2016-01-01

    We conducted pharmacovigilance data mining for a β-lactam antibiotics, amoxicillin, and compare the adverse events (AEs) with the drug labels of 9 countries including Korea, USA, UK, Japan, Germany, Swiss, Italy, France, and Laos. We used the Korea Adverse Event Reporting System (KAERS) database, a nationwide database of AE reports, between December 1988 and June 2014. Frequentist and Bayesian methods were used to calculate disproportionality distribution of drug-AE pairs. The AE which was detected by all the three indices of proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC) was defined as a signal. The KAERS database contained a total of 807,582 AE reports, among which 1,722 reports were attributed to amoxicillin. Among the 192,510 antibiotics-AE pairs, the number of amoxicillin-AE pairs was 2,913. Among 241 AEs, 52 adverse events were detected as amoxicillin signals. Comparing the drug labels of 9 countries, 12 adverse events including ineffective medicine, bronchitis, rhinitis, sinusitis, dry mouth, gastroesophageal reflux, hypercholesterolemia, gastric carcinoma, abnormal crying, induration, pulmonary carcinoma, and influenza-like symptoms were not listed on any of the labels of nine countries. In conclusion, we detected 12 new signals of amoxicillin which were not listed on the labels of 9 countries. Therefore, it should be followed by signal evaluation including causal association, clinical significance, and preventability. PMID:27510377

  7. Clinical Utility of Array Comparative Genomic Hybridization for Detection of Chromosomal Abnormalities in Pediatric Acute Lymphoblastic Leukemia

    PubMed Central

    Rabin, Karen R.; Man, Tsz-Kwong; Yu, Alexander; Folsom, Matthew R.; Zhao, Yi-Jue; Rao, Pulivarthi H.; Plon, Sharon E.; Naeem, Rizwan C.

    2014-01-01

    Background Accurate detection of recurrent chromosomal abnormalities is critical to assign patients to risk-based therapeutic regimens for pediatric acute lymphoblastic leukemia (ALL). Procedure We investigated the utility of array comparative genomic hybridization (aCGH) for detection of chromosomal abnormalities compared to standard clinical evaluation with karyotype and fluorescent in-situ hybridization (FISH). Fifty pediatric ALL diagnostic bone marrows were analyzed by bacterial artificial chromosome (BAC) array, and findings compared to standard clinical evaluation. Results Sensitivity of aCGH was 79% to detect karyotypic findings other than balanced translocations, which cannot be detected by aCGH because they involve no copy number change. aCGH also missed abnormalities occurring in subclones constituting less than 25% of cells. aCGH detected 44 additional abnormalities undetected or misidentified by karyotype, 21 subsequently validated by FISH, including abnormalities in 4 of 10 cases with uninformative cytogenetics. aCGH detected concurrent terminal deletions of both 9p and 20q in three cases, in two of which the 20q deletion was undetected by karyotype. A narrow region of loss at 7p21 was detected in two cases. Conclusions An array with increased BAC density over regions important in ALL, combined with PCR for fusion products of balanced translocations, could minimize labor- and time-intensive cytogenetic assays and provide key prognostic information in the approximately 35% of cases with uninformative cytogenetics. PMID:18253961

  8. Context-aware event detection smartphone application for first responders

    NASA Astrophysics Data System (ADS)

    Boddhu, Sanjay K.; Dave, Rakesh P.; McCartney, Matt; West, James A.; Williams, Robert L.

    2013-05-01

    The rise of social networking platforms like Twitter, Facebook, etc…, have provided seamless sharing of information (as chat, video and other media) among its user community on a global scale. Further, the proliferation of the smartphones and their connectivity networks has powered the ordinary individuals to share and acquire information regarding the events happening in his/her immediate vicinity in a real-time fashion. This human-centric sensed data being generated in "human-as-sensor" approach is tremendously valuable as it delivered mostly with apt annotations and ground truth that would be missing in traditional machine-centric sensors, besides high redundancy factor (same data thru multiple users). Further, when appropriately employed this real-time data can support in detecting localized events like fire, accidents, shooting, etc…, as they unfold and pin-point individuals being affected by those events. This spatiotemporal information, when made available for first responders in the event vicinity (or approaching it) can greatly assist them to make effective decisions to protect property and life in a timely fashion. In this vein, under SATE and YATE programs, the research team at AFRL Tec^Edge Discovery labs had demonstrated the feasibility of developing Smartphone applications, that can provide a augmented reality view of the appropriate detected events in a given geographical location (localized) and also provide an event search capability over a large geographic extent. In its current state, the application thru its backend connectivity utilizes a data (Text & Image) processing framework, which deals with data challenges like; identifying and aggregating important events, analyzing and correlating the events temporally and spatially and building a search enabled event database. Further, the smartphone application with its backend data processing workflow has been successfully field tested with live user generated feeds.

  9. B cells from patients with systemic lupus erythematosus display abnormal antigen receptor-mediated early signal transduction events.

    PubMed Central

    Liossis, S N; Kovacs, B; Dennis, G; Kammer, G M; Tsokos, G C

    1996-01-01

    To understand the molecular mechanisms that are responsible for the B cell overactivity that is observed in patients with SLE, we have conducted experiments in which the surface immunoglobulin (sIg)-mediated early cell signaling events were studied. The anti-sIgM-mediated free intracytoplasmic calcium ([Ca2+]i) responses were significantly higher in SLE B cells compared with responses of normal individuals and to those of patients with other systemic autoimmune rheumatic diseases. The anti-IgD mAb induced [Ca2+]i responses were also higher in lupus B cells than in controls. The magnitude of anti-sIgM-mediated Ca2+ release from intracellular stores was also increased in B cells from SLE patients compared with normal controls. The amount of inositol phosphate metabolites produced upon crosslinking of sIgM was slightly higher in patients with lupus than in normal controls, although the difference was not statistically significant. In contrast, the degree of anti-sIgM-induced protein tyrosine phosphorylation was obviously increased in lupus patients. Our study demonstrates clearly for the first time that SLE B cells exhibit aberrant early signal transduction events, including augmented calcium responses after crosslinking of the B cell receptor and increased antigen-receptor-mediated phosphorylation of protein tyrosine residues. Because the above abnormalities did not correlate with disease activity or treatment status, we propose that they may have pathogenic significance. PMID:8958217

  10. Summary of gas release events detected by hydrogen monitoring

    SciTech Connect

    MCCAIN, D.J.

    1999-05-18

    This paper summarizes the results of monitoring tank headspace for flammable gas release events. In over 40 tank years of monitoring the largest detected release in a single-shell tank is 2.4 cubic meters of Hydrogen. In the double-shell tanks the largest release is 19.3 cubic meters except in SY-101 pre mixer pump installation condition.

  11. Detection of abnormalities in febrile AIDS patients with In-111-labeled leukocyte and Ga-67 scintigraphy

    SciTech Connect

    Fineman, D.S.; Palestro, C.J.; Kim, C.K.; Needle, L.B.; Vallabhajosula, S.; Solomon, R.W.; Goldsmith, S.J.

    1989-03-01

    Thirty-six patients with acquired immunodeficiency syndrome (AIDS), who were febrile but without localizing signs, underwent indium-111 leukocyte scintigraphy 24 hours after injection of labeled white blood cells (WBCs) and were restudied 48 hours after injection of gallium-67 citrate. Fifty-six abnormalities were identified as possible sources of the fever; 27 were confirmed with biopsy. Of these 27, 15 were identified only on In-111 WBC scans (including colitis, sinusitis, and focal bacterial pneumonia); six, only on Ga-67 scans (predominantly Pneumocystis carinii pneumonia and lymphadenopathy); and six, on both studies (predominantly pulmonary lesions). In-111 WBC scanning revealed 21 of 27 abnormalities (78%) and gallium scanning, 12 of 27 (44%). If only one scintigraphic study has been performed, particularly with Ga-67, a significant number of lesions would not have been detected. The authors believe radionuclide evaluation of the febrile AIDS patient without localizing signs should begin with In-111 WBC scintigraphy. Gallium scanning may be used depending on results of In-111 WBC scans or if there is a high index of suspicion for P carinii pneumonia.

  12. Context and quality estimation in video for enhanced event detection

    NASA Astrophysics Data System (ADS)

    Irvine, John M.; Wood, Richard J.

    2015-05-01

    Numerous practical applications for automated event recognition in video rely on analysis of the objects and their associated motion, i.e., the kinematics of the scene. The ability to recognize events in practice depends on accurate tracking objects of interest in the video data and accurate recognition of changes relative to the background. Numerous factors can degrade the performance of automated algorithms. Our object detection and tracking algorithms estimate the object position and attributes within the context of a dynamic assessment of video quality, to provide more reliable event recognition under challenging conditions. We present an approach to robustly modeling the image quality which informs tuning parameters to use for a given video stream. The video quality model rests on a suite of image metrics computed in real-time from the video. We will describe the formulation of the image quality model. Results from a recent experiment will quantify the empirical performance for recognition of events of interest.

  13. Human visual system-based smoking event detection

    NASA Astrophysics Data System (ADS)

    Odetallah, Amjad D.; Agaian, Sos S.

    2012-06-01

    Human action (e.g. smoking, eating, and phoning) analysis is an important task in various application domains like video surveillance, video retrieval, human-computer interaction systems, and so on. Smoke detection is a crucial task in many video surveillance applications and could have a great impact to raise the level of safety of urban areas, public parks, airplanes, hospitals, schools and others. The detection task is challenging since there is no prior knowledge about the object's shape, texture and color. In addition, its visual features will change under different lighting and weather conditions. This paper presents a new scheme of a system for detecting human smoking events, or small smoke, in a sequence of images. In developed system, motion detection and background subtraction are combined with motion-region-saving, skin-based image segmentation, and smoke-based image segmentation to capture potential smoke regions which are further analyzed to decide on the occurrence of smoking events. Experimental results show the effectiveness of the proposed approach. As well, the developed method is capable of detecting the small smoking events of uncertain actions with various cigarette sizes, colors, and shapes.

  14. Early Lung Cancer Detection in Uranium Miners with Abnormal Sputum Cytology

    SciTech Connect

    Saccomanno, G.

    2000-06-30

    ''Early Lung Cancer Detection in Uranium Miners with Abnormal Sputum Cytology'' was funded by the Department of Energy to monitor the health effects of radon exposure and/or cigarette smoke on uranium workers from the Colorado Plateau. The resulting Saccomanno Uranium Workers Archive and data base has been used as a source of information to prove eligibility for compensation under the Radiation Exposure Compensation Act and as the source of primary data tissue for a subcontract and other collaborations with outside investigators. The latter includes a study of radon exposure and lung cancer risk in a non-smoking cohort of uranium miners (subcontract); a study of genetic markers for lung cancer susceptibility; and a study of {sup 210}Pb accumulation in the skull as a biomarker of radon exposure.

  15. Frequency of abnormal findings detected by comprehensive clinical evaluation at 1 year after allogeneic hematopoietic cell transplantation.

    PubMed

    Lee, Stephanie J; Seaborn, Travis; Mao, Frances J; Massey, Susan C; Luu, Ngoc Q; Schubert, Mary A; Chien, Jason W; Carpenter, Paul A; Moravec, Carina; Martin, Paul J; Flowers, Mary E D

    2009-04-01

    Consensus guidelines recommend various screening examinations for survivors after allogeneic hematopoietic cell transplantation (HCT), but how often these examinations detect abnormal findings is unknown. We reviewed the medical records of 118 patients who received comprehensive, standardized evaluations at 1 year after allogeneic HCT at Fred Hutchinson Cancer Research Center/Seattle Cancer Care Alliance. Abnormal findings were common, including moderate to severe pulmonary dysfunction (16%), fasting hyperlipidemia (56%), osteopenia (52%), osteoporosis (6%), and active chronic graft-versus-host disease (cGVHD) (64%). Recurrent malignancy (4%) and cGVHD (29%) were detected in previously unsuspected cases. Only 3% of patients had no abnormal findings. We conclude that comprehensive evaluation at 1 year after allogeneic HCT detects a high prevalence of medical problems. Longer follow-up is needed to determine whether early detection and intervention affect later morbidity and mortality. PMID:19285628

  16. Array-Based Comparative Genomic Hybridization for the Genomewide Detection of Submicroscopic Chromosomal Abnormalities

    PubMed Central

    Vissers, Lisenka E. L. M. ; de Vries, Bert B. A. ; Osoegawa, Kazutoyo ; Janssen, Irene M. ; Feuth, Ton ; Choy, Chik On ; Straatman, Huub ; van der Vliet, Walter ; Huys, Erik H. L. P. G. ; van Rijk, Anke ; Smeets, Dominique ; van Ravenswaaij-Arts, Conny M. A. ; Knoers, Nine V. ; van der Burgt, Ineke ; de Jong, Pieter J. ; Brunner, Han G. ; van Kessel, Ad Geurts ; Schoenmakers, Eric F. P. M. ; Veltman, Joris A. 

    2003-01-01

    Microdeletions and microduplications, not visible by routine chromosome analysis, are a major cause of human malformation and mental retardation. Novel high-resolution, whole-genome technologies can improve the diagnostic detection rate of these small chromosomal abnormalities. Array-based comparative genomic hybridization allows such a high-resolution screening by hybridizing differentially labeled test and reference DNAs to arrays consisting of thousands of genomic clones. In this study, we tested the diagnostic capacity of this technology using ∼3,500 flourescent in situ hybridization–verified clones selected to cover the genome with an average of 1 clone per megabase (Mb). The sensitivity and specificity of the technology were tested in normal-versus-normal control experiments and through the screening of patients with known microdeletion syndromes. Subsequently, a series of 20 cytogenetically normal patients with mental retardation and dysmorphisms suggestive of a chromosomal abnormality were analyzed. In this series, three microdeletions and two microduplications were identified and validated. Two of these genomic changes were identified also in one of the parents, indicating that these are large-scale genomic polymorphisms. Deletions and duplications as small as 1 Mb could be reliably detected by our approach. The percentage of false-positive results was reduced to a minimum by use of a dye-swap-replicate analysis, all but eliminating the need for laborious validation experiments and facilitating implementation in a routine diagnostic setting. This high-resolution assay will facilitate the identification of novel genes involved in human mental retardation and/or malformation syndromes and will provide insight into the flexibility and plasticity of the human genome. PMID:14628292

  17. Automatic event detection based on artificial neural networks

    NASA Astrophysics Data System (ADS)

    Doubravová, Jana; Wiszniowski, Jan; Horálek, Josef

    2015-04-01

    The proposed algorithm was developed to be used for Webnet, a local seismic network in West Bohemia. The Webnet network was built to monitor West Bohemia/Vogtland swarm area. During the earthquake swarms there is a large number of events which must be evaluated automatically to get a quick estimate of the current earthquake activity. Our focus is to get good automatic results prior to precise manual processing. With automatic data processing we may also reach a lower completeness magnitude. The first step of automatic seismic data processing is the detection of events. To get a good detection performance we require low number of false detections as well as high number of correctly detected events. We used a single layer recurrent neural network (SLRNN) trained by manual detections from swarms in West Bohemia in the past years. As inputs of the SLRNN we use STA/LTA of half-octave filter bank fed by vertical and horizontal components of seismograms. All stations were trained together to obtain the same network with the same neuron weights. We tried several architectures - different number of neurons - and different starting points for training. Networks giving the best results for training set must not be the optimal ones for unknown waveforms. Therefore we test each network on test set from different swarm (but still with similar characteristics, i.e. location, focal mechanisms, magnitude range). We also apply a coincidence verification for each event. It means that we can lower the number of false detections by rejecting events on one station only and force to declare an event on all stations in the network by coincidence on two or more stations. In further work we would like to retrain the network for each station individually so each station will have its own coefficients (neural weights) set. We would also like to apply this method to data from Reykjanet network located in Reykjanes peninsula, Iceland. As soon as we have a reliable detection, we can proceed to

  18. Real-Time Plasma Process Condition Sensing and Abnormal Process Detection

    PubMed Central

    Yang, Ryan; Chen, Rongshun

    2010-01-01

    The plasma process is often used in the fabrication of semiconductor wafers. However, due to the lack of real-time etching control, this may result in some unacceptable process performances and thus leads to significant waste and lower wafer yield. In order to maximize the product wafer yield, a timely and accurately process fault or abnormal detection in a plasma reactor is needed. Optical emission spectroscopy (OES) is one of the most frequently used metrologies in in-situ process monitoring. Even though OES has the advantage of non-invasiveness, it is required to provide a huge amount of information. As a result, the data analysis of OES becomes a big challenge. To accomplish real-time detection, this work employed the sigma matching method technique, which is the time series of OES full spectrum intensity. First, the response model of a healthy plasma spectrum was developed. Then, we defined a matching rate as an indictor for comparing the difference between the tested wafers response and the health sigma model. The experimental results showed that this proposal method can detect process faults in real-time, even in plasma etching tools. PMID:22219683

  19. Detecting rare gene transfer events in bacterial populations.

    PubMed

    Nielsen, Kaare M; Bøhn, Thomas; Townsend, Jeffrey P

    2014-01-01

    Horizontal gene transfer (HGT) enables bacteria to access, share, and recombine genetic variation, resulting in genetic diversity that cannot be obtained through mutational processes alone. In most cases, the observation of evolutionary successful HGT events relies on the outcome of initially rare events that lead to novel functions in the new host, and that exhibit a positive effect on host fitness. Conversely, the large majority of HGT events occurring in bacterial populations will go undetected due to lack of replication success of transformants. Moreover, other HGT events that would be highly beneficial to new hosts can fail to ensue due to lack of physical proximity to the donor organism, lack of a suitable gene transfer mechanism, genetic compatibility, and stochasticity in tempo-spatial occurrence. Experimental attempts to detect HGT events in bacterial populations have typically focused on the transformed cells or their immediate offspring. However, rare HGT events occurring in large and structured populations are unlikely to reach relative population sizes that will allow their immediate identification; the exception being the unusually strong positive selection conferred by antibiotics. Most HGT events are not expected to alter the likelihood of host survival to such an extreme extent, and will confer only minor changes in host fitness. Due to the large population sizes of bacteria and the time scales involved, the process and outcome of HGT are often not amenable to experimental investigation. Population genetic modeling of the growth dynamics of bacteria with differing HGT rates and resulting fitness changes is therefore necessary to guide sampling design and predict realistic time frames for detection of HGT, as it occurs in laboratory or natural settings. Here we review the key population genetic parameters, consider their complexity and highlight knowledge gaps for further research. PMID:24432015

  20. Detecting rare gene transfer events in bacterial populations

    PubMed Central

    Nielsen, Kaare M.; Bøhn, Thomas; Townsend, Jeffrey P.

    2014-01-01

    Horizontal gene transfer (HGT) enables bacteria to access, share, and recombine genetic variation, resulting in genetic diversity that cannot be obtained through mutational processes alone. In most cases, the observation of evolutionary successful HGT events relies on the outcome of initially rare events that lead to novel functions in the new host, and that exhibit a positive effect on host fitness. Conversely, the large majority of HGT events occurring in bacterial populations will go undetected due to lack of replication success of transformants. Moreover, other HGT events that would be highly beneficial to new hosts can fail to ensue due to lack of physical proximity to the donor organism, lack of a suitable gene transfer mechanism, genetic compatibility, and stochasticity in tempo-spatial occurrence. Experimental attempts to detect HGT events in bacterial populations have typically focused on the transformed cells or their immediate offspring. However, rare HGT events occurring in large and structured populations are unlikely to reach relative population sizes that will allow their immediate identification; the exception being the unusually strong positive selection conferred by antibiotics. Most HGT events are not expected to alter the likelihood of host survival to such an extreme extent, and will confer only minor changes in host fitness. Due to the large population sizes of bacteria and the time scales involved, the process and outcome of HGT are often not amenable to experimental investigation. Population genetic modeling of the growth dynamics of bacteria with differing HGT rates and resulting fitness changes is therefore necessary to guide sampling design and predict realistic time frames for detection of HGT, as it occurs in laboratory or natural settings. Here we review the key population genetic parameters, consider their complexity and highlight knowledge gaps for further research. PMID:24432015

  1. Comparison of Event Detection Methods for Centralized Sensor Networks

    NASA Technical Reports Server (NTRS)

    Sauvageon, Julien; Agogiono, Alice M.; Farhang, Ali; Tumer, Irem Y.

    2006-01-01

    The development of an Integrated Vehicle Health Management (IVHM) for space vehicles has become a great concern. Smart Sensor Networks is one of the promising technologies that are catching a lot of attention. In this paper, we propose to a qualitative comparison of several local event (hot spot) detection algorithms in centralized redundant sensor networks. The algorithms are compared regarding their ability to locate and evaluate the event under noise and sensor failures. The purpose of this study is to check if the ratio performance/computational power of the Mote Fuzzy Validation and Fusion algorithm is relevant compare to simpler methods.

  2. Detection and interpretation of seismoacoustic events at German infrasound stations

    NASA Astrophysics Data System (ADS)

    Pilger, Christoph; Koch, Karl; Ceranna, Lars

    2016-04-01

    Three infrasound arrays with collocated or nearby installed seismometers are operated by the Federal Institute for Geosciences and Natural Resources (BGR) as the German National Data Center (NDC) for the verification of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). Infrasound generated by seismoacoustic events is routinely detected at these infrasound arrays, but air-to-ground coupled acoustic waves occasionally show up in seismometer recordings as well. Different natural and artificial sources like meteoroids as well as industrial and mining activity generate infrasonic signatures that are simultaneously detected at microbarometers and seismometers. Furthermore, many near-surface sources like earthquakes and explosions generate both seismic and infrasonic waves that can be detected successively with both technologies. The combined interpretation of seismic and acoustic signatures provides additional information about the origin time and location of remote infrasound events or about the characterization of seismic events distinguishing man-made and natural origins. Furthermore, seismoacoustic studies help to improve the modelling of infrasound propagation and ducting in the atmosphere and allow quantifying the portion of energy coupled into ground and into air by seismoacoustic sources. An overview of different seismoacoustic sources and their detection by German infrasound stations as well as some conclusions on the benefit of a combined seismoacoustic analysis are presented within this study.

  3. Prenatal aneupioidy detection by fluorencence in situ hybridization (FISH) in 1,068 second trimester pregnancies with fetal ultrasound abnormalities

    SciTech Connect

    Ward, B.E.; Wright, M.; Lytle, C. |

    1994-09-01

    One indication for rapid prenatal aneuploidy detection in uncultured amniocytes by FISH is the identification of fetal abnormalities by ultrasound. We analyzed 1,068 consecutive specimens from second trimester pregnancies with fetal ultrasound abnormalities referred for FISH plus cytogenetics. These specimens are a subset (14.7%) of the most recent 7,240 clinical referrals for these combined analyses. Hybridization with specific probes for chromosomes 21, 18, 13, X and Y were used to detect common aneuploidies. As defined by previously described criteria, specimens were reported as informative disomic, informative trisomic, or uninformative within two days of receipt. The rate of informative results from acceptable specimens was 90.1%. The vast majority of uninformative results was due to maternal cell contamination which precluded analysis. Within the informative group there were no false positives, false negatives nor reports of incorrect gender. Of the 1,068 tested specimens with ultrasound abnormalities, 135 (12.5%) were cytogenetically diagnosed as aneuploid. Prior to the cytogenetic analysis, a total of 107 aneuploidies were correctly identified by FISH. The remaining 26 aneuploidies generated an uninformative FISH result. The overall FISH detection rate for aneuploidy (including informative and uninformative results) was 79%. Other unbalanced chromosome abnormalities were present in 2.1% of specimens and 0.7% had balanced chromosome abnormalities. The inclusive total cytogenetic abnormality rate was 15.4%, of which 85% were potentially detectable by our FISH protocol. This clinical experience demonstrates that aneuploidy detection by FISH on uncultured amniocytes can provide accurate and rapid identification of aneuploidies, especially when such abnormalities are suspected following the diagnosis of fetal anomalies by ultrasound examination.

  4. PMU Data Event Detection: A User Guide for Power Engineers

    SciTech Connect

    Allen, A.; Singh, M.; Muljadi, E.; Santoso, S.

    2014-10-01

    This user guide is intended to accompany a software package containing a Matrix Laboratory (MATLAB) script and related functions for processing phasor measurement unit (PMU) data. This package and guide have been developed by the National Renewable Energy Laboratory and the University of Texas at Austin. The objective of this data processing exercise is to discover events in the vast quantities of data collected by PMUs. This document attempts to cover some of the theory behind processing the data to isolate events as well as the functioning of the MATLAB scripts. The report describes (1) the algorithms and mathematical background that the accompanying MATLAB codes use to detect events in PMU data and (2) the inputs required from the user and the outputs generated by the scripts.

  5. Look what else we found - clinically significant abnormalities detected during routine ROP screening

    PubMed Central

    Jayadev, Chaitra; Vinekar, Anand; Bauer, Noel; Mangalesh, Shwetha; Mahendradas, Padmamalini; Kemmanu, Vasudha; Mallipatna, Ashwin; Shetty, Bhujang

    2015-01-01

    Purpose: The purpose of this study was to report the spectrum of anterior and posterior segment diagnoses in Asian Indian premature infants detected serendipitously during routine retinopathy of prematurity (ROP) screening during a 1 year period. Methods: A retrospective review of all Retcam (Clarity MSI, USA) imaging sessions during the year 2011 performed on infants born either <2001 g at birth and/or <34.1 weeks of gestation recruited for ROP screening was performed. All infants had a minimum of seven images at each session, which included the dilated anterior segment, disc, and macula center and the four quadrants using the 130° lens. Results: Of the 8954 imaging sessions of 1450 new infants recruited in 2011, there were 111 (7.66%) with a diagnosis other than ROP. Anterior segment diagnoses seen in 31 (27.9%) cases included clinically significant cataract, lid abnormalities, anophthalmos, microphthalmos, and corneal diseases. Posterior segment diagnoses in 80 (72.1%) cases included retinal hemorrhages, cherry red spots, and neonatal uveitis of infective etiologies. Of the 111 cases, 15 (13.5%) underwent surgical procedures and 24 (21.6%) underwent medical procedures; importantly, two eyes with retinoblastoma were detected which were managed timely. Conclusions: This study emphasizes the importance of ocular digital imaging in premature infants. Visually significant, potentially life-threatening, and even treatable conditions were detected serendipitously during routine ROP screening that may be missed or detected late otherwise. This pilot data may be used to advocate for a possible universal infant eye screening program using digital imaging. PMID:26139795

  6. Detection of abnormal cardiac activity using principal component analysis--a theoretical study.

    PubMed

    Greisas, Ariel; Zafrir, Zohar; Zlochiver, Sharon

    2015-01-01

    Electrogram-guided ablation has been recently developed for allowing better detection and localization of abnormal atrial activity that may be the source of arrhythmogeneity. Nevertheless, no clear indication for the benefit of using electrograms guided ablation over empirical ablation was established thus far, and there is a clear need of improving the localization of cardiac arrhythmogenic targets for ablation. In this paper, we propose a new approach for detection and localization of irregular cardiac activity during ablation procedures that is based on dimension reduction algorithms and principal component analysis (PCA). Using an 8×8 electrode array, our method produces manifolds that allow easy visualization and detection of possible arrhythmogenic ablation targets characterized by irregular conduction. We employ mathematical modeling and computer simulations to demonstrate the feasibility of the new approach for two well established arrhythmogenic sources for irregular conduction--spiral waves and patchy fibrosis. Our results show that the PCA method can differentiate between focal ectopic activity and spiral wave activity, as these two types of activity produce substantially different manifold shapes. Moreover, the technique allows the detection of spiral wave cores and their general meandering and drifting pattern. Fibrotic patches larger than 2 mm(2) could also be visualized using the PCA method, both for quiescent atrial tissue and for tissue exhibiting spiral wave activity. We envision that this method, contingent to further numerical and experimental validation studies in more complex, realistic geometrical configurations and with clinical data, can improve existing atrial ablation mapping capabilities, thus increasing success rates and optimizing arrhythmia management. PMID:25073163

  7. Gait Event Detection during Stair Walking Using a Rate Gyroscope

    PubMed Central

    Formento, Paola Catalfamo; Acevedo, Ruben; Ghoussayni, Salim; Ewins, David

    2014-01-01

    Gyroscopes have been proposed as sensors for ambulatory gait analysis and functional electrical stimulation systems. These applications often require detection of the initial contact (IC) of the foot with the floor and/or final contact or foot off (FO) from the floor during outdoor walking. Previous investigations have reported the use of a single gyroscope placed on the shank for detection of IC and FO on level ground and incline walking. This paper describes the evaluation of a gyroscope placed on the shank for determination of IC and FO in subjects ascending and descending a set of stairs. Performance was compared with a reference pressure measurement system. The absolute mean difference between the gyroscope and the reference was less than 45 ms for IC and better than 135 ms for FO for both activities. Detection success was over 93%. These results provide preliminary evidence supporting the use of a gyroscope for gait event detection when walking up and down stairs. PMID:24651724

  8. Integrated Seismic Event Detection and Location by Advanced Array Processing

    SciTech Connect

    Kvaerna, T; Gibbons, S J; Ringdal, F; Harris, D B

    2007-02-09

    The principal objective of this two-year study is to develop and test a new advanced, automatic approach to seismic detection/location using array processing. We address a strategy to obtain significantly improved precision in the location of low-magnitude events compared with current fully-automatic approaches, combined with a low false alarm rate. We have developed and evaluated a prototype automatic system which uses as a basis regional array processing with fixed, carefully calibrated, site-specific parameters in conjuction with improved automatic phase onset time estimation. We have in parallel developed tools for Matched Field Processing for optimized detection and source-region identification of seismic signals. This narrow-band procedure aims to mitigate some of the causes of difficulty encountered using the standard array processing system, specifically complicated source-time histories of seismic events and shortcomings in the plane-wave approximation for seismic phase arrivals at regional arrays.

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

    SciTech Connect

    Botvinick, E.H.; Frais, M.A.; Shosa, D.W.; O'Connell, J.W.; Pacheco-Alvarez, J.A.; Scheinman, M.; Hattner, R.S.; Morady, F.; Faulkner, D.B.

    1982-08-01

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

  10. Detection of Abnormal Hemoglobin Variants by HPLC Method: Common Problems with Suggested Solutions

    PubMed Central

    Pant, Leela; Kalita, Dipti; Singh, Sompal; Kudesia, Madhur; Mendiratta, Sumanlata; Mittal, Meenakshi; Mathur, Alka

    2014-01-01

    Thalassemia and thalassemic hemoglobinopathies pose serious health problem leading to severe morbidity and mortality in Indian population. Plethora of hemoglobin variants is prevalent in multiethnic Indian population. The aim of the present study was to analyze laboratory aspects, namely, hematological profile and HPLC findings of the hemoglobin variants detected, and to discuss problems that we faced in diagnosis in a routine clinical laboratory. We screened a total of 4800 cases in a hospital based population of North India in a 2-years period of by automated HPLC method using the Variant Hemoglobin Testing System (Variant II Beta Thalassemia Short Program, Bio-Rad Laboratories) under the experimental conditions specified by the manufacturer. Whole blood in EDTA was used and red cell indices were determined using automated hematology analyzer. We detected 290 cases with abnormal variants in which beta thalassemia was the most common followed by hemoglobin E. Here, we discuss the laboratory aspects of various hemoglobin disorders and diagnostic difficulties in cases like borderline HbA2 values, presence of silent mutation, alpha thalassemia gene, and few rare variants which at times require correlation with genetic study. Special attention was given to HbA2 level even in presence of a structural variant to rule out coinheritance of beta thalassemia gene. PMID:27351019

  11. Basic Characteristics of a Macroscopic Measure for Detecting Abnormal Changes in a Multiagent System

    PubMed Central

    Kinoshita, Tetsuo

    2015-01-01

    Multiagent application systems must deal with various changes in both the system and the system environment at runtime. Generally, such changes have undesirable negative effects on the system. To manage and control the system, it is important to observe and detect negative effects using an appropriate observation function of the system’s behavior. This paper focuses on the design of this function and proposes a new macroscopic measure with which to observe behavioral characteristics of a runtime multiagent system. The proposed measure is designed as the variance of fluctuation of a macroscopic activity factor of the whole system, based on theoretical analysis of the macroscopic behavioral model of a multiagent system. Experiments are conducted to investigate basic characteristics of the proposed measure, using a test bed system. The results of experiments show that the proposed measure reacts quickly and increases drastically in response to abnormal changes in the system. Hence, the proposed measure is considered a measure that can be used to detect undesirable changes in a multiagent system. PMID:25897499

  12. Detection of Abnormal Operation Noise Using CHLAC of Sound Spectrograph and Continuous DP Matching

    NASA Astrophysics Data System (ADS)

    Hattori, Koosuke; Ohmi, Taishi; Taguchi, Ryo; Umezaki, Taizo

    It is a general way that the industrial product is tested by individual inspector. If the product involves sound factors, each inspector will evaluate the test product to sort out a strange engine noise from the natural sound. However, it is hard to cover the consistency in evaluation criteria due to the personal equation referred to the idea that every individual had an inherent bias, plus a physical and mental conditions can be a negative effect on his/her evaluation criteria. It would be ideal if the criteria would not be affected by anyone, anywhere, circumstances; accordingly the quality of products must be equated. In this paper, we propose a noise detection method based on Cubic Higher-order Local Auto-Correlation (CHLAC) scheme and DP Matching provided by Cepstrum Analysis to extract the correct solution. This technique is practically used for detecting any human abnormal movements out of a monitored video clip and identifying individual persons by voice. The study results are shown to be highly effective in our proposed method.

  13. Abnormal Ventral and Dorsal Attention Network Activity during Single and Dual Target Detection in Schizophrenia

    PubMed Central

    Jimenez, Amy M.; Lee, Junghee; Wynn, Jonathan K.; Cohen, Mark S.; Engel, Stephen A.; Glahn, David C.; Nuechterlein, Keith H.; Reavis, Eric A.; Green, Michael F.

    2016-01-01

    Early visual perception and attention are impaired in schizophrenia, and these deficits can be observed on target detection tasks. These tasks activate distinct ventral and dorsal brain networks which support stimulus-driven and goal-directed attention, respectively. We used single and dual target rapid serial visual presentation (RSVP) tasks during fMRI with an ROI approach to examine regions within these networks associated with target detection and the attentional blink (AB) in 21 schizophrenia outpatients and 25 healthy controls. In both tasks, letters were targets and numbers were distractors. For the dual target task, the second target (T2) was presented at three different lags after the first target (T1) (lag1 = 100 ms, lag3 = 300 ms, lag7 = 700ms). For both single and dual target tasks, patients identified fewer targets than controls. For the dual target task, both groups showed the expected AB effect with poorer performance at lag 3 than at lags 1 or 7, and there was no group by lag interaction. During the single target task, patients showed abnormally increased deactivation of the temporo-parietal junction (TPJ), a key region of the ventral network. When attention demands were increased during the dual target task, patients showed overactivation of the posterior intraparietal cortex, a key dorsal network region, along with failure to deactivate TPJ. Results suggest inefficient and faulty suppression of salience-oriented processing regions, resulting in increased sensitivity to stimuli in general, and difficulty distinguishing targets from non-targets. PMID:27014135

  14. Statistical language analysis for automatic exfiltration event detection.

    SciTech Connect

    Robinson, David Gerald

    2010-04-01

    This paper discusses the recent development a statistical approach for the automatic identification of anomalous network activity that is characteristic of exfiltration events. This approach is based on the language processing method eferred to as latent dirichlet allocation (LDA). Cyber security experts currently depend heavily on a rule-based framework for initial detection of suspect network events. The application of the rule set typically results in an extensive list of uspect network events that are then further explored manually for suspicious activity. The ability to identify anomalous network events is heavily dependent on the experience of the security personnel wading through the network log. Limitations f this approach are clear: rule-based systems only apply to exfiltration behavior that has previously been observed, and experienced cyber security personnel are rare commodities. Since the new methodology is not a discrete rule-based pproach, it is more difficult for an insider to disguise the exfiltration events. A further benefit is that the methodology provides a risk-based approach that can be implemented in a continuous, dynamic or evolutionary fashion. This permits uspect network activity to be identified early with a quantifiable risk associated with decision making when responding to suspicious activity.

  15. Hidden Markov Models for Detecting Aseismic Events in Southern California

    NASA Astrophysics Data System (ADS)

    Granat, R.

    2004-12-01

    We employ a hidden Markov model (HMM) to segment surface displacement time series collection by the Southern California Integrated Geodetic Network (SCIGN). These segmented time series are then used to detect regional events by observing the number of simultaneous mode changes across the network; if a large number of stations change at the same time, that indicates an event. The hidden Markov model (HMM) approach assumes that the observed data has been generated by an unobservable dynamical statistical process. The process is of a particular form such that each observation is coincident with the system being in a particular discrete state, which is interpreted as a behavioral mode. The dynamics are the model are constructed so that the next state is directly dependent only on the current state -- it is a first order Markov process. The model is completely described by a set of parameters: the initial state probabilities, the first order Markov chain state-to-state transition probabilities, and the probability distribution of observable outputs associated with each state. The result of this approach is that our segmentation decisions are based entirely on statistical changes in the behavior of the observed daily displacements. In general, finding the optimal model parameters to fit the data is a difficult problem. We present an innovative model fitting method that is unsupervised (i.e., it requires no labeled training data) and uses a regularized version of the expectation-maximization (EM) algorithm to ensure that model solutions are both robust with respect to initial conditions and of high quality. We demonstrate the reliability of the method as compared to standard model fitting methods and show that it results in lower noise in the mode change correlation signal used to detect regional events. We compare candidate events detected by this method to the seismic record and observe that most are not correlated with a significant seismic event. Our analysis

  16. Model observer design for detecting multiple abnormalities in anatomical background images

    NASA Astrophysics Data System (ADS)

    Wen, Gezheng; Markey, Mia K.; Park, Subok

    2016-03-01

    As psychophysical studies are resource-intensive to conduct, model observers are commonly used to assess and optimize medical imaging quality. Existing model observers were typically designed to detect at most one signal. However, in clinical practice, there may be multiple abnormalities in a single image set (e.g., multifocal and multicentric breast cancers (MMBC)), which can impact treatment planning. Prevalence of signals can be different across anatomical regions, and human observers do not know the number or location of signals a priori. As new imaging techniques have the potential to improve multiple-signal detection (e.g., digital breast tomosynthesis may be more effective for diagnosis of MMBC than planar mammography), image quality assessment approaches addressing such tasks are needed. In this study, we present a model-observer mechanism to detect multiple signals in the same image dataset. To handle the high dimensionality of images, a novel implementation of partial least squares (PLS) was developed to estimate different sets of efficient channels directly from the images. Without any prior knowledge of the background or the signals, the PLS channels capture interactions between signals and the background which provide discriminant image information. Corresponding linear decision templates are employed to generate both image-level and location-specific scores on the presence of signals. Our preliminary results show that the model observer using PLS channels, compared to our first attempts with Laguerre-Gauss channels, can achieve high performance with a reasonably small number of channels, and the optimal design of the model observer may vary as the tasks of clinical interest change.

  17. Application of Kalman Filtering Techniques for Microseismic Event Detection

    NASA Astrophysics Data System (ADS)

    Baziw, E.; Weir-Jones, I.

    - Microseismic monitoring systems are generally installed in areas of induced seismicity caused by human activity. Induced seismicity results from changes in the state of stress which may occur as a result of excavation within the rock mass in mining (i.e., rockbursts), and changes in hydrostatic pressures and rock temperatures (e.g., during fluid injection or extraction) in oil exploitation, dam construction or fluid disposal. Microseismic monitoring systems determine event locations and important source parameters such as attenuation, seismic moment, source radius, static stress drop, peak particle velocity and seismic energy. An essential part of the operation of a microseismic monitoring system is the reliable detection of microseismic events. In the absence of reliable, automated picking techniques, operators rely upon manual picking. This is time-consuming, costly and, in the presence of background noise, very prone to error. The techniques described in this paper not only permit the reliable identification of events in cluttered signal environments they have also enabled the authors to develop reliable automated event picking procedures. This opens the way to use microseismic monitoring as a cost-effective production/operations procedure. It has been the experience of the authors that in certain noisy environments, the seismic monitoring system may trigger on and subsequently acquire substantial quantities of erroneous data, due to the high energy content of the ambient noise. Digital filtering techniques need to be applied on the microseismic data so that the ambient noise is removed and event detection simplified. The monitoring of seismic acoustic emissions is a continuous, real-time process and it is desirable to implement digital filters which can also be designed in the time domain and in real-time such as the Kalman Filter. This paper presents a real-time Kalman Filter which removes the statistically describable background noise from the recorded

  18. Multidimensional morphometric 3D MRI analyses for detecting brain abnormalities in children: impact of control population.

    PubMed

    Wilke, Marko; Rose, Douglas F; Holland, Scott K; Leach, James L

    2014-07-01

    Automated morphometric approaches are used to detect epileptogenic structural abnormalities in 3D MR images in adults, using the variance of a control population to obtain z-score maps in an individual patient. Due to the substantial changes the developing human brain undergoes, performing such analyses in children is challenging. This study investigated six features derived from high-resolution T1 datasets in four groups: normal children (1.5T or 3T data), normal clinical scans (3T data), and patients with structural brain lesions (3T data), with each n = 10. Normative control data were obtained from the NIH study on normal brain development (n = 401). We show that control group size substantially influences the captured variance, directly impacting the patient's z-scores. Interestingly, matching on gender does not seem to be beneficial, which was unexpected. Using data obtained at higher field scanners produces slightly different base rates of suprathreshold voxels, as does using clinically derived normal studies, suggesting a subtle but systematic effect of both factors. Two approaches for controlling suprathreshold voxels in a multidimensional approach (combining features and requiring a minimum cluster size) were shown to be substantial and effective in reducing this number. Finally, specific strengths and limitations of such an approach could be demonstrated in individual cases. PMID:25050423

  19. Swarm intelligence for detecting interesting events in crowded environments.

    PubMed

    Kaltsa, Vagia; Briassouli, Alexia; Kompatsiaris, Ioannis; Hadjileontiadis, Leontios J; Strintzis, Michael Gerasimos

    2015-07-01

    This paper focuses on detecting and localizing anomalous events in videos of crowded scenes, i.e., divergences from a dominant pattern. Both motion and appearance information are considered, so as to robustly distinguish different kinds of anomalies, for a wide range of scenarios. A newly introduced concept based on swarm theory, histograms of oriented swarms (HOS), is applied to capture the dynamics of crowded environments. HOS, together with the well-known histograms of oriented gradients, are combined to build a descriptor that effectively characterizes each scene. These appearance and motion features are only extracted within spatiotemporal volumes of moving pixels to ensure robustness to local noise, increase accuracy in the detection of local, nondominant anomalies, and achieve a lower computational cost. Experiments on benchmark data sets containing various situations with human crowds, as well as on traffic data, led to results that surpassed the current state of the art (SoA), confirming the method's efficacy and generality. Finally, the experiments show that our approach achieves significantly higher accuracy, especially for pixel-level event detection compared to SoA methods, at a low computational cost. PMID:25769154

  20. Endmember detection in marine environment with oil spill event

    NASA Astrophysics Data System (ADS)

    Andreou, Charoula; Karathanassi, Vassilia

    2011-11-01

    Oil spill events are a crucial environmental issue. Detection of oil spills is important for both oil exploration and environmental protection. In this paper, investigation of hyperspectral remote sensing is performed for the detection of oil spills and the discrimination of different oil types. Spectral signatures of different oil types are very useful, since they may serve as endmembers in unmixing and classification models. Towards this direction, an oil spectral library, resulting from spectral measurements of artificial oil spills as well as of look-alikes in marine environment was compiled. Samples of four different oil types were used; two crude oils, one marine residual fuel oil, and one light petroleum product. Lookalikes comprise sea water, river discharges, shallow water and water with algae. Spectral measurements were acquired with spectro-radiometer GER1500. Moreover, oil and look-alikes spectral signatures have been examined whether they can be served as endmembers. This was accomplished by testifying their linear independence. After that, synthetic hyperspectral images based on the relevant oil spectral library were created. Several simplex-based endmember algorithms such as sequential maximum angle convex cone (SMACC), vertex component analysis (VCA), n-finder algorithm (N-FINDR), and automatic target generation process (ATGP) were applied on the synthetic images in order to evaluate their effectiveness for detecting oil spill events occurred from different oil types. Results showed that different types of oil spills with various thicknesses can be extracted as endmembers.

  1. Robust cardiac event change detection method for long-term healthcare monitoring applications.

    PubMed

    Satija, Udit; Ramkumar, Barathram; Manikandan, M Sabarimalai

    2016-06-01

    A long-term continuous cardiac health monitoring system highly demands more battery power for real-time transmission of electrocardiogram (ECG) signals and increases bandwidth, treatment costs and traffic load of the diagnostic server. In this Letter, the authors present an automated low-complexity robust cardiac event change detection (CECD) method that can continuously detect specific changes in PQRST morphological patterns and heart rhythms and then enable transmission/storing of the recorded ECG signals. The proposed CECD method consists of four stages: ECG signal quality assessment, R-peak detection and beat waveform extraction, temporal and RR interval feature extraction and cardiac event change decision. The proposed method is tested and validated using both normal and abnormal ECG signals including different types of arrhythmia beats, heart rates and signal quality. Results show that the method achieves an average sensitivity of 99.76%, positive predictivity of 94.58% and overall accuracy of 94.32% in determining the changes in heartbeat waveforms of the ECG signals. PMID:27382480

  2. An Automated Visual Event Detection System for Cabled Observatory Video

    NASA Astrophysics Data System (ADS)

    Edgington, D. R.; Cline, D. E.; Mariette, J.

    2007-12-01

    The permanent presence of underwater cameras on oceanic cabled observatories, such as the Victoria Experimental Network Under the Sea (VENUS) and Eye-In-The-Sea (EITS) on Monterey Accelerated Research System (MARS), will generate valuable data that can move forward the boundaries of understanding the underwater world. However, sightings of underwater animal activities are rare, resulting in the recording of many hours of video with relatively few events of interest. The burden of video management and analysis often requires reducing the amount of video recorded and later analyzed. Sometimes enough human resources do not exist to analyze the video; the strains on human attention needed to analyze video demand an automated way to assist in video analysis. Towards this end, an Automated Visual Event Detection System (AVED) is in development at the Monterey Bay Aquarium Research Institute (MBARI) to address the problem of analyzing cabled observatory video. Here we describe the overall design of the system to process video data and enable science users to analyze the results. We present our results analyzing video from the VENUS observatory and test data from EITS deployments. This automated system for detecting visual events includes a collection of custom and open source software that can be run three ways: through a Web Service, through a Condor managed pool of AVED enabled compute servers, or locally on a single computer. The collection of software also includes a graphical user interface to preview or edit detected results and to setup processing options. To optimize the compute-intensive AVED algorithms, a parallel program has been implemented for high-data rate applications like the EITS instrument on MARS.

  3. Use of sonification in the detection of anomalous events

    NASA Astrophysics Data System (ADS)

    Ballora, Mark; Cole, Robert J.; Kruesi, Heidi; Greene, Herbert; Monahan, Ganesh; Hall, David L.

    2012-06-01

    In this paper, we describe the construction of a soundtrack that fuses stock market data with information taken from tweets. This soundtrack, or auditory display, presents the numerical and text data in such a way that anomalous events may be readily detected, even by untrained listeners. The soundtrack generation is flexible, allowing an individual listener to create a unique audio mix from the available information sources. Properly constructed, the display exploits the auditory system's sensitivities to periodicities, to dynamic changes, and to patterns. This type of display could be valuable in environments that demand high levels of situational awareness based on multiple sources of incoming information.

  4. The waveform correlation event detection system global prototype software design

    SciTech Connect

    Beiriger, J.I.; Moore, S.G.; Trujillo, J.R.; Young, C.J.

    1997-12-01

    The WCEDS prototype software system was developed to investigate the usefulness of waveform correlation methods for CTBT monitoring. The WCEDS prototype performs global seismic event detection and has been used in numerous experiments. This report documents the software system design, presenting an overview of the system operation, describing the system functions, tracing the information flow through the system, discussing the software structures, and describing the subsystem services and interactions. The effectiveness of the software design in meeting project objectives is considered, as well as opportunities for code refuse and lessons learned from the development process. The report concludes with recommendations for modifications and additions envisioned for regional waveform-correlation-based detector.

  5. Occurrence of maxillary sinus abnormalities detected by cone beam CT in asymptomatic patients

    PubMed Central

    2012-01-01

    Background Although cone beam computed tomography (CBCT) images of the maxillofacial region allow the inspection of the entire volume of the maxillary sinus (MS), identifying anatomic variations and abnormalities in the image volume, this is frequently neglected by oral radiologists when interpreting images of areas at a distance from the dentoalveolar region, such as the full anatomical aspect of the MS. The aim of this study was to investigate maxillary sinus abnormalities in asymptomatic patients by using CBCT. Methods 1113 CBCT were evaluated by two examiners and identification of abnormalities, the presence of periapical lesions and proximity to the lower sinus wall were recorded. Data were analyzed using descriptive statistics, chi-square tests and Kappa statistics. Results Abnormalities were diagnosed in 68.2% of cases (kappa = 0.83). There was a significant difference between genders (p < 0.001) and there was no difference in age groups. Mucosal thickening was the most prevalent abnormality (66%), followed by retention cysts (10.1%) and opacification (7.8%). No association was observed between the proximity of periapical lesions and the presence and type of inflammatory abnormalities (p = 0.124). Conclusions Abnormalities in maxillary sinus emphasizes how important it is for the dentomaxillofacial radiologist to undertake an interpretation of the whole volume of CBCT images. PMID:22883529

  6. Automatic adverse drug events detection using letters to the editor.

    PubMed

    Yang, Chao; Srinivasan, Padmini; Polgreen, Philip M

    2012-01-01

    We present and test the intuition that letters to the editor in journals carry early signals of adverse drug events (ADEs). Surprisingly these letters have not yet been exploited for automatic ADE detection unlike for example, clinical records and PubMed. Part of the challenge is that it is not easy to access the full-text of letters (for the most part these do not appear in PubMed). Also letters are likely underrated in comparison with full articles. Besides demonstrating that this intuition holds we contribute techniques for post market drug surveillance. Specifically, we test an automatic approach for ADE detection from letters using off-the-shelf machine learning tools. We also involve natural language processing for feature definitions. Overall we achieve high accuracy in our experiments and our method also works well on a second new test set. Our results encourage us to further pursue this line of research. PMID:23304379

  7. Automatic Adverse Drug Events Detection Using Letters to the Editor

    PubMed Central

    Yang, Chao; Srinivasan, Padmini; Polgreen, Philip M.

    2012-01-01

    We present and test the intuition that letters to the editor in journals carry early signals of adverse drug events (ADEs). Surprisingly these letters have not yet been exploited for automatic ADE detection unlike for example, clinical records and PubMed. Part of the challenge is that it is not easy to access the full-text of letters (for the most part these do not appear in PubMed). Also letters are likely underrated in comparison with full articles. Besides demonstrating that this intuition holds we contribute techniques for post market drug surveillance. Specifically, we test an automatic approach for ADE detection from letters using off-the-shelf machine learning tools. We also involve natural language processing for feature definitions. Overall we achieve high accuracy in our experiments and our method also works well on a second new test set. Our results encourage us to further pursue this line of research. PMID:23304379

  8. DASAF: An R Package for Deep Sequencing-Based Detection of Fetal Autosomal Abnormalities from Maternal Cell-Free DNA

    PubMed Central

    Tang, Xiaoyan; Qiu, Feng; Tao, Chunmei; Gao, Junhui; Ma, Mengmeng; Zhong, Tingyan; Cai, JianPing; Li, Yixue

    2016-01-01

    Background. With the development of massively parallel sequencing (MPS), noninvasive prenatal diagnosis using maternal cell-free DNA is fast becoming the preferred method of fetal chromosomal abnormality detection, due to its inherent high accuracy and low risk. Typically, MPS data is parsed to calculate a risk score, which is used to predict whether a fetal chromosome is normal or not. Although there are several highly sensitive and specific MPS data-parsing algorithms, there are currently no tools that implement these methods. Results. We developed an R package, detection of autosomal abnormalities for fetus (DASAF), that implements the three most popular trisomy detection methods—the standard Z-score (STDZ) method, the GC correction Z-score (GCCZ) method, and the internal reference Z-score (IRZ) method—together with one subchromosome abnormality identification method (SCAZ). Conclusions. With the cost of DNA sequencing declining and with advances in personalized medicine, the demand for noninvasive prenatal testing will undoubtedly increase, which will in turn trigger an increase in the tools available for subsequent analysis. DASAF is a user-friendly tool, implemented in R, that supports identification of whole-chromosome as well as subchromosome abnormalities, based on maternal cell-free DNA sequencing data after genome mapping. PMID:27437397

  9. DASAF: An R Package for Deep Sequencing-Based Detection of Fetal Autosomal Abnormalities from Maternal Cell-Free DNA.

    PubMed

    Liu, Baohong; Tang, Xiaoyan; Qiu, Feng; Tao, Chunmei; Gao, Junhui; Ma, Mengmeng; Zhong, Tingyan; Cai, JianPing; Li, Yixue; Ding, Guohui

    2016-01-01

    Background. With the development of massively parallel sequencing (MPS), noninvasive prenatal diagnosis using maternal cell-free DNA is fast becoming the preferred method of fetal chromosomal abnormality detection, due to its inherent high accuracy and low risk. Typically, MPS data is parsed to calculate a risk score, which is used to predict whether a fetal chromosome is normal or not. Although there are several highly sensitive and specific MPS data-parsing algorithms, there are currently no tools that implement these methods. Results. We developed an R package, detection of autosomal abnormalities for fetus (DASAF), that implements the three most popular trisomy detection methods-the standard Z-score (STDZ) method, the GC correction Z-score (GCCZ) method, and the internal reference Z-score (IRZ) method-together with one subchromosome abnormality identification method (SCAZ). Conclusions. With the cost of DNA sequencing declining and with advances in personalized medicine, the demand for noninvasive prenatal testing will undoubtedly increase, which will in turn trigger an increase in the tools available for subsequent analysis. DASAF is a user-friendly tool, implemented in R, that supports identification of whole-chromosome as well as subchromosome abnormalities, based on maternal cell-free DNA sequencing data after genome mapping. PMID:27437397

  10. Increased SERS detection efficiency for characterizing rare events in flow.

    PubMed

    Jacobs, Kevin T; Schultz, Zachary D

    2015-08-18

    Improved surface-enhanced Raman scattering (SERS) measurements of a flowing aqueous sample are accomplished by combining line focus optics with sheath-flow SERS detection. The straightforward introduction of a cylindrical lens into the optical path of the Raman excitation laser increases the efficiency of SERS detection and the reproducibility of SERS signals at low concentrations. The width of the line focus is matched to the width of the sample capillary from which the analyte elutes under hydrodynamic focusing conditions, allowing for increased collection across the SERS substrate while maintaining the power density below the damage threshold at any specific point. We show that a 4× increase in power spread across the line increases the signal-to-noise ratio by a factor of 2 for a variety of analytes, such as rhodamine 6G, amino acids, and lipid vesicles, without any detectable photodamage. COMSOL simulations and Raman maps elucidate the hydrodynamic focusing properties of the flow cell, providing a clearer picture of the confinement effects at the surface where the sample exits the capillary. The lipid vesicle results suggest that the combination of hydrodynamic focusing and increased optical collection enables the reproducible detection of rare events, in this case individual lipid vesicles. PMID:26168151

  11. Local Seismic Event Detection Using Image Processing Techniques

    NASA Astrophysics Data System (ADS)

    West, J. D.; Fouch, M. J.

