WorldWideScience

Sample records for classifying electrophysiologically-defined classes

  1. Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level

    Directory of Open Access Journals (Sweden)

    Shehzad Khalid

    2014-01-01

    Full Text Available We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes.

  2. Ensemble Clustering Classification Applied to Competing SVM and One-Class Classifiers Exemplified by Plant MicroRNAs Data

    Directory of Open Access Journals (Sweden)

    Yousef Malik

    2016-12-01

    Full Text Available The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how they co-cluster. This distance is then used within the framework of the kNN algorithm to define a classifier named ensemble clustering kNN classifier (EC-kNN. In many instances in our experiments we achieved highest accuracy while SVM failed to perform as well. In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. The comparison was applied to seven different plant microRNA species considering eight feature selection methods. In this study, the averaged results show that EC-kNN outperforms all other methods employed here and previously published results for the same data. In conclusion, this study shows that the chosen classifier shows high performance when the distance metric is carefully chosen.

  3. Class-specific Error Bounds for Ensemble Classifiers

    Energy Technology Data Exchange (ETDEWEB)

    Prenger, R; Lemmond, T; Varshney, K; Chen, B; Hanley, W

    2009-10-06

    The generalization error, or probability of misclassification, of ensemble classifiers has been shown to be bounded above by a function of the mean correlation between the constituent (i.e., base) classifiers and their average strength. This bound suggests that increasing the strength and/or decreasing the correlation of an ensemble's base classifiers may yield improved performance under the assumption of equal error costs. However, this and other existing bounds do not directly address application spaces in which error costs are inherently unequal. For applications involving binary classification, Receiver Operating Characteristic (ROC) curves, performance curves that explicitly trade off false alarms and missed detections, are often utilized to support decision making. To address performance optimization in this context, we have developed a lower bound for the entire ROC curve that can be expressed in terms of the class-specific strength and correlation of the base classifiers. We present empirical analyses demonstrating the efficacy of these bounds in predicting relative classifier performance. In addition, we specify performance regions of the ROC curve that are naturally delineated by the class-specific strengths of the base classifiers and show that each of these regions can be associated with a unique set of guidelines for performance optimization of binary classifiers within unequal error cost regimes.

  4. Defining and Classifying Interest Groups

    DEFF Research Database (Denmark)

    Baroni, Laura; Carroll, Brendan; Chalmers, Adam

    2014-01-01

    The interest group concept is defined in many different ways in the existing literature and a range of different classification schemes are employed. This complicates comparisons between different studies and their findings. One of the important tasks faced by interest group scholars engaged...... in large-N studies is therefore to define the concept of an interest group and to determine which classification scheme to use for different group types. After reviewing the existing literature, this article sets out to compare different approaches to defining and classifying interest groups with a sample...... in the organizational attributes of specific interest group types. As expected, our comparison of coding schemes reveals a closer link between group attributes and group type in narrower classification schemes based on group organizational characteristics than those based on a behavioral definition of lobbying....

  5. A joint latent class model for classifying severely hemorrhaging trauma patients.

    Science.gov (United States)

    Rahbar, Mohammad H; Ning, Jing; Choi, Sangbum; Piao, Jin; Hong, Chuan; Huang, Hanwen; Del Junco, Deborah J; Fox, Erin E; Rahbar, Elaheh; Holcomb, John B

    2015-10-24

    In trauma research, "massive transfusion" (MT), historically defined as receiving ≥10 units of red blood cells (RBCs) within 24 h of admission, has been routinely used as a "gold standard" for quantifying bleeding severity. Due to early in-hospital mortality, however, MT is subject to survivor bias and thus a poorly defined criterion to classify bleeding trauma patients. Using the data from a retrospective trauma transfusion study, we applied a latent-class (LC) mixture model to identify severely hemorrhaging (SH) patients. Based on the joint distribution of cumulative units of RBCs and binary survival outcome at 24 h of admission, we applied an expectation-maximization (EM) algorithm to obtain model parameters. Estimated posterior probabilities were used for patients' classification and compared with the MT rule. To evaluate predictive performance of the LC-based classification, we examined the role of six clinical variables as predictors using two separate logistic regression models. Out of 471 trauma patients, 211 (45 %) were MT, while our latent SH classifier identified only 127 (27 %) of patients as SH. The agreement between the two classification methods was 73 %. A non-ignorable portion of patients (17 out of 68, 25 %) who died within 24 h were not classified as MT but the SH group included 62 patients (91 %) who died during the same period. Our comparison of the predictive models based on MT and SH revealed significant differences between the coefficients of potential predictors of patients who may be in need of activation of the massive transfusion protocol. The traditional MT classification does not adequately reflect transfusion practices and outcomes during the trauma reception and initial resuscitation phase. Although we have demonstrated that joint latent class modeling could be used to correct for potential bias caused by misclassification of severely bleeding patients, improvement in this approach could be made in the presence of time to event

  6. Dynamic cluster generation for a fuzzy classifier with ellipsoidal regions.

    Science.gov (United States)

    Abe, S

    1998-01-01

    In this paper, we discuss a fuzzy classifier with ellipsoidal regions that dynamically generates clusters. First, for the data belonging to a class we define a fuzzy rule with an ellipsoidal region. Namely, using the training data for each class, we calculate the center and the covariance matrix of the ellipsoidal region for the class. Then we tune the fuzzy rules, i.e., the slopes of the membership functions, successively until there is no improvement in the recognition rate of the training data. Then if the number of the data belonging to a class that are misclassified into another class exceeds a prescribed number, we define a new cluster to which those data belong and the associated fuzzy rule. Then we tune the newly defined fuzzy rules in the similar way as stated above, fixing the already obtained fuzzy rules. We iterate generation of clusters and tuning of the newly generated fuzzy rules until the number of the data belonging to a class that are misclassified into another class does not exceed the prescribed number. We evaluate our method using thyroid data, Japanese Hiragana data of vehicle license plates, and blood cell data. By dynamic cluster generation, the generalization ability of the classifier is improved and the recognition rate of the fuzzy classifier for the test data is the best among the neural network classifiers and other fuzzy classifiers if there are no discrete input variables.

  7. A Novel Design of 4-Class BCI Using Two Binary Classifiers and Parallel Mental Tasks

    Directory of Open Access Journals (Sweden)

    Tao Geng

    2008-01-01

    Full Text Available A novel 4-class single-trial brain computer interface (BCI based on two (rather than four or more binary linear discriminant analysis (LDA classifiers is proposed, which is called a “parallel BCI.” Unlike other BCIs where mental tasks are executed and classified in a serial way one after another, the parallel BCI uses properly designed parallel mental tasks that are executed on both sides of the subject body simultaneously, which is the main novelty of the BCI paradigm used in our experiments. Each of the two binary classifiers only classifies the mental tasks executed on one side of the subject body, and the results of the two binary classifiers are combined to give the result of the 4-class BCI. Data was recorded in experiments with both real movement and motor imagery in 3 able-bodied subjects. Artifacts were not detected or removed. Offline analysis has shown that, in some subjects, the parallel BCI can generate a higher accuracy than a conventional 4-class BCI, although both of them have used the same feature selection and classification algorithms.

  8. Identification and optimization of classifier genes from multi-class earthworm microarray dataset.

    Directory of Open Access Journals (Sweden)

    Ying Li

    Full Text Available Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT and RDX. One important goal of microarray experiments is to discover novel biomarkers for toxicity evaluation. We have developed an earthworm microarray containing 15,208 unique oligo probes and have used it to profile gene expression in 248 earthworms exposed to TNT, RDX or neither. We assembled a new machine learning pipeline consisting of several well-established feature filtering/selection and classification techniques to analyze the 248-array dataset in order to construct classifier models that can separate earthworm samples into three groups: control, TNT-treated, and RDX-treated. First, a total of 869 genes differentially expressed in response to TNT or RDX exposure were identified using a univariate statistical algorithm of class comparison. Then, decision tree-based algorithms were applied to select a subset of 354 classifier genes, which were ranked by their overall weight of significance. A multiclass support vector machine (MC-SVM method and an unsupervised K-mean clustering method were applied to independently refine the classifier, producing a smaller subset of 39 and 30 classifier genes, separately, with 11 common genes being potential biomarkers. The combined 58 genes were considered the refined subset and used to build MC-SVM and clustering models with classification accuracy of 83.5% and 56.9%, respectively. This study demonstrates that the machine learning approach can be used to identify and optimize a small subset of classifier/biomarker genes from high dimensional datasets and generate classification models of acceptable precision for multiple classes.

  9. Single-Pol Synthetic Aperture Radar Terrain Classification using Multiclass Confidence for One-Class Classifiers

    Energy Technology Data Exchange (ETDEWEB)

    Koch, Mark William [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Steinbach, Ryan Matthew [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Moya, Mary M [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-10-01

    Except in the most extreme conditions, Synthetic aperture radar (SAR) is a remote sensing technology that can operate day or night. A SAR can provide surveillance over a long time period by making multiple passes over a wide area. For object-based intelligence it is convenient to segment and classify the SAR images into objects that identify various terrains and man-made structures that we call “static features.” In this paper we introduce a novel SAR image product that captures how different regions decorrelate at different rates. Using superpixels and their first two moments we develop a series of one-class classification algorithms using a goodness-of-fit metric. P-value fusion is used to combine the results from different classes. We also show how to combine multiple one-class classifiers to get a confidence about a classification. This can be used by downstream algorithms such as a conditional random field to enforce spatial constraints.

  10. Defining Social Class Across Time and Between Groups.

    Science.gov (United States)

    Cohen, Dov; Shin, Faith; Liu, Xi; Ondish, Peter; Kraus, Michael W

    2017-11-01

    We examined changes over four decades and between ethnic groups in how people define their social class. Changes included the increasing importance of income, decreasing importance of occupational prestige, and the demise of the "Victorian bargain," in which poor people who subscribed to conservative sexual and religious norms could think of themselves as middle class. The period also saw changes (among Whites) and continuity (among Black Americans) in subjective status perceptions. For Whites (and particularly poor Whites), their perceptions of enhanced social class were greatly reduced. Poor Whites now view their social class as slightly but significantly lower than their poor Black and Latino counterparts. For Black respondents, a caste-like understanding of social class persisted, as they continued to view their class standing as relatively independent of their achieved education, income, and occupation. Such achievement indicators, however, predicted Black respondents' self-esteem more than they predicted self-esteem for any other group.

  11. Anomaly detection in forward looking infrared imaging using one-class classifiers

    Science.gov (United States)

    Popescu, Mihail; Stone, Kevin; Havens, Timothy; Ho, Dominic; Keller, James

    2010-04-01

    In this paper we describe a method for generating cues of possible abnormal objects present in the field of view of an infrared (IR) camera installed on a moving vehicle. The proposed method has two steps. In the first step, for each frame, we generate a set of possible points of interest using a corner detection algorithm. In the second step, the points related to the background are discarded from the point set using an one class classifier (OCC) trained on features extracted from a local neighborhood of each point. The advantage of using an OCC is that we do not need examples from the "abnormal object" class to train the classifier. Instead, OCC is trained using corner points from images known to be abnormal object free, i.e., that contain only background scenes. To further reduce the number of false alarms we use a temporal fusion procedure: a region has to be detected as "interesting" in m out of n, mGM). The comparison is performed using a set of about 900 background point neighborhoods for training and 400 for testing. The best performing OCC is then used to detect abnormal objects in a set of IR video sequences obtained on a 1 mile long country road.

  12. Error minimizing algorithms for nearest eighbor classifiers

    Energy Technology Data Exchange (ETDEWEB)

    Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory; Zimmer, G. Beate [TEXAS A& M

    2011-01-03

    Stack Filters define a large class of discrete nonlinear filter first introd uced in image and signal processing for noise removal. In recent years we have suggested their application to classification problems, and investigated their relationship to other types of discrete classifiers such as Decision Trees. In this paper we focus on a continuous domain version of Stack Filter Classifiers which we call Ordered Hypothesis Machines (OHM), and investigate their relationship to Nearest Neighbor classifiers. We show that OHM classifiers provide a novel framework in which to train Nearest Neighbor type classifiers by minimizing empirical error based loss functions. We use the framework to investigate a new cost sensitive loss function that allows us to train a Nearest Neighbor type classifier for low false alarm rate applications. We report results on both synthetic data and real-world image data.

  13. Serial electrophysiological findings in Guillain-Barré syndrome not fulfilling AIDP or AMAN criteria.

    Science.gov (United States)

    Hosokawa, Takafumi; Nakajima, Hideto; Unoda, Kiichi; Yamane, Kazushi; Doi, Yoshimitsu; Ishida, Shimon; Kimura, Fumiharu; Hanafusa, Toshiaki

    2016-09-01

    Guillain-Barré syndrome (GBS) is categorized into two major subtypes: acute inflammatory demyelinating polyneuropathy (AIDP) and acute motor axonal neuropathy (AMAN). However, a proportion of patients are electrophysiologically unclassified because of electrophysiological findings that do not fulfil AIDP or AMAN criteria, and underlying pathophysiological mechanisms and lesion distributions of unclassified patients are not well defined. The aims of this study are to elucidate disease pathophysiology and lesion distribution in unclassified patients. We retrospectively studied 48 consecutive GBS patients. Patients were classified on the basis of initial electrophysiological findings according to Ho's criteria. Clinical and serial electrophysiological examinations of unclassified patients were conducted. Twelve (25 %) GBS patients were unclassified. All unclassified patients were able to walk independently at 21 days after onset. No unclassified patients, except one patient with diabetes mellitus, had sensory nerve involvement. Eight patients underwent a follow-up study within 15 days of the initial study. Distal motor latencies (DMLs) of the left median motor nerve were found to be significantly and uniformly decreased compared with initial studies (p = 0.008). DMLs (p < 0.0001) and distal compound action potential (CMAP) durations (p = 0.002) of all nerves were significantly decreased, and distal CMAP amplitudes (p = 0.026) significantly increased compared with initial studies. In unclassified GBS patients, DML values during initial electrophysiological studies would be prolonged compared with expected values in the same patient unaffected by GBS and later improve rapidly with increased distal CMAP amplitudes without the development of excessive temporal dispersions. Lesions are also present in distal nerve segments caused by reversible conduction failure.

  14. Approach to defining de minimis, intermediate, and other classes of radioactive waste

    International Nuclear Information System (INIS)

    Cohen, J.J.; Smith, C.F.

    1986-01-01

    This study has developed a framework within which the complete spectrum of radioactive wastes can be defined. An approach has been developed that reflects both concerns in the framework of a radioactive waste classification system. In this approach, the class of any radioactive waste stream is dependent on its degree of radioactivity and its persistence. To be consistent with conventional systems, four waste classes are defined. In increasing order of concern due to radioactivity and/or duration, these are: 1. De Minimis Wastes: This waste has such a low content of radioactive material that it can be considered essentially nonradioactive and managed according to its nonradiological characteristics. 2. Low-Level Waste (LLW): Maximum concentrations for wastes considered to be in this class are prescribed in 10CFR61 as wastes that can be disposed of by shallow land burial methods. 3. Intermediate Level Waste (ILW): This category defines a class of waste whose content exceeds class C (10CFR61) levels, yet does not pose a sufficient hazard to justify management as a high-level waste (i.e., permanent isolation by deep geologic disposal). 4. High-Level Waste: HLW poses the most serious management problem and requires the most restrictive disposal methods. It is defined in NWPA as waste derived from the reprocessing of nuclear fuel and/or as highly radioactive wastes that require permanent isolation

  15. Multi-label classifier based on histogram of gradients for predicting the anatomical therapeutic chemical class/classes of a given compound.

    Science.gov (United States)

    Nanni, Loris; Brahnam, Sheryl

    2017-09-15

    Given an unknown compound, is it possible to predict its Anatomical Therapeutic Chemical class/classes? This is a challenging yet important problem since such a prediction could be used to deduce not only a compound's possible active ingredients but also its therapeutic, pharmacological and chemical properties, thereby substantially expediting the pace of drug development. The problem is challenging because some drugs and compounds belong to two or more ATC classes, making machine learning extremely difficult. In this article a multi-label classifier system is proposed that incorporates information about a compound's chemical-chemical interaction and its structural and fingerprint similarities to other compounds belonging to the different ATC classes. The proposed system reshapes a 1D feature vector to obtain a 2D matrix representation of the compound. This matrix is then described by a histogram of gradients that is fed into a Multi-Label Learning with Label-Specific Features classifier. Rigorous cross-validations demonstrate the superior prediction quality of this method compared with other state-of-the-art approaches developed for this problem, a superiority that is reflected particularly in the absolute true rate, the most important and harshest metric for assessing multi-label systems. The MATLAB code for replicating the experiments presented in this article is available at https://www.dropbox.com/s/7v1mey48tl9bfgz/ToolPaperATC.rar?dl=0 . loris.nanni@unipd.it. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  16. Defining and classifying syncope

    NARCIS (Netherlands)

    Thijs, Roland D.; Wieling, Wouter; Kaufmann, Horacio; van Dijk, Gert

    2004-01-01

    There is no widely adopted definition or classification of syncope and related disorders. This lack of uniformity harms patient care, research, and medical education. In this article, syncope is defined as a form of transient loss of consciousness (TLOC) due to cerebral hypoperfusion. Differences

  17. A New Class of Analytic Functions Defined by Using Salagean Operator

    Directory of Open Access Journals (Sweden)

    R. M. El-Ashwah

    2013-01-01

    Full Text Available We derive some results for a new class of analytic functions defined by using Salagean operator. We give some properties of functions in this class and obtain numerous sharp results including for example, coefficient estimates, distortion theorem, radii of star-likeness, convexity, close-to-convexity, extreme points, integral means inequalities, and partial sums of functions belonging to this class. Finally, we give an application involving certain fractional calculus operators that are also considered.

  18. Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers

    LENUS (Irish Health Repository)

    Dakna, Mohammed

    2010-12-10

    Abstract Background The purpose of this manuscript is to provide, based on an extensive analysis of a proteomic data set, suggestions for proper statistical analysis for the discovery of sets of clinically relevant biomarkers. As tractable example we define the measurable proteomic differences between apparently healthy adult males and females. We choose urine as body-fluid of interest and CE-MS, a thoroughly validated platform technology, allowing for routine analysis of a large number of samples. The second urine of the morning was collected from apparently healthy male and female volunteers (aged 21-40) in the course of the routine medical check-up before recruitment at the Hannover Medical School. Results We found that the Wilcoxon-test is best suited for the definition of potential biomarkers. Adjustment for multiple testing is necessary. Sample size estimation can be performed based on a small number of observations via resampling from pilot data. Machine learning algorithms appear ideally suited to generate classifiers. Assessment of any results in an independent test-set is essential. Conclusions Valid proteomic biomarkers for diagnosis and prognosis only can be defined by applying proper statistical data mining procedures. In particular, a justification of the sample size should be part of the study design.

  19. Electrophysiologic Findings and Pain in Carpal Tunnel Syndrome

    Directory of Open Access Journals (Sweden)

    Hava Dönmez Keklikoğlu

    2009-12-01

    Full Text Available OBJECTIVE: Carpal tunnel syndrome (CTS is defined as median nerve entrapment within the carpal tunnel at the wrist. Pain and paresthesia are the most common presenting symptoms of the patients. In this study, our aim was to identify the association between intensity of presenting symptoms and electrophysiologic findings in patients referred to the electrophysiology laboratory with prediagnosis of CTS. METHODS: Sixty-two consecutive patients who were referred to the electrophysiology laboratory with the diagnosis of CTS were enrolled in the study. The intensity of pain was determined by visual analog scale, the findings of Tinel-Phalen tests were assessed, and clinico-demographic findings were recorded. Nerve conduction studies were performed bilaterally in median and ulnar nerves. The severity of CTS was determined with electrophysiologic evaluation, and the association between electrophysiologic findings and symptoms were analyzed statistically. RESULTS: Sixty-two (57 female, 5 male patients were examined in the study. CTS was bilateral in 53 patients and unilateral in 9 patients (total 115 hands. Mean pain score was 5.78 ± 3.50. In 28 hands with a clinical diagnosis of CTS, no electrophysiologic CTS findings were found, whereas in 32 hands mild, in 41 hands moderate and in 14 hands severe findings were obtained. CONCLUSION: According to our study, there was no statistically significant association between severity of symptoms and severity of electrophysiologic findings in CTS

  20. Landscape object-based analysis of wetland plant functional types: the effects of spatial scale, vegetation classes and classifier methods

    Science.gov (United States)

    Dronova, I.; Gong, P.; Wang, L.; Clinton, N.; Fu, W.; Qi, S.

    2011-12-01

    Remote sensing-based vegetation classifications representing plant function such as photosynthesis and productivity are challenging in wetlands with complex cover and difficult field access. Recent advances in object-based image analysis (OBIA) and machine-learning algorithms offer new classification tools; however, few comparisons of different algorithms and spatial scales have been discussed to date. We applied OBIA to delineate wetland plant functional types (PFTs) for Poyang Lake, the largest freshwater lake in China and Ramsar wetland conservation site, from 30-m Landsat TM scene at the peak of spring growing season. We targeted major PFTs (C3 grasses, C3 forbs and different types of C4 grasses and aquatic vegetation) that are both key players in system's biogeochemical cycles and critical providers of waterbird habitat. Classification results were compared among: a) several object segmentation scales (with average object sizes 900-9000 m2); b) several families of statistical classifiers (including Bayesian, Logistic, Neural Network, Decision Trees and Support Vector Machines) and c) two hierarchical levels of vegetation classification, a generalized 3-class set and more detailed 6-class set. We found that classification benefited from object-based approach which allowed including object shape, texture and context descriptors in classification. While a number of classifiers achieved high accuracy at the finest pixel-equivalent segmentation scale, the highest accuracies and best agreement among algorithms occurred at coarser object scales. No single classifier was consistently superior across all scales, although selected algorithms of Neural Network, Logistic and K-Nearest Neighbors families frequently provided the best discrimination of classes at different scales. The choice of vegetation categories also affected classification accuracy. The 6-class set allowed for higher individual class accuracies but lower overall accuracies than the 3-class set because

  1. Classifying Radio Galaxies with the Convolutional Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Aniyan, A. K.; Thorat, K. [Department of Physics and Electronics, Rhodes University, Grahamstown (South Africa)

    2017-06-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

  2. Classifying Radio Galaxies with the Convolutional Neural Network

    Science.gov (United States)

    Aniyan, A. K.; Thorat, K.

    2017-06-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff-Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ˜200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

  3. Classifying Radio Galaxies with the Convolutional Neural Network

    International Nuclear Information System (INIS)

    Aniyan, A. K.; Thorat, K.

    2017-01-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

  4. Responsibility for proving and defining in abstract algebra class

    Science.gov (United States)

    Fukawa-Connelly, Timothy

    2016-07-01

    There is considerable variety in inquiry-oriented instruction, but what is common is that students assume roles in mathematical activity that in a traditional, lecture-based class are either assumed by the teacher (or text) or are not visible at all in traditional math classrooms. This paper is a case study of the teaching of an inquiry-based undergraduate abstract algebra course. In particular, gives a theoretical account of the defining and proving processes. The study examines the intellectual responsibility for the processes of defining and proving that the professor devolved to the students. While the professor wanted the students to engage in all aspects of defining and proving, he was only successful at devolving responsibility for certain aspects and much more successful at devolving responsibility for proving than conjecturing or defining. This study suggests that even a well-intentioned instructor may not be able to devolve responsibility to students for some aspects of mathematical practice without using a research-based curriculum or further professional development.

  5. Case based reasoning applied to medical diagnosis using multi-class classifier: A preliminary study

    Directory of Open Access Journals (Sweden)

    D. Viveros-Melo

    2017-02-01

    Full Text Available Case-based reasoning (CBR is a process used for computer processing that tries to mimic the behavior of a human expert in making decisions regarding a subject and learn from the experience of past cases. CBR has demonstrated to be appropriate for working with unstructured domains data or difficult knowledge acquisition situations, such as medical diagnosis, where it is possible to identify diseases such as: cancer diagnosis, epilepsy prediction and appendicitis diagnosis. Some of the trends that may be developed for CBR in the health science are oriented to reduce the number of features in highly dimensional data. An important contribution may be the estimation of probabilities of belonging to each class for new cases. In this paper, in order to adequately represent the database and to avoid the inconveniences caused by the high dimensionality, noise and redundancy, a number of algorithms are used in the preprocessing stage for performing both variable selection and dimension reduction procedures. Also, a comparison of the performance of some representative multi-class classifiers is carried out to identify the most effective one to include within a CBR scheme. Particularly, four classification techniques and two reduction techniques are employed to make a comparative study of multiclass classifiers on CBR

  6. Class prediction for high-dimensional class-imbalanced data

    Directory of Open Access Journals (Sweden)

    Lusa Lara

    2010-10-01

    Full Text Available Abstract Background The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of the variables available for each subject: the main characteristic of high-dimensional data is that the number of variables greatly exceeds the number of samples. Frequently the classifiers are developed using class-imbalanced data, i.e., data sets where the number of samples in each class is not equal. Standard classification methods used on class-imbalanced data often produce classifiers that do not accurately predict the minority class; the prediction is biased towards the majority class. In this paper we investigate if the high-dimensionality poses additional challenges when dealing with class-imbalanced prediction. We evaluate the performance of six types of classifiers on class-imbalanced data, using simulated data and a publicly available data set from a breast cancer gene-expression microarray study. We also investigate the effectiveness of some strategies that are available to overcome the effect of class imbalance. Results Our results show that the evaluated classifiers are highly sensitive to class imbalance and that variable selection introduces an additional bias towards classification into the majority class. Most new samples are assigned to the majority class from the training set, unless the difference between the classes is very large. As a consequence, the class-specific predictive accuracies differ considerably. When the class imbalance is not too severe, down-sizing and asymmetric bagging embedding variable selection work well, while over-sampling does not. Variable normalization can further worsen the performance of the classifiers. Conclusions Our results show that matching the prevalence of the classes in training and test set does not guarantee good performance of classifiers and that the problems related to classification with class

  7. A Novel Approach for Multi Class Fault Diagnosis in Induction Machine Based on Statistical Time Features and Random Forest Classifier

    Science.gov (United States)

    Sonje, M. Deepak; Kundu, P.; Chowdhury, A.

    2017-08-01

    Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.

  8. Anatomical and Electrophysiological Clustering of Superficial Medial Entorhinal Cortex Interneurons

    Science.gov (United States)

    2017-01-01

    Abstract Local GABAergic interneurons regulate the activity of spatially-modulated principal cells in the medial entorhinal cortex (MEC), mediating stellate-to-stellate connectivity and possibly enabling grid formation via recurrent inhibitory circuitry. Despite the important role interneurons seem to play in the MEC cortical circuit, the combination of low cell counts and functional diversity has made systematic electrophysiological studies of these neurons difficult. For these reasons, there remains a paucity of knowledge on the electrophysiological profiles of superficial MEC interneuron populations. Taking advantage of glutamic acid decarboxylase 2 (GAD2)-IRES-tdTomato and PV-tdTomato transgenic mice, we targeted GABAergic interneurons for whole-cell patch-clamp recordings and characterized their passive membrane features, basic input/output properties and action potential (AP) shape. These electrophysiologically characterized cells were then anatomically reconstructed, with emphasis on axonal projections and pial depth. K-means clustering of interneuron anatomical and electrophysiological data optimally classified a population of 106 interneurons into four distinct clusters. The first cluster is comprised of layer 2- and 3-projecting, slow-firing interneurons. The second cluster is comprised largely of PV+ fast-firing interneurons that project mainly to layers 2 and 3. The third cluster contains layer 1- and 2-projecting interneurons, and the fourth cluster is made up of layer 1-projecting horizontal interneurons. These results, among others, will provide greater understanding of the electrophysiological characteristics of MEC interneurons, help guide future in vivo studies, and may aid in uncovering the mechanism of grid field formation. PMID:29085901

  9. Application of a latent class analysis to empirically define eating disorder phenotypes.

    Science.gov (United States)

    Keel, Pamela K; Fichter, Manfred; Quadflieg, Norbert; Bulik, Cynthia M; Baxter, Mark G; Thornton, Laura; Halmi, Katherine A; Kaplan, Allan S; Strober, Michael; Woodside, D Blake; Crow, Scott J; Mitchell, James E; Rotondo, Alessandro; Mauri, Mauro; Cassano, Giovanni; Treasure, Janet; Goldman, David; Berrettini, Wade H; Kaye, Walter H

    2004-02-01

    Diagnostic criteria for eating disorders influence how we recognize, research, and treat eating disorders, and empirically valid phenotypes are required for revealing their genetic bases. To empirically define eating disorder phenotypes. Data regarding eating disorder symptoms and features from 1179 individuals with clinically significant eating disorders were submitted to a latent class analysis. The resulting latent classes were compared on non-eating disorder variables in a series of validation analyses. Multinational, collaborative study with cases ascertained through diverse clinical settings (inpatient, outpatient, and community). Members of affected relative pairs recruited for participation in genetic studies of eating disorders in which probands met DSM-IV-TR criteria for anorexia nervosa (AN) or bulimia nervosa and had at least 1 biological relative with a clinically significant eating disorder. Main Outcome Measure Number and clinical characterization of latent classes. A 4-class solution provided the best fit. Latent class 1 (LC1) resembled restricting AN; LC2, AN and bulimia nervosa with the use of multiple methods of purging; LC3, restricting AN without obsessive-compulsive features; and LC4, bulimia nervosa with self-induced vomiting as the sole form of purging. Biological relatives were significantly likely to belong to the same latent class. Across validation analyses, LC2 demonstrated the highest levels of psychological disturbance, and LC3 demonstrated the lowest. The presence of obsessive-compulsive features differentiates among individuals with restricting AN. Similarly, the combination of low weight and multiple methods of purging distinguishes among individuals with binge eating and purging behaviors. These results support some of the distinctions drawn within the DSM-IV-TR among eating disorder subtypes, while introducing new features to define phenotypes.

  10. The reliability of commonly used electrophysiology measures.

    Science.gov (United States)

    Brown, K E; Lohse, K R; Mayer, I M S; Strigaro, G; Desikan, M; Casula, E P; Meunier, S; Popa, T; Lamy, J-C; Odish, O; Leavitt, B R; Durr, A; Roos, R A C; Tabrizi, S J; Rothwell, J C; Boyd, L A; Orth, M

    Electrophysiological measures can help understand brain function both in healthy individuals and in the context of a disease. Given the amount of information that can be extracted from these measures and their frequent use, it is essential to know more about their inherent reliability. To understand the reliability of electrophysiology measures in healthy individuals. We hypothesized that measures of threshold and latency would be the most reliable and least susceptible to methodological differences between study sites. Somatosensory evoked potentials from 112 control participants; long-latency reflexes, transcranial magnetic stimulation with resting and active motor thresholds, motor evoked potential latencies, input/output curves, and short-latency sensory afferent inhibition and facilitation from 84 controls were collected at 3 visits over 24 months at 4 Track-On HD study sites. Reliability was assessed using intra-class correlation coefficients for absolute agreement, and the effects of reliability on statistical power are demonstrated for different sample sizes and study designs. Measures quantifying latencies, thresholds, and evoked responses at high stimulator intensities had the highest reliability, and required the smallest sample sizes to adequately power a study. Very few between-site differences were detected. Reliability and susceptibility to between-site differences should be evaluated for electrophysiological measures before including them in study designs. Levels of reliability vary substantially across electrophysiological measures, though there are few between-site differences. To address this, reliability should be used in conjunction with theoretical calculations to inform sample size and ensure studies are adequately powered to detect true change in measures of interest. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Gene-Based Multiclass Cancer Diagnosis with Class-Selective Rejections

    Science.gov (United States)

    Jrad, Nisrine; Grall-Maës, Edith; Beauseroy, Pierre

    2009-01-01

    Supervised learning of microarray data is receiving much attention in recent years. Multiclass cancer diagnosis, based on selected gene profiles, are used as adjunct of clinical diagnosis. However, supervised diagnosis may hinder patient care, add expense or confound a result. To avoid this misleading, a multiclass cancer diagnosis with class-selective rejection is proposed. It rejects some patients from one, some, or all classes in order to ensure a higher reliability while reducing time and expense costs. Moreover, this classifier takes into account asymmetric penalties dependant on each class and on each wrong or partially correct decision. It is based on ν-1-SVM coupled with its regularization path and minimizes a general loss function defined in the class-selective rejection scheme. The state of art multiclass algorithms can be considered as a particular case of the proposed algorithm where the number of decisions is given by the classes and the loss function is defined by the Bayesian risk. Two experiments are carried out in the Bayesian and the class selective rejection frameworks. Five genes selected datasets are used to assess the performance of the proposed method. Results are discussed and accuracies are compared with those computed by the Naive Bayes, Nearest Neighbor, Linear Perceptron, Multilayer Perceptron, and Support Vector Machines classifiers. PMID:19584932

  12. Minimum Information about a Cardiac Electrophysiology Experiment (MICEE): standardised reporting for model reproducibility, interoperability, and data sharing

    NARCIS (Netherlands)

    Quinn, T. A.; Granite, S.; Allessie, M. A.; Antzelevitch, C.; Bollensdorff, C.; Bub, G.; Burton, R. A. B.; Cerbai, E.; Chen, P. S.; Delmar, M.; DiFrancesco, D.; Earm, Y. E.; Efimov, I. R.; Egger, M.; Entcheva, E.; Fink, M.; Fischmeister, R.; Franz, M. R.; Garny, A.; Giles, W. R.; Hannes, T.; Harding, S. E.; Hunter, P. J.; Iribe, G.; Jalife, J.; Johnson, C. R.; Kass, R. S.; Kodama, I.; Koren, G.; Lord, P.; Markhasin, V. S.; Matsuoka, S.; McCulloch, A. D.; Mirams, G. R.; Morley, G. E.; Nattel, S.; Noble, D.; Olesen, S. P.; Panfilov, A. V.; Trayanova, N. A.; Ravens, U.; Richard, S.; Rosenbaum, D. S.; Rudy, Y.; Sachs, F.; Sachse, F. B.; Saint, D. A.; Schotten, U.; Solovyova, O.; Taggart, P.; Tung, L.; Varró, A.; Volders, P. G.; Wang, K.; Weiss, J. N.; Wettwer, E.; White, E.; Wilders, R.; Winslow, R. L.; Kohl, P.

    2011-01-01

    Cardiac experimental electrophysiology is in need of a well-defined Minimum Information Standard for recording, annotating, and reporting experimental data. As a step towards establishing this, we present a draft standard, called Minimum Information about a Cardiac Electrophysiology Experiment

  13. How do older adult drivers self-regulate? Characteristics of self-regulation classes defined by latent class analysis.

    Science.gov (United States)

    Bergen, Gwen; West, Bethany A; Luo, Feijun; Bird, Donna C; Freund, Katherine; Fortinsky, Richard H; Staplin, Loren

    2017-06-01

    Motor-vehicle crashes were the second leading cause of injury death for adults aged 65-84years in 2014. Some older drivers choose to self-regulate their driving to maintain mobility while reducing driving risk, yet the process remains poorly understood. Data from 729 older adults (aged ≥60years) who joined an older adult ride service program between April 1, 2010 and November 8, 2013 were analyzed to define and describe classes of driving self-regulation. Latent class analysis was employed to characterize older adult driving self-regulation classes using driving frequency and avoidance of seven driving situations. Logistic regression was used to explore associations between characteristics affecting mobility and self-regulation class. Three classes were identified (low, medium, and high self-regulation). High self-regulating participants reported the highest proportion of always avoiding seven risky driving situations and the lowest driving frequency followed by medium and low self-regulators. Those who were female, aged 80years or older, visually impaired, assistive device users, and those with special health needs were more likely to be high self-regulating compared with low self-regulating. Avoidance of certain driving situations and weekly driving frequency are valid indicators for describing driving self-regulation classes in older adults. Understanding the unique characteristics and mobility limitations of each class can guide optimal transportation strategies for older adults. Published by Elsevier Ltd.

  14. Predicting Alzheimer's disease by classifying 3D-Brain MRI images using SVM and other well-defined classifiers

    International Nuclear Information System (INIS)

    Matoug, S; Abdel-Dayem, A; Passi, K; Gross, W; Alqarni, M

    2012-01-01

    Alzheimer's disease (AD) is the most common form of dementia affecting seniors age 65 and over. When AD is suspected, the diagnosis is usually confirmed with behavioural assessments and cognitive tests, often followed by a brain scan. Advanced medical imaging and pattern recognition techniques are good tools to create a learning database in the first step and to predict the class label of incoming data in order to assess the development of the disease, i.e., the conversion from prodromal stages (mild cognitive impairment) to Alzheimer's disease, which is the most critical brain disease for the senior population. Advanced medical imaging such as the volumetric MRI can detect changes in the size of brain regions due to the loss of the brain tissues. Measuring regions that atrophy during the progress of Alzheimer's disease can help neurologists in detecting and staging the disease. In the present investigation, we present a pseudo-automatic scheme that reads volumetric MRI, extracts the middle slices of the brain region, performs segmentation in order to detect the region of brain's ventricle, generates a feature vector that characterizes this region, creates an SQL database that contains the generated data, and finally classifies the images based on the extracted features. For our results, we have used the MRI data sets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

  15. Working hand syndrome: A new definition of non-classified polyneuropathy condition.

    Science.gov (United States)

    Özdemir, Gökhan

    2017-06-01

    The aim of this paper was to define an unexplained non-classified polyneuropathy condition as a new neurological disease. This new diagnosis of occupation related polyneuropathy has been named as "WORKING HAND SYNDROME (WHS)."This study collected and compared clinic and electrophysiological analyze data from healthy controls, WHS patients, carpal tunnel syndrome (CTS) patients and polyneuropathy patients. The WHS patients presented to the clinic with pain, numbness, tingling, and burning sensations in their hands that increased significantly during rest and nighttime. However, there was no weakness in the muscles, and the deep tendon reflexes were normal in this disease. The patients had all been working in physically demanding jobs requiring the use of their hands/arms for at least 1 year, but no vibrating tools were used by the patients. All of the cases were men. I supposed that overload caused by an action repeated chronically by the hand/arm may impair the sensory nerves in mentioned hand/arm. In patients with these complaints, for a definitive diagnosis, similar diseases must be excluded. Nonetheless, the specific electrophysiological finding that the sural nerves are normal on the lower sides, as well as the occurrence of sensory axonal polyneuropathy in the sensory nerves without a significant effect on velocity and latency in the work-ups of the upper extremity are enough to make a diagnosis.In conclusion, WHS has been defined as a polyneuropathy and occupational disease. Patients with WHS present with pain, numbness, tingling, and burning sensations in their hands that increases significantly during rest and nighttime. They also use their arms/hands for jobs that require heavy labor. The neurological examinations of patients with WHS are normal. Only the sensory nerves in the upper extremities are affected. This article is suggested to serve as a resource for patients, health care professionals, and members of the neurology community at large.