    2013-12-01

    The large footprint of regularly-spaced broadband seismometers afforded by EarthScope's USArray Transportable Array (TA) [www.usarray.org] presents an unprecedented opportunity to develop novel seismic array processing methods. Here we report preliminary results from a new automated method for detecting small local seismic events within the footprint of the TA using image processing techniques. The overarching goal is to develop a new methodology for automated searches of large seismic datasets for signals that are difficult to detect by traditional means, such as STA/LTA triggering algorithms. We first process the raw broadband data for each station by bandpass filtering at 7-19 Hz and integrating the absolute value of the velocity waveform over a sequence of 5-second intervals. We further combine the integrated values of all three orthogonal channels into a single new time series with a 5-second sampling rate. This new time series is analogous to a measurement of the total seismic energy recorded at the station in each 5-second interval; we call this time series Integrated Ground Motion (IGM). Each sample is compared to a sliding longer-term average to remove diurnal and long-term noise effects. We create an image file by mapping each station location to an equivalent position in a blank image array, and use a modified Voronoi tessellation algorithm to assign each pixel in the image to the IGM value of the nearest station. We assign a value of zero if the pixel is more than a maximum distance from the nearest station. We apply 2-dimensional spatial image filtering techniques to remove large-scale features affecting much of the image, as we assume these likely result from teleseismic events. We also filter the time series to remove very small-scale features from noise spikes affecting a single seismic station. The resulting image contains only features of regional scale affecting 2 or more stations. For each of the remaining image features, we find the center

  12. Detecting and mitigating abnormal events in large scale networks: budget constrained placement on smart grids

    SciTech Connect

    Santhi, Nandakishore; Pan, Feng

    2010-10-19

    Several scenarios exist in the modern interconnected world which call for an efficient network interdiction algorithm. Applications are varied, including various monitoring and load shedding applications on large smart energy grids, computer network security, preventing the spread of Internet worms and malware, policing international smuggling networks, and controlling the spread of diseases. In this paper we consider some natural network optimization questions related to the budget constrained interdiction problem over general graphs, specifically focusing on the sensor/switch placement problem for large-scale energy grids. Many of these questions turn out to be computationally hard to tackle. We present a particular form of the interdiction question which is practically relevant and which we show as computationally tractable. A polynomial-time algorithm will be presented for solving this problem.

  13. Efficient hemodynamic event detection utilizing relational databases and wavelet analysis

    NASA Technical Reports Server (NTRS)

    Saeed, M.; Mark, R. G.

    2001-01-01

    Development of a temporal query framework for time-oriented medical databases has hitherto been a challenging problem. We describe a novel method for the detection of hemodynamic events in multiparameter trends utilizing wavelet coefficients in a MySQL relational database. Storage of the wavelet coefficients allowed for a compact representation of the trends, and provided robust descriptors for the dynamics of the parameter time series. A data model was developed to allow for simplified queries along several dimensions and time scales. Of particular importance, the data model and wavelet framework allowed for queries to be processed with minimal table-join operations. A web-based search engine was developed to allow for user-defined queries. Typical queries required between 0.01 and 0.02 seconds, with at least two orders of magnitude improvement in speed over conventional queries. This powerful and innovative structure will facilitate research on large-scale time-oriented medical databases.

  14. Detecting Rare Events in the Time-Domain

    SciTech Connect

    Rest, A; Garg, A

    2008-10-31

    One of the biggest challenges in current and future time-domain surveys is to extract the objects of interest from the immense data stream. There are two aspects to achieving this goal: detecting variable sources and classifying them. Difference imaging provides an elegant technique for identifying new transients or changes in source brightness. Much progress has been made in recent years toward refining the process. We discuss a selection of pitfalls that can afflict an automated difference imagine pipeline and describe some solutions. After identifying true astrophysical variables, we are faced with the challenge of classifying them. For rare events, such as supernovae and microlensing, this challenge is magnified because we must balance having selection criteria that select for the largest number of objects of interest against a high contamination rate. We discuss considerations and techniques for developing classification schemes.

  15. Detecting and characterising ramp events in wind power time series

    NASA Astrophysics Data System (ADS)

    Gallego, Cristóbal; Cuerva, Álvaro; Costa, Alexandre

    2014-12-01

    In order to implement accurate models for wind power ramp forecasting, ramps need to be previously characterised. This issue has been typically addressed by performing binary ramp/non-ramp classifications based on ad-hoc assessed thresholds. However, recent works question this approach. This paper presents the ramp function, an innovative wavelet- based tool which detects and characterises ramp events in wind power time series. The underlying idea is to assess a continuous index related to the ramp intensity at each time step, which is obtained by considering large power output gradients evaluated under different time scales (up to typical ramp durations). The ramp function overcomes some of the drawbacks shown by the aforementioned binary classification and permits forecasters to easily reveal specific features of the ramp behaviour observed at a wind farm. As an example, the daily profile of the ramp-up and ramp-down intensities are obtained for the case of a wind farm located in Spain.

  16. Barometric pressure and triaxial accelerometry-based falls event detection.

    PubMed

    Bianchi, Federico; Redmond, Stephen J; Narayanan, Michael R; Cerutti, Sergio; Lovell, Nigel H

    2010-12-01

    Falls and fall related injuries are a significant cause of morbidity, disability, and health care utilization, particularly among the age group of 65 years and over. The ability to detect falls events in an unsupervised manner would lead to improved prognoses for falls victims. Several wearable accelerometry and gyroscope-based falls detection devices have been described in the literature; however, they all suffer from unacceptable false positive rates. This paper investigates the augmentation of such systems with a barometric pressure sensor, as a surrogate measure of altitude, to assist in discriminating real fall events from normal activities of daily living. The acceleration and air pressure data are recorded using a wearable device attached to the subject's waist and analyzed offline. The study incorporates several protocols including simulated falls onto a mattress and simulated activities of daily living, in a cohort of 20 young healthy volunteers (12 male and 8 female; age: 23.7 ±3.0 years). A heuristically trained decision tree classifier is used to label suspected falls. The proposed system demonstrated considerable improvements in comparison to an existing accelerometry-based technique; showing an accuracy, sensitivity and specificity of 96.9%, 97.5%, and 96.5%, respectively, in the indoor environment, with no false positives generated during extended testing during activities of daily living. This is compared to 85.3%, 75%, and 91.5% for the same measures, respectively, when using accelerometry alone. The increased specificity of this system may enhance the usage of falls detectors among the elderly population. PMID:20805056

  17. Event Detection and Spatial Analysis for Characterizing Extreme Precipitation

    NASA Astrophysics Data System (ADS)

    Jeon, S.; Prabhat, M.; Byna, S.; Collins, W.; Wehner, M. F.

    2013-12-01

    Atmospheric Rivers (ARs) are large spatially coherent weather systems with high concentrations of elevated water vapor that often cause severe downpours and flooding over western coastal United States. With the availability of more atmospheric moisture in the future under global warming, we expect ARs to play an important role as a potential cause of extreme precipitation. We have recently developed TECA software for automatically identifying and tracking features in climate datasets. In particular, we are able to identify ARs that make landfall on the western coast of North America. This detection tool examines integrated water vapor field above a certain threshold and performs geometric analysis. Based on the detection procedure, we investigate impacts of ARs by exploring spatial extent of AR precipitation for CMIP5 simulations, and characterize spatial pattern of dependence for future projections under climate change within the framework of extreme value theory. The results show that AR events in RCP8.5 scenario (2076-2100) tend to produce heavier rainfall with higher frequency and longer duration than the events from historical run (1981-2005). Range of spatial dependence between extreme precipitations is concentrated on smaller localized area in California under the highest emission scenario than present day. Preliminary results are illustrated in Figure 1 and 2. Fig 1: Boxplot of annual max precipitation (left two) and max AR precipitation (right two) from GFDL-ESM2M during 25-year time period by station in California, US. Fig 2: Spatial dependence of max AR precipitation calculated from Station 4 (triangle) for historical run (left) and for future projections of RCP8.5 (right) from GFDL-ESM2M. Green and orange colors represent complete dependence and independence between two stations respectively.

  18. Identification of new events in Apollo 16 lunar seismic data by Hidden Markov Model-based event detection and classification

    NASA Astrophysics Data System (ADS)

    Knapmeyer-Endrun, Brigitte; Hammer, Conny

    2015-10-01

    Detection and identification of interesting events in single-station seismic data with little prior knowledge and under tight time constraints is a typical scenario in planetary seismology. The Apollo lunar seismic data, with the only confirmed events recorded on any extraterrestrial body yet, provide a valuable test case. Here we present the application of a stochastic event detector and classifier to the data of station Apollo 16. Based on a single-waveform example for each event class and some hours of background noise, the system is trained to recognize deep moonquakes, impacts, and shallow moonquakes and performs reliably over 3 years of data. The algorithm's demonstrated ability to detect rare events and flag previously undefined signal classes as new event types is of particular interest in the analysis of the first seismic recordings from a completely new environment. We are able to classify more than 50% of previously unclassified lunar events, and additionally find over 200 new events not listed in the current lunar event catalog. These events include deep moonquakes as well as impacts and could be used to update studies on temporal variations in event rate or deep moonquakes stacks used in phase picking for localization. No unambiguous new shallow moonquake was detected, but application to data of the other Apollo stations has the potential for additional new discoveries 40 years after the data were recorded. Besides, the classification system could be useful for future seismometer missions to other planets, e.g., the InSight mission to Mars.

  19. Detecting Tidal Disruption Events (TDEs) with the Einstein Probe

    NASA Astrophysics Data System (ADS)

    Yuan, W.; Komossa, S.; Zhang, C.; Feng, H.; Zhang, S.; Osborne, J.; O'Brien, P.; Watson, M.; Fraser, G.

    2014-07-01

    Stars are tidally disrupted and accreted when they approach supermassive black holes (SMBHs) closely, producing a flare of electromagnetic radiation. The majority of the (approximately two dozen) tidal disruption events (TDEs) identified so far have been discovered by their luminous, transient X-ray emission. Once TDEs are detected in much larger numbers, in future dedicated transient surveys, a wealth of new applications will become possible. Including (1) TDE rate measurements in dependence of host galaxy types, (2) an assessment of the population of IMBHs, and (3) new probes of general relativity and accretion processes. Here, we present the proposed X-ray mission Einstein Probe}, which aims at detecting TDEs in large numbers. The mission consists of a wide-field micro-pore Lobster-eye imager (60deg x 60deg, or ˜1 ster), and is designed to carry out an all-sky transient survey at energies of 0.5-4 keV. It will also carry an X-ray telescope of the same micro-pore optics for follow-ups, with a smaller field-of-view. It will be capable of issuing public transient alerts rapidly.

  20. Communication of ALS Patients by Detecting Event-Related Potential

    NASA Astrophysics Data System (ADS)

    Kanou, Naoyuki; Sakuma, Kenji; Nakashima, Kenji

    Amyotrophic Lateral Sclerosis(ALS) patients are unable to successfully communicate their desires, although their mental capacity is the same as non-affected persons. Therefore, the authors put emphasis on Event-Related Potential(ERP) which elicits the highest outcome for the target visual and hearing stimuli. P300 is one component of ERP. It is positive potential that is elicited when the subject focuses attention on stimuli that appears infrequently. In this paper, the authors focused on P200 and N200 components, in addition to P300, for their great improvement in the rate of correct judgment in the target word-specific experiment. Hence the authors propose the algorithm that specifies target words by detecting these three components. Ten healthy subjects and ALS patient underwent the experiment in which a target word out of five words, was specified by this algorithm. The rates of correct judgment in nine of ten healthy subjects were more than 90.0%. The highest rate was 99.7%. The highest rate of ALS patient was 100.0%. Through these results, the authors found the possibility that ALS patients could communicate with surrounding persons by detecting ERP(P200, N200 and P300) as their desire.

  1. Sonic Hedgehog: A Good Gene Gone Bad? Detection and Treatment of Genetic Abnormalities.

    ERIC Educational Resources Information Center

    Yaich, Lauren E.

    2001-01-01

    Presents a case of a baby born with the genetic condition holoprosencephaly in which students explore the "Sonic hedgehog" gene, signal transduction, and the ethics of body and tissue donation. Presents a two-part assignment that features students writing an informed consent document that explains the science behind this congenital abnormality,…

  2. Apparatus and method for detecting full-capture radiation events

    DOEpatents

    Odell, Daniel M. C.

    1994-01-01

    An apparatus and method for sampling the output signal of a radiation detector and distinguishing full-capture radiation events from Compton scattering events. The output signal of a radiation detector is continuously sampled. The samples are converted to digital values and input to a discriminator where samples that are representative of events are identified. The discriminator transfers only event samples, that is, samples representing full-capture events and Compton events, to a signal processor where the samples are saved in a three-dimensional count matrix with time (from the time of onset of the pulse) on the first axis, sample pulse current amplitude on the second axis, and number of samples on the third axis. The stored data are analyzed to separate the Compton events from full-capture events, and the energy of the full-capture events is determined without having determined the energies of any of the individual radiation detector events.

  3. Solar activity cycle and the incidence of foetal chromosome abnormalities detected at prenatal diagnosis

    NASA Astrophysics Data System (ADS)

    Halpern, Gabrielle J.; Stoupel, Eliahu G.; Barkai, Gad; Chaki, Rina; Legum, Cyril; Fejgin, Moshe D.; Shohat, Mordechai

    1995-06-01

    We studied 2001 foetuses during the period of minimal solar activity of solar cycle 21 and 2265 foetuses during the period of maximal solar activity of solar cycle 22, in all women aged 37 years and over who underwent free prenatal diagnosis in four hospitals in the greater Tel Aviv area. There were no significant differences in the total incidence of chromosomal abnormalities or of trisomy between the two periods (2.15% and 1.8% versus 2.34% and 2.12%, respectively). However, the trend of excessive incidence of chromosomal abnormalities in the period of maximal solar activity suggests that a prospective study in a large population would be required to rule out any possible effect of extreme solar activity.

  4. Detection of Local/Regional Events in Kuwait Using Next-Generation Detection Algorithms

    SciTech Connect

    Gok, M. Rengin; Al-Jerri, Farra; Dodge, Douglas; Al-Enezi, Abdullah; Hauk, Terri; Mellors, R.

    2014-12-10

    Seismic networks around the world use conventional triggering algorithms to detect seismic signals in order to locate local/regional seismic events. Kuwait National Seismological Network (KNSN) of Kuwait Institute of Scientific Research (KISR) is operating seven broad-band and short-period three-component stations in Kuwait. The network is equipped with Nanometrics digitizers and uses Antelope and Guralp acquisition software for processing and archiving the data. In this study, we selected 10 days of archived hourly-segmented continuous data of five stations (Figure 1) and 250 days of continuous recording at MIB. For the temporary deployment our selection criteria was based on KNSN catalog intensity for the period of time we test the method. An autonomous event detection and clustering framework is employed to test a more complete catalog of this short period of time. The goal is to illustrate the effectiveness of the technique and pursue the framework for longer period of time.

  5. Automatic detection of volcano-seismic events by modeling state and event duration in hidden Markov models

    NASA Astrophysics Data System (ADS)

    Bhatti, Sohail Masood; Khan, Muhammad Salman; Wuth, Jorge; Huenupan, Fernando; Curilem, Millaray; Franco, Luis; Yoma, Nestor Becerra

    2016-09-01

    In this paper we propose an automatic volcano event detection system based on Hidden Markov Model (HMM) with state and event duration models. Since different volcanic events have different durations, therefore the state and whole event durations learnt from the training data are enforced on the corresponding state and event duration models within the HMM. Seismic signals from the Llaima volcano are used to train the system. Two types of events are employed in this study, Long Period (LP) and Volcano-Tectonic (VT). Experiments show that the standard HMMs can detect the volcano events with high accuracy but generates false positives. The results presented in this paper show that the incorporation of duration modeling can lead to reductions in false positive rate in event detection as high as 31% with a true positive accuracy equal to 94%. Further evaluation of the false positives indicate that the false alarms generated by the system were mostly potential events based on the signal-to-noise ratio criteria recommended by a volcano expert.

  6. An a contrario approach for the detection of patient-specific brain perfusion abnormalities with arterial spin labelling.

    PubMed

    Maumet, Camille; Maurel, Pierre; Ferré, Jean-Christophe; Barillot, Christian

    2016-07-01

    In this paper, we introduce a new locally multivariate procedure to quantitatively extract voxel-wise patterns of abnormal perfusion in individual patients. This a contrario approach uses a multivariate metric from the computer vision community that is suitable to detect abnormalities even in the presence of closeby hypo- and hyper-perfusions. This method takes into account local information without applying Gaussian smoothing to the data. Furthermore, to improve on the standard a contrario approach, which assumes white noise, we introduce an updated a contrario approach that takes into account the spatial coherency of the noise in the probability estimation. Validation is undertaken on a dataset of 25 patients diagnosed with brain tumours and 61 healthy volunteers. We show how the a contrario approach outperforms the massively univariate general linear model usually employed for this type of analysis. PMID:27039702

  7. Usefulness of noninvasive detection of left ventricular diastolic abnormalities during isometric stress in hypertrophic cardiomyopathy and in athletes.

    PubMed

    Manolas, J; Kyriakidis, M; Anastasakis, A; Pegas, P; Rigopoulos, A; Theopistou, A; Toutouzas, P

    1998-02-01

    We showed previously that the handgrip apexcardiographic test (HAT) is a useful method for detecting left ventricular (LV) diastolic abnormalities in patients with coronary artery disease and systemic hypertension. This study evaluates the use of HAT for assessing the prevalence and types of exercise-induced diastolic abnormalities in patients with obstructive (n = 31) and nonobstructive (n = 35) hypertrophic cardiomyopathy (HC) as well as its potential value for separating healthy subjects and athletes from patients with HC. We obtained a HAT in 66 consecutive patients with HC and in 72 controls (52 healthy volunteers and 20 athletes). A positive HAT was defined by the presence of one of the following: (1) relative A wave to total height (A/H) during or after handgrip > 21% (compliance type), (2) total apexcardiographic relaxation time (TART) > 143 ms or the heart rate corrected TART (TARTI) during handgrip < 0.14, (relaxation type), (3) both types present (mixed type), and (4) diastolic amplitude time index (DATI = TARTI/[A/D]) during handgrip < 0.27. Of the controls, only 1 of 52 healthy subjects and 1 of 20 athletes showed a positive HAT, whereas of the total HC cohort 63 of 66 patients (95%) had a positive result. There was no significant difference in the distribution of these types between obstructive and nonobstructive HC. Further, no LV diastolic abnormalities were present in 10 of 35 patients (29%) with nonobstructive HC at rest and in 3 of 35 patients (9%) during handgrip, whereas of the patients with obstructive HC only 1 of 31 (3%) had no LV diastolic abnormalities at rest and none during handgrip. Based on HAT data, our study demonstrates that in HC (1) LV diastolic abnormalities are very frequent during handgrip; (2) patients with nonobstructive HC show significantly fewer LV diastolic abnormalities at rest than those with obstructive HC; and (3) no significant difference exists between obstructive and nonobstructive HC in the prevalence of types of

  8. Direct phosphorescent detection of primary event of photodynamic action

    NASA Astrophysics Data System (ADS)

    Losev, Anatoly P.; Knukshto, Valentin N.; Zhuravkin, Ivan N.

    1994-07-01

    Highly phosphorescent photosensitizer Pd-tetra (o-methoxy-p-sulfo) phenyl porphyrin (Pd-MSPP) was used to follow the primary events of photodynamic action - quenching of triplet states by free oxygen in different systems: water solutions of proteins, cells and tissues in vivo and in vitro. The photosensitizer forms complexes with proteins in solutions and biosystems showing remarkable hypsochromic shifts of band and an increase of the quantum yield and lifetime of phosphorescence at the binding to proteins. In absence of oxygen the lifetime of phosphorescence is almost single exponential, and depends on the energy of lowest triplet state of the sensitizer. The photochemical quenching of the triplets by cell components is negligible. In presence of free oxygen the quenching of the sensitizer triplets takes place. The emission spectrum of singlet oxygen with maximum 1271 nm was recorded in water protein solutions and quantum yield of sensitized luminescence was measured. In the systems studied, oxygen consumption was detected and oxygen concentration was estimated in the course of photodynamics by an increase in photosensitizer phosphorescence lifetime, using laser flash photolysis technique. At least two exponential kinetics of the phosphorescence decay shows that the distribution of the free oxygen is not uniform in tissues.

  9. Visual traffic surveillance framework: classification to event detection

    NASA Astrophysics Data System (ADS)

    Ambardekar, Amol; Nicolescu, Mircea; Bebis, George; Nicolescu, Monica

    2013-10-01

    Visual traffic surveillance using computer vision techniques can be noninvasive, automated, and cost effective. Traffic surveillance systems with the ability to detect, count, and classify vehicles can be employed in gathering traffic statistics and achieving better traffic control in intelligent transportation systems. However, vehicle classification poses a difficult problem as vehicles have high intraclass variation and relatively low interclass variation. Five different object recognition techniques are investigated: principal component analysis (PCA)+difference from vehicle space, PCA+difference in vehicle space, PCA+support vector machine, linear discriminant analysis, and constellation-based modeling applied to the problem of vehicle classification. Three of the techniques that performed well were incorporated into a unified traffic surveillance system for online classification of vehicles, which uses tracking results to improve the classification accuracy. To evaluate the accuracy of the system, 31 min of traffic video containing multilane traffic intersection was processed. It was possible to achieve classification accuracy as high as 90.49% while classifying correctly tracked vehicles into four classes: cars, SUVs/vans, pickup trucks, and buses/semis. While processing a video, our system also recorded important traffic parameters such as the appearance, speed, trajectory of a vehicle, etc. This information was later used in a search assistant tool to find interesting traffic events.

  10. Detection of structural and numerical chomosomal abnormalities by ACM-FISH analysis in sperm of oligozoospermic infertility patients

    SciTech Connect

    Schmid, T E; Brinkworth, M H; Hill, F; Sloter, E; Kamischke, A; Marchetti, F; Nieschlag, E; Wyrobek, A J

    2003-11-10

    Modern reproductive technologies are enabling the treatment of infertile men with severe disturbances of spermatogenesis. The possibility of elevated frequencies of genetically and chromosomally defective sperm has become an issue of concern with the increased usage of intracytoplasmic sperm injection (ICSI), which can enable men with severely impaired sperm production to father children. Several papers have been published about aneuploidy in oligozoospermic patients, but relatively little is known about chromosome structural aberrations in the sperm of these patients. We examined sperm from infertile, oligozoospermic individuals for structural and numerical chromosomal abnormalities using a multicolor ACM FISH assay that utilizes DNA probes specific for three regions of chromosome 1 to detect human sperm that carry numerical chromosomal abnormalities plus two categories of structural aberrations: duplications and deletions of 1pter and 1cen, and chromosomal breaks within the 1cen-1q12 region. There was a significant increase in the average frequencies of sperm with duplications and deletions in the infertility patients compared with the healthy concurrent controls. There was also a significantly elevated level of breaks within the 1cen-1q12 region. There was no evidence for an increase in chromosome-1 disomy, or in diploidy. Our data reveal that oligozoospermia is associated with chromosomal structural abnormalities suggesting that, oligozoospermic men carry a higher burden of transmissible, chromosome damage. The findings raise the possibility of elevated levels of transmissible chromosomal defects following ICSI treatment.

  11. Large Time Projection Chambers for Rare Event Detection

    SciTech Connect

    Heffner, M

    2009-11-03

    The Time Projection Chamber (TPC) concept [add ref to TPC section] has been applied to many projects outside of particle physics and the accelerator based experiments where it was initially developed. TPCs in non-accelerator particle physics experiments are principally focused on rare event detection (e.g. neutrino and darkmater experiments) and the physics of these experiments can place dramatically different constraints on the TPC design (only extensions to the traditional TPCs are discussed here). The drift gas, or liquid, is usually the target or matter under observation and due to very low signal rates a TPC with the largest active mass is desired. The large mass complicates particle tracking of short and sometimes very low energy particles. Other special design issues include, efficient light collection, background rejection, internal triggering and optimal energy resolution. Backgrounds from gamma-rays and neutrons are significant design issues in the construction of these TPCs. They are generally placed deep underground to shield from cosmogenic particles and surrounded with shielding to reduce radiation from the local surroundings. The construction materials have to be carefully screened for radiopurity as they are in close contact with the active mass and can be a signification source of background events. The TPC excels in reducing this internal background because the mass inside the fieldcage forms one monolithic volume from which fiducial cuts can be made ex post facto to isolate quiet drift mass, and can be circulated and purified to a very high level. Self shielding in these large mass systems can be significant and the effect improves with density. The liquid phase TPC can obtain a high density at low pressure which results in very good self-shielding and compact installation with a lightweight containment. The down sides are the need for cryogenics, slower charge drift, tracks shorter than the typical electron diffusion, lower energy resolution (e

  12. A neural network learned information measures for heart motion abnormality detection

    NASA Astrophysics Data System (ADS)

    Nambakhsh, M. S.; Punithakumar, Kumaradevan; Ben Ayed, Ismail; Goela, Aashish; Islam, Ali; Peters, Terry; Li, Shuo

    2011-03-01

    In this study, we propose an information theoretic neural network for normal/abnormal left ventricular motion classification which outperforms significantly other recent methods in the literature. The proposed framework consists of a supervised 3-layer artificial neural network (ANN) which uses hyperbolic tangent sigmoid and linear transfer functions for hidden and output layers, respectively. The ANN is fed by information theoretic measures of left ventricular wall motion such as Shannon's differential entropy (SDE), Rényi entropy and Fisher information, which measure global information of subjects distribution. Using 395×20 segmented LV cavities of short-axis magnetic resonance images (MRI) acquired from 48 subjects, the experimental results show that the proposed method outperforms Support Vector Machine (SVM) and thresholding based information theoretic classifiers. It yields a specificity equal to 90%, a sensitivity of 91%, and a remarkable Area Under Curve (AUC) for Receiver Operating Characteristic (ROC), equal to 93.2%.

  13. Robust algorithmic detection of the developed cardiac pathologies and emerging or transient abnormalities from short periods of RR data

    NASA Astrophysics Data System (ADS)

    Gavrishchaka, Valeriy V.; Senyukova, Olga

    2011-06-01

    Numerous research efforts and clinical testing have confirmed validity of heart rate variability (HRV) analysis as one of the cardiac diagnostics modalities. The majority of HRV analysis tools currently used in practice are based on linear indicators. Methods from nonlinear dynamics (NLD) provide more natural modeling framework for adaptive biological systems with multiple feedback loops. Compared to linear indicators, many NLD-based measures are much less sensitive to data artifacts and non-stationarity. However, majority of NLD measures require long time series for stable calculation. Similar restrictions also apply for linear indicators. Such requirements could drastically limit practical usability of HRV analysis in many applications, including express diagnostics, early indication of subtle directional changes during personalization of medical treatment, and robust detection of emerging or transient abnormalities. Recently we have illustrated that these challenges could be overcome by using classification framework based on boosting-like ensemble learning techniques that are capable of discovering robust meta-indicators from existing HRV measures and other incomplete empirical knowledge. In this paper we demonstrate universality of such meta-indicators and discuss operational details of their practical usage. Using such pathology examples as congestive heart failure (CHF) and arrhythmias, we show that classifiers trained on short RR segments (down to several minutes) could achieve reasonable classification accuracy (˜80-85% and higher). These indicators calculated from longer RR segments could be applicable for accurate diagnostics with classification accuracy approaching 100%. In addition, it is feasible to discover single "normal-abnormal" meta-classifier capable of detecting multiple abnormalities.

  14. Detection of an Abnormal Myeloid Clone by Flow Cytometry in Familial Platelet Disorder With Propensity to Myeloid Malignancy

    PubMed Central

    Ok, Chi Young; Leventaki, Vasiliki; Wang, Sa A.; Dinardo, Courtney; Medeiros, L. Jeffrey; Konoplev, Sergej

    2016-01-01

    Objectives To report aberrant myeloblasts detected by flow cytometry immunophenotypic studies in an asymptomatic patient with familial platelet disorder with propensity to myeloid malignancy, a rare autosomal dominant disease caused by germline heterozygous mutations in Runt-related transcription factor 1. Methods Morphologic evaluation, flow cytometry immunophenotypic studies, nanofluidics-based qualitative multiplex reverse transcriptase polymerase chain reaction, Sanger sequencing, and next-generation sequencing-based mutational hotspot analysis of 53 genes were performed on bone marrow biopsy and aspirate samples. Results Flow cytometry immunophenotypic analysis showed 0.6% CD34+ blasts with an abnormal immunophenotype: CD13 increased, CD33+, CD38 decreased, CD117 increased, and CD123 increased. Conclusions The acquisition of new phenotypic aberrancies in myeloblasts as detected by flow cytometry immunophenotypic studies might be a harbinger of impending myelodysplastic syndrome or acute myeloid leukemia in a patient with familial platelet disorder with propensity to myeloid malignancy. PMID:26800764

  15. The waveform correlation event detection system project: Issues in system refinement, tuning, and operation

    SciTech Connect

    Young, C.J.; Beiriger, J.I.; Harris, J.M.; Moore, S.G.; Trujillo, J.R.; Withers, M.M.; Aster, R.C.

    1996-08-01

    The goal of the Waveform Correlation Event Detection System (WCEDS) Project at Sandia Labs has been to develop a prototype of a full-waveform correlation based seismic event detection system which could be used to assess potential usefulness for CTBT monitoring. The current seismic event detection system in use at the IDC is very sophisticated and provides good results but there is still significant room for improvement, particularly in reducing the number of false events (currently being nearly equal to the number of real events). Our first prototype was developed last year and since then we have used it for extensive testing from which we have gained considerable insight. The original prototype was based on a long-period detector designed by Shearer (1994), but it has been heavily modified to address problems encountered in application to a data set from the Incorporated Research Institutes for Seismology (IRIS) broadband global network. Important modifications include capabilities for event masking and iterative event detection, continuous near-real time execution, improved Master Image creation, and individualized station pre-processing. All have been shown to improve bulletin quality. In some cases the system has detected marginal events which may not be detectable by traditional detection systems, but definitive conclusions cannot be made without direct comparisons. For this reason future work will focus on using the system to process GSETT3 data for comparison with current event detection systems at the IDC.

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

    PubMed

    Oliker, Nurit; Ostfeld, Avi

    2015-09-01

    Event detection is one of the current most challenging topics in water distribution systems analysis: how regular on-line hydraulic (e.g., pressure, flow) and water quality (e.g., pH, residual chlorine, turbidity) measurements at different network locations can be efficiently utilized to detect water quality contamination events. This study describes an integrated event detection model which combines multiple sensor stations data with network hydraulics. To date event detection modelling is likely limited to single sensor station location and dataset. Single sensor station models are detached from network hydraulics insights and as a result might be significantly exposed to false positive alarms. This work is aimed at decreasing this limitation through integrating local and spatial hydraulic data understanding into an event detection model. The spatial analysis complements the local event detection effort through discovering events with lower signatures by exploring the sensors mutual hydraulic influences. The unique contribution of this study is in incorporating hydraulic simulation information into the overall event detection process of spatially distributed sensors. The methodology is demonstrated on two example applications using base runs and sensitivity analyses. Results show a clear advantage of the suggested model over single-sensor event detection schemes. PMID:25996752

  17. Detection of liver cancer and abnormal liver tissue by Raman spectroscopy and fluorescence

    NASA Astrophysics Data System (ADS)

    Li, Xiaozhou; Ding, Jianhua; Zhang, Xiujun; Lin, Junxiu; Wang, Deli

    2005-01-01

    In this paper, laser induced human serum Raman spectra of liver cancer are measured. The spectra differences in serum from normal people and liver disease patients are analyzed. For the typical spectrum of normal serum, there are three sharp Raman peaks and relative intensity of Raman peaks excited by 514.5nm is higher than that excited by 488.0nm. For the Raman spectrum of liver cancer serum there are no peaks or very weak Raman peaks at the same positions. Results from more than two hundred case measurements show that clinical diagnostic accuracy is 92.86%. And then, the liver fibrosis and liver cirrhosis are studied applying the technology of LIF. To liver cirrhosis, the shape of Raman peak is similar to normal and fluorescence spectrum is similar to that of liver cancer from statistic data. The experiment indicates that there is notable fluorescence difference between the abnormal and normal liver tissue and have blue shift in fluorescence peak. Except for human serum, we use rats serum for researching either. Compared with results of path al examination, we analyze the spectra of normal cases, hepatic fibrosis and hepatocirrhosis respectively in an attempt to find some difference between them. Red shift of fluorescence peak is observed with disease evolution using 514.5nm excitation of an Ar-ion laser. However, no distinct changes happen with 488.0nm excitation. These results have important reference values to explore the method of laser spectrum diagnosis.

  18. Detection of abnormalities in ultrasound lung image using multi-level RVM classification.

    PubMed

    Veeramani, Senthil Kumar; Muthusamy, Ezhilarasi

    2016-06-01

    The classification of abnormalities in ultrasound images is the monitoring tool of fluid to air passage in the lung. In this study, the adaptive median filtering technique is employed for the preprocessing step. The preprocessed image is then extracted the features by the convoluted local tetra pattern, histogram of oriented gradient, Haralick feature extraction and the complete local binary pattern. The extracted features are selected by applying particle swarm optimization and differential evolution feature selection. In the final stage, classifiers namely relevance vector machine (RVM), and multi-level RVM are employed to perform classification of the lung diseases. The diseases respiratory distress syndrome (RDS), transient tachypnea of the new born, meconium aspiration syndrome, pneumothorax, bronchiolitis, pneumonia, and lung cancer are used for training and testing. The experimental analysis exhibits better accuracy, sensitivity, specificity, pixel count and fitness value than the other existing methods. The classification accuracy of above 90% is accomplished by multi-level RVM classifier. The system has been tested with a number of ultrasound lung images and has achieved satisfactory results in classifying the lung diseases. PMID:26135771

  19. Computer-aided detection of interstitial abnormalities in chest radiographs using a reference standard based on computed tomography

    SciTech Connect

    Arzhaeva, Yulia; Prokop, Mathias; Tax, David M. J.; De Jong, Pim A.; Schaefer-Prokop, Cornelia M.; Ginneken, Bram van

    2007-12-15

    A computer-aided detection (CAD) system is presented for the localization of interstitial lesions in chest radiographs. The system analyzes the complete lung fields using a two-class supervised pattern classification approach to distinguish between normal texture and texture affected by interstitial lung disease. Analysis is done pixel-wise and produces a probability map for an image where each pixel in the lung fields is assigned a probability of being abnormal. Interstitial lesions are often subtle and ill defined on x-rays and hence difficult to detect, even for expert radiologists. Therefore a new, semiautomatic method is proposed for setting a reference standard for training and evaluating the CAD system. The proposed method employs the fact that interstitial lesions are more distinct on a computed tomography (CT) scan than on a radiograph. Lesion outlines, manually drawn on coronal slices of a CT scan of the same patient, are automatically transformed to corresponding outlines on the chest x-ray, using manually indicated correspondences for a small set of anatomical landmarks. For the texture analysis, local structures are described by means of the multiscale Gaussian filter bank. The system performance is evaluated with ROC analysis on a database of digital chest radiographs containing 44 abnormal and 8 normal cases. The best performance is achieved for the linear discriminant and support vector machine classifiers, with an area under the ROC curve (A{sub z}) of 0.78. Separate ROC curves are built for classification of abnormalities of different degrees of subtlety versus normal class. Here the best performance in terms of A{sub z} is 0.90 for differentiation between obviously abnormal and normal pixels. The system is compared with two human observers, an expert chest radiologist and a chest radiologist in training, on evaluation of regions. Each lung field is divided in four regions, and the reference standard and the probability maps are converted into

  20. Automated Detection of Brain Abnormalities in Neonatal Hypoxia Ischemic Injury from MR Images

    PubMed Central

    Ghosh, Nirmalya; Sun, Yu; Bhanu, Bir; Ashwal, Stephen; Obenaus, Andre

    2014-01-01

    We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HII). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to check the temporal efficacy of our computational approaches. Sensitivity, specificity, similarity were used as performance metrics based on manual (‘gold standard’) injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while HRS for 33% data. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects. PMID:25000294

  1. Detection of Abnormal Muscle Activations during Walking Following Spinal Cord Injury (SCI)

    ERIC Educational Resources Information Center

    Wang, Ping; Low, K. H.; McGregor, Alison H.; Tow, Adela

    2013-01-01

    In order to identify optimal rehabilitation strategies for spinal cord injury (SCI) participants, assessment of impaired walking is required to detect, monitor and quantify movement disorders. In the proposed assessment, ten healthy and seven SCI participants were recruited to perform an over-ground walking test at slow walking speeds. SCI…

  2. Method and apparatus for detecting and determining event characteristics with reduced data collection

    NASA Technical Reports Server (NTRS)

    Totman, Peter D. (Inventor); Everton, Randy L. (Inventor); Egget, Mark R. (Inventor); Macon, David J. (Inventor)

    2007-01-01

    A method and apparatus for detecting and determining event characteristics such as, for example, the material failure of a component, in a manner which significantly reduces the amount of data collected. A sensor array, including a plurality of individual sensor elements, is coupled to a programmable logic device (PLD) configured to operate in a passive state and an active state. A triggering event is established such that the PLD records information only upon detection of the occurrence of the triggering event which causes a change in state within one or more of the plurality of sensor elements. Upon the occurrence of the triggering event, the change in state of the one or more sensor elements causes the PLD to record in memory which sensor element detected the event and at what time the event was detected. The PLD may be coupled with a computer for subsequent downloading and analysis of the acquired data.

  3. Reading Times and the Detection of Event Shift Processing

    ERIC Educational Resources Information Center

    Radvansky, Gabriel A.; Copeland, David E.

    2010-01-01

    When people read narratives, they often need to update their situation models as the described events change. Previous research has shown little to no increases in reading times for spatial shifts but consistent increases for temporal shifts. On this basis, researchers have suggested that spatial updating does not regularly occur, whereas temporal…

  4. Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application.

    PubMed

    Mur, Angel; Dormido, Raquel; Vega, Jesús; Duro, Natividad; Dormido-Canto, Sebastian

    2016-01-01

    In this paper, we propose a new unsupervised method to automatically characterize and detect events in multichannel signals. This method is used to identify artifacts in electroencephalogram (EEG) recordings of brain activity. The proposed algorithm has been evaluated and compared with a supervised method. To this end an example of the performance of the algorithm to detect artifacts is shown. The results show that although both methods obtain similar classification, the proposed method allows detecting events without training data and can also be applied in signals whose events are unknown a priori. Furthermore, the proposed method provides an optimal window whereby an optimal detection and characterization of events is found. The detection of events can be applied in real-time. PMID:27120605

  5. Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application

    PubMed Central

    Mur, Angel; Dormido, Raquel; Vega, Jesús; Duro, Natividad; Dormido-Canto, Sebastian

    2016-01-01

    In this paper, we propose a new unsupervised method to automatically characterize and detect events in multichannel signals. This method is used to identify artifacts in electroencephalogram (EEG) recordings of brain activity. The proposed algorithm has been evaluated and compared with a supervised method. To this end an example of the performance of the algorithm to detect artifacts is shown. The results show that although both methods obtain similar classification, the proposed method allows detecting events without training data and can also be applied in signals whose events are unknown a priori. Furthermore, the proposed method provides an optimal window whereby an optimal detection and characterization of events is found. The detection of events can be applied in real-time. PMID:27120605

  6. Quantitative Gait Analysis Using a Motorized Treadmill System Sensitively Detects Motor Abnormalities in Mice Expressing ATPase Defective Spastin

    PubMed Central

    Connell, James W.; Allison, Rachel; Reid, Evan

    2016-01-01

    The hereditary spastic paraplegias (HSPs) are genetic conditions in which there is progressive axonal degeneration in the corticospinal tract. Autosomal dominant mutations, including nonsense, frameshift and missense changes, in the gene encoding the microtubule severing ATPase spastin are the most common cause of HSP in North America and northern Europe. In this study we report quantitative gait analysis using a motorized treadmill system, carried out on mice knocked-in for a disease-associated mutation affecting a critical residue in the Walker A motif of the spastin ATPase domain. At 4 months and at one year of age homozygous mutant mice had a number of abnormal gait parameters, including in stride length and stride duration, compared to heterozygous and wild-type littermates. Gait parameters in heterozygous animals did not differ from wild-type littermates. We conclude that quantitative gait analysis using the DigiGait system sensitively detects motor abnormalities in a hereditary spastic paraplegia model, and would be a useful method for analyzing the effects of pharmacological treatments for HSP. PMID:27019090

  7. Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects.

    PubMed

    Ziegler, G; Ridgway, G R; Dahnke, R; Gaser, C

    2014-08-15

    Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18-94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease. PMID:24742919

  8. Setting objective thresholds for rare event detection in flow cytometry.

    PubMed

    Richards, Adam J; Staats, Janet; Enzor, Jennifer; McKinnon, Katherine; Frelinger, Jacob; Denny, Thomas N; Weinhold, Kent J; Chan, Cliburn

    2014-07-01

    The accurate identification of rare antigen-specific cytokine positive cells from peripheral blood mononuclear cells (PBMC) after antigenic stimulation in an intracellular staining (ICS) flow cytometry assay is challenging, as cytokine positive events may be fairly diffusely distributed and lack an obvious separation from the negative population. Traditionally, the approach by flow operators has been to manually set a positivity threshold to partition events into cytokine-positive and cytokine-negative. This approach suffers from subjectivity and inconsistency across different flow operators. The use of statistical clustering methods does not remove the need to find an objective threshold between between positive and negative events since consistent identification of rare event subsets is highly challenging for automated algorithms, especially when there is distributional overlap between the positive and negative events ("smear"). We present a new approach, based on the Fβ measure, that is similar to manual thresholding in providing a hard cutoff, but has the advantage of being determined objectively. The performance of this algorithm is compared with results obtained by expert visual gating. Several ICS data sets from the External Quality Assurance Program Oversight Laboratory (EQAPOL) proficiency program were used to make the comparisons. We first show that visually determined thresholds are difficult to reproduce and pose a problem when comparing results across operators or laboratories, as well as problems that occur with the use of commonly employed clustering algorithms. In contrast, a single parameterization for the Fβ method performs consistently across different centers, samples, and instruments because it optimizes the precision/recall tradeoff by using both negative and positive controls. PMID:24727143

  9. Solar 3He-rich events and abnormal enhancements of heavy-ion isotopes accelerated in two stages

    NASA Astrophysics Data System (ADS)

    Zhang, T. X.; Wang, J. X.; Tan, A.

    2005-12-01

    Heating and acceleration of neon (20Ne), magnesium (24Mg), and their rare isotopes (22Ne and 26Mg) in solar 3He-rich events are investigated according to the two-stage acceleration mechanism. It is shown that 20Ne+8, 22Ne+9, 24Mg+10, and 26Mg+11 can be preferentially heated by H-cyclotron waves with a frequency close to twice the 3He-cyclotron frequency that also heat 3He, through the third harmonic cyclotron resonance. If the initial electron temperature is in the range of ˜2-10 MK, the abundance ratios 22Ne/20Ne and 26Mg/24Mg in high-energy particles due to the second-stage acceleration can be enhanced by a factor of ˜2-6 relative to the solar corona, which are consistent with the measurements of the University of Maryland particle instrument on the Solar Anomalous and Magnetospheric Particle Explorer and the Ultra Lower Energy Isotope Spectrometer particle instrument on the Advanced Composition Explorer.

  10. Transmission of the BSE agent to mice in the absence of detectable abnormal prion protein.

    PubMed

    Lasmézas, C I; Deslys, J P; Robain, O; Jaegly, A; Beringue, V; Peyrin, J M; Fournier, J G; Hauw, J J; Rossier, J; Dormont, D

    1997-01-17

    The agent responsible for transmissible spongiform encephalopathies (TSEs) is thought to be a malfolded, protease-resistant version (PrPres) of the normal cellular prion protein (PrP). The interspecies transmission of bovine spongiform encephalopathy (BSE) to mice was studied. Although all of the mice injected with homogenate from BSE-infected cattle brain exhibited neurological symptoms and neuronal death, more than 55 percent had no detectable PrPres. During serial passage, PrPres appeared after the agent became adapted to the new host. Thus, PrPres may be involved in species adaptation, but a further unidentified agent may actually transmit BSE. PMID:8994041

  11. A Multiscale Decomposition Approach to Detect Abnormal Vasculature in the Optic Disc

    PubMed Central

    Agurto, Carla; Yu, Honggang; Murray, Victor; Pattichis, Marios S.; Nemeth, Sheila; Barriga, Simon; Soliz, Peter

    2015-01-01

    This paper presents a multiscale method to detect neovascularization in the optic disc (NVD) using fundus images. Our method is applied to a manually selected region of interest (ROI) containing the optic disc. All the vessels in the ROI are segmented by adaptively combining contrast enhancement methods with a vessel segmentation technique. Textural features extracted using multiscale amplitude-modulation frequency-modulation, morphological granulometry, and fractal dimension are used. A linear SVM is used to perform the classification, which is tested by means of 10-fold cross-validation. The performance is evaluated using 300 images achieving an AUC of 0.93 with maximum accuracy of 88%. PMID:25698545

  12. Motor task event detection using Subthalamic Nucleus Local Field Potentials.

    PubMed

    Niketeghad, Soroush; Hebb, Adam O; Nedrud, Joshua; Hanrahan, Sara J; Mahoor, Mohammad H

    2015-08-01

    Deep Brain Stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson's disease. Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and DBS side effects. In such systems, DBS parameters are adjusted based on patient's behavior, which means that behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local Field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. A practical behavior detection method should be able to detect behaviors asynchronously meaning that it should not use any prior knowledge of behavior onsets. In this paper, we introduce a behavior detection method that is able to asynchronously detect the finger movements of Parkinson patients. As a result of this study, we learned that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We used non-linear regression method to measure this connectivity and use it to detect the finger movements. Performance of this method is evaluated using Receiver Operating Characteristic (ROC). PMID:26737550

  13. Simulation-Based Evaluation of the Performances of an Algorithm for Detecting Abnormal Disease-Related Features in Cattle Mortality Records

    PubMed Central

    Perrin, Jean-Baptiste; Durand, Benoît; Gay, Emilie; Ducrot, Christian; Hendrikx, Pascal; Calavas, Didier; Hénaux, Viviane

    2015-01-01

    We performed a simulation study to evaluate the performances of an anomaly detection algorithm considered in the frame of an automated surveillance system of cattle mortality. The method consisted in a combination of temporal regression and spatial cluster detection which allows identifying, for a given week, clusters of spatial units showing an excess of deaths in comparison with their own historical fluctuations. First, we simulated 1,000 outbreaks of a disease causing extra deaths in the French cattle population (about 200,000 herds and 20 million cattle) according to a model mimicking the spreading patterns of an infectious disease and injected these disease-related extra deaths in an authentic mortality dataset, spanning from January 2005 to January 2010. Second, we applied our algorithm on each of the 1,000 semi-synthetic datasets to identify clusters of spatial units showing an excess of deaths considering their own historical fluctuations. Third, we verified if the clusters identified by the algorithm did contain simulated extra deaths in order to evaluate the ability of the algorithm to identify unusual mortality clusters caused by an outbreak. Among the 1,000 simulations, the median duration of simulated outbreaks was 8 weeks, with a median number of 5,627 simulated deaths and 441 infected herds. Within the 12-week trial period, 73% of the simulated outbreaks were detected, with a median timeliness of 1 week, and a mean of 1.4 weeks. The proportion of outbreak weeks flagged by an alarm was 61% (i.e. sensitivity) whereas one in three alarms was a true alarm (i.e. positive predictive value). The performances of the detection algorithm were evaluated for alternative combination of epidemiologic parameters. The results of our study confirmed that in certain conditions automated algorithms could help identifying abnormal cattle mortality increases possibly related to unidentified health events. PMID:26536596

  14. Cortical shell unwrapping for vertebral body abnormality detection on computed tomography.

    PubMed

    Yao, Jianhua; Burns, Joseph E; Muñoz, Hector; Summers, Ronald M

    2014-10-01

    The vertebral body is the main axial load-bearing structure of the spinal vertebra. Assessment of acute injury and chronic deformity of the vertebral body is difficult to assess accurately and quantitatively by simple visual inspection. We propose a cortical shell unwrapping method to examine the vertebral body for injury such as fractures and degenerative osteophytes. The spine is first segmented and partitioned into vertebrae. Then the cortical shell of the vertebral body is extracted using deformable dual-surface models. The cortical shell is then unwrapped onto a 2D map and the complex 3D detection problem is effectively converted to a pattern recognition problem on a 2D plane. Characteristic features adapted for different applications are computed and sent to a committee of support vector machines for classification. The system was evaluated on two applications, one for fracture detection on trauma CT datasets and the other on degenerative osteophyte assessment on sodium fluoride PET/CT. The fracture CAD achieved 93.6% sensitivity at 3.2 false positive per patient and the degenerative osteophyte CAD achieved 82% sensitivity at 4.7 false positive per patient. PMID:24815367

  15. Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease

    PubMed Central

    Zhan, Liang; Zhou, Jiayu; Wang, Yalin; Jin, Yan; Jahanshad, Neda; Prasad, Gautam; Nir, Talia M.; Leonardo, Cassandra D.; Ye, Jieping; Thompson, Paul M.; for the Alzheimer’s Disease Neuroimaging Initiative

    2015-01-01

    Alzheimer’s disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods used. Here we compared several tractography and feature extraction methods to see which ones gave best diagnostic classification for 202 people with AD, mild cognitive impairment or normal cognition, scanned with 41-gradient diffusion-weighted magnetic resonance imaging as part of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. We computed brain networks based on whole brain tractography with nine different methods – four of them tensor-based deterministic (FACT, RK2, SL, and TL), two orientation distribution function (ODF)-based deterministic (FACT, RK2), two ODF-based probabilistic approaches (Hough and PICo), and one “ball-and-stick” approach (Probtrackx). Brain networks derived from different tractography algorithms did not differ in terms of classification performance on ADNI, but performing principal components analysis on networks helped classification in some cases. Small differences may still be detectable in a truly vast cohort, but these experiments help assess the relative advantages of different tractography algorithms, and different post-processing choices, when used for classification. PMID:25926791

  16. The GRACE satellites detect recent extreme climate events in China

    NASA Astrophysics Data System (ADS)

    Tang, Jingshi; Liu, Lin

    2012-07-01

    As the climate changes, the extreme climates are occurring more frequenly over the globe. In China, drought or flood recently strikes almost every year and there have been several disastrous events in these years. We show that some of the disastrous events are so strong that corresponding gravity change can be observed by geodetic satellies. We use the Gravity Recovery and Climate Experiment (GRACE), which is a joint mission between NASA and DLR. One primary job of GRACE is to map Earth temporal gravity field with high resolution. Over the years the twin satellites have observed the loss of mass in Antarctic and Greenland, strong earthquakes, severe climate change in South America and so on, which provides a unique way to study the geophysical or climatological process. In this report, the Level-2 product in recent few years from Center for Space Research is used and specific areas in China are focused on. It is shown that after decorrelation, filter and other processes, the gravity anomalies observed by GRACE match the extreme climate events and the hydrological data from the Global Land Data Assimilation System (GLDAS).

  17. Electroencephalographic detection of respiratory-related cortical activity in humans: from event-related approaches to continuous connectivity evaluation.

    PubMed

    Hudson, Anna L; Navarro-Sune, Xavier; Martinerie, Jacques; Pouget, Pierre; Raux, Mathieu; Chavez, Mario; Similowski, Thomas

    2016-04-01

    The presence of a respiratory-related cortical activity during tidal breathing is abnormal and a hallmark of respiratory difficulties, but its detection requires superior discrimination and temporal resolution. The aim of this study was to validate a computational method using EEG covariance (or connectivity) matrices to detect a change in brain activity related to breathing. In 17 healthy subjects, EEG was recorded during resting unloaded breathing (RB), voluntary sniffs, and breathing against an inspiratory threshold load (ITL). EEG were analyzed by the specially developed covariance-based classifier, event-related potentials, and time-frequency (T-F) distributions. Nine subjects repeated the protocol. The classifier could accurately detect ITL and sniffs compared with the reference period of RB. For ITL, EEG-based detection was superior to airflow-based detection (P < 0.05). A coincident improvement in EEG-airflow correlation in ITL compared with RB (P < 0.05) confirmed that EEG detection relates to breathing. Premotor potential incidence was significantly higher before inspiration in sniffs and ITL compared with RB (P < 0.05), but T-F distributions revealed a significant difference between sniffs and RB only (P < 0.05). Intraclass correlation values ranged from poor (-0.2) to excellent (1.0). Thus, as for conventional event-related potential analysis, the covariance-based classifier can accurately predict a change in brain state related to a change in respiratory state, and given its capacity for near "real-time" detection, it is suitable to monitor the respiratory state in respiratory and critically ill patients in the development of a brain-ventilator interface. PMID:26864771

  18. Nonthreshold-based event detection for 3d environment monitoring in sensor networks

    SciTech Connect

    Li, M.; Liu, Y.H.; Chen, L.

    2008-12-15

    Event detection is a crucial task for wireless sensor network applications, especially environment monitoring. Existing approaches for event detection are mainly based on some predefined threshold values and, thus, are often inaccurate and incapable of capturing complex events. For example, in coal mine monitoring scenarios, gas leakage or water osmosis can hardly be described by the overrun of specified attribute thresholds but some complex pattern in the full-scale view of the environmental data. To address this issue, we propose a nonthreshold-based approach for the real 3D sensor monitoring environment. We employ energy-efficient methods to collect a time series of data maps from the sensor network and detect complex events through matching the gathered data to spatiotemporal data patterns. Finally, we conduct trace-driven simulations to prove the efficacy and efficiency of this approach on detecting events of complex phenomena from real-life records.

  19. Detection of abnormal muscle activations during walking following spinal cord injury (SCI).

    PubMed

    Wang, Ping; Low, K H; McGregor, Alison H; Tow, Adela

    2013-04-01

    In order to identify optimal rehabilitation strategies for spinal cord injury (SCI) participants, assessment of impaired walking is required to detect, monitor and quantify movement disorders. In the proposed assessment, ten healthy and seven SCI participants were recruited to perform an over-ground walking test at slow walking speeds. SCI participants were given assistance from physiotherapists, if required, while they were walking. In agreement with other research, larger cadence and smaller step length and swing phase of SCI gait were observed as a result of muscle weakness and resultant gait instability. Muscle activation patterns of seven major leg muscles were collected. The EMG signal was processed by the RMS in frequency domain to represent the muscle activation power, and the distribution of muscle activation was compared between healthy and SCI participants. The alternations of muscle activation within the phases of the gait cycle are highlighted to facilitate our understanding of the underlying muscular activation following SCI. Key differences were observed (p-value=0.0006) in the reduced activation of tibialis anterior (TA) in single stance phase and rectus femoris (RF) in swing phase (p-value=0.0011). We can then conclude that the proposed assessment approach of gait provides valuable information that can be used to target and define therapeutic interventions and their evaluation; hence impacting the functional outcome of SCI individuals. PMID:23396198

  20. Detection of zones of abnormal strains in structures using Gaussian curvature analysis

    SciTech Connect

    Lisle, R.J.

    1994-12-01

    Whereas some folds, such as those produced by flexural slip, do not theoretically entail strain within the folded surfaces, any surface involving double curvature (such as domes and saddles) cannot form without some stretching or contraction of the bedding. Whether straining of the surfaces is required during folding depends on the three-dimensional fold shape and, in particular, on the Gaussian curvature at points on the folded surface. Using this as a basis, I present a method for detecting zones of anomalously high strain in oil-field structures from Gaussian curvature analysis (GCA) of natural structures. The new method of GCA is suitable for analyzing surfaces that have been mapped seismically. A Gaussian curvature map of the structure is a principal outcome of the analysis and can be used to predict the density of strain-related subseismic structures, such as small-scale fracturing. The Goose Egg dome, near Casper, Wyoming, is analyzed and provides an example of GCA. In this structure, a relationship is observed between fracture densities and Gaussian curvature.

  1. Neuro-evolutionary event detection technique for downhole microseismic surveys

    NASA Astrophysics Data System (ADS)

    Maity, Debotyam; Salehi, Iraj

    2016-01-01

    Recent years have seen a significant increase in borehole microseismic data acquisition programs associated with unconventional reservoir developments such as hydraulic fracturing programs for shale oil and gas. The data so acquired is used for hydraulic fracture monitoring and diagnostics and therefore, the quality of the data in terms of resolution and accuracy has a significant impact on its value to the industry. Borehole microseismic data acquired in such environments typically suffer from propagation effects due to the presence of thin interbedded shale layers as well as noise and interference effects. Moreover, acquisition geometry has significant impact on detectability across portions of the sensor array. Our work focuses on developing robust first arrival detection and pick selection workflow for both P and S waves specifically designed for such environments. We introduce a novel workflow for refinement of picks with immunity towards significant noise artifacts and applicability over data with very low signal-to-noise ratio provided some accurate picks have already been made. This workflow utilizes multi-step hybrid detection and classification routine which makes use of a neural network based autopicker for initial picking and an evolutionary algorithm for pick refinement. We highlight the results from an actual field case study including multiple examples demonstrating immunity towards noise and compare the effectiveness of the workflow with two contemporary autopicking routines without the application of the shared detection/refinement procedure. Finally, we use a windowed waveform cross-correlation based uncertainty estimation method for potential quality control purposes. While the workflow was developed to work with the neural network based autopicker, it can be used with any other traditional autopicker and provides significant improvements in pick detection across seismic gathers.

  2. MLPA: A Rapid, Reliable, and Sensitive Method for Detection and Analysis of Abnormalities of 22q

    PubMed Central

    Vorstman, J.A.S.; Jalali, G.R.; Rappaport, E.F.; Hacker, A.M.; Scott, C.; Emanuel, B.S.

    2010-01-01

    In this study, essential test characteristics of the recently described multiplex ligation-dependent probe amplification (MLPA) method are presented, using chromosome 22 as a model. This novel method allows the relative quantification of ~40–45 different target DNA sequences in a single reaction. For the purpose of this study, MLPA was performed in a blinded manner on a training set containing over 50 samples, including typical 22q11.2 deletions, various atypical deletions, duplications (trisomy and tetrasomy), and unbalanced translocations. All samples in the training set have been previously characterized by fluorescence in situ hybridization (FISH) with cosmid or BAC clones and/or cytogenetic studies. MLPA findings were consistent with cytogenetic and FISH studies, no rearrangement went undetected and repeated tests gave consistent results. At a relative change in comparative signal strength of 30% or more, sensitivity and specificity values were 0.95 and 0.99, respectively. Given that MLPA is likely to be used as an initial screening method, a higher sensitivity, at the cost of a lower specificity, was deemed more appropriate. A receiver operator characteristic (ROC) curve analysis was performed to calculate the most optimal threshold range, with associated sensitivity and specificity values of 0.99 and 0.97, respectively. Finally, performance of each individual probe was analyzed, providing further useful information for the interpretation of MLPA results. In conclusion, MLPA has proven to be a highly sensitive and accurate tool for detecting copy number changes in the 22q11.2 region, making it a fast and economic alternative to currently used methods. The current study provides valuable and detailed information on the characteristics of this novel method. PMID:16791841

  3. White matter microstructural abnormality in children with hydrocephalus detected by probabilistic diffusion tractography

    PubMed Central

    Rajagopal, Akila; Shimony, Joshua S.; McKinstry, Robert C.; Altaye, Mekibib; Maloney, Tom; Mangano, Francesco T.; Limbrick, David D.; Holland, Scott K.; Jones, Blaise V.; Simpson, Sarah; Mercer, Deanna; Yuan, Weihong

    2014-01-01

    Background and Purpose Hydrocephalus is a severe pathologic condition in which WM damage is a major factor associated with poor outcomes. The goal of the study was to investigate tract-based WM connectivity and DTI measurements in children with hydrocephalus using PDT method. Methods Twelve children with hydrocephalus and 16 age matched controls were included in the study. PDT was conducted to generate tract-based connectivity distribution and DTI measures for the gCC and mCST. Tract-based summary measurements, included connectivity index and DTI measures (FA, MD, AD, and RDs) were calculated and compared between the two study groups. Results Tract-based summary measurement showed that there was a higher percentage of voxels with lower normalized CI values in the WM tracts from children with hydrocephalus. In gCC, left mCST and right mCST, the normalized CI value in children with hydrocephalus was found to be significantly lower (p<0.05, corrected). The tract based DTI measures showed that the children with hydrocephalus had significantly higher MD, AD, and RD in gCC, left mCST, and right mCST, and lower FA in gCC (p<0.05, corrected). Conclusions The analysis of WM connectivity showed that PDT method is a sensitive tool to detect the decreased continuity in WM tracts that are under the direct influence of mechanical distortion and increased intracranial pressure in hydrocephalus. This voxel-based connectivity method can provide quantitative information complementary to the standard DTI summary measures. PMID:24072621

  4. Spatial-temporal event detection in climate parameter imagery.

    SciTech Connect

    McKenna, Sean Andrew; Gutierrez, Karen A.

    2011-10-01

    Previously developed techniques that comprise statistical parametric mapping, with applications focused on human brain imaging, are examined and tested here for new applications in anomaly detection within remotely-sensed imagery. Two approaches to analysis are developed: online, regression-based anomaly detection and conditional differences. These approaches are applied to two example spatial-temporal data sets: data simulated with a Gaussian field deformation approach and weekly NDVI images derived from global satellite coverage. Results indicate that anomalies can be identified in spatial temporal data with the regression-based approach. Additionally, la Nina and el Nino climatic conditions are used as different stimuli applied to the earth and this comparison shows that el Nino conditions lead to significant decreases in NDVI in both the Amazon Basin and in Southern India.

  5. A new paradigm of dielectric relaxation spectroscopy for non-invasive detection of breast abnormalities: a preliminary feasibility analysis

    NASA Astrophysics Data System (ADS)

    Dhurjaty, Sreeram; Qiu, Yuchen; Tan, Maxine; Qian, Wei; Zheng, Bin

    2016-03-01

    In order to improve efficacy of screening mammography, in recent years, we have been investigating the feasibility of applying a resonance-frequency based electrical impedance spectroscopy (REIS) technology to noninvasively detect breast abnormalities that may lead to the development of cancer in the near-term. Despite promising study-results, we found that REIS suffered from relatively poor reproducibility due to perturbations in electrode placement, contact pressure variation on the breast, as well as variation of the resonating inductor. To overcome this limitation, in this study, we propose and analyze a new paradigm of Dielectric Relaxation Spectroscopy (DRS) that measures polarization-lag of dielectric signals in breast-capacitance when excited by the pulses or sine waves. Unlike conventional DRS that operates using the signals at very high frequencies (GHz) to examine changes in polarization, our new method detects and characterizes the dielectric properties of tissue at low frequencies (<=10 MHz) due to the advent of inexpensive oscillators that are accurate to 1 pico-second (used in GPS receivers) as well as measurement of amplitudes of 1 ppm or better. From theoretical analysis, we have proved that the sensitivity of new DRS in detecting permittivity of water increased by >=80 times as compared to conventional DRS, which operates at frequencies around 4GHz. By analyzing and comparing the relationship between the new DRS and REIS, we found that this DRS has potential advantages in enhancing repeatability from various readings, including temperature-insensitive detection, and yielding higher resolution or sensitivity (up to 100 Femtofarads).

  6. Detection of intermittent events in atmospheric time series

    NASA Astrophysics Data System (ADS)

    Paradisi, P.; Cesari, R.; Palatella, L.; Contini, D.; Donateo, A.

    2009-04-01

    The modeling approach in atmospheric sciences is based on the assumption that local fluxes of mass, momentum, heat, etc... can be described as linear functions of the local gradient of some intensive property (concentration, flow strain, temperature,...). This is essentially associated with Gaussian statistics and short range (exponential) correlations. However, the atmosphere is a complex dynamical system displaying a wide range of spatial and temporal scales. A global description of the atmospheric dynamics should include a great number of degrees of freedom, strongly interacting on several temporal and spatial scales, thus generating long range (power-law) correlations and non-Gaussian distribution of fluctuations (Lévy flights, Lévy walks, Continuous Time Random Walks) [1]. This is typically associated with anomalous diffusion and scaling, non-trivial memory features and correlation decays and, especially, with the emergence of flux-gradient relationships that are non-linear and/or non-local in time and/or space. Actually, the local flux-gradient relationship is greatly preferred due to a more clear physical meaning, allowing to perform direct comparisons with experimental data, and, especially, to smaller computational costs in numerical models. In particular, the linearity of this relationship allows to define a transport coefficient (e.g., turbulent diffusivity) and the modeling effort is usually focused on this coefficient. However, the validity of the local (and linear) flux-gradient model is strongly dependent on the range of spatial and temporal scales represented by the model and, consequently, by the sub-grid processes included in the flux-gradient relationship. In this work, in order to check the validity of local and linear flux-gradient relationships, an approach based on the concept of renewal critical events [2] is introduced. In fact, in renewal theory [2], the dynamical origin of anomalous behaviour and non-local flux-gradient relation is

  7. Automated identification of abnormal metaphase chromosome cells for the detection of chronic myeloid leukemia using microscopic images

    NASA Astrophysics Data System (ADS)

    Wang, Xingwei; Zheng, Bin; Li, Shibo; Mulvihill, John J.; Chen, Xiaodong; Liu, Hong

    2010-07-01

    Karyotyping is an important process to classify chromosomes into standard classes and the results are routinely used by the clinicians to diagnose cancers and genetic diseases. However, visual karyotyping using microscopic images is time-consuming and tedious, which reduces the diagnostic efficiency and accuracy. Although many efforts have been made to develop computerized schemes for automated karyotyping, no schemes can get be performed without substantial human intervention. Instead of developing a method to classify all chromosome classes, we develop an automatic scheme to detect abnormal metaphase cells by identifying a specific class of chromosomes (class 22) and prescreen for suspicious chronic myeloid leukemia (CML). The scheme includes three steps: (1) iteratively segment randomly distributed individual chromosomes, (2) process segmented chromosomes and compute image features to identify the candidates, and (3) apply an adaptive matching template to identify chromosomes of class 22. An image data set of 451 metaphase cells extracted from bone marrow specimens of 30 positive and 30 negative cases for CML is selected to test the scheme's performance. The overall case-based classification accuracy is 93.3% (100% sensitivity and 86.7% specificity). The results demonstrate the feasibility of applying an automated scheme to detect or prescreen the suspicious cancer cases.

  8. A Fuzzy-Decision Based Approach for Composite Event Detection in Wireless Sensor Networks

    PubMed Central

    Zhang, Shukui; Chen, Hao; Zhu, Qiaoming

    2014-01-01

    The event detection is one of the fundamental researches in wireless sensor networks (WSNs). Due to the consideration of various properties that reflect events status, the Composite event is more consistent with the objective world. Thus, the research of the Composite event becomes more realistic. In this paper, we analyze the characteristics of the Composite event; then we propose a criterion to determine the area of the Composite event and put forward a dominating set based network topology construction algorithm under random deployment. For the unreliability of partial data in detection process and fuzziness of the event definitions in nature, we propose a cluster-based two-dimensional τ-GAS algorithm and fuzzy-decision based composite event decision mechanism. In the case that the sensory data of most nodes are normal, the two-dimensional τ-GAS algorithm can filter the fault node data effectively and reduce the influence of erroneous data on the event determination. The Composite event judgment mechanism which is based on fuzzy-decision holds the superiority of the fuzzy-logic based algorithm; moreover, it does not need the support of a huge rule base and its computational complexity is small. Compared to CollECT algorithm and CDS algorithm, this algorithm improves the detection accuracy and reduces the traffic. PMID:25136690

  9. Detection of Upper Airway Status and Respiratory Events by a Current Generation Positive Airway Pressure Device

    PubMed Central

    Li, Qing Yun; Berry, Richard B.; Goetting, Mark G.; Staley, Bethany; Soto-Calderon, Haideliza; Tsai, Sheila C.; Jasko, Jeffrey G.; Pack, Allan I.; Kuna, Samuel T.

    2015-01-01

    Study Objectives: To compare a positive airway pressure (PAP) device's detection of respiratory events and airway status during device-detected apneas with events scored on simultaneous polysomnography (PSG). Design: Prospective PSGs of patients with sleep apnea using a new-generation PAP device. Settings: Four clinical and academic sleep centers. Patients: Forty-five patients with obstructive sleep apnea (OSA) and complex sleep apnea (Comp SA) performed a PSG on PAP levels adjusted to induce respiratory events. Interventions: None. Measurements and Results: PAP device data identifying the type of respiratory event and whether the airway during a device-detected apnea was open or obstructed were compared to time-synced, manually scored respiratory events on simultaneous PSG recording. Intraclass correlation coefficients between device-detected and PSG scored events were 0.854 for apnea-hypopnea index (AHI), 0.783 for apnea index, 0.252 for hypopnea index, and 0.098 for respiratory event-related arousals index. At a device AHI (AHIFlow) of 10 events/h, area under the receiver operating characteristic curve was 0.98, with sensitivity 0.92 and specificity 0.84. AHIFlow tended to overestimate AHI on PSG at values less than 10 events/h. The device detected that the airway was obstructed in 87.4% of manually scored obstructive apneas. Of the device-detected apneas with clear airway, a minority (15.8%) were manually scored as obstructive apneas. Conclusions: A device-detected apnea-hypopnea index (AHIFlow) < 10 events/h on a positive airway pressure device is strong evidence of good treatment efficacy. Device-detected airway status agrees closely with the presumed airway status during polysomnography scored events, but should not be equated with a specific type of respiratory event. Citation: Li QY, Berry RB, Goetting MG, Staley B, Soto-Calderon H, Tsai SC, Jasko JG, Pack AI, Kuna ST. Detection of upper airway status and respiratory events by a current generation positive

  10. Unsupervised Spatial Event Detection in Targeted Domains with Applications to Civil Unrest Modeling

    PubMed Central

    Zhao, Liang; Chen, Feng; Dai, Jing; Hua, Ting; Lu, Chang-Tien; Ramakrishnan, Naren

    2014-01-01

    Twitter has become a popular data source as a surrogate for monitoring and detecting events. Targeted domains such as crime, election, and social unrest require the creation of algorithms capable of detecting events pertinent to these domains. Due to the unstructured language, short-length messages, dynamics, and heterogeneity typical of Twitter data streams, it is technically difficult and labor-intensive to develop and maintain supervised learning systems. We present a novel unsupervised approach for detecting spatial events in targeted domains and illustrate this approach using one specific domain, viz. civil unrest modeling. Given a targeted domain, we propose a dynamic query expansion algorithm to iteratively expand domain-related terms, and generate a tweet homogeneous graph. An anomaly identification method is utilized to detect spatial events over this graph by jointly maximizing local modularity and spatial scan statistics. Extensive experiments conducted in 10 Latin American countries demonstrate the effectiveness of the proposed approach. PMID:25350136

  11. Seismic network detection probability assessment using waveforms and accounting to event association logic

    NASA Astrophysics Data System (ADS)

    Pinsky, Vladimir; Shapira, Avi

    2016-05-01

    The geographical area where a seismic event of magnitude M ≥ M t is detected by a seismic station network, for a defined probability is derived from a station probability of detection estimated as a function of epicentral distance. The latter is determined from both the bulletin data and the waveforms recorded by the station during the occurrence of the event with and without band-pass filtering. For simulating the real detection process, the waveforms are processed using the conventional Carl Johnson detection and association algorithm. The attempt is presented to account for the association time criterion in addition to the conventional approach adopted by the known PMC method.

  12. Diagnostic accuracy of myocardial deformation indices for detecting high risk coronary artery disease in patients without regional wall motion abnormality

    PubMed Central

    Rostamzadeh, Alireza; Shojaeifard, Maryam; Rezaei, Yousef; Dehghan, Kasra

    2015-01-01

    Background: The prediction of coronary artery disease (CAD) by conventional echocardiographic measurements is principally based on the estimation of ejection fraction and regional wall motion abnormality (RWMA). This study aimed to determine whether strain echocardiography of left ventricle measured by velocity vector imaging (VVI) method could detect patients with a high-risk CAD. Methods: In a prospective study, a total of 119 consecutive patients who were assessed for eligibility were categorized into three groups: (1) without CAD as normal (n=59), (2) 1- or 2-vessel disease as low-risk (n=29), and (3) left main and/or 3-vessel disease as high-risk (n=31). The peaks of systolic strain and strain rate from 18 curves of apical views were averaged as global longitudinal strain and strain rate (GLS and GLSR), respectively; the 6 systolic peaks of strain and strain rate at base- and mid-ventricular of short axis views were averaged as mean radial strain rate (MRSR). Results: GLS, GLSR, and basal MRSR of left ventricle were significantly lower in the high-risk group (P=0.047, P=0.004 and P=0.030, respectively). Receiver operating characteristics curve showed that the optimal values of GLS, GLSR, and basal MRSR for detecting the severe CAD were -17%, -1 s-1, and 1.45 s-1 with the sensitivities of 77%, 71%, and 71% and the specificities of 63%, 67%, and 62%, respectively. Conclusion: Decrements in the GLS, GLSR, and basal MRSR of the left ventricle can detect the high-risk CAD cases among patients without RWMA at rest. PMID:26309603

  13. Detection of Bartonella spp. in neotropical felids and evaluation of risk factors and hematological abnormalities associated with infection.

    PubMed

    Guimaraes, A M S; Brandão, P E; Moraes, W; Kiihl, S; Santos, L C; Filoni, C; Cubas, Z S; Robes, R R; Marques, L M; Neto, R L; Yamaguti, M; Oliveira, R C; Catão-Dias, J L; Richtzenhain, L J; Messick, J B; Biondo, A W; Timenetsky, J

    2010-05-19

    Although antibodies to Bartonella henselae have been described in all neotropical felid species, DNA has been detected in only one species, Leopardus wiedii. The aim of this study was to determine whether DNA of Bartonella spp. could be detected in blood of other captive neotropical felids and evaluate risk factors and hematological findings associated with infection. Blood samples were collected from 57 small felids, including 1 Leopardus geoffroyi, 17 L. wiedii, 22 Leopardus tigrinus, 14 Leopardus pardalis, and 3 Puma yagouaroundi; 10 blood samples from Panthera onca were retrieved from blood banks. Complete blood counts were performed on blood samples from small felids, while all samples were evaluated by PCR. DNA extraction was confirmed by amplification of the cat GAPDH gene. Bartonella spp. were assessed by amplifying a fragment of their 16S-23S rRNA intergenic spacer region; PCR products were purified and sequenced. For the small neotropical felids, risk factors [origin (wild-caught or zoo-born), gender, felid species, and flea exposure] were evaluated using exact multiple logistic regression. Hematological findings (anemia, polycythemia/hyperproteinemia, leukocytosis and leukopenia) were tested for association with infection using Fisher's exact test. The 635bp product amplified from 10 samples (10/67=14.92%) was identified as B. henselae by sequencing. Small neotropical felid males were more likely to be positive than females (95% CI=0.00-0.451, p=0.0028), however other analyzed variables were not considered risk factors (p>0.05). Hematological abnormalities were not associated with infection (p>0.05). This is the first report documenting B. henselae detection by PCR in several species of neotropical felids. PMID:19913372

  14. Find Your Manners: How Do Infants Detect the Invariant Manner of Motion in Dynamic Events?

    ERIC Educational Resources Information Center

    Pruden, Shannon M.; Goksun, Tilbe; Roseberry, Sarah; Hirsh-Pasek, Kathy; Golinkoff, Roberta M.

    2012-01-01

    To learn motion verbs, infants must be sensitive to the specific event features lexicalized in their language. One event feature important for the acquisition of English motion verbs is the manner of motion. This article examines when and how infants detect manners of motion across variations in the figure's path. Experiment 1 shows that 13- to…

  15. MCD for detection of event-based landslides

    NASA Astrophysics Data System (ADS)

    Mondini, A. C.; Chang, K.; Guzzetti, F.

    2011-12-01

    Landslides play an important role in the landscape evolution of mountainous terrain. They also present a socioeconomic problem in terms of risk for people and properties. Landslide inventory maps are not available for many areas affected by slope instabilities, resulting in a lack of primary information for the comprehension of the phenomenon, evaluation of relative landslide statistics, and civil protection operations on large scales. Traditional methods for the preparation of landslide inventory maps are based on the geomorphological interpretation of stereoscopic aerial photography and field surveys. These methods are expensive and time consuming. The exploitation of new remote sensing data, in particular very high resolution (VHR) satellite images, and new dedicated methods present an alternative to the traditional methods and are at the forefront of modern landslide research. Recent studies have showed the possibility to produce accurate landslide maps, reducing the time and resources required for their compilation and systematic update. This paper presents the Multiple Change Detection (MCD) technique, a new method that has shown promising results in landslide mapping. Through supervised or unsupervised classifiers, MCD combines different algorithms of change detection metrics, such as change in Normalized Differential Vegetation Index, spectral angle, principal component analysis, and independent component analysis, and applies them to a multi-temporal set of VHR satellite images to distinguish new landslides from stable areas. MCD has been applied with success in different geographical areas and with different satellite images, suggesting it is a reliable and robust technique. The technique can distinguish old from new landslides and capture runout features. Results of these case studies will be presented in the conference. Also to be presented are new developments of MCD involving the introduction of a priori information on landslide susceptibility within

  16. An integrated logit model for contamination event detection in water distribution systems.

    PubMed

    Housh, Mashor; Ostfeld, Avi

    2015-05-15

    The problem of contamination event detection in water distribution systems has become one of the most challenging research topics in water distribution systems analysis. Current attempts for event detection utilize a variety of approaches including statistical, heuristics, machine learning, and optimization methods. Several existing event detection systems share a common feature in which alarms are obtained separately for each of the water quality indicators. Unifying those single alarms from different indicators is usually performed by means of simple heuristics. A salient feature of the current developed approach is using a statistically oriented model for discrete choice prediction which is estimated using the maximum likelihood method for integrating the single alarms. The discrete choice model is jointly calibrated with other components of the event detection system framework in a training data set using genetic algorithms. The fusing process of each indicator probabilities, which is left out of focus in many existing event detection system models, is confirmed to be a crucial part of the system which could be modelled by exploiting a discrete choice model for improving its performance. The developed methodology is tested on real water quality data, showing improved performances in decreasing the number of false positive alarms and in its ability to detect events with higher probabilities, compared to previous studies. PMID:25770443

  17. Comparison of the STA/LTA and power spectral density methods for microseismic event detection

    NASA Astrophysics Data System (ADS)

    Vaezi, Yoones; Van der Baan, Mirko

    2015-12-01

    Robust event detection and picking is a prerequisite for reliable (micro-) seismic interpretations. Detection of weak events is a common challenge among various available event detection algorithms. In this paper we compare the performance of two event detection methods, the short-term average/long-term average (STA/LTA) method, which is the most commonly used technique in industry, and a newly introduced method that is based on the power spectral density (PSD) measurements. We have applied both techniques to a 1-hr long segment of the vertical component of some raw continuous data recorded at a borehole geophone in a hydraulic fracturing experiment. The PSD technique outperforms the STA/LTA technique by detecting a higher number of weak events while keeping the number of false alarms at a reasonable level. The time-frequency representations obtained through the PSD method can also help define a more suitable bandpass filter which is usually required for the STA/LTA method. The method offers thus much promise for automated event detection in industrial, local, regional and global seismological data sets.

  18. Probabilistic approaches to fault detection in networked discrete event systems.

    PubMed

    Athanasopoulou, Eleftheria; Hadjicostis, Christoforos N

    2005-09-01

    In this paper, we consider distributed systems that can be modeled as finite state machines with known behavior under fault-free conditions, and we study the detection of a general class of faults that manifest themselves as permanent changes in the next-state transition functionality of the system. This scenario could arise in a variety of situations encountered in communication networks, including faults occurred due to design or implementation errors during the execution of communication protocols. In our approach, fault diagnosis is performed by an external observer/diagnoser that functions as a finite state machine and which has access to the input sequence applied to the system but has only limited access to the system state or output. In particular, we assume that the observer/diagnoser is only able to obtain partial information regarding the state of the given system at intermittent time intervals that are determined by certain synchronizing conditions between the system and the observer/diagnoser. By adopting a probabilistic framework, we analyze ways to optimally choose these synchronizing conditions and develop adaptive strategies that achieve a low probability of aliasing, i.e., a low probability that the external observer/diagnoser incorrectly declares the system as fault-free. An application of these ideas in the context of protocol testing/classification is provided as an example. PMID:16252815

  19. The waveform correlation event detection system project, Phase I: Issues in prototype development and testing

    SciTech Connect

    Young, C.; Harris, M.; Beiriger, J.; Moore, S.; Trujillo, J.; Withers, M.; Aster, R.

    1996-08-01

    A study using long-period seismic data showed that seismic events can be detected and located based on correlations of processed waveform profiles with the profile expected for an event. In this technique both time and space are discretized and events are found by forming profiles and calculating correlations for all time-distance points. events are declared at points with large correlations. In the first phase of the Waveform Correlation Event Detection System (WCEDS) Project at Sandia Labs we have developed a prototype automatic event detection system based on Shearer`s work which shows promise for treaty monitoring applications. Many modifications have been made to meet the requirements of the monitoring environment. A new full matrix multiplication has been developed which can reduce the number of computations needed for the data correlation by as much as two orders of magnitude for large grids. New methodology has also been developed to deal with the problems caused by false correlations (sidelobes) generated during the correlation process. When an event has been detected, masking matrices are set up which will mask all correlation sidelobes due to the event, allowing other events with intermingled phases to be found. This process is repeated until a detection threshold is reached. The system was tested on one hour of Incorporated Research Institutions for Seismology (IRIS) broadband data and built all 4 of the events listed in the National Earthquake Information Center (NEIC) Preliminary Determination of Epicenters (PDE) which were observable by the IRIS network. A continuous execution scheme has been developed for the system but has not yet been implemented. Improvements to the efficiency of the code are in various stages of development. Many refinements would have to be made to the system before it could be used as part of an actual monitoring system, but at this stage we know of no clear barriers which would prevent an eventual implementation of the system.

  20. Theta responses are abnormal in mild cognitive impairment: evidence from analysis of theta event-related synchronization during a temporal expectancy task.

    PubMed

    Caravaglios, Giuseppe; Muscoso, Emma Gabriella; Di Maria, Giulia; Costanzo, Erminio

    2013-07-01

    We examined the hypothesis that the attention/executive deficits in mild cognitive impairment (MCI) due to Alzheimer's disease is associated to an abnormal cortical activation, revealed by the method of event-related synchronization/desynchronization (ERS/ERD) in the theta band during a paradigm of temporal orienting of attention. MCI patients (n = 25) and healthy elderly (HE) matched controls (n = 15) performed a task in which periodically omitted tones had to be predicted and their virtual onset time had to be marked by pressing a button. Single-trial theta responses were measured, respectively, before and after the motor response. Then, theta responses were compared to theta power during eyes closed resting state (ERD/ERS method).The temporal course of the task was characterized by two different behavioural conditions: (1) a pre-event epoch, in which the subject awaited the virtual onset of the omitted tone, (2) a post-event (after button pressing) epoch, in which the subject was in a post-motor response condition. The most important findings are summarized as follows: (1) in both groups, the pre-event epoch was characterized by theta ERS on temporal electrodes, but HE had a greater theta ERS compared to that of MCI group; (2) in both groups, during the post-motor condition, there was a theta ERS on prefrontal regions, and, also in this case, HE showed a greater theta enhancement compared to that of MCI patients; (3) HE showed evidence of lateralization: during the waiting epoch, theta ERS was dominant on the right posterior temporal lead (T6), whilst, during the post-motor epoch, theta ERS was greater on the left, as well as the midline prefrontal leads. Compared to the traditional neuropsychological measures for the episodic memory, these theta ERS indicators were less accurate in differentiating MCI patients from healthy elderly. The clinical relevance of these findings is that the weaker theta reactivity in MCI would indicate an early impairment in the

  1. Abnormal cortical sensorimotor activity during "Target" sound detection in subjects with acute acoustic trauma sequelae: an fMRI study.

    PubMed

    Job, Agnès; Pons, Yoann; Lamalle, Laurent; Jaillard, Assia; Buck, Karl; Segebarth, Christoph; Delon-Martin, Chantal

    2012-03-01

    The most common consequences of acute acoustic trauma (AAT) are hearing loss at frequencies above 3 kHz and tinnitus. In this study, we have used functional Magnetic Resonance Imaging (fMRI) to visualize neuronal activation patterns in military adults with AAT and various tinnitus sequelae during an auditory "oddball" attention task. AAT subjects displayed overactivities principally during reflex of target sound detection, in sensorimotor areas and in emotion-related areas such as the insula, anterior cingulate and prefrontal cortex, in premotor area, in cross-modal sensory associative areas, and, interestingly, in a region of the Rolandic operculum that has recently been shown to be involved in tympanic movements due to air pressure. We propose further investigations of this brain area and fine middle ear investigations, because our results might suggest a model in which AAT tinnitus may arise as a proprioceptive illusion caused by abnormal excitability of middle-ear muscle spindles possibly link with the acoustic reflex and associated with emotional and sensorimotor disturbances. PMID:22574285

  2. Qualitative and event-specific real-time PCR detection methods for Bt brinjal event EE-1.

    PubMed

    Randhawa, Gurinder Jit; Sharma, Ruchi; Singh, Monika

    2012-01-01

    Bt brinjal event EE-1 with cry1Ac gene, expressing insecticidal protein against fruit and shoot borer, is the first genetically modified food crop in the pipeline for commercialization in India. Qualitative polymerase chain reaction (PCR) along with event-specific conventional as well as real-time PCR methods to characterize the event EE-1 is reported. A multiplex (pentaplex) PCR system simultaneously amplifying cry1Ac transgene, Cauliflower Mosaic Virus (CaMV) 35S promoter, nopaline synthase (nos) terminator, aminoglycoside adenyltransferase (aadA) marker gene, and a taxon-specific beta-fructosidase gene in event EE-1 has been developed. Furthermore, construct-specific PCR, targeting the approximate 1.8 kb region of inserted gene construct comprising the region of CaMV 35S promoter and cry1Ac gene has also been developed. The LOD of developed EE-1 specific conventional PCR assay is 0.01%. The method performance of the reported real-time PCR assay was consistent with the acceptance criteria of Codex Alimentarius Commission ALINORM 10/33/23, with the LOD and LOQ values of 0.05%. The developed detection methods would not only facilitate effective regulatory compliance for identification of genetic traits, risk assessment, management, and postrelease monitoring, but also address consumer concerns and resolution of legal disputes. PMID:23451391

  3. Real-time detection and classification of anomalous events in streaming data

    DOEpatents

    Ferragut, Erik M.; Goodall, John R.; Iannacone, Michael D.; Laska, Jason A.; Harrison, Lane T.

    2016-04-19

    A system is described for receiving a stream of events and scoring the events based on anomalousness and maliciousness (or other classification). The events can be displayed to a user in user-defined groupings in an animated fashion. The system can include a plurality of anomaly detectors that together implement an algorithm to identify low probability events and detect atypical traffic patterns. The atypical traffic patterns can then be classified as being of interest or not. In one particular example, in a network environment, the classification can be whether the network traffic is malicious or not.

  4. Investigation of EMIC Waves During Balloon Detected Relativistic Electron Precipitation Events

    NASA Astrophysics Data System (ADS)

    Woodger, L. A.; Millan, R. M.

    2009-12-01

    Multiple relativistic electron precipitation (REP) events were detected by balloon-borne instrumentation during the MAXIS 2000 and MINIS 2005 campaigns. It has been suggested that resonance with EMIC waves caused these precipitation events (Lorentzen et al, 2000 and Millan et al, 2002) due to their location in the dusk sector. We present observations of dusk-side relativistic electron precipitation events, and use supporting satellite and theoretical data to investigate the relationship between EMIC waves and the detected REP. Satellite data can provide direct measurements of not only the waves themselves but also important resonance condition parameters. The data will be presented collectively with each event to showcase similarities and differences between events and the challenges that arise in trying to understand the relationship between dusk-side relativistic electron precipitation and EMIC waves.

  5. Detection of stick-slip events within the Whillans Ice Stream using an artificial neural network

    NASA Astrophysics Data System (ADS)

    Bernsen, S. P.

    2014-12-01

    Temporal changes in the periodic stick-slip events on the Whillans Ice Stream (WIS) help to understand the hydrosphere-cryosphere coupling in West Antarctica. Previous studies have shown that the periodic behavior has been ongoing for a number of years but the record of slip events is incomplete. Rayleigh waves from WIS grounding line events exhibit different patterns than events from the interior of the glacier. An algorithm using a backpropagation neural network is proposed to efficiently extract surface waves that are a result of stick slip events. A neural network approach has its advantages of machine learning, simplified mathematics, and eliminates the need for an analyst to correctly pick first arrivals. Training data has been assembled using 107 events occuring during the 2010 austral summer that were previously identified to correspond to stick slip events at the grounding line as well as the interior of the WIS. A 0.1 s moving window of 3 s of each of the preprocessed attributes is input into the neural network for automated surface wave detection. Following surface wave detection a much longer 30 minute sliding window is used to classify surface wave detections as grounding line, interior, or non-stick slip events. Similar to the automatic detection algorithms for body waves, preprocessing using STA/LTA ratio, degree of polarization, variance, and skewness exhibit obvious patterns during the onset of surface waves. The The automated event detection could lead to more cost effective means of data collection in future seismic experiments especially with an increase in array density in cold weather regions.

  6. Early snowmelt events: detection, distribution, and significance in a major sub-arctic watershed

    NASA Astrophysics Data System (ADS)

    Alese Semmens, Kathryn; Ramage, Joan; Bartsch, Annett; Liston, Glen E.

    2013-03-01

    High latitude drainage basins are experiencing higher average temperatures, earlier snowmelt onset in spring, and an increase in rain on snow (ROS) events in winter, trends that climate models project into the future. Snowmelt-dominated basins are most sensitive to winter temperature increases that influence the frequency of ROS events and the timing and duration of snowmelt, resulting in changes to spring runoff. Of specific interest in this study are early melt events that occur in late winter preceding melt onset in the spring. The study focuses on satellite determination and characterization of these early melt events using the Yukon River Basin (Canada/USA) as a test domain. The timing of these events was estimated using data from passive (Advanced Microwave Scanning Radiometer—EOS (AMSR-E)) and active (SeaWinds on Quick Scatterometer (QuikSCAT)) microwave remote sensors, employing detection algorithms for brightness temperature (AMSR-E) and radar backscatter (QuikSCAT). The satellite detected events were validated with ground station meteorological and hydrological data, and the spatial and temporal variability of the events across the entire river basin was characterized. Possible causative factors for the detected events, including ROS, fog, and positive air temperatures, were determined by comparing the timing of the events to parameters from SnowModel and National Centers for Environmental Prediction North American Regional Reanalysis (NARR) outputs, and weather station data. All melt events coincided with above freezing temperatures, while a limited number corresponded to ROS (determined from SnowModel and ground data) and a majority to fog occurrence (determined from NARR). The results underscore the significant influence that warm air intrusions have on melt in some areas and demonstrate the large temporal and spatial variability over years and regions. The study provides a method for melt detection and a baseline from which to assess future change.

  7. Event Detection and Visualization of Ocean Eddies based on SSH and Velocity Field

    NASA Astrophysics Data System (ADS)

    Matsuoka, Daisuke; Araki, Fumiaki; Inoue, Yumi; Sasaki, Hideharu

    2016-04-01

    Numerical studies of ocean eddies have been progressed using high-resolution ocean general circulation models. In order to understand ocean eddies from simulation results with large amount of information volume, it is necessary to visualize not only distribution of eddies of each time step, but also events or phenomena of eddies. However, previous methods cannot precisely detect eddies, especially, during the events such as eddies' amalgamation, bifurcation. In the present study, we propose a new approach of eddy's detection, tracking and event visualization based on sea surface height (SSH) and velocity field. The proposed method detects eddies region as well as streams and currents region, and classifies detected eddies into several types. By tracking the time-varying change of classified eddies, it is possible to detect not only eddies event such as amalgamation and bifurcation but also the interaction between eddy and ocean current. As a result of visualizing detected eddies and events, we succeeded in creating the movie which enables us to intuitively understand the region of interest.

  8. Patients with mild cognitive impairment have an abnormal upper-alpha event-related desynchronization/synchronization (ERD/ERS) during a task of temporal attention.

    PubMed

    Caravaglios, Giuseppe; Muscoso, Emma Gabriella; Di Maria, Giulia; Costanzo, Erminio

    2015-03-01

    There are several evidences indicating that an impairment in attention-executive functions is present in prodromal Alzheimer's disease and predict future global cognitive decline. In particular, the issue of temporal orienting of attention in patients with mild cognitive impairment (MCI) due to Alzheimer's disease has been overlooked. The present research aimed to explore whether subtle deficits of cortical activation are present in these patients early in the course of the disease. We studied the upper-alpha event-related synchronization/desynchronization phenomenon during a paradigm of temporal orientation of attention. MCI patients (n = 27) and healthy elderly controls (n = 15) performed a task in which periodically omitted tones had to be predicted and their virtual onset time had to be marked by pressing a button. Single-trial responses were measured, respectively, before and after the motor response. Then, upper-alpha responses were compared to upper-alpha power during eyes-closed resting state. The time course of the task was characterized by two different behavioral conditions: (1) a pre-event epoch, in which the subject awaited the virtual onset of the omitted tone, (2) a post-event epoch (after button pressing), in which the subject was in a post-motor response condition. The principal findings are: (1) during the waiting epoch, only healthy elderly had an upper-alpha ERD at the level of both temporal and posterior brain regions; (2) during the post-motor epoch, the aMCI patients had a weaker upper-alpha ERS on prefrontal regions; (3) only healthy elderly showed a laterality effect: (a) during the waiting epoch, the upper-alpha ERD was greater at the level of the right posterior-temporal lead; during the post-motor epoch, the upper alpha ERS was greater on the left prefrontal lead. The relevance of these findings is that the weaker upper-alpha response observed in aMCI patients is evident even if the accuracy of the behavioral performance (i.e., button

  9. Event Detection in Aerospace Systems using Centralized Sensor Networks: A Comparative Study of Several Methodologies

    NASA Technical Reports Server (NTRS)

    Mehr, Ali Farhang; Sauvageon, Julien; Agogino, Alice M.; Tumer, Irem Y.

    2006-01-01

    Recent advances in micro electromechanical systems technology, digital electronics, and wireless communications have enabled development of low-cost, low-power, multifunctional miniature smart sensors. These sensors can be deployed throughout a region in an aerospace vehicle to build a network for measurement, detection and surveillance applications. Event detection using such centralized sensor networks is often regarded as one of the most promising health management technologies in aerospace applications where timely detection of local anomalies has a great impact on the safety of the mission. In this paper, we propose to conduct a qualitative comparison of several local event detection algorithms for centralized redundant sensor networks. The algorithms are compared with respect to their ability to locate and evaluate an event in the presence of noise and sensor failures for various node geometries and densities.

  10. Detection of gait events using an F-Scan in-shoe pressure measurement system.

    PubMed

    Catalfamo, Paola; Moser, David; Ghoussayni, Salim; Ewins, David

    2008-10-01

    A portable system capable of accurate detection of initial contact (IC) and foot off (FO) without adding encumbrance to the subject would be extremely useful in many gait analysis applications. Force platforms represent the gold standard method for determining these events and other methods including foot switches and kinematic data have also been proposed. These approaches, however, present limitations in terms of the number of steps that can be analysed per trial, the portability for outdoor measurements or the information needed beforehand. The purpose of this study was to evaluate the F-Scan((R)) Mobile pressure measurement system when detecting IC and FO. Two methods were used, one was the force detection (FD) in-built algorithm used by F-Scan software and a new area detection (AD) method using the loaded area during the gait cycle. Both methods were tested in ten healthy adults and compared with the detection provided by a kinetic detection (KT) algorithm. The absolute mean differences between KT and FD were (mean+/-standard deviation) 42+/-11 ms for IC and 37+/-11 ms for FO. The absolute mean differences between KT and AD were 22+/-9 ms for IC and 10+/-4 ms for FO. The AD method remained closer to KT detection for all subjects providing sufficiently accurate detection of both events and presenting advantages in terms of portability, number of steps analysed per trial and practicality as to make it a system of choice for gait event detection. PMID:18468441

  11. Evaluation of two methodologies for lameness detection in dairy cows based on postural and gait abnormalities observed during milking and while restrained at headlock stanchions.

    PubMed

    García-Muñoz, A; Vidal, G; Singh, N; Silva-Del-Río, N

    2016-06-01

    Lameness is a critical issue on dairies with an impact on production and animal welfare. Early lameness detection followed by effective treatments could improve prognosis and cure rate of lame cows. Current methods for lameness detection are based on locomotion score (LS) that requires observation of cows walking, preferably at the exit of the milking parlor. This is a time-consuming task that is difficult to implement on large dairies. Therefore, a common methodology for lameness detection is based on milkers' and cow pushers' observations of cows walking to the milking parlor or standing at the milking stall (MPP). Observation of postural abnormalities predictive of lameness while cows are locked at stanchions (S) can be used as an alternative detection method. The objective of this research was to study the association between postural and gait abnormalities observed with S and MPP methodologies and lameness using LS≥3 as the reference method, as well as to evaluate the epidemiological characteristics of those methods as a diagnostic test for lameness. A secondary objective was to describe the type of hoof lesions observed with postural and gait abnormalities detected with LS, MPP, and S methodologies. A cross-sectional study design was performed on 2274 cows from one farm in California (US). Arched back, cow-hocked, wide-stance, and favored-limb postures as well as uneven gait were observed. Both lameness detection methodologies, S and MPP, indicated that arched back and favored-limb were postural abnormalities associated with lameness. However, the epidemiological test characteristics for each of the postures evaluated as a diagnostic test for lameness indicated that both detection methods, S and MPP, had good specificity (>0.91) but poor sensitivity (0.04-0.39). A convenience sample of 104 cows, selected based on LS>3, favored-limb, presence of two or more abnormal postures, and gait anomalies with either S or MPP methods, received a hoof examination

  12. Application of the FICTION technique for the simultaneous detection of immunophenotype and chromosomal abnormalities in routinely fixed, paraffin wax embedded bone marrow trephines

    PubMed Central

    Korać, P; Jones, M; Dominis, M; Kušec, R; Mason, D Y; Banham, A H; Ventura, R A

    2005-01-01

    The use of interphase fluorescence in situ hybridisation (FISH) to study cytogenetic abnormalities in routinely fixed paraffin wax embedded tissue has become commonplace over the past decade. However, very few studies have applied FISH to routinely fixed bone marrow trephines (BMTs). This may be because of the acid based decalcification methods that are commonly used during the processing of BMTs, which may adversely affect the suitability of the sample for FISH analysis. For the first time, this report describes the simultaneous application of FISH and immunofluorescent staining (the FICTION technique) to formalin fixed, EDTA decalcified and paraffin wax embedded BMTs. This technique allows the direct correlation of genetic abnormalities to immunophenotype, and therefore will be particularly useful for the identification of genetic abnormalities in specific tumour cells present in BMTs. The application of this to routine clinical practice will assist diagnosis and the detection of minimal residual disease. PMID:16311361

  13. A canonical correlation analysis based method for contamination event detection in water sources.

    PubMed

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

    2016-06-15

    In this study, a general framework integrating a data-driven estimation model is employed for contamination event detection in water sources. Sequential canonical correlation coefficients are updated in the model using multivariate water quality time series. The proposed method utilizes canonical correlation analysis for studying the interplay between two sets of water quality parameters. The model is assessed by precision, recall and F-measure. The proposed method is tested using data from a laboratory contaminant injection experiment. The proposed method could detect a contamination event 1 minute after the introduction of 1.600 mg l(-1) acrylamide solution. With optimized parameter values, the proposed method can correctly detect 97.50% of all contamination events with no false alarms. The robustness of the proposed method can be explained using the Bauer-Fike theorem. PMID:27264637

  14. On-line Machine Learning and Event Detection in Petascale Data Streams

    NASA Astrophysics Data System (ADS)

    Thompson, David R.; Wagstaff, K. L.

    2012-01-01

    Traditional statistical data mining involves off-line analysis in which all data are available and equally accessible. However, petascale datasets have challenged this premise since it is often impossible to store, let alone analyze, the relevant observations. This has led the machine learning community to investigate adaptive processing chains where data mining is a continuous process. Here pattern recognition permits triage and followup decisions at multiple stages of a processing pipeline. Such techniques can also benefit new astronomical instruments such as the Large Synoptic Survey Telescope (LSST) and Square Kilometre Array (SKA) that will generate petascale data volumes. We summarize some machine learning perspectives on real time data mining, with representative cases of astronomical applications and event detection in high volume datastreams. The first is a "supervised classification" approach currently used for transient event detection at the Very Long Baseline Array (VLBA). It injects known signals of interest - faint single-pulse anomalies - and tunes system parameters to recover these events. This permits meaningful event detection for diverse instrument configurations and observing conditions whose noise cannot be well-characterized in advance. Second, "semi-supervised novelty detection" finds novel events based on statistical deviations from previous patterns. It detects outlier signals of interest while considering known examples of false alarm interference. Applied to data from the Parkes pulsar survey, the approach identifies anomalous "peryton" phenomena that do not match previous event models. Finally, we consider online light curve classification that can trigger adaptive followup measurements of candidate events. Classifier performance analyses suggest optimal survey strategies, and permit principled followup decisions from incomplete data. These examples trace a broad range of algorithm possibilities available for online astronomical data

  15. Method for detecting binding events using micro-X-ray fluorescence spectrometry

    DOEpatents

    Warner, Benjamin P.; Havrilla, George J.; Mann, Grace

    2010-12-28

    Method for detecting binding events using micro-X-ray fluorescence spectrometry. Receptors are exposed to at least one potential binder and arrayed on a substrate support. Each member of the array is exposed to X-ray radiation. The magnitude of a detectable X-ray fluorescence signal for at least one element can be used to determine whether a binding event between a binder and a receptor has occurred, and can provide information related to the extent of binding between the binder and receptor.

  16. Detecting, Monitoring, and Reporting Possible Adverse Drug Events Using an Arden-Syntax-based Rule Engine.

    PubMed

    Fehre, Karsten; Plössnig, Manuela; Schuler, Jochen; Hofer-Dückelmann, Christina; Rappelsberger, Andrea; Adlassnig, Klaus-Peter

    2015-01-01

    The detection of adverse drug events (ADEs) is an important aspect of improving patient safety. The iMedication system employs predefined triggers associated with significant events in a patient's clinical data to automatically detect possible ADEs. We defined four clinically relevant conditions: hyperkalemia, hyponatremia, renal failure, and over-anticoagulation. These are some of the most relevant ADEs in internal medical and geriatric wards. For each patient, ADE risk scores for all four situations are calculated, compared against a threshold, and judged to be monitored, or reported. A ward-based cockpit view summarizes the results. PMID:26262252

  17. Valvular Abnormalities Detected by Echocardiography in 5-Year Survivors of Childhood Cancer: A Long-Term Follow-Up Study

    SciTech Connect

    Pal, Helena J. van der; Caron, Huib N.; Kremer, Leontien C.; Dalen, Elvira C. van

    2015-01-01

    Purpose: To determine the prevalence of valvular abnormalities after radiation therapy involving the heart region and/or treatment with anthracyclines and to identify associated risk factors in a large cohort of 5-year childhood cancer survivors (CCS). Methods and Materials: The study cohort consisted of all 626 eligible 5-year CCS diagnosed with childhood cancer in the Emma Children's Hospital/Academic Medical Center between 1966 and 1996 and treated with radiation therapy involving the heart region and/or anthracyclines. We determined the presence of valvular abnormalities according to echocardiograms. Physical radiation dose was converted into the equivalent dose in 2-Gy fractions (EQD{sub 2}). Using multivariable logistic regression analyses, we examined the associations between cancer treatment and valvular abnormalities. Results: We identified 225 mainly mild echocardiographic valvular abnormalities in 169 of 545 CCS (31%) with a cardiac assessment (median follow-up time, 14.9 years [range, 5.1-36.8 years]; median attained age 22.0 years [range, 7.0-49.7 years]). Twenty-four CCS (4.4%) had 31 moderate or higher-graded abnormalities. Most common abnormalities were tricuspid valve disorders (n=119; 21.8%) and mitral valve disorders (n=73; 13.4%). The risk of valvular abnormalities was associated with increasing radiation dose (using EQD{sub 2}) involving the heart region (odds ratio 1.33 per 10 Gy) and the presence of congenital heart disease (odds ratio 3.43). We found no statistically significant evidence that anthracyclines increase the risk. Conclusions: Almost one-third of CCS treated with potentially cardiotoxic therapy had 1 or more asymptomatic, mostly mild valvular abnormalities after a median follow-up of nearly 15 years. The most important risk factors are higher EQD{sub 2} to the heart region and congenital heart disease. Studies with longer follow-up are necessary to investigate the clinical course of asymptomatic valvular abnormalities in

  18. Meiotic abnormalities

    SciTech Connect

    1993-12-31

    Chapter 19, describes meiotic abnormalities. These include nondisjunction of autosomes and sex chromosomes, genetic and environmental causes of nondisjunction, misdivision of the centromere, chromosomally abnormal human sperm, male infertility, parental age, and origin of diploid gametes. 57 refs., 2 figs., 1 tab.

  19. Detection of invisible and crucial events: from seismic fluctuations to the war against terrorism

    NASA Astrophysics Data System (ADS)

    Allegrini, Paolo; Fronzoni, Leone; Grigolini, Paolo; Latora, Vito; Mega, Mirko S.; Palatella, Luigi; Rapisarda, Andrea; Vinciguerra, Sergio

    2004-04-01

    We prove the efficiency of a new method for the detection of crucial events that might have useful applications to the war against terrorism. This has to do with the search for rare but significant events, a theme of research that has been made of extreme importance by the tragedy of September 11. This method is applied here to defining the statistics of seismic main-shocks, as done in cond-mat/0212529. The emphasis here is on the conceptual issues behind the results obtained in cond-mat/0212529 than on geophysics. This discussion suggests that the method has a wider range of validity. We support this general discussion with a dynamic model originally proposed in cond-mat/0107597 for purposes different from geophysical applications. However, it is a case where the crucial events to detect are under our control, thereby making it possible for us to check the accuracy of the method of detection of invisible and crucial events that we propose here for a general purpose, including the war against terrorism. For this model an analytical treatment has been recently found [cond-mat/0209038], supporting the claims that we make in this paper for the accuracy of the method of detection. For the reader's convenience, the results on the seismic fluctuations are suitably reviewed, and discussed in the light of the more general perspective of this paper. We also review the model for seismic fluctuations, proposed in the earlier work of cond-mat/0212529. This model shares with the model of cond-mat/0107597 the property that the crucial events are imbedded in a sea of secondary events, but it allows us to reveal with accuracy the statistics of the crucial events for different mathematical reasons.