  16. Analyzing clinical and electrophysiological characteristics of Paroxysmal Dyskinesia

    Directory of Open Access Journals (Sweden)

    Jue-qian Zhou

    2011-01-01

    Full Text Available The classification, clinical and electrophysiological characteristics, treatment outcome and pathogenesis of paroxysmal dyskinesia were summarized and analyzed. Paroxysmal dyskinesia was classified into three types. Different types had different incentives in clinical practice. Patients were mostly male adolescents, and the attacks, which were in various forms, manifested as dysmyotonia of choreoathetosis, body torsion and facemaking; no disturbance of consciousness during attacks. Electroencephalogram and other examinations showed no specific abnormalities during both the attacks and interictal period. Paroxysmal dyskinesia was an independent disease and different from epilepsy.

  17. Discrimination of Breast Tumors in Ultrasonic Images by Classifier Ensemble Trained with AdaBoost

    Science.gov (United States)

    Takemura, Atsushi; Shimizu, Akinobu; Hamamoto, Kazuhiko

    In this paper, we propose a novel method for acurate automated discrimination of breast tumors (carcinoma, fibroadenoma, and cyst). We defined 199 features related to diagnositic observations noticed when a doctor judges breast tumors, such as internal echo, shape, and boundary echo. These features included novel features based on a parameter of log-compressed K distribution, which reflect physical characteristics of ultrasonic B-mode imaging. Furthermore, we propose a discrimination method of breast tumors by using an ensemble classifier based on the multi-class AdaBoost algorithm with effective features selection. Verification by analyzing 200 carcinomas, 30 fibroadenomas and 30 cycts showed the usefulness of the newly defined features and the effectiveness of the discrimination by using an ensemble classifier trained by AdaBoost.

  18. 3D Bayesian contextual classifiers

    DEFF Research Database (Denmark)

    Larsen, Rasmus

    2000-01-01

    We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours.......We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours....

  19. Consistency Analysis of Nearest Subspace Classifier

    OpenAIRE

    Wang, Yi

    2015-01-01

    The Nearest subspace classifier (NSS) finds an estimation of the underlying subspace within each class and assigns data points to the class that corresponds to its nearest subspace. This paper mainly studies how well NSS can be generalized to new samples. It is proved that NSS is strongly consistent under certain assumptions. For completeness, NSS is evaluated through experiments on various simulated and real data sets, in comparison with some other linear model based classifiers. It is also ...

  20. Action Recognition Using 3D Histograms of Texture and A Multi-Class Boosting Classifier.

    Science.gov (United States)

    Zhang, Baochang; Yang, Yun; Chen, Chen; Yang, Linlin; Han, Jungong; Shao, Ling

    2017-10-01

    Human action recognition is an important yet challenging task. This paper presents a low-cost descriptor called 3D histograms of texture (3DHoTs) to extract discriminant features from a sequence of depth maps. 3DHoTs are derived from projecting depth frames onto three orthogonal Cartesian planes, i.e., the frontal, side, and top planes, and thus compactly characterize the salient information of a specific action, on which texture features are calculated to represent the action. Besides this fast feature descriptor, a new multi-class boosting classifier (MBC) is also proposed to efficiently exploit different kinds of features in a unified framework for action classification. Compared with the existing boosting frameworks, we add a new multi-class constraint into the objective function, which helps to maintain a better margin distribution by maximizing the mean of margin, whereas still minimizing the variance of margin. Experiments on the MSRAction3D, MSRGesture3D, MSRActivity3D, and UTD-MHAD data sets demonstrate that the proposed system combining 3DHoTs and MBC is superior to the state of the art.

  1. Improved Collaborative Representation Classifier Based on l2-Regularized for Human Action Recognition

    Directory of Open Access Journals (Sweden)

    Shirui Huo

    2017-01-01

    Full Text Available Human action recognition is an important recent challenging task. Projecting depth images onto three depth motion maps (DMMs and extracting deep convolutional neural network (DCNN features are discriminant descriptor features to characterize the spatiotemporal information of a specific action from a sequence of depth images. In this paper, a unified improved collaborative representation framework is proposed in which the probability that a test sample belongs to the collaborative subspace of all classes can be well defined and calculated. The improved collaborative representation classifier (ICRC based on l2-regularized for human action recognition is presented to maximize the likelihood that a test sample belongs to each class, then theoretical investigation into ICRC shows that it obtains a final classification by computing the likelihood for each class. Coupled with the DMMs and DCNN features, experiments on depth image-based action recognition, including MSRAction3D and MSRGesture3D datasets, demonstrate that the proposed approach successfully using a distance-based representation classifier achieves superior performance over the state-of-the-art methods, including SRC, CRC, and SVM.

  2. Hybrid classifiers methods of data, knowledge, and classifier combination

    CERN Document Server

    Wozniak, Michal

    2014-01-01

    This book delivers a definite and compact knowledge on how hybridization can help improving the quality of computer classification systems. In order to make readers clearly realize the knowledge of hybridization, this book primarily focuses on introducing the different levels of hybridization and illuminating what problems we will face with as dealing with such projects. In the first instance the data and knowledge incorporated in hybridization were the action points, and then a still growing up area of classifier systems known as combined classifiers was considered. This book comprises the aforementioned state-of-the-art topics and the latest research results of the author and his team from Department of Systems and Computer Networks, Wroclaw University of Technology, including as classifier based on feature space splitting, one-class classification, imbalance data, and data stream classification.

  3. Visual electrophysiology in children

    Directory of Open Access Journals (Sweden)

    Jelka Brecelj

    2005-10-01

    Full Text Available Background: Electrophysiological assessment of vision in children helps to recognise abnormal development of the visual system when it is still susceptible to medication and eventual correction. Visual electrophysiology provides information about the function of the retina (retinal pigment epithelium, cone and rod receptors, bipolar, amacrine, and ganglion cells, optic nerve, chiasmal and postchiasmal visual pathway, and visual cortex.Methods: Electroretinograms (ERG and visual evoked potentials (VEP are recorded non-invasively; in infants are recorded simultaneously ERG with skin electrodes, while in older children separately ERG with HK loop electrode in accordance with ISCEV (International Society for Clinical Electrophysiology of Vision recommendations.Results: Clinical and electrophysiological changes in children with nystagmus, Leber’s congenital amaurosis, achromatopsia, congenital stationary night blindness, progressive retinal dystrophies, optic nerve hypoplasia, albinism, achiasmia, optic neuritis and visual pathway tumours are presented.Conclusions: Electrophysiological tests can help to indicate the nature and the location of dysfunction in unclear ophthalmological and/or neurological cases.

  4. A Topic Model Approach to Representing and Classifying Football Plays

    KAUST Repository

    Varadarajan, Jagannadan

    2013-09-09

    We address the problem of modeling and classifying American Football offense teams’ plays in video, a challenging example of group activity analysis. Automatic play classification will allow coaches to infer patterns and tendencies of opponents more ef- ficiently, resulting in better strategy planning in a game. We define a football play as a unique combination of player trajectories. To this end, we develop a framework that uses player trajectories as inputs to MedLDA, a supervised topic model. The joint maximiza- tion of both likelihood and inter-class margins of MedLDA in learning the topics allows us to learn semantically meaningful play type templates, as well as, classify different play types with 70% average accuracy. Furthermore, this method is extended to analyze individual player roles in classifying each play type. We validate our method on a large dataset comprising 271 play clips from real-world football games, which will be made publicly available for future comparisons.

  5. Classifier Fusion With Contextual Reliability Evaluation.

    Science.gov (United States)

    Liu, Zhunga; Pan, Quan; Dezert, Jean; Han, Jun-Wei; He, You

    2018-05-01

    Classifier fusion is an efficient strategy to improve the classification performance for the complex pattern recognition problem. In practice, the multiple classifiers to combine can have different reliabilities and the proper reliability evaluation plays an important role in the fusion process for getting the best classification performance. We propose a new method for classifier fusion with contextual reliability evaluation (CF-CRE) based on inner reliability and relative reliability concepts. The inner reliability, represented by a matrix, characterizes the probability of the object belonging to one class when it is classified to another class. The elements of this matrix are estimated from the -nearest neighbors of the object. A cautious discounting rule is developed under belief functions framework to revise the classification result according to the inner reliability. The relative reliability is evaluated based on a new incompatibility measure which allows to reduce the level of conflict between the classifiers by applying the classical evidence discounting rule to each classifier before their combination. The inner reliability and relative reliability capture different aspects of the classification reliability. The discounted classification results are combined with Dempster-Shafer's rule for the final class decision making support. The performance of CF-CRE have been evaluated and compared with those of main classical fusion methods using real data sets. The experimental results show that CF-CRE can produce substantially higher accuracy than other fusion methods in general. Moreover, CF-CRE is robust to the changes of the number of nearest neighbors chosen for estimating the reliability matrix, which is appealing for the applications.

  6. Electrophysiological changes in patients with liver cirrhosis in a tertiary care hospital in karachi, pakistan

    International Nuclear Information System (INIS)

    Parkash, O.; Mohyuddin, G.R.; Ayub, A.; Nazir, I.

    2017-01-01

    Electrophysiological changes in cirrhosis are well known but least investigated especially in our country hence we wanted to see electrophysiological changes especially QT interval in cirrhotic patients. Methods: A cross-sectional study was conducted at Aga Khan University Hospital Karachi (AKUH) in which medical records (duration 2008-2010) of cirrhotic patients were reviewed. Results: Three hundred and eighty cirrhotic patients' charts were studied, 227 (59.7 percent) were male and mean age of this cohort was 52.8+-12.6 years. The most common cause for CLD was Hepatitis C (CHC) in 260 (68.4 percent), NBNC in 56(14.7 percent) and HBV in 51 (13.4 percent). Only 225 had complete ECG workup, the mean corrected QT interval was 0.44+-0.067 sec. Among the electrophysiological abnormalities, 79 (35 percent) had a prolonged corrected QT interval, 7 (3.1 percent) had a prolonged PR interval (>0.22s) and prolonged QRS duration was seen in 23 (10.4 percent) patients. QT prolongation was seen in 1 of the 5 patients with Child Class A (20 percent), 22 of the 73 patients with Child Class B (30.1 percent), and 25 of the 61 patients with Child Class C (41 percent). However, this difference however was not statistically significant. (p value=.331). Conclusion: We conclude that QT prolongation is more frequent in patients with liver cirrhosis especially when the disease is more advanced like in Child C hence these patients are more prone to sudden cardiac death. Moreover, this study shows that the risk associated with QT prolongation is present through all classes of liver cirrhosis. We recommend that routine cardiac screening with ECG of all cirrhotic patients be performed. (author)

  7. Bayesian Classifier for Medical Data from Doppler Unit

    Directory of Open Access Journals (Sweden)

    J. Málek

    2006-01-01

    Full Text Available Nowadays, hand-held ultrasonic Doppler units (probes are often used for noninvasive screening of atherosclerosis in the arteries of the lower limbs. The mean velocity of blood flow in time and blood pressures are measured on several positions on each lower limb. By listening to the acoustic signal generated by the device or by reading the signal displayed on screen, a specialist can detect peripheral arterial disease (PAD.This project aims to design software that will be able to analyze data from such a device and classify it into several diagnostic classes. At the Department of Functional Diagnostics at the Regional Hospital in Liberec a database of several hundreds signals was collected. In cooperation with the specialist, the signals were manually classified into four classes. For each class, selected signal features were extracted and then used for training a Bayesian classifier. Another set of signals was used for evaluating and optimizing the parameters of the classifier. Slightly above 84 % of successfully recognized diagnostic states, was recently achieved on the test data. 

  8. Electrophysiologic Assessments of Involuntary Movements: Tremor and Myoclonus

    Directory of Open Access Journals (Sweden)

    Hyun-Dong Park

    2009-05-01

    Full Text Available Tremor is defined as a rhythmical, involuntary oscillatory movement of a body part. Although neurological examination reveals information regarding its frequency, regularity, amplitude, and activation conditions, the electrophysiological investigations help in confirming the tremor, in differentiating it from other hyperkinetic disorders like myoclonus, and may provide etiological clues. Accelerometer with surface electromyogram (EMG can be used to document the dominant frequency of a tremor, which may be useful as certain frequencies are more characteristic of specific etiologies than others hyperkinetic disorders. It may show rhythmic bursts, duration and activation pattern (alternating or synchronous. Myoclonus is a quick, involuntary movement. Electrophysiological studies may helpful in the evaluation of myoclonus, not only for confirming the clinical diagnosis but also for understanding the underlying physiological mechanisms. Electroencephalogram (EEG-EMG correlates can give us important information about myoclonus. Jerk-locked back-averaging and evoked potentials with recording of the long-latency, long-loop reflexes are currently available to study the pathophysiology of myoclonus.

  9. Nonparametric, Coupled ,Bayesian ,Dictionary ,and Classifier Learning for Hyperspectral Classification.

    Science.gov (United States)

    Akhtar, Naveed; Mian, Ajmal

    2017-10-03

    We present a principled approach to learn a discriminative dictionary along a linear classifier for hyperspectral classification. Our approach places Gaussian Process priors over the dictionary to account for the relative smoothness of the natural spectra, whereas the classifier parameters are sampled from multivariate Gaussians. We employ two Beta-Bernoulli processes to jointly infer the dictionary and the classifier. These processes are coupled under the same sets of Bernoulli distributions. In our approach, these distributions signify the frequency of the dictionary atom usage in representing class-specific training spectra, which also makes the dictionary discriminative. Due to the coupling between the dictionary and the classifier, the popularity of the atoms for representing different classes gets encoded into the classifier. This helps in predicting the class labels of test spectra that are first represented over the dictionary by solving a simultaneous sparse optimization problem. The labels of the spectra are predicted by feeding the resulting representations to the classifier. Our approach exploits the nonparametric Bayesian framework to automatically infer the dictionary size--the key parameter in discriminative dictionary learning. Moreover, it also has the desirable property of adaptively learning the association between the dictionary atoms and the class labels by itself. We use Gibbs sampling to infer the posterior probability distributions over the dictionary and the classifier under the proposed model, for which, we derive analytical expressions. To establish the effectiveness of our approach, we test it on benchmark hyperspectral images. The classification performance is compared with the state-of-the-art dictionary learning-based classification methods.

  10. Investigation of the Development of 7th Grade Students’ Skills to Define, Construct and Classify Polygons with Cabri Geometry

    Directory of Open Access Journals (Sweden)

    Ahmet Yanık

    2013-03-01

    Full Text Available The aim of the study is to investigate the development of 7th Grade students’ skills to define, construct and classify polygons in geometry course with Cabri Geometry II Plus software geometry, an example of dynamic geometry software. The study used qualitative and quantitative research methods in accordance with the research objectives and focus, so it was designed as a mixed method research. The participants of the study were 21 7th Grade students, 11 girls and 10 boys, who were attending a secondary school in Eskişehir city center during 2012-2013 school year. As a source of qualitative data, four students in this class were selected for the interview. The data were collected with “Polygon Identification and Classification Scale”, one group pre-test and post-test in order to determine the level of development and significance level of the gender variable, and Cabri Geometry worksheets developed by the researchers. The quantitative data were analyzed with SPSS Statistics 20. Also, t-test and Wilcoxon test were used in data analysis. The data obtained from the interviews were analyzed through descriptive analysis. The qualitative data showed that the mean of correct answers given by the students to the questions in the Polygon Identification and Classification Scale was higher in the post-test than the pre-test. The ttest results for the pre-test and post-test mean scores and the results of the paired samples test showed a significant difference in favor of the post-test. There was no significant difference based on the gender variable. On the other hand, the data obtained from the interviews were coded under five different themes. The activities about the concept of formation showed that incorrect formations caused incorrect generalizations about the shapes. The study found that, as a result of the teaching practice in the study, hierarchical relations among polygons were expressed correctly. Finally, after the practice, the

  11. Memory-Based Specification of Verbal Features for Classifying Animals into Super-Ordinate and Sub-Ordinate Categories

    OpenAIRE

    Takahiro Soshi; Norio Fujimaki; Atsushi Matsumoto; Aya S. Ihara

    2017-01-01

    Accumulating evidence suggests that category representations are based on features. Distinguishing features are considered to define categories, because of all-or-none responses for objects in different categories; however, it is unclear how distinguishing features actually classify objects at various category levels. The present study included 75 animals within three classes (mammal, bird, and fish), along with 195 verbal features. Healthy adults participated in memory-based feature-animal m...

  12. Gene-Based Multiclass Cancer Diagnosis with Class-Selective Rejections

    Directory of Open Access Journals (Sweden)

    Nisrine Jrad

    2009-01-01

    rejection scheme. The state of art multiclass algorithms can be considered as a particular case of the proposed algorithm where the number of decisions is given by the classes and the loss function is defined by the Bayesian risk. Two experiments are carried out in the Bayesian and the class selective rejection frameworks. Five genes selected datasets are used to assess the performance of the proposed method. Results are discussed and accuracies are compared with those computed by the Naive Bayes, Nearest Neighbor, Linear Perceptron, Multilayer Perceptron, and Support Vector Machines classifiers.

  13. Solid-state NMR, electrophysiology and molecular dynamics characterization of human VDAC2

    International Nuclear Information System (INIS)

    Gattin, Zrinka; Schneider, Robert; Laukat, Yvonne; Giller, Karin; Maier, Elke; Zweckstetter, Markus; Griesinger, Christian; Benz, Roland; Becker, Stefan; Lange, Adam

    2015-01-01

    The voltage-dependent anion channel (VDAC) is the most abundant protein of the outer mitochondrial membrane and constitutes the major pathway for the transport of ADP, ATP, and other metabolites. In this multidisciplinary study we combined solid-state NMR, electrophysiology, and molecular dynamics simulations, to study the structure of the human VDAC isoform 2 in a lipid bilayer environment. We find that the structure of hVDAC2 is similar to the structure of hVDAC1, in line with recent investigations on zfVDAC2. However, hVDAC2 appears to exhibit an increased conformational heterogeneity compared to hVDAC1 which is reflected in broader solid-state NMR spectra and less defined electrophysiological profiles

  14. Solid-state NMR, electrophysiology and molecular dynamics characterization of human VDAC2

    Energy Technology Data Exchange (ETDEWEB)

    Gattin, Zrinka; Schneider, Robert; Laukat, Yvonne; Giller, Karin [Max Planck Institute for Biophysical Chemistry (Germany); Maier, Elke [Theodor-Boveri-Institut (Biozentrum) der Universität Würzburg, Lehrstuhl für Biotechnologie (Germany); Zweckstetter, Markus; Griesinger, Christian [Max Planck Institute for Biophysical Chemistry (Germany); Benz, Roland [Theodor-Boveri-Institut (Biozentrum) der Universität Würzburg, Lehrstuhl für Biotechnologie (Germany); Becker, Stefan; Lange, Adam, E-mail: alange@fmp-berlin.de [Max Planck Institute for Biophysical Chemistry (Germany)

    2015-04-15

    The voltage-dependent anion channel (VDAC) is the most abundant protein of the outer mitochondrial membrane and constitutes the major pathway for the transport of ADP, ATP, and other metabolites. In this multidisciplinary study we combined solid-state NMR, electrophysiology, and molecular dynamics simulations, to study the structure of the human VDAC isoform 2 in a lipid bilayer environment. We find that the structure of hVDAC2 is similar to the structure of hVDAC1, in line with recent investigations on zfVDAC2. However, hVDAC2 appears to exhibit an increased conformational heterogeneity compared to hVDAC1 which is reflected in broader solid-state NMR spectra and less defined electrophysiological profiles.

  15. Current concepts in nuclear pore electrophysiology.

    Science.gov (United States)

    Bustamante, José Omar

    2006-01-01

    Over 4 decades ago, microelectrode studies of in situ nuclei showed that, under certain conditions, the nuclear envelope (NE) behaves as a barrier opposing the nucleocytoplasmic flow of physiological ions. As the nuclear pore complexes (NPCs) of the NE are the only pathways for direct nucleocytoplasmic flow, those experiments implied that the NPCs are capable of restricting ion flow. These early studies validated electrophysiology as a useful approach to quantify some of the mechanisms by which NPCs mediate gene activity and expression. Since electron microscopy (EM) and other non-electrophysiological investigations, showed that the NPC lumen is a nanochannel, the opinion prevailed that the NPC could not oppose the flow of ions and, therefore, that electrophysiological observations resulted from technical artifacts. Consequently, the initial enthusiasm with nuclear electrophysiology faded out in less than a decade. In 1990, nuclear electrophysiology was revisited with patch-clamp, the most powerful electrophysiological technique to date. Patch-clamp has consistently demonstrated that the NE has intrinsic ion channel activity. Direct demonstrations of the NPC on-off ion channel gating behavior were published for artificial conditions in 1995 and for intact living nuclei in 2002. This on-off switching/gating behavior can be interpreted in terms of a metastable energy barrier. In the hope of advancing nuclear electrophysiology, and to complement the other papers contained in this special issue of the journal, here I review some of the main technical, experimental, and theoretical issues of the field, with special focus on NPCs.

  16. What's at Stake in the Lives of People with Intellectual Disability? Part I: The Power of Naming, Defining, Diagnosing, Classifying, and Planning Supports

    Science.gov (United States)

    Schalock, Robert L.; Luckasson, Ruth

    2013-01-01

    This article focuses on the power of naming, defining, diagnosing, classifying, and planning supports for people with intellectual disability. The article summarizes current thinking regarding these five functions, states the essential question addressed by the respective function, and provides an overview of the high stakes involved for people…

  17. Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images.

    Science.gov (United States)

    Alexandridis, Thomas K; Tamouridou, Afroditi Alexandra; Pantazi, Xanthoula Eirini; Lagopodi, Anastasia L; Kashefi, Javid; Ovakoglou, Georgios; Polychronos, Vassilios; Moshou, Dimitrios

    2017-09-01

    In the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaining high-resolution images. Four novelty detection classifiers were used to identify S. marianum between other vegetation in a field. The classifiers were One Class Support Vector Machine (OC-SVM), One Class Self-Organizing Maps (OC-SOM), Autoencoders and One Class Principal Component Analysis (OC-PCA). As input features to the novelty detection classifiers, the three spectral bands and texture were used. The S. marianum identification accuracy using OC-SVM reached an overall accuracy of 96%. The results show the feasibility of effective S. marianum mapping by means of novelty detection classifiers acting on multispectral UAV imagery.

  18. Classifying seismic waveforms from scratch: a case study in the alpine environment

    Science.gov (United States)

    Hammer, C.; Ohrnberger, M.; Fäh, D.

    2013-01-01

    Nowadays, an increasing amount of seismic data is collected by daily observatory routines. The basic step for successfully analyzing those data is the correct detection of various event types. However, the visually scanning process is a time-consuming task. Applying standard techniques for detection like the STA/LTA trigger still requires the manual control for classification. Here, we present a useful alternative. The incoming data stream is scanned automatically for events of interest. A stochastic classifier, called hidden Markov model, is learned for each class of interest enabling the recognition of highly variable waveforms. In contrast to other automatic techniques as neural networks or support vector machines the algorithm allows to start the classification from scratch as soon as interesting events are identified. Neither the tedious process of collecting training samples nor a time-consuming configuration of the classifier is required. An approach originally introduced for the volcanic task force action allows to learn classifier properties from a single waveform example and some hours of background recording. Besides a reduction of required workload this also enables to detect very rare events. Especially the latter feature provides a milestone point for the use of seismic devices in alpine warning systems. Furthermore, the system offers the opportunity to flag new signal classes that have not been defined before. We demonstrate the application of the classification system using a data set from the Swiss Seismological Survey achieving very high recognition rates. In detail we document all refinements of the classifier providing a step-by-step guide for the fast set up of a well-working classification system.

  19. Normal Values for Heart Electrophysiology Parameters of Healthy Swine Determined on Electrophysiology Study.

    Science.gov (United States)

    Noszczyk-Nowak, Agnieszka; Cepiel, Alicja; Janiszewski, Adrian; Pasławski, Robert; Gajek, Jacek; Pasławska, Urszula; Nicpoń, Józef

    2016-01-01

    Swine are a well-recognized animal model for human cardiovascular diseases. Despite the widespread use of porcine model in experimental electrophysiology, still no reference values for intracardiac electrical activity and conduction parameters determined during an invasive electrophysiology study (EPS) have been developed in this species thus far. The aim of the study was to develop a set of normal values for intracardiac electrical activity and conduction parameters determined during an invasive EPS of swine. The study included 36 healthy domestic swine (24-40 kg body weight). EPS was performed under a general anesthesia with midazolam, propofol and isoflurane. The reference values for intracardiac electrical activity and conduction parameters were calculated as arithmetic means ± 2 standard deviations. The reference values were determined for AH, HV and PA intervals, interatrial conduction time at its own and imposed rhythm, sinus node recovery time (SNRT), corrected sinus node recovery time (CSNRT), anterograde and retrograde Wenckebach points, atrial, atrioventricular node and ventricular refractory periods. No significant correlations were found between body weight and heart rate of the examined pigs and their electrophysiological parameters. The hereby presented reference values can be helpful in comparing the results of various studies, as well as in more accurately estimating the values of electrophysiological parameters that can be expected in a given experiment.

  20. Bayes classifiers for imbalanced traffic accidents datasets.

    Science.gov (United States)

    Mujalli, Randa Oqab; López, Griselda; Garach, Laura

    2016-03-01

    Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009-2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents. Copyright © 2015 Elsevier Ltd. All rights reserved.

  1. Luminosity class of neutron reflectometers

    Energy Technology Data Exchange (ETDEWEB)

    Pleshanov, N.K., E-mail: pnk@pnpi.spb.ru

    2016-10-21

    The formulas that relate neutron fluxes at reflectometers with differing q-resolutions are derived. The reference luminosity is defined as a maximum flux for measurements with a standard resolution. The methods of assessing the reference luminosity of neutron reflectometers are presented for monochromatic and white beams, which are collimated with either double diaphragm or small angle Soller systems. The values of the reference luminosity for unified parameters define luminosity class of reflectometers. The luminosity class characterizes (each operation mode of) the instrument by one number and can be used to classify operating reflectometers and optimize designed reflectometers. As an example the luminosity class of the neutron reflectometer NR-4M (reactor WWR-M, Gatchina) is found for four operation modes: 2.1 (monochromatic non-polarized beam), 1.9 (monochromatic polarized beam), 1.5 (white non-polarized beam), 1.1 (white polarized beam); it is shown that optimization of measurements may increase the flux at the sample up to two orders of magnitude with monochromatic beams and up to one order of magnitude with white beams. A fan beam reflectometry scheme with monochromatic neutrons is suggested, and the expected increase in luminosity is evaluated. A tuned-phase chopper with a variable TOF resolution is recommended for reflectometry with white beams.

  2. Comparison of electrophysiological findings in axonal and demyelinating Guillain-Barre syndrome

    Science.gov (United States)

    Yadegari, Samira; Nafissi, Shahriar; Kazemi, Neda

    2014-01-01

    Background: Incidence and predominant subtype of Guillain-Barre syndrome (GBS) differs geographically. Electrophysiology has an important role in early diagnosis and prediction of prognosis. This study is conducted to determine the frequent subtype of GBS in a large group of patients in Iran and compare nerve conduction studies in axonal and demyelinating forms of GBS. Methods: We retrospectively evaluated the medical records and electrodiagnostic study (EDS) of 121 GBS patients who were managed in our hospital during 11 years. After regarding the exclusion criteria, patients classified as three groups: acute inflammatory demyelinating polyneuropathy (AIDP), acute motor axonal neuropathy (AMAN), and acute motor sensory axonal neuropathy (AMSAN). The most frequent subtype and then electrophysiological characteristic based on the time of EDS and their cerebrospinal fluid (CSF) profile were assessed. Results: Among 70 patients finally included in the study, 67% were men. About 63%, 23%, and 14% had AIDP, AMAN, and AMSAN, respectively. AIDP patients represented a wider range of ages compared with other groups. Higher levels of CSF protein, abnormal late responses and sural sparing were more frequent in AIDP subtype. Five AMSAN patients also revealed sural sparing. Conduction block (CB) was observed in one AMAN patient. Prolonged F-wave latency was observed only in AIDP cases. CB and inexcitable sensory nerves were more frequent after 2 weeks, but reduced F-wave persistency was more prominent in the early phase. Conclusion: AIDP was the most frequent subtype. Although the electrophysiology and CSF are important diagnostic tools, classification should not be made based on a distinct finding. PMID:25422732

  3. Lyme carditis. Electrophysiologic and histopathologic study

    International Nuclear Information System (INIS)

    Reznick, J.W.; Braunstein, D.B.; Walsh, R.L.; Smith, C.R.; Wolfson, P.M.; Gierke, L.W.; Gorelkin, L.; Chandler, F.W.

    1986-01-01

    To further define the nature of Lyme carditis, electrophysiologic study and endomyocardial biopsy were performed in a patient with Lyme disease, whose principal cardiac manifestation was high-degree atrioventricular block. Intracardiac recording demonstrated supra-Hisian block and complete absence of an escape mechanism. Gallium 67 scanning demonstrated myocardial uptake, and right ventricular endomyocardial biopsy revealed active lymphocytic myocarditis. A structure compatible with a spirochetal organism was demonstrated in one biopsy specimen. It is concluded that Lyme disease can produce active myocarditis, as suggested by gallium 67 imaging and confirmed by endomyocardial biopsy. Furthermore, the presence of high-grade atrioventricular block in this disease requires aggressive management with temporary pacemaker and corticosteroid therapy

  4. Combining multiple classifiers for age classification

    CSIR Research Space (South Africa)

    Van Heerden, C

    2009-11-01

    Full Text Available The authors compare several different classifier combination methods on a single task, namely speaker age classification. This task is well suited to combination strategies, since significantly different feature classes are employed. Support vector...

  5. Fisher classifier and its probability of error estimation

    Science.gov (United States)

    Chittineni, C. B.

    1979-01-01

    Computationally efficient expressions are derived for estimating the probability of error using the leave-one-out method. The optimal threshold for the classification of patterns projected onto Fisher's direction is derived. A simple generalization of the Fisher classifier to multiple classes is presented. Computational expressions are developed for estimating the probability of error of the multiclass Fisher classifier.

  6. Just-in-time classifiers for recurrent concepts.

    Science.gov (United States)

    Alippi, Cesare; Boracchi, Giacomo; Roveri, Manuel

    2013-04-01

    Just-in-time (JIT) classifiers operate in evolving environments by classifying instances and reacting to concept drift. In stationary conditions, a JIT classifier improves its accuracy over time by exploiting additional supervised information coming from the field. In nonstationary conditions, however, the classifier reacts as soon as concept drift is detected; the current classification setup is discarded and a suitable one activated to keep the accuracy high. We present a novel generation of JIT classifiers able to deal with recurrent concept drift by means of a practical formalization of the concept representation and the definition of a set of operators working on such representations. The concept-drift detection activity, which is crucial in promptly reacting to changes exactly when needed, is advanced by considering change-detection tests monitoring both inputs and classes distributions.

  7. Classifying objects in LWIR imagery via CNNs

    Science.gov (United States)

    Rodger, Iain; Connor, Barry; Robertson, Neil M.

    2016-10-01

    The aim of the presented work is to demonstrate enhanced target recognition and improved false alarm rates for a mid to long range detection system, utilising a Long Wave Infrared (LWIR) sensor. By exploiting high quality thermal image data and recent techniques in machine learning, the system can provide automatic target recognition capabilities. A Convolutional Neural Network (CNN) is trained and the classifier achieves an overall accuracy of > 95% for 6 object classes related to land defence. While the highly accurate CNN struggles to recognise long range target classes, due to low signal quality, robust target discrimination is achieved for challenging candidates. The overall performance of the methodology presented is assessed using human ground truth information, generating classifier evaluation metrics for thermal image sequences.

  8. Automatic fitting of spiking neuron models to electrophysiological recordings

    Directory of Open Access Journals (Sweden)

    Cyrille Rossant

    2010-03-01

    Full Text Available Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to electrophysiological recordings. The fitting procedure can be very time consuming both in terms of computer simulations and in terms of code writing. We present algorithms to fit spiking models to electrophysiological data (time-varying input and spike trains that can run in parallel on graphics processing units (GPUs. The model fitting library is interfaced with Brian, a neural network simulator in Python. If a GPU is present it uses just-in-time compilation to translate model equations into optimized code. Arbitrary models can then be defined at script level and run on the graphics card. This tool can be used to obtain empirically validated spiking models of neurons in various systems. We demonstrate its use on public data from the INCF Quantitative Single-Neuron Modeling 2009 competition by comparing the performance of a number of neuron spiking models.

  9. The use of hyperspectral data for tree species discrimination: Combining binary classifiers

    CSIR Research Space (South Africa)

    Dastile, X

    2010-11-01

    Full Text Available classifier Classification system 7 class 1 class 2 new sample For 5-nearest neighbour classification: assign new sample to class 1. RU SASA 2010 ? Given learning task {(x1,t1),(x 2,t2),?,(x p,tp)} (xi ? Rn feature vectors, ti ? {?1,?, ?c...). A review on the combination of binary classifiers in multiclass problems. Springer science and Business Media B.V [7] Dietterich T.G and Bakiri G.(1995). Solving Multiclass Learning Problem via Error-Correcting Output Codes. AI Access Foundation...

  10. Molecular signatures define two main classes of meningiomas

    Directory of Open Access Journals (Sweden)

    Costello Joseph F

    2007-10-01

    Full Text Available Abstract Background Meningiomas are common brain tumors that are classified into three World Health Organization grades (benign, atypical and malignant and are molecularly ill-defined tumors. The purpose of this study was identify molecular signatures unique to the different grades of meningiomas and to unravel underlying molecular mechanisms driving meningioma tumorigenesis. Results We have used a combination of gene expression microarrays and array comparative genomic hybridization (aCGH to show that meningiomas of all three grades fall into two main molecular groups designated 'low-proliferative' and 'high-proliferative' meningiomas. While all benign meningiomas fall into the low-proliferative group and all malignant meningiomas fall into the high-proliferative group, atypical meningiomas distribute into either one of these groups. High-proliferative atypical meningiomas had an elevated median MIB-1 labeling index and a greater frequency of copy number aberrations (CNAs compared to low-proliferative atypical meningiomas. Additionally, losses on chromosome 6q, 9p, 13 and 14 were found exclusively in the high-proliferative meningiomas. We have identified genes that distinguish benign low-proliferative meningiomas from malignant high-proliferative meningiomas and have found that gain of cell-proliferation markers and loss of components of the transforming growth factor-beta signaling pathway were the major molecular mechanisms that distinguish these two groups. Conclusion Collectively, our data suggests that atypical meningiomas are not a molecularly distinct group but are similar to either benign or malignant meningiomas. It is anticipated that identified molecular and CNA markers will potentially be more accurate prognostic markers of meningiomas.

  11. LBM-EP: Lattice-Boltzmann method for fast cardiac electrophysiology simulation from 3D images.

    Science.gov (United States)

    Rapaka, S; Mansi, T; Georgescu, B; Pop, M; Wright, G A; Kamen, A; Comaniciu, Dorin

    2012-01-01

    Current treatments of heart rhythm troubles require careful planning and guidance for optimal outcomes. Computational models of cardiac electrophysiology are being proposed for therapy planning but current approaches are either too simplified or too computationally intensive for patient-specific simulations in clinical practice. This paper presents a novel approach, LBM-EP, to solve any type of mono-domain cardiac electrophysiology models at near real-time that is especially tailored for patient-specific simulations. The domain is discretized on a Cartesian grid with a level-set representation of patient's heart geometry, previously estimated from images automatically. The cell model is calculated node-wise, while the transmembrane potential is diffused using Lattice-Boltzmann method within the domain defined by the level-set. Experiments on synthetic cases, on a data set from CESC'10 and on one patient with myocardium scar showed that LBM-EP provides results comparable to an FEM implementation, while being 10 - 45 times faster. Fast, accurate, scalable and requiring no specific meshing, LBM-EP paves the way to efficient and detailed models of cardiac electrophysiology for therapy planning.