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

    PubMed

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

    2016-04-15

    Early warning systems are widely used to safeguard water security, but their effectiveness has raised many questions. To understand why conventional detection methods fail to identify contamination events, this study evaluates the performance of three contamination detection methods using data from a real contamination accident and two artificial datasets constructed using a widely applied contamination data construction approach. Results show that the Pearson correlation Euclidean distance (PE) based detection method performs better for real contamination incidents, while the Euclidean distance method (MED) and linear prediction filter (LPF) method are more suitable for detecting sudden spike-like variation. This analysis revealed why the conventional MED and LPF methods failed to identify existence of contamination events. The analysis also revealed that the widely used contamination data construction approach is misleading. PMID:26905801

  1. Development of an algorithm for automatic detection and rating of squeak and rattle events

    NASA Astrophysics Data System (ADS)

    Chandrika, Unnikrishnan Kuttan; Kim, Jay H.

    2010-10-01

    A new algorithm for automatic detection and rating of squeak and rattle (S&R) events was developed. The algorithm utilizes the perceived transient loudness (PTL) that approximates the human perception of a transient noise. At first, instantaneous specific loudness time histories are calculated over 1-24 bark range by applying the analytic wavelet transform and Zwicker loudness transform to the recorded noise. Transient specific loudness time histories are then obtained by removing estimated contributions of the background noise from instantaneous specific loudness time histories. These transient specific loudness time histories are summed to obtain the transient loudness time history. Finally, the PTL time history is obtained by applying Glasberg and Moore temporal integration to the transient loudness time history. Detection of S&R events utilizes the PTL time history obtained by summing only 18-24 barks components to take advantage of high signal-to-noise ratio in the high frequency range. A S&R event is identified when the value of the PTL time history exceeds the detection threshold pre-determined by a jury test. The maximum value of the PTL time history is used for rating of S&R events. Another jury test showed that the method performs much better if the PTL time history obtained by summing all frequency components is used. Therefore, r ating of S&R events utilizes this modified PTL time history. Two additional jury tests were conducted to validate the developed detection and rating methods. The algorithm developed in this work will enable automatic detection and rating of S&R events with good accuracy and minimum possibility of false alarm.

  2. Fast and robust microseismic event detection using very fast simulated annealing

    NASA Astrophysics Data System (ADS)

    Velis, Danilo R.; Sabbione, Juan I.; Sacchi, Mauricio D.

    2013-04-01

    The study of microseismic data has become an essential tool in many geoscience fields, including oil reservoir geophysics, mining and CO2 sequestration. In hydraulic fracturing, microseismicity studies permit the characterization and monitoring of the reservoir dynamics in order to optimize the production and the fluid injection process itself. As the number of events is usually large and the signal-to-noise ratio is in general very low, fast, automated, and robust detection algorithms are required for most applications. Also, real-time functionality is commonly needed to control the fluid injection in the field. Generally, events are located by means of grid search algorithms that rely on some approximate velocity model. These techniques are very effective and accurate, but computationally intensive when dealing with large three or four-dimensional grids. Here, we present a fast and robust method that allows to automatically detect and pick an event in 3C microseismic data without any input information about the velocity model. The detection is carried out by means of a very fast simulated annealing (VFSA) algorithm. To this end, we define an objective function that measures the energy of a potential microseismic event along the multichannel signal. This objective function is based on the stacked energy of the envelope of the signals calculated within a predefined narrow time window that depends on the source position, receivers geometry and velocity. Once an event has been detected, the source location can be estimated, in a second stage, by inverting the corresponding traveltimes using a standard technique, which would naturally require some knowledge of the velocity model. Since the proposed technique focuses on the detection of the microseismic events only, the velocity model is not required, leading to a fast algorithm that carries out the detection in real-time. Besides, the strategy is applicable to data with very low signal-to-noise ratios, for it relies

  3. SU-E-J-122: Detecting Treatment-Induced Metabolic Abnormalities in Craniopharyngioma Patients Undergoing Surgery and Proton Therapy

    SciTech Connect

    Hua, C; Shulkin, B; Li, Y; LI, X; Merchant, T; Indelicato, D; Boop, F

    2014-06-01

    Purpose: To identify treatment-induced defects in the brain of children with craniopharyngioma receiving surgery and proton therapy using fluorodeoxyglucose positron emission tomography (FDG PET). Methods: Forty seven patients were enrolled on a clinical trial for craniopharyngioma with serial imaging and functional evaluations. Proton therapy was delivered using the double-scattered beams with a prescribed dose of 54 Cobalt Gray Equivalent. FDG tracer uptake in each of 63 anatomical regions was computed after warping PET images to a 3D reference template in Talairach coordinates. Regional uptake was deemed significantly low or high if exceeding two standard deviations of normal population from the mean. For establishing the normal ranges, 132 children aged 1–20 years with noncentral nervous system related diseases and normal-appearing cerebral PET scans were analyzed. Age- and gender-dependent regional uptake models were developed by linear regression and confidence intervals were calculated. Results: Most common PET abnormality before proton therapy was significantly low uptake in the frontal lobe, the occipital lobe (particularly in cuneus), the medial and ventral temporal lobe, cingulate gyrus, caudate nuclei, and thalamus. They were related to injury from surgical corridors, tumor mass effect, insertion of a ventricular catheter, and the placement of an Ommaya reservoir. Surprisingly a significantly high uptake was observed in temporal gyri and the parietal lobe. In 13 patients who already completed 18-month PET scans, metabolic abnormalities improved in 11 patients from baseline. One patient had persistent abnormalities. Only one revealed new uptake abnormalities in thalamus, brainstem, cerebellum, and insula. Conclusion: Postoperative FDG PET of craniopharyngioma patients revealed metabolic abnormalities in specific regions of the brain. Proton therapy did not appear to exacerbate these surgery- and tumor-induced defects. In patients with persistent and

  4. Novel application of a multiscale entropy index as a sensitive tool for detecting subtle vascular abnormalities in the aged and diabetic.

    PubMed

    Wu, Hsien-Tsai; Lo, Men-Tzung; Chen, Guan-Hong; Sun, Cheuk-Kwan; Chen, Jian-Jung

    2013-01-01

    Although previous studies have shown the successful use of pressure-induced reactive hyperemia as a tool for the assessment of endothelial function, its sensitivity remains questionable. This study aims to investigate the feasibility and sensitivity of a novel multiscale entropy index (MEI) in detecting subtle vascular abnormalities in healthy and diabetic subjects. Basic anthropometric and hemodynamic parameters, serum lipid profiles, and glycosylated hemoglobin levels were recorded. Arterial pulse wave signals were acquired from the wrist with an air pressure sensing system (APSS), followed by MEI and dilatation index (DI) analyses. MEI succeeded in detecting significant differences among the four groups of subjects: healthy young individuals, healthy middle-aged or elderly individuals, well-controlled diabetic individuals, and poorly controlled diabetic individuals. A reduction in multiscale entropy reflected age- and diabetes-related vascular changes and may serve as a more sensitive indicator of subtle vascular abnormalities compared with DI in the setting of diabetes. PMID:23509600

  5. Novel Application of a Multiscale Entropy Index as a Sensitive Tool for Detecting Subtle Vascular Abnormalities in the Aged and Diabetic

    PubMed Central

    Wu, Hsien-Tsai; Lo, Men-Tzung; Chen, Guan-Hong; Sun, Cheuk-Kwan; Chen, Jian-Jung

    2013-01-01

    Although previous studies have shown the successful use of pressure-induced reactive hyperemia as a tool for the assessment of endothelial function, its sensitivity remains questionable. This study aims to investigate the feasibility and sensitivity of a novel multiscale entropy index (MEI) in detecting subtle vascular abnormalities in healthy and diabetic subjects. Basic anthropometric and hemodynamic parameters, serum lipid profiles, and glycosylated hemoglobin levels were recorded. Arterial pulse wave signals were acquired from the wrist with an air pressure sensing system (APSS), followed by MEI and dilatation index (DI) analyses. MEI succeeded in detecting significant differences among the four groups of subjects: healthy young individuals, healthy middle-aged or elderly individuals, well-controlled diabetic individuals, and poorly controlled diabetic individuals. A reduction in multiscale entropy reflected age- and diabetes-related vascular changes and may serve as a more sensitive indicator of subtle vascular abnormalities compared with DI in the setting of diabetes. PMID:23509600

  6. Wenchuan Event Detection And Localization Using Waveform Correlation Coupled With Double Difference

    NASA Astrophysics Data System (ADS)

    Slinkard, M.; Heck, S.; Schaff, D. P.; Young, C. J.; Richards, P. G.

    2014-12-01

    The well-studied Wenchuan aftershock sequence triggered by the May 12, 2008, Ms 8.0, mainshock offers an ideal test case for evaluating the effectiveness of using waveform correlation coupled with double difference relocation to detect and locate events in a large aftershock sequence. We use Sandia's SeisCorr detector to process 3 months of data recorded by permanent IRIS and temporary ASCENT stations using templates from events listed in a global catalog to find similar events in the raw data stream. Then we take the detections and relocate them using the double difference method. We explore both the performance that can be expected with using just a small number of stations, and, the benefits of reprocessing a well-studied sequence such as this one using waveform correlation to find even more events. We benchmark our results against previously published results describing relocations of regional catalog data. Before starting this project, we had examples where with just a few stations at far-regional distances, waveform correlation combined with double difference did and impressive job of detection and location events with precision at the few hundred and even tens of meters level.

  7. Secure Access Control and Large Scale Robust Representation for Online Multimedia Event Detection

    PubMed Central

    Liu, Changyu; Li, Huiling

    2014-01-01

    We developed an online multimedia event detection (MED) system. However, there are a secure access control issue and a large scale robust representation issue when we want to integrate traditional event detection algorithms into the online environment. For the first issue, we proposed a tree proxy-based and service-oriented access control (TPSAC) model based on the traditional role based access control model. Verification experiments were conducted on the CloudSim simulation platform, and the results showed that the TPSAC model is suitable for the access control of dynamic online environments. For the second issue, inspired by the object-bank scene descriptor, we proposed a 1000-object-bank (1000OBK) event descriptor. Feature vectors of the 1000OBK were extracted from response pyramids of 1000 generic object detectors which were trained on standard annotated image datasets, such as the ImageNet dataset. A spatial bag of words tiling approach was then adopted to encode these feature vectors for bridging the gap between the objects and events. Furthermore, we performed experiments in the context of event classification on the challenging TRECVID MED 2012 dataset, and the results showed that the robust 1000OBK event descriptor outperforms the state-of-the-art approaches. PMID:25147840

  8. Testing the ability of different seismic detections approaches to monitor aftershocks following a moderate magnitude event.

    NASA Astrophysics Data System (ADS)

    Romero, Paula; Díaz, Jordi; Ruiz, Mario; Cantavella, Juan Vicente; Gomez-García, Clara

    2016-04-01

    The detection and picking of seismic events is a permanent concern for seismic surveying, in particular when dealing with aftershocks of moderate magnitude events. Many efforts have been done to find the balance between computer efficiency and the robustness of the detection methods. In this work, data recorded by a high density seismic network deployed following a 5.2 magnitude event located close to Albacete, SE Spain, is used to test the ability of classical and recently proposed detection methodologies. Two days after the main shock, occurred the 23th February, a network formed by 11 stations from ICTJA-CSIC and 2 stations from IGN were deployed over the region, with inter-station distances ranging between 5 and 10 km. The network remained in operation until April 6th, 2015 and allowed to manually identify up to 552 events with magnitudes from 0.2 to 3.5 located in an area of just 25 km2 inside the network limits. The detection methods here studied applied are the classical STA/LTA, a power spectral method, a detector based in the Benford's law and a waveform similarity method. The STA/LTA method, based in the comparison of background noise and seismic signal amplitudes, is taken as a reference to evaluate the results arising from the other approaches. The power spectral density method is based in the inspection of the characteristic frequency pattern associated to seismic events. The Benford's Law detector analyses the distribution of the first-digit of displacement count in the histogram of a seismic waveform, considering that only the windows containing seismic wave arrivals will match the logarithmic law. Finally, the waveform similarity method is based in the analysis of the normalized waveform amplitude, detecting those events with waveform similar to a previously defined master event. The aim of this contribution is to inspect the ability of the different approaches to accurately detect the aftershocks events for this kind of seismic crisis and to

  9. Detecting Continuity Violations in Infancy: A New Account and New Evidence from Covering and Tube Events

    ERIC Educational Resources Information Center

    Wang, S.h.; Baillargeon, R.; Paterson, S.

    2005-01-01

    Recent research on infants' responses to occlusion and containment events indicates that, although some violations of the continuity principle are detected at an early age e.g. Aguiar, A., & Baillargeon, R. (1999). 2.5-month-old infants' reasoning about when objects should and should not be occluded. Cognitive Psychology 39, 116-157; Hespos, S.…

  10. A novel seizure detection algorithm informed by hidden Markov model event states

    NASA Astrophysics Data System (ADS)

    Baldassano, Steven; Wulsin, Drausin; Ung, Hoameng; Blevins, Tyler; Brown, Mesha-Gay; Fox, Emily; Litt, Brian

    2016-06-01

    Objective. Recently the FDA approved the first responsive, closed-loop intracranial device to treat epilepsy. Because these devices must respond within seconds of seizure onset and not miss events, they are tuned to have high sensitivity, leading to frequent false positive stimulations and decreased battery life. In this work, we propose a more robust seizure detection model. Approach. We use a Bayesian nonparametric Markov switching process to parse intracranial EEG (iEEG) data into distinct dynamic event states. Each event state is then modeled as a multidimensional Gaussian distribution to allow for predictive state assignment. By detecting event states highly specific for seizure onset zones, the method can identify precise regions of iEEG data associated with the transition to seizure activity, reducing false positive detections associated with interictal bursts. The seizure detection algorithm was translated to a real-time application and validated in a small pilot study using 391 days of continuous iEEG data from two dogs with naturally occurring, multifocal epilepsy. A feature-based seizure detector modeled after the NeuroPace RNS System was developed as a control. Main results. Our novel seizure detection method demonstrated an improvement in false negative rate (0/55 seizures missed versus 2/55 seizures missed) as well as a significantly reduced false positive rate (0.0012 h versus 0.058 h‑1). All seizures were detected an average of 12.1 ± 6.9 s before the onset of unequivocal epileptic activity (unequivocal epileptic onset (UEO)). Significance. This algorithm represents a computationally inexpensive, individualized, real-time detection method suitable for implantable antiepileptic devices that may considerably reduce false positive rate relative to current industry standards.

  11. Efficient, Decentralized Detection of Qualitative Spatial Events in a Dynamic Scalar Field.

    PubMed

    Jeong, Myeong-Hun; Duckham, Matt

    2015-01-01

    This paper describes an efficient, decentralized algorithm to monitor qualitative spatial events in a dynamic scalar field. The events of interest involve changes to the critical points (i.e., peak, pits and passes) and edges of the surface network derived from the field. Four fundamental types of event (appearance, disappearance, movement and switch) are defined. Our algorithm is designed to rely purely on qualitative information about the neighborhoods of nodes in the sensor network and does not require information about nodes' coordinate positions. Experimental investigations confirm that our algorithm is efficient, with O(n) overall communication complexity (where n is the number of nodes in the sensor network), an even load balance and low operational latency. The accuracy of event detection is comparable to established centralized algorithms for the identification of critical points of a surface network. Our algorithm is relevant to a broad range of environmental monitoring applications of sensor networks. PMID:26343672

  12. Efficient, Decentralized Detection of Qualitative Spatial Events in a Dynamic Scalar Field

    PubMed Central

    Jeong, Myeong-Hun; Duckham, Matt

    2015-01-01

    This paper describes an efficient, decentralized algorithm to monitor qualitative spatial events in a dynamic scalar field. The events of interest involve changes to the critical points (i.e., peak, pits and passes) and edges of the surface network derived from the field. Four fundamental types of event (appearance, disappearance, movement and switch) are defined. Our algorithm is designed to rely purely on qualitative information about the neighborhoods of nodes in the sensor network and does not require information about nodes’ coordinate positions. Experimental investigations confirm that our algorithm is efficient, with O(n) overall communication complexity (where n is the number of nodes in the sensor network), an even load balance and low operational latency. The accuracy of event detection is comparable to established centralized algorithms for the identification of critical points of a surface network. Our algorithm is relevant to a broad range of environmental monitoring applications of sensor networks. PMID:26343672

  13. Utilization of Human Papillomavirus DNA Detection for Cervical Cancer Screening in Women Presenting With Abnormal Cytology in Lokoja, Nigeria

    PubMed Central

    Kolawole, Olatunji; Ogah, Jeremiah; Alabi, Olatunde; Suleiman, Mustapha; Amuda, Oluwatomi; Kolawole, Folashade

    2015-01-01

    Background: Cervical cancer is regarded as the second highest cause of cancer deaths in Nigeria, with an overall prevalence similar to most developing countries. Screening for cervical cancer is primarily performed using papanicolaou (PAP) staining procedure, in Nigeria. Objectives: This study aimed to use human papillomavirus (HPV) DNA typing, as a means of ascertaining the presence of high risk HPV in cytology samples, which are positive for the presence of cervical intraepithelial neoplasia (CIN), using the PAP screening procedure. Patients and Methods: Amplification of DNA was done using polymerase chain reaction. Gene sequencing was carried out to determine the presence of high risk HPV from cervical smears that were positive for abnormal cytology, from a cross-sectional study involving women between the ages of 16 - 65 years, screened for CIN and cervical cancer, in Lokoja, Nigeria. Results: Result showed a 100% presence of high risk HPV in all the samples with abnormal cytology. The HPV genotype 35 accounted for the highest percentage of the HPVs cases, with a 40% incidence. The HPV genotype 31 accounted for 30% of samples, while HPV genotype 16 and 18 accounted for 20% and 10% of samples, respectively. Conclusions: The high prevalence of HPV in abnormal cytology underlines to the fact that the presence of HPV is a critical factor in the development of cervical cancer. The use of HPV DNA techniques could actually become an effective and fast means of ascertaining the presence of HPV in abnormal cytology. PMID:26568803

  14. Performance of the CellaVision® DM96 system for detecting red blood cell morphologic abnormalities

    PubMed Central

    Horn, Christopher L.; Mansoor, Adnan; Wood, Brenda; Nelson, Heather; Higa, Diane; Lee, Lik Hang; Naugler, Christopher

    2015-01-01

    Background: Red blood cell (RBC) analysis is a key feature in the evaluation of hematological disorders. The gold standard light microscopy technique has high sensitivity, but is a relativity time-consuming and labor intensive procedure. This study tested the sensitivity and specificity of gold standard light microscopy manual differential to the CellaVision® DM96 (CCS; CellaVision, Lund, Sweden) automated image analysis system, which takes digital images of samples at high magnification and compares these images with an artificial neural network based on a database of cells and preclassified according to RBC morphology. Methods: In this study, 212 abnormal peripheral blood smears within the Calgary Laboratory Services network of hospital laboratories were selected and assessed for 15 different RBC morphologic abnormalities by manual microscopy. The same samples were reassessed as a manual addition from the instrument screen using the CellaVision® DM96 system with 8 microscope high power fields (×100 objective and a 22 mm ocular). The results of the investigation were then used to calculate the sensitivity and specificity of the CellaVision® DM96 system in reference to light microscopy. Results: The sensitivity ranged from a low of 33% (RBC agglutination) to a high of 100% (sickle cells, stomatocytes). The remainder of the RBC abnormalities tested somewhere between these two extremes. The specificity ranged from 84% (schistocytes) to 99.5% (sickle cells, stomatocytes). Conclusions: Our results showed generally high specificities but variable sensitivities for RBC morphologic abnormalities. PMID:25774322

  15. Feature selection of seismic waveforms for long period event detection at Cotopaxi Volcano

    NASA Astrophysics Data System (ADS)

    Lara-Cueva, R. A.; Benítez, D. S.; Carrera, E. V.; Ruiz, M.; Rojo-Álvarez, J. L.

    2016-04-01

    Volcano Early Warning Systems (VEWS) have become a research topic in order to preserve human lives and material losses. In this setting, event detection criteria based on classification using machine learning techniques have proven useful, and a number of systems have been proposed in the literature. However, to the best of our knowledge, no comprehensive and principled study has been conducted to compare the influence of the many different sets of possible features that have been used as input spaces in previous works. We present an automatic recognition system of volcano seismicity, by considering feature extraction, event classification, and subsequent event detection, in order to reduce the processing time as a first step towards a high reliability automatic detection system in real-time. We compiled and extracted a comprehensive set of temporal, moving average, spectral, and scale-domain features, for separating long period seismic events from background noise. We benchmarked two usual kinds of feature selection techniques, namely, filter (mutual information and statistical dependence) and embedded (cross-validation and pruning), each of them by using suitable and appropriate classification algorithms such as k Nearest Neighbors (k-NN) and Decision Trees (DT). We applied this approach to the seismicity presented at Cotopaxi Volcano in Ecuador during 2009 and 2010. The best results were obtained by using a 15 s segmentation window, feature matrix in the frequency domain, and DT classifier, yielding 99% of detection accuracy and sensitivity. Selected features and their interpretation were consistent among different input spaces, in simple terms of amplitude and spectral content. Our study provides the framework for an event detection system with high accuracy and reduced computational requirements.

  16. Detection, tracking and event localization of interesting features in 4-D atmospheric data

    NASA Astrophysics Data System (ADS)

    Limbach, S.; Schömer, E.; Wernli, H.

    2011-11-01

    We introduce a novel algorithm for the efficient detection and tracking of interesting features in spatial-temporal atmospheric data, as well as for the precise localization of the occurring genesis, lysis, merging and splitting events. The algorithm is based on the well-known region growing segmentation method. We extended the basic idea towards the analysis of the complete 4-D dataset, identifying segments representing the spatial features and their development over time. Each segment consists of one set of distinct 3-D features per time step. The algorithm keeps track of the successors of each 3-D feature, constructing the so-called event graph of each segment. The precise localization of the splitting events is based on a search for all grid points inside the initial 3-D feature which have a similar distance to all successive 3-D features of the next time step. The merging event is localized analogously considering inverted direction of time. We tested the implementation on a four-dimensional field of wind speed data from European Centre for Medium-Range Weather Forecasts (ECMWF) analyses and computed a climatology of upper-tropospheric jet streams and their events. We compare our results with a previous climatology, investigate the statistical distribution of the merging and splitting events, and illustrate the meteorological significance of the jet splitting events with a case study. A brief outlook is given on additional potential applications of the 4-D data segmentation technique.

  17. Detection and analysis of microseismic events using a Matched Filtering Algorithm (MFA)

    NASA Astrophysics Data System (ADS)

    Caffagni, Enrico; Eaton, David W.; Jones, Joshua P.; van der Baan, Mirko

    2016-07-01

    A new Matched Filtering Algorithm (MFA) is proposed for detecting and analysing microseismic events recorded by downhole monitoring of hydraulic fracturing. This method requires a set of well-located template (`parent') events, which are obtained using conventional microseismic processing and selected on the basis of high signal-to-noise (S/N) ratio and representative spatial distribution of the recorded microseismicity. Detection and extraction of `child' events are based on stacked, multichannel cross-correlation of the continuous waveform data, using the parent events as reference signals. The location of a child event relative to its parent is determined using an automated process, by rotation of the multicomponent waveforms into the ray-centred co-ordinates of the parent and maximizing the energy of the stacked amplitude envelope within a search volume around the parent's hypocentre. After correction for geometrical spreading and attenuation, the relative magnitude of the child event is obtained automatically using the ratio of stacked envelope peak with respect to its parent. Since only a small number of parent events require interactive analysis such as picking P- and S-wave arrivals, the MFA approach offers the potential for significant reduction in effort for downhole microseismic processing. Our algorithm also facilitates the analysis of single-phase child events, that is, microseismic events for which only one of the S- or P-wave arrivals is evident due to unfavourable S/N conditions. A real-data example using microseismic monitoring data from four stages of an open-hole slickwater hydraulic fracture treatment in western Canada demonstrates that a sparse set of parents (in this case, 4.6 per cent of the originally located events) yields a significant (more than fourfold increase) in the number of located events compared with the original catalogue. Moreover, analysis of the new MFA catalogue suggests that this approach leads to more robust interpretation

  18. Detection and analysis of microseismic events using a Matched Filtering Algorithm (MFA)

    NASA Astrophysics Data System (ADS)

    Caffagni, Enrico; Eaton, David W.; Jones, Joshua P.; van der Baan, Mirko

    2016-05-01

    A new Matched Filtering Algorithm (MFA) is proposed for detecting and analyzing microseismic events recorded by downhole monitoring of hydraulic fracturing. This method requires a set of well-located template (`parent') events, which are obtained using conventional microseismic processing and selected on the basis of high signal-to-noise (S/N) ratio and representative spatial distribution of the recorded microseismicity. Detection and extraction of `child' events are based on stacked, multi-channel cross-correlation of the continuous waveform data, using the parent events as reference signals. The location of a child event relative to its parent is determined using an automated process, by rotation of the multi-component waveforms into the ray-centered co-ordinates of the parent and maximizing the energy of the stacked amplitude envelope within a search volume around the parent's hypocentre. After correction for geometrical spreading and attenuation, the relative magnitude of the child event is obtained automatically using the ratio of stacked envelope peak with respect to its parent. Since only a small number of parent events require interactive analysis such as picking P- and S-wave arrivals, the MFA approach offers the potential for significant reduction in effort for downhole microseismic processing. Our algorithm also facilitates the analysis of single-phase child events, i.e. microseismic events for which only one of the S- or P-wave arrival is evident due to unfavorable S/N conditions. A real-data example using microseismic monitoring data from 4 stages of an open-hole slickwater hydraulic fracture treatment in western Canada demonstrates that a sparse set of parents (in this case, 4.6 per cent of the originally located events) yields a significant (more than four-fold increase) in the number of located events compared with the original catalog. Moreover, analysis of the new MFA catalog suggests that this approach leads to more robust interpretation of the

  19. Covert Network Analysis for Key Player Detection and Event Prediction Using a Hybrid Classifier

    PubMed Central

    Akram, M. Usman; Khan, Shoab A.; Javed, Muhammad Younus

    2014-01-01

    National security has gained vital importance due to increasing number of suspicious and terrorist events across the globe. Use of different subfields of information technology has also gained much attraction of researchers and practitioners to design systems which can detect main members which are actually responsible for such kind of events. In this paper, we present a novel method to predict key players from a covert network by applying a hybrid framework. The proposed system calculates certain centrality measures for each node in the network and then applies novel hybrid classifier for detection of key players. Our system also applies anomaly detection to predict any terrorist activity in order to help law enforcement agencies to destabilize the involved network. As a proof of concept, the proposed framework has been implemented and tested using different case studies including two publicly available datasets and one local network. PMID:25136674

  20. Drivers of Emerging Infectious Disease Events as a Framework for Digital Detection

    PubMed Central

    Olson, Sarah H.; Benedum, Corey M.; Mekaru, Sumiko R.; Preston, Nicholas D.; Mazet, Jonna A.K.; Joly, Damien O.

    2015-01-01

    The growing field of digital disease detection, or epidemic intelligence, attempts to improve timely detection and awareness of infectious disease (ID) events. Early detection remains an important priority; thus, the next frontier for ID surveillance is to improve the recognition and monitoring of drivers (antecedent conditions) of ID emergence for signals that precede disease events. These data could help alert public health officials to indicators of elevated ID risk, thereby triggering targeted active surveillance and interventions. We believe that ID emergence risks can be anticipated through surveillance of their drivers, just as successful warning systems of climate-based, meteorologically sensitive diseases are supported by improved temperature and precipitation data. We present approaches to driver surveillance, gaps in the current literature, and a scientific framework for the creation of a digital warning system. Fulfilling the promise of driver surveillance will require concerted action to expand the collection of appropriate digital driver data. PMID:26196106

  1. Drivers of Emerging Infectious Disease Events as a Framework for Digital Detection.

    PubMed

    Olson, Sarah H; Benedum, Corey M; Mekaru, Sumiko R; Preston, Nicholas D; Mazet, Jonna A K; Joly, Damien O; Brownstein, John S

    2015-08-01

    The growing field of digital disease detection, or epidemic intelligence, attempts to improve timely detection and awareness of infectious disease (ID) events. Early detection remains an important priority; thus, the next frontier for ID surveillance is to improve the recognition and monitoring of drivers (antecedent conditions) of ID emergence for signals that precede disease events. These data could help alert public health officials to indicators of elevated ID risk, thereby triggering targeted active surveillance and interventions. We believe that ID emergence risks can be anticipated through surveillance of their drivers, just as successful warning systems of climate-based, meteorologically sensitive diseases are supported by improved temperature and precipitation data. We present approaches to driver surveillance, gaps in the current literature, and a scientific framework for the creation of a digital warning system. Fulfilling the promise of driver surveillance will require concerted action to expand the collection of appropriate digital driver data. PMID:26196106

  2. Congenital Abnormalities

    MedlinePlus

    ... serious health problems (e.g. Down syndrome ). Single-Gene Abnormalities Sometimes the chromosomes are normal in number, ... blood flow to the fetus impair fetal growth. Alcohol consumption and certain drugs during pregnancy significantly increase ...

  3. Craniofacial Abnormalities

    MedlinePlus

    ... of the skull and face. Craniofacial abnormalities are birth defects of the face or head. Some, like cleft ... palate, are among the most common of all birth defects. Others are very rare. Most of them affect ...

  4. Walking abnormalities

    MedlinePlus

    ... include: Arthritis of the leg or foot joints Conversion disorder (a psychological disorder) Foot problems (such as a ... injuries. For an abnormal gait that occurs with conversion disorder, counseling and support from family members are strongly ...

  5. Chromosome Abnormalities

    MedlinePlus

    ... decade, newer techniques have been developed that allow scientists and doctors to screen for chromosomal abnormalities without using a microscope. These newer methods compare the patient's DNA to a normal DNA ...

  6. Nail abnormalities

    MedlinePlus

    Nail abnormalities are problems with the color, shape, texture, or thickness of the fingernails or toenails. ... Fungus or yeast cause changes in the color, texture, and shape of the nails. Bacterial infection may ...

  7. A novel adaptive, real-time algorithm to detect gait events from wearable sensors.

    PubMed

    Chia Bejarano, Noelia; Ambrosini, Emilia; Pedrocchi, Alessandra; Ferrigno, Giancarlo; Monticone, Marco; Ferrante, Simona

    2015-05-01

    A real-time, adaptive algorithm based on two inertial and magnetic sensors placed on the shanks was developed for gait-event detection. For each leg, the algorithm detected the Initial Contact (IC), as the minimum of the flexion/extension angle, and the End Contact (EC) and the Mid-Swing (MS), as minimum and maximum of the angular velocity, respectively. The algorithm consisted of calibration, real-time detection, and step-by-step update. Data collected from 22 healthy subjects (21 to 85 years) walking at three self-selected speeds were used to validate the algorithm against the GaitRite system. Comparable levels of accuracy and significantly lower detection delays were achieved with respect to other published methods. The algorithm robustness was tested on ten healthy subjects performing sudden speed changes and on ten stroke subjects (43 to 89 years). For healthy subjects, F1-scores of 1 and mean detection delays lower than 14 ms were obtained. For stroke subjects, F1-scores of 0.998 and 0.944 were obtained for IC and EC, respectively, with mean detection delays always below 31 ms. The algorithm accurately detected gait events in real time from a heterogeneous dataset of gait patterns and paves the way for the design of closed-loop controllers for customized gait trainings and/or assistive devices. PMID:25069118

  8. Sparse conditional mixture model: late fusion with missing scores for multimedia event detection

    NASA Astrophysics Data System (ADS)

    Nallapati, Ramesh; Yeh, Eric; Myers, Gregory

    2013-03-01

    The problem of event detection in multimedia clips is typically handled by modeling each of the component modalities independently, then combining their detection scores in a late fusion approach. One of the problems of a late fusion model in the multimedia setting is that the detection scores may be missing from one or more components for a given clip; e.g., when there is no speech in the clip; or when there is no overlay text. Standard fusion techniques typically address this problem by assuming a default backoff score for a component when its detection score is missing for a clip. This may potentially bias the fusion model, especially if there are many missing detections from a given component. In this work, we present the Sparse Conditional Mixture Model (SCMM) which models only the observed detection scores for each example, thereby avoiding making any assumptions about the distributions of the scores that are made by backoff models. Our experiments in multi-media event detection using the TRECVID-2011 corpus demonstrates that SCMM achieves statistically significant performance gains over standard late fusion techniques. The SCMM model is very general and is applicable to fusion problems with missing data in any domain.

  9. Event Detection and Location of Earthquakes Using the Cascadia Initiative Dataset

    NASA Astrophysics Data System (ADS)

    Morton, E.; Bilek, S. L.; Rowe, C. A.

    2015-12-01

    The Cascadia subduction zone (CSZ) produces a range of slip behavior along the plate boundary megathrust, from great earthquakes to episodic slow slip and tremor (ETS). Unlike other subduction zones that produce great earthquakes and ETS, the CSZ is notable for the lack of small and moderate magnitude earthquakes recorded. The seismogenic zone extent is currently estimated to be primarily offshore, thus the lack of observed small, interplate earthquakes may be partially due to the use of only land seismometers. The Cascadia Initiative (CI) community seismic experiment seeks to address this issue by including ocean bottom seismometers (OBS) deployed directly over the locked seismogenic zone, in addition to land seismometers. We use these seismic data to explore whether small magnitude earthquakes are occurring on the plate interface, but have gone undetected by the land-based seismic networks. We select a subset of small magnitude (M0.1-3.7) earthquakes from existing earthquake catalogs, based on land seismic data, whose preliminary hypocentral locations suggest they may have occurred on the plate interface. We window the waveforms on CI OBS and land seismometers around the phase arrival times for these earthquakes to generate templates for subspace detection, which allows for additional flexibility over traditional matched filter detection methods. Here we present event detections from the first year of CI deployment and preliminary locations for the detected events. Initial results of scanning the first year of the CI deployment using one cluster of template events, located near a previously identified subducted seamount, include 473 detections on OBS station M08A (~61.6 km offshore) and 710 detections on OBS station J25A (~44.8 km northeast of M08A). Ongoing efforts include detection using additional OBS stations along the margin, as well as determining locations of clusters detected in the first year of deployment.

  10. Event-specific quantitative detection of nine genetically modified maizes using one novel standard reference molecule.

    PubMed

    Yang, Litao; Guo, Jinchao; Pan, Aihu; Zhang, Haibo; Zhang, Kewei; Wang, Zhengming; Zhang, Dabing

    2007-01-10

    With the development of genetically modified organism (GMO) detection techniques, the Polymerase Chain Reaction (PCR) technique has been the mainstay for GMO detection, and real-time PCR is the most effective and important method for GMO quantification. An event-specific detection strategy based on the unique and specific integration junction sequences between the host plant genome DNA and the integrated gene is being developed for its high specificity. This study establishes the event-specific detection methods for TC1507 and CBH351 maizes. In addition, the event-specific TaqMan real-time PCR detection methods for another seven GM maize events (Bt11, Bt176, GA21, MON810, MON863, NK603, and T25) were systematically optimized and developed. In these PCR assays, the fluorescent quencher, TAMRA, was dyed on the T-base of the probe at the internal position to improve the intensity of the fluorescent signal. To overcome the difficulties in obtaining the certified reference materials of these GM maizes, one novel standard reference molecule containing all nine specific integration junction sequences of these GM maizes and the maize endogenous reference gene, zSSIIb, was constructed and used for quantitative analysis. The limits of detection of these methods were 20 copies for these different GM maizes, the limits of quantitation were about 20 copies, and the dynamic ranges for quantification were from 0.05 to 100% in 100 ng of DNA template. Furthermore, nine groups of the mixed maize samples of these nine GM maize events were quantitatively analyzed to evaluate the accuracy and precision. The accuracy expressed as bias varied from 0.67 to 28.00% for the nine tested groups of GM maize samples, and the precision expressed as relative standard deviations was from 0.83 to 26.20%. All of these indicated that the established event-specific real-time PCR detection systems and the reference molecule in this study are suitable for the identification and quantification of these GM

  11. Detection, tracking and event localization of jet stream features in 4-D atmospheric data

    NASA Astrophysics Data System (ADS)

    Limbach, S.; Schömer, E.; Wernli, H.

    2012-04-01

    We introduce a novel algorithm for the efficient detection and tracking of features in spatiotemporal atmospheric data, as well as for the precise localization of the occurring genesis, lysis, merging and splitting events. The algorithm works on data given on a four-dimensional structured grid. Feature selection and clustering are based on adjustable local and global criteria, feature tracking is predominantly based on spatial overlaps of the feature's full volumes. The resulting 3-D features and the identified correspondences between features of consecutive time steps are represented as the nodes and edges of a directed acyclic graph, the event graph. Merging and splitting events appear in the event graph as nodes with multiple incoming or outgoing edges, respectively. The precise localization of the splitting events is based on a search for all grid points inside the initial 3-D feature that have a similar distance to two successive 3-D features of the next time step. The merging event is localized analogously, operating backward in time. As a first application of our method we present a climatology of upper-tropospheric jet streams and their events, based on four-dimensional wind speed data from European Centre for Medium-Range Weather Forecasts (ECMWF) analyses. We compare our results with a climatology from a previous study, investigate the statistical distribution of the merging and splitting events, and illustrate the meteorological significance of the jet splitting events with a case study. A brief outlook is given on additional potential applications of the 4-D data segmentation technique.

  12. Detection of abnormal resting-state networks in individual patients suffering from focal epilepsy: an initial step toward individual connectivity assessment

    PubMed Central

    Dansereau, Christian L.; Bellec, Pierre; Lee, Kangjoo; Pittau, Francesca; Gotman, Jean; Grova, Christophe

    2014-01-01

    The spatial coherence of spontaneous slow fluctuations in the blood-oxygen-level dependent (BOLD) signal at rest is routinely used to characterize the underlying resting-state networks (RSNs). Studies have demonstrated that these patterns are organized in space and highly reproducible from subject to subject. Moreover, RSNs reorganizations have been suggested in pathological conditions. Comparisons of RSNs organization have been performed between groups of subjects but have rarely been applied at the individual level, a step required for clinical application. Defining the notion of modularity as the organization of brain activity in stable networks, we propose Detection of Abnormal Networks in Individuals (DANI) to identify modularity changes at the individual level. The stability of each RSN was estimated using a spatial clustering method: Bootstrap Analysis of Stable Clusters (BASC) (Bellec et al., 2010). Our contributions consisted in (i) providing functional maps of the most stable cores of each networks and (ii) in detecting “abnormal” individual changes in networks organization when compared to a population of healthy controls. DANI was first evaluated using realistic simulated data, showing that focussing on a conservative core size (50% most stable regions) improved the sensitivity to detect modularity changes. DANI was then applied to resting state fMRI data of six patients with focal epilepsy who underwent multimodal assessment using simultaneous EEG/fMRI acquisition followed by surgery. Only patient with a seizure free outcome were selected and the resected area was identified using a post-operative MRI. DANI automatically detected abnormal changes in 5 out of 6 patients, with excellent sensitivity, showing for each of them at least one “abnormal” lateralized network closely related to the epileptic focus. For each patient, we also detected some distant networks as abnormal, suggesting some remote reorganization in the epileptic brain. PMID

  13. A method for detecting and locating geophysical events using groups of arrays

    NASA Astrophysics Data System (ADS)

    de Groot-Hedlin, Catherine D.; Hedlin, Michael A. H.

    2015-11-01

    We have developed a novel method to detect and locate geophysical events that makes use of any sufficiently dense sensor network. This method is demonstrated using acoustic sensor data collected in 2013 at the USArray Transportable Array (TA). The algorithm applies Delaunay triangulation to divide the sensor network into a mesh of three-element arrays, called triads. Because infrasound waveforms are incoherent between the sensors within each triad, the data are transformed into envelopes, which are cross-correlated to find signals that satisfy a consistency criterion. The propagation azimuth, phase velocity and signal arrival time are computed for each signal. Triads with signals that are consistent with a single source are bundled as an event group. The ensemble of arrival times and azimuths of detected signals within each group are used to locate a common source in space and time. A total of 513 infrasonic stations that were active for part or all of 2013 were divided into over 2000 triads. Low (0.5-2 Hz) and high (2-8 Hz) catalogues of infrasonic events were created for the eastern USA. The low-frequency catalogue includes over 900 events and reveals several highly active source areas on land that correspond with coal mining regions. The high-frequency catalogue includes over 2000 events, with most occurring offshore. Although their cause is not certain, most events are clearly anthropogenic as almost all occur during regular working hours each week. The regions to which the TA is most sensitive vary seasonally, with the direction of reception dependent on the direction of zonal winds. The catalogue has also revealed large acoustic events that may provide useful insight into the nature of long-range infrasound propagation in the atmosphere.

  14. A Heuristic Indication and Warning Staging Model for Detection and Assessment of Biological Events

    PubMed Central

    Wilson, James M.; Polyak, Marat G.; Blake, Jane W.; Collmann, Jeff

    2008-01-01

    Objective This paper presents a model designed to enable rapid detection and assessment of biological threats that may require swift intervention by the international public health community. Design We utilized Strauss’ grounded theory to develop an expanded model of social disruption due to biological events based on retrospective and prospective case studies. We then applied this model to the temporal domain and propose a heuristic staging model, the Wilson–Collmann Scale for assessing biological event evolution. Measurements We retrospectively and manually examined hard copy archival local media reports in the native vernacular for three biological events associated with substantial social disruption. The model was then tested prospectively through media harvesting based on keywords corresponding to the model parameters. Results Our heuristic staging model provides valuable information about the features of a biological event that can be used to determine the level of concern warranted, such as whether the pathogen in question is responding to established public health disease control measures, including the use of antimicrobials or vaccines; whether the public health and medical infrastructure of the country involved is adequate to mount the necessary response; whether the country’s officials are providing an appropriate level of information to international public health authorities; and whether the event poses a international threat. The approach is applicable for monitoring open-source (public-domain) media for indications and warnings of such events, and specifically for markers of the social disruption that commonly occur as these events unfold. These indications and warnings can then be used as the basis for staging the biological threat in the same manner that the United States National Weather Service currently uses storm warning models (such as the Saffir-Simpson Hurricane Scale) to detect and assess threatening weather conditions. Conclusion

  15. Persistent homological sparse network approach to detecting white matter abnormality in maltreated children: MRI and DTI multimodal study.

    PubMed

    Chung, Moo K; Hanson, Jamie L; Lee, Hyekyoung; Adluru, Nagesh; Alexander, Andrew L; Davidson, Richard J; Pollak, Seth D

    2013-01-01

    We present a novel persistent homological sparse network analysis framework for characterizing white matter abnormalities in tensor-based morphometry (TBM) in magnetic resonance imaging (MRI). Traditionally TBM is used in quantifying tissue volume change in each voxel in a massive univariate fashion. However, this obvious approach cannot be used in testing, for instance, if the change in one voxel is related to other voxels. To address this limitation of univariate-TBM, we propose a new persistent homological approach to testing more complex relational hypotheses across brain regions. The proposed methods are applied to characterize abnormal white matter in maltreated children. The results are further validated using fractional anisotropy (FA) values in diffusion tensor imaging (DTI). PMID:24505679

  16. Characterization and Analysis of Networked Array of Sensors for Event Detection (CANARY-EDS)

    2011-05-27

    CANARY -EDS provides probabilistic event detection based on analysis of time-series data from water quality or other sensors. CANARY also can compare patterns against a library of previously seen data to indicate that a certain pattern has reoccurred, suppressing what would otherwise be considered an event. CANARY can be configured to analyze previously recorded data from files or databases, or it can be configured to run in real-time mode directory from a database, or throughmore » the US EPA EDDIES software.« less

  17. Shared clonal cytogenetic abnormalities in aberrant mast cells and leukemic myeloid blasts detected by single nucleotide polymorphism microarray-based whole-genome scanning.

    PubMed

    Frederiksen, John K; Shao, Lina; Bixby, Dale L; Ross, Charles W

    2016-04-01

    Systemic mastocytosis (SM) is characterized by a clonal proliferation of aberrant mast cells within extracutaneous sites. In a subset of SM cases, a second associated hematologic non-mast cell disease (AHNMD) is also present, usually of myeloid origin. Polymerase chain reaction and targeted fluorescence in situ hybridization studies have provided evidence that, in at least some cases, the aberrant mast cells are related clonally to the neoplastic cells of the AHNMD. In this work, a single nucleotide polymorphism microarray (SNP-A) was used to characterize the cytogenetics of the aberrant mast cells from a patient with acute myeloid leukemia and concomitant mast cell leukemia associated with a KIT D816A mutation. The results demonstrate the presence of shared cytogenetic abnormalities between the mast cells and myeloid blasts, as well as additional abnormalities within mast cells (copy-neutral loss of heterozygosity) not detectable by routine karyotypic analysis. To our knowledge, this work represents the first application of SNP-A whole-genome scanning to the detection of shared cytogenetic abnormalities between the two components of a case of SM-AHNMD. The findings provide additional evidence of a frequent clonal link between aberrant mast cells and cells of myeloid AHNMDs, and also highlight the importance of direct sequencing for identifying uncommon activating KIT mutations. PMID:26865278

  18. CTBT infrasound network performance to detect the 2013 Russian fireball event

    NASA Astrophysics Data System (ADS)

    Pilger, Christoph; Ceranna, Lars; Ross, J. Ole; Le Pichon, Alexis; Mialle, Pierrick; Garcés, Milton A.

    2015-04-01

    The explosive fragmentation of the 2013 Chelyabinsk meteorite generated a large airburst with an equivalent yield of 500 kT TNT. It is the most energetic event recorded by the infrasound component of the Comprehensive Nuclear-Test-Ban Treaty-International Monitoring System (CTBT-IMS), globally detected by 20 out of 42 operational stations. This study performs a station-by-station estimation of the IMS detection capability to explain infrasound detections and nondetections from short to long distances, using the Chelyabinsk meteorite as global reference event. Investigated parameters influencing the detection capability are the directivity of the line source signal, the ducting of acoustic energy, and the individual noise conditions at each station. Findings include a clear detection preference for stations perpendicular to the meteorite trajectory, even over large distances. Only a weak influence of stratospheric ducting is observed for this low-frequency case. Furthermore, a strong dependence on the diurnal variability of background noise levels at each station is observed, favoring nocturnal detections.

  19. Comparison of myocardial contrast echocardiography with NC100100 and 99mTc sestamibi SPECT for detection of resting myocardial perfusion abnormalities in patients with previous myocardial infarction

    PubMed Central

    Jucquois, I; Nihoyannopoulos, P; D'Hondt, A; Roelants, V; Robert, A; Melin, J; Glass, D; Vanoverschelde, J

    2000-01-01

    OBJECTIVE—To determine whether myocardial contrast echocardiography (MCE) following intravenous injection of perfluorocarbon microbubbles permits identification of resting myocardial perfusion abnormalities in patients who have had a previous myocardial infarction.
PATIENTS AND INTERVENTIONS—22 patients (mean (SD) age 66 (11) years) underwent MCE after intravenous injection of NC100100, a novel perfluorocarbon containing contrast agent, and resting 99mTc sestamibi single photon emission computed tomography (SPECT). With both methods, myocardial perfusion was graded semiquantitatively as 1 = normal, 0.5 = mild defect, and 0 = severe defect.
RESULTS—Among the 203 normally contracting segments, 151 (74%) were normally perfused by SPECT and 145 (71%) by MCE. With SPECT, abnormal tracer uptake was mainly found among normally contracting segments from the inferior wall. By contrast, with MCE poor myocardial opacification was noted essentially among the normally contracting segments from the anterior and lateral walls. Of the 142 dysfunctional segments, 87 (61%) showed perfusion defects by SPECT, and 94 (66%) by MCE. With both methods, perfusion abnormalities were seen more frequently among akinetic than hypokinetic segments. MCE correctly identified 81/139 segments that exhibited a perfusion defect by SPECT (58%), and 135/206 segments that were normally perfused by SPECT (66%). Exclusion of segments with attenuation artefacts (defined as abnormal myocardial opacification or sestamibi uptake but normal contraction) by either MCE or SPECT improved both the sensitivity (76%) and the specificity (83%) of the detection of SPECT perfusion defects by MCE.
CONCLUSIONS—The data suggest that MCE allows identification of myocardial perfusion abnormalities in patients who have had a previous myocardial infarction, provided that regional wall motion is simultaneously taken into account.


Keywords: myocardial contrast echocardiography; NC100100

  20. Hazardous Traffic Event Detection Using Markov Blanket and Sequential Minimal Optimization (MB-SMO)

    PubMed Central

    Yan, Lixin; Zhang, Yishi; He, Yi; Gao, Song; Zhu, Dunyao; Ran, Bin; Wu, Qing

    2016-01-01

    The ability to identify hazardous traffic events is already considered as one of the most effective solutions for reducing the occurrence of crashes. Only certain particular hazardous traffic events have been studied in previous studies, which were mainly based on dedicated video stream data and GPS data. The objective of this study is twofold: (1) the Markov blanket (MB) algorithm is employed to extract the main factors associated with hazardous traffic events; (2) a model is developed to identify hazardous traffic event using driving characteristics, vehicle trajectory, and vehicle position data. Twenty-two licensed drivers were recruited to carry out a natural driving experiment in Wuhan, China, and multi-sensor information data were collected for different types of traffic events. The results indicated that a vehicle’s speed, the standard deviation of speed, the standard deviation of skin conductance, the standard deviation of brake pressure, turn signal, the acceleration of steering, the standard deviation of acceleration, and the acceleration in Z (G) have significant influences on hazardous traffic events. The sequential minimal optimization (SMO) algorithm was adopted to build the identification model, and the accuracy of prediction was higher than 86%. Moreover, compared with other detection algorithms, the MB-SMO algorithm was ranked best in terms of the prediction accuracy. The conclusions can provide reference evidence for the development of dangerous situation warning products and the design of intelligent vehicles. PMID:27420073

  1. Hazardous Traffic Event Detection Using Markov Blanket and Sequential Minimal Optimization (MB-SMO).

    PubMed

    Yan, Lixin; Zhang, Yishi; He, Yi; Gao, Song; Zhu, Dunyao; Ran, Bin; Wu, Qing

    2016-01-01

    The ability to identify hazardous traffic events is already considered as one of the most effective solutions for reducing the occurrence of crashes. Only certain particular hazardous traffic events have been studied in previous studies, which were mainly based on dedicated video stream data and GPS data. The objective of this study is twofold: (1) the Markov blanket (MB) algorithm is employed to extract the main factors associated with hazardous traffic events; (2) a model is developed to identify hazardous traffic event using driving characteristics, vehicle trajectory, and vehicle position data. Twenty-two licensed drivers were recruited to carry out a natural driving experiment in Wuhan, China, and multi-sensor information data were collected for different types of traffic events. The results indicated that a vehicle's speed, the standard deviation of speed, the standard deviation of skin conductance, the standard deviation of brake pressure, turn signal, the acceleration of steering, the standard deviation of acceleration, and the acceleration in Z (G) have significant influences on hazardous traffic events. The sequential minimal optimization (SMO) algorithm was adopted to build the identification model, and the accuracy of prediction was higher than 86%. Moreover, compared with other detection algorithms, the MB-SMO algorithm was ranked best in terms of the prediction accuracy. The conclusions can provide reference evidence for the development of dangerous situation warning products and the design of intelligent vehicles. PMID:27420073

  2. Collaborative-Comparison Learning for Complex Event Detection Using Distributed Hierarchical Graph Neuron (DHGN) Approach in Wireless Sensor Network

    NASA Astrophysics Data System (ADS)

    Muhamad Amin, Anang Hudaya; Khan, Asad I.

    Research trends in existing event detection schemes using Wireless Sensor Network (WSN) have mainly focused on routing and localisation of nodes for optimum coordination when retrieving sensory information. Efforts have also been put in place to create schemes that are able to provide learning mechanisms for event detection using classification or clustering approaches. These schemes entail substantial communication and computational overheads owing to the event-oblivious nature of data transmissions. In this paper, we present an event detection scheme that has the ability to distribute detection processes over the resource-constrained wireless sensor nodes and is suitable for events with spatio-temporal characteristics. We adopt a pattern recognition algorithm known as Distributed Hierarchical Graph Neuron (DHGN) with collaborative-comparison learning for detecting critical events in WSN. The scheme demonstrates good accuracy for binary classification and offers low-complexity and high-scalability in terms of its processing requirements.

  3. Support Vector Machine Model for Automatic Detection and Classification of Seismic Events

    NASA Astrophysics Data System (ADS)

    Barros, Vesna; Barros, Lucas

    2016-04-01

    The automated processing of multiple seismic signals to detect, localize and classify seismic events is a central tool in both natural hazards monitoring and nuclear treaty verification. However, false detections and missed detections caused by station noise and incorrect classification of arrivals are still an issue and the events are often unclassified or poorly classified. Thus, machine learning techniques can be used in automatic processing for classifying the huge database of seismic recordings and provide more confidence in the final output. Applied in the context of the International Monitoring System (IMS) - a global sensor network developed for the Comprehensive Nuclear-Test-Ban Treaty (CTBT) - we propose a fully automatic method for seismic event detection and classification based on a supervised pattern recognition technique called the Support Vector Machine (SVM). According to Kortström et al., 2015, the advantages of using SVM are handleability of large number of features and effectiveness in high dimensional spaces. Our objective is to detect seismic events from one IMS seismic station located in an area of high seismicity and mining activity and classify them as earthquakes or quarry blasts. It is expected to create a flexible and easily adjustable SVM method that can be applied in different regions and datasets. Taken a step further, accurate results for seismic stations could lead to a modification of the model and its parameters to make it applicable to other waveform technologies used to monitor nuclear explosions such as infrasound and hydroacoustic waveforms. As an authorized user, we have direct access to all IMS data and bulletins through a secure signatory account. A set of significant seismic waveforms containing different types of events (e.g. earthquake, quarry blasts) and noise is being analysed to train the model and learn the typical pattern of the signal from these events. Moreover, comparing the performance of the support

  4. Surface-Wave Multiple-Event Relocation and Detection of Earthquakes along the Romanche Fracture Zone

    NASA Astrophysics Data System (ADS)

    Cleveland, M.; Ammon, C. J.; VanDeMark, T. F.