  12. 4 cases of 'ataxic hemiparesis'. A comparative study of computed tomography and electrophysiological findings

    Energy Technology Data Exchange (ETDEWEB)

    Eguchi, Kiyoshi; Kamei, Hidekazu; Kitamura, Eiko; Komatsuzaki, Satoshi; Yamane, Kiyomi; Takemiya, Toshiko; Kobayashi, Itsuro; Maruyama, Shoichi

    1984-10-01

    Ataxic hemiparesis is described as a syndrome in which pyramidal and cerebellar signs occur ipsilaterally. Fisher who suggested the designation ''ataxic hemiparesis'' for this syndrome confirmed by pathological study that causative lesion was in the basis pontis at the level of the junction of the upper one third and lower two thirds on the opposite side of the neurological deficit and he also reported that CT might fail to show the lesion. We observed 4 patients with ataxic hemiparesis and examined them in auditory brainstem response (ABR), somatosensory evoked potential (SEP), and blink reflex as electrophysiological study. Their CT and electrophysiological findings were compared with each others to define the responsible lesion more clearly. Essentially, these abnormal electrophysiological findings were recognized only in the case of pontine hemorrhage, and these findings recovered to normal as clinical and CT findings were improved. In the other cases, the electrophysiological findings were not prominent and CT revealed the lesions in deep frontal region, internal capsule and cerebellar hemispheres respectively. These results might show that many cases of extra-pontine lesions could develop the syndrome of ataxic hemiparesis. However, the relation between responsible lesions for ataxic hemiparesis and electrophysiological findings are still uncertain. Further evidences including clinicopathological studies will be required to clarify this relation and to get the more accurate anatomical interpretation of ataxic hemiparesis from lesions besides the pontine region. (author).

  13. Cohort of Patients Referred for Brugada Syndrome Investigation in an Electrophysiology Service - 19-Year Registry

    Directory of Open Access Journals (Sweden)

    Stefan Warpechowski Neto

    2018-06-01

    Full Text Available Abstract Background: Brugada syndrome (SBr is an arrhythmic condition characterized by ST-T segment abnormalities in the right precordial leads associated with a high risk of ventricular arrhythmias and sudden death. Local data regarding the clinical characteristics of patients with a typical electrocardiographic (ECG pattern undergoing electrophysiological study are scarce. Objective: To evaluate patients with an ECG pattern suggestive of SBr referred for electrophysiological evaluation in a specialized center. Methods: Cohort study of patients referred for electrophysiological study because of an ECG pattern compatible with SBr between January 1998 and March 2017. Results: Of the 5506 procedures, 35 (0.64% were for SBr investigation, 25 of which (71.42% were performed in men. The mean age was 43.89 ± 13.1 years. The ECG patterns were as follows: type I, 22 (62.85%; type II, 12 (34.30%; and type III, 1 (2.85%. Twenty-three patients (65.7% were asymptomatic, 6 (17.14% had palpitations, 5 (14.3% had syncope, and 3 (8.6% had a family history of sudden death. Electrophysiological study induced ventricular tachyarrhythmias in 16 cases (45.7%, the mean ventricular refractory period being 228 ± 36 ms. Ajmaline / procainamide was used in 11 cases (31.4%, changing the ECG pattern to type I in 7 (63.6%. Sixteen cases (45.7% received an implantable cardioverter defibrillator (ICD. In a mean 5-year follow-up, 1 of the 16 patients (6.25% with ICD had appropriate therapy for ventricular fibrillation. There was no death. Other arrhythmias occurred in 4 (11.4% cases. Conclusions: Most patients are men, and a type I ECG pattern is the main indication for electrophysiological study. Class IA drugs have a high ECG conversion rate. The ICD event rate was 6%. (Arq Bras Cardiol. 2018; [online].ahead print, PP.0-0

  14. We are all ordinary people : Perceptions of class and class differences in personal relationships

    NARCIS (Netherlands)

    Van Eijk, G.

    2009-01-01

    This paper examines people’s perceptions of class and class differences—in general and with regard to personal relationships. Data from an original survey on personal networks (n=195) shows that most people think they are middle class, although many lower class respondents classify themselves as

  15. The Ia.2 Epitope Defines a Subset of Lipid Raft Resident MHC Class II Molecules Crucial to Effective Antigen Presentation1

    Science.gov (United States)

    Busman-Sahay, Kathleen; Sargent, Elizabeth; Harton, Jonathan A.; Drake, James R.

    2016-01-01

    Previous work has established that binding of the 11-5.2 anti-I-Ak mAb, which recognizes the Ia.2 epitope on I-Ak class II molecules, elicits MHC class II signaling, whereas binding of two other anti-I-Ak mAb that recognize the Ia.17 epitope fail to elicit signaling. Using a biochemical approach, we establish that the Ia.2 epitope recognized by the widely used 11-5.2 mAb defines a subset of cell surface I-Ak molecules predominantly found within membrane lipid rafts. Functional studies demonstrate that the Ia.2 bearing subset of I-Ak class II molecules is critically necessary for effective B cell–T cell interactions especially at low antigen doses, a finding consistent with published studies on the role of raft-resident class II molecules in CD4 T cell activation. Interestingly, B cells expressing recombinant I-Ak class II molecules possessing a β chain-tethered HEL peptide lack the Ia.2 epitope and fail to partition into lipid rafts. Moreover, cells expressing Ia.2 negative tethered peptide-class II molecules are severely impaired in their ability to present both tethered peptide or peptide derived from exogenous antigen to CD4 T cells. These results establish the Ia.2 epitope as defining a lipid raft-resident MHC class II confomer vital to the initiation of MHC class II restricted B cell–T cell interactions. PMID:21543648

  16. Electrophysiological and structural remodeling in heart failure modulate arrhythmogenesis. 2D simulation study.

    Directory of Open Access Journals (Sweden)

    Juan F Gomez

    Full Text Available Heart failure is operationally defined as the inability of the heart to maintain blood flow to meet the needs of the body and it is the final common pathway of various cardiac pathologies. Electrophysiological remodeling, intercellular uncoupling and a pro-fibrotic response have been identified as major arrhythmogenic factors in heart failure.In this study we investigate vulnerability to reentry under heart failure conditions by incorporating established electrophysiological and anatomical remodeling using computer simulations.The electrical activity of human transmural ventricular tissue (5 cm × 5 cm was simulated using the human ventricular action potential model Grandi et al. under control and heart failure conditions. The MacCannell et al. model was used to model fibroblast electrical activity, and their electrotonic interactions with myocytes. Selected degrees of diffuse fibrosis and variations in intercellular coupling were considered and the vulnerable window (VW for reentry was evaluated following cross-field stimulation.No reentry was observed in normal conditions or in the presence of HF ionic remodeling. However, defined amount of fibrosis and/or cellular uncoupling were sufficient to elicit reentrant activity. Under conditions where reentry was generated, HF electrophysiological remodeling did not alter the width of the VW. However, intermediate fibrosis and cellular uncoupling significantly widened the VW. In addition, biphasic behavior was observed, as very high fibrotic content or very low tissue conductivity hampered the development of reentry. Detailed phase analysis of reentry dynamics revealed an increase of phase singularities with progressive fibrotic components.Structural remodeling is a key factor in the genesis of vulnerability to reentry. A range of intermediate levels of fibrosis and intercellular uncoupling can combine to favor reentrant activity.

  17. Derivation of LDA log likelihood ratio one-to-one classifier

    NARCIS (Netherlands)

    Spreeuwers, Lieuwe Jan

    2014-01-01

    The common expression for the Likelihood Ratio classifier using LDA assumes that the reference class mean is available. In biometrics, this is often not the case and only a single sample of the reference class is available. In this paper expressions are derived for biometric comparison between

  18. The Electrophysiological Phenomenon of Alzheimer's Disease: A Psychopathology Theory.

    Science.gov (United States)

    Holston, Ezra C

    2015-08-01

    The current understanding of Alzheimer's disease (AD) is based on the Aβ and tau pathology and the resulting neuropathological changes, which are associated with manifested clinical symptoms. However, electrophysiological brain changes may provide a more expansive understanding of AD. Hence, the objective of this systematic review is to propose a theory about the electrophysiological phenomenon of Alzheimer's disease (EPAD). The review of literature resulted from an extensive search of PubMed and MEDLINE databases. One-hundred articles were purposively selected. They provided an understanding of the concepts establishing the theory of EPAD (neuropathological changes, neurochemical changes, metabolic changes, and electrophysiological brain changes). Changes in the electrophysiology of the brain are foundational to the association or interaction of the concepts. Building on Berger's Psychophysical Model, it is evident that electrophysiological brain changes occur and affect cortical areas to generate or manifest symptoms from onset and across the stages of AD, which may be prior to pathological changes. Therefore, the interaction of the concepts demonstrates how the psychopathology results from affected electrophysiology of the brain. The theory of the EPAD provides a theoretical foundation for appropriate measurements of AD without dependence on neuropathological changes. Future research is warranted to further test this theory. Ultimately, this theory contributes to existing knowledge because it shows how electrophysiological changes are useful in understanding the risk and progression of AD across the stages.

  19. Beyond "implementation strategies": classifying the full range of strategies used in implementation science and practice.

    Science.gov (United States)

    Leeman, Jennifer; Birken, Sarah A; Powell, Byron J; Rohweder, Catherine; Shea, Christopher M

    2017-11-03

    Strategies are central to the National Institutes of Health's definition of implementation research as "the study of strategies to integrate evidence-based interventions into specific settings." Multiple scholars have proposed lists of the strategies used in implementation research and practice, which they increasingly are classifying under the single term "implementation strategies." We contend that classifying all strategies under a single term leads to confusion, impedes synthesis across studies, and limits advancement of the full range of strategies of importance to implementation. To address this concern, we offer a system for classifying implementation strategies that builds on Proctor and colleagues' (2013) reporting guidelines, which recommend that authors not only name and define their implementation strategies but also specify who enacted the strategy (i.e., the actor) and the level and determinants that were targeted (i.e., the action targets). We build on Wandersman and colleagues' Interactive Systems Framework to distinguish strategies based on whether they are enacted by actors functioning as part of a Delivery, Support, or Synthesis and Translation System. We build on Damschroder and colleague's Consolidated Framework for Implementation Research to distinguish the levels that strategies target (intervention, inner setting, outer setting, individual, and process). We then draw on numerous resources to identify determinants, which are conceptualized as modifiable factors that prevent or enable the adoption and implementation of evidence-based interventions. Identifying actors and targets resulted in five conceptually distinct classes of implementation strategies: dissemination, implementation process, integration, capacity-building, and scale-up. In our descriptions of each class, we identify the level of the Interactive System Framework at which the strategy is enacted (actors), level and determinants targeted (action targets), and outcomes used to

  20. 29 CFR 1910.307 - Hazardous (classified) locations.

    Science.gov (United States)

    2010-07-01

    ... equipment at the location. (c) Electrical installations. Equipment, wiring methods, and installations of... covers the requirements for electric equipment and wiring in locations that are classified depending on... provisions of this section. (4) Division and zone classification. In Class I locations, an installation must...

  1. The decision tree classifier - Design and potential. [for Landsat-1 data

    Science.gov (United States)

    Hauska, H.; Swain, P. H.

    1975-01-01

    A new classifier has been developed for the computerized analysis of remote sensor data. The decision tree classifier is essentially a maximum likelihood classifier using multistage decision logic. It is characterized by the fact that an unknown sample can be classified into a class using one or several decision functions in a successive manner. The classifier is applied to the analysis of data sensed by Landsat-1 over Kenosha Pass, Colorado. The classifier is illustrated by a tree diagram which for processing purposes is encoded as a string of symbols such that there is a unique one-to-one relationship between string and decision tree.

  2. Comparisons and Selections of Features and Classifiers for Short Text Classification

    Science.gov (United States)

    Wang, Ye; Zhou, Zhi; Jin, Shan; Liu, Debin; Lu, Mi

    2017-10-01

    Short text is considerably different from traditional long text documents due to its shortness and conciseness, which somehow hinders the applications of conventional machine learning and data mining algorithms in short text classification. According to traditional artificial intelligence methods, we divide short text classification into three steps, namely preprocessing, feature selection and classifier comparison. In this paper, we have illustrated step-by-step how we approach our goals. Specifically, in feature selection, we compared the performance and robustness of the four methods of one-hot encoding, tf-idf weighting, word2vec and paragraph2vec, and in the classification part, we deliberately chose and compared Naive Bayes, Logistic Regression, Support Vector Machine, K-nearest Neighbor and Decision Tree as our classifiers. Then, we compared and analysed the classifiers horizontally with each other and vertically with feature selections. Regarding the datasets, we crawled more than 400,000 short text files from Shanghai and Shenzhen Stock Exchanges and manually labeled them into two classes, the big and the small. There are eight labels in the big class, and 59 labels in the small class.

  3. Least Square Support Vector Machine Classifier vs a Logistic Regression Classifier on the Recognition of Numeric Digits

    Directory of Open Access Journals (Sweden)

    Danilo A. López-Sarmiento

    2013-11-01

    Full Text Available In this paper is compared the performance of a multi-class least squares support vector machine (LSSVM mc versus a multi-class logistic regression classifier to problem of recognizing the numeric digits (0-9 handwritten. To develop the comparison was used a data set consisting of 5000 images of handwritten numeric digits (500 images for each number from 0-9, each image of 20 x 20 pixels. The inputs to each of the systems were vectors of 400 dimensions corresponding to each image (not done feature extraction. Both classifiers used OneVsAll strategy to enable multi-classification and a random cross-validation function for the process of minimizing the cost function. The metrics of comparison were precision and training time under the same computational conditions. Both techniques evaluated showed a precision above 95 %, with LS-SVM slightly more accurate. However the computational cost if we found a marked difference: LS-SVM training requires time 16.42 % less than that required by the logistic regression model based on the same low computational conditions.

  4. Context-sensitive intra-class clustering

    KAUST Repository

    Yu, Yingwei

    2014-02-01

    This paper describes a new semi-supervised learning algorithm for intra-class clustering (ICC). ICC partitions each class into sub-classes in order to minimize overlap across clusters from different classes. This is achieved by allowing partitioning of a certain class to be assisted by data points from other classes in a context-dependent fashion. The result is that overlap across sub-classes (both within- and across class) is greatly reduced. ICC is particularly useful when combined with algorithms that assume that each class has a unimodal Gaussian distribution (e.g., Linear Discriminant Analysis (LDA), quadratic classifiers), an assumption that is not always true in many real-world situations. ICC can help partition non-Gaussian, multimodal distributions to overcome such a problem. In this sense, ICC works as a preprocessor. Experiments with our ICC algorithm on synthetic data sets and real-world data sets indicated that it can significantly improve the performance of LDA and quadratic classifiers. We expect our approach to be applicable to a broader class of pattern recognition problems where class-conditional densities are significantly non-Gaussian or multi-modal. © 2013 Elsevier Ltd. All rights reserved.

  5. Beyond “implementation strategies”: classifying the full range of strategies used in implementation science and practice

    Directory of Open Access Journals (Sweden)

    Jennifer Leeman

    2017-11-01

    Full Text Available Abstract Background Strategies are central to the National Institutes of Health’s definition of implementation research as “the study of strategies to integrate evidence-based interventions into specific settings.” Multiple scholars have proposed lists of the strategies used in implementation research and practice, which they increasingly are classifying under the single term “implementation strategies.” We contend that classifying all strategies under a single term leads to confusion, impedes synthesis across studies, and limits advancement of the full range of strategies of importance to implementation. To address this concern, we offer a system for classifying implementation strategies that builds on Proctor and colleagues’ (2013 reporting guidelines, which recommend that authors not only name and define their implementation strategies but also specify who enacted the strategy (i.e., the actor and the level and determinants that were targeted (i.e., the action targets. Main body We build on Wandersman and colleagues’ Interactive Systems Framework to distinguish strategies based on whether they are enacted by actors functioning as part of a Delivery, Support, or Synthesis and Translation System. We build on Damschroder and colleague’s Consolidated Framework for Implementation Research to distinguish the levels that strategies target (intervention, inner setting, outer setting, individual, and process. We then draw on numerous resources to identify determinants, which are conceptualized as modifiable factors that prevent or enable the adoption and implementation of evidence-based interventions. Identifying actors and targets resulted in five conceptually distinct classes of implementation strategies: dissemination, implementation process, integration, capacity-building, and scale-up. In our descriptions of each class, we identify the level of the Interactive System Framework at which the strategy is enacted (actors, level and

  6. A Virtual Class Calculus

    DEFF Research Database (Denmark)

    Ernst, Erik; Ostermann, Klaus; Cook, William Randall

    2006-01-01

    Virtual classes are class-valued attributes of objects. Like virtual methods, virtual classes are defined in an object's class and may be redefined within subclasses. They resemble inner classes, which are also defined within a class, but virtual classes are accessed through object instances...... model for virtual classes has been a long-standing open question. This paper presents a virtual class calculus, vc, that captures the essence of virtual classes in these full-fledged programming languages. The key contributions of the paper are a formalization of the dynamic and static semantics of vc...

  7. Radiation dose electrophysiology procedures

    International Nuclear Information System (INIS)

    Hernandez-Armas, J.; Rodriguez, A.; Catalan, A.; Hernandez Armas, O.; Luque Japon, L.; Moral, S.; Barroso, L.; Rfuez-Hdez, R.

    2006-01-01

    The aim of this paper has been to measure and analyse some of the parameters which are directly related with the doses given to patients in two electrophysiology procedures: diagnosis and ablation with radiofrequency. 16 patients were considered in this study. 13 them had an ablation with radiofrequency at the Unit of Electrophysiology at the University Hospital of the Canaries, La Laguna., Tenerife. The results of skin doses, in the ablation cases, were higher than 2 Gy (threshold of some deterministic effects). The average value was 1.1 Gy. The personal doses, measured under the lead apron, for physician and nurses were 4 and 3 micro Sievert. These results emphasised the necessity of radiation protection measures in order to reduce, ad much as possible, the doses to patients. (Author)

  8. On the Fibration Defined by the Field Lines of a Knotted Class of Electromagnetic Fields at a Particular Time

    Directory of Open Access Journals (Sweden)

    Manuel Arrayás

    2017-10-01

    Full Text Available A class of vacuum electromagnetic fields in which the field lines are knotted curves are reviewed. The class is obtained from two complex functions at a particular instant t = 0 so they inherit the topological properties of red the level curves of these functions. We study the complete topological structure defined by the magnetic and electric field lines at t = 0 . This structure is not conserved in time in general, although it is possible to red find special cases in which the field lines are topologically equivalent for every value of t.

  9. Classifying galaxy spectra at 0.5 < z < 1 with self-organizing maps

    Science.gov (United States)

    Rahmani, S.; Teimoorinia, H.; Barmby, P.

    2018-05-01

    The spectrum of a galaxy contains information about its physical properties. Classifying spectra using templates helps elucidate the nature of a galaxy's energy sources. In this paper, we investigate the use of self-organizing maps in classifying galaxy spectra against templates. We trained semi-supervised self-organizing map networks using a set of templates covering the wavelength range from far ultraviolet to near infrared. The trained networks were used to classify the spectra of a sample of 142 galaxies with 0.5 K-means clustering, a supervised neural network, and chi-squared minimization. Spectra corresponding to quiescent galaxies were more likely to be classified similarly by all methods while starburst spectra showed more variability. Compared to classification using chi-squared minimization or the supervised neural network, the galaxies classed together by the self-organizing map had more similar spectra. The class ordering provided by the one-dimensional self-organizing maps corresponds to an ordering in physical properties, a potentially important feature for the exploration of large datasets.

  10. Autoclave Sterilization of PEDOT:PSS Electrophysiology Devices.

    Science.gov (United States)

    Uguz, Ilke; Ganji, Mehran; Hama, Adel; Tanaka, Atsunori; Inal, Sahika; Youssef, Ahmed; Owens, Roisin M; Quilichini, Pascale P; Ghestem, Antoine; Bernard, Christophe; Dayeh, Shadi A; Malliaras, George G

    2016-12-01

    Autoclaving, the most widely available sterilization method, is applied to poly(3,4-ethylenedioxythiophene) doped with polystyrene sulfonate (PEDOT:PSS) electrophysiology devices. The process does not harm morphology or electrical properties, while it effectively kills E. coli intentionally cultured on the devices. This finding paves the way to widespread introduction of PEDOT:PSS electrophysiology devices to the clinic. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. Imagining class: A study into material social class position, subjective identification, and voting behavior across Europe.

    Science.gov (United States)

    D'Hooge, Lorenzo; Achterberg, Peter; Reeskens, Tim

    2018-02-01

    The traditional approach to class voting has largely ignored the question whether material class positions coincide with subjective class identification. Following Sosnaud et al. (2013), this study evaluates party preferences when Europeans' material and subjective social class do not coincide. Seminal studies on voting behavior have suggested that members of lower classes are more likely to vote for the economic left and cultural right and that higher classes demonstrate the opposite pattern. Yet, these studies have on the one hand overlooked the possibility that there is a mismatch between the material class people can be classified in and the class they think they are part of, and on the other hand the consequences of this discordant class identification on voting behavior. Analyzing the 2009 wave of the European Elections Study, we find that the majority of the Europeans discordantly identify with the middle class, whereas only a minority of the lower and higher classes concordantly identify with their material social class. Further, material class only seems to predict economic voting behavior when it coincides with subjective class; for instance, individuals who have an inflated class identification are more likely to vote for the economic left, even when they materially can be classified as middle or high class. We conclude this paper with a discussion on scholarly debates concerning class and politics. Copyright © 2017 Elsevier Inc. All rights reserved.

  12. Silicon nanowire arrays as learning chemical vapour classifiers

    International Nuclear Information System (INIS)

    Niskanen, A O; Colli, A; White, R; Li, H W; Spigone, E; Kivioja, J M

    2011-01-01

    Nanowire field-effect transistors are a promising class of devices for various sensing applications. Apart from detecting individual chemical or biological analytes, it is especially interesting to use multiple selective sensors to look at their collective response in order to perform classification into predetermined categories. We show that non-functionalised silicon nanowire arrays can be used to robustly classify different chemical vapours using simple statistical machine learning methods. We were able to distinguish between acetone, ethanol and water with 100% accuracy while methanol, ethanol and 2-propanol were classified with 96% accuracy in ambient conditions.

  13. Electrophysiological evaluation of Wolff-Parkinson-White Syndrome

    Science.gov (United States)

    Brembilla-Perrot, Beatrice

    2002-01-01

    Sudden death might complicate the follow-up of symptomatic patients with the Wolff-Parkinson-White syndrome (WPW) and might be the first event in patients with asymptomatic WPW. The risk of sudden death is increased in some clinical situations. Generally, the noninvasive studies are unable to predict the risk of sudden death correctly . The electrophysiological study is the best means to detect the risk of sudden death and to evaluate the nature of symptoms. Methods used to define the prognosis of WPW are well-defined. At first the maximal rate of conduction through the accessory pathway is evaluated; programmed atrial stimulation using 1 and 2 extrastimuli delivered at different cycle lengths is then used to determine the accessory pathway refractory period and to induce a supraventricular tachycardia. These methods should be performed in the control state and repeated in adrenergic situations either during exercise test or more simply during a perfusion of small doses of isoproterenol. The induction of an atrial fibrillation with rapid conduction through the accessory pathway (> 240/min in control state, > 300/min after isoproterenol) is the sign of a form of WPW at risk of sudden death. PMID:16951730

  14. Electrophysiological evaluation of Wolff-Parkinson-White Syndrome

    Directory of Open Access Journals (Sweden)

    Béatrice Brembilla-Perrot

    2002-10-01

    Full Text Available Sudden death might complicate the follow-up of symptomatic patients with the Wolff-Parkinson-White syndrome (WPW and might be the first event in patients with asymptomatic WPW. The risk of sudden death is increased in some clinical situations. Generally, the noninvasive studies are unable to predict the risk of sudden death correctly . The electrophysiological study is the best means to detect the risk of sudden death and to evaluate the nature of symptoms. Methods used to define the prognosis of WPW are well-defined. At first the maximal rate of conduction through the accessory pathway is evaluated; programmed atrial stimulation using 1 and 2 extrastimuli delivered at different cycle lengths is then used to determine the accessory pathway refractory period and to induce a supraventricular tachycardia. These methods should be performed in the control state and repeated in adrenergic situations either during exercise test or more simply during a perfusion of small doses of isoproterenol. The induction of an atrial fibrillation with rapid conduction through the accessory pathway (> 240/min in control state, > 300/min after isoproterenol is the sign of a form of WPW at risk of sudden death.

  15. A contextual classifier that only requires one prototype pixel for each class

    DEFF Research Database (Denmark)

    Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær; Conradsen, Knut

    2001-01-01

    constructed with experimental data is used in this stage. The algorithm was tested with the Kappa coefficient k on synthetical images and compared with K-means (k~=0.41) and a similar scheme that uses spectral means (k~=0.75) instead of histograms (k~=0.90). Results are shown on a dermatological image......A three stage scheme for classification of multi-spectral images is proposed. In each stage, statistics of each class present in the image are estimated. The user is required to provide only one prototype pixel for each class to be seeded into a homogeneous region. The algorithm starts...... by generating optimum initial training sets, one for each class, maximizing the redundancy in the data sets. These sets are the realizations of the maximal discs centered on the prototype pixels for which it is true that all the elements belong to the same class as the center one. Afterwards a region growing...

  16. A Contextual Classifier That Only Requires One Prototype Pixel for Each Class

    DEFF Research Database (Denmark)

    Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær; Conradsen, Knut

    2002-01-01

    constructed with experimental data is used in this stage. The algorithm was tested with the Kappa coefficient κ on synthetic images and compared with K-means (κ~=0.41) and a similar scheme that uses spectral means (κ~=0.75) instead of histograms (κ~=0.90). The results are shown on a dermatological image......A three-stage scheme for the classification of multispectral images is proposed. In each stage, the statistics of each class present in the image are estimated. The user is required to provide only one prototype pixel for each class to be seeded into a homogeneous region. The algorithm starts...... by generating optimum initial training sets, one for each class, maximizing the redundancy in the data sets. These sets are the realizations of the maximal discs centered on the prototype pixels for which it is true that all the elements belong to the same class as the center one. Afterwards, a region...

  17. Building gene expression profile classifiers with a simple and efficient rejection option in R.

    Science.gov (United States)

    Benso, Alfredo; Di Carlo, Stefano; Politano, Gianfranco; Savino, Alessandro; Hafeezurrehman, Hafeez

    2011-01-01

    The collection of gene expression profiles from DNA microarrays and their analysis with pattern recognition algorithms is a powerful technology applied to several biological problems. Common pattern recognition systems classify samples assigning them to a set of known classes. However, in a clinical diagnostics setup, novel and unknown classes (new pathologies) may appear and one must be able to reject those samples that do not fit the trained model. The problem of implementing a rejection option in a multi-class classifier has not been widely addressed in the statistical literature. Gene expression profiles represent a critical case study since they suffer from the curse of dimensionality problem that negatively reflects on the reliability of both traditional rejection models and also more recent approaches such as one-class classifiers. This paper presents a set of empirical decision rules that can be used to implement a rejection option in a set of multi-class classifiers widely used for the analysis of gene expression profiles. In particular, we focus on the classifiers implemented in the R Language and Environment for Statistical Computing (R for short in the remaining of this paper). The main contribution of the proposed rules is their simplicity, which enables an easy integration with available data analysis environments. Since in the definition of a rejection model tuning of the involved parameters is often a complex and delicate task, in this paper we exploit an evolutionary strategy to automate this process. This allows the final user to maximize the rejection accuracy with minimum manual intervention. This paper shows how the use of simple decision rules can be used to help the use of complex machine learning algorithms in real experimental setups. The proposed approach is almost completely automated and therefore a good candidate for being integrated in data analysis flows in labs where the machine learning expertise required to tune traditional

  18. Distinct retroelement classes define evolutionary breakpoints demarcating sites of evolutionary novelty

    Science.gov (United States)

    Longo, Mark S; Carone, Dawn M; Green, Eric D; O'Neill, Michael J; O'Neill, Rachel J

    2009-01-01

    Background Large-scale genome rearrangements brought about by chromosome breaks underlie numerous inherited diseases, initiate or promote many cancers and are also associated with karyotype diversification during species evolution. Recent research has shown that these breakpoints are nonrandomly distributed throughout the mammalian genome and many, termed "evolutionary breakpoints" (EB), are specific genomic locations that are "reused" during karyotypic evolution. When the phylogenetic trajectory of orthologous chromosome segments is considered, many of these EB are coincident with ancient centromere activity as well as new centromere formation. While EB have been characterized as repeat-rich regions, it has not been determined whether specific sequences have been retained during evolution that would indicate previous centromere activity or a propensity for new centromere formation. Likewise, the conservation of specific sequence motifs or classes at EBs among divergent mammalian taxa has not been determined. Results To define conserved sequence features of EBs associated with centromere evolution, we performed comparative sequence analysis of more than 4.8 Mb within the tammar wallaby, Macropus eugenii, derived from centromeric regions (CEN), euchromatic regions (EU), and an evolutionary breakpoint (EB) that has undergone convergent breakpoint reuse and past centromere activity in marsupials. We found a dramatic enrichment for long interspersed nucleotide elements (LINE1s) and endogenous retroviruses (ERVs) and a depletion of short interspersed nucleotide elements (SINEs) shared between CEN and EBs. We analyzed the orthologous human EB (14q32.33), known to be associated with translocations in many cancers including multiple myelomas and plasma cell leukemias, and found a conserved distribution of similar repetitive elements. Conclusion Our data indicate that EBs tracked within the class Mammalia harbor sequence features retained since the divergence of marsupials

  19. Distinct retroelement classes define evolutionary breakpoints demarcating sites of evolutionary novelty

    Directory of Open Access Journals (Sweden)

    Green Eric D

    2009-07-01

    Full Text Available Abstract Background Large-scale genome rearrangements brought about by chromosome breaks underlie numerous inherited diseases, initiate or promote many cancers and are also associated with karyotype diversification during species evolution. Recent research has shown that these breakpoints are nonrandomly distributed throughout the mammalian genome and many, termed "evolutionary breakpoints" (EB, are specific genomic locations that are "reused" during karyotypic evolution. When the phylogenetic trajectory of orthologous chromosome segments is considered, many of these EB are coincident with ancient centromere activity as well as new centromere formation. While EB have been characterized as repeat-rich regions, it has not been determined whether specific sequences have been retained during evolution that would indicate previous centromere activity or a propensity for new centromere formation. Likewise, the conservation of specific sequence motifs or classes at EBs among divergent mammalian taxa has not been determined. Results To define conserved sequence features of EBs associated with centromere evolution, we performed comparative sequence analysis of more than 4.8 Mb within the tammar wallaby, Macropus eugenii, derived from centromeric regions (CEN, euchromatic regions (EU, and an evolutionary breakpoint (EB that has undergone convergent breakpoint reuse and past centromere activity in marsupials. We found a dramatic enrichment for long interspersed nucleotide elements (LINE1s and endogenous retroviruses (ERVs and a depletion of short interspersed nucleotide elements (SINEs shared between CEN and EBs. We analyzed the orthologous human EB (14q32.33, known to be associated with translocations in many cancers including multiple myelomas and plasma cell leukemias, and found a conserved distribution of similar repetitive elements. Conclusion Our data indicate that EBs tracked within the class Mammalia harbor sequence features retained since the

  20. z-CLASSES IN FINITE GROUPS OF CONJUGATE TYPE (n,1) 1 ...

    Indian Academy of Sciences (India)

    37

    Following this motivation to use the z-classes to classify. “dynamical types” of transformations, the z-classes of real hyperbolic isometries have been classified and counted by Gongopadhyay and Kulkarni [GK09]. Apart from geometric motivations, the z-classes are important objects in their own right. Characterizations of the ...

  1. Generation and customization of biosynthetic excitable tissues for electrophysiological studies and cell-based therapies.

    Science.gov (United States)

    Nguyen, Hung X; Kirkton, Robert D; Bursac, Nenad

    2018-05-01

    We describe a two-stage protocol to generate electrically excitable and actively conducting cell networks with stable and customizable electrophysiological phenotypes. Using this method, we have engineered monoclonally derived excitable tissues as a robust and reproducible platform to investigate how specific ion channels and mutations affect action potential (AP) shape and conduction. In the first stage of the protocol, we combine computational modeling, site-directed mutagenesis, and electrophysiological techniques to derive optimal sets of mammalian and/or prokaryotic ion channels that produce specific AP shape and conduction characteristics. In the second stage of the protocol, selected ion channels are stably expressed in unexcitable human cells by means of viral or nonviral delivery, followed by flow cytometry or antibiotic selection to purify the desired phenotype. This protocol can be used with traditional heterologous expression systems or primary excitable cells, and application of this method to primary fibroblasts may enable an alternative approach to cardiac cell therapy. Compared with existing methods, this protocol generates a well-defined, relatively homogeneous electrophysiological phenotype of excitable cells that facilitates experimental and computational studies of AP conduction and can decrease arrhythmogenic risk upon cell transplantation. Although basic cell culture and molecular biology techniques are sufficient to generate excitable tissues using the described protocol, experience with patch-clamp techniques is required to characterize and optimize derived cell populations.

  2. Predicted MHC peptide binding promiscuity explains MHC class I 'hotspots' of antigen presentation defined by mass spectrometry eluted ligand data.

    Science.gov (United States)

    Jappe, Emma Christine; Kringelum, Jens; Trolle, Thomas; Nielsen, Morten

    2018-02-15

    Peptides that bind to and are presented by MHC class I and class II molecules collectively make up the immunopeptidome. In the context of vaccine development, an understanding of the immunopeptidome is essential, and much effort has been dedicated to its accurate and cost-effective identification. Current state-of-the-art methods mainly comprise in silico tools for predicting MHC binding, which is strongly correlated with peptide immunogenicity. However, only a small proportion of the peptides that bind to MHC molecules are, in fact, immunogenic, and substantial work has been dedicated to uncovering additional determinants of peptide immunogenicity. In this context, and in light of recent advancements in mass spectrometry (MS), the existence of immunological hotspots has been given new life, inciting the hypothesis that hotspots are associated with MHC class I peptide immunogenicity. We here introduce a precise terminology for defining these hotspots and carry out a systematic analysis of MS and in silico predicted hotspots. We find that hotspots defined from MS data are largely captured by peptide binding predictions, enabling their replication in silico. This leads us to conclude that hotspots, to a great degree, are simply a result of promiscuous HLA binding, which disproves the hypothesis that the identification of hotspots provides novel information in the context of immunogenic peptide prediction. Furthermore, our analyses demonstrate that the signal of ligand processing, although present in the MS data, has very low predictive power to discriminate between MS and in silico defined hotspots. © 2018 John Wiley & Sons Ltd.

  3. Early electrophysiological findings in Fisher-Bickerstaff syndrome.

    Science.gov (United States)

    Alberti, M A; Povedano, M; Montero, J; Casasnovas, C

    2017-09-06

    The term Fisher-Bickerstaff syndrome (FBS) has been proposed to describe the clinical spectrum encompassing Miller-Fisher syndrome (MFS) and Bickerstaff brainstem encephalitis. The pathophysiology of FBS and the nature of the underlying neuropathy (demyelinating or axonal) are still subject to debate. This study describes the main findings of an early neurophysiological study on 12 patients diagnosed with FBS. Retrospective evaluation of clinical characteristics and electrophysiological findings of 12 patients with FBS seen in our neurology department within 10 days of disease onset. Follow-up electrophysiological studies were also evaluated, where available. The most frequent electrophysiological finding, present in 5 (42%) patients, was reduced sensory nerve action potential (SNAP) amplitude in one or more nerves. Abnormalities were rarely found in motor neurography, with no signs of demyelination. The cranial nerve exam revealed abnormalities in 3 patients (facial neurography and/or blink reflex test). Three patients showed resolution of SNAP amplitude reduction in serial neurophysiological studies, suggesting the presence of reversible sensory nerve conduction block. Results from cranial MRI scans were normal in all patients. An electrophysiological pattern of sensory axonal neuropathy, with no associated signs of demyelination, is an early finding of FBS. Early neurophysiological evaluation and follow-up are essential for diagnosing patients with FBS. Copyright © 2017 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.

  4. Electrophysiological study for comparing the effect of biological activity between type A botulinum toxins in rat gastrocnemius muscle.

    Science.gov (United States)

    Kim, C-S; Jang, W S; Son, I P; Nam, S H; Kim, Y I; Park, K Y; Kim, B J; Kim, M N

    2013-09-01

    New cosmetic applications and products based on the effects of botulinum toxin (BTX) treatment have stimulated demand for this class of natural compounds. This demand generates the need for appropriate standardized protocols to test and compare the effectiveness of new BTX preparations. Based on the previously described electrophysiological methods, we measured and compared the inhibitory effects of two BTX type A (BTX-A) preparations on neuromuscular transmission through split-body test. The effectiveness was evaluated in terms of the compound muscle action potential (CMAP) and conduction velocity after BTX-A injection. We used a split-body method to compare two different BTX-As in the rat. Based on the changes in the CMAP, the two different BTX-As induced paralytic effect on the rat tibialis anterior muscle. However, the two different BTX-A preparations did not differ significantly in effectiveness and did not induce a delay in conduction velocity. The new BTX-A preparation used in this electrophysiological study had similar effect compared with the previously marketed BTX-A.[AQ: Please approve the edits made to the sentence "The new BTX-A preparation…") We propose that a split-body electrophysiological protocol will be useful in establishing the comparative effectiveness of new BTX products.

  5. Electrophysiologic studies of neronal activities under ischemia condition.

    Science.gov (United States)

    Huang, Shun-Ho; Wang, Ping-Hsien; Chen, Jia-Jin Jason

    2008-01-01

    Substrate with integrated microelectrode arrays (MEAs) provides an alternative electrophysiological method. With MEAS, one can measure the impedance and elicit electrical stimulation from multiple sites of MEAs to determine the electrophysiological conditions of cells. The aims of this research were to construct an impedance and action potential measurement system for neurons cultured on MEAs for observing the electrophysiological signal transmission in neuronal network during glucose and oxygen deprivation (OGD). An extracellular stimulator producing the biphasic micro-current pulse for neuron stimulation was built in this study. From the time-course recording of impedance, OGD condition effectively induced damage in neurons in vitro. It is known that the results of cell stimulation are affected by electrode impedance, so does the result of neuron cells covered on the electrode can measure the sealing resistance. For extracellular stimulation study, cortical neuronal activity was recorded and the suitable stimulation window was determined. However, the stimulation results were affected by electrode impedance as well as sealing impedance resulting from neuron cells covering the electrode. Further development of surface modification for cultured neuron network should provide a better way for in vitro impedance and electrophysiological measurements.

  6. Using Neural Networks to Classify Digitized Images of Galaxies

    Science.gov (United States)

    Goderya, S. N.; McGuire, P. C.

    2000-12-01

    Automated classification of Galaxies into Hubble types is of paramount importance to study the large scale structure of the Universe, particularly as survey projects like the Sloan Digital Sky Survey complete their data acquisition of one million galaxies. At present it is not possible to find robust and efficient artificial intelligence based galaxy classifiers. In this study we will summarize progress made in the development of automated galaxy classifiers using neural networks as machine learning tools. We explore the Bayesian linear algorithm, the higher order probabilistic network, the multilayer perceptron neural network and Support Vector Machine Classifier. The performance of any machine classifier is dependant on the quality of the parameters that characterize the different groups of galaxies. Our effort is to develop geometric and invariant moment based parameters as input to the machine classifiers instead of the raw pixel data. Such an approach reduces the dimensionality of the classifier considerably, and removes the effects of scaling and rotation, and makes it easier to solve for the unknown parameters in the galaxy classifier. To judge the quality of training and classification we develop the concept of Mathews coefficients for the galaxy classification community. Mathews coefficients are single numbers that quantify classifier performance even with unequal prior probabilities of the classes.

  7. Software and hardware infrastructure for research in electrophysiology.

    Science.gov (United States)

    Mouček, Roman; Ježek, Petr; Vařeka, Lukáš; Rondík, Tomáš; Brůha, Petr; Papež, Václav; Mautner, Pavel; Novotný, Jiří; Prokop, Tomáš; Stěbeták, Jan

    2014-01-01

    As in other areas of experimental science, operation of electrophysiological laboratory, design and performance of electrophysiological experiments, collection, storage and sharing of experimental data and metadata, analysis and interpretation of these data, and publication of results are time consuming activities. If these activities are well organized and supported by a suitable infrastructure, work efficiency of researchers increases significantly. This article deals with the main concepts, design, and development of software and hardware infrastructure for research in electrophysiology. The described infrastructure has been primarily developed for the needs of neuroinformatics laboratory at the University of West Bohemia, the Czech Republic. However, from the beginning it has been also designed and developed to be open and applicable in laboratories that do similar research. After introducing the laboratory and the whole architectural concept the individual parts of the infrastructure are described. The central element of the software infrastructure is a web-based portal that enables community researchers to store, share, download and search data and metadata from electrophysiological experiments. The data model, domain ontology and usage of semantic web languages and technologies are described. Current data publication policy used in the portal is briefly introduced. The registration of the portal within Neuroscience Information Framework is described. Then the methods used for processing of electrophysiological signals are presented. The specific modifications of these methods introduced by laboratory researches are summarized; the methods are organized into a laboratory workflow. Other parts of the software infrastructure include mobile and offline solutions for data/metadata storing and a hardware stimulator communicating with an EEG amplifier and recording software.

  8. Snoring classified: The Munich-Passau Snore Sound Corpus.

    Science.gov (United States)

    Janott, Christoph; Schmitt, Maximilian; Zhang, Yue; Qian, Kun; Pandit, Vedhas; Zhang, Zixing; Heiser, Clemens; Hohenhorst, Winfried; Herzog, Michael; Hemmert, Werner; Schuller, Björn

    2018-03-01

    Snoring can be excited in different locations within the upper airways during sleep. It was hypothesised that the excitation locations are correlated with distinct acoustic characteristics of the snoring noise. To verify this hypothesis, a database of snore sounds is developed, labelled with the location of sound excitation. Video and audio recordings taken during drug induced sleep endoscopy (DISE) examinations from three medical centres have been semi-automatically screened for snore events, which subsequently have been classified by ENT experts into four classes based on the VOTE classification. The resulting dataset containing 828 snore events from 219 subjects has been split into Train, Development, and Test sets. An SVM classifier has been trained using low level descriptors (LLDs) related to energy, spectral features, mel frequency cepstral coefficients (MFCC), formants, voicing, harmonic-to-noise ratio (HNR), spectral harmonicity, pitch, and microprosodic features. An unweighted average recall (UAR) of 55.8% could be achieved using the full set of LLDs including formants. Best performing subset is the MFCC-related set of LLDs. A strong difference in performance could be observed between the permutations of train, development, and test partition, which may be caused by the relatively low number of subjects included in the smaller classes of the strongly unbalanced data set. A database of snoring sounds is presented which are classified according to their sound excitation location based on objective criteria and verifiable video material. With the database, it could be demonstrated that machine classifiers can distinguish different excitation location of snoring sounds in the upper airway based on acoustic parameters. Copyright © 2018 Elsevier Ltd. All rights reserved.

  9. Software and Hardware Infrastructure for Research in Electrophysiology

    Directory of Open Access Journals (Sweden)

    Roman eMouček

    2014-03-01

    Full Text Available As in other areas of experimental science, operation of electrophysiological laboratory, design and performance of electrophysiological experiments, collection, storage and sharing of experimental data and metadata, analysis and interpretation of these data, and publication of results are time consuming activities. If these activities are well organized and supported by a suitable infrastructure, work efficiency of researchers increases significantly.This article deals with the main concepts, design, and development of software and hardware infrastructure for research in electrophysiology. The described infrastructure has been primarily developed for the needs of neuroinformatics laboratory at the University of West Bohemia, the Czech Republic. However, from the beginning it has been also designed and developed to be open and applicable in laboratories that do similar research.After introducing the laboratory and the whole architectural concept the individual parts of the infrastructure are described. The central element of the software infrastructure is a web-based portal that enables community researchers to store, share, download and search data and metadata from electrophysiological experiments. The data model, domain ontology and usage of semantic web languages and technologies are described. Current data publication policy used in the portal is briefly introduced. The registration of the portal within Neuroscience Information Framework is described. Then the methods used for processing of electrophysiological signals are presented. The specific modifications of these methods introduced by laboratory researches are summarized; the methods are organized into a laboratory workflow. Other parts of the software infrastructure include mobile and offline solutions for data/metadata storing and a hardware stimulator communicating with an EEG amplifier and recording software.

  10. Electrophysiological Evidence in Schizophrenia in Relation to Treatment Response

    Directory of Open Access Journals (Sweden)

    Kazuki Sueyoshi

    2018-06-01

    Full Text Available Several domains of cognitive function, e.g., verbal memory, information processing, fluency, attention, and executive function are impaired in patients with schizophrenia. Cognitive impairments in schizophrenia have attracted interests as a treatment target, because they are considered to greatly affect functional outcome. Electrophysiological markers, including electroencephalogram (EEG, particularly, event-related potentials, have contributed to psychiatric research and clinical practice. In this review, we provide a summary of studies relating electrophysiological findings to cognitive performance in schizophrenia. Electrophysiological indices may provide an objective marker of cognitive processes, contributing to the development of effective interventions to improve cognitive and social outcomes. Further efforts to understand biological mechanisms of cognitive disturbances, and develop effective therapeutics are warranted.

  11. Acceptability and characteristics of 124 human bioequivalence studies with active substances classified according to the Biopharmaceutic Classification System

    Science.gov (United States)

    Ramirez, Elena; Laosa, Olga; Guerra, Pedro; Duque, Blanca; Mosquera, Beatriz; Borobia, Alberto M; Lei, Suhua H; Carcas, Antonio J; Frias, Jesus

    2010-01-01

    AIM The aim of this study was to evaluate the acceptability of 124 bioequivalence (BE) studies with 80 active substances categorized according to the Biopharmaceutics Classification System (BCS) in order to establish if there were different probabilities of proving BE between the different BCS classes. METHODS We evaluated the differences between pharmaceutical products with active substances from different BCS classes in terms of acceptability, number of subjects in the study (n), the point estimates, and intra- and inter-subject coefficients of variation data from BE studies with generic products. RESULTS Out of 124 BE studies 89 (71.77%) were performed with pharmaceutical products containing active substances classified by the BCS. In all BCS classes there were non-bioequivalent pharmaceutical products: 4 out of 26 (15.38%) in class 1, 14 out of 28 (50%) in class 2, 3 out of 22 (13.63%) in class 3 and 1 out of 13 (7.69%) in class 4. When we removed those pharmaceutical products in which intra-subject variability was higher than predicted (2 in class 1 active substances, 9 in class 2 and 2 in class 3) there were still non-BE pharmaceutical products in classes 1, 2 and 3. CONCLUSIONS Comparisons between pharmaceutical products with active substances from the four BCS classes have not allowed us to define differential characteristics of each class in terms of n, inter and intra-subject variability for Cmax or AUC. Despite the usually employed test dissolution methodology proposed as quality control, pharmaceutical products with active substances from the four classes of BCS showed non-BE studies. PMID:21039763

  12. Vandalism Detection in Wikipedia: a Bag-of-Words Classifier Approach

    OpenAIRE

    Belani, Amit

    2010-01-01

    A bag-of-words based probabilistic classifier is trained using regularized logistic regression to detect vandalism in the English Wikipedia. Isotonic regression is used to calibrate the class membership probabilities. Learning curve, reliability, ROC, and cost analysis are performed.

  13. Lung Nodule Image Classification Based on Local Difference Pattern and Combined Classifier.

    Science.gov (United States)

    Mao, Keming; Deng, Zhuofu

    2016-01-01

    This paper proposes a novel lung nodule classification method for low-dose CT images. The method includes two stages. First, Local Difference Pattern (LDP) is proposed to encode the feature representation, which is extracted by comparing intensity difference along circular regions centered at the lung nodule. Then, the single-center classifier is trained based on LDP. Due to the diversity of feature distribution for different class, the training images are further clustered into multiple cores and the multicenter classifier is constructed. The two classifiers are combined to make the final decision. Experimental results on public dataset show the superior performance of LDP and the combined classifier.

  14. Lung Nodule Image Classification Based on Local Difference Pattern and Combined Classifier

    Directory of Open Access Journals (Sweden)

    Keming Mao

    2016-01-01

    Full Text Available This paper proposes a novel lung nodule classification method for low-dose CT images. The method includes two stages. First, Local Difference Pattern (LDP is proposed to encode the feature representation, which is extracted by comparing intensity difference along circular regions centered at the lung nodule. Then, the single-center classifier is trained based on LDP. Due to the diversity of feature distribution for different class, the training images are further clustered into multiple cores and the multicenter classifier is constructed. The two classifiers are combined to make the final decision. Experimental results on public dataset show the superior performance of LDP and the combined classifier.

  15. Spatio-Spectral Method for Estimating Classified Regions with High Confidence using MODIS Data

    International Nuclear Information System (INIS)

    Katiyal, Anuj; Rajan, Dr K S

    2014-01-01

    In studies like change analysis, the availability of very high resolution (VHR)/high resolution (HR) imagery for a particular period and region is a challenge due to the sensor revisit times and high cost of acquisition. Therefore, most studies prefer lower resolution (LR) sensor imagery with frequent revisit times, in addition to their cost and computational advantages. Further, the classification techniques provide us a global estimate of the class accuracy, which limits its utility if the accuracy is low. In this work, we focus on the sub-classification problem of LR images and estimate regions of higher confidence than the global classification accuracy within its classified region. The spectrally classified data was mined into spatially clustered regions and further refined and processed using statistical measures to arrive at local high confidence regions (LHCRs), for every class. Rabi season MODIS data of January 2006 and 2007 was used for this study and the evaluation of LHCR was done using the APLULC 2005 classified data. For Jan-2007, the global class accuracies for water bodies (WB), forested regions (FR) and Kharif crops and barren lands (KB) were 89%, 71.7% and 71.23% respectively, while the respective LHCRs had accuracies of 96.67%, 89.4% and 80.9% covering an area of 46%, 29% and 14.5% of the initially classified areas. Though areas are reduced, LHCRs with higher accuracies help in extracting more representative class regions. Identification of such regions can facilitate in improving the classification time and processing for HR images when combined with the more frequently acquired LR imagery, isolate pure vs. mixed/impure pixels and as training samples locations for HR imagery

  16. Electrophysiology in visually impaired children

    NARCIS (Netherlands)

    Genderen, Maria Michielde van

    2006-01-01

    Inherited retinal disorders and posterior visual pathway abnormalities are important causes of visual impairment in children. Visual electrophysiology often is indispensable in diagnosing these conditions. This thesis shows the wide range of use of pediatric electro-ophthalmology, and demonstrates

  17. Defining greater-than-class-C low-level radioactive waste

    International Nuclear Information System (INIS)

    Knecht, M.A.; Oztunali, O.I.

    1986-01-01

    The Low-Level Radioactive Waste Policy Amendments Act of 1985 (LLRWPAA) was signed by President Reagan on January 15, 1986. This act requires the federal government to be responsible for the disposal of greater-than-class-C low-level radioactive waste (LLRW) that is generated commercially by state agencies and by federal entities (other than waste generated by atomic weapons research, development, or testing, or by decommissioning of vessels of the nuclear navy). To plan for disposal, the federal government will require estimates of the volume of waste involved and characterization of this waste. A clear definition of greater-than-class-C LLRW is the first step in determining what wastes will be included in the waste to be received by the federal government. This definition will influence major policy decisions to be made for management of such waste. The purpose of this paper is to examine the existing information on greater-than-class-C LLRW in view of the current definition of such waste and potential changes in this definition - for example, an upper limit on the concentrations of radionuclides in LLRW. The paper identifies further information needs to develop a clear definition of such waste for use in federal planning for acceptance of responsibility for disposal of such waste

  18. Optimizing the phenotyping of rodent ASD models: enrichment analysis of mouse and human neurobiological phenotypes associated with high-risk autism genes identifies morphological, electrophysiological, neurological, and behavioral features

    Directory of Open Access Journals (Sweden)

    Buxbaum Joseph D

    2012-02-01

    Full Text Available Abstract Background There is interest in defining mouse neurobiological phenotypes useful for studying autism spectrum disorders (ASD in both forward and reverse genetic approaches. A recurrent focus has been on high-order behavioral analyses, including learning and memory paradigms and social paradigms. However, well-studied mouse models, including for example Fmr1 knockout mice, do not show dramatic deficits in such high-order phenotypes, raising a question as to what constitutes useful phenotypes in ASD models. Methods To address this, we made use of a list of 112 disease genes etiologically involved in ASD to survey, on a large scale and with unbiased methods as well as expert review, phenotypes associated with a targeted disruption of these genes in mice, using the Mammalian Phenotype Ontology database. In addition, we compared the results with similar analyses for human phenotypes. Findings We observed four classes of neurobiological phenotypes associated with disruption of a large proportion of ASD genes, including: (1 Changes in brain and neuronal morphology; (2 electrophysiological changes; (3 neurological changes; and (4 higher-order behavioral changes. Alterations in brain and neuronal morphology represent quantitative measures that can be more widely adopted in models of ASD to understand cellular and network changes. Interestingly, the electrophysiological changes differed across different genes, indicating that excitation/inhibition imbalance hypotheses for ASD would either have to be so non-specific as to be not falsifiable, or, if specific, would not be supported by the data. Finally, it was significant that in analyses of both mouse and human databases, many of the behavioral alterations were neurological changes, encompassing sensory alterations, motor abnormalities, and seizures, as opposed to higher-order behavioral changes in learning and memory and social behavior paradigms. Conclusions The results indicated that mutations

  19. Classifying risk status of non-clinical adolescents using psychometric indicators for psychosis spectrum disorders.

    Science.gov (United States)

    Fonseca-Pedrero, Eduardo; Gooding, Diane C; Ortuño-Sierra, Javier; Pflum, Madeline; Paino, Mercedes; Muñiz, José

    2016-09-30

    This study is an attempt to evaluate extant psychometric indicators using latent profile analysis for classifying community-derived individuals based on a set of clinical, behavioural, and personality traits considered risk markers for psychosis spectrum disorders. The present investigation included four hundred and forty-nine high-school students between the ages of 12 and 19. We used the following to assess risk: the Prodromal Questionnaire-Brief (PQ-B), Oviedo Schizotypy Assessment Questionnaire (ESQUIZO-Q), Anticipatory and Consummatory Interpersonal Pleasure Scale-Adolescent version (ACIPS-A), and General Health Questionnaire 12 (GHQ-12). Using Latent profile analysis six latent classes (LC) were identified: participants in class 1 (LC1) displayed little or no symptoms and accounted for 38.53% of the sample; class 2 (LC2), who accounted for 28.06%, also produced low mean scores across most measures though they expressed somewhat higher levels of subjective distress; LC3, a positive schizotypy group (10.24%); LC4 (13.36%), a psychosis high-risk group; LC5, a high positive and negative schizotypy group (4.45%); and LC6, a very high distress, severe clinical high-risk group, comprised 5.34% of the sample. The current research indicates that different latent classes of early individuals at risk can be empirically defined in adolescent community samples using psychometric indicators for psychosis spectrum disorders. These findings may have implications for early detection and prevention strategies in psychosis spectrum disorders. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  20. Clinical and electrophysiological evaluation of pediatric Wolff-Parkinson-White patients

    Science.gov (United States)

    Yıldırım, Işıl; Özer, Sema; Karagöz, Tevfik; Şahin, Murat; Özkutlu, Süheyla; Alehan, Dursun; Çeliker, Alpay

    2015-01-01

    Objective: Wolff-Parkinson-White (WPW) syndrome presents with paroxysmal supraventricular tachycardia and is characterized by electrocardiographic (ECG) findings of a short PR interval and a delta wave. The objective of this study was to evaluate the electrophysiological properties of children with WPW syndrome and to develop an algorithm for the management of these patients with limited access to electrophysiological study. Methods: A retrospective review of all pediatric patients who underwent electrophysiological evaluation for WPW syndrome was performed. Results: One hundred nine patients underwent electrophysiological evaluation at a single tertiary center between 1997 and 2011. The median age of the patients was 11 years (0.1-18). Of the 109 patients, 82 presented with tachycardia (median age 11 (0.1-18) years), and 14 presented with syncope (median age 12 (6-16) years); 13 were asymptomatic (median age 10 (2-13) years). Induced AF degenerated to ventricular fibrillation (VF) in 2 patients. Of the 2 patients with VF, 1 was asymptomatic and the other had syncope; the accessory pathway effective refractory period was ≤180 ms in both. An intracardiac electrophysiological study was performed in 92 patients, and ablation was not attempted for risk of atrioventricular block in 8 (8.6%). The success and recurrence rate of ablation were 90.5% and 23.8% respectively. Conclusion: The induction of VF in 2 of 109 patients in our study suggests that the prognosis of WPW in children is not as benign as once thought. All patients with a WPW pattern on the ECG should be assessed electrophysiologically and risk-stratified. Ablation of patients with risk factors can prevent sudden death in this population. PMID:26006136

  1. Word classes

    DEFF Research Database (Denmark)

    Rijkhoff, Jan

    2007-01-01

    in grammatical descriptions of some 50 languages, which together constitute a representative sample of the world’s languages (Hengeveld et al. 2004: 529). It appears that there are both quantitative and qualitative differences between word class systems of individual languages. Whereas some languages employ...... a parts-of-speech system that includes the categories Verb, Noun, Adjective and Adverb, other languages may use only a subset of these four lexical categories. Furthermore, quite a few languages have a major word class whose members cannot be classified in terms of the categories Verb – Noun – Adjective...... – Adverb, because they have properties that are strongly associated with at least two of these four traditional word classes (e.g. Adjective and Adverb). Finally, this article discusses some of the ways in which word class distinctions interact with other grammatical domains, such as syntax and morphology....

  2. Optimizing Dynamic Class Composition in a Statically Typed Language

    DEFF Research Database (Denmark)

    Nielsen, Anders Bach; Ernst, Erik

    2008-01-01

    -enable a type safe treatment of classifiers and their associated types and instances, even in the case where classifiers are created dynamically. This opens the opportunity to make dynamic class computations available as an integrated part of the language semantics. The language gbeta is an example where...... this is achieved based on mixins and linearization. In this paper we focus on the virtual machine related challenges of supporting dynamic class composition. In particular we present some core algorithms used for creating new classes, as well as some performance enhancements in these algorithms....

  3. Electrophysiological measurements of diabetic peripheral neuropathy: A systematic review.

    Science.gov (United States)

    Shabeeb, Dheyauldeen; Najafi, Masoud; Hasanzadeh, Gholamreza; Hadian, Mohammed Reza; Musa, Ahmed Eleojio; Shirazi, Alireza

    2018-03-28

    Peripheral neuropathy is one of the main complications of diabetes mellitus. One of the features of diabetic nerve damage is abnormality of sensory and motor nerve conduction study. An electrophysiological examination can be reproduced and is also a non-invasive approach in the assessment of peripheral nerve function. Population-based and clinical studies have been conducted to validate the sensitivity of these methods. When the diagnosis was based on clinical electrophysiological examination, abnormalities were observed in all patients. In this research, using a review design, we reviewed the issue of clinical electrophysiological examination of diabetic peripheral neuropathy in articles from 2008 to 2017. For this purpose, PubMed, Scopus and Embase databases of journals were used for searching articles. The researchers indicated that diabetes (both types) is a very disturbing health issue in the modern world and should be given serious attention. Based on conducted studies, it was demonstrated that there are different procedures for prevention and treatment of diabetes-related health problems such as diabetic polyneuropathy (DPN). The first objective quantitative indication of the peripheral neuropathy is abnormality of sensory and motor nerve conduction tests. Electrophysiology is accurate, reliable and sensitive. It can be reproduced and also is a noninvasive approach in the assessment of peripheral nerve function. The methodological review has found that the best method for quantitative indication of the peripheral neuropathy compared with all other methods is clinical electrophysiological examination. For best results, standard protocols such as temperature control and equipment calibration are recommended. Copyright © 2018. Published by Elsevier Ltd.

  4. A supervised contextual classifier based on a region-growth algorithm

    DEFF Research Database (Denmark)

    Lira, Jorge; Maletti, Gabriela Mariel

    2002-01-01

    A supervised classification scheme to segment optical multi-spectral images has been developed. In this classifier, an automated region-growth algorithm delineates the training sets. This algorithm handles three parameters: an initial pixel seed, a window size and a threshold for each class. A su...

  5. Where can pixel counting area estimates meet user-defined accuracy requirements?

    Science.gov (United States)

    Waldner, François; Defourny, Pierre

    2017-08-01

    Pixel counting is probably the most popular way to estimate class areas from satellite-derived maps. It involves determining the number of pixels allocated to a specific thematic class and multiplying it by the pixel area. In the presence of asymmetric classification errors, the pixel counting estimator is biased. The overarching objective of this article is to define the applicability conditions of pixel counting so that the estimates are below a user-defined accuracy target. By reasoning in terms of landscape fragmentation and spatial resolution, the proposed framework decouples the resolution bias and the classifier bias from the overall classification bias. The consequence is that prior to any classification, part of the tolerated bias is already committed due to the choice of the spatial resolution of the imagery. How much classification bias is affordable depends on the joint interaction of spatial resolution and fragmentation. The method was implemented over South Africa for cropland mapping, demonstrating its operational applicability. Particular attention was paid to modeling a realistic sensor's spatial response by explicitly accounting for the effect of its point spread function. The diagnostic capabilities offered by this framework have multiple potential domains of application such as guiding users in their choice of imagery and providing guidelines for space agencies to elaborate the design specifications of future instruments.

  6. A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

    Science.gov (United States)

    Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

    2013-01-01

    In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

  7. Expressiveness and definability in circumscription

    Directory of Open Access Journals (Sweden)

    Francicleber Martins Ferreira

    2011-06-01

    Full Text Available We investigate expressiveness and definability issues with respect to minimal models, particularly in the scope of Circumscription. First, we give a proof of the failure of the Löwenheim-Skolem Theorem for Circumscription. Then we show that, if the class of P; Z-minimal models of a first-order sentence is Δ-elementary, then it is elementary. That is, whenever the circumscription of a first-order sentence is equivalent to a first-order theory, then it is equivalent to a finitely axiomatizable one. This means that classes of models of circumscribed theories are either elementary or not Δ-elementary. Finally, using the previous result, we prove that, whenever a relation Pi is defined in the class of P; Z-minimal models of a first-order sentence Φ and whenever such class of P; Z-minimal models is Δ-elementary, then there is an explicit definition ψ for Pi such that the class of P; Z-minimal models of Φ is the class of models of Φ ∧ ψ. In order words, the circumscription of P in Φ with Z varied can be replaced by Φ plus this explicit definition ψ for Pi.

  8. The assessment of visually impaired persons working capacities using electrophysiological and ophthalmic ergonomics methods

    Directory of Open Access Journals (Sweden)

    M. I. Razumovsky

    2014-07-01

    Full Text Available Aim was to analyze working capacities of visually impaired persons by means of complex electrophysiological and ophthalmic ergonomics eye examination.Materials and methods. Standard clinical ophthalmologic examination (visual acuity measurement, refractometry, biomicroscopy, ophthalmoscopy as well as electrophysiological (electrooculography, electrical sensitivity of the eye, critical flicker fusion frequency and ophthalmic ergonomics tests (accommodation measurement, professional testing using automated system «Proftest-1» were performed.Results. Complex electrophysiological and ophthalmic ergonomics tests were performed in 20 visually impaired persons. Their results revealed direct correlation between electrophysiological and ophthalmic ergonomics indices.Conclusion. Working capacities of visually impaired persons can be assessed reliably using complex electrophysiological and ophthalmic ergonomics eye examination only.

  9. The assessment of visually impaired persons working capacities using electrophysiological and ophthalmic ergonomics methods

    Directory of Open Access Journals (Sweden)

    M. I. Razumovsky

    2014-01-01

    Full Text Available Aim was to analyze working capacities of visually impaired persons by means of complex electrophysiological and ophthalmic ergonomics eye examination.Materials and methods. Standard clinical ophthalmologic examination (visual acuity measurement, refractometry, biomicroscopy, ophthalmoscopy as well as electrophysiological (electrooculography, electrical sensitivity of the eye, critical flicker fusion frequency and ophthalmic ergonomics tests (accommodation measurement, professional testing using automated system «Proftest-1» were performed.Results. Complex electrophysiological and ophthalmic ergonomics tests were performed in 20 visually impaired persons. Their results revealed direct correlation between electrophysiological and ophthalmic ergonomics indices.Conclusion. Working capacities of visually impaired persons can be assessed reliably using complex electrophysiological and ophthalmic ergonomics eye examination only.

  10. Novel electrophysiological approaches to clinical epilepsy. Diagnosis and treatment

    International Nuclear Information System (INIS)

    Kanazawa, Kyoko; Matsumoto, Riki; Ikeda, Akio; Kinoshita, Masako

    2011-01-01

    Seizure onset zone (SOZ) is currently defined by ictal epileptiform discharges, which are most commonly recorded as regional low-voltage fast waves or repetitive spikes. Interictal epileptiform discharges, on the other hand, are not specific enough for SOZ as they are recorded at zones other than the SOZ; they are also recorded from areas that do not generate the ictal pattern and from areas to which ictal discharges propagate. Besides spikes and sharp waves, a novel index of human epileptogenicity has been investigated in association with wide-band electroencephalography (EEG) analysis. We primarily noted the following during clinical neurophysiological analysis for clinical epilepsy. Recent development of digital EEG technology enabled us to record wide-band EEG in a clinical setting. Thus, high frequency (>200 Hz) and low frequency (<1 Hz) components can be reliably recorded using subdural electrodes. Direct current shift, slow shift, ripple, and fast ripple can be well delineated, and they will be potentially useful in the diagnosis and management of epileptic patients. Fiber tractography (morphological parameter) and cortico-cortical-evoked potentials with single cortical stimulation (electrophysiological parameter) elucidated cortico-cortical connections in human brain. The data thus obtained can help us understand the mechanism of seizure propagation and normal cortical functional connectivity. Non-invasive simultaneous recording of EEG and functional magnetic resonance imaging (fMRI) provided information on the roles of deep brain structures associated with scalp-recorded epileptiform discharges. Interventional neurophysiology can shed light on the non-pharmacological treatment of epilepsy. In this report, we discuss these novel electrophysiological approaches to the diagnosis and treatment of clinical epilepsy. (author)

  11. Combination of minimum enclosing balls classifier with SVM in coal-rock recognition

    Science.gov (United States)

    Song, QingJun; Jiang, HaiYan; Song, Qinghui; Zhao, XieGuang; Wu, Xiaoxuan

    2017-01-01

    Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition. PMID:28937987

  12. Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

    Science.gov (United States)

    Song, QingJun; Jiang, HaiYan; Song, Qinghui; Zhao, XieGuang; Wu, Xiaoxuan

    2017-01-01

    Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.

  13. Distance-Based Image Classification: Generalizing to New Classes at Near Zero Cost

    NARCIS (Netherlands)

    Mensink, T.; Verbeek, J.; Perronnin, F.; Csurka, G.

    2013-01-01

    We study large-scale image classification methods that can incorporate new classes and training images continuously over time at negligible cost. To this end, we consider two distance-based classifiers, the k-nearest neighbor (k-NN) and nearest class mean (NCM) classifiers, and introduce a new

  14. Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers

    Directory of Open Access Journals (Sweden)

    M. Al-Rousan

    2005-08-01

    Full Text Available Building an accurate automatic sign language recognition system is of great importance in facilitating efficient communication with deaf people. In this paper, we propose the use of polynomial classifiers as a classification engine for the recognition of Arabic sign language (ArSL alphabet. Polynomial classifiers have several advantages over other classifiers in that they do not require iterative training, and that they are highly computationally scalable with the number of classes. Based on polynomial classifiers, we have built an ArSL system and measured its performance using real ArSL data collected from deaf people. We show that the proposed system provides superior recognition results when compared with previously published results using ANFIS-based classification on the same dataset and feature extraction methodology. The comparison is shown in terms of the number of misclassified test patterns. The reduction in the rate of misclassified patterns was very significant. In particular, we have achieved a 36% reduction of misclassifications on the training data and 57% on the test data.

  15. Dynamics of intrinsic electrophysiological properties in spinal cord neurones

    DEFF Research Database (Denmark)

    Russo, R E; Hounsgaard, J

    1999-01-01

    The spinal cord is engaged in a wide variety of functions including generation of motor acts, coding of sensory information and autonomic control. The intrinsic electrophysiological properties of spinal neurones represent a fundamental building block of the spinal circuits executing these tasks. ....... Specialised, cell specific electrophysiological phenotypes gradually differentiate during development and are continuously adjusted in the adult animal by metabotropic synaptic interactions and activity-dependent plasticity to meet a broad range of functional demands....

  16. Classified facilities for environmental protection

    International Nuclear Information System (INIS)

    Anon.

    1993-02-01

    The legislation of the classified facilities governs most of the dangerous or polluting industries or fixed activities. It rests on the law of 9 July 1976 concerning facilities classified for environmental protection and its application decree of 21 September 1977. This legislation, the general texts of which appear in this volume 1, aims to prevent all the risks and the harmful effects coming from an installation (air, water or soil pollutions, wastes, even aesthetic breaches). The polluting or dangerous activities are defined in a list called nomenclature which subjects the facilities to a declaration or an authorization procedure. The authorization is delivered by the prefect at the end of an open and contradictory procedure after a public survey. In addition, the facilities can be subjected to technical regulations fixed by the Environment Minister (volume 2) or by the prefect for facilities subjected to declaration (volume 3). (A.B.)

  17. The three-dimensional origin of the classifying algebra

    International Nuclear Information System (INIS)

    Fuchs, Juergen; Schweigert, Christoph; Stigner, Carl

    2010-01-01

    It is known that reflection coefficients for bulk fields of a rational conformal field theory in the presence of an elementary boundary condition can be obtained as representation matrices of irreducible representations of the classifying algebra, a semisimple commutative associative complex algebra. We show how this algebra arises naturally from the three-dimensional geometry of factorization of correlators of bulk fields on the disk. This allows us to derive explicit expressions for the structure constants of the classifying algebra as invariants of ribbon graphs in the three-manifold S 2 xS 1 . Our result unravels a precise relation between intertwiners of the action of the mapping class group on spaces of conformal blocks and boundary conditions in rational conformal field theories.

  18. Why Does Rebalancing Class-Unbalanced Data Improve AUC for Linear Discriminant Analysis?

    Science.gov (United States)

    Xue, Jing-Hao; Hall, Peter

    2015-05-01

    Many established classifiers fail to identify the minority class when it is much smaller than the majority class. To tackle this problem, researchers often first rebalance the class sizes in the training dataset, through oversampling the minority class or undersampling the majority class, and then use the rebalanced data to train the classifiers. This leads to interesting empirical patterns. In particular, using the rebalanced training data can often improve the area under the receiver operating characteristic curve (AUC) for the original, unbalanced test data. The AUC is a widely-used quantitative measure of classification performance, but the property that it increases with rebalancing has, as yet, no theoretical explanation. In this note, using Gaussian-based linear discriminant analysis (LDA) as the classifier, we demonstrate that, at least for LDA, there is an intrinsic, positive relationship between the rebalancing of class sizes and the improvement of AUC. We show that the largest improvement of AUC is achieved, asymptotically, when the two classes are fully rebalanced to be of equal sizes.

  19. Classifying decommissioning wastes for allocation to appropriate final repositories

    International Nuclear Information System (INIS)

    Alder, J.C.; Tunaboylu, K.

    1982-01-01

    For the safe disposal of radioactive wastes in different repositories, it is of advantage to classify them in well-defined conditioned categories, appropriate for final disposal. These categories, the so-called waste sorts are characterized by similar radionuclide distribution, similar nuclide-specific activity concentrations and similar waste matrix. A methodology is presented for classifying decommissioning wastes and is applied to the decommissioning wastes arising from a Swiss program of 6 GWe. The amounts and nuclide-specific activity inventories of the decommissioning waste sorts have been estimated. A first allocation into two different repository types has been performed. Such a classification enables one to define the source parameters for repository safety analysis and allows one to allocate the different waste categories into appropriate final repositories. This work presents a first iteration to determine which waste sorts belong to which repository type. The characteristics of waste sorts have to be better defined and the protective strength of the repository barriers has to be optimized. 7 references, 2 figures, 4 tables

  20. [Automated processing of electrophysiologic signals].

    Science.gov (United States)

    Korenevskiĭ, N A; Gubanov, V V

    1995-01-01

    The paper outlines a diagram of a multichannel analyzer of electrophysiological signals while are significantly non-stationary (such as those of electroencephalograms, myograms, etc.), by using a method based on the ranging procedure by the change-over points which may be the points of infection, impaired locality, minima, maxima, discontinuity, etc.

  1. An Ensemble Based Evolutionary Approach to the Class Imbalance Problem with Applications in CBIR

    Directory of Open Access Journals (Sweden)

    Aun Irtaza

    2018-03-01

    Full Text Available In order to lower the dependence on textual annotations for image searches, the content based image retrieval (CBIR has become a popular topic in computer vision. A wide range of CBIR applications consider classification techniques, such as artificial neural networks (ANN, support vector machines (SVM, etc. to understand the query image content to retrieve relevant output. However, in multi-class search environments, the retrieval results are far from optimal due to overlapping semantics amongst subjects of various classes. The classification through multiple classifiers generate better results, but as the number of negative examples increases due to highly correlated semantic classes, classification bias occurs towards the negative class, hence, the combination of the classifiers become even more unstable particularly in one-against-all classification scenarios. In order to resolve this issue, a genetic algorithm (GA based classifier comity learning (GCCL method is presented in this paper to generate stable classifiers by combining ANN with SVMs through asymmetric and symmetric bagging. The proposed approach resolves the classification disagreement amongst different classifiers and also resolves the class imbalance problem in CBIR. Once the stable classifiers are generated, the query image is presented to the trained model to understand the underlying semantic content of the query image for association with the precise semantic class. Afterwards, the feature similarity is computed within the obtained class to generate the semantic response of the system. The experiments reveal that the proposed method outperforms various state-of-the-art methods and significantly improves the image retrieval performance.

  2. A scalable pairwise class interaction framework for multidimensional classification

    DEFF Research Database (Denmark)

    Arias, Jacinto; Gámez, Jose A.; Nielsen, Thomas Dyhre

    2016-01-01

    We present a general framework for multidimensional classification that cap- tures the pairwise interactions between class variables. The pairwise class inter- actions are encoded using a collection of base classifiers (Phase 1), for which the class predictions are combined in a Markov random fie...