    2011-12-01

    The Romanche Transform system, located along the equatorial Mid-Atlantic Ridge, is approximately 900 km in length and separates plates moving with a relative plate speed of three cm/yr. We use cross-correlation of globally recorded Rayleigh waves to estimate precise relative epicentroids of moderate-size earthquakes along the Romanche Fracture Zone system. The Romanche transform has an even distribution of large events along its entire length that provide a good base of events with excellent signal-to-noise observations. Two distinct moderate-magnitude event clusters occur along the eastern half of the transform and the region between the clusters hosted a large event in the last decade. Based on initial results (Van DeMark, 2006), unlike those of shorter transform systems, the events along the Romanche do not follow narrow features, the event clusters seem to spread perpendicular as well as laterally to the transform trend. These patterns are consistent with parallel, en echelon and/or braided fault systems, which have been previously observed on the Romanche through the use of side scanning sonar (Parson and Searle, 1986). We also explore the character and potential of seismic body waves to extend the method to help improve relative event depth estimates. Relying on a good base of larger and moderate-magnitude seismicity, we attempt to extend the analysis by processing continuous data streams through processes measuring waveform similarity (e.g. cross-correlation) in an attempt to detect smaller events using a subset of nearest seismic stations.

  5. Detection of cryptic subtelomeric chromosome abnormalities and identification of anonymous chromatin using a quantitative multiplex ligation-dependent probe amplification (MLPA) assay.

    PubMed

    Northrop, Emma L; Ren, Hua; Bruno, Damien L; McGhie, James D R; Coffa, Jordi; Schouten, Jan; Choo, K H Andy; Slater, Howard R

    2005-11-01

    The need to detect clinically significant segmental aneuploidies beyond the range of light microscopy demands the development of new cost-efficient, sensitive, and robust analytical techniques. Multiplex ligation-dependent probe amplification (MLPA) has already been shown to be particularly effective and flexible for measuring copy numbers in a multiplex format. Previous attempts to develop a reliable MLPA to assay all chromosome subtelomeric regions have been confounded by unforeseen copy number variation in some genes that are very close to the telomeres in healthy individuals. We addressed this shortcoming by substituting all known polymorphic probes and using two complementary multiplex assays to minimize the likelihood of false results. We developed this new quantitative MLPA strategy for two important diagnostic applications. First, in a group of cases with high clinical suspicion of a chromosome abnormality but normal, high-resolution karyotypes, MLPA detected subtelomeric abnormalities in three patients. Two were de novo terminal deletions (del(4p) and del(1p)), and one was a derivative chromosome 1 from a maternal t(1p;17p). The range of these segmental aneuploidies was 1.8-6.6 Mb, and none were visible on retrospective microscopy. Second, in a group of six patients with apparently de novo single-chromosome abnormalities containing anonymous chromatin, MLPA identified two cases with simple intrachromosomal duplications: dup(6p) and dup(8q). Three cases showed derivative chromosomes from translocations involving the distal regions of 9q and 4q, 5p and 11q, and 6q and 3p. One case showed a nonreciprocal, interchromosomal translocation of the distal region of 10p-7p. All abnormalities in both groups were confirmed by fluorescence in situ hybridization (FISH) using bacterial artificial chromosomes (BACs). This quantitative MLPA technique for subtelomeric assays is compared with previously described alternative techniques. PMID:16170807

  6. Single-subject-based whole-brain MEG slow-wave imaging approach for detecting abnormality in patients with mild traumatic brain injury.

    PubMed

    Huang, Ming-Xiong; Nichols, Sharon; Baker, Dewleen G; Robb, Ashley; Angeles, Annemarie; Yurgil, Kate A; Drake, Angela; Levy, Michael; Song, Tao; McLay, Robert; Theilmann, Rebecca J; Diwakar, Mithun; Risbrough, Victoria B; Ji, Zhengwei; Huang, Charles W; Chang, Douglas G; Harrington, Deborah L; Muzzatti, Laura; Canive, Jose M; Christopher Edgar, J; Chen, Yu-Han; Lee, Roland R

    2014-01-01

    Traumatic brain injury (TBI) is a leading cause of sustained impairment in military and civilian populations. However, mild TBI (mTBI) can be difficult to detect using conventional MRI or CT. Injured brain tissues in mTBI patients generate abnormal slow-waves (1-4 Hz) that can be measured and localized by resting-state magnetoencephalography (MEG). In this study, we develop a voxel-based whole-brain MEG slow-wave imaging approach for detecting abnormality in patients with mTBI on a single-subject basis. A normative database of resting-state MEG source magnitude images (1-4 Hz) from 79 healthy control subjects was established for all brain voxels. The high-resolution MEG source magnitude images were obtained by our recent Fast-VESTAL method. In 84 mTBI patients with persistent post-concussive symptoms (36 from blasts, and 48 from non-blast causes), our method detected abnormalities at the positive detection rates of 84.5%, 86.1%, and 83.3% for the combined (blast-induced plus with non-blast causes), blast, and non-blast mTBI groups, respectively. We found that prefrontal, posterior parietal, inferior temporal, hippocampus, and cerebella areas were particularly vulnerable to head trauma. The result also showed that MEG slow-wave generation in prefrontal areas positively correlated with personality change, trouble concentrating, affective lability, and depression symptoms. Discussion is provided regarding the neuronal mechanisms of MEG slow-wave generation due to deafferentation caused by axonal injury and/or blockages/limitations of cholinergic transmission in TBI. This study provides an effective way for using MEG slow-wave source imaging to localize affected areas and supports MEG as a tool for assisting the diagnosis of mTBI. PMID:25009772

  7. Single-subject-based whole-brain MEG slow-wave imaging approach for detecting abnormality in patients with mild traumatic brain injury

    PubMed Central

    Huang, Ming-Xiong; Nichols, Sharon; Baker, Dewleen G.; Robb, Ashley; Angeles, Annemarie; Yurgil, Kate A.; Drake, Angela; Levy, Michael; Song, Tao; McLay, Robert; Theilmann, Rebecca J.; Diwakar, Mithun; Risbrough, Victoria B.; Ji, Zhengwei; Huang, Charles W.; Chang, Douglas G.; Harrington, Deborah L.; Muzzatti, Laura; Canive, Jose M.; Christopher Edgar, J.; Chen, Yu-Han; Lee, Roland R.

    2014-01-01

    Traumatic brain injury (TBI) is a leading cause of sustained impairment in military and civilian populations. However, mild TBI (mTBI) can be difficult to detect using conventional MRI or CT. Injured brain tissues in mTBI patients generate abnormal slow-waves (1–4 Hz) that can be measured and localized by resting-state magnetoencephalography (MEG). In this study, we develop a voxel-based whole-brain MEG slow-wave imaging approach for detecting abnormality in patients with mTBI on a single-subject basis. A normative database of resting-state MEG source magnitude images (1–4 Hz) from 79 healthy control subjects was established for all brain voxels. The high-resolution MEG source magnitude images were obtained by our recent Fast-VESTAL method. In 84 mTBI patients with persistent post-concussive symptoms (36 from blasts, and 48 from non-blast causes), our method detected abnormalities at the positive detection rates of 84.5%, 86.1%, and 83.3% for the combined (blast-induced plus with non-blast causes), blast, and non-blast mTBI groups, respectively. We found that prefrontal, posterior parietal, inferior temporal, hippocampus, and cerebella areas were particularly vulnerable to head trauma. The result also showed that MEG slow-wave generation in prefrontal areas positively correlated with personality change, trouble concentrating, affective lability, and depression symptoms. Discussion is provided regarding the neuronal mechanisms of MEG slow-wave generation due to deafferentation caused by axonal injury and/or blockages/limitations of cholinergic transmission in TBI. This study provides an effective way for using MEG slow-wave source imaging to localize affected areas and supports MEG as a tool for assisting the diagnosis of mTBI. PMID:25009772

  8. Detection of Unusual Events and Trends in Complex Non-Stationary Data Streams

    SciTech Connect

    Perez, Rafael B; Protopopescu, Vladimir A; Worley, Brian Addison; Perez, Cristina

    2006-01-01

    The search for unusual events and trends hidden in multi-component, nonlinear, non-stationary, noisy signals is extremely important for a host of different applications, ranging from nuclear power plant and electric grid operation to internet traffic and implementation of non-proliferation protocols. In the context of this work, we define an unusual event as a local signal disturbance and a trend as a continuous carrier of information added to and different from the underlying baseline dynamics. The goal of this paper is to investigate the feasibility of detecting hidden intermittent events inside non-stationary signal data sets corrupted by high levels of noise, by using the Hilbert-Huang empirical mode decomposition method.

  9. Use of a clinical event monitor to prevent and detect medication errors.

    PubMed Central

    Payne, T. H.; Savarino, J.; Marshall, R.; Hoey, C. T.

    2000-01-01

    Errors in health care facilities are common and often unrecognized. We have used our clinical event monitor to prevent and detect medication errors by scrutinizing electronic messages sent to it when any medication order is written in our facility. A growing collection of medication safety rules covering dose limit errors, laboratory monitoring, and other topics may be applied to each medication order message to provide an additional layer of protection beyond existing order checks, reminders, and alerts available within our computer-based record system. During a typical day the event monitor receives 4802 messages, of which 4719 pertain to medication orders. We have found the clinical event monitor to be a valuable tool for clinicians and quality management groups charged with improving medication safety. PMID:11079962

  10. Efficient In Planta Detection and Dissection of De Novo Mutation Events in the Arabidopsis thaliana Disease Resistance Gene UNI.

    PubMed

    Ogawa, Tomohiko; Mori, Akiko; Igari, Kadunari; Morita, Miyo Terao; Tasaka, Masao; Uchida, Naoyuki

    2016-06-01

    Plants possess disease resistance (R) proteins encoded by R genes, and each R protein recognizes a specific pathogen factor(s) for immunity. Interestingly, a remarkably high degree of polymorphisms in R genes, which are traces of past mutation events during evolution, suggest the rapid diversification of R genes. However, little is known about molecular aspects that facilitate the rapid change of R genes because of the lack of tools that enable us to monitor de novo R gene mutations efficiently in an experimentally feasible time scale, especially in living plants. Here we introduce a model assay system that enables efficient in planta detection of de novo mutation events in the Arabidopsis thaliana R gene UNI in one generation. The uni-1D mutant harbors a gain-of-function allele of the UNI gene. uni-1D heterozygous individuals originally exhibit dwarfism with abnormally short stems. However, interestingly, morphologically normal stems sometimes emerge spontaneously from the uni-1D plants, and the morphologically reverted tissues carry additional de novo mutations in the UNI gene. Strikingly, under an extreme condition, almost half of the examined population shows the reversion phenomenon. By taking advantage of this phenomenon, we demonstrate that the reversion frequency is remarkably sensitive to a variety of fluctuations in DNA stability, underlying a mutable tendency of the UNI gene. We also reveal that activities of the salicylic acid pathway and DNA damage sensor pathway are involved in the reversion phenomenon. Thus, we provide an experimentally feasible model tool to explore factors and conditions that significantly affect the R gene mutation phenomenon. PMID:27016096

  11. Detection of Severe Rain on Snow events using passive microwave remote sensing

    NASA Astrophysics Data System (ADS)

    Grenfell, T. C.; Putkonen, J.

    2007-12-01

    Severe wintertime rain-on-snow (ROS) events create a strong ice layer or layers in the snow on arctic tundra that act as a barrier to ungulate grazing. These events are linked with large-scale ungulate herd declines via starvation and reduced calf production rate when the animals are unable to penetrate through the resulting ice layer. ROS events also produce considerable perturbation in the mean wintertime soil temperature beneath the snow pack. ROS is a sporadic but well-known and significant phenomenon that is currently very poorly documented. Characterization of the distribution and occurrence of severe rain-on-snow events is based only on anecdotal evidence, indirect observations of carcasses found adjacent to iced snow packs, and irregular detection by a sparse observational weather network. We have analyzed in detail a particular well-identified ROS event that took place on Banks Island in early October 2003 that resulted in the death of 20,000 musk oxen. We make use of multifrequency passive microwave imagery from the special sensing microwave imager satellite sensor suite (SSM/I) in conjunction with a strong-fluctuation-theory (SFT) emissivity model. We show that a combination of time series analysis and cluster analysis based on microwave spectral gradients and polarization ratios provides a means to detect the stages of the ROS event resulting from the modification of the vertical structure of the snow pack, specifically wetting the snow, the accumulation of liquid water at the base of the snow during the rain event, and the subsequent modification of the snowpack after refreezing. SFT model analysis provides quantitative confirmation of our interpretation of the evolution of the microwave properties of the snowpack as a result of the ROS event. In particular, in addition to the grain coarsening due to destructive metamorphism, we detect the presence of the internal water and ice layers, directly identifying the physical properties producing the

  12. Detection and analysis of high-temperature events in the BIRD mission

    NASA Astrophysics Data System (ADS)

    Zhukov, Boris; Briess, Klaus; Lorenz, Eckehard; Oertel, Dieter; Skrbek, Wolfgang

    2005-01-01

    The primary mission objective of a new small Bi-spectral InfraRed Detection (BIRD) satellite is detection and quantitative analysis of high-temperature events like fires and volcanoes. An absence of saturation in the BIRD infrared channels makes it possible to improve false alarm rejection as well as to retrieve quantitative characteristics of hot targets, including their effective fire temperature, area and the radiative energy release. Examples are given of detection and analysis of wild and coal seam fires, of volcanic activity as well as of oil fires in Iraq. The smallest fires detected by BIRD, which were verified on ground, had an area of 12m2 at daytime and 4m2 at night.

  13. Assessing Reliability of Medical Record Reviews for the Detection of Hospital Adverse Events

    PubMed Central

    Ock, Minsu; Lee, Sang-il; Jo, Min-Woo; Lee, Jin Yong; Kim, Seon-Ha

    2015-01-01

    Objectives: The purpose of this study was to assess the inter-rater reliability and intra-rater reliability of medical record review for the detection of hospital adverse events. Methods: We conducted two stages retrospective medical records review of a random sample of 96 patients from one acute-care general hospital. The first stage was an explicit patient record review by two nurses to detect the presence of 41 screening criteria (SC). The second stage was an implicit structured review by two physicians to identify the occurrence of adverse events from the positive cases on the SC. The inter-rater reliability of two nurses and that of two physicians were assessed. The intra-rater reliability was also evaluated by using test-retest method at approximately two weeks later. Results: In 84.2% of the patient medical records, the nurses agreed as to the necessity for the second stage review (kappa, 0.68; 95% confidence interval [CI], 0.54 to 0.83). In 93.0% of the patient medical records screened by nurses, the physicians agreed about the absence or presence of adverse events (kappa, 0.71; 95% CI, 0.44 to 0.97). When assessing intra-rater reliability, the kappa indices of two nurses were 0.54 (95% CI, 0.31 to 0.77) and 0.67 (95% CI, 0.47 to 0.87), whereas those of two physicians were 0.87 (95% CI, 0.62 to 1.00) and 0.37 (95% CI, -0.16 to 0.89). Conclusions: In this study, the medical record review for detecting adverse events showed intermediate to good level of inter-rater and intra-rater reliability. Well organized training program for reviewers and clearly defining SC are required to get more reliable results in the hospital adverse event study. PMID:26429290

  14. Flow detection via sparse frame analysis for suspicious event recognition in infrared imagery

    NASA Astrophysics Data System (ADS)

    Fernandes, Henrique C.; Batista, Marcos A.; Barcelos, Celia A. Z.; Maldague, Xavier P. V.

    2013-05-01

    It is becoming increasingly evident that intelligent systems are very bene¯cial for society and that the further development of such systems is necessary to continue to improve society's quality of life. One area that has drawn the attention of recent research is the development of automatic surveillance systems. In our work we outline a system capable of monitoring an uncontrolled area (an outside parking lot) using infrared imagery and recognizing suspicious events in this area. The ¯rst step is to identify moving objects and segment them from the scene's background. Our approach is based on a dynamic background-subtraction technique which robustly adapts detection to illumination changes. It is analyzed only regions where movement is occurring, ignoring in°uence of pixels from regions where there is no movement, to segment moving objects. Regions where movement is occurring are identi¯ed using °ow detection via sparse frame analysis. During the tracking process the objects are classi¯ed into two categories: Persons and Vehicles, based on features such as size and velocity. The last step is to recognize suspicious events that may occur in the scene. Since the objects are correctly segmented and classi¯ed it is possible to identify those events using features such as velocity and time spent motionless in one spot. In this paper we recognize the suspicious event suspicion of object(s) theft from inside a parked vehicle at spot X by a person" and results show that the use of °ow detection increases the recognition of this suspicious event from 78:57% to 92:85%.

  15. Energy Reconstruction for Events Detected in TES X-ray Detectors

    NASA Astrophysics Data System (ADS)

    Ceballos, M. T.; Cardiel, N.; Cobo, B.

    2015-09-01

    The processing of the X-ray events detected by a TES (Transition Edge Sensor) device (such as the one that will be proposed in the ESA AO call for instruments for the Athena mission (Nandra et al. 2013) as a high spectral resolution instrument, X-IFU (Barret et al. 2013)), is a several step procedure that starts with the detection of the current pulses in a noisy signal and ends up with their energy reconstruction. For this last stage, an energy calibration process is required to convert the pseudo energies measured in the detector to the real energies of the incoming photons, accounting for possible nonlinearity effects in the detector. We present the details of the energy calibration algorithm we implemented as the last part of the Event Processing software that we are developing for the X-IFU instrument, that permits the calculation of the calibration constants in an analytical way.

  16. How unusual are the "unusual events" detected by control chart techniques in healthcare settings?

    PubMed

    Borckardt, Jeffrey J; Nash, Michael R; Hardesty, Susan; Herbert, Joan; Cooney, Harriet; Pelic, Christopher

    2006-01-01

    Statistical process control (SPC) charts have become widely implemented tools for quality monitoring and assurance in healthcare settings across the United States. SPC methods have been successfully used in industrial settings to track the quality of products manufactured by machines and to detect deviations from acceptable Levels of product quality. However, problems may arise when SPC methods are used to evaluate human behavior. Specifically, when human behavior is tracked over time, the data stream generated usually exhibits periodicity and gradualism with respect to behavioral changes over time. These tendencies can be quantified and are recognized in the statistical field as autocorrelation. When autocorrelation is present, conventional SPC methods too often identify events as "unusuaL" when they really should be understood as products of random fluctuation. This article discusses the concept of autocorrelation and demonstrates the negative impact of autocorrelation on traditional SPC methods, with a specific focus on the use of SPC charts to detect unusual events. PMID:16944647

  17. Automatic Detection and Classification of Unsafe Events During Power Wheelchair Use

    PubMed Central

    Moghaddam, Athena K.; Yuen, Hiu Kim; Archambault, Philippe S.; Routhier, François; Michaud, François; Boissy, Patrick

    2014-01-01

    Using a powered wheelchair (PW) is a complex task requiring advanced perceptual and motor control skills. Unfortunately, PW incidents and accidents are not uncommon and their consequences can be serious. The objective of this paper is to develop technological tools that can be used to characterize a wheelchair user’s driving behavior under various settings. In the experiments conducted, PWs are outfitted with a datalogging platform that records, in real-time, the 3-D acceleration of the PW. Data collection was conducted over 35 different activities, designed to capture a spectrum of PW driving events performed at different speeds (collisions with fixed or moving objects, rolling on incline plane, and rolling across multiple types obstacles). The data was processed using time-series analysis and data mining techniques, to automatically detect and identify the different events. We compared the classification accuracy using four different types of time-series features: 1) time-delay embeddings; 2) time-domain characterization; 3) frequency-domain features; and 4) wavelet transforms. In the analysis, we compared the classification accuracy obtained when distinguishing between safe and unsafe events during each of the 35 different activities. For the purposes of this study, unsafe events were defined as activities containing collisions against objects at different speed, and the remainder were defined as safe events. We were able to accurately detect 98% of unsafe events, with a low (12%) false positive rate, using only five examples of each activity. This proof-of-concept study shows that the proposed approach has the potential of capturing, based on limited input from embedded sensors, contextual information on PW use, and of automatically characterizing a user’s PW driving behavior. PMID:27170879

  18. Automatic Detection and Classification of Unsafe Events During Power Wheelchair Use.

    PubMed

    Pineau, Joelle; Moghaddam, Athena K; Yuen, Hiu Kim; Archambault, Philippe S; Routhier, François; Michaud, François; Boissy, Patrick

    2014-01-01

    Using a powered wheelchair (PW) is a complex task requiring advanced perceptual and motor control skills. Unfortunately, PW incidents and accidents are not uncommon and their consequences can be serious. The objective of this paper is to develop technological tools that can be used to characterize a wheelchair user's driving behavior under various settings. In the experiments conducted, PWs are outfitted with a datalogging platform that records, in real-time, the 3-D acceleration of the PW. Data collection was conducted over 35 different activities, designed to capture a spectrum of PW driving events performed at different speeds (collisions with fixed or moving objects, rolling on incline plane, and rolling across multiple types obstacles). The data was processed using time-series analysis and data mining techniques, to automatically detect and identify the different events. We compared the classification accuracy using four different types of time-series features: 1) time-delay embeddings; 2) time-domain characterization; 3) frequency-domain features; and 4) wavelet transforms. In the analysis, we compared the classification accuracy obtained when distinguishing between safe and unsafe events during each of the 35 different activities. For the purposes of this study, unsafe events were defined as activities containing collisions against objects at different speed, and the remainder were defined as safe events. We were able to accurately detect 98% of unsafe events, with a low (12%) false positive rate, using only five examples of each activity. This proof-of-concept study shows that the proposed approach has the potential of capturing, based on limited input from embedded sensors, contextual information on PW use, and of automatically characterizing a user's PW driving behavior. PMID:27170879

  19. EUROarray human papillomavirus (HPV) assay is highly concordant with other commercial assays for detection of high-risk HPV genotypes in women with high grade cervical abnormalities.

    PubMed

    Cornall, A M; Poljak, M; Garland, S M; Phillips, S; Machalek, D A; Tan, J H; Quinn, M A; Tabrizi, S N

    2016-06-01

    The purpose of this study was to evaluate the performance of the EUROIMMUN EUROArray HPV genotyping assay against the Roche Cobas 4800, Roche HPV Amplicor, Roche Linear Array and Qiagen Hybrid Capture 2 assays in the detection of high-risk HPV (HR-HPV) from liquid based cervical cytology samples collected from women undergoing follow-up for abnormal cervical cytology results. Cervical specimens from 404 women undergoing management of high-grade cytological abnormality were evaluated by EUROarray HPV for detection of HR-HPV genotypes and prediction of histologically-confirmed cervical intraepithelial neoplasia grade 2 or higher (≥CIN2). The results were compared to Hybrid Capture 2, Cobas 4800 HPV, Amplicor and Linear Array HPV. Positivity for 14 HR-HPV types was 80.0 % for EUROarray (95 % CI; 75.7-83.8 %). Agreement (κ, 95 % CI) between the EUROarray and other HPV tests for detection of HR-HPV was good to very good [Hybrid Capture κ = 0.62 (0.54-0.71); Cobas κ = 0.81 (0.74-0.88); Amplicor κ = 0.68 (0.60-0.77); Linear Array κ = 0.77 (0.70-0.85)]. For detection of HR-HPV, agreement with EUROarray was 87.90 % (Hybrid Capture), 93.58 % (Cobas), 92.84 % (Amplicor) and 92.59 % (Linear Array). Detection of HR-HPV was not significantly different between EUROarray and any other test (p < 0.001). EUROarray was concordant with other assays evaluated for detection of high-risk HPV and showed sensitivity and specificity for detection of ≥ CIN2 of 86 % and 71 %, respectively. PMID:27048314

  20. A multivariate based event detection method and performance comparison with two baseline methods.

    PubMed

    Liu, Shuming; Smith, Kate; Che, Han

    2015-09-01

    Early warning systems have been widely deployed to protect water systems from accidental and intentional contamination events. Conventional detection algorithms are often criticized for having high false positive rates and low true positive rates. This mainly stems from the inability of these methods to determine whether variation in sensor measurements is caused by equipment noise or the presence of contamination. This paper presents a new detection method that identifies the existence of contamination by comparing Euclidean distances of correlation indicators, which are derived from the correlation coefficients of multiple water quality sensors. The performance of the proposed method was evaluated using data from a contaminant injection experiment and compared with two baseline detection methods. The results show that the proposed method can differentiate between fluctuations caused by equipment noise and those due to the presence of contamination. It yielded higher possibility of detection and a lower false alarm rate than the two baseline methods. With optimized parameter values, the proposed method can correctly detect 95% of all contamination events with a 2% false alarm rate. PMID:25996758

  1. Temporal and Spatial Predictability of an Irrelevant Event Differently Affect Detection and Memory of Items in a Visual Sequence

    PubMed Central

    Ohyama, Junji; Watanabe, Katsumi

    2016-01-01

    We examined how the temporal and spatial predictability of a task-irrelevant visual event affects the detection and memory of a visual item embedded in a continuously changing sequence. Participants observed 11 sequentially presented letters, during which a task-irrelevant visual event was either present or absent. Predictabilities of spatial location and temporal position of the event were controlled in 2 × 2 conditions. In the spatially predictable conditions, the event occurred at the same location within the stimulus sequence or at another location, while, in the spatially unpredictable conditions, it occurred at random locations. In the temporally predictable conditions, the event timing was fixed relative to the order of the letters, while in the temporally unpredictable condition; it could not be predicted from the letter order. Participants performed a working memory task and a target detection reaction time (RT) task. Memory accuracy was higher for a letter simultaneously presented at the same location as the event in the temporally unpredictable conditions, irrespective of the spatial predictability of the event. On the other hand, the detection RTs were only faster for a letter simultaneously presented at the same location as the event when the event was both temporally and spatially predictable. Thus, to facilitate ongoing detection processes, an event must be predictable both in space and time, while memory processes are enhanced by temporally unpredictable (i.e., surprising) events. Evidently, temporal predictability has differential effects on detection and memory of a visual item embedded in a sequence of images. PMID:26869966

  2. Temporal and Spatial Predictability of an Irrelevant Event Differently Affect Detection and Memory of Items in a Visual Sequence.

    PubMed

    Ohyama, Junji; Watanabe, Katsumi

    2016-01-01

    We examined how the temporal and spatial predictability of a task-irrelevant visual event affects the detection and memory of a visual item embedded in a continuously changing sequence. Participants observed 11 sequentially presented letters, during which a task-irrelevant visual event was either present or absent. Predictabilities of spatial location and temporal position of the event were controlled in 2 × 2 conditions. In the spatially predictable conditions, the event occurred at the same location within the stimulus sequence or at another location, while, in the spatially unpredictable conditions, it occurred at random locations. In the temporally predictable conditions, the event timing was fixed relative to the order of the letters, while in the temporally unpredictable condition; it could not be predicted from the letter order. Participants performed a working memory task and a target detection reaction time (RT) task. Memory accuracy was higher for a letter simultaneously presented at the same location as the event in the temporally unpredictable conditions, irrespective of the spatial predictability of the event. On the other hand, the detection RTs were only faster for a letter simultaneously presented at the same location as the event when the event was both temporally and spatially predictable. Thus, to facilitate ongoing detection processes, an event must be predictable both in space and time, while memory processes are enhanced by temporally unpredictable (i.e., surprising) events. Evidently, temporal predictability has differential effects on detection and memory of a visual item embedded in a sequence of images. PMID:26869966

  3. Testing the waveform correlation event detection system: Teleseismic, regional, and local distances

    SciTech Connect

    Young, C.J.; Beiriger, J.I.; Harris, J.M.

    1997-08-01

    Waveform Correlation Event Detection System (WCEDS) prototypes have now been developed for both global and regional networks and the authors have extensively tested them to assess the potential usefulness of this technology for CTBT (Comprehensive Test Ban Treaty) monitoring. In this paper they present the results of tests on data sets from the IDC (International Data Center) Primary Network and the New Mexico Tech Seismic Network. The data sets span a variety of event types and noise conditions. The results are encouraging at both scales but show particular promise for regional networks. The global system was developed at Sandia Labs and has been tested on data from the IDC Primary Network. The authors have found that for this network the system does not perform at acceptable levels for either detection or location unless directional information (azimuth and slowness) is used. By incorporating directional information, however, both areas can be improved substantially suggesting that WCEDS may be able to offer a global detection capability which could complement that provided by the GA (Global Association) system in use at the IDC and USNDC (United States National Data Center). The local version of WCEDS (LWCEDS) has been developed and tested at New Mexico Tech using data from the New Mexico Tech Seismic Network (NMTSN). Results indicate that the WCEDS technology works well at this scale, despite the fact that the present implementation of LWCEDS does not use directional information. The NMTSN data set is a good test bed for the development of LWCEDS because of a typically large number of observed local phases and near network-wide recording of most local and regional events. Detection levels approach those of trained analysts, and locations are within 3 km of manually determined locations for local events.

  4. Myelodysplastic syndromes: pathogenesis, functional abnormalities, and clinical implications.

    PubMed Central

    Jacobs, A

    1985-01-01

    The myelodysplastic syndromes represent a preleukaemic state in which a clonal abnormality of haemopoietic stem cell is characterised by a variety of phenotypic manifestations with varying degrees of ineffective haemopoiesis. This state probably develops as a sequence of events in which the earliest stages may be difficult to detect by conventional pathological techniques. The process is characterised by genetic changes leading to abnormal control of cell proliferation and differentiation. Expansion of an abnormal clone may be related to independence from normal growth factors, insensitivity to normal inhibitory factors, suppression of normal clonal growth, or changes in the immunological or nutritional condition of the host. The haematological picture is of peripheral blood cytopenias: a cellular bone marrow, and functional abnormalities of erythroid, myeloid, and megakaryocytic cells. In most cases marrow cells have an abnormal DNA content, often with disturbances of the cell cycle: an abnormal karyotype is common in premalignant clones. Growth abnormalities of erythroid or granulocyte-macrophage progenitors are common in marrow cultures, and lineage specific surface membrane markers indicate aberrations of differentiation. Progression of the disorder may occur through clonal expansion or through clonal evolution with a greater degree of malignancy. Current attempts to influence abnormal growth and differentiation have had only limited success. Clinical recognition of the syndrome depends on an acute awareness of the signs combined with the identification of clonal and functional abnormalities. PMID:2999194

  5. Event Detection for Hydrothermal Plumes: A case study at Grotto Vent

    NASA Astrophysics Data System (ADS)

    Bemis, K. G.; Ozer, S.; Xu, G.; Rona, P. A.; Silver, D.

    2012-12-01

    Evidence is mounting that geologic events such as volcanic eruptions (and intrusions) and earthquakes (near and far) influence the flow rates and temperatures of hydrothermal systems. Connecting such suppositions to observations of hydrothermal output is challenging, but new ongoing time series have the potential to capture such events. This study explores using activity detection, a technique modified from computer vision, to identify pre-defined events within an extended time series recorded by COVIS (Cabled Observatory Vent Imaging Sonar) and applies it to a time series, with gaps, from Sept 2010 to the present; available measurements include plume orientation, plume rise rate, and diffuse flow area at the NEPTUNE Canada Observatory at Grotto Vent, Main Endeavour Field, Juan de Fuca Ridge. Activity detection is the process of finding a pattern (activity) in a data set containing many different types of patterns. Among many approaches proposed to model and detect activities, we have chosen a graph-based technique, Petri Nets, as they do not require training data to model the activity. They use the domain expert's knowledge to build the activity as a combination of feature states and their transitions (actions). Starting from a conceptual model of how hydrothermal plumes respond to daily tides, we have developed a Petri Net based detection algorithm that identifies deviations from the specified response. Initially we assumed that the orientation of the plume would change smoothly and symmetrically in a consistent daily pattern. However, results indicate that the rate of directional changes varies. The present Petri Net detects unusually large and rapid changes in direction or amount of bending; however inspection of Figure 1 suggests that many of the events detected may be artifacts resulting from gaps in the data or from the large temporal spacing. Still, considerable complexity overlies the "normal" tidal response pattern (the data has a dominant frequency of

  6. Real-time gait event detection for transfemoral amputees during ramp ascending and descending.

    PubMed

    Maqbool, H F; Husman, M A B; Awad, M I; Abouhossein, A; Dehghani-Sanij, A A

    2015-01-01

    Events and phases detection of the human gait are vital for controlling prosthesis, orthosis and functional electrical stimulation (FES) systems. Wearable sensors are inexpensive, portable and have fast processing capability. They are frequently used to assess spatio-temporal, kinematic and kinetic parameters of the human gait which in turn provide more details about the human voluntary control and ampute-eprosthesis interaction. This paper presents a reliable real-time gait event detection algorithm based on simple heuristics approach, applicable to signals from tri-axial gyroscope for lower limb amputees during ramp ascending and descending. Experimental validation is done by comparing the results of gyroscope signal with footswitches. For healthy subjects, the mean difference between events detected by gyroscope and footswitches is 14 ms and 10.5 ms for initial contact (IC) whereas for toe off (TO) it is -5 ms and -25 ms for ramp up and down respectively. For transfemoral amputee, the error is slightly higher either due to the placement of footswitches underneath the foot or the lack of proper knee flexion and ankle plantarflexion/dorsiflexion during ramp up and down. Finally, repeatability tests showed promising results. PMID:26737364

  7. Method for the depth corrected detection of ionizing events from a co-planar grids sensor

    DOEpatents

    De Geronimo, Gianluigi; Bolotnikov, Aleksey E.; Carini, Gabriella

    2009-05-12

    A method for the detection of ionizing events utilizing a co-planar grids sensor comprising a semiconductor substrate, cathode electrode, collecting grid and non-collecting grid. The semiconductor substrate is sensitive to ionizing radiation. A voltage less than 0 Volts is applied to the cathode electrode. A voltage greater than the voltage applied to the cathode is applied to the non-collecting grid. A voltage greater than the voltage applied to the non-collecting grid is applied to the collecting grid. The collecting grid and the non-collecting grid are summed and subtracted creating a sum and difference respectively. The difference and sum are divided creating a ratio. A gain coefficient factor for each depth (distance between the ionizing event and the collecting grid) is determined, whereby the difference between the collecting electrode and the non-collecting electrode multiplied by the corresponding gain coefficient is the depth corrected energy of an ionizing event. Therefore, the energy of each ionizing event is the difference between the collecting grid and the non-collecting grid multiplied by the corresponding gain coefficient. The depth of the ionizing event can also be determined from the ratio.

  8. Advanced Geospatial Hydrodynamic Signals Analysis for Tsunami Event Detection and Warning

    NASA Astrophysics Data System (ADS)

    Arbab-Zavar, Banafshe; Sabeur, Zoheir

    2013-04-01

    Current early tsunami warning can be issued upon the detection of a seismic event which may occur at a given location offshore. This also provides an opportunity to predict the tsunami wave propagation and run-ups at potentially affected coastal zones by selecting the best matching seismic event from a database of pre-computed tsunami scenarios. Nevertheless, it remains difficult and challenging to obtain the rupture parameters of the tsunamigenic earthquakes in real time and simulate the tsunami propagation with high accuracy. In this study, we propose a supporting approach, in which the hydrodynamic signal is systematically analysed for traces of a tsunamigenic signal. The combination of relatively low amplitudes of a tsunami signal at deep waters and the frequent occurrence of background signals and noise contributes to a generally low signal to noise ratio for the tsunami signal; which in turn makes the detection of this signal difficult. In order to improve the accuracy and confidence of detection, a re-identification framework in which a tsunamigenic signal is detected via the scan of a network of hydrodynamic stations with water level sensing is performed. The aim is to attempt the re-identification of the same signatures as the tsunami wave spatially propagates through the hydrodynamic stations sensing network. The re-identification of the tsunamigenic signal is technically possible since the tsunami signal at the open ocean itself conserves its birthmarks relating it to the source event. As well as supporting the initial detection and improving the confidence of detection, a re-identified signal is indicative of the spatial range of the signal, and thereby it can be used to facilitate the identification of certain background signals such as wind waves which do not have as large a spatial reach as tsunamis. In this paper, the proposed methodology for the automatic detection of tsunamigenic signals has been achieved using open data from NOAA with a recorded

  9. BioSense: implementation of a National Early Event Detection and Situational Awareness System.

    PubMed

    Bradley, Colleen A; Rolka, H; Walker, D; Loonsk, J

    2005-08-26

    BioSense is a CDC initiative to support enhanced early detection, quantification, and localization of possible biologic terrorism attacks and other events of public health concern on a national level. The goals of the BioSense initiative are to advance early detection by providing the standards, infrastructure, and data acquisition for near real-time reporting, analytic evaluation and implementation, and early event detection support for state and local public health officials. BioSense collects and analyzes Department of Defense and Department of Veterans Affairs ambulatory clinical diagnoses and procedures and Laboratory Corporation of America laboratory-test orders. The application summarizes and presents analytical results and data visualizations by source, day, and syndrome for each ZIP code, state, and metropolitan area through maps, graphs, and tables. An initial proof of a concept evaluation project was conducted before the system was made available to state and local users in April 2004. User recruitment involved identifying and training BioSense administrators and users from state and local health departments. User support has been an essential component of the implementation and enhancement process. CDC initiated the BioIntelligence Center (BIC) in June 2004 to conduct internal monitoring of BioSense national data daily. BIC staff have supported state and local system monitoring, conducted data anomaly inquiries, and communicated with state and local public health officials. Substantial investments will be made in providing regional, state, and local data for early event detection and situational awareness, test beds for data and algorithm evaluation, detection algorithm development, and data management technologies, while maintaining the focus on state and local public health needs. PMID:16177687

  10. Group localisation and unsupervised detection and classification of basic crowd behaviour events for surveillance applications

    NASA Astrophysics Data System (ADS)

    Roubtsova, Nadejda S.; de With, Peter H. N.

    2013-02-01

    Technology for monitoring crowd behaviour is in demand for surveillance and security applications. The trend in research is to tackle detection of complex crowd behaviour events (panic, ght, evacuation etc.) directly using machine learning techniques. In this paper, we present a contrary, bottom-up approach seeking basic group information: (1) instantaneous location and (2) the merge, split and lateral slide-by events - the three basic motion patterns comprising any crowd behaviour. The focus on such generic group information makes our algorithm suitable as a building block in a variety of surveillance systems, possibly integrated with static content analysis solutions. Our feature extraction framework has optical ow in its core. The framework is universal being motion-based, rather than object-detection-based and generates a large variety of motion-blob- characterising features useful for an array of classi cation problems. Motion-based characterisation is performed on a group as an atomic whole and not by means of superposition of individual human motions. Within that feature space, our classi cation system makes decisions based on heuristic rules and thresholds, without machine learning. Our system performs well on group localisation, consistently generating contours around both moving and halted groups. The visual output of our periodical group localisation is equivalent to tracking and the group contour accuracy ranges from adequate to exceptionally good. The system successfully detects and classi es within our merge/split/slide-by event space in surveillance-type video sequences, di ering in resolution, scale, quality and motion content. Quantitatively, its performance is characterised by a good recall: 83% on detection and 71% on combined detection and classi cation.

  11. Final Scientific Report, Integrated Seismic Event Detection and Location by Advanced Array Processing

    SciTech Connect

    Kvaerna, T.; Gibbons. S.J.; Ringdal, F; Harris, D.B.

    2007-01-30

    In the field of nuclear explosion monitoring, it has become a priority to detect, locate, and identify seismic events down to increasingly small magnitudes. The consideration of smaller seismic events has implications for a reliable monitoring regime. Firstly, the number of events to be considered increases greatly; an exponential increase in naturally occurring seismicity is compounded by large numbers of seismic signals generated by human activity. Secondly, the signals from smaller events become more difficult to detect above the background noise and estimates of parameters required for locating the events may be subject to greater errors. Thirdly, events are likely to be observed by a far smaller number of seismic stations, and the reliability of event detection and location using a very limited set of observations needs to be quantified. For many key seismic stations, detection lists may be dominated by signals from routine industrial explosions which should be ascribed, automatically and with a high level of confidence, to known sources. This means that expensive analyst time is not spent locating routine events from repeating seismic sources and that events from unknown sources, which could be of concern in an explosion monitoring context, are more easily identified and can be examined with due care. We have obtained extensive lists of confirmed seismic events from mining and other artificial sources which have provided an excellent opportunity to assess the quality of existing fully-automatic event bulletins and to guide the development of new techniques for online seismic processing. Comparing the times and locations of confirmed events from sources in Fennoscandia and NW Russia with the corresponding time and location estimates reported in existing automatic bulletins has revealed substantial mislocation errors which preclude a confident association of detected signals with known industrial sources. The causes of the errors are well understood and are

  12. Application of data cubes for improving detection of water cycle extreme events

    NASA Astrophysics Data System (ADS)

    Teng, W. L.; Albayrak, A.

    2015-12-01

    As part of an ongoing NASA-funded project to remove a longstanding barrier to accessing NASA data (i.e., accessing archived time-step array data as point-time series), for the hydrology and other point-time series-oriented communities, "data cubes" are created from which time series files (aka "data rods") are generated on-the-fly and made available as Web services from the Goddard Earth Sciences Data and Information Services Center (GES DISC). Data cubes are data as archived rearranged into spatio-temporal matrices, which allow for easy access to the data, both spatially and temporally. A data cube is a specific case of the general optimal strategy of reorganizing data to match the desired means of access. The gain from such reorganization is greater the larger the data set. As a use case for our project, we are leveraging existing software to explore the application of the data cubes concept to machine learning, for the purpose of detecting water cycle extreme (WCE) events, a specific case of anomaly detection, requiring time series data. We investigate the use of the sequential probability ratio test (SPRT) for anomaly detection and support vector machines (SVM) for anomaly classification. We show an example of detection of WCE events, using the Global Land Data Assimilation Systems (GLDAS) data set.

  13. Non Conventional Seismic Events Along the Himalayan Arc Detected in the Hi-Climb Dataset

    NASA Astrophysics Data System (ADS)

    Vergne, J.; Nàbĕlek, J. L.; Rivera, L.; Bollinger, L.; Burtin, A.

    2008-12-01

    From September 2002 to August 2005, more than 200 broadband seismic stations were operated across the Himalayan arc and the southern Tibetan plateau in the framework of the Hi-Climb project. Here, we take advantage of the high density of stations along the main profile to look for coherent seismic wave arrivals that can not be attributed to ordinary tectonic events. An automatic detection algorithm is applied to the continuous data streams filtered between 1 and 10 Hz, followed by a visual inspection of all detections. We discovered about one hundred coherent signals that cannot be attributed to local, regional or teleseismic earthquakes and which are characterized by emergent arrivals and long durations ranging from one minute to several hours. Most of these non conventional seismic events have a low signal to noise ratio and are thus only observed above 1 Hz in the frequency band where the seismic noise is the lowest. However, a small subset of them are strong enough to be observed in a larger frequency band and show an enhancement of long periods compared to standard earthquakes. Based on the analysis of the relative amplitude measured at each station or, when possible, on the correlation of the low frequency part of the signals, most of these events appear to be located along the High Himalayan range. But, because of their emergent character and the main orientation of the seismic profile, their longitude and depth remain poorly constrained. The origin of these non conventional seismic events is still unsealed but their seismic signature shares several characteristics with non volcanic tremors, glacial earthquakes and/or debris avalanches. All these phenomena may occur along the Himalayan range but were not seismically detected before. Here we discuss the pros and cons for each of these postulated candidates based on the analysis of the recorded waveforms and slip models.

  14. Global Detection of Protein Kinase D-dependent Phosphorylation Events in Nocodazole-treated Human Cells*

    PubMed Central

    Franz-Wachtel, Mirita; Eisler, Stephan A.; Krug, Karsten; Wahl, Silke; Carpy, Alejandro; Nordheim, Alfred; Pfizenmaier, Klaus; Hausser, Angelika; Macek, Boris

    2012-01-01

    Protein kinase D (PKD) is a cytosolic serine/threonine kinase implicated in regulation of several cellular processes such as response to oxidative stress, directed cell migration, invasion, differentiation, and fission of the vesicles at the trans-Golgi network. Its variety of functions must be mediated by numerous substrates; however, only a couple of PKD substrates have been identified so far. Here we perform stable isotope labeling of amino acids in cell culture-based quantitative phosphoproteomic analysis to detect phosphorylation events dependent on PKD1 activity in human cells. We compare relative phosphorylation levels between constitutively active and kinase dead PKD1 strains of HEK293 cells, both treated with nocodazole, a microtubule-depolymerizing reagent that disrupts the Golgi complex and activates PKD1. We identify 124 phosphorylation sites that are significantly down-regulated upon decrease of PKD1 activity and show that the PKD target motif is significantly enriched among down-regulated phosphorylation events, pointing to the presence of direct PKD1 substrates. We further perform PKD1 target motif analysis, showing that a proline residue at position +1 relative to the phosphorylation site serves as an inhibitory cue for PKD1 activity. Among PKD1-dependent phosphorylation events, we detect predominantly proteins with localization at Golgi membranes and function in protein sorting, among them several sorting nexins and members of the insulin-like growth factor 2 receptor pathway. This study presents the first global detection of PKD1-dependent phosphorylation events and provides a wealth of information for functional follow-up of PKD1 activity upon disruption of the Golgi network in human cells. PMID:22496350

  15. Event Detection Using Mobile Phone Mass GPS Data and Their Reliavility Verification by Dmsp/ols Night Light Image

    NASA Astrophysics Data System (ADS)

    Yuki, Akiyama; Satoshi, Ueyama; Ryosuke, Shibasaki; Adachi, Ryuichiro

    2016-06-01

    In this study, we developed a method to detect sudden population concentration on a certain day and area, that is, an "Event," all over Japan in 2012 using mass GPS data provided from mobile phone users. First, stay locations of all phone users were detected using existing methods. Second, areas and days where Events occurred were detected by aggregation of mass stay locations into 1-km-square grid polygons. Finally, the proposed method could detect Events with an especially large number of visitors in the year by removing the influences of Events that occurred continuously throughout the year. In addition, we demonstrated reasonable reliability of the proposed Event detection method by comparing the results of Event detection with light intensities obtained from the night light images from the DMSP/OLS night light images. Our method can detect not only positive events such as festivals but also negative events such as natural disasters and road accidents. These results are expected to support policy development of urban planning, disaster prevention, and transportation management.

  16. Fault detection and isolation in manufacturing systems with an identified discrete event model

    NASA Astrophysics Data System (ADS)

    Roth, Matthias; Schneider, Stefan; Lesage, Jean-Jacques; Litz, Lothar

    2012-10-01

    In this article a generic method for fault detection and isolation (FDI) in manufacturing systems considered as discrete event systems (DES) is presented. The method uses an identified model of the closed-loop of plant and controller built on the basis of observed fault-free system behaviour. An identification algorithm known from literature is used to determine the fault detection model in form of a non-deterministic automaton. New results of how to parameterise this algorithm are reported. To assess the fault detection capability of an identified automaton, probabilistic measures are proposed. For fault isolation, the concept of residuals adapted for DES is used by defining appropriate set operations representing generic fault symptoms. The method is applied to a case study system.

  17. An evaluation of generalized likelihood Ratio Outlier Detection to identification of seismic events in Western China

    SciTech Connect

    Taylor, S.R.; Hartse, H.E.

    1996-09-24

    The Generalized Likelihood Ratio Outlier Detection Technique for seismic event identification is evaluated using synthetic test data and frequency-dependent P{sub g}/L{sub g} measurements from western China. For most seismic stations that are to be part of the proposed International Monitoring System for the Comprehensive Test Ban Treaty, there will be few or no nuclear explosions in the magnitude range of interest (e.g. M{sub b} < 4) on which to base an event-identification system using traditional classification techniques. Outlier detection is a reasonable alternative approach to the seismic discrimination problem when no calibration explosions are available. Distance-corrected P{sub g}/L{sub g} data in seven different frequency bands ranging from 0.5 to 8 Hz from the Chinese Digital Seismic Station WMQ are used to evaluate the technique. The data are collected from 157 known earthquakes, 215 unknown events (presumed earthquakes and possibly some industrial explosions), and 18 known nuclear explosions (1 from the Chinese Lop Nor test site and 17 from the East Kazakh test site). A feature selection technique is used to find the best combination of discriminants to use for outlier detection. Good discrimination performance is found by combining a low-frequency (0.5 to 1 Hz) P{sub g}/L{sub g} ratio with high-frequency ratios (e.g. 2 to 4 and 4 to 8 Hz). Although the low-frequency ratio does not discriminate between earthquakes and nuclear explosions well by itself, it can be effectively combined with the high-frequency discriminants. Based on the tests with real and synthetic data, the outlier detection technique appears to be an effective approach to seismic monitoring in uncalibrated regions.

  18. Event detection of hydrological processes with passive L-band data from SMOS

    NASA Astrophysics Data System (ADS)

    Al Bitar, Ahmad; Jacquette, Elsa; Kerr, Yann; Mialon, Arnaud; Cabot, Francois; Quesney, Arnaud; Merlin, Olivier; Richaume, Philippe

    2010-10-01

    Since it's launch, the ESA's Soil Moisture and Ocean Salinity (SMOS) satellite, is delivering new data from its LBand 1.4Ghz 2D interferometer [1]. The observations from SMOS are used to retrieve soil moisture in the first centimeters and ocean salinity at the surface of the water. The observations are multi-angular with a 3 days maximum revisit time. The spatial resolution of SMOS data is 40km. In this paper we present on event detection algorithm implemented at CATDS (Centre Aval de Traitement des Données SMOS) the CNES level 3 and level 4 SMOS enter. This algorithm is a three stage change detection algorithm. At stage one the possibility/probability of occurrence of the event is evaluated. This is done via spatiotemporal constraints maps. These maps are obtained from the analysis of NSIDC's freezing index products over the last century. Climate data from ancillary files are tested will taking into consideration the uncertainty of the data. Some selected retrieved variables are also tested. At stage two a time series analysis is applied. In the current version of the algorithm a direct change detection algorithm is used. The tests make use of available variables of polarization index, retrieved soil moisture...Finally at stage three a simple fuzzy logic approach is used to decide if the event occurred. This approaches takes into consideration the separation time of the data. Ascending and descending orbits are taken into consideration. In this study freezing detection is presented over central CONUS. The temporal and angular signature of SMOS will be presented. Comparison is done with the SCAN network

  19. Predictive modeling of structured electronic health records for adverse drug event detection

    PubMed Central

    2015-01-01

    Background The digitization of healthcare data, resulting from the increasingly widespread adoption of electronic health records, has greatly facilitated its analysis by computational methods and thereby enabled large-scale secondary use thereof. This can be exploited to support public health activities such as pharmacovigilance, wherein the safety of drugs is monitored to inform regulatory decisions about sustained use. To that end, electronic health records have emerged as a potentially valuable data source, providing access to longitudinal observations of patient treatment and drug use. A nascent line of research concerns predictive modeling of healthcare data for the automatic detection of adverse drug events, which presents its own set of challenges: it is not yet clear how to represent the heterogeneous data types in a manner conducive to learning high-performing machine learning models. Methods Datasets from an electronic health record database are used for learning predictive models with the purpose of detecting adverse drug events. The use and representation of two data types, as well as their combination, are studied: clinical codes, describing prescribed drugs and assigned diagnoses, and measurements. Feature selection is conducted on the various types of data to reduce dimensionality and sparsity, while allowing for an in-depth feature analysis of the usefulness of each data type and representation. Results Within each data type, combining multiple representations yields better predictive performance compared to using any single representation. The use of clinical codes for adverse drug event detection significantly outperforms the use of measurements; however, there is no significant difference over datasets between using only clinical codes and their combination with measurements. For certain adverse drug events, the combination does, however, outperform using only clinical codes. Feature selection leads to increased predictive performance for both

  20. Exupery volcano fast response system - The event detection and waveform classification system

    NASA Astrophysics Data System (ADS)

    Hammer, Conny; Ohrnberger, Matthias

    2010-05-01

    Volcanic eruptions are often preceded by seismic activity which can be used to quantify the volcanic activity since the number and the size of certain types of seismic events usually increase before periods of volcanic crisis. The implementation of an automatic detection and classification system for seismic signals of volcanic origin allows not only for the processing of large amounts of data in short time, but also provides consistent and time-invariant results. Here, we have developed a system based upon a combination of different methods. To enable a first robust event detection in the continuous data stream different modules are implemented in the real time system Earthworm which is widely distributed in active volcano monitoring observatories worldwide. Among those software modules are classical trigger algorithm like STA/LTA and cross-correlation master event matching which is also used to detect different classes of signals. Furthermore an additional module is implemented in the real time system to compute continuous activity parameters which are also used to quantify the volcanic activity. Most automatic classification systems need a sufficiently large pre-classified data set for training the system. However in case of a volcanic crisis we are often confronted with a lack of training data due to insufficient prior observations because prior data acquisition might be carried out with different equipment at a low number of sites and due to the imminent crisis there might be no time for the time-consuming and tedious process of preparing a training data set. For this reason we have developed a novel seismic event spotting technique in order to be less dependent on the existence of previously acquired data bases of event classes. One main goal is therefore to provide observatory staff with a robust event classification based on a minimum number of reference waveforms. By using a "learning-while-recording" approach we are allowing for the fast build-up of a

  1. Detection and identification of multiple genetically modified events using DNA insert fingerprinting.

    PubMed

    Raymond, Philippe; Gendron, Louis; Khalf, Moustafa; Paul, Sylvianne; Dibley, Kim L; Bhat, Somanath; Xie, Vicki R D; Partis, Lina; Moreau, Marie-Eve; Dollard, Cheryl; Coté, Marie-José; Laberge, Serge; Emslie, Kerry R

    2010-03-01

    Current screening and event-specific polymerase chain reaction (PCR) assays for the detection and identification of genetically modified organisms (GMOs) in samples of unknown composition or for the detection of non-regulated GMOs have limitations, and alternative approaches are required. A transgenic DNA fingerprinting methodology using restriction enzyme digestion, adaptor ligation, and nested PCR was developed where individual GMOs are distinguished by the characteristic fingerprint pattern of the fragments generated. The inter-laboratory reproducibility of the amplified fragment sizes using different capillary electrophoresis platforms was compared, and reproducible patterns were obtained with an average difference in fragment size of 2.4 bp. DNA insert fingerprints for 12 different maize events, including two maize hybrids and one soy event, were generated that reflected the composition of the transgenic DNA constructs. Once produced, the fingerprint profiles were added to a database which can be readily exchanged and shared between laboratories. This approach should facilitate the process of GMO identification and characterization. PMID:19943159

  2. Piezoelectric energy-harvesting power source and event detection sensors for gun-fired munitions

    NASA Astrophysics Data System (ADS)

    Rastegar, Jahangir; Feng, Dake; Pereira, Carlos M.