  3. Transfer Learning for Class Imbalance Problems with Inadequate Data.

    Science.gov (United States)

    Al-Stouhi, Samir; Reddy, Chandan K

    2016-07-01

    A fundamental problem in data mining is to effectively build robust classifiers in the presence of skewed data distributions. Class imbalance classifiers are trained specifically for skewed distribution datasets. Existing methods assume an ample supply of training examples as a fundamental prerequisite for constructing an effective classifier. However, when sufficient data is not readily available, the development of a representative classification algorithm becomes even more difficult due to the unequal distribution between classes. We provide a unified framework that will potentially take advantage of auxiliary data using a transfer learning mechanism and simultaneously build a robust classifier to tackle this imbalance issue in the presence of few training samples in a particular target domain of interest. Transfer learning methods use auxiliary data to augment learning when training examples are not sufficient and in this paper we will develop a method that is optimized to simultaneously augment the training data and induce balance into skewed datasets. We propose a novel boosting based instance-transfer classifier with a label-dependent update mechanism that simultaneously compensates for class imbalance and incorporates samples from an auxiliary domain to improve classification. We provide theoretical and empirical validation of our method and apply to healthcare and text classification applications.

  4. Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics.

    Science.gov (United States)

    He, Bin; Sohrabpour, Abbas; Brown, Emery; Liu, Zhongming

    2018-06-04

    Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.

  5. An Improvement To The k-Nearest Neighbor Classifier For ECG Database

    Science.gov (United States)

    Jaafar, Haryati; Hidayah Ramli, Nur; Nasir, Aimi Salihah Abdul

    2018-03-01

    The k nearest neighbor (kNN) is a non-parametric classifier and has been widely used for pattern classification. However, in practice, the performance of kNN often tends to fail due to the lack of information on how the samples are distributed among them. Moreover, kNN is no longer optimal when the training samples are limited. Another problem observed in kNN is regarding the weighting issues in assigning the class label before classification. Thus, to solve these limitations, a new classifier called Mahalanobis fuzzy k-nearest centroid neighbor (MFkNCN) is proposed in this study. Here, a Mahalanobis distance is applied to avoid the imbalance of samples distribition. Then, a surrounding rule is employed to obtain the nearest centroid neighbor based on the distributions of training samples and its distance to the query point. Consequently, the fuzzy membership function is employed to assign the query point to the class label which is frequently represented by the nearest centroid neighbor Experimental studies from electrocardiogram (ECG) signal is applied in this study. The classification performances are evaluated in two experimental steps i.e. different values of k and different sizes of feature dimensions. Subsequently, a comparative study of kNN, kNCN, FkNN and MFkCNN classifier is conducted to evaluate the performances of the proposed classifier. The results show that the performance of MFkNCN consistently exceeds the kNN, kNCN and FkNN with the best classification rates of 96.5%.

  6. NeuroElectro: A Window to the World's Neuron Electrophysiology Data

    Directory of Open Access Journals (Sweden)

    Shreejoy J Tripathy

    2014-04-01

    Full Text Available The behavior of neural circuits is determined largely by the electrophysiological properties of the neurons they contain. Understanding the relationships of these properties requires the ability to first identify and catalog each property. However, information about such properties is largely locked away in decades of closed-access journal articles with heterogeneous conventions for reporting results, making it difficult to utilize the underlying data. We solve this problem through the NeuroElectro project: a Python library, RESTful API, and web application (at http://neuroelectro.org for the extraction, visualization, and summarization of published data on neurons' electrophysiological properties. Information is organized both by neuron type (using neuron definitions provided by NeuroLex and by electrophysiological property (using a newly developed ontology. We describe the techniques and challenges associated with the automated extraction of tabular electrophysiological data and methodological metadata from journal articles. We further discuss strategies for how to best combine, normalize and organize data across these heterogeneous sources. NeuroElectro is a valuable resource for experimental physiologists looking to supplement their own data, for computational modelers looking to constrain their model parameters, and for theoreticians searching for undiscovered relationships among neurons and their properties.

  7. DECISION TREE CLASSIFIERS FOR STAR/GALAXY SEPARATION

    International Nuclear Information System (INIS)

    Vasconcellos, E. C.; Ruiz, R. S. R.; De Carvalho, R. R.; Capelato, H. V.; Gal, R. R.; LaBarbera, F. L.; Frago Campos Velho, H.; Trevisan, M.

    2011-01-01

    We study the star/galaxy classification efficiency of 13 different decision tree algorithms applied to photometric objects in the Sloan Digital Sky Survey Data Release Seven (SDSS-DR7). Each algorithm is defined by a set of parameters which, when varied, produce different final classification trees. We extensively explore the parameter space of each algorithm, using the set of 884,126 SDSS objects with spectroscopic data as the training set. The efficiency of star-galaxy separation is measured using the completeness function. We find that the Functional Tree algorithm (FT) yields the best results as measured by the mean completeness in two magnitude intervals: 14 ≤ r ≤ 21 (85.2%) and r ≥ 19 (82.1%). We compare the performance of the tree generated with the optimal FT configuration to the classifications provided by the SDSS parametric classifier, 2DPHOT, and Ball et al. We find that our FT classifier is comparable to or better in completeness over the full magnitude range 15 ≤ r ≤ 21, with much lower contamination than all but the Ball et al. classifier. At the faintest magnitudes (r > 19), our classifier is the only one that maintains high completeness (>80%) while simultaneously achieving low contamination (∼2.5%). We also examine the SDSS parametric classifier (psfMag - modelMag) to see if the dividing line between stars and galaxies can be adjusted to improve the classifier. We find that currently stars in close pairs are often misclassified as galaxies, and suggest a new cut to improve the classifier. Finally, we apply our FT classifier to separate stars from galaxies in the full set of 69,545,326 SDSS photometric objects in the magnitude range 14 ≤ r ≤ 21.

  8. Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

    Directory of Open Access Journals (Sweden)

    QingJun Song

    Full Text Available Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB algorithm plus Support vector machine (SVM is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.

  9. Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Bach Phi Duong

    2018-04-01

    Full Text Available The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs. The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance.

  10. Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis.

    Science.gov (United States)

    Duong, Bach Phi; Kim, Jong-Myon

    2018-04-07

    The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance.

  11. Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis

    Science.gov (United States)

    Kim, Jong-Myon

    2018-01-01

    The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance. PMID:29642466

  12. Denervation syndromes of the shoulder girdle: MR imaging with electrophysiologic correlation

    International Nuclear Information System (INIS)

    Bredella, M.A.; Wischer, T.K.; Stork, A.; Genant, H.K.; Tirman, P.F.J.; Fritz, R.C.

    1999-01-01

    Objective. To investigate the use of MR imaging in the characterization of denervated muscle of the shoulder correlated with electrophysiologic studies.Design and patients. We studied with MR imaging five patients who presented with shoulder weakness and pain and who underwent electrophysiologic studies. On MR imaging the distribution of muscle edema and fatty infiltration was recorded, as was the presence of masses impinging on a regional nerve.Results. Acute/subacute denervation was best seen on T2-weighted fast spin-echo images with fat saturation, showing increased SI related to neurogenic edema. Chronic denervation was best seen on T1-weighted spin-echo images, demonstrating loss of muscle bulk and diffuse areas of increased signal intensity within the muscle. Three patients showed MR imaging and electrophysiologic findings of Parsonage Turner syndrome. One patient demonstrated an arteriovenous malformation within the spinoglenoid notch, impinging on the suprascapular nerve with associated atrophy of the infraspinatus muscle. The fifth patient demonstrated fatty atrophy of the teres minor muscle caused by compression by a cyst of the axillary nerve and electrophysiologic findings of an incomplete axillary nerve block.Conclusion. MR imaging is useful in detecting and characterizing denervation atrophy and neurogenic edema in shoulder muscles. MR imaging can provide additional information to electrophysiologic studies by estimating the age (acute/chronic) and identifying morphologic causes for shoulder pain and atrophy. (orig.)

  13. Stack filter classifiers

    Energy Technology Data Exchange (ETDEWEB)

    Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory

    2009-01-01

    Just as linear models generalize the sample mean and weighted average, weighted order statistic models generalize the sample median and weighted median. This analogy can be continued informally to generalized additive modeels in the case of the mean, and Stack Filters in the case of the median. Both of these model classes have been extensively studied for signal and image processing but it is surprising to find that for pattern classification, their treatment has been significantly one sided. Generalized additive models are now a major tool in pattern classification and many different learning algorithms have been developed to fit model parameters to finite data. However Stack Filters remain largely confined to signal and image processing and learning algorithms for classification are yet to be seen. This paper is a step towards Stack Filter Classifiers and it shows that the approach is interesting from both a theoretical and a practical perspective.

  14. Wearable carbon nanotube based dry-electrodes for electrophysiological sensors

    Science.gov (United States)

    Kang, Byeong-Cheol; Ha, Tae-Jun

    2018-05-01

    In this paper, we demonstrate all-solution-processed carbon nanotube (CNT) dry-electrodes for the detection of electrophysiological signals such as electrocardiograms (ECG) and electromyograms (EMG). The key parameters of P, Q, R, S, and T peaks are successfully extracted by such CNT based dry-electrodes, which is comparable with conventional silver/chloride (Ag/AgCl) wet-electrodes with a conducting gel film for the ECG recording. Furthermore, the sensing performance of CNT based dry-electrodes is secured during the bending test of 200 cycles, which is essential for wearable electrophysiological sensors in a non-invasive method on human skin. We also investigate the application of wearable CNT based dry-electrodes directly attached to the human skins such as forearm for sensing the electrophysiological signals. The accurate and rapid sensing response can be achieved by CNT based dry-electrodes to supervise the health condition affected by excessive physical movements during the real-time measurements.

  15. Representative Vector Machines: A Unified Framework for Classical Classifiers.

    Science.gov (United States)

    Gui, Jie; Liu, Tongliang; Tao, Dacheng; Sun, Zhenan; Tan, Tieniu

    2016-08-01

    Classifier design is a fundamental problem in pattern recognition. A variety of pattern classification methods such as the nearest neighbor (NN) classifier, support vector machine (SVM), and sparse representation-based classification (SRC) have been proposed in the literature. These typical and widely used classifiers were originally developed from different theory or application motivations and they are conventionally treated as independent and specific solutions for pattern classification. This paper proposes a novel pattern classification framework, namely, representative vector machines (or RVMs for short). The basic idea of RVMs is to assign the class label of a test example according to its nearest representative vector. The contributions of RVMs are twofold. On one hand, the proposed RVMs establish a unified framework of classical classifiers because NN, SVM, and SRC can be interpreted as the special cases of RVMs with different definitions of representative vectors. Thus, the underlying relationship among a number of classical classifiers is revealed for better understanding of pattern classification. On the other hand, novel and advanced classifiers are inspired in the framework of RVMs. For example, a robust pattern classification method called discriminant vector machine (DVM) is motivated from RVMs. Given a test example, DVM first finds its k -NNs and then performs classification based on the robust M-estimator and manifold regularization. Extensive experimental evaluations on a variety of visual recognition tasks such as face recognition (Yale and face recognition grand challenge databases), object categorization (Caltech-101 dataset), and action recognition (Action Similarity LAbeliNg) demonstrate the advantages of DVM over other classifiers.

  16. Comparing cosmic web classifiers using information theory

    Energy Technology Data Exchange (ETDEWEB)

    Leclercq, Florent [Institute of Cosmology and Gravitation (ICG), University of Portsmouth, Dennis Sciama Building, Burnaby Road, Portsmouth PO1 3FX (United Kingdom); Lavaux, Guilhem; Wandelt, Benjamin [Institut d' Astrophysique de Paris (IAP), UMR 7095, CNRS – UPMC Université Paris 6, Sorbonne Universités, 98bis boulevard Arago, F-75014 Paris (France); Jasche, Jens, E-mail: florent.leclercq@polytechnique.org, E-mail: lavaux@iap.fr, E-mail: j.jasche@tum.de, E-mail: wandelt@iap.fr [Excellence Cluster Universe, Technische Universität München, Boltzmannstrasse 2, D-85748 Garching (Germany)

    2016-08-01

    We introduce a decision scheme for optimally choosing a classifier, which segments the cosmic web into different structure types (voids, sheets, filaments, and clusters). Our framework, based on information theory, accounts for the design aims of different classes of possible applications: (i) parameter inference, (ii) model selection, and (iii) prediction of new observations. As an illustration, we use cosmographic maps of web-types in the Sloan Digital Sky Survey to assess the relative performance of the classifiers T-WEB, DIVA and ORIGAMI for: (i) analyzing the morphology of the cosmic web, (ii) discriminating dark energy models, and (iii) predicting galaxy colors. Our study substantiates a data-supported connection between cosmic web analysis and information theory, and paves the path towards principled design of analysis procedures for the next generation of galaxy surveys. We have made the cosmic web maps, galaxy catalog, and analysis scripts used in this work publicly available.

  17. Comparing cosmic web classifiers using information theory

    International Nuclear Information System (INIS)

    Leclercq, Florent; Lavaux, Guilhem; Wandelt, Benjamin; Jasche, Jens

    2016-01-01

    We introduce a decision scheme for optimally choosing a classifier, which segments the cosmic web into different structure types (voids, sheets, filaments, and clusters). Our framework, based on information theory, accounts for the design aims of different classes of possible applications: (i) parameter inference, (ii) model selection, and (iii) prediction of new observations. As an illustration, we use cosmographic maps of web-types in the Sloan Digital Sky Survey to assess the relative performance of the classifiers T-WEB, DIVA and ORIGAMI for: (i) analyzing the morphology of the cosmic web, (ii) discriminating dark energy models, and (iii) predicting galaxy colors. Our study substantiates a data-supported connection between cosmic web analysis and information theory, and paves the path towards principled design of analysis procedures for the next generation of galaxy surveys. We have made the cosmic web maps, galaxy catalog, and analysis scripts used in this work publicly available.

  18. Hemilingual spasm: defining a new entity, its electrophysiological correlates and surgical treatment through microvascular decompression.

    Science.gov (United States)

    Osburn, Leisha L; Møller, Aage R; Bhatt, Jay R; Cohen-Gadol, Aaron A

    2010-07-01

    We report on vascular compression syndrome of the 12th cranial nerve (hypoglossal), an occurrence not previously reported, and demonstrate, through corresponding objective electrophysiological evidence, that microvascular decompression of the hypoglossal nerve root can cure hemilingual spasm. A 52-year-old man had lower face muscle twitching and tongue spasms, which worsened with talking, chewing, or emotional stress. Carbamazepine offered only temporary relief, and relief from injections of botulinum toxin was insignificant. He was referred for surgical treatment. High-resolution magnetic resonance imaging of his posterior fossa contents revealed no obvious evidence of any compressive vessel along the facial nerve, but a compressive vessel along the hypoglossal nerve was apparent. The presence of preoperative tongue spasms encouraged interoperative monitoring of tongue motor responses. The facial nerve exit zone was explored, but microsurgical inspection of the seventh/eighth cranial nerve complex did not reveal any compressive vessel. However, at the anterolateral aspect of the medulla oblongata, the hypoglossal nerve was clearly compressed and distorted laterally by a large tortuous vertebral artery. When the artery was mobilized away from the nerve, the abnormal late electromyographic response to transcranial electrical stimulation disappeared; immediately after shredded Teflon was interpositioned between the artery and the nerve, the abnormal spontaneous tongue fasciculation also disappeared. The patient has remained spasm free 6 months after surgery. Hemilingual spasm may be caused by vascular contact/compression along cranial nerve XII at the lower brainstem and belong to the same family of cranial nerve hyperactivity disorders as hemifacial spasm.

  19. Re-visiting the electrophysiology of language.

    Science.gov (United States)

    Obleser, Jonas

    2015-09-01

    This editorial accompanies a special issue of Brain and Language re-visiting old themes and new leads in the electrophysiology of language. The event-related potential (ERP) as a series of characteristic deflections ("components") over time and their distribution on the scalp has been exploited by speech and language researchers over decades to find support for diverse psycholinguistic models. Fortunately, methodological and statistical advances have allowed human neuroscience to move beyond some of the limitations imposed when looking at the ERP only. Most importantly, we currently witness a refined and refreshed look at "event-related" (in the literal sense) brain activity that relates itself more closely to the actual neurobiology of speech and language processes. It is this imminent change in handling and interpreting electrophysiological data of speech and language experiments that this special issue intends to capture. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Conductive Hearing Loss during Infancy: Effects on Later Auditory Brain Stem Electrophysiology.

    Science.gov (United States)

    Gunnarson, Adele D.; Finitzo, Terese

    1991-01-01

    Long-term effects on auditory electrophysiology from early fluctuating hearing loss were studied in 27 children, aged 5 to 7 years, who had been evaluated originally in infancy. Findings suggested that early fluctuating hearing loss disrupts later auditory brain stem electrophysiology. (Author/DB)

  1. Class 2 piping rules in elevated temperature applications compared with Class 1 prescriptions for LMFBRs

    International Nuclear Information System (INIS)

    Capello, R.; Stretti, G.; Cesari, F.G.

    1989-01-01

    An LMFBR plant has many piping systems subjected to elevated temperature (> 427 o C) which, depending on their function and safety criteria, are classified as of quality level 1 or 2. The design of class 1 and class 2 piping for elevated temperatures is performed in accordance with ASME CCN-47 and CCN-253 respectively. This paper discusses what level of knowledge and analysis is necessary, to apply the rules of class 2 (CCN-253) rather than those of class 1 (CCN-47) for the design analysis of piping systems. From the designer viewpoint the burden of verification is much greater in class 1 than in class 2. This paper also examines the reliability of class 2 rules for elevated temperature when used to obtain structural results and justify the design of class 1 systems. In fact it can be shown that in some cases it is possible to design class 1 piping systems using class 2 rules. (author)

  2. Use of artificial neural networks and geographic objects for classifying remote sensing imagery

    Directory of Open Access Journals (Sweden)

    Pedro Resende Silva

    2014-06-01

    Full Text Available The aim of this study was to develop a methodology for mapping land use and land cover in the northern region of Minas Gerais state, where, in addition to agricultural land, the landscape is dominated by native cerrado, deciduous forests, and extensive areas of vereda. Using forest inventory data, as well as RapidEye, Landsat TM and MODIS imagery, three specific objectives were defined: 1 to test use of image segmentation techniques for an object-based classification encompassing spectral, spatial and temporal information, 2 to test use of high spatial resolution RapidEye imagery combined with Landsat TM time series imagery for capturing the effects of seasonality, and 3 to classify data using Artificial Neural Networks. Using MODIS time series and forest inventory data, time signatures were extracted from the dominant vegetation formations, enabling selection of the best periods of the year to be represented in the classification process. Objects created with the segmentation of RapidEye images, along with the Landsat TM time series images, were classified by ten different Multilayer Perceptron network architectures. Results showed that the methodology in question meets both the purposes of this study and the characteristics of the local plant life. With excellent accuracy values for native classes, the study showed the importance of a well-structured database for classification and the importance of suitable image segmentation to meet specific purposes.

  3. Retinal dysfunction and refractive errors: an electrophysiological study of children

    Science.gov (United States)

    Flitcroft, D I; Adams, G G W; Robson, A G; Holder, G E

    2005-01-01

    Aims: To evaluate the relation between refractive error and electrophysiological retinal abnormalities in children referred for investigation of reduced vision. Methods: The study group comprised 123 consecutive patients referred over a 14 month period from the paediatric service of Moorfields Eye Hospital for electrophysiological investigation of reduced vision. Subjects were divided into five refractive categories according to their spectacle correction: high myopia (⩽−6D), low myopia (>−6D and ⩽−0.75D), emmetropia (>−0.75 and 1.5D) and ERG abnormalities (18/35 with high astigmatism v 20/88 without, χ2 test, p = 0.002). There was no significant variation in frequency of abnormalities between low myopes, emmetropes, and low hyperopes. The rate of abnormalities was very similar in both high myopes (8/15) and high hyperopes (5/10). Conclusions: High ametropia and astigmatism in children being investigated for poor vision are associated with a higher rate of retinal electrophysiological abnormalities. An increased rate of refractive errors in the presence of retinal pathology is consistent with the hypothesis that the retina is involved in the process of emmetropisation. Electrophysiological testing should be considered in cases of high ametropia in childhood to rule out associated retinal pathology. PMID:15774929

  4. Electrophysiological Monitoring of Brain Injury and Recovery after Cardiac Arrest

    Directory of Open Access Journals (Sweden)

    Ruoxian Deng

    2015-10-01

    Full Text Available Reliable prognostic methods for cerebral functional outcome of post cardiac-arrest (CA patients are necessary, especially since therapeutic hypothermia (TH as a standard treatment. Traditional neurophysiological prognostic indicators, such as clinical examination and chemical biomarkers, may result in indecisive outcome predictions and do not directly reflect neuronal activity, though they have remained the mainstay of clinical prognosis. The most recent advances in electrophysiological methods—electroencephalography (EEG pattern, evoked potential (EP and cellular electrophysiological measurement—were developed to complement these deficiencies, and will be examined in this review article. EEG pattern (reactivity and continuity provides real-time and accurate information for early-stage (particularly in the first 24 h hypoxic-ischemic (HI brain injury patients with high sensitivity. However, the signal is easily affected by external stimuli, thus the measurements of EP should be combined with EEG background to validate the predicted neurologic functional result. Cellular electrophysiology, such as multi-unit activity (MUA and local field potentials (LFP, has strong potential for improving prognostication and therapy by offering additional neurophysiologic information to understand the underlying mechanisms of therapeutic methods. Electrophysiology provides reliable and precise prognostication on both global and cellular levels secondary to cerebral injury in cardiac arrest patients treated with TH.

  5. A highly versatile and easily configurable system for plant electrophysiology.

    Science.gov (United States)

    Gunsé, Benet; Poschenrieder, Charlotte; Rankl, Simone; Schröeder, Peter; Rodrigo-Moreno, Ana; Barceló, Juan

    2016-01-01

    In this study we present a highly versatile and easily configurable system for measuring plant electrophysiological parameters and ionic flow rates, connected to a computer-controlled highly accurate positioning device. The modular software used allows easy customizable configurations for the measurement of electrophysiological parameters. Both the operational tests and the experiments already performed have been fully successful and rendered a low noise and highly stable signal. Assembly, programming and configuration examples are discussed. The system is a powerful technique that not only gives precise measuring of plant electrophysiological status, but also allows easy development of ad hoc configurations that are not constrained to plant studies. •We developed a highly modular system for electrophysiology measurements that can be used either in organs or cells and performs either steady or dynamic intra- and extracellular measurements that takes advantage of the easiness of visual object-oriented programming.•High precision accuracy in data acquisition under electrical noisy environments that allows it to run even in a laboratory close to electrical equipment that produce electrical noise.•The system makes an improvement of the currently used systems for monitoring and controlling high precision measurements and micromanipulation systems providing an open and customizable environment for multiple experimental needs.

  6. Clinical and electrophysiological characteristics of patients with paroxysmal intra-His block with narrow QRS complexes.

    Science.gov (United States)

    Ragupathi, Loheetha; Johnson, Drew; Greenspon, Arnold; Frisch, Daniel; Ho, Reginald T; Pavri, Behzad B

    2018-04-18

    Atrioventricular (AV) block is usually due to infranodal disease and associated with a wide QRS complex; such patients often progress to complete AV block and pacemaker dependency. Uncommonly, infranodal AV block can occur within the His bundle with a narrow QRS complex. The aims of this study were to define clinical/echocardiographic characteristics of patients with AV block within the His bundle and report progression to pacemaker dependency. We retrospectively identified patients with narrow QRS complexes and documented intra-His delay or block at electrophysiology study (group A) or with electrocardiogram-documented Mobitz II AV block/paroxysmal AV block (group B). Clinical, electrophysiological, and echocardiographic variables at presentation and pacemaker parameters at the last follow-up visit were evaluated. Twenty-seven patients (19 women) were identified (mean age 64 ± 13 years; range, 38-85 years). Four patients who had block with narrow QRS complexes rarely progress to pacemaker dependency and require infrequent pacing. This entity is more common in women, with a higher prevalence of aortic and/or mitral annular calcification. If confirmed by additional studies, single-chamber pacemaker may be sufficient. Copyright © 2018 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

  7. An expert computer program for classifying stars on the MK spectral classification system

    International Nuclear Information System (INIS)

    Gray, R. O.; Corbally, C. J.

    2014-01-01

    This paper describes an expert computer program (MKCLASS) designed to classify stellar spectra on the MK Spectral Classification system in a way similar to humans—by direct comparison with the MK classification standards. Like an expert human classifier, the program first comes up with a rough spectral type, and then refines that spectral type by direct comparison with MK standards drawn from a standards library. A number of spectral peculiarities, including barium stars, Ap and Am stars, λ Bootis stars, carbon-rich giants, etc., can be detected and classified by the program. The program also evaluates the quality of the delivered spectral type. The program currently is capable of classifying spectra in the violet-green region in either the rectified or flux-calibrated format, although the accuracy of the flux calibration is not important. We report on tests of MKCLASS on spectra classified by human classifiers; those tests suggest that over the entire HR diagram, MKCLASS will classify in the temperature dimension with a precision of 0.6 spectral subclass, and in the luminosity dimension with a precision of about one half of a luminosity class. These results compare well with human classifiers.

  8. An expert computer program for classifying stars on the MK spectral classification system

    Energy Technology Data Exchange (ETDEWEB)

    Gray, R. O. [Department of Physics and Astronomy, Appalachian State University, Boone, NC 26808 (United States); Corbally, C. J. [Vatican Observatory Research Group, Tucson, AZ 85721-0065 (United States)

    2014-04-01

    This paper describes an expert computer program (MKCLASS) designed to classify stellar spectra on the MK Spectral Classification system in a way similar to humans—by direct comparison with the MK classification standards. Like an expert human classifier, the program first comes up with a rough spectral type, and then refines that spectral type by direct comparison with MK standards drawn from a standards library. A number of spectral peculiarities, including barium stars, Ap and Am stars, λ Bootis stars, carbon-rich giants, etc., can be detected and classified by the program. The program also evaluates the quality of the delivered spectral type. The program currently is capable of classifying spectra in the violet-green region in either the rectified or flux-calibrated format, although the accuracy of the flux calibration is not important. We report on tests of MKCLASS on spectra classified by human classifiers; those tests suggest that over the entire HR diagram, MKCLASS will classify in the temperature dimension with a precision of 0.6 spectral subclass, and in the luminosity dimension with a precision of about one half of a luminosity class. These results compare well with human classifiers.

  9. Comparing methods of classifying life courses: Sequence analysis and latent class analysis

    NARCIS (Netherlands)

    Elzinga, C.H.; Liefbroer, Aart C.; Han, Sapphire

    2017-01-01

    We compare life course typology solutions generated by sequence analysis (SA) and latent class analysis (LCA). First, we construct an analytic protocol to arrive at typology solutions for both methodologies and present methods to compare the empirical quality of alternative typologies. We apply this

  10. Comparing methods of classifying life courses: sequence analysis and latent class analysis

    NARCIS (Netherlands)

    Han, Y.; Liefbroer, A.C.; Elzinga, C.

    2017-01-01

    We compare life course typology solutions generated by sequence analysis (SA) and latent class analysis (LCA). First, we construct an analytic protocol to arrive at typology solutions for both methodologies and present methods to compare the empirical quality of alternative typologies. We apply this

  11. Pelvic floor electrophysiology patterns associated with faecal ...

    African Journals Online (AJOL)

    Hussein Al-Moghazy Sultan

    2012-12-28

    Dec 28, 2012 ... pelvic floor electrophysiological abnormalities associated with. FI were illustrated in ... detection of a localized anal sphincter defect clinically and ..... Woods R, Voyvodic F, Schloithe A, Sage M, Wattchow D. Anal sphincter ...

  12. Imbalanced Class Learning in Epigenetics

    OpenAIRE

    Haque, M. Muksitul; Skinner, Michael K.; Holder, Lawrence B.

    2014-01-01

    In machine learning, one of the important criteria for higher classification accuracy is a balanced dataset. Datasets with a large ratio between minority and majority classes face hindrance in learning using any classifier. Datasets having a magnitude difference in number of instances between the target concept result in an imbalanced class distribution. Such datasets can range from biological data, sensor data, medical diagnostics, or any other domain where labeling any instances of the mino...

  13. Heterogeneity of Monosymptomatic Resting Tremor in a Prospective Study: Clinical Features, Electrophysiological Test, and Dopamine Transporter Positron Emission Tomography.

    Science.gov (United States)

    Zheng, Hua-Guang; Zhang, Rong; Li, Xin; Li, Fang-Fei; Wang, Ya-Chen; Wang, Xue-Mei; Lu, Ling-Long; Feng, Tao

    2015-07-05

    The relationship between monosymptomatic resting tremor (mRT) and Parkinson's disease (PD) remains controversial. In this study, we aimed to assess the function of presynaptic dopaminergic neurons in patients with mRT by dopamine transporter positron emission tomography (DAT-PET) and to evaluate the utility of clinical features or electrophysiological studies in differential diagnosis. Thirty-three consecutive patients with mRT were enrolled prospectively. The Unified Parkinson's Disease Rating Scale and electromyography were tested before DAT-PET. Striatal asymmetry index (SAI) was calculated, and a normal DAT-PET was defined as a SAI of hygiene score, walking in motor experiences of daily living (Part II) and motor examination (Part III) were significant different between two groups (P postural tremor tend to be higher in the SWEDDs group (P = 0.08 and P = 0.05, respectively). mRT is heterogeneous in presynaptic nigrostriatal dopaminergic degeneration, which can be determined by DAT-PET brain imaging. Clinical and electrophysiological features may provide clues to distinguish PD from SWEDDs.

  14. Two subgroups of antipsychotic-naive, first-episode schizophrenia patients identified with a Gaussian mixture model on cognition and electrophysiology

    DEFF Research Database (Denmark)

    Bak, N.; Ebdrup, B.H.; Oranje, B

    2017-01-01

    Deficits in information processing and cognition are among the most robust findings in schizophrenia patients. Previous efforts to translate group-level deficits into clinically relevant and individualized information have, however, been non-successful, which is possibly explained by biologically...... different disease subgroups. We applied machine learning algorithms on measures of electrophysiology and cognition to identify potential subgroups of schizophrenia. Next, we explored subgroup differences regarding treatment response. Sixty-six antipsychotic-naive first-episode schizophrenia patients...... be used to classify subgroups of schizophrenia patients. The two distinct subgroups, which we identified, were psychopathologically inseparable before treatment, yet their response to dopaminergic blockade was predicted with significant accuracy. This proof of principle encourages further endeavors...

  15. Acquired Class D β-Lactamases

    Directory of Open Access Journals (Sweden)

    Nuno T. Antunes

    2014-08-01

    Full Text Available The Class D β-lactamases have emerged as a prominent resistance mechanism against β-lactam antibiotics that previously had efficacy against infections caused by pathogenic bacteria, especially by Acinetobacter baumannii and the Enterobacteriaceae. The phenotypic and structural characteristics of these enzymes correlate to activities that are classified either as a narrow spectrum, an extended spectrum, or a carbapenemase spectrum. We focus on Class D β-lactamases that are carried on plasmids and, thus, present particular clinical concern. Following a historical perspective, the susceptibility and kinetics patterns of the important plasmid-encoded Class D β-lactamases and the mechanisms for mobilization of the chromosomal Class D β-lactamases are discussed.

  16. Block of GABA(A) receptor ion channel by penicillin: electrophysiological and modeling insights toward the mechanism.

    Science.gov (United States)

    Rossokhin, Alexey V; Sharonova, Irina N; Bukanova, Julia V; Kolbaev, Sergey N; Skrebitsky, Vladimir G

    2014-11-01

    GABA(A) receptors (GABA(A)R) mainly mediate fast inhibitory neurotransmission in the central nervous system. Different classes of modulators target GABA(A)R properties. Penicillin G (PNG) belongs to the class of noncompetitive antagonists blocking the open GABA(A)R and is a prototype of β-lactam antibiotics. In this study, we combined electrophysiological and modeling approaches to investigate the peculiarities of PNG blockade of GABA-activated currents recorded from isolated rat Purkinje cells and to predict the PNG binding site. Whole-cell patch-сlamp recording and fast application system was used in the electrophysiological experiments. PNG block developed after channel activation and increased with membrane depolarization suggesting that the ligand binds within the open channel pore. PNG blocked stationary component of GABA-activated currents in a concentration-dependent manner with IC50 value of 1.12mM at -70mV. The termination of GABA and PNG co-application was followed by a transient tail current. Protection of the tail current from bicuculline block and dependence of its kinetic parameters on agonist affinity suggest that PNG acts as a sequential open channel blocker that prevents agonist dissociation while the channel remains blocked. We built the GABA(A)R models based on nAChR and GLIC structures and performed an unbiased systematic search of the PNG binding site. Monte-Carlo energy minimization was used to find the lowest energy binding modes. We have shown that PNG binds close to the intracellular vestibule. In both models the maximum contribution to the energy of ligand-receptor interactions revealed residues located on the level of 2', 6' and 9' rings formed by a bundle of M2 transmembrane segments, indicating that these residues most likely participate in PNG binding. The predicted structural models support the described mechanism of PNG block. Copyright © 2014 Elsevier Inc. All rights reserved.

  17. PERIPHERAL NEUROPATHY ELECTROPHYSIOLOGICAL SCREENING IN CHILDREN WITH CELIAC DISEASE

    Directory of Open Access Journals (Sweden)

    Şedat IŞIKAY

    2015-06-01

    Full Text Available Background The involvement of the peripheral nervous system in children with celiac disease is particularly rare. Objective The aim of this study was to assess the need for neurophysiological testing in celiac disease patients without neurological symptoms in order to detect early subclinical neuropathy and its possible correlations with clinical and demographic characteristics. Methods Two hundred and twenty consecutive children with celiac disease were screened for neurological symptoms and signs, and those without symptoms or signs were included. Also, patients with comorbidities associated with peripheral neuropathy or a history of neurological disease were excluded. The remaining 167 asymptomatic patients as well as 100 control cases were tested electro-physiologically for peripheral nervous system diseases. Motor nerve conduction studies, including F-waves, were performed for the median, ulnar, peroneal, and tibial nerves, and sensory nerve conduction studies were performed for the median, ulnar, and sural nerves with H reflex of the soleus muscle unilaterally. All studies were carried out using surface recording electrodes. Normative values established in our laboratory were used. Results Evidence for subclinical neuropathy was not determined with electrophysiological studies in any of the participants. Conclusion In this highly selective celiac disease group without any signs, symptoms as well as the predisposing factors for polyneuropathy, we did not determine any cases with neuropathy. With these results we can conclude that in asymptomatic cases with celiac disease electrophysiological studies are not necessary. However, larger studies with the electrophysiological studies performed at different stages of disease at follow-ups are warranted.

  18. A theoretical formulation of the electrophysiological inverse problem on the sphere.

    Science.gov (United States)

    Riera, Jorge J; Valdés, Pedro A; Tanabe, Kunio; Kawashima, Ryuta

    2006-04-07

    The construction of three-dimensional images of the primary current density (PCD) produced by neuronal activity is a problem of great current interest in the neuroimaging community, though being initially formulated in the 1970s. There exist even now enthusiastic debates about the authenticity of most of the inverse solutions proposed in the literature, in which low resolution electrical tomography (LORETA) is a focus of attention. However, in our opinion, the capabilities and limitations of the electro and magneto encephalographic techniques to determine PCD configurations have not been extensively explored from a theoretical framework, even for simple volume conductor models of the head. In this paper, the electrophysiological inverse problem for the spherical head model is cast in terms of reproducing kernel Hilbert spaces (RKHS) formalism, which allows us to identify the null spaces of the implicated linear integral operators and also to define their representers. The PCD are described in terms of a continuous basis for the RKHS, which explicitly separates the harmonic and non-harmonic components. The RKHS concept permits us to bring LORETA into the scope of the general smoothing splines theory. A particular way of calculating the general smoothing splines is illustrated, avoiding a brute force discretization prematurely. The Bayes information criterion is used to handle dissimilarities in the signal/noise ratios and physical dimensions of the measurement modalities, which could affect the estimation of the amount of smoothness required for that class of inverse solution to be well specified. In order to validate the proposed method, we have estimated the 3D spherical smoothing splines from two data sets: electric potentials obtained from a skull phantom and magnetic fields recorded from subjects performing an experiment of human faces recognition.

  19. Neural Dissociation in the Production of Lexical versus Classifier Signs in ASL: Distinct Patterns of Hemispheric Asymmetry

    Science.gov (United States)

    Hickok, Gregory; Pickell, Herbert; Klima, Edward; Bellugi, Ursula

    2009-01-01

    We examine the hemispheric organization for the production of two classes of ASL signs, lexical signs and classifier signs. Previous work has found strong left hemisphere dominance for the production of lexical signs, but several authors have speculated that classifier signs may involve the right hemisphere to a greater degree because they can…

  20. 19 CFR 151.25 - Mixing classes of sugar.

    Science.gov (United States)

    2010-04-01

    ... 19 Customs Duties 2 2010-04-01 2010-04-01 false Mixing classes of sugar. 151.25 Section 151.25... TREASURY (CONTINUED) EXAMINATION, SAMPLING, AND TESTING OF MERCHANDISE Sugars, Sirups, and Molasses § 151.25 Mixing classes of sugar. No regulations relative to the weighing, taring, sampling, classifying...