    2015-05-01

    This paper presents a review of piezoelectric based energy harvesting devices and their charge collection electronics for use in very harsh environment of gun-fired munitions. A number of novel classes of such energy harvesting power sources have been developed for gun-fired munitions and similar applications, including those with integrated safety and firing setback event detection electronics and logic circuitry. The power sources are designed to harvest energy from firing acceleration and vibratory motions during the flight. As an example, the application of the developed piezoelectric based energy harvesting devices with event detection circuitry for the development of self-powered initiators with full no-fire safety circuitry for protection against accidental drops, transportation vibration, and other similar low amplitude accelerations and/or high amplitude but short duration acceleration events is presented. The design allows the use of a very small piezoelectric element, thereby allowing such devices to be highly miniaturized. These devices can be readily hardened to withstand very high G firing setback accelerations in excess of 100,000 G and the harsh firing environment. The design of prototypes and testing under realistic conditions are presented.

  3. Chromosomal Abnormalities and Schizophrenia

    PubMed Central

    BASSETT, ANNE S.; CHOW, EVA W.C.; WEKSBERG, ROSANNA

    2011-01-01

    Schizophrenia is a common and serious psychiatric illness with strong evidence for genetic causation, but no specific loci yet identified. Chromosomal abnormalities associated with schizophrenia may help to understand the genetic complexity of the illness. This paper reviews the evidence for associations between chromosomal abnormalities and schizophrenia and related disorders. The results indicate that 22q11.2 microdeletions detected by fluorescence in-situ hybridization (FISH) are significantly associated with schizophrenia. Sex chromosome abnormalities seem to be increased in schizophrenia but insufficient data are available to indicate whether schizophrenia or related disorders are increased in patients with sex chromosome aneuploidies. Other reports of chromosomal abnormalities associated with schizophrenia have the potential to be important adjuncts to linkage studies in gene localization. Advances in molecular cytogenetic techniques (i.e., FISH) have produced significant increases in rates of identified abnormalities in schizophrenia, particularly in patients with very early age at onset, learning difficulties or mental retardation, or dysmorphic features. The results emphasize the importance of considering behavioral phenotypes, including adult onset psychiatric illnesses, in genetic syndromes and the need for clinicians to actively consider identifying chromosomal abnormalities and genetic syndromes in selected psychiatric patients. PMID:10813803

  4. Detection of Events in Biomedical Signals by a Rényi Entropy Measure

    NASA Astrophysics Data System (ADS)

    Gabarda, S.; Cristóbal, G.; Martínez-Alajarín, J.; Ruiz, R.

    2006-10-01

    Biomedical signals contain important information about the healthy condition of human beings. Anomalous events in these signals are commonly associated to diseases. The information content enclosed by time-frequency representations (TFR) of biomedical signals can be explored by means of different Rényi entropy measures. To be precise, Rényi entropy can be approached under different normalizations, producing different outcomes. The best choice depends upon the particularities of the application considered. In this paper we propose a new processing scheme to the problem of events detection in biomedical signals, based on a particular normalization of the Rény entropy measurement. As in the case of another TFR's, the pseudo-Wigner distribution (PWD) of a biomedical signal can take negative values and thus it cannot be properly interpreted as a probability density function. Therefore a complexity measure based on the classical Shannon entropy cannot be used and a generalized measure such as the Rényi entropy is required. Our method allows the identification of the events as the moments having the highest amount of information (entropy) along the temporal data. This provides localized information about normal and pathological events in biomedical signals. Therefore, the diagnosis of diseases is facilitated in this way. The method is illustrated with examples of application to phonocardiograms and electrocardiograms and result are discussed.

  5. Detecting regular sound changes in linguistics as events of concerted evolution

    SciTech Connect

    Hruschka, Daniel  J.; Branford, Simon; Smith, Eric  D.; Wilkins, Jon; Meade, Andrew; Pagel, Mark; Bhattacharya, Tanmoy

    2014-12-18

    Background: Concerted evolution is normally used to describe parallel changes at different sites in a genome, but it is also observed in languages where a specific phoneme changes to the same other phoneme in many words in the lexicon—a phenomenon known as regular sound change. We develop a general statistical model that can detect concerted changes in aligned sequence data and apply it to study regular sound changes in the Turkic language family. Results: Linguistic evolution, unlike the genetic substitutional process, is dominated by events of concerted evolutionary change. Our model identified more than 70 historical events of regular sound change that occurred throughout the evolution of the Turkic language family, while simultaneously inferring a dated phylogenetic tree. Including regular sound changes yielded an approximately 4-fold improvement in the characterization of linguistic change over a simpler model of sporadic change, improved phylogenetic inference, and returned more reliable and plausible dates for events on the phylogenies. The historical timings of the concerted changes closely follow a Poisson process model, and the sound transition networks derived from our model mirror linguistic expectations. Conclusions: We demonstrate that a model with no prior knowledge of complex concerted or regular changes can nevertheless infer the historical timings and genealogical placements of events of concerted change from the signals left in contemporary data. Our model can be applied wherever discrete elements—such as genes, words, cultural trends, technologies, or morphological traits—can change in parallel within an organism or other evolving group.

  6. Comprehensive temporal information detection from clinical text: medical events, time, and TLINK identification

    PubMed Central

    Sohn, Sunghwan; Wagholikar, Kavishwar B; Li, Dingcheng; Jonnalagadda, Siddhartha R; Tao, Cui; Komandur Elayavilli, Ravikumar; Liu, Hongfang

    2013-01-01

    Background Temporal information detection systems have been developed by the Mayo Clinic for the 2012 i2b2 Natural Language Processing Challenge. Objective To construct automated systems for EVENT/TIMEX3 extraction and temporal link (TLINK) identification from clinical text. Materials and methods The i2b2 organizers provided 190 annotated discharge summaries as the training set and 120 discharge summaries as the test set. Our Event system used a conditional random field classifier with a variety of features including lexical information, natural language elements, and medical ontology. The TIMEX3 system employed a rule-based method using regular expression pattern match and systematic reasoning to determine normalized values. The TLINK system employed both rule-based reasoning and machine learning. All three systems were built in an Apache Unstructured Information Management Architecture framework. Results Our TIMEX3 system performed the best (F-measure of 0.900, value accuracy 0.731) among the challenge teams. The Event system produced an F-measure of 0.870, and the TLINK system an F-measure of 0.537. Conclusions Our TIMEX3 system demonstrated good capability of regular expression rules to extract and normalize time information. Event and TLINK machine learning systems required well-defined feature sets to perform well. We could also leverage expert knowledge as part of the machine learning features to further improve TLINK identification performance. PMID:23558168

  7. Detecting Regular Sound Changes in Linguistics as Events of Concerted Evolution

    PubMed Central

    Hruschka, Daniel J.; Branford, Simon; Smith, Eric D.; Wilkins, Jon; Meade, Andrew; Pagel, Mark; Bhattacharya, Tanmoy

    2015-01-01

    Summary Background Concerted evolution is normally used to describe parallel changes at different sites in a genome, but it is also observed in languages where a specific phoneme changes to the same other phoneme in many words in the lexicon—a phenomenon known as regular sound change. We develop a general statistical model that can detect concerted changes in aligned sequence data and apply it to study regular sound changes in the Turkic language family. Results Linguistic evolution, unlike the genetic substitutional process, is dominated by events of concerted evolutionary change. Our model identified more than 70 historical events of regular sound change that occurred throughout the evolution of the Turkic language family, while simultaneously inferring a dated phylogenetic tree. Including regular sound changes yielded an approximately 4-fold improvement in the characterization of linguistic change over a simpler model of sporadic change, improved phylogenetic inference, and returned more reliable and plausible dates for events on the phylogenies. The historical timings of the concerted changes closely follow a Poisson process model, and the sound transition networks derived from our model mirror linguistic expectations. Conclusions We demonstrate that a model with no prior knowledge of complex concerted or regular changes can nevertheless infer the historical timings and genealogical placements of events of concerted change from the signals left in contemporary data. Our model can be applied wherever discrete elements—such as genes, words, cultural trends, technologies, or morphological traits—can change in parallel within an organism or other evolving group. PMID:25532895

  8. Detection of Visual Events in Underwater Video Using a Neuromorphic Saliency-based Attention System

    NASA Astrophysics Data System (ADS)

    Edgington, D. R.; Walther, D.; Cline, D. E.; Sherlock, R.; Salamy, K. A.; Wilson, A.; Koch, C.

    2003-12-01

    The Monterey Bay Aquarium Research Institute (MBARI) uses high-resolution video equipment on remotely operated vehicles (ROV) to obtain quantitative data on the distribution and abundance of oceanic animals. High-quality video data supplants the traditional approach of assessing the kinds and numbers of animals in the oceanic water column through towing collection nets behind ships. Tow nets are limited in spatial resolution, and often destroy abundant gelatinous animals resulting in species undersampling. Video camera-based quantitative video transects (QVT) are taken through the ocean midwater, from 50m to 4000m, and provide high-resolution data at the scale of the individual animals and their natural aggregation patterns. However, the current manual method of analyzing QVT video by trained scientists is labor intensive and poses a serious limitation to the amount of information that can be analyzed from ROV dives. Presented here is an automated system for detecting marine animals (events) visible in the videos. Automated detection is difficult due to the low contrast of many translucent animals and due to debris ("marine snow") cluttering the scene. Video frames are processed with an artificial intelligence attention selection algorithm that has proven a robust means of target detection in a variety of natural terrestrial scenes. The candidate locations identified by the attention selection module are tracked across video frames using linear Kalman filters. Typically, the occurrence of visible animals in the video footage is sparse in space and time. A notion of "boring" video frames is developed by detecting whether or not there is an interesting candidate object for an animal present in a particular sequence of underwater video -- video frames that do not contain any "interesting" events. If objects can be tracked successfully over several frames, they are stored as potentially "interesting" events. Based on low-level properties, interesting events are

  9. Analysis of grain boundary dynamics using event detection and cumulative averaging.

    PubMed

    Gautam, A; Ophus, C; Lançon, F; Denes, P; Dahmen, U

    2015-04-01

    To analyze extended time series of high resolution images, we have employed automated frame-by-frame comparisons that are able to detect dynamic changes in the structure of a grain boundary in Au. Using cumulative averaging of images between events allowed high resolution measurements of the atomic relaxation in the interface with sufficient accuracy for comparison with atomistic models. Cumulative averaging was also used to observe the structural rearrangement of atomic columns at a moving step in the grain boundary. The technique of analyzing changing features in high resolution images by averaging between incidents can be used to deconvolute stochastic events that occur at random intervals and on time scales well beyond that accessible to single-shot imaging. PMID:25498139

  10. Detecting consciousness in a total locked-in syndrome: an active event-related paradigm.

    PubMed

    Schnakers, Caroline; Perrin, Fabien; Schabus, Manuel; Hustinx, Roland; Majerus, Steve; Moonen, Gustave; Boly, Melanie; Vanhaudenhuyse, Audrey; Bruno, Marie-Aurelie; Laureys, Steven

    2009-08-01

    Total locked-in syndrome is characterized by tetraplegia, anarthria and paralysis of eye motility. In this study, consciousness was detected in a 21-year-old woman who presented a total locked-in syndrome after a basilar artery thrombosis (49 days post-injury) using an active event-related paradigm. The patient was presented sequences of names containing the patient's own name and other names. The patient was instructed to count her own name or to count another target name. Similar to 4 age- and gender-matched healthy controls, the P3 response recorded for the voluntarily counted own name was larger than while passively listening. This P3 response was observed 14 days before the first behavioral signs of consciousness. This study shows that our active event-related paradigm allowed to identify voluntary brain activity in a patient who would behaviorally be diagnosed as comatose. PMID:19241281

  11. Application of Data Cubes for Improving Detection of Water Cycle Extreme Events

    NASA Technical Reports Server (NTRS)

    Albayrak, Arif; Teng, William

    2015-01-01

    As part of an ongoing NASA-funded project to remove a longstanding barrier to accessing NASA data (i.e., accessing archived time-step array data as point-time series), for the hydrology and other point-time series-oriented communities, "data cubes" are created from which time series files (aka "data rods") are generated on-the-fly and made available as Web services from the Goddard Earth Sciences Data and Information Services Center (GES DISC). Data cubes are data as archived rearranged into spatio-temporal matrices, which allow for easy access to the data, both spatially and temporally. A data cube is a specific case of the general optimal strategy of reorganizing data to match the desired means of access. The gain from such reorganization is greater the larger the data set. As a use case of our project, we are leveraging existing software to explore the application of the data cubes concept to machine learning, for the purpose of detecting water cycle extreme events, a specific case of anomaly detection, requiring time series data. We investigate the use of support vector machines (SVM) for anomaly classification. We show an example of detection of water cycle extreme events, using data from the Tropical Rainfall Measuring Mission (TRMM).

  12. Detecting tidal disruption events of massive black holes in normal galaxies with the Einstein Probe

    NASA Astrophysics Data System (ADS)

    Yuan, W.; Komossa, S.; Zhang, C.; Feng, H.; Ling, Z.-X.; Zhao, D. H.; Zhang, S.-N.; Osborne, J. P.; O'Brien, P.; Willingale, R.; Lapington, J.; Lapington

    2016-02-01

    Stars are tidally disrupted and accreted when they approach massive black holes (MBHs) closely, producing a flare of electromagnetic radiation. The majority of the (approximately two dozen) tidal disruption events (TDEs) identified so far have been discovered by their luminous, transient X-ray emission. Once TDEs are detected in much larger numbers, in future dedicated transient surveys, a wealth of new applications will become possible. Here, we present the proposed Einstein Probe mission, which is a dedicated time-domain soft X-ray all-sky monitor aiming at detecting X-ray transients including TDEs in large numbers. The mission consists of a wide-field micro-pore Lobster-eye imager (60° × 60°), and is designed to carry out an all-sky transient survey at energies of 0.5-4 keV. It will also carry a more sensitive telescope for X-ray follow-ups, and will be capable of issuing public transient alerts rapidly. Einstein Probe is expected to revolutionise the field of TDE research by detecting several tens to hundreds of events per year from the early phase of flares, many with long-term, well sampled lightcurves.

  13. Multiscale vision model for event detection and reconstruction in two-photon imaging data.

    PubMed

    Brazhe, Alexey; Mathiesen, Claus; Lind, Barbara; Rubin, Andrey; Lauritzen, Martin

    2014-07-01

    Reliable detection of calcium waves in multiphoton imaging data is challenging because of the low signal-to-noise ratio and because of the unpredictability of the time and location of these spontaneous events. This paper describes our approach to calcium wave detection and reconstruction based on a modified multiscale vision model, an object detection framework based on the thresholding of wavelet coefficients and hierarchical trees of significant coefficients followed by nonlinear iterative partial object reconstruction, for the analysis of two-photon calcium imaging data. The framework is discussed in the context of detection and reconstruction of intercellular glial calcium waves. We extend the framework by a different decomposition algorithm and iterative reconstruction of the detected objects. Comparison with several popular state-of-the-art image denoising methods shows that performance of the multiscale vision model is similar in the denoising, but provides a better segmenation of the image into meaningful objects, whereas other methods need to be combined with dedicated thresholding and segmentation utilities. PMID:26157968

  14. Automated detection and analysis of depolarization events in human cardiomyocytes using MaDEC.

    PubMed

    Szymanska, Agnieszka F; Heylman, Christopher; Datta, Rupsa; Gratton, Enrico; Nenadic, Zoran

    2016-08-01

    Optical imaging-based methods for assessing the membrane electrophysiology of in vitro human cardiac cells allow for non-invasive temporal assessment of the effect of drugs and other stimuli. Automated methods for detecting and analyzing the depolarization events (DEs) in image-based data allow quantitative assessment of these different treatments. In this study, we use 2-photon microscopy of fluorescent voltage-sensitive dyes (VSDs) to capture the membrane voltage of actively beating human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). We built a custom and freely available Matlab software, called MaDEC, to detect, quantify, and compare DEs of hiPS-CMs treated with the β-adrenergic drugs, propranolol and isoproterenol. The efficacy of our software is quantified by comparing detection results against manual DE detection by expert analysts, and comparing DE analysis results to known drug-induced electrophysiological effects. The software accurately detected DEs with true positive rates of 98-100% and false positive rates of 1-2%, at signal-to-noise ratios (SNRs) of 5 and above. The MaDEC software was also able to distinguish control DEs from drug-treated DEs both immediately as well as 10min after drug administration. PMID:27281718

  15. Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.

    PubMed

    Cron, Andrew; Gouttefangeas, Cécile; Frelinger, Jacob; Lin, Lin; Singh, Satwinder K; Britten, Cedrik M; Welters, Marij J P; van der Burg, Sjoerd H; West, Mike; Chan, Cliburn

    2013-01-01

    Flow cytometry is the prototypical assay for multi-parameter single cell analysis, and is essential in vaccine and biomarker research for the enumeration of antigen-specific lymphocytes that are often found in extremely low frequencies (0.1% or less). Standard analysis of flow cytometry data relies on visual identification of cell subsets by experts, a process that is subjective and often difficult to reproduce. An alternative and more objective approach is the use of statistical models to identify cell subsets of interest in an automated fashion. Two specific challenges for automated analysis are to detect extremely low frequency event subsets without biasing the estimate by pre-processing enrichment, and the ability to align cell subsets across multiple data samples for comparative analysis. In this manuscript, we develop hierarchical modeling extensions to the Dirichlet Process Gaussian Mixture Model (DPGMM) approach we have previously described for cell subset identification, and show that the hierarchical DPGMM (HDPGMM) naturally generates an aligned data model that captures both commonalities and variations across multiple samples. HDPGMM also increases the sensitivity to extremely low frequency events by sharing information across multiple samples analyzed simultaneously. We validate the accuracy and reproducibility of HDPGMM estimates of antigen-specific T cells on clinically relevant reference peripheral blood mononuclear cell (PBMC) samples with known frequencies of antigen-specific T cells. These cell samples take advantage of retrovirally TCR-transduced T cells spiked into autologous PBMC samples to give a defined number of antigen-specific T cells detectable by HLA-peptide multimer binding. We provide open source software that can take advantage of both multiple processors and GPU-acceleration to perform the numerically-demanding computations. We show that hierarchical modeling is a useful probabilistic approach that can provide a consistent labeling

  16. An analog cell to detect single event transients in voltage references

    NASA Astrophysics Data System (ADS)

    Franco, F. J.; Palomar, C.; Izquierdo, J. G.; Agapito, J. A.

    2015-01-01

    A reliable voltage reference is mandatory in mixed-signal systems. However, this family of components can undergo very long single event transients when operating in radiation environments such as space and nuclear facilities due to the impact of heavy ions. The purpose of the present paper is to demonstrate how a simple cell can be used to detect these transients. The cell was implemented with typical COTS components and its behavior was verified by SPICE simulations and in a laser facility. Different applications of the cell are explored as well.

  17. Brain abnormalities in male children and adolescents with hemophilia: detection with MR imaging. The Hemophilia Growth and Development Study Group.

    PubMed

    Wilson, D A; Nelson, M D; Fenstermacher, M J; Bohan, T P; Hopper, K D; Tilton, A; Mitchell, W G; Contant, C F; Maeder, M A; Donfield, S M

    1992-11-01

    Cranial magnetic resonance (MR) imaging was performed in 124 male patients (aged 7-19 years), from 14 institutions, in whom a diagnosis of moderate to severe hemophilia was made. Blood tests in all subjects were negative for human immunodeficiency virus. Findings in MR studies were abnormal in 25 (20.2%) subjects. Six lesions in five subjects were classified as congenital. The most commonly identified congenital lesion was a posterior fossa collection of cerebrospinal fluid (five cases). Twenty-two subjects had acquired lesions that were probably related to the hemophilia or its treatment. The most commonly acquired lesions were single- or multifocal areas of high signal intensity within the white matter on T2-weighted images noted in 14 (11.3%) subjects. Two subjects had large focal areas of brain atrophy, and six had some degree of diffuse cerebral cortical atrophy. Three subjects (2.4%) had hemorrhagic lesions. To the authors' knowledge, the unexpected finding of small, focal, nonhemorrhagic white matter lesions has not previously been reported. PMID:1410372

  18. A novel progressive signal association algorithm for detecting teleseismic/network-outside events using regional seismic networks

    NASA Astrophysics Data System (ADS)

    Jin, Ping; Pan, Changzhou; Zhang, Chengliu; Shen, Xufeng; Wang, Hongchun; Lu, Na

    2015-06-01

    Regional seismic networks may and in some cases need to be used to monitor teleseismic or network-outside events. For detecting and localizing teleseismic events automatically and reliably in this case, in this paper we present a novel progressive association algorithm for teleseismic signals recorded by a regional seismic network. The algorithm takes triangle station arrays as the starting point to search for P waves of teleseismic events progressively by that, as detections from different stations actually are from the same teleseismic event, their arrival times should be linearly related to the average slowness vector with which the signal propagates across the network, and the slowness of direct teleseismic P wave basically is different from other major seismic phases. We have tested this algorithm using data recorded by Xinjiang Seismic Network of China (XJSN) for 16 d. The results show that the algorithm can effectively and reliably detect and localize earthquakes outside of the network. For the period of the test data, as all mb 4.0+ events with Δc < 30° and all mb 4.5+ events with Δc < 60° referring to the International Data Center-Reviewed Event Bulletin (IDC REB) were detected, where Δc is the epicentral distance relative to the network's geographical centre, the rate of false events only accounted for 2.4 per cent, suggesting that the new association algorithm has good application prospect for situations when regional seismic networks need to be used to monitor teleseismic events.

  19. Optimized Swinging Door Algorithm for Wind Power Ramp Event Detection: Preprint

    SciTech Connect

    Cui, Mingjian; Zhang, Jie; Florita, Anthony R.; Hodge, Bri-Mathias; Ke, Deping; Sun, Yuanzhang

    2015-08-06

    Significant wind power ramp events (WPREs) are those that influence the integration of wind power, and they are a concern to the continued reliable operation of the power grid. As wind power penetration has increased in recent years, so has the importance of wind power ramps. In this paper, an optimized swinging door algorithm (SDA) is developed to improve ramp detection performance. Wind power time series data are segmented by the original SDA, and then all significant ramps are detected and merged through a dynamic programming algorithm. An application of the optimized SDA is provided to ascertain the optimal parameter of the original SDA. Measured wind power data from the Electric Reliability Council of Texas (ERCOT) are used to evaluate the proposed optimized SDA.

  20. Label-Free Detection of Single Living Bacteria via Electrochemical Collision Event.

    PubMed

    Lee, Ji Young; Kim, Byung-Kwon; Kang, Mijeong; Park, Jun Hui

    2016-01-01

    We detected single living bacterial cells on ultramicroelectrode (UME) using a single-particle collision method and optical microscopic methods. The number of collision events involving the bacterial cells indicated in current-time (i-t) curves corresponds to the number of bacterial cells (i.e., Escherichia coli) on the UME surface, as observed visually. Simulations were performed to determine the theoretical current response (75 pA) and frequency (0.47 pM(-1) s(-1)) of single Escherichia coli collisions. The experimental current response (83 pA) and frequency (0.26 pM(-1) s(-1)) were on the same order of magnitude as the theoretical values. This single-particle collision approach facilitates detecting living bacteria and determining their concentration in solution and could be widely applied to studying other bacteria and biomolecules. PMID:27435527

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

    PubMed

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

    2015-01-01

    The enzyme-linked probe hybridization chip utilizes a method based on ligase-hybridizing probe chip technology, with the principle of using thio-primers for protection against enzyme digestion, and using lambda DNA exonuclease to cut multiple PCR products obtained from the sample being tested into single-strand chains for hybridization. The 5'-end amino-labeled probe was fixed onto the aldehyde chip, and hybridized with the single-stranded PCR product, followed by addition of a fluorescent-modified probe that was then enzymatically linked with the adjacent, substrate-bound probe in order to achieve highly specific, parallel, and high-throughput detection. Specificity and sensitivity testing demonstrated that enzyme-linked probe hybridization technology could be applied to the specific detection of eight genetic modification events at the same time, with a sensitivity reaching 0.1% and the achievement of accurate, efficient, and stable results. PMID:26345863

  2. Label-Free Detection of Single Living Bacteria via Electrochemical Collision Event

    PubMed Central

    Lee, Ji Young; Kim, Byung-Kwon; Kang, Mijeong; Park, Jun Hui

    2016-01-01

    We detected single living bacterial cells on ultramicroelectrode (UME) using a single-particle collision method and optical microscopic methods. The number of collision events involving the bacterial cells indicated in current-time (i-t) curves corresponds to the number of bacterial cells (i.e., Escherichia coli) on the UME surface, as observed visually. Simulations were performed to determine the theoretical current response (75 pA) and frequency (0.47 pM−1 s−1) of single Escherichia coli collisions. The experimental current response (83 pA) and frequency (0.26 pM−1 s−1) were on the same order of magnitude as the theoretical values. This single-particle collision approach facilitates detecting living bacteria and determining their concentration in solution and could be widely applied to studying other bacteria and biomolecules. PMID:27435527

  3. Detecting event-related recurrences by symbolic analysis: applications to human language processing

    PubMed Central

    beim Graben, Peter; Hutt, Axel

    2015-01-01

    Quasi-stationarity is ubiquitous in complex dynamical systems. In brain dynamics, there is ample evidence that event-related potentials (ERPs) reflect such quasi-stationary states. In order to detect them from time series, several segmentation techniques have been proposed. In this study, we elaborate a recent approach for detecting quasi-stationary states as recurrence domains by means of recurrence analysis and subsequent symbolization methods. We address two pertinent problems of contemporary recurrence analysis: optimizing the size of recurrence neighbourhoods and identifying symbols from different realizations for sequence alignment. As possible solutions for these problems, we suggest a maximum entropy criterion and a Hausdorff clustering algorithm. The resulting recurrence domains for single-subject ERPs are obtained as partition cells reflecting quasi-stationary brain states. PMID:25548270

  4. Label-Free Detection of Single Living Bacteria via Electrochemical Collision Event

    NASA Astrophysics Data System (ADS)

    Lee, Ji Young; Kim, Byung-Kwon; Kang, Mijeong; Park, Jun Hui

    2016-07-01

    We detected single living bacterial cells on ultramicroelectrode (UME) using a single-particle collision method and optical microscopic methods. The number of collision events involving the bacterial cells indicated in current-time (i-t) curves corresponds to the number of bacterial cells (i.e., Escherichia coli) on the UME surface, as observed visually. Simulations were performed to determine the theoretical current response (75 pA) and frequency (0.47 pM‑1 s‑1) of single Escherichia coli collisions. The experimental current response (83 pA) and frequency (0.26 pM‑1 s‑1) were on the same order of magnitude as the theoretical values. This single-particle collision approach facilitates detecting living bacteria and determining their concentration in solution and could be widely applied to studying other bacteria and biomolecules.

  5. Detection of planets in extremely weak central perturbation microlensing events via next-generation ground-based surveys

    SciTech Connect

    Chung, Sun-Ju; Lee, Chung-Uk; Koo, Jae-Rim E-mail: leecu@kasi.re.kr

    2014-04-20

    Even though the recently discovered high-magnification event MOA-2010-BLG-311 had complete coverage over its peak, confident planet detection did not happen due to extremely weak central perturbations (EWCPs, fractional deviations of ≲ 2%). For confident detection of planets in EWCP events, it is necessary to have both high cadence monitoring and high photometric accuracy better than those of current follow-up observation systems. The next-generation ground-based observation project, Korea Microlensing Telescope Network (KMTNet), satisfies these conditions. We estimate the probability of occurrence of EWCP events with fractional deviations of ≤2% in high-magnification events and the efficiency of detecting planets in the EWCP events using the KMTNet. From this study, we find that the EWCP events occur with a frequency of >50% in the case of ≲ 100 M {sub E} planets with separations of 0.2 AU ≲ d ≲ 20 AU. We find that for main-sequence and sub-giant source stars, ≳ 1 M {sub E} planets in EWCP events with deviations ≤2% can be detected with frequency >50% in a certain range that changes with the planet mass. However, it is difficult to detect planets in EWCP events of bright stars like giant stars because it is easy for KMTNet to be saturated around the peak of the events because of its constant exposure time. EWCP events are caused by close, intermediate, and wide planetary systems with low-mass planets and close and wide planetary systems with massive planets. Therefore, we expect that a much greater variety of planetary systems than those already detected, which are mostly intermediate planetary systems, regardless of the planet mass, will be significantly detected in the near future.

  6. The ADENOMA Study. Accuracy of Detection using Endocuff Vision™ Optimization of Mucosal Abnormalities: study protocol for randomized controlled trial

    PubMed Central

    Bevan, Roisin; Ngu, Wee Sing; Saunders, Brian P.; Tsiamoulos, Zacharias; Bassett, Paul; Hoare, Zoe; Rees, Colin J.

    2016-01-01

    Background: Colonoscopy is the gold standard investigation for the diagnosis of bowel pathology and colorectal cancer screening. Adenoma detection rate is a marker of high quality colonoscopy and a high adenoma detection rate is associated with a lower incidence of interval cancers. Several technological advancements have been explored to improve adenoma detection rate. A new device called Endocuff Vision™ has been shown to improve adenoma detection rate in pilot studies. Methods/Design: This is a prospective, multicenter, randomized controlled trial comparing the adenoma detection rate in patients undergoing Endocuff Vision™-assisted colonoscopy with standard colonoscopy. All patients above 18 years of age referred for screening, surveillance, or diagnostic colonoscopy who are able to consent are invited to the study. Patients with absolute contraindications to colonoscopy, large bowel obstruction or pseudo-obstruction, colon cancer or polyposis syndromes, colonic strictures, severe diverticular segments, active colitis, anticoagulant therapy, or pregnancy are excluded. Patients are randomized according to site, age, sex, and bowel cancer screening status to receive Endocuff Vision™-assisted colonoscopy or standard colonoscopy on the day of procedure. Baseline data, colonoscopy, and polyp data including histology are collected. Nurse assessment of patient comfort and patient comfort questionnaires are completed post procedure. Patients are followed up at 21 days and complete a patient experience questionnaire. This study will take place across seven NHS Hospital Trusts: one in London and six within the Northern Region Endoscopy Group. A maximum of 10 colonoscopists per site will recruit a total of 1772 patients, with a maximum of four bowel screening colonoscopists permitted per site. Discussion: This is the first trial to evaluate the adenoma detection rate of Endocuff Vision™ in all screening, surveillance, and diagnostic patient groups. This timely

  7. Automatic Detection of Whole Night Snoring Events Using Non-Contact Microphone

    PubMed Central

    Dafna, Eliran; Tarasiuk, Ariel; Zigel, Yaniv

    2013-01-01

    Objective Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology. Design Sounds during polysomnography (PSG) were recorded using a directional condenser microphone placed 1 m above the bed. An AdaBoost classifier was trained and validated on manually labeled snoring and non-snoring acoustic events. Patients Sixty-seven subjects (age 52.5±13.5 years, BMI 30.8±4.7 kg/m2, m/f 40/27) referred for PSG for obstructive sleep apnea diagnoses were prospectively and consecutively recruited. Twenty-five subjects were used for the design study; the validation study was blindly performed on the remaining forty-two subjects. Measurements and Results To train the proposed sound detector, >76,600 acoustic episodes collected in the design study were manually classified by three scorers into snore and non-snore episodes (e.g., bedding noise, coughing, environmental). A feature selection process was applied to select the most discriminative features extracted from time and spectral domains. The average snore/non-snore detection rate (accuracy) for the design group was 98.4% based on a ten-fold cross-validation technique. When tested on the validation group, the average detection rate was 98.2% with sensitivity of 98.0% (snore as a snore) and specificity of 98.3% (noise as noise). Conclusions Audio-based features extracted from time and spectral domains can accurately discriminate between snore and non-snore acoustic events. This audio analysis approach enables detection and analysis of snoring sounds from a full night in order to produce quantified measures for objective follow-up of patients. PMID:24391903

  8. Hierarchical representation and machine learning from faulty jet engine behavioral examples to detect real time abnormal conditions

    NASA Technical Reports Server (NTRS)

    Gupta, U. K.; Ali, M.

    1988-01-01

    The theoretical basis and operation of LEBEX, a machine-learning system for jet-engine performance monitoring, are described. The behavior of the engine is modeled in terms of four parameters (the rotational speeds of the high- and low-speed sections and the exhaust and combustion temperatures), and parameter variations indicating malfunction are transformed into structural representations involving instances and events. LEBEX extracts descriptors from a set of training data on normal and faulty engines, represents them hierarchically in a knowledge base, and uses them to diagnose and predict faults on a real-time basis. Diagrams of the system architecture and printouts of typical results are shown.

  9. Identification of a cleavage site directing the immunochemical detection of molecular abnormalities in type IIA von Willebrand factor.

    PubMed Central

    Dent, J A; Berkowitz, S D; Ware, J; Kasper, C K; Ruggeri, Z M

    1990-01-01

    Proteolytic cleavage of the von Willebrand factor subunit may be important for processing and/or function of the molecule and is altered in certain subtypes of von Willebrand disease. It results in the generation of two main fragments with apparent molecular masses of 140 kDa and 176 kDa from the 225-kDa subunit. We have now obtained chemical evidence to locate the protease-sensitive bond between residues Tyr-842 and Met-843, a site that appears to reflect the specificity of calcium-dependent neutral proteases (calpains). Antibodies were raised against four synthetic peptides that represented sequences immediately preceding or following or including the cleavage site. One antibody (against the fragment from Ala-837 through Asp-851) reacted only with the intact subunit, and its epitope included the cleavage site. All others reacted specifically with either the 140-kDa or the 176-kDa fragment, demonstrating their origin from a single cleavage. In samples of purified von Willebrand factor from four of five patients with type IIA von Willebrand disease, the anti-peptide antibodies showed markedly decreased reactivity with either the 140-kDa or the 176-kDa fragment, suggesting the existence of distinct molecular abnormalities clustered around the cleavage site. Thus, in the majority of type IIA patients, a common pathogenetic mechanism may lead to the disappearance of the larger multimers as a consequence of structural changes that may expose a sensitive bond to the action of specific proteases. These studies demonstrate the use of anti-peptide antibodies directed at a relevant structural domain for the immunochemical differentiation of normal and mutant molecules. Images PMID:2385594

  10. Large-Scale Disturbance Events in Terrestrial Ecosystems Detected using Global Satellite Data Sets

    NASA Astrophysics Data System (ADS)

    Potter, C.; Tan, P.; Kumar, V.; Klooster, S.

    2004-12-01

    Studies are being conducted to evaluate patterns in a 19-year record of global satellite observations of vegetation phenology from the Advanced Very High Resolution Radiometer (AVHRR), as a means to characterize large-scale ecosystem disturbance events and regimes. The fraction absorbed of photosynthetically active radiation (FPAR) by vegetation canopies worldwide has been computed at a monthly time interval from 1982 to 2000 and gridded at a spatial resolution of 8-km globally. Potential disturbance events were identified in the FPAR time series by locating anomalously low values (FPAR-LO) that lasted longer than 12 consecutive months at any 8-km pixel. We can find verifiable evidence of numerous disturbance types across North America, including major regional patterns of cold and heat waves, forest fires, tropical storms, and large-scale forest logging. Based on this analysis, an historical picture is emerging of periodic droughts and heat waves, possibly coupled with herbivorous insect outbreaks, as among the most important causes of ecosystem disturbance in North America. In South America, large areas of northeastern Brazil appear to have been impacted in the early 1990s by severe drought. Amazon tropical forest disturbance can be detected at large scales particularly in the mid 1990s. In Asia, large-scale disturbance events appear in the mid 1980s and the late 1990s across boreal and temperate forest zones, as well as in cropland areas of western India. In northern Europe and central Africa, large-scale forest disturbance appears in the mid 1990s.

  11. Endpoint Visual Detection of Three Genetically Modified Rice Events by Loop-Mediated Isothermal Amplification

    PubMed Central

    Chen, Xiaoyun; Wang, Xiaofu; Jin, Nuo; Zhou, Yu; Huang, Sainan; Miao, Qingmei; Zhu, Qing; Xu, Junfeng

    2012-01-01

    Genetically modified (GM) rice KMD1, TT51-1, and KF6 are three of the most well known transgenic Bt rice lines in China. A rapid and sensitive molecular assay for risk assessment of GM rice is needed. Polymerase chain reaction (PCR), currently the most common method for detecting genetically modified organisms, requires temperature cycling and relatively complex procedures. Here we developed a visual and rapid loop-mediated isothermal amplification (LAMP) method to amplify three GM rice event-specific junction sequences. Target DNA was amplified and visualized by two indicators (SYBR green or hydroxy naphthol blue [HNB]) within 60 min at an isothermal temperature of 63 °C. Different kinds of plants were selected to ensure the specificity of detection and the results of the non-target samples were negative, indicating that the primer sets for the three GM rice varieties had good levels of specificity. The sensitivity of LAMP, with detection limits at low concentration levels (0.01%–0.005% GM), was 10- to 100-fold greater than that of conventional PCR. Additionally, the LAMP assay coupled with an indicator (SYBR green or HNB) facilitated analysis. These findings revealed that the rapid detection method was suitable as a simple field-based test to determine the status of GM crops. PMID:23203072

  12. Detection of Pharmacovigilance-Related adverse Events Using Electronic Health Records and automated Methods

    PubMed Central

    Haerian, K; Varn, D; Vaidya, S; Ena, L; Chase, HS; Friedman, C

    2013-01-01

    Electronic health records (EHRs) are an important source of data for detection of adverse drug reactions (ADRs). However, adverse events are frequently due not to medications but to the patients’ underlying conditions. Mining to detect ADRs from EHR data must account for confounders. We developed an automated method using natural-language processing (NLP) and a knowledge source to differentiate cases in which the patient’s disease is responsible for the event rather than a drug. Our method was applied to 199,920 hospitalization records, concentrating on two serious ADRs: rhabdomyolysis (n = 687) and agranulocytosis (n = 772). Our method automatically identified 75% of the cases, those with disease etiology. The sensitivity and specificity were 93.8% (confidence interval: 88.9-96.7%) and 91.8% (confidence interval: 84.0-96.2%), respectively. The method resulted in considerable saving of time: for every 1 h spent in development, there was a saving of at least 20 h in manual review. The review of the remaining 25% of the cases therefore became more feasible, allowing us to identify the medications that had caused the ADRs. PMID:22713699

  13. A Simple and Robust Event-Detection Algorithm for Single-Cell Impedance Cytometry.

    PubMed

    Caselli, Federica; Bisegna, Paolo

    2016-02-01

    Microfluidic impedance cytometry is emerging as a powerful label-free technique for the characterization of single biological cells. In order to increase the sensitivity and the specificity of the technique, suited digital signal processing methods are required to extract meaningful information from measured impedance data. In this study, a simple and robust event-detection algorithm for impedance cytometry is presented. Since a differential measuring scheme is generally adopted, the signal recorded when a cell passes through the sensing region of the device exhibits a typical odd-symmetric pattern. This feature is exploited twice by the proposed algorithm: first, a preliminary segmentation, based on the correlation of the data stream with the simplest odd-symmetric template, is performed; then, the quality of detected events is established by evaluating their E2O index, that is, a measure of the ratio between their even and odd parts. A thorough performance analysis is reported, showing the robustness of the algorithm with respect to parameter choice and noise level. In terms of sensitivity and positive predictive value, an overall performance of 94.9% and 98.5%, respectively, was achieved on two datasets relevant to microfluidic chips with very different characteristics, considering three noise levels. The present algorithm can foster the role of impedance cytometry in single-cell analysis, which is the new frontier in "Omics." PMID:26241968

  14. AKSED: adaptive knowledge-based system for event detection using collaborative unmanned aerial vehicles

    NASA Astrophysics Data System (ADS)

    Wang, X. Sean; Lee, Byung Suk; Sadjadi, Firooz

    2006-05-01

    Advances in sensor technology and image processing have made it possible to equip unmanned aerial vehicles (UAVs) with economical, high-resolution, energy-efficient sensors. Despite the improvements, current UAVs lack autonomous and collaborative operation capabilities, due to limited bandwidth and limited on-board image processing abilities. The situation, however, is changing. In the next generation of UAVs, much image processing can be carried out onboard and communication bandwidth problem will improve. More importantly, with more processing power, collaborative operations among a team of autonomous UAVs can provide more intelligent event detection capabilities. In this paper, we present ideas for developing a system enabling target recognitions by collaborative operations of autonomous UAVs. UAVs are configured in three stages: manufacturing, mission planning, and deployment. Different sets of information are needed at different stages, and the resulting outcome is an optimized event detection code deployed onto a UAV. The envisioned system architecture and the contemplated methodology, together with problems to be addressed, are presented.

  15. The Waveform Correlation Event Detection System project, Phase II: Testing with the IDC primary network

    SciTech Connect

    Young, C.J.; Beiriger, J.I.; Moore, S.G.

    1998-04-01

    Further improvements to the Waveform Correlation Event Detection System (WCEDS) developed by Sandia Laboratory have made it possible to test the system on the accepted Comprehensive Test Ban Treaty (CTBT) seismic monitoring network. For our test interval we selected a 24-hour period from December 1996, and chose to use the Reviewed Event Bulletin (REB) produced by the Prototype International Data Center (PIDC) as ground truth for evaluating the results. The network is heterogeneous, consisting of array and three-component sites, and as a result requires more flexible waveform processing algorithms than were available in the first version of the system. For simplicity and superior performance, we opted to use the spatial coherency algorithm of Wagner and Owens (1996) for both types of sites. Preliminary tests indicated that the existing version of WCEDS, which ignored directional information, could not achieve satisfactory detection or location performance for many of the smaller events in the REB, particularly those in the south Pacific where the network coverage is unusually sparse. To achieve an acceptable level of performance, we made modifications to include directional consistency checks for the correlations, making the regions of high correlation much less ambiguous. These checks require the production of continuous azimuth and slowness streams for each station, which is accomplished by means of FK processing for the arrays and power polarization processing for the three-component sites. In addition, we added the capability to use multiple frequency-banded data streams for each site to increase sensitivity to phases whose frequency content changes as a function of distance.

  16. Unreported seismic events found far off-shore Mexico using full-waveform, cross-correlation detection method.

    NASA Astrophysics Data System (ADS)

    Solano, ErickaAlinne; Hjorleifsdottir, Vala; Perez-Campos, Xyoli

    2015-04-01

    A large subset of seismic events do not have impulsive arrivals, such as low frequency events in volcanoes, earthquakes in the shallow part of the subduction interface and further down dip from the traditional seismogenic part, glacial events, volcanic and non-volcanic tremors and landslides. A suite of methods can be used to detect these non-impulsive events. One of this methods is the full-waveform detection based on time reversal methods (Solano, et al , submitted to GJI). The method uses continuous observed seismograms, together with Greens functions and moment tensor responses calculated for an arbitrary 3D structure. This method was applied to the 2012 Ometepec-Pinotepa Nacional earthquake sequence in Guerrero, Mexico. During the span time of the study, we encountered three previously unknown events. One of this events was an impulsive earthquake in the Ometepec area, that only has clear arrivals on three stations and was therefore not located and reported by the SSN. The other two events are previously undetected events, very depleted in high frequencies, that occurred far outside the search area. A very rough estimate gives the location of this two events in the portion of the East Pacific Rise around 9 N. These two events are detected despite their distance from the search area, due to favorable move-out on the array of the Mexican National Seismological Service network (SSN). We are expanding the study area to the EPR and to a larger period of time, with the objective of finding more events in that region. We will present an analysis of the newly detected events, as well as any further findings at the meeting.

  17. EEG-based event detection using optimized echo state networks with leaky integrator neurons.

    PubMed

    Ayyagari, Sudhanshu S D P; Jones, Richard D; Weddell, Stephen J

    2014-01-01

    This study investigates the classification ability of linear and nonlinear classifiers on biological signals using the electroencephalogram (EEG) and examines the impact of architectural changes within the classifier in order to enhance the classification. Consequently, artificial events were used to validate a prototype EEG-based microsleep detection system based around an echo state network (ESN) and a linear discriminant analysis (LDA) classifier. The artificial events comprised infrequent 2-s long bursts of 15 Hz sinusoids superimposed on prerecorded 16-channel EEG data which provided a means of determining and optimizing the accuracy of overall classifier on `gold standard' events. The performance of this system was tested on different signal-to-noise amplitude ratios (SNRs) ranging from 16 down to 0.03. Results from several feature selection/reduction and pattern classification modules indicated that training the classifier using a leaky-integrator neuron ESN structure yielded highest classification accuracy. For datasets with a low SNR of 0.3, training the leaky-neuron ESN using only those features which directly correspond to the underlying event, resulted in a phi correlation of 0.92 compared to 0.37 that employed principal component analysis (PCA). On the same datasets, other classifiers such as LDA and simple ESNs using PCA performed weakly with a correlation of 0.05 and 0 respectively. These results suggest that ESNs with leaky neuron architectures have superior pattern recognition properties. This, in turn, may reflect their superior ability to exploit differences in state dynamics and, hence, provide superior temporal characteristics in learning. PMID:25571328

  18. Slip-Related Changes in Plantar Pressure Distribution, and Parameters for Early Detection of Slip Events

    PubMed Central

    Choi, Seungyoung; Cho, Hyungpil; Kang, Boram; Lee, Dong Hun; Kim, Mi Jung

    2015-01-01

    Objective To investigate differences in plantar pressure distribution between a normal gait and unpredictable slip events to predict the initiation of the slipping process. Methods Eleven male participants were enrolled. Subjects walked onto a wooden tile, and two layers of oily vinyl sheet were placed on the expected spot of the 4th step to induce a slip. An insole pressure-measuring system was used to monitor plantar pressure distribution. This system measured plantar pressure in four regions (the toes, metatarsal head, arch, and heel) for three events: the step during normal gait; the recovered step, when the subject recovered from a slip; and the uncorrected, harmful slipped step. Four variables were analyzed: peak pressure (PP), contact time (CT), the pressure-time integral (PTI), and the instant of peak pressure (IPP). Results The plantar pressure pattern in the heel was unique, as compared with other parts of the sole. In the heel, PP, CT, and PTI values were high in slipped and recovered steps compared with normal steps. The IPP differed markedly among the three steps. The IPPs in the heel for the three events were, in descending order (from latest to earliest), slipped, recovered, and normal steps, whereas in the other regions the order was normal, recovered, and slipped steps. Finally, the metatarsal head-to-heel IPP ratios for the normal, recovered, and slipped steps were 6.1±2.9, 3.1±3.0, and 2.2±2.5, respectively. Conclusion A distinctive plantar pressure pattern in the heel might be useful for early detection of a slip event to prevent slip-related injuries. PMID:26798603

  19. Detecting regular sound changes in linguistics as events of concerted evolution

    DOE PAGESBeta

    Hruschka, Daniel  J.; Branford, Simon; Smith, Eric  D.; Wilkins, Jon; Meade, Andrew; Pagel, Mark; Bhattacharya, Tanmoy

    2014-12-18

    Background: Concerted evolution is normally used to describe parallel changes at different sites in a genome, but it is also observed in languages where a specific phoneme changes to the same other phoneme in many words in the lexicon—a phenomenon known as regular sound change. We develop a general statistical model that can detect concerted changes in aligned sequence data and apply it to study regular sound changes in the Turkic language family. Results: Linguistic evolution, unlike the genetic substitutional process, is dominated by events of concerted evolutionary change. Our model identified more than 70 historical events of regular soundmore » change that occurred throughout the evolution of the Turkic language family, while simultaneously inferring a dated phylogenetic tree. Including regular sound changes yielded an approximately 4-fold improvement in the characterization of linguistic change over a simpler model of sporadic change, improved phylogenetic inference, and returned more reliable and plausible dates for events on the phylogenies. The historical timings of the concerted changes closely follow a Poisson process model, and the sound transition networks derived from our model mirror linguistic expectations. Conclusions: We demonstrate that a model with no prior knowledge of complex concerted or regular changes can nevertheless infer the historical timings and genealogical placements of events of concerted change from the signals left in contemporary data. Our model can be applied wherever discrete elements—such as genes, words, cultural trends, technologies, or morphological traits—can change in parallel within an organism or other evolving group.« less

  20. [Familial translocation t(3;22) detected in a carrier child with mental retardation and other abnormalities].

    PubMed

    Gregori Romero, M; Gil Benso, R; López Ginés, C; Pellín Pérez, A; Barberá Guillem, E

    1984-10-31

    A case of familial translocation t(3;22) (q21;q13) detected through a boy carrier with various functional and phenotypical alterations is described. The caryotype study (G bands) showed that the mother, one maternal aunt, two maternal uncles, and the brother of the proband were likewise carriers of the same translocation. We discuss the type of segregation and the high familial incidence of the translocation. PMID:6524770

  1. Automatic Event Detection and Characterization of solar events with IRIS, SDO/AIA and Hi-C

    NASA Astrophysics Data System (ADS)

    Alexander, Caroline; Fayock, Brian; Winebarger, Amy

    2016-05-01

    Dynamic, low-lying loops with peak temperatures <1 MK are observed throughout the solar transition region. These loops can be observed in SDO/AIA data due to some lower temperature spectral lines in the passbands, but have not been studied in great detail. We have developed a technique to automatically identify events (i.e., brightenings) on a pixel-by-pixel basis applying a set of selection criteria. The pixels are then grouped according to their proximity in space and relative progression of the event. This method allows us to characterize their overall lifetime and the rate at which these events occur. Our current progress includes identification of these groups of events in IRIS data, determination of their existence in AIA data, and characterization based on a comparison between the two. This technique has also been used on Hi-C data in preparation for the rocket re-flight in July 2016. Results on the success of this technique at identifying real structures and sources of heating will be shown.

  2. Using tensor-based morphometry to detect structural brain abnormalities in rats with adolescent intermittent alcohol exposure

    NASA Astrophysics Data System (ADS)

    Paniagua, Beatriz; Ehlers, Cindy; Crews, Fulton; Budin, Francois; Larson, Garrett; Styner, Martin; Oguz, Ipek

    2011-03-01

    Understanding the effects of adolescent binge drinking that persist into adulthood is a crucial public health issue. Adolescent intermittent ethanol exposure (AIE) is an animal model that can be used to investigate these effects in rodents. In this work, we investigate the application of a particular image analysis technique, tensor-based morphometry, for detecting anatomical differences between AIE and control rats using Diffusion Tensor Imaging (DTI). Deformation field analysis is a popular method for detecting volumetric changes analyzing Jacobian determinants calculated on deformation fields. Recent studies showed that computing deformation field metrics on the full deformation tensor, often referred to as tensor-based morphometry (TBM), increases the sensitivity to anatomical differences. In this paper we conduct a comprehensive TBM study for precisely locating differences between control and AIE rats. Using a DTI RARE sequence designed for minimal geometric distortion, 12-directional images were acquired postmortem for control and AIE rats (n=9). After preprocessing, average images for the two groups were constructed using an unbiased atlas building approach. We non-rigidly register the two atlases using Large Deformation Diffeomorphic Metric Mapping, and analyze the resulting deformation field using TBM. In particular, we evaluate the tensor determinant, geodesic anisotropy, and deformation direction vector (DDV) on the deformation field to detect structural differences. This yields data on the local amount of growth, shrinkage and the directionality of deformation between the groups. We show that TBM can thus be used to measure group morphological differences between rat populations, demonstrating the potential of the proposed framework.