  1. Gearbox Condition Monitoring Using Advanced Classifiers

    Directory of Open Access Journals (Sweden)

    P. Večeř

    2010-01-01

    Full Text Available New efficient and reliable methods for gearbox diagnostics are needed in automotive industry because of growing demand for production quality. This paper presents the application of two different classifiers for gearbox diagnostics – Kohonen Neural Networks and the Adaptive-Network-based Fuzzy Interface System (ANFIS. Two different practical applications are presented. In the first application, the tested gearboxes are separated into two classes according to their condition indicators. In the second example, ANFIS is applied to label the tested gearboxes with a Quality Index according to the condition indicators. In both applications, the condition indicators were computed from the vibration of the gearbox housing. 

  2. An update of the DEF database of protein fold class predictions

    DEFF Research Database (Denmark)

    Reczko, Martin; Karras, Dimitris; Bohr, Henrik

    1997-01-01

    An update is given on the Database of Expected Fold classes (DEF) that contains a collection of fold-class predictions made from protein sequences and a mail server that provides new predictions for new sequences. To any given sequence one of 49 fold-classes is chosen to classify the structure re...... related to the sequence with high accuracy. The updated predictions system is developed using data from the new version of the 3D-ALI database of aligned protein structures and thus is giving more reliable and more detailed predictions than the previous DEF system.......An update is given on the Database of Expected Fold classes (DEF) that contains a collection of fold-class predictions made from protein sequences and a mail server that provides new predictions for new sequences. To any given sequence one of 49 fold-classes is chosen to classify the structure...

  3. Scopolamine Reduces Electrophysiological Indices of Distractor Suppression: Evidence from a Contingent Capture Task

    Directory of Open Access Journals (Sweden)

    Inga Laube

    2017-12-01

    Full Text Available Limited resources for the in-depth processing of external stimuli make it necessary to select only relevant information from our surroundings and to ignore irrelevant stimuli. Attentional mechanisms facilitate this selection via top-down modulation of stimulus representations in the brain. Previous research has indicated that acetylcholine (ACh modulates this influence of attention on stimulus processing. However, the role of muscarinic receptors as well as the specific mechanism of cholinergic modulation remains unclear. Here we investigated the influence of ACh on feature-based, top-down control of stimulus processing via muscarinic receptors by using a contingent capture paradigm which specifically tests attentional shifts toward uninformative cue stimuli which display one of the target defining features In a double-blind, placebo controlled study we measured the impact of the muscarinic receptor antagonist scopolamine on behavioral and electrophysiological measures of contingent attentional capture. The results demonstrated all the signs of functional contingent capture, i.e., attentional shifts toward cued locations reflected in increased amplitudes of N1 and N2Pc components, under placebo conditions. However, scopolamine did not affect behavioral or electrophysiological measures of contingent capture. Instead, scopolamine reduced the amplitude of the distractor-evoked Pd component which has recently been associated with active suppression of irrelevant distractor information. The findings suggest a general cholinergic modulation of top-down control during distractor processing.

  4. Contributions of neuroimaging, balance testing, electrophysiology and blood markers to the assessment of sport-related concussion.

    Science.gov (United States)

    Davis, G A; Iverson, G L; Guskiewicz, K M; Ptito, A; Johnston, K M

    2009-05-01

    To review the diagnostic tests and investigations used in the management of sports concussion, in the adult and paediatric populations, to (a) monitor the severity of symptoms and deficits, (b) track recovery and (c) advance knowledge relating to the natural history and neurobiology of the injury. Qualitative literature review of the neuroimaging, balance testing, electrophysiology, blood marker and concussion literature. PubMed and Medline databases were reviewed for investigations used in the management of adult and paediatric concussion, including structural imaging (computerised tomography, magnetic resonance imaging, diffusion tensor imaging), functional imaging (single photon emission computerised tomography, positron emission tomography, functional magnetic resonance imaging), spectroscopy (magnetic resonance spectroscopy, near infrared spectroscopy), balance testing (Balance Error Scoring System, Sensory Organization Test, gait testing, virtual reality), electrophysiological tests (electroencephalography, evoked potentials, event related potentials, magnetoencephalography, heart rate variability), genetics (apolipoprotein E4, channelopathies) and blood markers (S100, neuron-specific enolase, cleaved Tau protein, glutamate). For the adult and paediatric populations, each test has been classified as being: (1) clinically useful, (2) a research tool only or (3) not useful in sports-related concussion. The current status of the diagnostic tests and investigations is analysed, and potential directions for future research are provided. Currently, all tests and investigations, with the exception of clinical balance testing, remain experimental. There is accumulating research, however, that shows promise for the future clinical application of functional magnetic resonance imaging in sport concussion assessment and management.

  5. Hybrid automata models of cardiac ventricular electrophysiology for real-time computational applications.

    Science.gov (United States)

    Andalam, Sidharta; Ramanna, Harshavardhan; Malik, Avinash; Roop, Parthasarathi; Patel, Nitish; Trew, Mark L

    2016-08-01

    Virtual heart models have been proposed for closed loop validation of safety-critical embedded medical devices, such as pacemakers. These models must react in real-time to off-the-shelf medical devices. Real-time performance can be obtained by implementing models in computer hardware, and methods of compiling classes of Hybrid Automata (HA) onto FPGA have been developed. Models of ventricular cardiac cell electrophysiology have been described using HA which capture the complex nonlinear behavior of biological systems. However, many models that have been used for closed-loop validation of pacemakers are highly abstract and do not capture important characteristics of the dynamic rate response. We developed a new HA model of cardiac cells which captures dynamic behavior and we implemented the model in hardware. This potentially enables modeling the heart with over 1 million dynamic cells, making the approach ideal for closed loop testing of medical devices.

  6. Machine learning classifiers and fMRI: a tutorial overview.

    Science.gov (United States)

    Pereira, Francisco; Mitchell, Tom; Botvinick, Matthew

    2009-03-01

    Interpreting brain image experiments requires analysis of complex, multivariate data. In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviours and other variables of interest from fMRI data and thereby show the data contain information about them. In this tutorial overview we review some of the key choices faced in using this approach as well as how to derive statistically significant results, illustrating each point from a case study. Furthermore, we show how, in addition to answering the question of 'is there information about a variable of interest' (pattern discrimination), classifiers can be used to tackle other classes of question, namely 'where is the information' (pattern localization) and 'how is that information encoded' (pattern characterization).

  7. The Impact of Oversampling with SMOTE on the Performance of 3 Classifiers in Prediction of Type 2 Diabetes.

    Science.gov (United States)

    Ramezankhani, Azra; Pournik, Omid; Shahrabi, Jamal; Azizi, Fereidoun; Hadaegh, Farzad; Khalili, Davood

    2016-01-01

    To evaluate the impact of the synthetic minority oversampling technique (SMOTE) on the performance of probabilistic neural network (PNN), naïve Bayes (NB), and decision tree (DT) classifiers for predicting diabetes in a prospective cohort of the Tehran Lipid and Glucose Study (TLGS). . Data of the 6647 nondiabetic participants, aged 20 years or older with more than 10 years of follow-up, were used to develop prediction models based on 21 common risk factors. The minority class in the training dataset was oversampled using the SMOTE technique, at 100%, 200%, 300%, 400%, 500%, 600%, and 700% of its original size. The original and the oversampled training datasets were used to establish the classification models. Accuracy, sensitivity, specificity, precision, F-measure, and Youden's index were used to evaluated the performance of classifiers in the test dataset. To compare the performance of the 3 classification models, we used the ROC convex hull (ROCCH). Oversampling the minority class at 700% (completely balanced) increased the sensitivity of the PNN, DT, and NB by 64%, 51%, and 5%, respectively, but decreased the accuracy and specificity of the 3 classification methods. NB had the best Youden's index before and after oversampling. The ROCCH showed that PNN is suboptimal for any class and cost conditions. To determine a classifier with a machine learning algorithm like the PNN and DT, class skew in data should be considered. The NB and DT were optimal classifiers in a prediction task in an imbalanced medical database. © The Author(s) 2014.

  8. Feature extraction using convolutional neural network for classifying breast density in mammographic images

    Science.gov (United States)

    Thomaz, Ricardo L.; Carneiro, Pedro C.; Patrocinio, Ana C.

    2017-03-01

    Breast cancer is the leading cause of death for women in most countries. The high levels of mortality relate mostly to late diagnosis and to the direct proportionally relationship between breast density and breast cancer development. Therefore, the correct assessment of breast density is important to provide better screening for higher risk patients. However, in modern digital mammography the discrimination among breast densities is highly complex due to increased contrast and visual information for all densities. Thus, a computational system for classifying breast density might be a useful tool for aiding medical staff. Several machine-learning algorithms are already capable of classifying small number of classes with good accuracy. However, machinelearning algorithms main constraint relates to the set of features extracted and used for classification. Although well-known feature extraction techniques might provide a good set of features, it is a complex task to select an initial set during design of a classifier. Thus, we propose feature extraction using a Convolutional Neural Network (CNN) for classifying breast density by a usual machine-learning classifier. We used 307 mammographic images downsampled to 260x200 pixels to train a CNN and extract features from a deep layer. After training, the activation of 8 neurons from a deep fully connected layer are extracted and used as features. Then, these features are feedforward to a single hidden layer neural network that is cross-validated using 10-folds to classify among four classes of breast density. The global accuracy of this method is 98.4%, presenting only 1.6% of misclassification. However, the small set of samples and memory constraints required the reuse of data in both CNN and MLP-NN, therefore overfitting might have influenced the results even though we cross-validated the network. Thus, although we presented a promising method for extracting features and classifying breast density, a greater database is

  9. Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets.

    Science.gov (United States)

    Sankari, E Siva; Manimegalai, D

    2017-12-21

    Predicting membrane protein types is an important and challenging research area in bioinformatics and proteomics. Traditional biophysical methods are used to classify membrane protein types. Due to large exploration of uncharacterized protein sequences in databases, traditional methods are very time consuming, expensive and susceptible to errors. Hence, it is highly desirable to develop a robust, reliable, and efficient method to predict membrane protein types. Imbalanced datasets and large datasets are often handled well by decision tree classifiers. Since imbalanced datasets are taken, the performance of various decision tree classifiers such as Decision Tree (DT), Classification And Regression Tree (CART), C4.5, Random tree, REP (Reduced Error Pruning) tree, ensemble methods such as Adaboost, RUS (Random Under Sampling) boost, Rotation forest and Random forest are analysed. Among the various decision tree classifiers Random forest performs well in less time with good accuracy of 96.35%. Another inference is RUS boost decision tree classifier is able to classify one or two samples in the class with very less samples while the other classifiers such as DT, Adaboost, Rotation forest and Random forest are not sensitive for the classes with fewer samples. Also the performance of decision tree classifiers is compared with SVM (Support Vector Machine) and Naive Bayes classifier. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. How to achieve ultrasound-guided femoral venous access: the new standard of care in the electrophysiology laboratory.

    Science.gov (United States)

    Wiles, Benedict M; Child, Nicholas; Roberts, Paul R

    2017-06-01

    Bedside vascular ultrasound machines are increasingly available. They are used to facilitate safer vascular access across a number of different specialties. In the electrophysiology laboratory however, where patients are frequently anticoagulated and require the insertion of multiple venous sheaths, anatomical landmark techniques predominate. Despite the high number of vascular complications associated with electrophysiological procedures and the increasing evidence to support its use in electrophysiology, ultrasound remains underutilised. A new standard of care is required. A comprehensive technical report, providing a detailed explanation of this important technique, will provide other electrophysiology centres with the knowledge and justification for adopting ultrasound guidance as their standard practice. We review the increasing body of evidence which demonstrates that routine ultrasound usage can substantially improve the safety of femoral venous access in the electrophysiology laboratory. We offer a comprehensive technical report to guide operators through the process of ultrasound-guided venous access, with a specific focus on the electrophysiology laboratory. Additionally, we detail a novel technique which utilises real-time colour Doppler ultrasound to accurately identify needle tip location during venous puncture. The use of vascular ultrasound to guide femoral venous cannulation is rapid, inexpensive and easily learnt. Ultrasound is readily available and offers the potential to significantly reduce vascular complications in the unique setting of the electrophysiology laboratory. Ultrasound guidance to achieve femoral venous access should be the new standard of care in electrophysiology.

  11. Electrophysiological properties and calcium handling of embryonic stem cell-derived cardiomyocytes

    Directory of Open Access Journals (Sweden)

    Jae Boum Youm

    2016-03-01

    Full Text Available Embryonic stem cell-derived cardiomyocytes (ESC-CMs hold great interest in many fields of research including clinical applications such as stem cell and gene therapy for cardiac repair or regeneration. ESC-CMs are also used as a platform tool for pharmacological tests or for investigations of cardiac remodeling. ESC-CMs have many different aspects of morphology, electrophysiology, calcium handling, and bioenergetics compared with adult cardiomyocytes. They are immature in morphology, similar to sinus nodal-like in the electrophysiology, higher contribution of trans-sarcolemmal Ca2+ influx to Ca2+ handling, and higher dependence on anaerobic glycolysis. Here, I review a detailed electrophysiology and Ca2+ handling features of ESC-CMs during differentiation into adult cardiomyocytes to gain insights into how all the developmental changes are related to each other to display cardinal features of developing cardiomyocytes.

  12. AN IMPLEMENTATION OF EIS-SVM CLASSIFIER USING RESEARCH ARTICLES FOR TEXT CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    B Ramesh

    2016-04-01

    Full Text Available Automatic text classification is a prominent research topic in text mining. The text pre-processing is a major role in text classifier. The efficiency of pre-processing techniques is increasing the performance of text classifier. In this paper, we are implementing ECAS stemmer, Efficient Instance Selection and Pre-computed Kernel Support Vector Machine for text classification using recent research articles. We are using better pre-processing techniques such as ECAS stemmer to find root word, Efficient Instance Selection for dimensionality reduction of text data and Pre-computed Kernel Support Vector Machine for classification of selected instances. In this experiments were performed on 750 research articles with three classes such as engineering article, medical articles and educational articles. The EIS-SVM classifier provides better performance in real-time research articles classification.

  13. Electrophysiological gap detection thresholds: effects of age and comparison with a behavioral measure.

    Science.gov (United States)

    Palmer, Shannon B; Musiek, Frank E

    2014-01-01

    Temporal processing ability has been linked to speech understanding ability and older adults often complain of difficulty understanding speech in difficult listening situations. Temporal processing can be evaluated using gap detection procedures. There is some research showing that gap detection can be evaluated using an electrophysiological procedure. However, there is currently no research establishing gap detection threshold using the N1-P2 response. The purposes of the current study were to 1) determine gap detection thresholds in younger and older normal-hearing adults using an electrophysiological measure, 2) compare the electrophysiological gap detection threshold and behavioral gap detection threshold within each group, and 3) investigate the effect of age on each gap detection measure. This study utilized an older adult group and younger adult group to compare performance on an electrophysiological and behavioral gap detection procedure. The subjects in this study were 11 younger, normal-hearing adults (mean = 22 yrs) and 11 older, normal-hearing adults (mean = 64.36 yrs). All subjects completed an adaptive behavioral gap detection procedure in order to determine their behavioral gap detection threshold (BGDT). Subjects also completed an electrophysiologic gap detection procedure to determine their electrophysiologic gap detection threshold (EGDT). Older adults demonstrated significantly larger gap detection thresholds than the younger adults. However, EGDT and BGDT were not significantly different in either group. The mean difference between EGDT and BGDT for all subjects was 0.43 msec. Older adults show poorer gap detection ability when compared to younger adults. However, this study shows that gap detection thresholds can be measured using evoked potential recordings and yield results similar to a behavioral measure. American Academy of Audiology.

  14. Classifying Microorganisms

    DEFF Research Database (Denmark)

    Sommerlund, Julie

    2006-01-01

    This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological characteris......This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological...... characteristics. The coexistence of the classification systems does not lead to a conflict between them. Rather, the systems seem to co-exist in different configurations, through which they are complementary, contradictory and inclusive in different situations-sometimes simultaneously. The systems come...

  15. Defining Virtual Reality: Dimensions Determining Telepresence.

    Science.gov (United States)

    Steuer, Jonathan

    1992-01-01

    Defines virtual reality as a particular type of experience (in terms of "presence" and "telepresence") rather than as a collection of hardware. Maintains that media technologies can be classified and studied in terms of vividness and interactivity, two attributes on which virtual reality ranks very high. (SR)

  16. Evaluation of Optogenetic Electrophysiology Tools in Human Stem Cell-Derived Cardiomyocytes

    Directory of Open Access Journals (Sweden)

    Susann Björk

    2017-11-01

    Full Text Available Current cardiac drug safety assessments focus on hERG channel block and QT prolongation for evaluating arrhythmic risks, whereas the optogenetic approach focuses on the action potential (AP waveform generated by a monolayer of human cardiomyocytes beating synchronously, thus assessing the contribution of several ion channels on the overall drug effect. This novel tool provides arrhythmogenic sensitizing by light-induced pacing in combination with non-invasive, all-optical measurements of cardiomyocyte APs and will improve assessment of drug-induced electrophysiological aberrancies. With the help of patch clamp electrophysiology measurements, we aimed to investigate whether the optogenetic modifications alter human cardiomyocytes' electrophysiology and how well the optogenetic analyses perform against this gold standard. Patch clamp electrophysiology measurements of non-transduced stem cell-derived cardiomyocytes compared to cells expressing the commercially available optogenetic constructs Optopatch and CaViar revealed no significant changes in action potential duration (APD parameters. Thus, inserting the optogenetic constructs into cardiomyocytes does not significantly affect the cardiomyocyte's electrophysiological properties. When comparing the two methods against each other (patch clamp vs. optogenetic imaging we found no significant differences in APD parameters for the Optopatch transduced cells, whereas the CaViar transduced cells exhibited modest increases in APD-values measured with optogenetic imaging. Thus, to broaden the screen, we combined optogenetic measurements of membrane potential and calcium transients with contractile motion measured by video motion tracking. Furthermore, to assess how optogenetic measurements can predict changes in membrane potential, or early afterdepolarizations (EADs, cells were exposed to cumulating doses of E-4031, a hERG potassium channel blocker, and drug effects were measured at both spontaneous and

  17. Ensembles of novelty detection classifiers for structural health monitoring using guided waves

    Science.gov (United States)

    Dib, Gerges; Karpenko, Oleksii; Koricho, Ermias; Khomenko, Anton; Haq, Mahmoodul; Udpa, Lalita

    2018-01-01

    Guided wave structural health monitoring uses sparse sensor networks embedded in sophisticated structures for defect detection and characterization. The biggest challenge of those sensor networks is developing robust techniques for reliable damage detection under changing environmental and operating conditions (EOC). To address this challenge, we develop a novelty classifier for damage detection based on one class support vector machines. We identify appropriate features for damage detection and introduce a feature aggregation method which quadratically increases the number of available training observations. We adopt a two-level voting scheme by using an ensemble of classifiers and predictions. Each classifier is trained on a different segment of the guided wave signal, and each classifier makes an ensemble of predictions based on a single observation. Using this approach, the classifier can be trained using a small number of baseline signals. We study the performance using Monte-Carlo simulations of an analytical model and data from impact damage experiments on a glass fiber composite plate. We also demonstrate the classifier performance using two types of baseline signals: fixed and rolling baseline training set. The former requires prior knowledge of baseline signals from all EOC, while the latter does not and leverages the fact that EOC vary slowly over time and can be modeled as a Gaussian process.

  18. Characteristics, treatment, and outcomes of periprocedural cerebrovascular accidents during electrophysiologic procedures.

    Science.gov (United States)

    Harb, Serge C; Thomas, George; Saliba, Walid I; Nakhoul, Georges N; Hussein, Ayman A; Duarte, Valeria E; Bhargava, Mandeep; Baranowski, Bryan; Tchou, Patrick; Dresing, Thomas; Callahan, Thomas; Kanj, Mohamed; Natale, Andrea; Lindsay, Bruce D; Wazni, Oussama M

    2013-06-01

    We sought to identify the characteristics, treatment, and outcomes of periprocedural cerebrovascular accident (PCVA) during electrophysiologic (EP) procedures. Periprocedural cerebrovascular accident is one of the most feared complications during EP procedures with very few data regarding its characteristics, management, and outcomes. Between January 1998 and December 2008, we reviewed 30,032 invasive EP procedures for PCVA occurrence and characteristics. Management and outcomes were also determined. Thirty-eight CVAs were identified. Twenty (53 %) were intraprocedural and 18 (47 %) postprocedural. Thirty-two (84 %) were classified as strokes and six (16 %) as transient ischemic attacks. All CVAs except one (37, 97 %) were ischemic and the vast majority occurred during ablation procedures (36, 95 %). Among the 31 patients with ischemic stroke, 11 (35 %) were treated with reperfusion (eight catheter-based therapy and three intravenous t-PA) of whom five (46 %) had complete recovery, three (27 %) had partial recovery, and three (27 %) had no recovery. No hemorrhagic transformations occurred. Periprocedural cerebrovascular accident during EP procedures is rare and is almost always ischemic. It occurs more frequently during ablation procedures. Reperfusion therapy is feasible and safe.

  19. Voltage imaging in vivo with a new class of rhodopsin-based indicators

    Science.gov (United States)

    Douglass, Adam

    2013-03-01

    Reliable, optical detection of single action potentials in an intact brain is one of the longest-standing challenges in neuroscience. We have recently shown that a number of microbial rhodopsins exhibit intrinsic fluorescence that is sensitive to transmembrane potential. One class of indicator, derived from Archaerhodopsin-3 (Arch), responds to voltage transients with a speed and sensitivity that enable near-perfect identification of single action potentials in cultured neurons [Nat Methods. (2011). 9:90-5]. We have extended the use of these indicators to an in vivo context through the application of advanced imaging techniques to the larval zebrafish. Using planar-illumination, spinning-disk confocal, and epifluorescence imaging modalities, we have successfully recorded electrical activity in a variety of fish structures, including the brain and heart, in a completely noninvasive manner. Transgenic lines expressing Arch variants in defined cells enable comprehensive measurements to be made from specific target populations. In parallel, we have also extended the capabilities of our indicators by improving their multiphoton excitability and overall brightness. Microbial rhodopsin-based voltage indicators now enable optical interrogation of complex neural circuits, and electrophysiology in systems for which electrode-based techniques are challenging.

  20. Construction of Pancreatic Cancer Classifier Based on SVM Optimized by Improved FOA

    Science.gov (United States)

    Ma, Xiaoqi

    2015-01-01

    A novel method is proposed to establish the pancreatic cancer classifier. Firstly, the concept of quantum and fruit fly optimal algorithm (FOA) are introduced, respectively. Then FOA is improved by quantum coding and quantum operation, and a new smell concentration determination function is defined. Finally, the improved FOA is used to optimize the parameters of support vector machine (SVM) and the classifier is established by optimized SVM. In order to verify the effectiveness of the proposed method, SVM and other classification methods have been chosen as the comparing methods. The experimental results show that the proposed method can improve the classifier performance and cost less time. PMID:26543867

  1. Multi-class machine classification of suicide-related communication on Twitter.

    Science.gov (United States)

    Burnap, Pete; Colombo, Gualtiero; Amery, Rosie; Hodorog, Andrei; Scourfield, Jonathan

    2017-08-01

    The World Wide Web, and online social networks in particular, have increased connectivity between people such that information can spread to millions of people in a matter of minutes. This form of online collective contagion has provided many benefits to society, such as providing reassurance and emergency management in the immediate aftermath of natural disasters. However, it also poses a potential risk to vulnerable Web users who receive this information and could subsequently come to harm. One example of this would be the spread of suicidal ideation in online social networks, about which concerns have been raised. In this paper we report the results of a number of machine classifiers built with the aim of classifying text relating to suicide on Twitter. The classifier distinguishes between the more worrying content, such as suicidal ideation, and other suicide-related topics such as reporting of a suicide, memorial, campaigning and support. It also aims to identify flippant references to suicide. We built a set of baseline classifiers using lexical, structural, emotive and psychological features extracted from Twitter posts. We then improved on the baseline classifiers by building an ensemble classifier using the Rotation Forest algorithm and a Maximum Probability voting classification decision method, based on the outcome of base classifiers. This achieved an F-measure of 0.728 overall (for 7 classes, including suicidal ideation) and 0.69 for the suicidal ideation class. We summarise the results by reflecting on the most significant predictive principle components of the suicidal ideation class to provide insight into the language used on Twitter to express suicidal ideation. Finally, we perform a 12-month case study of suicide-related posts where we further evaluate the classification approach - showing a sustained classification performance and providing anonymous insights into the trends and demographic profile of Twitter users posting content of this type.

  2. Computationally efficient SVM multi-class image recognition with confidence measures

    International Nuclear Information System (INIS)

    Makili, Lazaro; Vega, Jesus; Dormido-Canto, Sebastian; Pastor, Ignacio; Murari, Andrea

    2011-01-01

    Typically, machine learning methods produce non-qualified estimates, i.e. the accuracy and reliability of the predictions are not provided. Transductive predictors are very recent classifiers able to provide, simultaneously with the prediction, a couple of values (confidence and credibility) to reflect the quality of the prediction. Usually, a drawback of the transductive techniques for huge datasets and large dimensionality is the high computational time. To overcome this issue, a more efficient classifier has been used in a multi-class image classification problem in the TJ-II stellarator database. It is based on the creation of a hash function to generate several 'one versus the rest' classifiers for every class. By using Support Vector Machines as the underlying classifier, a comparison between the pure transductive approach and the new method has been performed. In both cases, the success rates are high and the computation time with the new method is up to 0.4 times the old one.

  3. BIOPHARMACEUTICS CLASSIFICATION SYSTEM: A STRATEGIC TOOL FOR CLASSIFYING DRUG SUBSTANCES

    OpenAIRE

    Rohilla Seema; Rohilla Ankur; Marwaha RK; Nanda Arun

    2011-01-01

    The biopharmaceutical classification system (BCS) is a scientific approach for classifying drug substances based on their dose/solubility ratio and intestinal permeability. The BCS has been developed to allow prediction of in vivo pharmacokinetic performance of drug products from measurements of permeability and solubility. Moreover, the drugs can be categorized into four classes of BCS on the basis of permeability and solubility namely; high permeability high solubility, high permeability lo...

  4. Building an automated SOAP classifier for emergency department reports.

    Science.gov (United States)

    Mowery, Danielle; Wiebe, Janyce; Visweswaran, Shyam; Harkema, Henk; Chapman, Wendy W

    2012-02-01

    Information extraction applications that extract structured event and entity information from unstructured text can leverage knowledge of clinical report structure to improve performance. The Subjective, Objective, Assessment, Plan (SOAP) framework, used to structure progress notes to facilitate problem-specific, clinical decision making by physicians, is one example of a well-known, canonical structure in the medical domain. Although its applicability to structuring data is understood, its contribution to information extraction tasks has not yet been determined. The first step to evaluating the SOAP framework's usefulness for clinical information extraction is to apply the model to clinical narratives and develop an automated SOAP classifier that classifies sentences from clinical reports. In this quantitative study, we applied the SOAP framework to sentences from emergency department reports, and trained and evaluated SOAP classifiers built with various linguistic features. We found the SOAP framework can be applied manually to emergency department reports with high agreement (Cohen's kappa coefficients over 0.70). Using a variety of features, we found classifiers for each SOAP class can be created with moderate to outstanding performance with F(1) scores of 93.9 (subjective), 94.5 (objective), 75.7 (assessment), and 77.0 (plan). We look forward to expanding the framework and applying the SOAP classification to clinical information extraction tasks. Copyright © 2011. Published by Elsevier Inc.

  5. Sedation for paediatric auditory electrophysiology in South Africa

    African Journals Online (AJOL)

    emergency departments and nuclear medicine.1 Added to this is the periodic need ... electrophysiology in the paediatric population in South Africa were not found. ..... to inadequate information technology infrastructure as well as limited data ...

  6. Multi-Range Conditional Random Field for Classifying Railway Electrification System Objects Using Mobile Laser Scanning Data

    Directory of Open Access Journals (Sweden)

    Jaewook Jung

    2016-12-01

    Full Text Available Railways have been used as one of the most crucial means of transportation in public mobility and economic development. For safe railway operation, the electrification system in the railway infrastructure, which supplies electric power to trains, is an essential facility for stable train operation. Due to its important role, the electrification system needs to be rigorously and regularly inspected and managed. This paper presents a supervised learning method to classify Mobile Laser Scanning (MLS data into ten target classes representing overhead wires, movable brackets and poles, which are key objects in the electrification system. In general, the layout of the railway electrification system shows strong spatial regularity relations among object classes. The proposed classifier is developed based on Conditional Random Field (CRF, which characterizes not only labeling homogeneity at short range, but also the layout compatibility between different object classes at long range in the probabilistic graphical model. This multi-range CRF model consists of a unary term and three pairwise contextual terms. In order to gain computational efficiency, MLS point clouds are converted into a set of line segments to which the labeling process is applied. Support Vector Machine (SVM is used as a local classifier considering only node features for producing the unary potentials of the CRF model. As the short-range pairwise contextual term, the Potts model is applied to enforce a local smoothness in the short-range graph; while long-range pairwise potentials are designed to enhance the spatial regularities of both horizontal and vertical layouts among railway objects. We formulate two long-range pairwise potentials as the log posterior probability obtained by the naive Bayes classifier. The directional layout compatibilities are characterized in probability look-up tables, which represent the co-occurrence rate of spatial relations in the horizontal and vertical

  7. How large a training set is needed to develop a classifier for microarray data?

    Science.gov (United States)

    Dobbin, Kevin K; Zhao, Yingdong; Simon, Richard M

    2008-01-01

    A common goal of gene expression microarray studies is the development of a classifier that can be used to divide patients into groups with different prognoses, or with different expected responses to a therapy. These types of classifiers are developed on a training set, which is the set of samples used to train a classifier. The question of how many samples are needed in the training set to produce a good classifier from high-dimensional microarray data is challenging. We present a model-based approach to determining the sample size required to adequately train a classifier. It is shown that sample size can be determined from three quantities: standardized fold change, class prevalence, and number of genes or features on the arrays. Numerous examples and important experimental design issues are discussed. The method is adapted to address ex post facto determination of whether the size of a training set used to develop a classifier was adequate. An interactive web site for performing the sample size calculations is provided. We showed that sample size calculations for classifier development from high-dimensional microarray data are feasible, discussed numerous important considerations, and presented examples.

  8. Sentiment analysis system for movie review in Bahasa Indonesia using naive bayes classifier method

    Science.gov (United States)

    Nurdiansyah, Yanuar; Bukhori, Saiful; Hidayat, Rahmad

    2018-04-01

    There are many ways of implementing the use of sentiments often found in documents; one of which is the sentiments found on the product or service reviews. It is so important to be able to process and extract textual data from the documents. Therefore, we propose a system that is able to classify sentiments from review documents into two classes: positive sentiment and negative sentiment. We use Naive Bayes Classifier method in this document classification system that we build. We choose Movienthusiast, a movie reviews in Bahasa Indonesia website as the source of our review documents. From there, we were able to collect 1201 movie reviews: 783 positive reviews and 418 negative reviews that we use as the dataset for this machine learning classifier. The classifying accuracy yields an average of 88.37% from five times of accuracy measuring attempts using aforementioned dataset.

  9. An electrophysiological approach to the diagnosis of neurogenic dysphagia: implications for botulinum toxin treatment.

    Science.gov (United States)

    Alfonsi, E; Merlo, I M; Ponzio, M; Montomoli, C; Tassorelli, C; Biancardi, C; Lozza, A; Martignoni, E

    2010-01-01

    Botulinum toxin (BTX) injection into the cricopharyngeal (CP) muscle has been proposed for the treatment of neurogenic dysphagia due to CP hyperactivity. The aim was to determine whether an electrophysiological method exploring oropharyngeal swallowing could guide treatment and discriminate responders from non-responders, based on the association of CP dysfunction with other electrophysiological abnormalities of swallowing. Patients with different neurological disorders were examined: Parkinson disease, progressive supranuclear palsy, multiple system atrophy-Parkinson variant, multiple system atrophy cerebellar variant, stroke, multiple sclerosis and ataxia telangiectasia. All patients presented with clinical dysphagia, and with complete absence of CP muscle inhibition during the hypopharyngeal phase of swallowing. Each patient underwent clinical and electrophysiological investigations before and after treatment with BTX into the CP muscle of one side (15 units of Botox). Clinical and electrophysiological procedures were performed in a blind manner by two different investigators. The following electrophysiological measures were analysed: (1) duration of EMG activity of suprahyoid/submental muscles (SHEMG-D); (2) duration of laryngopharyngeal mechanogram (LPM-D); (3) duration of the inhibition of the CP muscle EMG activity (CPEMG-ID); and (4) interval between onset of EMG activity of suprahyoid/submental muscles and onset of laryngopharyngeal mechanogram (I-SHEMG-LPM). Two months after treatment, 50% of patients showed a significant improvement. Patients with prolonged or reduced SHEMG-D values and prolonged I-SHEMG-LPM values did not respond to BTX. Therefore, values for which BTX had no effect (warning values) were identified. This electrophysiological method can recognise swallowing abnormalities which may affect the outcome of the therapeutic approach to dysphagia with BTX treatment.

  10. Electrophysiological Evaluation of Oropharyngeal Dysphagia in Parkinson’s Disease

    Science.gov (United States)

    Ertekin, Cumhur

    2014-01-01

    Parkinson’s disease (PD) is a chronic, neurodegenerative movement disorder that typically affects elderly patients. Swallowing disorders are highly prevalent in PD and can have grave consequences, including pneumonia, malnutrition, dehydration and mortality. Neurogenic dysphagia in PD can manifest with both overt clinical symptoms or silent dysphagia. Regardless, early diagnosis and objective follow-up of dysphagia in PD is crucial for timely and appropriate care for these patients. In this review, we provide a comprehensive summary of the electrophysiological methods that can be used to objectively evaluate dysphagia in PD. We discuss the electrophysiological abnormalities that can be observed in PD, their clinical correlates and the pathophysiology underlying these findings. PMID:25360228

  11. On the identification of multiple space dependent ionic parameters in cardiac electrophysiology modelling

    Science.gov (United States)

    Abidi, Yassine; Bellassoued, Mourad; Mahjoub, Moncef; Zemzemi, Nejib

    2018-03-01

    In this paper, we consider the inverse problem of space dependent multiple ionic parameters identification in cardiac electrophysiology modelling from a set of observations. We use the monodomain system known as a state-of-the-art model in cardiac electrophysiology and we consider a general Hodgkin-Huxley formalism to describe the ionic exchanges at the microscopic level. This formalism covers many physiological transmembrane potential models including those in cardiac electrophysiology. Our main result is the proof of the uniqueness and a Lipschitz stability estimate of ion channels conductance parameters based on some observations on an arbitrary subdomain. The key idea is a Carleman estimate for a parabolic operator with multiple coefficients and an ordinary differential equation system.

  12. Scalable electrophysiology in intact small animals with nanoscale suspended electrode arrays

    Science.gov (United States)

    Gonzales, Daniel L.; Badhiwala, Krishna N.; Vercosa, Daniel G.; Avants, Benjamin W.; Liu, Zheng; Zhong, Weiwei; Robinson, Jacob T.

    2017-07-01

    Electrical measurements from large populations of animals would help reveal fundamental properties of the nervous system and neurological diseases. Small invertebrates are ideal for these large-scale studies; however, patch-clamp electrophysiology in microscopic animals typically requires invasive dissections and is low-throughput. To overcome these limitations, we present nano-SPEARs: suspended electrodes integrated into a scalable microfluidic device. Using this technology, we have made the first extracellular recordings of body-wall muscle electrophysiology inside an intact roundworm, Caenorhabditis elegans. We can also use nano-SPEARs to record from multiple animals in parallel and even from other species, such as Hydra littoralis. Furthermore, we use nano-SPEARs to establish the first electrophysiological phenotypes for C. elegans models for amyotrophic lateral sclerosis and Parkinson's disease, and show a partial rescue of the Parkinson's phenotype through drug treatment. These results demonstrate that nano-SPEARs provide the core technology for microchips that enable scalable, in vivo studies of neurobiology and neurological diseases.

  13. Decoding speech perception by native and non-native speakers using single-trial electrophysiological data.

    Directory of Open Access Journals (Sweden)

    Alex Brandmeyer

    Full Text Available Brain-computer interfaces (BCIs are systems that use real-time analysis of neuroimaging data to determine the mental state of their user for purposes such as providing neurofeedback. Here, we investigate the feasibility of a BCI based on speech perception. Multivariate pattern classification methods were applied to single-trial EEG data collected during speech perception by native and non-native speakers. Two principal questions were asked: 1 Can differences in the perceived categories of pairs of phonemes be decoded at the single-trial level? 2 Can these same categorical differences be decoded across participants, within or between native-language groups? Results indicated that classification performance progressively increased with respect to the categorical status (within, boundary or across of the stimulus contrast, and was also influenced by the native language of individual participants. Classifier performance showed strong relationships with traditional event-related potential measures and behavioral responses. The results of the cross-participant analysis indicated an overall increase in average classifier performance when trained on data from all participants (native and non-native. A second cross-participant classifier trained only on data from native speakers led to an overall improvement in performance for native speakers, but a reduction in performance for non-native speakers. We also found that the native language of a given participant could be decoded on the basis of EEG data with accuracy above 80%. These results indicate that electrophysiological responses underlying speech perception can be decoded at the single-trial level, and that decoding performance systematically reflects graded changes in the responses related to the phonological status of the stimuli. This approach could be used in extensions of the BCI paradigm to support perceptual learning during second language acquisition.