  3. Validity assessment of the detection method of maize event Bt10 through investigation of its molecular structure.

    PubMed

    Milcamps, Anne; Rabe, Scott; Cade, Rebecca; De Framond, Anic J; Henriksson, Peter; Kramer, Vance; Lisboa, Duarte; Pastor-Benito, Susana; Willits, Michael G; Lawrence, David; Van den Eede, Guy

    2009-04-22

    In March 2005, U.S. authorities informed the European Commission of the inadvertent release of unauthorized maize GM event Bt10 in their market and subsequently the grain channel. In the United States measures were taken to eliminate Bt10 from seed and grain supplies; in the European Union an embargo for maize gluten and brewer's grain import was implemented unless certified of Bt10 absence with a Bt10-specific PCR detection method. With the aim of assessing the validity of the Bt10 detection method, an in-depth analysis of the molecular organization of the genetic modification of this event was carried out by both the company Syngenta, who produced the event, and the European Commission Joint Research Centre, who validated the detection method. Using a variety of molecular analytical tools, both organizations found the genetic modification of event Bt10 to be very complex in structure, with rearrangements, inversions, and multiple copies of the structural elements (cry1Ab, pat, and the amp gene), interspersed with small genomic maize fragments. Southern blot analyses demonstrated that all Bt10 elements were found tightly linked on one large fragment, including the region that would generate the event-specific PCR amplicon of the Bt10 detection method. This study proposes a hypothetical map of the insert of event Bt10 and concludes that the validated detection method for event Bt10 is fit for its purpose. PMID:19368351

  4. Real-time detection of pathological cardiac events in the electrocardiogram.

    PubMed

    Iliev, Ivo; Krasteva, Vessela; Tabakov, Serafim

    2007-03-01

    The development of accurate and fast methods for real-time electrocardiogram (ECG) analysis is mandatory in handheld fully automated monitoring devices for high-risk cardiac patients. The present work describes a simple software method for fast detection of pathological cardiac events. It implements real-time procedures for QRS detection, interbeat RR-intervals analysis, QRS waveform evaluation and a decision-tree beat classifier. Two QRS descriptors are defined to assess (i) the RR interval deviation from the mean RR interval and (ii) the QRS waveform deviation from the QRS pattern of the sustained rhythm. The calculation of the second parameter requires a specific technique, in order to satisfy the demand for straight signal processing with minimum iterations and small memory size. This technique includes fast and resource efficient estimation of a histogram matrix, which accumulates dynamically the amplitude-temporal distribution of the successive QRS pattern waveforms. The pilot version of the method is developed in Matlab and it is tested with internationally recognized ECG databases. The assessment of the online single lead QRS detector showed sensitivity and positive predictivity of above 99%. The classification rules for detection of pathological ventricular beats were defined empirically by statistical analysis. The attained specificity and sensitivity are about 99.5% and 95.7% for all databases and about 99.81% and 98.87% for the noise free dataset. The method is applicable in low computational cost systems for long-term ECG monitoring, such as intelligent holters, automatic event/alarm recorders or personal devices with intermittent wireless data transfer to a central terminal. PMID:17322591

  5. Predictors of Arrhythmic Events Detected by Implantable Loop Recorders in Renal Transplant Candidates

    PubMed Central

    Silva, Rodrigo Tavares; Martinelli Filho, Martino; Peixoto, Giselle de Lima; de Lima, José Jayme Galvão; de Siqueira, Sérgio Freitas; Costa, Roberto; Gowdak, Luís Henrique Wolff; de Paula, Flávio Jota; Kalil Filho, Roberto; Ramires, José Antônio Franchini

    2015-01-01

    Background The recording of arrhythmic events (AE) in renal transplant candidates (RTCs) undergoing dialysis is limited by conventional electrocardiography. However, continuous cardiac rhythm monitoring seems to be more appropriate due to automatic detection of arrhythmia, but this method has not been used. Objective We aimed to investigate the incidence and predictors of AE in RTCs using an implantable loop recorder (ILR). Methods A prospective observational study conducted from June 2009 to January 2011 included 100 consecutive ambulatory RTCs who underwent ILR and were followed-up for at least 1 year. Multivariate logistic regression was applied to define predictors of AE. Results During a mean follow-up of 424 ± 127 days, AE could be detected in 98% of patients, and 92% had more than one type of arrhythmia, with most considered potentially not serious. Sustained atrial tachycardia and atrial fibrillation occurred in 7% and 13% of patients, respectively, and bradyarrhythmia and non-sustained or sustained ventricular tachycardia (VT) occurred in 25% and 57%, respectively. There were 18 deaths, of which 7 were sudden cardiac events: 3 bradyarrhythmias, 1 ventricular fibrillation, 1 myocardial infarction, and 2 undetermined. The presence of a long QTc (odds ratio [OR] = 7.28; 95% confidence interval [CI], 2.01–26.35; p = 0.002), and the duration of the PR interval (OR = 1.05; 95% CI, 1.02–1.08; p < 0.001) were independently associated with bradyarrhythmias. Left ventricular dilatation (LVD) was independently associated with non-sustained VT (OR = 2.83; 95% CI, 1.01–7.96; p = 0.041). Conclusions In medium-term follow-up of RTCs, ILR helped detect a high incidence of AE, most of which did not have clinical relevance. The PR interval and presence of long QTc were predictive of bradyarrhythmias, whereas LVD was predictive of non-sustained VT. PMID:26351983

  6. Arrhythmia Detection in Pediatric Patients: ECG Quality and Diagnostic Yield of a Patient-Triggered Einthoven Lead-I Event Recorder (Zenicor EKG-2™).

    PubMed

    Usadel, Lea; Haverkämper, Guido; Herrmann, Susanne; Löber, Rebekka; Weiss, Katja; Opgen-Rhein, Bernd; Berger, Felix; Will, Joachim C

    2016-03-01

    Symptoms that may be caused by arrhythmia are common in pediatric outpatient departments, though it remains challenging to reveal paroxysmal tachycardia. This investigation evaluated prospectively the quality and diagnostic yield of a newly available handheld patient-activated event recorder (ER) in children. In 226 children (pts) aged 0-17 years with or without congenital heart defects, pacemaker/ICDs or arrhythmia, a lead-I ER ECG was created. ER ECGs were recorded by pressing the patients' thumbs on the device and were analyzed in comparison with a lead-12 ECG, as gold standard. Event recording and data transmission were possible in all cases. ECG quality of the ER showed a high accordance in measuring heart rate (ICC = 0.962), duration of QRS complexes (κ = 0.686), and PR interval (ICC = 0.750) (p < 0.001) although P wave detection remained challenging (p = 0.120). 36 % (n = 82) of the pts had heart rhythm disturbances. The ER yielded 92 % sensitivity in diagnosing supraventricular tachycardia plus 77 % sensitivity and 92 % specificity in identifying abnormal ECGs. In children, the application of the tested ER was suitable. ECGs of good quality could be performed and transmitted easily, and also complex arrhythmia analysis was possible. This ER is an excellent diagnostic device for the detection and exclusion of tachycardia in children. PMID:26573815

  7. “Indefinite for Dysplasia” in Barrett's Esophagus: Inflammation and DNA Content Abnormality are Significant Predictors of Early Detection of Neoplasia

    PubMed Central

    Choi, Won-Tak; Emond, Mary J; Rabinovitch, Peter S; Ahn, Joseph; Upton, Melissa P; Westerhoff, Maria

    2015-01-01

    Background: Dysplasia arising from Barrett's esophagus precedes esophageal adenocarcinoma (EAC). Cases that are difficult to diagnose as dysplastic, especially in the setting of inflammation, may be designated “indefinite for dysplasia (IND).” Although flow cytometric analysis of DNA content has shown some promise in detecting EAC, there are few reports that have specifically evaluated the outcome of IND. Aims and methods: We analyzed a series of 96 IND patients seen at the University of Washington between 2005 and 2013 to determine the outcome of IND and to identify factors (including histologic features and DNA flow cytometric data) associated with subsequent detection of neoplasia. Results: Twenty-five percent of IND cases were found to have low-grade dysplasia, high-grade dysplasia (HGD), or EAC within 1 year, with 37% and 47% detected within 2 and 3 years, respectively. The 1-, 2-, and 3-year detection rates of HGD or EAC were 10%, 13%, and 20%, respectively. Active inflammation (hazard ratio (HR)=3.4, P=0.0005) and abnormal DNA content (HR=5.7, P=0.003) were significant risk factors of neoplasia. When active inflammation and DNA flow cytometric results were considered together, the HR for the combined markers was 18.8 (P<0.0001). The sensitivity and specificity of the combined markers for predicting detection of subsequent neoplasia within 3 years were 100% and 60%, respectively, with 100% negative and 89% positive predictive values. Conclusions: Histology with the support of DNA flow cytometry can identify a subset of IND patients who may have a higher risk for subsequent detection of neoplasia. PMID:25761942

  8. Abnormal FDG and MIBG Activity in the Bones in a Patient With Neuroblastoma Without Detectable Primary Tumor.

    PubMed

    Zhang, Wei; Zhuang, Hongming; Servaes, Sabah

    2016-08-01

    Neuroblastoma is among the most common extracranial solid tumors in pediatric patients and typically arises anywhere from the neck to pelvis but most commonly in the adrenal glands. It is extremely rare for a patient to have extensive metastases from neuroblastoma without primary tumor being identified. We present a 3-year-old with widespread bone and bone marrow involvement of the disease revealed on both FDG PET/CT and MIBG scan, which was pathologically proven as neuroblastoma. However, extensive imaging did not detect primary tumor anywhere. PMID:26825196

  9. The WISE Detection of an Infrared Echo in Tidal Disruption Event ASASSN-14li

    NASA Astrophysics Data System (ADS)

    Jiang, Ning; Dou, Liming; Wang, Tinggui; Yang, Chenwei; Lyu, Jianwei; Zhou, Hongyan

    2016-09-01

    We report the detection of a significant infrared variability of the nearest tidal disruption event (TDE) ASASSN-14li using Wide-field Infrared Survey Explorer and newly released Near-Earth Object WISE Reactivation data. In comparison with the quiescent state, the infrared flux is brightened by 0.12 and 0.16 mag in the W1 (3.4 μm) and W2 (4.6 μm) bands at 36 days after the optical discovery (or ∼110 days after the peak disruption date). The flux excess is still detectable ∼170 days later. Assuming that the flare-like infrared emission is from the dust around the black hole, its blackbody temperature is estimated to be ∼2.1 × 103 K, slightly higher than the dust sublimation temperature, indicating that the dust is likely located close to the dust sublimation radius. The equilibrium between the heating and radiation of the dust claims a bolometric luminosity of ∼1043–1045 erg s‑1, comparable with the observed peak luminosity. This result has for the first time confirmed the detection of infrared emission from the dust echoes of TDEs.

  10. Automatic Detection of Swallowing Events by Acoustical Means for Applications of Monitoring of Ingestive Behavior

    PubMed Central

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

    2010-01-01

    Our understanding of etiology of obesity and overweight is incomplete due to lack of objective and accurate methods for Monitoring of Ingestive Behavior (MIB) in the free living population. Our research has shown that frequency of swallowing may serve as a predictor for detecting food intake, differentiating liquids and solids, and estimating ingested mass. This paper proposes and compares two methods of acoustical swallowing detection from sounds contaminated by motion artifacts, speech and external noise. Methods based on mel-scale Fourier spectrum, wavelet packets, and support vector machines are studied considering the effects of epoch size, level of decomposition and lagging on classification accuracy. The methodology was tested on a large dataset (64.5 hours with a total of 9,966 swallows) collected from 20 human subjects with various degrees of adiposity. Average weighted epoch recognition accuracy for intra-visit individual models was 96.8% which resulted in 84.7% average weighted accuracy in detection of swallowing events. These results suggest high efficiency of the proposed methodology in separation of swallowing sounds from artifacts that originate from respiration, intrinsic speech, head movements, food ingestion, and ambient noise. The recognition accuracy was not related to body mass index, suggesting that the methodology is suitable for obese individuals. PMID:19789095

  11. Solar Power Ramp Events Detection Using an Optimized Swinging Door Algorithm

    SciTech Connect

    Cui, Mingjian; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Ke, Deping; Sun, Yuanzhang

    2015-08-05

    Solar power ramp events (SPREs) significantly influence the integration of solar power on non-clear days and threaten the reliable and economic operation of power systems. Accurately extracting solar power ramps becomes more important with increasing levels of solar power penetrations in power systems. In this paper, we develop an optimized swinging door algorithm (OpSDA) to enhance the state of the art in SPRE detection. First, the swinging door algorithm (SDA) is utilized to segregate measured solar power generation into consecutive segments in a piecewise linear fashion. Then we use a dynamic programming approach to combine adjacent segments into significant ramps when the decision thresholds are met. In addition, the expected SPREs occurring in clear-sky solar power conditions are removed. Measured solar power data from Tucson Electric Power is used to assess the performance of the proposed methodology. OpSDA is compared to two other ramp detection methods: the SDA and the L1-Ramp Detect with Sliding Window (L1-SW) method. The statistical results show the validity and effectiveness of the proposed method. OpSDA can significantly improve the performance of the SDA, and it can perform as well as or better than L1-SW with substantially less computation time.

  12. Solar Power Ramp Events Detection Using an Optimized Swinging Door Algorithm: Preprint

    SciTech Connect

    Cui, Mingjian; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Ke, Deping; Sun, Yuanzhang

    2015-08-07

    Solar power ramp events (SPREs) are those that significantly influence the integration of solar power on non-clear days and threaten the reliable and economic operation of power systems. Accurately extracting solar power ramps becomes more important with increasing levels of solar power penetrations in power systems. In this paper, we develop an optimized swinging door algorithm (OpSDA) to detection. First, the swinging door algorithm (SDA) is utilized to segregate measured solar power generation into consecutive segments in a piecewise linear fashion. Then we use a dynamic programming approach to combine adjacent segments into significant ramps when the decision thresholds are met. In addition, the expected SPREs occurring in clear-sky solar power conditions are removed. Measured solar power data from Tucson Electric Power is used to assess the performance of the proposed methodology. OpSDA is compared to two other ramp detection methods: the SDA and the L1-Ramp Detect with Sliding Window (L1-SW) method. The statistical results show the validity and effectiveness of the proposed method. OpSDA can significantly improve the performance of the SDA, and it can perform as well as or better than L1-SW with substantially less computation time.

  13. High-Performance Signal Detection for Adverse Drug Events using MapReduce Paradigm.

    PubMed

    Fan, Kai; Sun, Xingzhi; Tao, Ying; Xu, Linhao; Wang, Chen; Mao, Xianling; Peng, Bo; Pan, Yue

    2010-01-01

    Post-marketing pharmacovigilance is important for public health, as many Adverse Drug Events (ADEs) are unknown when those drugs were approved for marketing. However, due to the large number of reported drugs and drug combinations, detecting ADE signals by mining these reports is becoming a challenging task in terms of computational complexity. Recently, a parallel programming model, MapReduce has been introduced by Google to support large-scale data intensive applications. In this study, we proposed a MapReduce-based algorithm, for common ADE detection approach, Proportional Reporting Ratio (PRR), and tested it in mining spontaneous ADE reports from FDA. The purpose is to investigate the possibility of using MapReduce principle to speed up biomedical data mining tasks using this pharmacovigilance case as one specific example. The results demonstrated that MapReduce programming model could improve the performance of common signal detection algorithm for pharmacovigilance in a distributed computation environment at approximately liner speedup rates. PMID:21347109

  14. Orbit Determination and Maneuver Detection Using Event Representation with Thrust-Fourier-Coefficients

    NASA Astrophysics Data System (ADS)

    Lubey, D.; Ko, H.; Scheeres, D.

    The classical orbit determination (OD) method of dealing with unknown maneuvers is to restart the OD process with post-maneuver observations. However, it is also possible to continue the OD process through such unknown maneuvers by representing those unknown maneuvers with an appropriate event representation. It has been shown in previous work (Ko & Scheeres, JGCD 2014) that any maneuver performed by a satellite transitioning between two arbitrary orbital states can be represented as an equivalent maneuver connecting those two states using Thrust-Fourier-Coefficients (TFCs). Event representation using TFCs rigorously provides a unique control law that can generate the desired secular behavior for a given unknown maneuver. This paper presents applications of this representation approach to orbit prediction and maneuver detection problem across unknown maneuvers. The TFCs are appended to a sequential filter as an adjoint state to compensate unknown perturbing accelerations and the modified filter estimates the satellite state and thrust coefficients by processing OD across the time of an unknown maneuver. This modified sequential filter with TFCs is capable of fitting tracking data and maintaining an OD solution in the presence of unknown maneuvers. Also, the modified filter is found effective in detecting a sudden change in TFC values which indicates a maneuver. In order to illustrate that the event representation approach with TFCs is robust and sufficiently general to be easily adjustable, different types of measurement data are processed with the filter in a realistic LEO setting. Further, cases with mis-modeling of non-gravitational force are included in our study to verify the versatility and efficiency of our presented algorithm. Simulation results show that the modified sequential filter with TFCs can detect and estimate the orbit and thrust parameters in the presence of unknown maneuvers with or without measurement data during maneuvers. With no measurement

  15. The Event Detection and the Apparent Velocity Estimation Based on Computer Vision

    NASA Astrophysics Data System (ADS)

    Shimojo, M.

    2012-08-01

    The high spatial and time resolution data obtained by the telescopes aboard Hinode revealed the new interesting dynamics in solar atmosphere. In order to detect such events and estimate the velocity of dynamics automatically, we examined the estimation methods of the optical flow based on the OpenCV that is the computer vision library. We applied the methods to the prominence eruption observed by NoRH, and the polar X-ray jet observed by XRT. As a result, it is clear that the methods work well for solar images if the images are optimized for the methods. It indicates that the optical flow estimation methods in the OpenCV library are very useful to analyze the solar phenomena.

  16. Event detection and localization for small mobile robots using reservoir computing.

    PubMed

    Antonelo, E A; Schrauwen, B; Stroobandt, D

    2008-08-01

    Reservoir Computing (RC) techniques use a fixed (usually randomly created) recurrent neural network, or more generally any dynamic system, which operates at the edge of stability, where only a linear static readout output layer is trained by standard linear regression methods. In this work, RC is used for detecting complex events in autonomous robot navigation. This can be extended to robot localization tasks which are solely based on a few low-range, high-noise sensory data. The robot thus builds an implicit map of the environment (after learning) that is used for efficient localization by simply processing the input stream of distance sensors. These techniques are demonstrated in both a simple simulation environment and in the physically realistic Webots simulation of the commercially available e-puck robot, using several complex and even dynamic environments. PMID:18662855

  17. Energy efficient data representation and aggregation with event region detection in wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Banerjee, Torsha

    Detection (PERD) for WSNs. When a single event occurs, a child of the tree sends a Flagged Polynomial (FP) to its parent, if the readings approximated by it falls outside the data range defining the existing phenomenon. After the aggregation process is over, the root having the two polynomials, P and FP can be queried for FP (approximating the new event region) instead of flooding the whole network. For multiple such events, instead of computing a polynomial corresponding to each new event, areas with same data range are combined by the corresponding tree nodes and the aggregated coefficients are passed on. Results reveal that a new event can be detected by PERD while error in detection remains constant and is less than a threshold of 10%. As the node density increases, accuracy and delay for event detection are found to remain almost constant, making PERD highly scalable. Whenever an event occurs in a WSN, data is generated by closeby sensors and relaying the data to the base station (BS) make sensors closer to the BS run out of energy at a much faster rate than sensors in other parts of the network. This gives rise to an unequal distribution of residual energy in the network and makes those sensors with lower remaining energy level die at much faster rate than others. We propose a scheme for enhancing network Lifetime using mobile cluster heads (CH) in a WSN. To maintain remaining energy more evenly, some energy-rich nodes are designated as CHs which move in a controlled manner towards sensors rich in energy and data. This eliminates multihop transmission required by the static sensors and thus increases the overall lifetime of the WSN. We combine the idea of clustering and mobile CH to first form clusters of static sensor nodes. A collaborative strategy among the CHs further increases the lifetime of the network. Time taken for transmitting data to the BS is reduced further by making the CHs follow a connectivity strategy that always maintain a connected path to the BS

  18. Possible Detection of Volcanic Activity on Europa: Analysis of An Optical Transient Event

    NASA Astrophysics Data System (ADS)

    de La Fuente Marcos, R.; Nissar, A.

    2002-06-01

    Europa's low crater density suggests that geological activity has continued to the present epoch, leading to the possibility that current resurfacing events might be detectable. CCD observations were carried out with a ST-6 camera at the 0.5 m Mons Cassegrain telescope (Izaña Observatory, Tenerife,Canary Islands, Spain) during the night between 2 3 October 1999. Our images show a transient bright feature on the Galilean satellite. These images are analyzed here with the purpose of understanding the nature of the transient phenomena as it could be the result of explosive venting on the surface of the Jovian satellite. By comparison, we use NASA Infrared Telescope Facility images of two Io hot spots taken on12 October 1990. Although we mainly restrict our discussion on apossible eruptive nature of the observed spots, we also consider other alternative mechanisms able to produce bright events. In particular, an interaction between charged material being ejected from Europa and the Jovian magnetosphere cannot be entirely ruled out. If confirmed, this result would lend support for the existence of active resurfacing in Europa.

  19. Fusion of waveform events and radionuclide detections with the help of atmospheric transport modelling

    NASA Astrophysics Data System (ADS)

    Krysta, Monika; Kushida, Noriyuki; Kotselko, Yuriy; Carter, Jerry

    2016-04-01

    Possibilities of associating information from four pillars constituting CTBT monitoring and verification regime, namely seismic, infrasound, hydracoustic and radionuclide networks, have been explored by the International Data Centre (IDC) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) for a long time. Based on a concept of overlying waveform events with the geographical regions constituting possible sources of the detected radionuclides, interactive and non-interactive tools were built in the past. Based on the same concept, a design of a prototype of a Fused Event Bulletin was proposed recently. One of the key design elements of the proposed approach is the ability to access fusion results from either the radionuclide or from the waveform technologies products, which are available on different time scales and through various different automatic and interactive products. To accommodate various time scales a dynamic product evolving while the results of the different technologies are being processed and compiled is envisioned. The product would be available through the Secure Web Portal (SWP). In this presentation we describe implementation of the data fusion functionality in the test framework of the SWP. In addition, we address possible refinements to the already implemented concepts.

  20. Using AHRQ patient safety indicators to detect postdischarge adverse events in the Veterans Health Administration.

    PubMed

    Mull, Hillary J; Borzecki, Ann M; Chen, Qi; Shin, Marlena H; Rosen, Amy K

    2014-01-01

    Patient safety indicators (PSIs) use inpatient administrative data to flag cases with potentially preventable adverse events (AEs) attributable to hospital care. This study explored how many AEs the PSIs identified in the 30 days post discharge. PSI software was run on Veterans Health Administration 2003-2007 administrative data for 10 recently validated PSIs. Among PSI-eligible index hospitalizations not flagged with an AE, this study evaluated how many AEs occurred within 1 to 14 and 15 to 30 days post discharge using inpatient and outpatient administrative data. Considering all PSI-eligible index hospitalizations, 11 141 postdischarge AEs were identified, compared with 40 578 inpatient-flagged AEs. More than 60% of postdischarge AEs were detected within 14 days of discharge. The majority of postdischarge AEs were decubitus ulcers and postoperative pulmonary embolisms or deep vein thromboses. Extending PSI algorithms to the postdischarge period may provide a more complete picture of hospital quality. Future work should use chart review to validate postdischarge PSI events. PMID:23939485

  1. Applying a New Event Detection Algorithm to an Ocean Bottom Seismometer Dataset Recorded Offshore Southern California

    NASA Astrophysics Data System (ADS)

    Bishop, J.; Kohler, M. D.; Bunn, J.; Chandy, K. M.

    2015-12-01

    A number of active southern California offshore faults are capable of M>6 earthquakes, and the only permanent Southern California Seismic Network stations that can contribute to ongoing, small-magnitude earthquake detection and location are those located on the coastline and islands. To obtain a more detailed picture of the seismicity of the region, an array of 34 ocean bottom seismometers (OBSs) was deployed to record continuous waveform data off the coast of Southern California for 12 months (2010-2011) as part of the ALBACORE (Asthenospheric and Lithospheric Broadband Architecture from the California Offshore Region Experiment) project. To obtain a local event catalog based on OBS data, we make use of a newly developed data processing platform based on Python. The data processing procedure comprises a multi-step analysis that starts with the identification of significant signals above the time-adjusted noise floor for each sensor. This is followed by a time-dependent statistical estimate of the likelihood of an earthquake based on the aggregated signals in the array. For periods with elevated event likelihood, an adaptive grid-fitting procedure is used that yields candidate earthquake hypocenters with confidence estimates that best match the observed sensor signals. The results are validated with synthetic travel times and manual picks. Using results from ALBACORE, we have created a more complete view of active faulting in the California Borderland.

  2. Application of remote sensing in coastal change detection after the tsunami event in Indonesia

    NASA Astrophysics Data System (ADS)

    Lim, H. S.; MatJafri, M. Z.; Abdullah, K.; Saleh, N. Mohd.; Surbakti, M. S.

    2008-10-01

    Shoreline mapping and shoreline change detection are critical in many coastal zone applications. This study focuses on applying remote sensing technology to identify and assess coastal changes in the Banda Aceh, Indonesia. Major changes to land cover were found along the coastal line. Using remote sensing data to detect coastal line change requires high spatial resolution data. In this study, two high spatial data with 30 meter resolution of Landsat TM images captured before and after the Tsunami event were acquired for this purpose. The two satellite images was overlain and compared with pre-Tsunami imagery and with after Tsunami. The two Landsat TM images also were used to generate land cover classification maps for the 24 December 2004 and 27 March 2005, before and after the Tsunami event respectively. The standard supervised classifier was performed to the satellite images such as the Maximum Likelihood, Minimum Distance-to-mean and Parallelepiped. High overall accuracy (>80%) and Kappa coefficient (>0.80) was achieved by the Maximum Likelihood classifier in this study. Estimation of the damage areas between the two dated was estimated from the different between the two classified land cover maps. Visible damage could be seen in either before and after image pair. The visible damage land areas were determined and draw out using the polygon tool included in the PCI Geomatica image processing software. The final set of polygons containing the major changes in the coastal line. An overview of the coastal line changes using Landsat TM images is also presented in this study. This study provided useful information that helps local decision makers make better plan and land management choices.

  3. Automated Visual Event Detection, Tracking, and Data Management System for Cabled- Observatory Video

    NASA Astrophysics Data System (ADS)

    Edgington, D. R.; Cline, D. E.; Schlining, B.; Raymond, E.

    2008-12-01

    Ocean observatories and underwater video surveys have the potential to unlock important discoveries with new and existing camera systems. Yet the burden of video management and analysis often requires reducing the amount of video recorded through time-lapse video or similar methods. It's unknown how many digitized video data sets exist in the oceanographic community, but we suspect that many remain under analyzed due to lack of good tools or human resources to analyze the video. To help address this problem, the Automated Visual Event Detection (AVED) software and The Video Annotation and Reference System (VARS) have been under development at MBARI. For detecting interesting events in the video, the AVED software has been developed over the last 5 years. AVED is based on a neuromorphic-selective attention algorithm, modeled on the human vision system. Frames are decomposed into specific feature maps that are combined into a unique saliency map. This saliency map is then scanned to determine the most salient locations. The candidate salient locations are then segmented from the scene using algorithms suitable for the low, non-uniform light and marine snow typical of deep underwater video. For managing the AVED descriptions of the video, the VARS system provides an interface and database for describing, viewing, and cataloging the video. VARS was developed by the MBARI for annotating deep-sea video data and is currently being used to describe over 3000 dives by our remotely operated vehicles (ROV), making it well suited to this deepwater observatory application with only a few modifications. To meet the compute and data intensive job of video processing, a distributed heterogeneous network of computers is managed using the Condor workload management system. This system manages data storage, video transcoding, and AVED processing. Looking to the future, we see high-speed networks and Grid technology as an important element in addressing the problem of processing and

  4. Detection of events of public health importance under the international health regulations: a toolkit to improve reporting of unusual events by frontline healthcare workers

    PubMed Central

    2011-01-01

    Background The International Health Regulations (IHR (2005)) require countries to notify WHO of any event which may constitute a public health emergency of international concern. This notification relies on reports of events occurring at the local level reaching the national public health authorities. By June 2012 WHO member states are expected to have implemented the capacity to "detect events involving disease or death above expected levels for the particular time and place" on the local level and report essential information to the appropriate level of public health authority. Our objective was to develop tools to assist European countries improve the reporting of unusual events of public health significance from frontline healthcare workers to public health authorities. Methods We investigated obstacles and incentives to event reporting through a systematic literature review and expert consultations with national public health officials from various European countries. Multi-day expert meetings and qualitative interviews were used to gather experiences and examples of public health event reporting. Feedback on specific components of the toolkit was collected from healthcare workers and public health officials throughout the design process. Results Evidence from 79 scientific publications, two multi-day expert meetings and seven qualitative interviews stressed the need to clarify concepts and expectations around event reporting in European countries between the frontline and public health authorities. An analytical framework based on three priority areas for improved event reporting (professional engagement, communication and infrastructure) was developed and guided the development of the various tools. We developed a toolkit adaptable to country-specific needs that includes a guidance document for IHR National Focal Points and nine tool templates targeted at clinicians and laboratory staff: five awareness campaign tools, three education and training tools, and

  5. Beat-by-Beat Quantification of Cardiac Cycle Events Detected From Three-Dimensional Precordial Acceleration Signals.

    PubMed

    Paukkunen, Mikko; Parkkila, Petteri; Hurnanen, Tero; Pankaala, Mikko; Koivisto, Tero; Nieminen, Tuomo; Kettunen, Raimo; Sepponen, Raimo

    2016-03-01

    The vibrations produced by the cardiovascular system that are coupled to the precordium can be noninvasively detected using accelerometers. This technique is called seismocardiography. Although clinical applications have been proposed for seismocardiography, the physiology underlying the signal is still not clear. The relationship of seismocardiograms of on the back-to-front axis and cardiac events is fairly well known. However, the 3-D seismocardiograms detectable with modern accelerometers have not been quantified in terms of cardiac cycle events. A major reason for this might be the degree of intersubject variability observed in 3-D seismocardiograms. We present a method to quantify 3-D seismocardiography in terms of cardiac cycle events. First, cardiac cycle events are identified from the seismocardiograms, and then, assigned a number based on the location in which the corresponding event was found. 396 cardiac cycle events from 9 healthy subjects and 120 cardiac cycle events from patients suffering from atrial flutter were analyzed. Despite the weak intersubject correlation of the waveforms (0.05, 0.27, and 0.15 for the x-, y-, and z-axes, respectively), the present method managed to find latent similarities in the seismocardiograms of healthy subjects. We observed that in healthy subjects the distribution of cardiac cycle event coordinates was centered on specific locations. These locations were different in patients with atrial flutter. The results suggest that spatial distribution of seismocardiographic cardiac cycle events might be used to discriminate healthy individuals and those with a failing heart. PMID:25594987

  6. Detection of whole-brain abnormalities in temporal lobe epilepsy using tensor-based morphometry with DARTEL

    NASA Astrophysics Data System (ADS)

    Li, Wenjing; He, Huiguang; Lu, Jingjing; Lv, Bin; Li, Meng; Jin, Zhengyu

    2009-10-01

    Tensor-based morphometry (TBM) is an automated technique for detecting the anatomical differences between populations by examining the gradients of the deformation fields used to nonlinearly warp MR images. The purpose of this study was to investigate the whole-brain volume changes between the patients with unilateral temporal lobe epilepsy (TLE) and the controls using TBM with DARTEL, which could achieve more accurate inter-subject registration of brain images. T1-weighted images were acquired from 21 left-TLE patients, 21 right-TLE patients and 21 healthy controls, which were matched in age and gender. The determinants of the gradient of deformation fields at voxel level were obtained to quantify the expansion or contraction for individual images relative to the template, and then logarithmical transformation was applied on it. A whole brain analysis was performed using general lineal model (GLM), and the multiple comparison was corrected by false discovery rate (FDR) with p<0.05. For left-TLE patients, significant volume reductions were found in hippocampus, cingulate gyrus, precentral gyrus, right temporal lobe and cerebellum. These results potentially support the utility of TBM with DARTEL to study the structural changes between groups.

  7. [Comparison of detectability of liquid crystal displays (LCDs) and film using phantoms of small adenocarcinomas as abnormalities].

    PubMed

    Mochizuki, Yasuo; Abe, Shinji; Monma, Masahiko; Yamaguchi, Kojirou; Adachi, Toshiki

    2011-01-01

    Following the trend of the digitalization of the modalities used for diagnostic imaging, the devices for such imaging have increasingly included monitors. The present study was undertaken to evaluate the usefulness of soft-copy (liquid crystal display; LCD) images of phantoms of small adenocarcinomas using receiver operating characteristic (ROC) analysis of two different display systems: LCD and hard copy (film). A two-tailed paired t-test and the jackknife method (parametric methods) were performed, and no significant differences were found in the area under the ROC curve (AUC) for the pulmonary fields, lungs, ribs, or mediastinum between the film and LCD display systems, and the detectability did not differ between the film and LCD monitors. A Mann-Whitney U test, which is a non-parametric method that applies to the analysis of a small sample, also showed no significant differences in the AUC. The results of this study suggest that LCDs can replace hard-copy film as a display system if the signals. PMID:21532242

  8. Using GPS to Rapidly Detect and Model Earthquakes and Transient Deformation Events

    NASA Astrophysics Data System (ADS)

    Crowell, Brendan W.

    The rapid modeling and detection of earthquakes and transient deformation is a problem of extreme societal importance for earthquake early warning and rapid hazard response. To date, GPS data is not used in earthquake early warning or rapid source modeling even in Japan or California where the most extensive geophysical networks exist. This dissertation focuses on creating algorithms for automated modeling of earthquakes and transient slip events using GPS data in the western United States and Japan. First, I focus on the creation and use of high-rate GPS and combined seismogeodetic data for applications in earthquake early warning and rapid slip inversions. Leveraging data from earthquakes in Japan and southern California, I demonstrate that an accurate magnitude estimate can be made within seconds using P wave displacement scaling, and that a heterogeneous static slip model can be generated within 2-3 minutes. The preliminary source characterization is sufficiently robust to independently confirm the extent of fault slip used for rapid assessment of strong ground motions and improved tsunami warning in subduction zone environments. Secondly, I investigate the automated detection of transient slow slip events in Cascadia using daily positional estimates from GPS. Proper geodetic characterization of transient deformation is necessary for studies of regional interseismic, coseismic and postseismic tectonics, and miscalculations can affect our understanding of the regional stress field. I utilize the relative strength index (RSI) from financial forecasting to create a complete record of slow slip from continuous GPS stations in the Cascadia subduction zone between 1996 and 2012. I create a complete history of slow slip across the Cascadia subduction zone, fully characterizing the timing, progression, and magnitude of events. Finally, using a combination of continuous and campaign GPS measurements, I characterize the amount of extension, shear and subsidence in the

  9. Detecting Visual Function Abnormality with a Contrast-Dependent Visual Test in Patients with Type 2 Diabetes.

    PubMed

    Tsai, Li-Ting; Liao, Kuo-Meng; Jang, Yuh; Hu, Fu-Chang; Wu, Wei-Chi

    2016-01-01

    In addition to diabetic retinopathy, diabetes also causes early retinal neurodegeneration and other eye problems, which cause various types of visual deficits. This study used a computer-based visual test (Macular Multi-Function Assessment (MMFA)) to assess contrast-dependent macular visual function in patients with type 2 diabetes to collect more visual information than possible with only the visual acuity test. Because the MMFA is a newly developed test, this study first compared the agreement and discriminative ability of the MMFA and the Early Treatment Diabetic Retinopathy Study (ETDRS) contrast acuity charts. Then symbol discrimination performances of diabetic patients and controls were evaluated at 4 contrast levels using the MMFA. Seventy-seven patients and 45 controls participated. The agreement between MMFA and ETDRS scores was examined by fitting three-level linear mixed-effect models to estimate the intraclass correlation coefficients (ICCs). The estimated areas under the receiver operating characteristic (ROC) curve were used to compare the discriminative ability of diseased versus non-diseased participants between the two tests. The MMFA scores of patients and controls were compared with multiple linear regression analysis after adjusting the effects of age, sex, hypertension and cataract. Results showed that the scores of the MMFA and ETDRS tests displayed high levels of agreement and acceptable and similar discriminative ability. The MMFA performance was correlated with the severity of diabetic retinopathy. Most of the MMFA scores differed significantly between the diabetic patients and controls. In the low contrast condition, the MMFA scores were significantly lower for 006Eon-DR patients than for controls. The potential utility of the MMFA as an easy screening tool for contrast-dependent visual function and for detecting early functional visual change in patients with type 2 diabetes is discussed. PMID:27611680

  10. PREFACE: Fourth Symposium on Large TPCs for Low Energy Rare Event Detection

    NASA Astrophysics Data System (ADS)

    Irastorza, Igor G.; Colas, Paul; Giomataris, Ioannis

    2009-07-01

    The Fourth International Symposium on large TPCs for low-energy rare-event detection was held at the Hermite auditorium of the Insitute Henri Poincaréte, 11 rue Pierre et Marie Curie in Paris on 18-19 December 2008. As in previous instances of the meeting, held always in Paris in 2006, 2004 and 2002, it gathered a significant community of physicists involved in rare event searches and/or development of time projection chambers (TPCs). The purpose of the meeting was to present and discuss the status of current experiments or projects involving the use of large TPCs for the search of rare events, like low-energy neutrinos, double beta decay, dark matter or axion experiments, as well as to discuss new results and ideas in the framework of the last developments of Micro Pattern Gaseous Detectors (MPGD), and how these are being - or could be - applied to the mentioned searches. The rapid evolvement of these devices and the relevance of their latest results need to be efficiently transferred to the rare event community. The creation of this series of meetings followed the motivation of bringing together both know-hows and it is proving to be a fruitful area of collaboration. Once more, the format of the meeting proved to be a success. A short (2 days) and relatively informal program with some recent highlighted results, rather than exhaustive reviews, attracted the interest of the audience. The symposium, fourth of the series, is becoming consolidated as a regular meeting place for the synergic interplay between the fields of rare events and TPC development. Apart from the usual topics central to the conference subject, like the status of some low-energy neutrino physics and double beta decay experiments, dark matter experiments (and in general physics in underground laboratories), axion searches, or development results, every year the conference programme is enriched with original slightly off-topic contributions that trigger the curiosity and stimulate further thought

  11. Improvement of spectral density-based activation detection of event-related fMRI data.

    PubMed

    Ngan, Shing-Chung; Hu, Xiaoping; Tan, Li-Hai; Khong, Pek-Lan

    2009-09-01

    For event-related data obtained from an experimental paradigm with a periodic design, spectral density at the fundamental frequency of the paradigm has been used as a template-free activation detection measure. In this article, we build and expand upon this detection measure to create an improved, integrated measure. Such an integrated measure linearly combines information contained in the spectral densities at the fundamental frequency as well as the harmonics of the paradigm and in a spatial correlation function characterizing the degree of co-activation among neighboring voxels. Several figures of merit are described and used to find appropriate values for the coefficients in the linear combination. Using receiver-operating characteristic analysis on simulated functional magnetic resonance imaging (fMRI) data sets, we quantify and validate the improved performance of the integrated measure over the spectral density measure based on the fundamental frequency as well as over some other popular template-free data analysis methods. We then demonstrate the application of the new method on an experimental fMRI data set. Finally, several extensions to this work are suggested. PMID:19535208

  12. Detecting concealed information using feedback related event-related brain potentials.

    PubMed

    Sai, Liyang; Lin, Xiaohong; Hu, Xiaoqing; Fu, Genyue

    2014-10-01

    Employing an event-related potential (ERP)-based concealed information test (CIT), the present study investigated (1) the neurocognitive processes when people received feedbacks regarding their deceptive/truthful responses and (2) whether such feedback-related ERP activities can be used to detect concealed information above and beyond the recognition-related P300. During the CIT, participants were presented with rare, meaningful probes (their own names) embedded within a series of frequent yet meaningless irrelevants (others' names). Participants were instructed to deny their recognition of the probes. Critically, following participants' responses, they were provided with feedbacks regarding whether they succeeded or failed in the CIT. Replicating previous ERP-based CITs, we found a larger P300 elicited by probe compared to irrelevant. Regarding feedback-related ERPs, a temporospatial Principle Component Analyses found two ERP components that were not only sensitive to feedback manipulations but also can discriminate probe from irrelevant: an earlier, central-distributed positivity that was elicited by "success" feedbacks peaked around 219ms; and a later, right central-distributed positivity that was also elicited by "success" feedbacks, peaked around 400ms. Importantly, the feedback ERPs were not correlated with P300 that was elicited by probe/irrelevant, suggesting that these two ERPs reflect independent processes underlying memory concealment. These findings illustrate the feasibility and promise of using feedback-related ERPs to detect concealed memory and thus deception. PMID:25058495

  13. Fully Autonomous Multiplet Event Detection: Application to Local-Distance Monitoring of Blood Falls Seismicity

    SciTech Connect

    Carmichael, Joshua Daniel; Carr, Christina; Pettit, Erin C.

    2015-06-18

    We apply a fully autonomous icequake detection methodology to a single day of high-sample rate (200 Hz) seismic network data recorded from the terminus of Taylor Glacier, ANT that temporally coincided with a brine release episode near Blood Falls (May 13, 2014). We demonstrate a statistically validated procedure to assemble waveforms triggered by icequakes into populations of clusters linked by intra-event waveform similarity. Our processing methodology implements a noise-adaptive power detector coupled with a complete-linkage clustering algorithm and noise-adaptive correlation detector. This detector-chain reveals a population of 20 multiplet sequences that includes ~150 icequakes and produces zero false alarms on the concurrent, diurnally variable noise. Our results are very promising for identifying changes in background seismicity associated with the presence or absence of brine release episodes. We thereby suggest that our methodology could be applied to longer time periods to establish a brine-release monitoring program for Blood Falls that is based on icequake detections.

  14. Novel Logistic Regression Model of Chest CT Attenuation Coefficient Distributions for the Automated Detection of Abnormal (Emphysema or ILD) versus Normal Lung

    PubMed Central

    Chan, Kung-Sik; Jiao, Feiran; Mikulski, Marek A.; Gerke, Alicia; Guo, Junfeng; Newell, John D; Hoffman, Eric A.; Thompson, Brad; Lee, Chang Hyun; Fuortes, Laurence J.

    2015-01-01

    Rationale and Objectives We evaluated the role of automated quantitative computed tomography (CT) scan interpretation algorithm in detecting Interstitial Lung Disease (ILD) and/or emphysema in a sample of elderly subjects with mild lung disease.ypothesized that the quantification and distributions of CT attenuation values on lung CT, over a subset of Hounsfield Units (HU) range [−1000 HU, 0 HU], can differentiate early or mild disease from normal lung. Materials and Methods We compared results of quantitative spiral rapid end-exhalation (functional residual capacity; FRC) and end-inhalation (total lung capacity; TLC) CT scan analyses in 52 subjects with radiographic evidence of mild fibrotic lung disease to 17 normal subjects. Several CT value distributions were explored, including (i) that from the peripheral lung taken at TLC (with peels at 15 or 65mm), (ii) the ratio of (i) to that from the core of lung, and (iii) the ratio of (ii) to its FRC counterpart. We developed a fused-lasso logistic regression model that can automatically identify sub-intervals of [−1000 HU, 0 HU] over which a CT value distribution provides optimal discrimination between abnormal and normal scans. Results The fused-lasso logistic regression model based on (ii) with 15 mm peel identified the relative frequency of CT values over [−1000, −900] and that over [−450,−200] HU as a means of discriminating abnormal versus normal, resulting in a zero out-sample false positive rate and 15%false negative rate of that was lowered to 12% by pooling information. Conclusions We demonstrated the potential usefulness of this novel quantitative imaging analysis method in discriminating ILD and/or emphysema from normal lungs. PMID:26776294

  15. Localized Detection of Frozen Precipitation Events and the Rain/Snow Transition

    NASA Astrophysics Data System (ADS)

    Strachan, S.

    2014-12-01

    Frozen precipitation in the mid-latitudes and semi-arid environments frequently serves a crucial role in the annual water budget. Often occurring along elevational gradients, the rain/snow transition (or, "snow line") in mountain systems determines the amount of water which enters the system slowly during melt phases as opposed to rain which immediately infiltrates or runs off to lower elevations. This in turn influences the location and composition of ecological communities such as conifer forests, as well as timing and nature of the entire mountain block annual hydrologic cycle. Characterization of the rain/snow transition is becoming a priority in mountainous semi-arid regions, as increasing human populations and repeated drought episodes combine to create water shortages. Atmospheric conditions (temperature and relative humidity) which signal the rain/snow transition have been described, but variability within the conditions window can create error in estimating true areal cover of frozen versus liquid precipitation. In populated, flood-prone regions, radar installations specifically tuned to the detection of the "bright band" transition elevation can be deployed; however these cannot be permanently installed at remote, solar-power-dependent climate stations or with fine geographical scale. Characterization of current trends in rain/snow transition can be made using automated weather stations placed along the elevational gradient fielding sensors for high-frequency (e.g. 1-10 minute) measurement of air temperature, relative humidity, liquid precipitation, and precipitation mass. Visual validation of precipitation modes detected through automated means is performed using time-series records from digital cameras placed at each station. Refinements of geographically-explicit relationships of atmospheric conditions to precipitation mode can be made over time, as well as detection of seasonally-anomalous but eco-hydrologically-significant frozen precipitation events

  16. FOREWORD: 3rd Symposium on Large TPCs for Low Energy Event Detection

    NASA Astrophysics Data System (ADS)

    Irastorza, Igor G.; Colas, Paul; Gorodetzky, Phillippe

    2007-05-01

    The Third International Symposium on large TPCs for low-energy rare-event detection was held at Carré des sciences, Poincaré auditorium, 25 rue de la Montagne Ste Geneviève in Paris on 11 12 December 2006. This prestigious location belonging to the Ministry of Research is hosted in the former Ecole Polytechnique. The meeting, held in Paris every two years, gathers a significant community of physicists involved in rare event detection. Its purpose is an extensive discussion of present and future projects using large TPCs for low energy, low background detection of rare events (low-energy neutrinos, dark matter, solar axions). The use of a new generation of Micro-Pattern Gaseous Detectors (MPGD) appears to be a promising way to reach this goal. The program this year was enriched by a new session devoted to the detection challenge of polarized gamma rays, relevant novel experimental techniques and the impact on particle physics, astrophysics and astronomy. A very particular feature of this conference is the large variety of talks ranging from purely theoretical to purely experimental subjects including novel technological aspects. This allows discussion and exchange of useful information and new ideas that are emerging to address particle physics experimental challenges. The scientific highlights at the Symposium came on many fronts: Status of low-energy neutrino physics and double-beta decay New ideas on double-beta decay experiments Gamma ray polarization measurement combining high-precision TPCs with MPGD read-out Dark Matter challenges in both axion and WIMP search with new emerging ideas for detection improvements Progress in gaseous and liquid TPCs for rare event detection Georges Charpak opened the meeting with a talk on gaseous detectors for applications in the bio-medical field. He also underlined the importance of new MPGD detectors for both physics and applications. There were about 100 registered participants at the symposium. The successful

  17. Impaired target detection in schizophrenia and the ventral attentional network: Findings from a joint event-related potential–functional MRI analysis

    PubMed Central

    Wynn, Jonathan K.; Jimenez, Amy M.; Roach, Brian J.; Korb, Alexander; Lee, Junghee; Horan, William P.; Ford, Judith M.; Green, Michael F.

    2015-01-01

    Schizophrenia patients have abnormal neural responses to salient, infrequent events. We integrated event-related potentials (ERP) and fMRI to examine the contributions of the ventral (salience) and dorsal (sustained) attention networks to this dysfunctional neural activation. Twenty-one schizophrenia patients and 22 healthy controls were assessed in separate sessions with ERP and fMRI during a visual oddball task. Visual P100, N100, and P300 ERP waveforms and fMRI activation were assessed. A joint independent components analysis (jICA) on the ERP and fMRI data were conducted. Patients exhibited reduced P300, but not P100 or N100, amplitudes to targets and reduced fMRI neural activation in both dorsal and ventral attentional networks compared with controls. However, the jICA revealed that the P300 was linked specifically to activation in the ventral (salience) network, including anterior cingulate, anterior insula, and temporal parietal junction, with patients exhibiting significantly lower activation. The P100 and N100 were linked to activation in the dorsal (sustained) network, with no group differences in level of activation. This joint analysis approach revealed the nature of target detection deficits that were not discernable by either imaging methodology alone, highlighting the utility of a multimodal fMRI and ERP approach to understand attentional network deficits in schizophrenia. PMID:26448909

  18. Impaired target detection in schizophrenia and the ventral attentional network: Findings from a joint event-related potential-functional MRI analysis.

    PubMed

    Wynn, Jonathan K; Jimenez, Amy M; Roach, Brian J; Korb, Alexander; Lee, Junghee; Horan, William P; Ford, Judith M; Green, Michael F

    2015-01-01

    Schizophrenia patients have abnormal neural responses to salient, infrequent events. We integrated event-related potentials (ERP) and fMRI to examine the contributions of the ventral (salience) and dorsal (sustained) attention networks to this dysfunctional neural activation. Twenty-one schizophrenia patients and 22 healthy controls were assessed in separate sessions with ERP and fMRI during a visual oddball task. Visual P100, N100, and P300 ERP waveforms and fMRI activation were assessed. A joint independent components analysis (jICA) on the ERP and fMRI data were conducted. Patients exhibited reduced P300, but not P100 or N100, amplitudes to targets and reduced fMRI neural activation in both dorsal and ventral attentional networks compared with controls. However, the jICA revealed that the P300 was linked specifically to activation in the ventral (salience) network, including anterior cingulate, anterior insula, and temporal parietal junction, with patients exhibiting significantly lower activation. The P100 and N100 were linked to activation in the dorsal (sustained) network, with no group differences in level of activation. This joint analysis approach revealed the nature of target detection deficits that were not discernable by either imaging methodology alone, highlighting the utility of a multimodal fMRI and ERP approach to understand attentional network deficits in schizophrenia. PMID:26448909

  19. Rapid and Highly Sensitive Detection of Variant Creutzfeldt - Jakob Disease Abnormal Prion Protein on Steel Surfaces by Protein Misfolding Cyclic Amplification: Application to Prion Decontamination Studies

    PubMed Central

    Belondrade, Maxime; Nicot, Simon; Béringue, Vincent; Coste, Joliette; Lehmann, Sylvain; Bougard, Daisy

    2016-01-01

    The prevalence of variant Creutzfeldt-Jakob disease (vCJD) in the population remains uncertain, although it has been estimated that 1 in 2000 people in the United Kingdom are positive for abnormal prion protein (PrPTSE) by a recent survey of archived appendix tissues. The prominent lymphotropism of vCJD prions raises the possibility that some surgical procedures may be at risk of iatrogenic vCJD transmission in healthcare facilities. It is therefore vital that decontamination procedures applied to medical devices before their reprocessing are thoroughly validated. A current limitation is the lack of a rapid model permissive to human prions. Here, we developed a prion detection assay based on protein misfolding cyclic amplification (PMCA) technology combined with stainless-steel wire surfaces as carriers of prions (Surf-PMCA). This assay allowed the specific detection of minute quantities (10−8 brain dilution) of either human vCJD or ovine scrapie PrPTSE adsorbed onto a single steel wire, within a two week timeframe. Using Surf-PMCA we evaluated the performance of several reference and commercially available prion-specific decontamination procedures. Surprisingly, we found the efficiency of several marketed reagents to remove human vCJD PrPTSE was lower than expected. Overall, our results demonstrate that Surf-PMCA can be used as a rapid and ultrasensitive assay for the detection of human vCJD PrPTSE adsorbed onto a metallic surface, therefore facilitating the development and validation of decontamination procedures against human prions. PMID:26800081

  20. Rapid and Highly Sensitive Detection of Variant Creutzfeldt-Jakob Disease Abnormal Prion Protein on Steel Surfaces by Protein Misfolding Cyclic Amplification: Application to Prion Decontamination Studies.