  14. Ultraconformable Temporary Tattoo Electrodes for Electrophysiology

    Science.gov (United States)

    Ferrari, Laura M.; Sudha, Sudha; Tarantino, Sergio; Esposti, Roberto; Bolzoni, Francesco; Cavallari, Paolo; Cipriani, Christian

    2018-01-01

    Abstract Electrically interfacing the skin for monitoring personal health condition is the basis of skin‐contact electrophysiology. In the clinical practice the use of stiff and bulky pregelled or dry electrodes, in contrast to the soft body tissues, imposes severe restrictions to user comfort and mobility while limiting clinical applications. Here, in this work dry, unperceivable temporary tattoo electrodes are presented. Customized single or multielectrode arrays are readily fabricated by inkjet printing of conducting polymer onto commercial decal transfer paper, which allows for easy transfer on the user's skin. Conformal adhesion to the skin is provided thanks to their ultralow thickness (Tattoo electrode–skin contact impedance is characterized on short‐ (1 h) and long‐term (48 h) and compared with standard pregelled and dry electrodes. The viability in electrophysiology is validated by surface electromyography and electrocardiography recordings on various locations on limbs and face. A novel concept of tattoo as perforable skin‐contact electrode, through which hairs can grow, is demonstrated, thus permitting to envision very long‐term recordings on areas with high hair density. The proposed materials and patterning strategy make this technology amenable for large‐scale production of low‐cost sensing devices. PMID:29593975

  15. Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains

    Directory of Open Access Journals (Sweden)

    Eils Roland

    2006-06-01

    Full Text Available Abstract Background The subcellular location of a protein is closely related to its function. It would be worthwhile to develop a method to predict the subcellular location for a given protein when only the amino acid sequence of the protein is known. Although many efforts have been made to predict subcellular location from sequence information only, there is the need for further research to improve the accuracy of prediction. Results A novel method called HensBC is introduced to predict protein subcellular location. HensBC is a recursive algorithm which constructs a hierarchical ensemble of classifiers. The classifiers used are Bayesian classifiers based on Markov chain models. We tested our method on six various datasets; among them are Gram-negative bacteria dataset, data for discriminating outer membrane proteins and apoptosis proteins dataset. We observed that our method can predict the subcellular location with high accuracy. Another advantage of the proposed method is that it can improve the accuracy of the prediction of some classes with few sequences in training and is therefore useful for datasets with imbalanced distribution of classes. Conclusion This study introduces an algorithm which uses only the primary sequence of a protein to predict its subcellular location. The proposed recursive scheme represents an interesting methodology for learning and combining classifiers. The method is computationally efficient and competitive with the previously reported approaches in terms of prediction accuracies as empirical results indicate. The code for the software is available upon request.

  16. Language effects in second-language learners: A longitudinal electrophysiological study of spanish classroom learning.

    Science.gov (United States)

    Soskey, Laura; Holcomb, Phillip J; Midgley, Katherine J

    2016-09-01

    How do the neural mechanisms involved in word recognition evolve over the course of word learning in adult learners of a new second language? The current study sought to closely track language effects, which are differences in electrophysiological indices of word processing between one's native and second languages, in beginning university learners over the course of a single semester of learning. Monolingual L1 English-speakers enrolled in introductory Spanish were first trained on a list of 228 Spanish words chosen from the vocabulary to be learned in class. Behavioral data from the training session and the following experimental sessions spaced over the course of the semester showed expected learning effects. In the three laboratory sessions participants read words in three lists (English, Spanish and mixed) while performing a go/no-go lexical decision task in which event-related potentials (ERPs) were recorded. As observed in previous studies there were ERP language effects with larger N400s to native than second language words. Importantly, this difference declined over the course of L2 learning with N400 amplitude increasing for new second language words. These results suggest that even over a single semester of learning that new second language words are rapidly incorporated into the word recognition system and begin to take on lexical and semantic properties similar to native language words. Moreover, the results suggest that electrophysiological measures can be used as sensitive measures for tracking the acquisition of new linguistic knowledge. Copyright © 2016 Elsevier B.V. All rights reserved.

  17. An automated approach to the design of decision tree classifiers

    Science.gov (United States)

    Argentiero, P.; Chin, R.; Beaudet, P.

    1982-01-01

    An automated technique is presented for designing effective decision tree classifiers predicated only on a priori class statistics. The procedure relies on linear feature extractions and Bayes table look-up decision rules. Associated error matrices are computed and utilized to provide an optimal design of the decision tree at each so-called 'node'. A by-product of this procedure is a simple algorithm for computing the global probability of correct classification assuming the statistical independence of the decision rules. Attention is given to a more precise definition of decision tree classification, the mathematical details on the technique for automated decision tree design, and an example of a simple application of the procedure using class statistics acquired from an actual Landsat scene.

  18. Optimal reduced-rank quadratic classifiers using the Fukunaga-Koontz transform with applications to automated target recognition

    Science.gov (United States)

    Huo, Xiaoming; Elad, Michael; Flesia, Ana G.; Muise, Robert R.; Stanfill, S. Robert; Friedman, Jerome; Popescu, Bogdan; Chen, Jihong; Mahalanobis, Abhijit; Donoho, David L.

    2003-09-01

    In target recognition applications of discriminant of classification analysis, each 'feature' is a result of a convolution of an imagery with a filter, which may be derived from a feature vector. It is important to use relatively few features. We analyze an optimal reduced-rank classifier under the two-class situation. Assuming each population is Gaussian and has zero mean, and the classes differ through the covariance matrices: ∑1 and ∑2. The following matrix is considered: Λ=(∑1+∑2)-1/2∑1(∑1+∑2)-1/2. We show that the k eigenvectors of this matrix whose eigenvalues are most different from 1/2 offer the best rank k approximation to the maximum likelihood classifier. The matrix Λ and its eigenvectors have been introduced by Fukunaga and Koontz; hence this analysis gives a new interpretation of the well known Fukunaga-Koontz transform. The optimality that is promised in this method hold if the two populations are exactly Guassian with the same means. To check the applicability of this approach to real data, an experiment is performed, in which several 'modern' classifiers were used on an Infrared ATR data. In these experiments, a reduced-rank classifier-Tuned Basis Functions-outperforms others. The competitive performance of the optimal reduced-rank quadratic classifier suggests that, at least for classification purposes, the imagery data behaves in a nearly-Gaussian fashion.

  19. Parent Involvement and Science Achievement: A Cross-Classified Multilevel Latent Growth Curve Analysis

    Science.gov (United States)

    Johnson, Ursula Y.; Hull, Darrell M.

    2014-01-01

    The authors examined science achievement growth at Grades 3, 5, and 8 and parent school involvement at the same time points using the Early Childhood Longitudinal Study-Kindergarten Class of 1998-1999. Data were analyzed using cross-classified multilevel latent growth curve modeling with time invariant and varying covariates. School-based…

  20. Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer

    Directory of Open Access Journals (Sweden)

    Doyle Scott

    2012-10-01

    Full Text Available Abstract Background Automated classification of histopathology involves identification of multiple classes, including benign, cancerous, and confounder categories. The confounder tissue classes can often mimic and share attributes with both the diseased and normal tissue classes, and can be particularly difficult to identify, both manually and by automated classifiers. In the case of prostate cancer, they may be several confounding tissue types present in a biopsy sample, posing as major sources of diagnostic error for pathologists. Two common multi-class approaches are one-shot classification (OSC, where all classes are identified simultaneously, and one-versus-all (OVA, where a “target” class is distinguished from all “non-target” classes. OSC is typically unable to handle discrimination of classes of varying similarity (e.g. with images of prostate atrophy and high grade cancer, while OVA forces several heterogeneous classes into a single “non-target” class. In this work, we present a cascaded (CAS approach to classifying prostate biopsy tissue samples, where images from different classes are grouped to maximize intra-group homogeneity while maximizing inter-group heterogeneity. Results We apply the CAS approach to categorize 2000 tissue samples taken from 214 patient studies into seven classes: epithelium, stroma, atrophy, prostatic intraepithelial neoplasia (PIN, and prostate cancer Gleason grades 3, 4, and 5. A series of increasingly granular binary classifiers are used to split the different tissue classes until the images have been categorized into a single unique class. Our automatically-extracted image feature set includes architectural features based on location of the nuclei within the tissue sample as well as texture features extracted on a per-pixel level. The CAS strategy yields a positive predictive value (PPV of 0.86 in classifying the 2000 tissue images into one of 7 classes, compared with the OVA (0.77 PPV and OSC

  1. Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds.

    Science.gov (United States)

    Cannon, Edward O; Amini, Ata; Bender, Andreas; Sternberg, Michael J E; Muggleton, Stephen H; Glen, Robert C; Mitchell, John B O

    2007-05-01

    We investigate the classification performance of circular fingerprints in combination with the Naive Bayes Classifier (MP2D), Inductive Logic Programming (ILP) and Support Vector Inductive Logic Programming (SVILP) on a standard molecular benchmark dataset comprising 11 activity classes and about 102,000 structures. The Naive Bayes Classifier treats features independently while ILP combines structural fragments, and then creates new features with higher predictive power. SVILP is a very recently presented method which adds a support vector machine after common ILP procedures. The performance of the methods is evaluated via a number of statistical measures, namely recall, specificity, precision, F-measure, Matthews Correlation Coefficient, area under the Receiver Operating Characteristic (ROC) curve and enrichment factor (EF). According to the F-measure, which takes both recall and precision into account, SVILP is for seven out of the 11 classes the superior method. The results show that the Bayes Classifier gives the best recall performance for eight of the 11 targets, but has a much lower precision, specificity and F-measure. The SVILP model on the other hand has the highest recall for only three of the 11 classes, but generally far superior specificity and precision. To evaluate the statistical significance of the SVILP superiority, we employ McNemar's test which shows that SVILP performs significantly (p < 5%) better than both other methods for six out of 11 activity classes, while being superior with less significance for three of the remaining classes. While previously the Bayes Classifier was shown to perform very well in molecular classification studies, these results suggest that SVILP is able to extract additional knowledge from the data, thus improving classification results further.

  2. Process management using component thermal-hydraulic function classes

    Science.gov (United States)

    Morman, J.A.; Wei, T.Y.C.; Reifman, J.

    1999-07-27

    A process management expert system where following malfunctioning of a component, such as a pump, for determining system realignment procedures such as for by-passing the malfunctioning component with on-line speeds to maintain operation of the process at full or partial capacity or to provide safe shut down of the system while isolating the malfunctioning component. The expert system uses thermal-hydraulic function classes at the component level for analyzing unanticipated as well as anticipated component malfunctions to provide recommended sequences of operator actions. Each component is classified according to its thermal-hydraulic function, and the generic and component-specific characteristics for that function. Using the diagnosis of the malfunctioning component and its thermal hydraulic class, the expert system analysis is carried out using generic thermal-hydraulic first principles. One aspect of the invention employs a qualitative physics-based forward search directed primarily downstream from the malfunctioning component in combination with a subsequent backward search directed primarily upstream from the serviced component. Generic classes of components are defined in the knowledge base according to the three thermal-hydraulic functions of mass, momentum and energy transfer and are used to determine possible realignment of component configurations in response to thermal-hydraulic function imbalance caused by the malfunctioning component. Each realignment to a new configuration produces the accompanying sequence of recommended operator actions. All possible new configurations are examined and a prioritized list of acceptable solutions is produced. 5 figs.

  3. Process management using component thermal-hydraulic function classes

    Science.gov (United States)

    Morman, James A.; Wei, Thomas Y. C.; Reifman, Jaques

    1999-01-01

    A process management expert system where following malfunctioning of a component, such as a pump, for determining system realignment procedures such as for by-passing the malfunctioning component with on-line speeds to maintain operation of the process at full or partial capacity or to provide safe shut down of the system while isolating the malfunctioning component. The expert system uses thermal-hydraulic function classes at the component level for analyzing unanticipated as well as anticipated component malfunctions to provide recommended sequences of operator actions. Each component is classified according to its thermal-hydraulic function, and the generic and component-specific characteristics for that function. Using the diagnosis of the malfunctioning component and its thermal hydraulic class, the expert system analysis is carried out using generic thermal-hydraulic first principles. One aspect of the invention employs a qualitative physics-based forward search directed primarily downstream from the malfunctioning component in combination with a subsequent backward search directed primarily upstream from the serviced component. Generic classes of components are defined in the knowledge base according to the three thermal-hydraulic functions of mass, momentum and energy transfer and are used to determine possible realignment of component configurations in response to thermal-hydraulic function imbalance caused by the malfunctioning component. Each realignment to a new configuration produces the accompanying sequence of recommended operator actions. All possible new configurations are examined and a prioritized list of acceptable solutions is produced.

  4. The EB factory project. I. A fast, neural-net-based, general purpose light curve classifier optimized for eclipsing binaries

    International Nuclear Information System (INIS)

    Paegert, Martin; Stassun, Keivan G.; Burger, Dan M.

    2014-01-01

    We describe a new neural-net-based light curve classifier and provide it with documentation as a ready-to-use tool for the community. While optimized for identification and classification of eclipsing binary stars, the classifier is general purpose, and has been developed for speed in the context of upcoming massive surveys such as the Large Synoptic Survey Telescope. A challenge for classifiers in the context of neural-net training and massive data sets is to minimize the number of parameters required to describe each light curve. We show that a simple and fast geometric representation that encodes the overall light curve shape, together with a chi-square parameter to capture higher-order morphology information results in efficient yet robust light curve classification, especially for eclipsing binaries. Testing the classifier on the ASAS light curve database, we achieve a retrieval rate of 98% and a false-positive rate of 2% for eclipsing binaries. We achieve similarly high retrieval rates for most other periodic variable-star classes, including RR Lyrae, Mira, and delta Scuti. However, the classifier currently has difficulty discriminating between different sub-classes of eclipsing binaries, and suffers a relatively low (∼60%) retrieval rate for multi-mode delta Cepheid stars. We find that it is imperative to train the classifier's neural network with exemplars that include the full range of light curve quality to which the classifier will be expected to perform; the classifier performs well on noisy light curves only when trained with noisy exemplars. The classifier source code, ancillary programs, a trained neural net, and a guide for use, are provided.

  5. Imbalanced class learning in epigenetics.

    Science.gov (United States)

    Haque, M Muksitul; Skinner, Michael K; Holder, Lawrence B

    2014-07-01

    In machine learning, one of the important criteria for higher classification accuracy is a balanced dataset. Datasets with a large ratio between minority and majority classes face hindrance in learning using any classifier. Datasets having a magnitude difference in number of instances between the target concept result in an imbalanced class distribution. Such datasets can range from biological data, sensor data, medical diagnostics, or any other domain where labeling any instances of the minority class can be time-consuming or costly or the data may not be easily available. The current study investigates a number of imbalanced class algorithms for solving the imbalanced class distribution present in epigenetic datasets. Epigenetic (DNA methylation) datasets inherently come with few differentially DNA methylated regions (DMR) and with a higher number of non-DMR sites. For this class imbalance problem, a number of algorithms are compared, including the TAN+AdaBoost algorithm. Experiments performed on four epigenetic datasets and several known datasets show that an imbalanced dataset can have similar accuracy as a regular learner on a balanced dataset.

  6. Memory-Based Specification of Verbal Features for Classifying Animals into Super-Ordinate and Sub-Ordinate Categories

    Directory of Open Access Journals (Sweden)

    Takahiro Soshi

    2017-09-01

    Full Text Available Accumulating evidence suggests that category representations are based on features. Distinguishing features are considered to define categories, because of all-or-none responses for objects in different categories; however, it is unclear how distinguishing features actually classify objects at various category levels. The present study included 75 animals within three classes (mammal, bird, and fish, along with 195 verbal features. Healthy adults participated in memory-based feature-animal matching verification tests. Analyses included a hierarchical clustering analysis, support vector machine, and independent component analysis to specify features effective for classifications. Quantitative and qualitative comparisons for significant features were conducted between super-ordinate and sub-ordinate levels. The number of significant features was larger for super-ordinate than sub-ordinate levels. Qualitatively, the proportion of biological features was larger than cultural/affective features in both the levels, while the proportion of affective features increased at the sub-ordinate level. To summarize, the two types of features differentially function to establish category representations.

  7. Class teacher’s gender culture

    OpenAIRE

    GOGOL-SAVRIY M.V.

    2012-01-01

    The article considers the gender approach in the professional culture of a class teacher. The nature, levels and sublevels of class teacher’s gender culture development are defined. Taking into consideration the concepts of leading researchers, the essence of components of class teacher’s gender culture is discovered according to the levels of its development as professional and educational activity. Proceeding from the results of the diagnostics of class teachers’ gender culture at comprehen...

  8. Mandibular condyle dimensions in Peruvian patients with Class II and Class III skeletal patterns.

    Directory of Open Access Journals (Sweden)

    Hugo Zegarra-Baquerizo

    2017-10-01

    Full Text Available Objective: To compare condylar dimensions of young adults with Class II and Class III skeletal patterns using cone-beam computed tomography (CBCT. Materials and methods: 124 CBCTs from 18-30 year-old patients, divided into 2 groups according to skeletal patterns (Class II and Class III were evaluated. Skeletal patterns were classified by measuring the ANB angle of each patient. The anteroposterior diameter (A and P of the right and left mandibular condyle was assessed from a sagittal view by a line drawn from point A (anterior to P (posterior. The coronal plane allowed the evaluation of the medio-lateral diameter by drawing a line from point M (medium to L (lateral; all distances were measured in mm. Results: In Class II the A-P diameter was 9.06±1.33 and 8.86±1.56 for the right and left condyles respectively, in Class III these values were 8.71±1.2 and 8.84±1.42. In Class II the M-L diameter was 17.94±2.68 and 17.67±2.44 for the right and left condyles respectively, in Class III these values were 19.16±2.75 and 19.16±2.54. Conclusion: Class III M-L dimensions showed higher values than Class II, whereas these differences were minimal in A-P.

  9. Textual and shape-based feature extraction and neuro-fuzzy classifier for nuclear track recognition

    Science.gov (United States)

    Khayat, Omid; Afarideh, Hossein

    2013-04-01

    Track counting algorithms as one of the fundamental principles of nuclear science have been emphasized in the recent years. Accurate measurement of nuclear tracks on solid-state nuclear track detectors is the aim of track counting systems. Commonly track counting systems comprise a hardware system for the task of imaging and software for analysing the track images. In this paper, a track recognition algorithm based on 12 defined textual and shape-based features and a neuro-fuzzy classifier is proposed. Features are defined so as to discern the tracks from the background and small objects. Then, according to the defined features, tracks are detected using a trained neuro-fuzzy system. Features and the classifier are finally validated via 100 Alpha track images and 40 training samples. It is shown that principle textual and shape-based features concomitantly yield a high rate of track detection compared with the single-feature based methods.

  10. The Consistency Between Clinical and Electrophysiological Diagnoses

    Directory of Open Access Journals (Sweden)

    Esra E. Okuyucu

    2009-09-01

    Full Text Available OBJECTIVE: The aim of this study was to provide information concerning the impact of electrophysiological tests in the clinical management and diagnosis of patients, and to evaluate the consistency between referring clinical diagnoses and electrophysiological diagnoses. METHODS: The study included 957 patients referred to the electroneuromyography (ENMG laboratory from different clinics with different clinical diagnoses in 2008. Demographic data, referring clinical diagnoses, the clinics where the requests wanted, and diagnoses after ENMG testing were recorded and statistically evaluated. RESULTS: In all, 957 patients [644 (67.3% female and 313 (32.7% male] were included in the study. Mean age of the patients was 45.40 ± 14.54 years. ENMG requests were made by different specialists; 578 (60.4% patients were referred by neurologists, 122 (12.8% by orthopedics, 140 (14.6% by neurosurgeons, and 117 (12.2% by physical treatment and rehabilitation departments. According to the results of ENMG testing, 513 (53.6% patients’ referrals were related to their referral diagnosis, whereas 397 (41.5% patients had normal ENMG test results, and 47 (4.9% patients had a diagnosis that differed from the referring diagnosis. Among the relation between the referral diagnosis and electrophysiological diagnosis according to the clinics where the requests were made, there was no statistical difference (p= 0.794, but there were statistically significant differences between the support of different clinical diagnoses, such as carpal tunnel syndrome, polyneuropathy, radiculopathy-plexopathy, entrapment neuropathy, and myopathy based on ENMG test results (p< 0.001. CONCLUSION: ENMG is a frequently used neurological examination. As such, referrals for ENMG can be made to either support the referring diagnosis or to exclude other diagnoses. This may explain the inconsistency between clinical referring diagnoses and diagnoses following ENMG

  11. Can single classifiers be as useful as model ensembles to produce benthic seabed substratum maps?

    Science.gov (United States)

    Turner, Joseph A.; Babcock, Russell C.; Hovey, Renae; Kendrick, Gary A.

    2018-05-01

    Numerous machine-learning classifiers are available for benthic habitat map production, which can lead to different results. This study highlights the performance of the Random Forest (RF) classifier, which was significantly better than Classification Trees (CT), Naïve Bayes (NB), and a multi-model ensemble in terms of overall accuracy, Balanced Error Rate (BER), Kappa, and area under the curve (AUC) values. RF accuracy was often higher than 90% for each substratum class, even at the most detailed level of the substratum classification and AUC values also indicated excellent performance (0.8-1). Total agreement between classifiers was high at the broadest level of classification (75-80%) when differentiating between hard and soft substratum. However, this sharply declined as the number of substratum categories increased (19-45%) including a mix of rock, gravel, pebbles, and sand. The model ensemble, produced from the results of all three classifiers by majority voting, did not show any increase in predictive performance when compared to the single RF classifier. This study shows how a single classifier may be sufficient to produce benthic seabed maps and model ensembles of multiple classifiers.

  12. Retinal Electrophysiology Is a Viable Preclinical Biomarker for Drug Penetrance into the Central Nervous System

    Directory of Open Access Journals (Sweden)

    Jason Charng

    2016-01-01

    Full Text Available Objective. To examine whether retinal electrophysiology is a useful surrogate marker of drug penetrance into the central nervous system (CNS. Materials and Methods. Brain and retinal electrophysiology were assessed with full-field visually evoked potentials and electroretinograms in conscious and anaesthetised rats following systemic or local administrations of centrally penetrant (muscimol or nonpenetrant (isoguvacine compounds. Results. Local injections into the eye/brain bypassed the blood neural barriers and produced changes in retinal/brain responses for both drugs. In conscious animals, systemic administration of muscimol resulted in retinal and brain biopotential changes, whereas systemic delivery of isoguvacine did not. General anaesthesia confounded these outcomes. Conclusions. Retinal electrophysiology, when recorded in conscious animals, shows promise as a viable biomarker of drug penetration into the CNS. In contrast, when conducted under anaesthetised conditions confounds can be induced in both cortical and retinal electrophysiological recordings.

  13. Conceptualizing "Homework" in Flipped Mathematics Classes

    Science.gov (United States)

    de Araujo, Zandra; Otten, Samuel; Birisci, Salih

    2017-01-01

    Flipped instruction is becoming more common in the United States, particularly in mathematics classes. One of the defining characteristics of this increasingly popular instructional format is the homework teachers assign. In contrast to traditional mathematics classes in which homework consists of problem sets, homework in flipped classes often…

  14. Problems of Indexing Classes of News Based on the Computed ...

    African Journals Online (AJOL)

    News is classified. Such classes as sports, politics, news on crime, gossips, business, etc., are common amongst newspapers in Nigeria. Interestingly most readers and patrons of newspapers adopt the rule of the thumb in choosing a suitable newspaper to read/buy. However, most newspapers try to cover all classes but ...

  15. Heart beats in the cloud: distributed analysis of electrophysiological 'Big Data' using cloud computing for epilepsy clinical research.

    Science.gov (United States)

    Sahoo, Satya S; Jayapandian, Catherine; Garg, Gaurav; Kaffashi, Farhad; Chung, Stephanie; Bozorgi, Alireza; Chen, Chien-Hun; Loparo, Kenneth; Lhatoo, Samden D; Zhang, Guo-Qiang

    2014-01-01

    The rapidly growing volume of multimodal electrophysiological signal data is playing a critical role in patient care and clinical research across multiple disease domains, such as epilepsy and sleep medicine. To facilitate secondary use of these data, there is an urgent need to develop novel algorithms and informatics approaches using new cloud computing technologies as well as ontologies for collaborative multicenter studies. We present the Cloudwave platform, which (a) defines parallelized algorithms for computing cardiac measures using the MapReduce parallel programming framework, (b) supports real-time interaction with large volumes of electrophysiological signals, and (c) features signal visualization and querying functionalities using an ontology-driven web-based interface. Cloudwave is currently used in the multicenter National Institute of Neurological Diseases and Stroke (NINDS)-funded Prevention and Risk Identification of SUDEP (sudden unexplained death in epilepsy) Mortality (PRISM) project to identify risk factors for sudden death in epilepsy. Comparative evaluations of Cloudwave with traditional desktop approaches to compute cardiac measures (eg, QRS complexes, RR intervals, and instantaneous heart rate) on epilepsy patient data show one order of magnitude improvement for single-channel ECG data and 20 times improvement for four-channel ECG data. This enables Cloudwave to support real-time user interaction with signal data, which is semantically annotated with a novel epilepsy and seizure ontology. Data privacy is a critical issue in using cloud infrastructure, and cloud platforms, such as Amazon Web Services, offer features to support Health Insurance Portability and Accountability Act standards. The Cloudwave platform is a new approach to leverage of large-scale electrophysiological data for advancing multicenter clinical research.

  16. Low default credit scoring using two-class non-parametric kernel density estimation

    CSIR Research Space (South Africa)

    Rademeyer, E

    2016-12-01

    Full Text Available This paper investigates the performance of two-class classification credit scoring data sets with low default ratios. The standard two-class parametric Gaussian and non-parametric Parzen classifiers are extended, using Bayes’ rule, to include either...

  17. Degeneration and height of cervical discs classified from MRI compared with precise height measurements from radiographs

    International Nuclear Information System (INIS)

    Kolstad, Frode; Myhr, Gunnar; Kvistad, Kjell Arne; Nygaard, Oystein P.; Leivseth, Gunnar

    2005-01-01

    Study design: Descriptive study comparing MRI classifications with measurements from radiographs. Objectives: 1.Define the relationship between MRI classified cervical disc degeneration and objectively measured disc height. 2.Assess the level of inter- and intra-observer errors using MRI in defining cervical disc degeneration. Summary of background data: Cervical spine degeneration has been defined radiologically by loss of disc height, decreased disc and bone marrow signal intensity and disc protrusion/herniation on MRI. The intra- and inter-observer error using MRI in defining cervical degeneration influences data interpretation. Few previous studies have addressed this source of error. The relation and time sequence between cervical disc degeneration classified by MRI and cervical disc height decrease measured from radiographs is unclear. Methods: The MRI classification of degeneration was based on nucleus signal, prolaps identification and bone marrow signal. Two neuro-radiologists evaluated the MR-images independently in a blinded fashion. The radiographic disc height measurements were done by a new computer-assisted method compensating for image distortion and permitting comparison with normal level-, age- and gender-appropriate disc height. Results/conclusions: 1.Progressing disc degeneration classified from MRI is on average significantly associated with a decrease of disc height as measured from radiographs. Within each MRI defined category of degeneration measured disc heights, however, scatter in a wide range. 2.The inter-observer agreement between two neuro-radiologists in both defining degeneration and disc height by MRI was only moderate. Studies addressing questions related to cervical disc degeneration should take this into consideration

  18. Degeneration and height of cervical discs classified from MRI compared with precise height measurements from radiographs

    Energy Technology Data Exchange (ETDEWEB)

    Kolstad, Frode [National Centre of Spinal Disorders, Norwegian University of Science and Technology, University Hospital of Trondheim, 7006 Trondheim (Norway)]. E-mail: frode.kolstad@medisin.ntnu.no; Myhr, Gunnar [Department of Radiology, University Hospital of Trondheim, 7006 Trondheim (Norway); Kvistad, Kjell Arne [Department of Radiology, University Hospital of Trondheim, 7006 Trondheim (Norway); Nygaard, Oystein P. [National Centre of Spinal Disorders, Norwegian University of Science and Technology, University Hospital of Trondheim, 7006 Trondheim (Norway); Leivseth, Gunnar [Department of Neuromedicine, Faculty of Medicine, Norwegian University of Science and Technology, University Hospital of Trondheim, 7006 Trondheim (Norway)

    2005-09-01

    Study design: Descriptive study comparing MRI classifications with measurements from radiographs. Objectives: 1.Define the relationship between MRI classified cervical disc degeneration and objectively measured disc height. 2.Assess the level of inter- and intra-observer errors using MRI in defining cervical disc degeneration. Summary of background data: Cervical spine degeneration has been defined radiologically by loss of disc height, decreased disc and bone marrow signal intensity and disc protrusion/herniation on MRI. The intra- and inter-observer error using MRI in defining cervical degeneration influences data interpretation. Few previous studies have addressed this source of error. The relation and time sequence between cervical disc degeneration classified by MRI and cervical disc height decrease measured from radiographs is unclear. Methods: The MRI classification of degeneration was based on nucleus signal, prolaps identification and bone marrow signal. Two neuro-radiologists evaluated the MR-images independently in a blinded fashion. The radiographic disc height measurements were done by a new computer-assisted method compensating for image distortion and permitting comparison with normal level-, age- and gender-appropriate disc height. Results/conclusions: 1.Progressing disc degeneration classified from MRI is on average significantly associated with a decrease of disc height as measured from radiographs. Within each MRI defined category of degeneration measured disc heights, however, scatter in a wide range. 2.The inter-observer agreement between two neuro-radiologists in both defining degeneration and disc height by MRI was only moderate. Studies addressing questions related to cervical disc degeneration should take this into consideration.

  19. Premature Ventricular Contraction Coupling Interval Variability Destabilizes Cardiac Neuronal and Electrophysiological Control: Insights From Simultaneous Cardioneural Mapping.

    Science.gov (United States)

    Hamon, David; Rajendran, Pradeep S; Chui, Ray W; Ajijola, Olujimi A; Irie, Tadanobu; Talebi, Ramin; Salavatian, Siamak; Vaseghi, Marmar; Bradfield, Jason S; Armour, J Andrew; Ardell, Jeffrey L; Shivkumar, Kalyanam

    2017-04-01

    Variability in premature ventricular contraction (PVC) coupling interval (CI) increases the risk of cardiomyopathy and sudden death. The autonomic nervous system regulates cardiac electrical and mechanical indices, and its dysregulation plays an important role in cardiac disease pathogenesis. The impact of PVCs on the intrinsic cardiac nervous system, a neural network on the heart, remains unknown. The objective was to determine the effect of PVCs and CI on intrinsic cardiac nervous system function in generating cardiac neuronal and electric instability using a novel cardioneural mapping approach. In a porcine model (n=8), neuronal activity was recorded from a ventricular ganglion using a microelectrode array, and cardiac electrophysiological mapping was performed. Neurons were functionally classified based on their response to afferent and efferent cardiovascular stimuli, with neurons that responded to both defined as convergent (local reflex processors). Dynamic changes in neuronal activity were then evaluated in response to right ventricular outflow tract PVCs with fixed short, fixed long, and variable CI. PVC delivery elicited a greater neuronal response than all other stimuli ( P <0.001). Compared with fixed short and long CI, PVCs with variable CI had a greater impact on neuronal response ( P <0.05 versus short CI), particularly on convergent neurons ( P <0.05), as well as neurons receiving sympathetic ( P <0.05) and parasympathetic input ( P <0.05). The greatest cardiac electric instability was also observed after variable (short) CI PVCs. Variable CI PVCs affect critical populations of intrinsic cardiac nervous system neurons and alter cardiac repolarization. These changes may be critical for arrhythmogenesis and remodeling, leading to cardiomyopathy. © 2017 American Heart Association, Inc.

  20. Symmetric mixed states of n qubits: Local unitary stabilizers and entanglement classes

    Energy Technology Data Exchange (ETDEWEB)

    Lyons, David W.; Walck, Scott N. [Lebanon Valley College, Annville, Pennsylvania 17003 (United States)

    2011-10-15

    We classify, up to local unitary equivalence, local unitary stabilizer Lie algebras for symmetric mixed states of n qubits into six classes. These include the stabilizer types of the Werner states, the Greenberger-Horne-Zeilinger state and its generalizations, and Dicke states. For all but the zero algebra, we classify entanglement types (local unitary equivalence classes) of symmetric mixed states that have those stabilizers. We make use of the identification of symmetric density matrices with polynomials in three variables with real coefficients and apply the representation theory of SO(3) on this space of polynomials.

  1. Class D management implementation approach of the first orbital mission of the Earth Venture series

    Science.gov (United States)

    Wells, James E.; Scherrer, John; Law, Richard; Bonniksen, Chris

    2013-09-01

    A key element of the National Research Council's Earth Science and Applications Decadal Survey called for the creation of the Venture Class line of low-cost research and application missions within NASA (National Aeronautics and Space Administration). One key component of the architecture chosen by NASA within the Earth Venture line is a series of self-contained stand-alone spaceflight science missions called "EV-Mission". The first mission chosen for this competitively selected, cost and schedule capped, Principal Investigator-led opportunity is the CYclone Global Navigation Satellite System (CYGNSS). As specified in the defining Announcement of Opportunity, the Principal Investigator is held responsible for successfully achieving the science objectives of the selected mission and the management approach that he/she chooses to obtain those results has a significant amount of freedom as long as it meets the intent of key NASA guidance like NPR 7120.5 and 7123. CYGNSS is classified under NPR 7120.5E guidance as a Category 3 (low priority, low cost) mission and carries a Class D risk classification (low priority, high risk) per NPR 8705.4. As defined in the NPR guidance, Class D risk classification allows for a relatively broad range of implementation strategies. The management approach that will be utilized on CYGNSS is a streamlined implementation that starts with a higher risk tolerance posture at NASA and that philosophy flows all the way down to the individual part level.

  2. Class D Management Implementation Approach of the First Orbital Mission of the Earth Venture Series

    Science.gov (United States)

    Wells, James E.; Scherrer, John; Law, Richard; Bonniksen, Chris

    2013-01-01

    A key element of the National Research Council's Earth Science and Applications Decadal Survey called for the creation of the Venture Class line of low-cost research and application missions within NASA (National Aeronautics and Space Administration). One key component of the architecture chosen by NASA within the Earth Venture line is a series of self-contained stand-alone spaceflight science missions called "EV-Mission". The first mission chosen for this competitively selected, cost and schedule capped, Principal Investigator-led opportunity is the CYclone Global Navigation Satellite System (CYGNSS). As specified in the defining Announcement of Opportunity, the Principal Investigator is held responsible for successfully achieving the science objectives of the selected mission and the management approach that he/she chooses to obtain those results has a significant amount of freedom as long as it meets the intent of key NASA guidance like NPR 7120.5 and 7123. CYGNSS is classified under NPR 7120.5E guidance as a Category 3 (low priority, low cost) mission and carries a Class D risk classification (low priority, high risk) per NPR 8705.4. As defined in the NPR guidance, Class D risk classification allows for a relatively broad range of implementation strategies. The management approach that will be utilized on CYGNSS is a streamlined implementation that starts with a higher risk tolerance posture at NASA and that philosophy flows all the way down to the individual part level.

  3. Review: electrophysiology of basal ganglia and cortex in models of Parkinson disease.

    Science.gov (United States)

    Ellens, Damien J; Leventhal, Daniel K

    2013-01-01

    Incomplete understanding of the systems-level pathophysiology of Parkinson Disease (PD) remains a significant barrier to improving its treatment. Substantial progress has been made, however, due to the availability of neurotoxins that selectively target monoaminergic (in particular, dopaminergic) neurons. This review discusses the in vivo electrophysiology of basal ganglia (BG), thalamic, and cortical regions after dopamine-depleting lesions. These include firing rate changes, neuronal burst-firing, neuronal oscillations, and neuronal synchrony that result from a combination of local microanatomic changes and network-level interactions. While much is known of the clinical and electrophysiological phenomenology of dopamine loss, a critical gap in our conception of PD pathophysiology is the link between them. We discuss potential mechanisms by which these systems-level electrophysiological changes may emerge, as well as how they may relate to clinical parkinsonism. Proposals for an updated understanding of BG function are reviewed, with an emphasis on how emerging frameworks will guide future research into the pathophysiology and treatment of PD.

  4. Breadboard Amplifier: Building and Using Simple Electrophysiology Equipment.

    Science.gov (United States)

    Crisp, Kevin M; Lin, Hunter; Prosper, Issa

    2016-01-01

    Electrophysiology is a valuable skill for the neuroscientist, but the learning curve for students can be steep. Here we describe a very simple electromyography (EMG) amplifier that can be built from scratch by students with no electronics experience in about 30 minutes, making it ideal for incorporating into a laboratory activity. With few parts and no adjustments except the gain, students can begin physiology experiments quickly while having the satisfaction of having built the equipment themselves. Because the output of the circuit goes to a computer sound card, students can listen to electrophysiological activity as they see it on the computer screen, a feature many of our students greatly appreciated. Various applications are discussed, including dual channel recording, using streaming media platforms with remote lab partners and acquiring data in the field on a smart phone. Our students reported that they enjoyed being able to build a working device and using it to record from their own muscles.