    PubMed

    Belondrade, Maxime; Nicot, Simon; Béringue, Vincent; Coste, Joliette; Lehmann, Sylvain; Bougard, Daisy

    2016-01-01

    The prevalence of variant Creutzfeldt-Jakob disease (vCJD) in the population remains uncertain, although it has been estimated that 1 in 2000 people in the United Kingdom are positive for abnormal prion protein (PrPTSE) by a recent survey of archived appendix tissues. The prominent lymphotropism of vCJD prions raises the possibility that some surgical procedures may be at risk of iatrogenic vCJD transmission in healthcare facilities. It is therefore vital that decontamination procedures applied to medical devices before their reprocessing are thoroughly validated. A current limitation is the lack of a rapid model permissive to human prions. Here, we developed a prion detection assay based on protein misfolding cyclic amplification (PMCA) technology combined with stainless-steel wire surfaces as carriers of prions (Surf-PMCA). This assay allowed the specific detection of minute quantities (10-8 brain dilution) of either human vCJD or ovine scrapie PrPTSE adsorbed onto a single steel wire, within a two week timeframe. Using Surf-PMCA we evaluated the performance of several reference and commercially available prion-specific decontamination procedures. Surprisingly, we found the efficiency of several marketed reagents to remove human vCJD PrPTSE was lower than expected. Overall, our results demonstrate that Surf-PMCA can be used as a rapid and ultrasensitive assay for the detection of human vCJD PrPTSE adsorbed onto a metallic surface, therefore facilitating the development and validation of decontamination procedures against human prions. PMID:26800081

  1. Sudden event recognition: a survey.

    PubMed

    Suriani, Nor Surayahani; Hussain, Aini; Zulkifley, Mohd Asyraf

    2013-01-01

    Event recognition is one of the most active research areas in video surveillance fields. Advancement in event recognition systems mainly aims to provide convenience, safety and an efficient lifestyle for humanity. A precise, accurate and robust approach is necessary to enable event recognition systems to respond to sudden changes in various uncontrolled environments, such as the case of an emergency, physical threat and a fire or bomb alert. The performance of sudden event recognition systems depends heavily on the accuracy of low level processing, like detection, recognition, tracking and machine learning algorithms. This survey aims to detect and characterize a sudden event, which is a subset of an abnormal event in several video surveillance applications. This paper discusses the following in detail: (1) the importance of a sudden event over a general anomalous event; (2) frameworks used in sudden event recognition; (3) the requirements and comparative studies of a sudden event recognition system and (4) various decision-making approaches for sudden event recognition. The advantages and drawbacks of using 3D images from multiple cameras for real-time application are also discussed. The paper concludes with suggestions for future research directions in sudden event recognition. PMID:23921828

  2. Sudden Event Recognition: A Survey

    PubMed Central

    Suriani, Nor Surayahani; Hussain, Aini; Zulkifley, Mohd Asyraf

    2013-01-01

    Event recognition is one of the most active research areas in video surveillance fields. Advancement in event recognition systems mainly aims to provide convenience, safety and an efficient lifestyle for humanity. A precise, accurate and robust approach is necessary to enable event recognition systems to respond to sudden changes in various uncontrolled environments, such as the case of an emergency, physical threat and a fire or bomb alert. The performance of sudden event recognition systems depends heavily on the accuracy of low level processing, like detection, recognition, tracking and machine learning algorithms. This survey aims to detect and characterize a sudden event, which is a subset of an abnormal event in several video surveillance applications. This paper discusses the following in detail: (1) the importance of a sudden event over a general anomalous event; (2) frameworks used in sudden event recognition; (3) the requirements and comparative studies of a sudden event recognition system and (4) various decision-making approaches for sudden event recognition. The advantages and drawbacks of using 3D images from multiple cameras for real-time application are also discussed. The paper concludes with suggestions for future research directions in sudden event recognition. PMID:23921828

  3. Automated detection of rare-event pathogens through time-gated luminescence scanning microscopy.

    PubMed

    Lu, Yiqing; Jin, Dayong; Leif, Robert C; Deng, Wei; Piper, James A; Yuan, Jingli; Duan, Yusheng; Huo, Yujing

    2011-05-01

    Many microorganisms have a very low threshold (<10 cells) to trigger infectious diseases, and, in these cases, it is important to determine the absolute cell count in a low-cost and speedy fashion. Fluorescent microscopy is a routine method; however, one fundamental problem has been associated with the existence in the sample of large numbers of nontarget particles, which are naturally autofluorescent, thereby obscuring the visibility of target organisms. This severely affects both direct visual inspection and the automated microscopy based on computer pattern recognition. We report a novel strategy of time-gated luminescent scanning for accurate counting of rare-event cells, which exploits the large difference in luminescence lifetimes between the lanthanide biolabels, >100 μs, and the autofluorescence backgrounds, <0.1 μs, to render background autofluorescence invisible to the detector. Rather than having to resort to sophisticated imaging analysis, the background-free feature allows a single-element photomultiplier to locate rare-event cells, so that requirements for data storage and analysis are minimized to the level of image confirmation only at the final step. We have evaluated this concept in a prototype instrument using a 2D scanning stage and applied it to rare-event Giardia detection labeled by a europium complex. For a slide area of 225 mm(2) , the time-gated scanning method easily reduced the original 40,000 adjacent elements (0.075 mm × 0.075 mm) down to a few "elements of interest" containing the Giardia cysts. We achieved an averaged signal-to-background ratio of 41.2 (minimum ratio of 12.1). Such high contrasts ensured the accurate mapping of all the potential Giardia cysts free of false positives or negatives. This was confirmed by the automatic retrieving and time-gated luminescence bioimaging of these Giardia cysts. Such automated microscopy based on time-gated scanning can provide novel solutions for quantitative diagnostics in advanced

  4. Detection of Rapid Events at Mantle Depth by Future Gravity Missions

    NASA Astrophysics Data System (ADS)

    Ivins, Erik; Watkins, Michael

    2015-04-01

    The robust detection of gravity changes associated with relatively shallow subduction zone earthquakes (0-50 km depth co-seismic rupture) has been one of the success stories of the GRACE (e.g., Han et al. 2013, JGR-B, doi:10.1002/jgrb.50116) and GOCE (e.g., Fuchs et al., 2013, JGR-B, doi: 10.1002/jgrb.50381) missions. This surprise is a testament to the sensitivity of the measurement system, for the satellites must map the gravity potential field changes while flying at orbital altitudes exceeding 400 km (in the case of GRACE). It is clear that these observations contribute to advancing our understanding of large subduction zone earthquakes, if for no other reason than they allow comprehensive observation over the ocean covered solid Earth. The observations aid studies of both the mass transport associated with coseismic and post-seismic. Measurement capability for missions proposed to be flown after GRACE-2 are anticipated to be an order of magnitude, or greater, in accuracy and resolution (e.g., Wiese et al., 2012, J. Geodesy, doi: 10.1007/s00190-011-0493-8; Elsaka et al. 2014, J. Geodesy, doi: 10.1007/s00190-013-0665-9). Deep subduction zone earthquakes have not been detected, nor have any other non-seismic solid Earth deformations - with the exception of the glacial isostatic adjustment vertical response to the last glacial age. We examine the possibility that earthquakes occurring at, or near, the major transition zone in the mantle should be detected in the region where mantle phases become unstable and undergoes transition to a stable perovskite phase below 660 km depth. The Mw 8.2 1994 Bolivian Earthquake and the May 24, 2013 Mw 8.3 earthquake beneath the Sea of Okhotsk, Russia, are prototypes of events that can be studied with future gravity missions. Observation of gravity changes associated with deep subduction zone earthquakes could provide new clues on the enigmatic questions currently in debate over faulting mechanism (e.g., Zhan et al., 2014, Science

  5. Wavelet based automated postural event detection and activity classification with single imu - biomed 2013.

    PubMed

    Lockhart, Thurmon E; Soangra, Rahul; Zhang, Jian; Wu, Xuefan

    2013-01-01

    Mobility characteristics associated with activity of daily living such as sitting down, lying down, rising up, and walking are considered to be important in maintaining functional independence and healthy life style especially for the growing elderly population. Characteristics of postural transitions such as sit-to-stand are widely used by clinicians as a physical indicator of health, and walking is used as an important mobility assessment tool. Many tools have been developed to assist in the assessment of functional levels and to detect a person’s activities during daily life. These include questionnaires, observation, diaries, kinetic and kinematic systems, and validated functional tests. These measures are costly and time consuming, rely on subjective patient recall and may not accurately reflect functional ability in the patient’s home. In order to provide a low-cost, objective assessment of functional ability, inertial measurement unit (IMU) using MEMS technology has been employed to ascertain ADLs. These measures facilitate long-term monitoring of activity of daily living using wearable sensors. IMU system are desirable in monitoring human postures since they respond to both frequency and the intensity of movements and measure both dc (gravitational acceleration vector) and ac (acceleration due to body movement) components at a low cost. This has enabled the development of a small, lightweight, portable system that can be worn by a free-living subject without motion impediment – TEMPO (Technology Enabled Medical Precision Observation). Using this IMU system, we acquired indirect measures of biomechanical variables that can be used as an assessment of individual mobility characteristics with accuracy and recognition rates that are comparable to the modern motion capture systems. In this study, five subjects performed various ADLs and mobility measures such as posture transitions and gait characteristics were obtained. We developed postural event detection

  6. Detection of air pollution events over Évora-Portugal during 2009

    NASA Astrophysics Data System (ADS)

    Filipa Domingues, Ana; Bortoli, Daniele; Silva, Ana Maria; Kulkarni, Pavan; Antón, Manuel

    2010-05-01

    All over the world pollutant industries, traffic and other natural and anthropogenic sources are responsible for air pollution affecting health and also the climate. At the moment the monitoring of air quality in urban and country regions become an urgent concern in the atmospheric studies due to the impact of global air pollution on climate and on the environment. One of the evidences of the global character of air pollution is that it not only affects industrialized countries but also reaches less developed countries with pollution gases and particles generated for elsewhere. The development and the employment of instruments and techniques for measure the variation of atmospheric trace gases and perform their monitoring are crucial for the improvement of the air quality and the control of pollutants emissions. One of the instruments able to perform the air quality monitoring is the Spectrometer for Atmospheric TRacers Measurements (SPATRAM) and it is installed at the CGÉs Observatory in Évora (38.5° N, 7.9° W, 300 m asl). This UV-VIS Spectrometer is used to carry out measurements of the zenith scattered radiation (290- 900 nm) to retrieve the vertical content of some atmospheric trace gases such as O3 and NO2 in stratosphere, using Differential Optical Absorption Spectroscopy (DOAS) methodology. Although SPATRAM, in its actual geometric and operational configuration - zenith sky looking and passive mode measurements, is not able to detect small variations of tracers in the troposphere it is possible to identify enhancements in the pollution loads due to air masses movements from polluted sites. In spite of the fact that Evora is a quite unpolluted city the deep analysis of the DOAS output, namely the quantity of gas (in this case NO2) present along the optical path of measurements (SCD - Slant Column Density) allows for the detection of unpredicted variations in the diurnal NO2 cycle. The SPATRAḾs data allows the identification of polluting events which

  7. An automated cross-correlation based event detection technique and its application to surface passive data set

    USGS Publications Warehouse

    Forghani-Arani, Farnoush; Behura, Jyoti; Haines, Seth S.; Batzle, Mike

    2013-01-01

    In studies on heavy oil, shale reservoirs, tight gas and enhanced geothermal systems, the use of surface passive seismic data to monitor induced microseismicity due to the fluid flow in the subsurface is becoming more common. However, in most studies passive seismic records contain days and months of data and manually analysing the data can be expensive and inaccurate. Moreover, in the presence of noise, detecting the arrival of weak microseismic events becomes challenging. Hence, the use of an automated, accurate and computationally fast technique for event detection in passive seismic data is essential. The conventional automatic event identification algorithm computes a running-window energy ratio of the short-term average to the long-term average of the passive seismic data for each trace. We show that for the common case of a low signal-to-noise ratio in surface passive records, the conventional method is not sufficiently effective at event identification. Here, we extend the conventional algorithm by introducing a technique that is based on the cross-correlation of the energy ratios computed by the conventional method. With our technique we can measure the similarities amongst the computed energy ratios at different traces. Our approach is successful at improving the detectability of events with a low signal-to-noise ratio that are not detectable with the conventional algorithm. Also, our algorithm has the advantage to identify if an event is common to all stations (a regional event) or to a limited number of stations (a local event). We provide examples of applying our technique to synthetic data and a field surface passive data set recorded at a geothermal site.

  8. An engineered nano-plasmonic biosensing surface for colorimetric and SERS detection of DNA-hybridization events

    NASA Astrophysics Data System (ADS)

    Heydari, Esmaeil; Thompson, David; Graham, Duncan; Cooper, Jonathan M.; Clark, Alasdair W.

    2015-03-01

    We report a versatile nanophotonic biosensing platform that enables both colorimetric detection and enhanced Raman spectroscopy detection of molecular binding events. Through the integration of electron-beam lithography, dip-pennanolithography and molecular self-assembly, we demonstrate plasmonic nanostructures which change geometry and plasmonic properties in response to molecularly-mediated nanoparticle binding events. These biologically-active nanostructured surfaces hold considerable potential for use as multiplexed sensor platforms for point-of-care diagnostics, and as scaffolds for a new generation of molecularly dynamic metamaterials.

  9. Elastomeric optical fiber sensors and method for detecting and measuring events occurring in elastic materials

    DOEpatents

    Muhs, Jeffrey D.; Capps, Gary J.; Smith, David B.; White, Clifford P.

    1994-01-01

    Fiber optic sensing means for the detection and measurement of events such as dynamic loadings imposed upon elastic materials including cementitious materials, elastomers, and animal body components and/or the attrition of such elastic materials are provided. One or more optical fibers each having a deformable core and cladding formed of an elastomeric material such as silicone rubber are embedded in the elastic material. Changes in light transmission through any of the optical fibers due the deformation of the optical fiber by the application of dynamic loads such as compression, tension, or bending loadings imposed on the elastic material or by the attrition of the elastic material such as by cracking, deterioration, aggregate break-up, and muscle, tendon, or organ atrophy provide a measurement of the dynamic loadings and attrition. The fiber optic sensors can be embedded in elastomers subject to dynamic loadings and attrition such as commonly used automobiles and in shoes for determining the amount and frequency of the dynamic loadings and the extent of attrition. The fiber optic sensors are also useable in cementitious material for determining the maturation thereof.

  10. First Satellite-detected Perturbations of Outgoing Longwave Radiation Associated with Blowing Snow Events over Antarctica

    NASA Technical Reports Server (NTRS)

    Yang, Yuekui; Palm, Stephen P.; Marshak, Alexander; Wu, Dong L.; Yu, Hongbin; Fu, Qiang

    2014-01-01

    We present the first satellite-detected perturbations of the outgoing longwave radiation (OLR) associated with blowing snow events over the Antarctic ice sheet using data from Cloud-Aerosol Lidar with Orthogonal Polarization and Clouds and the Earth's Radiant Energy System. Significant cloud-free OLR differences are observed between the clear and blowing snow sky, with the sign andmagnitude depending on season and time of the day. During nighttime, OLRs are usually larger when blowing snow is present; the average difference in OLRs between without and with blowing snow over the East Antarctic Ice Sheet is about 5.2 W/m2 for the winter months of 2009. During daytime, in contrast, the OLR perturbation is usually smaller or even has the opposite sign. The observed seasonal variations and day-night differences in the OLR perturbation are consistent with theoretical calculations of the influence of blowing snow on OLR. Detailed atmospheric profiles are needed to quantify the radiative effect of blowing snow from the satellite observations.

  11. PREFACE: 7th International Symposium on Large TPCs for Low-Energy Rare Event Detection

    NASA Astrophysics Data System (ADS)

    Colas, P.; Giomataris, I.; Irastorza, I.; Patzak, Th

    2015-11-01

    The seventh "International Symposium on Large TPCs for Low-Energy Rare Event Detection", took place in Paris between the 15th and 17th of December 2014 at the Institute of Astroparticle Physics (APC) campus - Paris Diderot University. As usual the conference was organized during the week before Christmas, which seems to be convenient for most of the people and occurs every two years with almost 120 participants attending. Many people contributed to the success of the conference, but the organizers would particularly like to thank the management of APC for providing the nice Buffon auditorium and infrastructure. We also acknowledge the valuable support of DSM-Irfu and the University of Zaragoza. The scientific program consisted of plenary sessions including the following topics with theoretical and experimental lectures: • Low energy neutrino physics • Neutrinoless double beta decay process • Dark matter searches • Axion and especially solar axion searches • Space experiments and gamma-ray polarimetry • New detector R&D and future experiments

  12. A convenient method for detecting electrolyte bridges in multichannel electroencephalogram and event-related potential recordings.

    PubMed

    Tenke, C E; Kayser, J

    2001-03-01

    Dense electrode arrays offer numerous advantages over single channel electroencephalogram/event-related potential (EEG/ERP) recordings, but also exaggerate the influence of common error sources arising from the preparation of scalp placements. Even with conventional low density recordings (e.g. 30-channel Electro-Cap), over-application of electrode gel may result in electrolyte leakage and create low impedance bridges, particularly at vertically-aligned sites (e.g. inferior-lateral). The ensuing electrical short produces an artificial similarity of ERPs at neighboring sites that distorts the ERP topography. This artifact is not immediately apparent in group averages, and may even go undetected after visual inspection of the individual ERP waveforms. Besides adding noise variance to the topography, this error source also has the capacity to introduce systematic, localized artifacts (e.g. add or remove evidence of lateralized activity). Electrolyte bridges causing these artifacts can be easily detected by a simple variant of the Hjorth algorithm (intrinsic Hjorth), in which spatial interelectrode distances are replaced by an electrical analog of distance (i.e. the variances of the difference waveforms for all pairwise combinations of electrodes). When a low impedance bridge exists, the Hjorth algorithm identifies all affected sites as flat lines that are readily distinguishable from Hjorth waveforms at unbridged electrodes. PMID:11222978

  13. Block-adaptive filtering and its application to seismic-event detection

    SciTech Connect

    Clark, G.A.

    1981-04-01

    Block digital filtering involves the calculation of a block or finite set of filter output samples from a block of input samples. The motivation for block processing arises from computational advantages of the technique. Block filters take good advantage of parallel processing architectures, which are becoming more and more attractive with the advent of very large scale integrated (VLSI) circuits. This thesis extends the block technique to Wiener and adaptive filters, both of which are statistical filters. The key ingredient to this extension turns out to be the definition of a new performance index, block mean square error (BMSE), which combines the well known sum square error (SSE) and mean square error (MSE). A block adaptive filtering procedure is derived in which the filter coefficients are adjusted once per each output block in accordance with a generalized block least mean-square (BLMS) algorithm. Convergence properties of the BLMS algorithm are studied, including conditions for guaranteed convergence, convergence speed, and convergence accuracy. Simulation examples are given for clarity. Convergence properties of the BLMS and LMS algorithms are analyzed and compared. They are shown to be analogous, and under the proper circumstances, equivalent. The block adaptive filter was applied to the problem of detecting small seismic events in microseismic background noise. The predictor outperformed the world-wide standardized seismograph network (WWSSN) seismometers in improving signal-to-noise ratio (SNR).

  14. Abnormal cortical sensorimotor activity during “Target” sound detection in subjects with acute acoustic trauma sequelae: an fMRI study

    PubMed Central

    Job, Agnès; Pons, Yoann; Lamalle, Laurent; Jaillard, Assia; Buck, Karl; Segebarth, Christoph; Delon-Martin, Chantal

    2012-01-01

    The most common consequences of acute acoustic trauma (AAT) are hearing loss at frequencies above 3 kHz and tinnitus. In this study, we have used functional Magnetic Resonance Imaging (fMRI) to visualize neuronal activation patterns in military adults with AAT and various tinnitus sequelae during an auditory “oddball” attention task. AAT subjects displayed overactivities principally during reflex of target sound detection, in sensorimotor areas and in emotion-related areas such as the insula, anterior cingulate and prefrontal cortex, in premotor area, in cross-modal sensory associative areas, and, interestingly, in a region of the Rolandic operculum that has recently been shown to be involved in tympanic movements due to air pressure. We propose further investigations of this brain area and fine middle ear investigations, because our results might suggest a model in which AAT tinnitus may arise as a proprioceptive illusion caused by abnormal excitability of middle-ear muscle spindles possibly link with the acoustic reflex and associated with emotional and sensorimotor disturbances. PMID:22574285

  15. Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals.

    PubMed

    Jasiewicz, Jan M; Allum, John H J; Middleton, James W; Barriskill, Andrew; Condie, Peter; Purcell, Brendan; Li, Raymond Che Tin

    2006-12-01

    We report on three different methods of gait event detection (toe-off and heel strike) using miniature linear accelerometers and angular velocity transducers in comparison to using standard pressure-sensitive foot switches. Detection was performed with normal and spinal-cord injured subjects. The detection of end contact (EC), normally toe-off, and initial contact (IC) normally, heel strike was based on either foot linear accelerations or foot sagittal angular velocity or shank sagittal angular velocity. The results showed that all three methods were as accurate as foot switches in estimating times of IC and EC for normal gait patterns. In spinal-cord injured subjects, shank angular velocity was significantly less accurate (p<0.02). We conclude that detection based on foot linear accelerations or foot angular velocity can correctly identify the timing of IC and EC events in both normal and spinal-cord injured subjects. PMID:16500102

  16. Applying a Hidden Markov Model-Based Event Detection and Classification Algorithm to Apollo Lunar Seismic Data

    NASA Astrophysics Data System (ADS)

    Knapmeyer-Endrun, B.; Hammer, C.

    2014-12-01

    The seismometers that the Apollo astronauts deployed on the Moon provide the only recordings of seismic events from any extra-terrestrial body so far. These lunar events are significantly different from ones recorded on Earth, in terms of both signal shape and source processes. Thus they are a valuable test case for any experiment in planetary seismology. In this study, we analyze Apollo 16 data with a single-station event detection and classification algorithm in view of NASA's upcoming InSight mission to Mars. InSight, scheduled for launch in early 2016, has the goal to investigate Mars' internal structure by deploying a seismometer on its surface. As the mission does not feature any orbiter, continuous data will be relayed to Earth at a reduced rate. Full range data will only be available by requesting specific time-windows within a few days after the receipt of the original transmission. We apply a recently introduced algorithm based on hidden Markov models that requires only a single example waveform of each event class for training appropriate models. After constructing the prototypes we detect and classify impacts and deep and shallow moonquakes. Initial results for 1972 (year of station installation with 8 months of data) indicate a high detection rate of over 95% for impacts, of which more than 80% are classified correctly. Deep moonquakes, which occur in large amounts, but often show only very weak signals, are detected with less certainty (~70%). As there is only one weak shallow moonquake covered, results for this event class are not statistically significant. Daily adjustments of the background noise model help to reduce false alarms, which are mainly erroneous deep moonquake detections, by about 25%. The algorithm enables us to classify events that were previously listed in the catalog without classification, and, through the combined use of long period and short period data, identify some unlisted local impacts as well as at least two yet unreported

  17. Unsupervised spatio-temporal detection of brain functional activation based on hidden Markov multiple event sequence models

    NASA Astrophysics Data System (ADS)

    Faisan, Sylvain; Thoraval, Laurent; Armspach, Jean-Paul; Heitz, Fabrice; Foucher, Jack

    2005-04-01

    This paper presents a novel, completely unsupervised fMRI brain mapping approach that addresses the three problems of hemodynamic response function (HRF) shape variability, neural event timing, and fMRI response linearity. To make it robust, the method takes into account spatial and temporal information directly into the core of the activation detection process. In practice, activation detection is formulated in terms of temporal alignment between the sequence of hemodynamic response onsets (HROs) detected in the fMRI signal at υ and in the spatial neighbourhood of υ, and the sequence of "off-on" transitions observed in the input blocked stimulation paradigm (when considering epoch-related fMRI data), or the sequence of stimuli of the event-based paradigm (when considering event-related fMRI data). This multiple event sequence alignment problem, which comes under multisensor data fusion, is solved within the probabilistic framework of hidden Markov multiple event sequence models (HMMESMs), a special class of hidden Markov models. Results obtained on real and synthetic data compete with those obtained with the popular statistical parametric mapping (SPM) approach, but without necessitating any prior definition of the expected activation patterns, the HMMESM mapping approach being completely unsupervised.

  18. Effects of rainfall events on the occurrence and detection efficiency of viruses in river water impacted by combined sewer overflows.

    PubMed

    Hata, Akihiko; Katayama, Hiroyuki; Kojima, Keisuke; Sano, Shoichi; Kasuga, Ikuro; Kitajima, Masaaki; Furumai, Hiroaki

    2014-01-15

    Rainfall events can introduce large amount of microbial contaminants including human enteric viruses into surface water by intermittent discharges from combined sewer overflows (CSOs). The present study aimed to investigate the effect of rainfall events on viral loads in surface waters impacted by CSO and the reliability of molecular methods for detection of enteric viruses. The reliability of virus detection in the samples was assessed by using process controls for virus concentration, nucleic acid extraction and reverse transcription (RT)-quantitative PCR (qPCR) steps, which allowed accurate estimation of virus detection efficiencies. Recovery efficiencies of poliovirus in river water samples collected during rainfall events (<10%) were lower than those during dry weather conditions (>10%). The log10-transformed virus concentration efficiency was negatively correlated with suspended solid concentration (r(2)=0.86) that increased significantly during rainfall events. Efficiencies of DNA extraction and qPCR steps determined with adenovirus type 5 and a primer sharing control, respectively, were lower in dry weather. However, no clear relationship was observed between organic water quality parameters and efficiencies of these two steps. Observed concentrations of indigenous enteric adenoviruses, GII-noroviruses, enteroviruses, and Aichi viruses increased during rainfall events even though the virus concentration efficiency was presumed to be lower than in dry weather. The present study highlights the importance of using appropriate process controls to evaluate accurately the concentration of water borne enteric viruses in natural waters impacted by wastewater discharge, stormwater, and CSOs. PMID:24064345

  19. Event detection by feature unpredictability in phase-contrast videos of cell cultures.

    PubMed

    Kandemir, Melih; Rubio, Jose C; Schmidt, Ute; Wojek, Christian; Welbl, Johannes; Ommer, Björn; Hamprecht, Fred A

    2014-01-01

    In this work we propose a novel framework for generic event monitoring in live cell culture videos, built on the assumption that unpredictable observations should correspond to biological events. We use a small set of event-free data to train a multioutput multikernel Gaussian process model that operates as an event predictor by performing autoregression on a bank of heterogeneous features extracted from consecutive frames of a video sequence. We show that the prediction error of this model can be used as a probability measure of the presence of relevant events, that can enable users to perform further analysis or monitoring of large-scale non-annotated data. We validate our approach in two phase-contrast sequence data sets containing mitosis and apoptosis events: a new private dataset of human bone cancer (osteosarcoma) cells and a benchmark dataset of stem cells. PMID:25485374

  20. A new method to detect anisotropic electron events with SOHO/EPHIN

    NASA Astrophysics Data System (ADS)

    Banjac, Saša; Kühl, Patrick; Heber, Bernd

    2016-07-01

    The EPHIN instrument (Electron Proton Helium INstrument) forms a part of the COSTEP experiment (COmprehensive SupraThermal and Energetic Particle Analyzer) within the CEPAC collaboration on board of the SOHO spacecraft (SOlar and Heliospheric Observatory). The EPHIN sensor is a stack of six solid-state detectors surrounded by an anti-coincidence. It measures energy spectra of electrons in the range 250 keV to >8.7 MeV, and hydrogen and helium isotopes in the range 4~MeV/n to >53~MeV/n. In order to improve the isotopic resolution, the first two detectors have been segmented: 5 segments form a ring enclosing a central segment. This does not only allow to correct the energy-losses in the detectors for the different path-length in the detectors but allows also an estimation of the arrival direction of the particles with respect to the sensor axis. Utilizing an extensive GEANT 4 Monte-Carlo simulation of the sensor head we computed the scattering-induced modifications to the input angular distribution and developed an inversion method that takes into account the poor counting statistics by optimizing the corresponding algorithm. This improvement makes it possible for the first time to detect long lasting anisotropies in the 1~MeV-3~MeV electron flux with a single telescope on a three-axis stabilized spacecraft. We present the method and its application to several events with strong anisotropies. For validation, we compare our data with the WIND-3DP results.

  1. Vision-Based Finger Detection, Tracking, and Event Identification Techniques for Multi-Touch Sensing and Display Systems

    PubMed Central

    Chen, Yen-Lin; Liang, Wen-Yew; Chiang, Chuan-Yen; Hsieh, Tung-Ju; Lee, Da-Cheng; Yuan, Shyan-Ming; Chang, Yang-Lang

    2011-01-01

    This study presents efficient vision-based finger detection, tracking, and event identification techniques and a low-cost hardware framework for multi-touch sensing and display applications. The proposed approach uses a fast bright-blob segmentation process based on automatic multilevel histogram thresholding to extract the pixels of touch blobs obtained from scattered infrared lights captured by a video camera. The advantage of this automatic multilevel thresholding approach is its robustness and adaptability when dealing with various ambient lighting conditions and spurious infrared noises. To extract the connected components of these touch blobs, a connected-component analysis procedure is applied to the bright pixels acquired by the previous stage. After extracting the touch blobs from each of the captured image frames, a blob tracking and event recognition process analyzes the spatial and temporal information of these touch blobs from consecutive frames to determine the possible touch events and actions performed by users. This process also refines the detection results and corrects for errors and occlusions caused by noise and errors during the blob extraction process. The proposed blob tracking and touch event recognition process includes two phases. First, the phase of blob tracking associates the motion correspondence of blobs in succeeding frames by analyzing their spatial and temporal features. The touch event recognition process can identify meaningful touch events based on the motion information of touch blobs, such as finger moving, rotating, pressing, hovering, and clicking actions. Experimental results demonstrate that the proposed vision-based finger detection, tracking, and event identification system is feasible and effective for multi-touch sensing applications in various operational environments and conditions. PMID:22163990

  2. Detection of water-quality contamination events based on multi-sensor fusion using an extented Dempster-Shafer method

    NASA Astrophysics Data System (ADS)

    Hou, Dibo; He, Huimei; Huang, Pingjie; Zhang, Guangxin; Loaiciga, Hugo

    2013-05-01

    This study presents a method for detecting contamination events of sources of drinking water based on the Dempster-Shafer (D-S) evidence theory. The detection method has the purpose of protecting water supply systems against accidental and intentional contamination events. This purpose is achieved by first predicting future water-quality parameters using an autoregressive (AR) model. The AR model predicts future water-quality parameters using recent measurements of these parameters made with automated (on-line) water-quality sensors. Next, a probabilistic method assigns probabilities to the time series of residuals formed by comparing predicted water-quality parameters with threshold values. Finally, the D-S fusion method searches for anomalous probabilities of the residuals and uses the result of that search to determine whether the current water quality is normal (that is, free of pollution) or contaminated. The D-S fusion method is extended and improved in this paper by weighted averaging of water-contamination evidence and by the analysis of the persistence of anomalous probabilities of water-quality parameters. The extended D-S fusion method makes determinations that have a high probability of being correct concerning whether or not a source of drinking water has been contaminated. This paper's method for detecting water-contamination events was tested with water-quality time series from automated (on-line) water quality sensors. In addition, a small-scale, experimental, water-pipe network was tested to detect water-contamination events. The two tests demonstrated that the extended D-S fusion method achieves a low false alarm rate and high probabilities of detecting water contamination events.

  3. The necessity of recognizing all events in X-ray detection.

    PubMed

    Papp, T; Maxwell, J A; Papp, A T

    2010-01-01

    In our work in studying properties of inner shell ionization, we are troubled that the experimental data used to determine the basic parameters of X-ray physics have a large and unexplainable scatter. As we looked into the problems we found that many of them contradict simple logic, elemental arithmetic, even parity and angular momentum conservation laws. We have identified that the main source of the problems, other than the human factor, is rooted in the signal processing electronics. To overcome these problems we have developed a fully digital signal processor, which not only has excellent resolution and line shape, but also allows proper accounting of all events. This is achieved by processing all events and separating them into two or more spectra (maximum 16), where the first spectrum is the accepted or good spectrum and the second spectrum is the spectrum of all rejected events. The availability of all the events allows one to see the other part of the spectrum. To our surprise the total information explains many of the shortcomings and contradictions of the X-ray database. The data processing methodology cannot be established on the partial and fractional information offered by other approaches. Comparing Monte Carlo detector modeling results with the partial spectra is ambiguous. It suggests that the metrology of calibration by radioactive sources as well as other X-ray measurements could be improved by the availability of the proper accounting of all events. It is not enough to know that an event was rejected and increment the input counter, it is necessary to know, what was rejected and why it happened, whether it was a noise or a disturbed event, a retarded event or a true event, or any pile up combination of these events. Such information is supplied by our processor reporting the events rejected by each discriminator in separate spectra. Several industrial applications of this quality assurance capable signal processor are presented. PMID:19910204

  4. Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier

    PubMed Central

    Kambhampati, Satya Samyukta; Singh, Vishal; Ramkumar, Barathram

    2015-01-01

    In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%. PMID:26609414

  5. Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.

    PubMed

    Kambhampati, Satya Samyukta; Singh, Vishal; Manikandan, M Sabarimalai; Ramkumar, Barathram

    2015-08-01

    In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%. PMID:26609414

  6. A new method for producing automated seismic bulletins: Probabilistic event detection, association, and location

    SciTech Connect

    Draelos, Timothy J.; Ballard, Sanford; Young, Christopher J.; Brogan, Ronald

    2015-10-01

    Given a set of observations within a specified time window, a fitness value is calculated at each grid node by summing station-specific conditional fitness values. Assuming each observation was generated by a refracted P wave, these values are proportional to the conditional probabilities that each observation was generated by a seismic event at the grid node. The node with highest fitness value is accepted as a hypothetical event location, subject to some minimal fitness value, and all arrivals within a longer time window consistent with that event are associated with it. During the association step, a variety of different phases are considered. In addition, once associated with an event, an arrival is removed from further consideration. While unassociated arrivals remain, the search for other events is repeated until none are identified.

  7. A new method for producing automated seismic bulletins: Probabilistic event detection, association, and location

    DOE PAGESBeta

    Draelos, Timothy J.; Ballard, Sanford; Young, Christopher J.; Brogan, Ronald

    2015-10-01

    Given a set of observations within a specified time window, a fitness value is calculated at each grid node by summing station-specific conditional fitness values. Assuming each observation was generated by a refracted P wave, these values are proportional to the conditional probabilities that each observation was generated by a seismic event at the grid node. The node with highest fitness value is accepted as a hypothetical event location, subject to some minimal fitness value, and all arrivals within a longer time window consistent with that event are associated with it. During the association step, a variety of different phasesmore » are considered. In addition, once associated with an event, an arrival is removed from further consideration. While unassociated arrivals remain, the search for other events is repeated until none are identified.« less

  8. Early Detection of Left Atrial Energy Loss and Mechanics Abnormalities in Diabetic Patients with Normal Left Atrial Size: A Study Combining Vector Flow Mapping and Tissue Tracking Echocardiography

    PubMed Central

    Wang, Yi; Hou, Dailun; Ma, Rongchuan; Ding, Geqi; Yin, Lixue; Zhang, Mei

    2016-01-01

    Background Whether left atrial (LA) functional abnormalities already exist when the LA is of normal size is unknown. The aim of this study was to explore LA energy loss and mechanics changes using vector flow mapping (VFM) and two-dimensional tissue tracking (2DTT) echocardiography in patients with diabetes and normal LA size. Material/Methods This study included 47 normotensive patients with diabetes and 45 controls. The following indexes were measured: LA energy loss during systole (LAELs), early diastole (LAELed), and atrial contraction (LAELac); atrial longitudinal strain during systole (SLAs), early diastole (SLAed) and late diastole (SLAac); and peak LA strain rate during systole (SRLAs), early diastole (SRLAed), and atrial contraction (SRLAac). Results The LAELs and LAELed decreased in diabetic patients compared with controls (P=0.002, P<0.01, respectively), whereas the LAELac increased in diabetic patients (P<0.001). The SLAs, SLAed, SRLAs, and SRLAed (all P<0.01) were all lower in diabetic patients than in controls. However, there was no difference in the SLAac and SRLAac between the two groups. Multivariate regression analysis showed that the LAELs, LAELac, and SRLAs were independently associated with HbA1c in the whole study population. Conclusions LA energy loss and deformation mechanics are already impaired in diabetic patients with normal LA size and the long-term parameter of glycemic control was correlated with them. VFM combined with 2DTT might be a promising tool for the early detection of LA dysfunction caused by impaired glucose metabolism. PMID:27005947

  9. Breath-by-breath detection of apneic events for OSA severity estimation using non-contact audio recordings.

    PubMed

    Rosenwein, T; Dafna, E; Tarasiuk, A; Zigel, Y

    2015-08-01

    Obstructive sleep apnea (OSA) is a prevalent sleep disorder, characterized by recurrent episodes of upper airway obstructions during sleep. We hypothesize that breath-by-breath audio analysis of the respiratory cycle (i.e., inspiration and expiration phases) during sleep can reliably estimate the apnea hypopnea index (AHI), a measure of OSA severity. The AHI is calculated as the average number of apnea (A)/hypopnea (H) events per hour of sleep. Audio signals recordings of 186 adults referred to OSA diagnosis were acquired in-laboratory and at-home conditions during polysomnography and WatchPat study, respectively. A/H events were automatically segmented and classified using a binary random forest classifier. Total accuracy rate of 86.3% and an agreement of κ=42.98% were achieved in A/H event detection. Correlation of r=0.87 (r=0.74), diagnostic agreement of 76% (81.7%), and average absolute difference AHI error of 7.4 (7.8) (events/hour) were achieved in in-laboratory (at-home) conditions, respectively. Here we provide evidence that A/H events can be reliably detected at their exact time locations during sleep using non-contact audio approach. This study highlights the potential of this approach to reliably evaluate AHI in at home conditions. PMID:26738073

  10. IRcall and IRclassifier: two methods for flexible detection of intron retention events from RNA-Seq data

    PubMed Central

    2015-01-01

    Background The emergence of next-generation RNA sequencing (RNA-Seq) provides tremendous opportunities for researchers to analyze alternative splicing on a genome-wide scale. However, accurate detection of intron retention (IR) events from RNA-Seq data has remained an unresolved challenge in next-generation sequencing (NGS) studies. Results We propose two new methods: IRcall and IRclassifier to detect IR events from RNA-Seq data. Our methods combine together gene expression information, read coverage within an intron, and read counts (within introns, within flanking exons, supporting splice junctions, and overlapping with 5' splice site/ 3' splice site), employing ranking strategy and classifiers to detect IR events. We applied our approaches to one published RNA-Seq data on contrasting skip mutant and wild-type in Arabidopsis thaliana. Compared with three state-of-the-art methods, IRcall and IRclassifier could effectively filter out false positives, and predict more accurate IR events. Availability The data and codes of IRcall and IRclassifier are available at http://mlg.hit.edu.cn/ybai/IR/IRcallAndIRclass.html PMID:25707295

  11. Accuracy and Precision of Equine Gait Event Detection during Walking with Limb and Trunk Mounted Inertial Sensors

    PubMed Central

    Olsen, Emil; Andersen, Pia Haubro; Pfau, Thilo

    2012-01-01

    The increased variations of temporal gait events when pathology is present are good candidate features for objective diagnostic tests. We hypothesised that the gait events hoof-on/off and stance can be detected accurately and precisely using features from trunk and distal limb-mounted Inertial Measurement Units (IMUs). Four IMUs were mounted on the distal limb and five IMUs were attached to the skin over the dorsal spinous processes at the withers, fourth lumbar vertebrae and sacrum as well as left and right tuber coxae. IMU data were synchronised to a force plate array and a motion capture system. Accuracy (bias) and precision (SD of bias) was calculated to compare force plate and IMU timings for gait events. Data were collected from seven horses. One hundred and twenty three (123) front limb steps were analysed; hoof-on was detected with a bias (SD) of −7 (23) ms, hoof-off with 0.7 (37) ms and front limb stance with −0.02 (37) ms. A total of 119 hind limb steps were analysed; hoof-on was found with a bias (SD) of −4 (25) ms, hoof-off with 6 (21) ms and hind limb stance with 0.2 (28) ms. IMUs mounted on the distal limbs and sacrum can detect gait events accurately and precisely. PMID:22969392

  12. Wavelet packet transform for detection of single events in acoustic emission signals

    NASA Astrophysics Data System (ADS)

    Bianchi, Davide; Mayrhofer, Erwin; Gröschl, Martin; Betz, Gerhard; Vernes, András

    2015-12-01

    Acoustic emission signals in tribology can be used for monitoring the state of bodies in contact and relative motion. The recorded signal includes information which can be associated with different events, such as the formation and propagation of cracks, appearance of scratches and so on. One of the major challenges in analyzing these acoustic emission signals is to identify parts of the signal which belong to such an event and discern it from noise. In this contribution, a wavelet packet decomposition within the framework of multiresolution analysis theory is considered to analyze acoustic emission signals to investigate the failure of tribological systems. By applying the wavelet packet transform a method for the extraction of single events in rail contact fatigue test is proposed. The extraction of such events at several stages of the test permits a classification and the analysis of the evolution of cracks in the rail.

  13. Validating administrative data for the detection of adverse events in older hospitalized patients

    PubMed Central

    Ackroyd-Stolarz, Stacy; Bowles, Susan K; Giffin, Lorri

    2014-01-01

    Older hospitalized patients are at risk of experiencing adverse events including, but not limited to, hospital-acquired pressure ulcers, fall-related injuries, and adverse drug events. A significant challenge in monitoring and managing adverse events is lack of readily accessible information on their occurrence. Purpose The objective of this retrospective cross-sectional study was to validate diagnostic codes for pressure ulcers, fall-related injuries, and adverse drug events found in routinely collected administrative hospitalization data. Methods All patients 65 years of age or older discharged between April 1, 2009 and March 31, 2011 from a provincial academic health sciences center in Canada were eligible for inclusion in the validation study. For each of the three types of adverse events, a random sample of 50 patients whose records were positive and 50 patients whose records were not positive for an adverse event was sought for review in the validation study (n=300 records in total). A structured health record review was performed independently by two health care providers with experience in geriatrics, both of whom were unaware of the patient’s status with respect to adverse event coding. A physician reviewed 40 records (20 reviewed by each health care provider) to establish interrater agreement. Results A total of 39 pressure ulcers, 56 fall-related injuries, and 69 adverse drug events were identified through health record review. Of these, 34 pressure ulcers, 54 fall-related injuries, and 47 adverse drug events were also identified in administrative data. Overall, the diagnostic codes for adverse events had a sensitivity and specificity exceeding 0.67 (95% confidence interval [CI]: 0.56–0.99) and 0.89 (95% CI: 0.72–0.99), respectively. Conclusion It is feasible and valid to identify pressure ulcers, fall-related injuries, and adverse drug events in older hospitalized patients using routinely collected administrative hospitalization data. The

  14. Hard X-ray Detectability of Small Impulsive Heating Events in the Solar Corona

    NASA Astrophysics Data System (ADS)

    Glesener, L.; Klimchuk, J. A.; Bradshaw, S. J.; Marsh, A.; Krucker, S.; Christe, S.

    2015-12-01

    Impulsive heating events ("nanoflares") are a candidate to supply the solar corona with its ~2 MK temperature. These transient events can be studied using extreme ultraviolet and soft X-ray observations, among others. However, the impulsive events may occur in tenuous loops on small enough timescales that the heating is essentially not observed due to ionization timescales, and only the cooling phase is observed. Bremsstrahlung hard X-rays could serve as a more direct and prompt indicator of transient heating events. A hard X-ray spacecraft based on the direct-focusing technology pioneered by the Focusing Optics X-ray Solar Imager (FOXSI) sounding rocket could search for these direct signatures. In this work, we use the hydrodynamical EBTEL code to simulate differential emission measures produced by individual heating events and by ensembles of such events. We then directly predict hard X-ray spectra and consider their observability by a future spaceborne FOXSI, and also by the RHESSI and NuSTAR spacecraft.

  15. A Time-Reversed Reciprocal Method for Detecting High-frequency events in Civil Structures

    NASA Astrophysics Data System (ADS)

    Kohler, M. D.; Heaton, T. H.

    2007-12-01

    A new method that uses the properties of wave propagation reciprocity and time-reversed reciprocal Green's functions is presented for identifying high-frequency events that occur within engineered structures. Wave propagation properties of a seismic source in an elastic medium are directly applicable to structural waveform data. The number of structures with dense seismic networks embedded in them is increasing, making it possible to develop new approaches to identifying failure events such as fracturing welds that take advantage of the large number of recordings. The event identification method is based on the hypothesis that a database can be compiled of pre-event, source-receiver Green's functions using experimental sources. For buildings it is assumed that the source-time excitation is a delta function, proportional to the displacement produced at the receiver site. In theory, if all the Green's functions for a structure are known for a complete set of potential failure event locations, forward modeling can be used to compute a range of displacements to identify the correct Green's functions, locations, and source times from the suite of displacements that recorded actual events. The method is applied to a 17-story, steel, moment-frame building using experimentally applied impulse-force hammer sources. The building has an embedded, 72-channel, accelerometer array that is continuously recorded by 24-bit data loggers at 100 and 500 sps. The focus of this particular application is the identification of brittle- fractured welds of beam-column connections.

  16. Accumulation rates or percentages? How to quantify Sporormiella and other coprophilous fungal spores to detect late Quaternary megafaunal extinction events

    NASA Astrophysics Data System (ADS)

    Wood, Jamie R.; Wilmshurst, Janet M.

    2013-10-01

    Spores of coprophilous fungi, and in particular those of Sporormiella, are a routinely used proxy for detecting late Quaternary herbivore extinction events in sedimentary records. Spore abundance is typically quantified as a percentage of the total, or dryland, pollen sum. Although this is a quick method that does not require the development of site-specific age-depth models, it relies on stable pollen accumulation rates and is therefore highly sensitive to changes in vegetation. This may lead to incorrect placement of extinction events in sedimentary records, particularly when they occur contemporaneously with major climatic/vegetation transitions. We suggest that the preferred method of quantification should be accumulation rate, and that pollen abundance data should also be presented, particularly for periods of major vegetation change. This approach provides a more reliable record of past herbivore abundance independent of vegetation change, allowing extinction events to be more accurately placed in stratigraphic sequences.

  17. Molecular abnormalities in Ewing's sarcoma.

    PubMed

    Burchill, Susan Ann

    2008-10-01

    Ewing's sarcoma is one of the few solid tumors for which the underlying molecular genetic abnormality has been described: rearrangement of the EWS gene on chromosome 22q12 with an ETS gene family member. These translocations define the Ewing's sarcoma family of tumors (ESFT) and provide a valuable tool for their accurate and unequivocal diagnosis. They also represent ideal targets for the development of tumor-specific therapeutics. Although secondary abnormalities occur in over 80% of primary ESFT the clinical utility of these is currently unclear. However, abnormalities in genes that regulate the G(1)/S checkpoint are frequently described and may be important in predicting outcome and response. Increased understanding of the molecular events that arise in ESFT and their role in the development and maintenance of the malignant phenotype will inform the improved stratification of patients for therapy and identify targets and pathways for the design of more effective cancer therapeutics. PMID:18925858

  18. Externally Sensitized Deprotection of PPG-Masked Carbonyls as a Spatial Proximity Probe in Photoamplified Detection of Binding Events

    PubMed Central

    Gustafson, Tiffany P.; Metzel, Greg A.

    2013-01-01

    Externally-sensitized electron-transfer fragmentation in dithiane PPG-protected carbonyls is adopted for detection and amplification of molecular recognition events. The new methodology allows for detection of as low as 50 attomoles of avidin utilizing an imager based on a low sensitivity mass-produced consumer CCD camera. Numeric modelling is carried out to demonstrate the intrinsic limitations of 2D amplification on surfaces and the advantages of unconstrained amplification in a compartmentalized volume of spatially addressable 3D solutions. PMID:22252455

  19. Event-specific detection of seven genetically modified soybean and maizes using multiplex-PCR coupled with oligonucleotide microarray.

    PubMed

    Xu, Jia; Zhu, Shuifang; Miao, Haizhen; Huang, Wensheng; Qiu, Minyan; Huang, Yan; Fu, Xuping; Li, Yao

    2007-07-11

    With the increasing development of genetically modified organism (GMO) detection techniques, the polymerase chain reaction (PCR) technique has been the mainstay for GMO detection. An oligonucleotide microarray is a glass chip to the surface of which an array of oligonucleotides was fixed as spots, each containing numerous copies of a sequence-specific probe that is complementary to a gene of interest. So it is used to detect ten or more targets synchronously. In this research, an event-specific detection strategy based on the unique and specific integration junction sequences between the host plant genome DNA and the integrated gene is being developed for its high specificity using multiplex-PCR together with oligonucleotide microarray. A commercial GM soybean (GTS 40-3-2) and six GM maize events (MON810, MON863, Bt176, Bt11, GA21, and T25) were detected by this method. The results indicate that it is a suitable method for the identification of these GM soybean and maizes. PMID:17559227

  20. Detecting Recent Atmospheric River Induced Flood Events over the Russian River Basin

    NASA Astrophysics Data System (ADS)

    Mehran, A.; Lettenmaier, D. P.; Ralph, F. M.; Lavers, D. A.

    2015-12-01

    Almost all major flood events in the coastal Western U.S. occur as a result of multi-day extreme precipitation during the winter and late fall, and most such events are now known to be Atmospheric Rivers (ARs). AR events are defined as having integrated water vapor (IWV) exceeding 2 cm in an area at least 2000 km long and no more than 1000 km wide. The dominant moisture source in many AR events, including those associated with most floods in the Russian River basin in Northern California, is the tropics. We report on a hydrological analysis of selected floods in the Russian River basin using the Distributed Hydrology Soil Vegetation Model (DHSVM), forced alternately by gridded station data, NWS WSR-88D radar data, and output from a regional atmospheric model. We also report results of river state forecasts using a river hydrodynamics model to reconstruct flood inundation from selected AR events. We diagnose errors in both the hydrological and river stage predictions, and discuss alternatives for future error reduction.

  1. Compression Algorithm Analysis of In-Situ (S)TEM Video: Towards Automatic Event Detection and Characterization

    SciTech Connect

    Teuton, Jeremy R.; Griswold, Richard L.; Mehdi, Beata L.; Browning, Nigel D.

    2015-09-23

    Precise analysis of both (S)TEM images and video are time and labor intensive processes. As an example, determining when crystal growth and shrinkage occurs during the dynamic process of Li dendrite deposition and stripping involves manually scanning through each frame in the video to extract a specific set of frames/images. For large numbers of images, this process can be very time consuming, so a fast and accurate automated method is desirable. Given this need, we developed software that uses analysis of video compression statistics for detecting and characterizing events in large data sets. This software works by converting the data into a series of images which it compresses into an MPEG-2 video using the open source “avconv” utility [1]. The software does not use the video itself, but rather analyzes the video statistics from the first pass of the video encoding that avconv records in the log file. This file contains statistics for each frame of the video including the frame quality, intra-texture and predicted texture bits, forward and backward motion vector resolution, among others. In all, avconv records 15 statistics for each frame. By combining different statistics, we have been able to detect events in various types of data. We have developed an interactive tool for exploring the data and the statistics that aids the analyst in selecting useful statistics for each analysis. Going forward, an algorithm for detecting and possibly describing events automatically can be written based on statistic(s) for each data type.

  2. Detection of numerical chromosomal abnormalities (chr. 1 and 18) before and after photodynamic therapy of human bladder carcinoma cells in vitro

    NASA Astrophysics Data System (ADS)

    Bachor, Ruediger; Reich, Ella D.; Kleinschmidt, Klaus; Hautmann, Richard E.

    1997-12-01

    The application of nonradioactive in situ hybridization with chromosome-specific probes for cytogenetic analysis has increased significantly in recent years. In the field of photodynamic therapy (PDT) the hypothesis is that after PDT the remaining viable malignant cells are potentially metastatic cells. Therefore, we performed in vitro experiments on human bladder carcinoma cells to evaluate numerical chromosomal abnormalities before and after PDT. The possible genotoxic effect of PDT with porphycene (AamTPPn) appears to be small based on criteria such as numerical chromosomal abnormalities for chromosome 1 and 18.

  3. PortVis: A Tool for Port-Based Detection of Security Events

    SciTech Connect

    McPherson, J; Ma, K; Krystosk, P; Bartoletti, T; Christensen, M

    2004-06-29

    Most visualizations of security-related network data require large amounts of finely detailed, high-dimensional data. However, in some cases, the data available can only be coarsely detailed because of security concerns or other limitations. How can interesting security events still be discovered in data that lacks important details, such as IP addresses, network security alarms, and labels? In this paper, we discuss a system we have designed that takes very coarsely detailed data-basic, summarized information of the activity on each TCP port during each given hour-and uses visualization to help uncover interesting security events.

  4. Wideband acoustic activation and detection of droplet vaporization events using a capacitive micromachined ultrasonic transducer.

    PubMed

    Novell, Anthony; Arena, Christopher B; Oralkan, Omer; Dayton, Paul A

    2016-06-01

    An ongoing challenge exists in understanding and optimizing the acoustic droplet vaporization (ADV) process to enhance contrast agent effectiveness for biomedical applications. Acoustic signatures from vaporization events can be identified and differentiated from microbubble or tissue signals based on their frequency content. The present study exploited the wide bandwidth of a 128-element capacitive micromachined ultrasonic transducer (CMUT) array for activation (8 MHz) and real-time imaging (1 MHz) of ADV events from droplets circulating in a tube. Compared to a commercial piezoelectric probe, the CMUT array provides a substantial increase of the contrast-to-noise ratio. PMID:27369143

  5. Strategy for in situ detection of natural transformation-based horizontal gene transfer events.

    PubMed

    Rizzi, Aurora; Pontiroli, Alessandra; Brusetti, Lorenzo; Borin, Sara; Sorlini, Claudia; Abruzzese, Alessandro; Sacchi, Gian Attilio; Vogel, Timothy M; Simonet, Pascal; Bazzicalupo, Marco; Nielsen, Kaare Magne; Monier, Jean-Michel; Daffonchio, Daniele

    2008-02-01

    A strategy is described that enables the in situ detection of natural transformation in Acinetobacter baylyi BD413 by the expression of a green fluorescent protein. Microscale detection of bacterial transformants growing on plant tissues was shown by fluorescence microscopy and indicated that cultivation-based selection of transformants on antibiotic-containing agar plates underestimates transformation frequencies. PMID:18165369

  6. Dune Detective, Using Ecological Studies to Reconstruct Events Which Shaped a Barrier Island.

    ERIC Educational Resources Information Center

    Godfrey, Paul J.; Hon, Will

    This publication is designed for use as part of a curriculum series developed by the Regional Marine Science Project. Students in grades 11 and 12 are exposed to research methods through a series of field exercises guiding investigators in reconstructing the events which have shaped the natural communities of a barrier beach. Background…

  7. Monkeying around with the gorillas in our midst: familiarity with an inattentional-blindness task does not improve the detection of unexpected events.

    PubMed

    Simons, Daniel J

    2010-01-01

    When people know to look for an unexpected event (eg, a gorilla in a basketball game), they tend to notice that event. But does knowledge that an unexpected event might occur improve the detection of other unexpected events in a similar scene? Subjects watched a new video in which, in addition to the gorilla, two other unexpected events occurred: a curtain changed color, and one player left the scene. Subjects who knew about videos like this one consistently spotted the gorilla in the new video, but they were slightly less likely to notice the other events. Foreknowledge that unexpected events might occur does not enhance the ability to detect other such events. PMID:23397479

  8. Detecting specific health-related events using an integrated sensor system for vital sign monitoring.

    PubMed

    Adnane, Mourad; Jiang, Zhongwei; Choi, Samjin; Jang, Hoyoung

    2009-01-01

    In this paper, a new method for the detection of apnea/hypopnea periods in physiological data is presented. The method is based on the intelligent combination of an integrated sensor system for long-time cardiorespiratory signal monitoring and dedicated signal-processing packages. Integrated sensors are a PVDF film and conductive fabric sheets. The signal processing package includes dedicated respiratory cycle (RC) and QRS complex detection algorithms and a new method using the respiratory cycle variability (RCV) for detecting apnea/hypopnea periods in physiological data. Results show that our method is suitable for online analysis of long time series data. PMID:22399978

  9. A new PCR-CGE (size and color) method for simultaneous detection of genetically modified maize events.

    PubMed

    Nadal, Anna; Coll, Anna; La Paz, Jose-Luis; Esteve, Teresa; Pla, Maria

    2006-10-01

    We present a novel multiplex PCR assay for simultaneous detection of multiple transgenic events in maize. Initially, five PCR primers pairs specific to events Bt11, GA21, MON810, and NK603, and Zea mays L. (alcohol dehydrogenase) were included. The event specificity was based on amplification of transgene/plant genome flanking regions, i.e., the same targets as for validated real-time PCR assays. These short and similarly sized amplicons were selected to achieve high and similar amplification efficiency for all targets; however, its unambiguous identification was a technical challenge. We achieved a clear distinction by a novel CGE approach that combined the identification by size and color (CGE-SC). In one single step, all five targets were amplified and specifically labeled with three different fluorescent dyes. The assay was specific and displayed an LOD of 0.1% of each genetically modified organism (GMO). Therefore, it was adequate to fulfill legal thresholds established, e.g., in the European Union. Our CGE-SC based strategy in combination with an adequate labeling design has the potential to simultaneously detect higher numbers of targets. As an example, we present the detection of up to eight targets in a single run. Multiplex PCR-CGE-SC only requires a conventional sequencer device and enables automation and high throughput. In addition, it proved to be transferable to a different laboratory. The number of authorized GMO events is rapidly growing; and the acreage of genetically modified (GM) varieties cultivated and commercialized worldwide is rapidly increasing. In this context, our multiplex PCR-CGE-SC can be suitable for screening GM contents in food. PMID:16972302

  10. The use of molecular and cytogenetic methods as a valuable tool in the detection of chromosomal abnormalities in horses: a case of sex chromosome chimerism in a Spanish purebred colt.

    PubMed

    Demyda-Peyrás, S; Membrillo, A; Bugno-Poniewierska, M; Pawlina, K; Anaya, G; Moreno-Millán, M

    2013-01-01

    Chromosomal abnormalities associated to sex chromosomes are reported as a problem more co