  5. Mappings on Neutrosophic Soft Classes

    Directory of Open Access Journals (Sweden)

    Shawkat Alkhazaleh

    2014-03-01

    Full Text Available In 1995 Smarandache introduced the concept of neutrosophic set which is a mathematical tool for handling problems involving imprecise, indeterminacy and inconsistent data. In 2013 Maji introduced the concept of neutrosophic soft set theory as a general mathematical tool for dealing with uncertainty. In this paper we define the notion of a mapping on classes where the neutrosophic soft classes are collections of neutrosophic soft set. We also define and study the properties of neutrosophic soft images and neutrosophic soft inverse images of neutrosophic soft sets.

  6. Electrophysiological mapping of novel prefrontal - cerebellar pathways

    Directory of Open Access Journals (Sweden)

    Thomas C Watson

    2009-08-01

    Full Text Available Whilst the cerebellum is predominantly considered a sensorimotor control structure, accumulating evidence suggests that it may also subserve non motor functions during cognition. However, this possibility is not universally accepted, not least because the nature and pattern of links between higher cortical structures and the cerebellum are poorly characterized. We have therefore used in vivo electrophysiological methods in anaesthetized rats to directly investigate connectivity between the medial prefrontal cortex (prelimbic subdivision, PrL and the cerebellum. Stimulation of deep layers of PrL evoked distinct field potentials in the cerebellar cortex with a mean latency to peak of approximately 35ms. These responses showed a well-defined topography, and were maximal in lobule VII of the contralateral vermis (a known oculomotor centre; they were not attenuated by local anesthesia of the overlying M2 motor cortex, though M2 stimulation did evoke field potentials in lobule VII with a shorter latency. Single-unit recordings showed that prelimbic cortical stimulation elicits complex spikes in lobule VII Purkinje cells, indicating transmission via a previously undescribed cerebro-olivocerebellar pathway. Our results therefore establish a physiological basis for communication between PrL and the cerebellum. The role(s of this pathway remain to be resolved, but presumably relate to control of eye movements and/or distributed networks associated with integrated prefrontal cortical functions.

  7. Distinct electrophysiological potentials for intention in action and prior intention for action

    DEFF Research Database (Denmark)

    Vinding, Mikkel C; Jensen, Mads; Overgaard, Morten

    2014-01-01

    The role of conscious intention in relation to motoric movements has become a major topic of investigation in neuroscience. Traditionally, reports of conscious intention have been compared to various features of the readiness-potential (RP) – an electrophysiological signal that appears before...... electrophysiological “intention potential” above the mid-frontal areas at the time participants formed a distal intention. This potential was only found when the distal intention was self-paced and not when the intention was formed in response to an external cue....

  8. Flexible Word Classes

    DEFF Research Database (Denmark)

    • First major publication on the phenomenon • Offers cross-linguistic, descriptive, and diverse theoretical approaches • Includes analysis of data from different language families and from lesser studied languages This book is the first major cross-linguistic study of 'flexible words', i.e. words...... that cannot be classified in terms of the traditional lexical categories Verb, Noun, Adjective or Adverb. Flexible words can - without special morphosyntactic marking - serve in functions for which other languages must employ members of two or more of the four traditional, 'specialised' word classes. Thus......, flexible words are underspecified for communicative functions like 'predicating' (verbal function), 'referring' (nominal function) or 'modifying' (a function typically associated with adjectives and e.g. manner adverbs). Even though linguists have been aware of flexible world classes for more than...

  9. Peripheral Neuropathy – Clinical and Electrophysiological Considerations

    Science.gov (United States)

    Chung, Tae; Prasad, Kalpana; Lloyd, Thomas E.

    2013-01-01

    This article is a primer on the pathophysiology and clinical evaluation of peripheral neuropathy for the radiologist. Magnetic resonance neurography (MRN) has utility in the diagnosis of many focal peripheral nerve lesions. When combined with history, examination, electrophysiology, and laboratory data, future advancements in high-field MRN may play an increasingly important role in the evaluation of patients with peripheral neuropathy. PMID:24210312

  10. When do latent class models overstate accuracy for diagnostic and other classifiers in the absence of a gold standard?

    Science.gov (United States)

    Spencer, Bruce D

    2012-06-01

    Latent class models are increasingly used to assess the accuracy of medical diagnostic tests and other classifications when no gold standard is available and the true state is unknown. When the latent class is treated as the true class, the latent class models provide measures of components of accuracy including specificity and sensitivity and their complements, type I and type II error rates. The error rates according to the latent class model differ from the true error rates, however, and empirical comparisons with a gold standard suggest the true error rates often are larger. We investigate conditions under which the true type I and type II error rates are larger than those provided by the latent class models. Results from Uebersax (1988, Psychological Bulletin 104, 405-416) are extended to accommodate random effects and covariates affecting the responses. The results are important for interpreting the results of latent class analyses. An error decomposition is presented that incorporates an error component from invalidity of the latent class model. © 2011, The International Biometric Society.

  11. Resuscitation great. Luigi Galvani and the foundations of electrophysiology.

    Science.gov (United States)

    Cajavilca, Christian; Varon, Joseph; Sternbach, George L

    2009-02-01

    Luigi Galvani became one of the greatest scientists of the 18th century with his research and the development of his theory on animal electricity. His work was appreciated by many scientists. Nevertheless, it gave rise to one of the most passionate scientific debates in history when Alessandro Volta postulated that Galvani had confused intrinsic animal electricity with small currents produced by metals. This debate would result in the creation of electrophysiology, electromagnetism, electrochemistry and the electrical battery. Galvani responded to each of the postulated theories of Volta giving irrefutable proof of the involvement of electricity in the contraction of muscles. However, his work was subsequently abandoned and silenced for many years but his ideas and theories were finally confirmed by the creation of new instruments and the interest of new scientists who helped position Galvani as the father of electrophysiology.

  12. Classes and Theories of Trees Associated with a Class Of Linear Orders

    DEFF Research Database (Denmark)

    Goranko, Valentin; Kellerman, Ruaan

    2011-01-01

    Given a class of linear order types C, we identify and study several different classes of trees, naturally associated with C in terms of how the paths in those trees are related to the order types belonging to C. We investigate and completely determine the set-theoretic relationships between...... these classes of trees and between their corresponding first-order theories. We then obtain some general results about the axiomatization of the first-order theories of some of these classes of trees in terms of the first-order theory of the generating class C, and indicate the problems obstructing such general...... results for the other classes. These problems arise from the possible existence of nondefinable paths in trees, that need not satisfy the first-order theory of C, so we have started analysing first order definable and undefinable paths in trees....

  13. Just-in-time adaptive classifiers-part II: designing the classifier.

    Science.gov (United States)

    Alippi, Cesare; Roveri, Manuel

    2008-12-01

    Aging effects, environmental changes, thermal drifts, and soft and hard faults affect physical systems by changing their nature and behavior over time. To cope with a process evolution adaptive solutions must be envisaged to track its dynamics; in this direction, adaptive classifiers are generally designed by assuming the stationary hypothesis for the process generating the data with very few results addressing nonstationary environments. This paper proposes a methodology based on k-nearest neighbor (NN) classifiers for designing adaptive classification systems able to react to changing conditions just-in-time (JIT), i.e., exactly when it is needed. k-NN classifiers have been selected for their computational-free training phase, the possibility to easily estimate the model complexity k and keep under control the computational complexity of the classifier through suitable data reduction mechanisms. A JIT classifier requires a temporal detection of a (possible) process deviation (aspect tackled in a companion paper) followed by an adaptive management of the knowledge base (KB) of the classifier to cope with the process change. The novelty of the proposed approach resides in the general framework supporting the real-time update of the KB of the classification system in response to novel information coming from the process both in stationary conditions (accuracy improvement) and in nonstationary ones (process tracking) and in providing a suitable estimate of k. It is shown that the classification system grants consistency once the change targets the process generating the data in a new stationary state, as it is the case in many real applications.

  14. SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition.

    Science.gov (United States)

    Melvin, Iain; Ie, Eugene; Kuang, Rui; Weston, Jason; Stafford, William Noble; Leslie, Christina

    2007-05-22

    Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at http://svm-fold.c2b2.columbia.edu. Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach

  15. Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    PANIGRAHY, P. S.

    2018-02-01

    Full Text Available Data driven approach for multi-class fault diagnosis of induction motor using MCSA at steady state condition is a complex pattern classification problem. This investigation has exploited the built-in ensemble process of non-iterative classifiers to resolve the most challenging issues in this area, including bearing and stator fault detection. Non-iterative techniques exhibit with an average 15% of increased fault classification accuracy against their iterative counterparts. Particularly RF has shown outstanding performance even at less number of training samples and noisy feature space because of its distributive feature model. The robustness of the results, backed by the experimental verification shows that the non-iterative individual classifiers like RF is the optimum choice in the area of automatic fault diagnosis of induction motor.

  16. Self-organizing map classifier for stressed speech recognition

    Science.gov (United States)

    Partila, Pavol; Tovarek, Jaromir; Voznak, Miroslav

    2016-05-01

    This paper presents a method for detecting speech under stress using Self-Organizing Maps. Most people who are exposed to stressful situations can not adequately respond to stimuli. Army, police, and fire department occupy the largest part of the environment that are typical of an increased number of stressful situations. The role of men in action is controlled by the control center. Control commands should be adapted to the psychological state of a man in action. It is known that the psychological changes of the human body are also reflected physiologically, which consequently means the stress effected speech. Therefore, it is clear that the speech stress recognizing system is required in the security forces. One of the possible classifiers, which are popular for its flexibility, is a self-organizing map. It is one type of the artificial neural networks. Flexibility means independence classifier on the character of the input data. This feature is suitable for speech processing. Human Stress can be seen as a kind of emotional state. Mel-frequency cepstral coefficients, LPC coefficients, and prosody features were selected for input data. These coefficients were selected for their sensitivity to emotional changes. The calculation of the parameters was performed on speech recordings, which can be divided into two classes, namely the stress state recordings and normal state recordings. The benefit of the experiment is a method using SOM classifier for stress speech detection. Results showed the advantage of this method, which is input data flexibility.

  17. Electrophysiological studies in thyrotoxicosis with and without associated sick sinus syndrome

    International Nuclear Information System (INIS)

    Talwar, K.K.; Gupta, V.; Kaul, U.; Ahuja, M.M.; Bhatia, M.L.

    1987-01-01

    Electrophysiological studies in 13 patients with thyrotoxicosis (5 men and 8 women, aged 17 to 76 years) are reported. Five patients presented with features of sick sinus syndrome (SSS) (Group A) while the remaining 8 patients (Group B) had no detectable cardiovascular abnormality. Sinus node function (corrected sinus node recovery and sinoatrial conduction time) was abnormal in all Group A but normal in Group B patients. Intra-atrial, artioventricular (AV) nodal, and infranodal conduction time and effective refractory period of atrium were normal in all patients in both groups. Effective refractory period of AV node was decreased in 6 patients (3 in each group). All Group A patients received radioiodine with complete clinical remission of sick sinus state in 4 subjects. Repeat electrophysiological studies in two of these patients, 6 and 12 months after treatment, showed complete normalization of sinus node function. This is the first reported electrophysiological study documenting the occurrence of SSS in thyrotoxicosis reversed by effective antithyroid treatment. We suggest that attempts should be made to identify underlying thyrotoxicosis in all patients with SSS, especially in the older age group. Appropriate medical treatment may prevent unnecessary implantation of permanent pacemakers in such patients

  18. Electrophysiological evidence for enhanced representation of food stimuli in working memory.

    Science.gov (United States)

    Rutters, Femke; Kumar, Sanjay; Higgs, Suzanne; Humphreys, Glyn W

    2015-02-01

    Studies from our laboratory have shown that, relative to neutral objects, food-related objects kept in working memory (WM) are particularly effective in guiding attention to food stimuli (Higgs et al. in Appetite, 2012). Here, we used electrophysiological measurements to investigate the neural representation of food versus non-food items in WM. Subjects were presented with a cue (food or non-food item) to either attend to or hold in WM. Subsequently, they had to search for a target, while the target and distractor were each flanked by a picture of a food or non-food item. Behavioural data showed that a food cue held in WM modulated the deployment of visual attention to a search target more than a non-food cue, even though the cue was irrelevant for target selection. Electrophysiological measures of attention, memory and retention of memory (the P3, LPP and SPCN components) were larger when food was kept in WM, compared to non-food items. No such effect was observed in a priming task, when the initial cue was merely identified. Overall, our electrophysiological data are consistent with the suggestion that food stimuli are particularly strongly represented in the WM system.

  19. Electrophysiological Monitoring of Injury ProgressionIn the Rat Cerebellar Cortex

    Directory of Open Access Journals (Sweden)

    Gokhan eOrdek

    2014-10-01

    Full Text Available The changes of excitability in affected neural networks can be used as a marker to study the temporal course of traumatic brain injury (TBI. The cerebellum is an ideal platform to study brain injury mechanisms at the network level using the electrophysiological methods. Within its crystalline morphology, the cerebellar cortex contains highly organized topographical subunits that are defined by two main inputs, the climbing and mossy fibers. Here we demonstrate the use of cerebellar evoked potentials (EPs mediated through these afferent systems for monitoring the injury progression in a rat model of fluid percussion injury (FPI. A mechanical tap on the dorsal hand was used as a stimulus, and EPs were recorded from the paramedian lobule (PML of the posterior cerebellum via multi-electrode arrays (MEA. Post-injury evoked response amplitudes (EPAs were analyzed on a daily basis for one week and compared with pre-injury values. We found a trend of consistently decreasing EPAs in all nine animals, losing as much as 72±4% of baseline amplitudes measured before the injury. Notably, our results highlighted two particular time windows; the first 24 hours of injury in the acute period and day-3 to day-7 in the delayed period where the largest drops (~50% and 24% were observed in the EPAs. In addition, cross-correlations of spontaneous signals between electrode pairs declined (from 0.47±0.1 to 0.35±0.04, p<0.001 along with the EPAs throughout the week of injury. In support of the electrophysiological findings, immunohistochemical analysis at day-7 post-injury showed detectable Purkinje cell loss at low FPI pressures and more with the largest pressures used. Our results suggest that sensory evoked potentials recorded from the cerebellar surface can be a useful technique to monitor the course of cerebellar injury and identify the phases of injury progression even at mild levels.

  20. Existence of a sex pheromone in Triatoma infestans (Hemiptera: Reduvidae: II. Electrophysiological correlates

    Directory of Open Access Journals (Sweden)

    Maria G. de Brito Sanchez

    1995-10-01

    Full Text Available The stimulus provided by a copulating pair of Triatoma infestans significantly affects the electrical activity of the nervous system of Triatoma infestans. Electrophysiological recordings were perfomed on stationary adult males presented with stimuli of an air current carrying odors from males, females, non-copulating pairs and mating pairs. The electrophysiological response was characterized by the low frequency occurrence of biphasic compound impulses. A significant increase in the frequency of the impulses occurred in stationary males when exposed to air currents of mating pairs, when compared to that evoked by a clean air stream. Analysis of the time course of the assays, showed that the electrophisiological activity during the copula was higher than prior to or after copula. The electrophysiological evidence presented here strongly supports the existence of pheromone(s released by one or both sexes during mating and which is perceived by male chemoreceptors located on the antennae.

  1. Premature Ventricular Contraction Coupling Interval Variability Destabilizes Cardiac Neuronal and Electrophysiological Control: Insights from Simultaneous Cardio-Neural Mapping

    Science.gov (United States)

    Hamon, David; Rajendran, Pradeep S.; Chui, Ray W.; Ajijola, Olujimi A.; Irie, Tadanobu; Talebi, Ramin; Salavatian, Siamak; Vaseghi, Marmar; Bradfield, Jason S.; Armour, J. Andrew; Ardell, Jeffrey L.; Shivkumar, Kalyanam

    2017-01-01

    Background Variability in premature ventricular contraction (PVC) coupling interval (CI) increases the risk of cardiomyopathy and sudden death. The autonomic nervous system regulates cardiac electrical and mechanical indices, and its dysregulation plays an important role in cardiac disease pathogenesis. The impact of PVCs on the intrinsic cardiac nervous system (ICNS), a neural network on the heart, remains unknown. The objective was to determine the effect of PVCs and CI on ICNS function in generating cardiac neuronal and electrical instability using a novel cardio-neural mapping approach. Methods and Results In a porcine model (n=8) neuronal activity was recorded from a ventricular ganglion using a microelectrode array, and cardiac electrophysiological mapping was performed. Neurons were functionally classified based on their response to afferent and efferent cardiovascular stimuli, with neurons that responded to both defined as convergent (local reflex processors). Dynamic changes in neuronal activity were then evaluated in response to right ventricular outflow tract PVCs with fixed short, fixed long, and variable CI. PVC delivery elicited a greater neuronal response than all other stimuli (P<0.001). Compared to fixed short and long CI, PVCs with variable CI had a greater impact on neuronal response (P<0.05 versus short CI), particularly on convergent neurons (P<0.05), as well as neurons receiving sympathetic (P<0.05) and parasympathetic input (P<0.05). The greatest cardiac electrical instability was also observed following variable (short) CI PVCs. Conclusions Variable CI PVCs affect critical populations of ICNS neurons and alter cardiac repolarization. These changes may be critical for arrhythmogenesis and remodeling leading to cardiomyopathy. PMID:28408652

  2. Electrophysiological studies in healthy subjects involving caffeine

    OpenAIRE

    Carvalho, Mamede de; Marcelino, Erica; Mendonça, Alexandre de

    2010-01-01

    Copyright ©2012 IOS Press All rights reserved. We review the electrophysiological studies concerning the effects of caffeine on muscle, lower and upper motor neuron excitability and cognition. Several different methods have been used, such as electromyography, recruitment analysis, H-reflex, transcranial magnetic stimulation (TMS), electroencephalography and event-related potentials. The positive effect of caffeine on vigilance, attention, speed of reaction, information processing and arou...

  3. Automatic Parameterization Strategy for Cardiac Electrophysiology Simulations

    OpenAIRE

    Costa, Caroline Mendonca; Hoetzl, Elena; Rocha, Bernardo Martins; Prassl, Anton J; Plank, Gernot

    2013-01-01

    Driven by recent advances in medical imaging, image segmentation and numerical techniques, computer models of ventricular electrophysiology account for increasingly finer levels of anatomical and biophysical detail. However, considering the large number of model parameters involved parameterization poses a major challenge. A minimum requirement in combined experimental and modeling studies is to achieve good agreement in activation and repolarization sequences between model and experiment or ...

  4. Asymptotic performance of regularized quadratic discriminant analysis based classifiers

    KAUST Repository

    Elkhalil, Khalil

    2017-12-13

    This paper carries out a large dimensional analysis of the standard regularized quadratic discriminant analysis (QDA) classifier designed on the assumption that data arise from a Gaussian mixture model. The analysis relies on fundamental results from random matrix theory (RMT) when both the number of features and the cardinality of the training data within each class grow large at the same pace. Under some mild assumptions, we show that the asymptotic classification error converges to a deterministic quantity that depends only on the covariances and means associated with each class as well as the problem dimensions. Such a result permits a better understanding of the performance of regularized QDA and can be used to determine the optimal regularization parameter that minimizes the misclassification error probability. Despite being valid only for Gaussian data, our theoretical findings are shown to yield a high accuracy in predicting the performances achieved with real data sets drawn from popular real data bases, thereby making an interesting connection between theory and practice.

  5. Electrophysiological evidence for phenomenal consciousness.

    Science.gov (United States)

    Revonsuo, Antti; Koivisto, Mika

    2010-09-01

    Abstract Recent evidence from event-related brain potentials (ERPs) lends support to two central theses in Lamme's theory. The earliest ERP correlate of visual consciousness appears over posterior visual cortex around 100-200 ms after stimulus onset. Its scalp topography and time window are consistent with recurrent processing in the visual cortex. This electrophysiological correlate of visual consciousness is mostly independent of later ERPs reflecting selective attention and working memory functions. Overall, the ERP evidence supports the view that phenomenal consciousness of a visual stimulus emerges earlier than access consciousness, and that attention and awareness are served by distinct neural processes.

  6. Two classes of metric spaces

    Directory of Open Access Journals (Sweden)

    Isabel Garrido

    2016-04-01

    Full Text Available The class of metric spaces (X,d known as small-determined spaces, introduced by Garrido and Jaramillo, are properly defined by means of some type of real-valued Lipschitz functions on X. On the other hand, B-simple metric spaces introduced by Hejcman are defined in terms of some kind of bornologies of bounded subsets of X. In this note we present a common framework where both classes of metric spaces can be studied which allows us to see not only the relationships between them but also to obtain new internal characterizations of these metric properties.

  7. Software Defined Cyberinfrastructure

    Energy Technology Data Exchange (ETDEWEB)

    Foster, Ian; Blaiszik, Ben; Chard, Kyle; Chard, Ryan

    2017-07-17

    Within and across thousands of science labs, researchers and students struggle to manage data produced in experiments, simulations, and analyses. Largely manual research data lifecycle management processes mean that much time is wasted, research results are often irreproducible, and data sharing and reuse remain rare. In response, we propose a new approach to data lifecycle management in which researchers are empowered to define the actions to be performed at individual storage systems when data are created or modified: actions such as analysis, transformation, copying, and publication. We term this approach software-defined cyberinfrastructure because users can implement powerful data management policies by deploying rules to local storage systems, much as software-defined networking allows users to configure networks by deploying rules to switches.We argue that this approach can enable a new class of responsive distributed storage infrastructure that will accelerate research innovation by allowing any researcher to associate data workflows with data sources, whether local or remote, for such purposes as data ingest, characterization, indexing, and sharing. We report on early experiments with this approach in the context of experimental science, in which a simple if-trigger-then-action (IFTA) notation is used to define rules.

  8. History of Bioelectrical Study and the Electrophysiology of the Primo Vascular System

    Directory of Open Access Journals (Sweden)

    Sang Hyun Park

    2013-01-01

    Full Text Available Background. Primo vascular system is a new anatomical structure whose research results have reported the possibility of a new circulatory system similar to the blood vascular system and cells. Electrophysiology, which measures and analyzes bioelectrical signals tissues and cells, is an important research area for investigating the function of tissues and cells. The bioelectrical study of the primo vascular system has been reported by using modern techniques since the early 1960s by Bonghan Kim. This paper reviews the research result of the electrophysiological study of the primo vascular system for the discussion of the circulatory function. We hope it would help to study the electrophysiology of the primo vascular system for researchers. This paper will use the following exchangeable expressions: Kyungrak system = Bonghan system = Bonghan circulatory system = primo vascular system = primo system; Bonghan corpuscle = primo node; Bonghan duct = primo vessel. We think that objective descriptions of reviewed papers are more important than unified expressions when citing the papers. That said, this paper will unify the expressions of the primo vascular system.

  9. Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method.

    Science.gov (United States)

    Zhang, Jian-Hua; Peng, Xiao-Di; Liu, Hua; Raisch, Jörg; Wang, Ru-Bin

    2013-12-01

    The human operator's ability to perform their tasks can fluctuate over time. Because the cognitive demands of the task can also vary it is possible that the capabilities of the operator are not sufficient to satisfy the job demands. This can lead to serious errors when the operator is overwhelmed by the task demands. Psychophysiological measures, such as heart rate and brain activity, can be used to monitor operator cognitive workload. In this paper, the most influential psychophysiological measures are extracted to characterize Operator Functional State (OFS) in automated tasks under a complex form of human-automation interaction. The fuzzy c-mean (FCM) algorithm is used and tested for its OFS classification performance. The results obtained have shown the feasibility and effectiveness of the FCM algorithm as well as the utility of the selected input features for OFS classification. Besides being able to cope with nonlinearity and fuzzy uncertainty in the psychophysiological data it can provide information about the relative importance of the input features as well as the confidence estimate of the classification results. The OFS pattern classification method developed can be incorporated into an adaptive aiding system in order to enhance the overall performance of a large class of safety-critical human-machine cooperative systems.

  10. Electrophysiological assessment in patients with Mobius syndrome and clumsiness.

    NARCIS (Netherlands)

    Verzijl, H.T.F.M.; Padberg, G.W.A.M.; Zwarts, M.J.

    2005-01-01

    The authors studied the nature of clumsiness in Mobius syndrome in terms of motor or sensory deficits, and sought to clarify the pathophysiological mechanism of the syndrome. Standardized electrophysiologic studies were conducted, with special emphasis on the long motor and sensory tracts and

  11. Optimizing Nanoelectrode Arrays for Scalable Intracellular Electrophysiology.

    Science.gov (United States)

    Abbott, Jeffrey; Ye, Tianyang; Ham, Donhee; Park, Hongkun

    2018-03-20

    Electrode technology for electrophysiology has a long history of innovation, with some decisive steps including the development of the voltage-clamp measurement technique by Hodgkin and Huxley in the 1940s and the invention of the patch clamp electrode by Neher and Sakmann in the 1970s. The high-precision intracellular recording enabled by the patch clamp electrode has since been a gold standard in studying the fundamental cellular processes underlying the electrical activities of neurons and other excitable cells. One logical next step would then be to parallelize these intracellular electrodes, since simultaneous intracellular recording from a large number of cells will benefit the study of complex neuronal networks and will increase the throughput of electrophysiological screening from basic neurobiology laboratories to the pharmaceutical industry. Patch clamp electrodes, however, are not built for parallelization; as for now, only ∼10 patch measurements in parallel are possible. It has long been envisioned that nanoscale electrodes may help meet this challenge. First, nanoscale electrodes were shown to enable intracellular access. Second, because their size scale is within the normal reach of the standard top-down fabrication, the nanoelectrodes can be scaled into a large array for parallelization. Third, such a nanoelectrode array can be monolithically integrated with complementary metal-oxide semiconductor (CMOS) electronics to facilitate the large array operation and the recording of the signals from a massive number of cells. These are some of the central ideas that have motivated the research activity into nanoelectrode electrophysiology, and these past years have seen fruitful developments. This Account aims to synthesize these findings so as to provide a useful reference. Summing up from the recent studies, we will first elucidate the morphology and associated electrical properties of the interface between a nanoelectrode and a cellular membrane

  12. Finger vein identification using fuzzy-based k-nearest centroid neighbor classifier

    Science.gov (United States)

    Rosdi, Bakhtiar Affendi; Jaafar, Haryati; Ramli, Dzati Athiar

    2015-02-01

    In this paper, a new approach for personal identification using finger vein image is presented. Finger vein is an emerging type of biometrics that attracts attention of researchers in biometrics area. As compared to other biometric traits such as face, fingerprint and iris, finger vein is more secured and hard to counterfeit since the features are inside the human body. So far, most of the researchers focus on how to extract robust features from the captured vein images. Not much research was conducted on the classification of the extracted features. In this paper, a new classifier called fuzzy-based k-nearest centroid neighbor (FkNCN) is applied to classify the finger vein image. The proposed FkNCN employs a surrounding rule to obtain the k-nearest centroid neighbors based on the spatial distributions of the training images and their distance to the test image. Then, the fuzzy membership function is utilized to assign the test image to the class which is frequently represented by the k-nearest centroid neighbors. Experimental evaluation using our own database which was collected from 492 fingers shows that the proposed FkNCN has better performance than the k-nearest neighbor, k-nearest-centroid neighbor and fuzzy-based-k-nearest neighbor classifiers. This shows that the proposed classifier is able to identify the finger vein image effectively.

  13. nRC: non-coding RNA Classifier based on structural features.

    Science.gov (United States)

    Fiannaca, Antonino; La Rosa, Massimo; La Paglia, Laura; Rizzo, Riccardo; Urso, Alfonso

    2017-01-01

    Non-coding RNA (ncRNA) are small non-coding sequences involved in gene expression regulation of many biological processes and diseases. The recent discovery of a large set of different ncRNAs with biologically relevant roles has opened the way to develop methods able to discriminate between the different ncRNA classes. Moreover, the lack of knowledge about the complete mechanisms in regulative processes, together with the development of high-throughput technologies, has required the help of bioinformatics tools in addressing biologists and clinicians with a deeper comprehension of the functional roles of ncRNAs. In this work, we introduce a new ncRNA classification tool, nRC (non-coding RNA Classifier). Our approach is based on features extraction from the ncRNA secondary structure together with a supervised classification algorithm implementing a deep learning architecture based on convolutional neural networks. We tested our approach for the classification of 13 different ncRNA classes. We obtained classification scores, using the most common statistical measures. In particular, we reach an accuracy and sensitivity score of about 74%. The proposed method outperforms other similar classification methods based on secondary structure features and machine learning algorithms, including the RNAcon tool that, to date, is the reference classifier. nRC tool is freely available as a docker image at https://hub.docker.com/r/tblab/nrc/. The source code of nRC tool is also available at https://github.com/IcarPA-TBlab/nrc.

  14. Subthalamic stimulation: toward a simplification of the electrophysiological procedure.

    Science.gov (United States)

    Fetter, Damien; Derrey, Stephane; Lefaucheur, Romain; Borden, Alaina; Wallon, David; Chastan, Nathalie; Maltete, David

    2016-06-01

    The aim of the present study was to assess the consequences of a simplification of the electrophysiological procedure on the post-operative clinical outcome after subthalamic nucleus implantation in Parkinson disease. Microelectrode recordings were performed on 5 parallel trajectories in group 1 and less than 5 trajectories in group 2. Clinical evaluations were performed 1 month before and 6 months after surgery. After surgery, the UPDRS III score in the off-drug/on-stimulation and on-drug/on-stimulation conditions significantly improved by 66,9% and 82%, respectively in group 1, and by 65.8% and 82.3% in group 2 (P<0.05). Meanwhile, the total number of words (P<0.05) significantly decreased for fluency tasks in both groups. Motor disability improvement and medication reduction were similar in both groups. Our results suggest that the electrophysiological procedure should be simplified as the team's experience increases.

  15. Generating prior probabilities for classifiers of brain tumours using belief networks

    Directory of Open Access Journals (Sweden)

    Arvanitis Theodoros N

    2007-09-01

    Full Text Available Abstract Background Numerous methods for classifying brain tumours based on magnetic resonance spectra and imaging have been presented in the last 15 years. Generally, these methods use supervised machine learning to develop a classifier from a database of cases for which the diagnosis is already known. However, little has been published on developing classifiers based on mixed modalities, e.g. combining imaging information with spectroscopy. In this work a method of generating probabilities of tumour class from anatomical location is presented. Methods The method of "belief networks" is introduced as a means of generating probabilities that a tumour is any given type. The belief networks are constructed using a database of paediatric tumour cases consisting of data collected over five decades; the problems associated with using this data are discussed. To verify the usefulness of the networks, an application of the method is presented in which prior probabilities were generated and combined with a classification of tumours based solely on MRS data. Results Belief networks were constructed from a database of over 1300 cases. These can be used to generate a probability that a tumour is any given type. Networks are presented for astrocytoma grades I and II, astrocytoma grades III and IV, ependymoma, pineoblastoma, primitive neuroectodermal tumour (PNET, germinoma, medulloblastoma, craniopharyngioma and a group representing rare tumours, "other". Using the network to generate prior probabilities for classification improves the accuracy when compared with generating prior probabilities based on class prevalence. Conclusion Bayesian belief networks are a simple way of using discrete clinical information to generate probabilities usable in classification. The belief network method can be robust to incomplete datasets. Inclusion of a priori knowledge is an effective way of improving classification of brain tumours by non-invasive methods.

  16. Electrophysiological Evaluation of People With Volatile Substance Addiction

    Directory of Open Access Journals (Sweden)

    Nurten Uzun

    2008-02-01

    Full Text Available OBJECTIVE: Scientific BACKGROUND: There is an increase in addiction of volatile substances in recent years. Miscellaneous electrophysiological pathological findings are determined in volatile substance abusers. OBJECTIVE: In this study, we aim to examine the neurologic effects of these substances by electrophysiologic methods. METHODS: Cases and METHOD: Twenty-three patients from Bakirkoy Psychiatry Hospital, Alcohol and Substance Addiction Research and Treatment Center were included in this study. Motor and sensory nerve conduction studies, somatosensorial, visual and auditory evoked potentials (SEP, VEP, BAEP as well as electroencephalography (EEG were studied in all 23 patients. The results were compared with the published data and the values of age matched 19 normal controls. RESULTS: RESULTS: In nerve conduction studies, there were pathological findings in 14 (60.9% cases, in three (13% mild sensorimotor polyneuropathy was determined. Tibial nerve motor distal latencies as well as median nerve sensorial and sural nerve distal latencies were longer in patients compared to controls (p<0.05. SEP findings were pathological in six (26.1% cases, VEP in two (8.7% cases and BAEP in eight (34.8% cases. Scalp SEP distal latency by tibial nerve stimulation as well as distal latencies of right and left V. wave, left III-V interpeak latency, right and left interpeak latencies and I-V interaural latency difference in BAEP were longer in abusers (p<0.05. Although it was not statistically significant, the ratio of pathological findings was higher if the exposure time was over 2 years. EEG was found to be normal in all patients. CONCLUSION: YORUM: Our results showed that toluene results in slowly progressive multifocal central nervous system damage and subclinical damage could be determined in early stages by electrophysiologic methods

  17. Electrophysiological biomarkers of epileptogenicity after traumatic brain injury.

    Science.gov (United States)

    Perucca, Piero; Smith, Gregory; Santana-Gomez, Cesar; Bragin, Anatol; Staba, Richard

    2018-06-05

    Post-traumatic epilepsy is the architype of acquired epilepsies, wherein a brain insult initiates an epileptogenic process culminating in an unprovoked seizure after weeks, months or years. Identifying biomarkers of such process is a prerequisite for developing and implementing targeted therapies aimed at preventing the development of epilepsy. Currently, there are no validated electrophysiological biomarkers of post-traumatic epileptogenesis. Experimental EEG studies using the lateral fluid percussion injury model have identified three candidate biomarkers of post-traumatic epileptogenesis: pathological high-frequency oscillations (HFOs, 80-300 Hz); repetitive HFOs and spikes (rHFOSs); and reduction in sleep spindle duration and dominant frequency at the transition from stage III to rapid eye movement sleep. EEG studies in humans have yielded conflicting data; recent evidence suggests that epileptiform abnormalities detected acutely after traumatic brain injury carry a significantly increased risk of subsequent epilepsy. Well-designed studies are required to validate these promising findings, and ultimately establish whether there are post-traumatic electrophysiological features which can guide the development of 'antiepileptogenic' therapies. Copyright © 2017. Published by Elsevier Inc.

  18. Pyramidal cell development: postnatal spinogenesis, dendritic growth, axon growth, and electrophysiology.

    Directory of Open Access Journals (Sweden)

    Guy eElston

    2014-08-01

    Full Text Available Here we review recent findings related to postnatal spinogenesis, dendritic and axon growth, pruning and electrophysiology of neocortical pyramidal cells in the developing primate brain. Pyramidal cells in sensory, association and executive cortex grow dendrites, spines and axons at different rates, and vary in the degree of pruning. Of particular note is the fact that pyramidal cells in primary visual area (V1 prune more spines than they grow during postnatal development, whereas those in inferotemporal (TEO and TE and granular prefrontal cortex (gPFC; Brodmann’s area 12 grow more than they prune. Moreover, pyramidal cells in TEO, TE and the gPFC continue to grow larger dendritic territories from birth into adulthood, replete with spines, whereas those in V1 become smaller during this time. The developmental profile of intrinsic axons also varies between cortical areas: those in V1, for example, undergo an early proliferation followed by pruning and local consolidation into adulthood, whereas those in area TE tend to establish their territory and consolidate it into adulthood with little pruning. We correlate the anatomical findings with the electrophysiological properties of cells in the different cortical areas, including membrane time constant, depolarizing sag, duration of individual action potentials, and spike-frequency adaptation. All of the electrophysiological variables ramped up before 7 months of age in V1, but continued to ramp up over a protracted period of time in area TE. These data suggest that the anatomical and electrophysiological profiles of pyramidal cells vary among cortical areas at birth, and continue to diverge into adulthood. Moreover, the data reveal that the use it or lose it notion of synaptic reinforcement may speak to only part of the story, use it but you still might lose it may be just as prevalent in the cerebral cortex.

  19. Subjective symptoms of carpal tunnel syndrome correlate more with psychological factors than electrophysiological severity

    Directory of Open Access Journals (Sweden)

    Firosh Khan

    2017-01-01

    Full Text Available Aim: Carpal tunnel syndrome (CTS is the most common entrapment neuropathy and is one of the most common requests for electrodiagnosis. We aimed to note the relationship of subjective symptom severity of CTS, with objective electrophysiological severity and psychological status of patients. Patients and Methods: One hundred and forty-four consecutive patients of CTS referred to neurophysiology laboratory of a tertiary care hospital over 1 year were prospectively studied. Boston CTS Assessment Questionnaire (BCTSAQ and visual analog scale (VAS were used to assess subjective symptom severity. Psychological status was assessed by Hospital Anxiety and Depression Scale (HADS. Electrophysiological severity of CTS was estimated by median motor distal latency and median to ulnar peak sensory latency difference across the wrist. Each parameter in both hands was scored from 0 to 3 depending on the severity grade, and a composite electrophysiological severity score (CEPSS was calculated for each patient by summing up the scores in both hands. Statistical analysis was done by Spearman's rank correlation test. Results: There was significant correlation of BCTSAQ with VAS (P = 0.001, HADS anxiety score (P < 0.001, and HADS depression score (P = 0.01. CEPSS had no significant correlation with VAS (P = 0.103, HADS anxiety score (P = 0.211, or HADS depression score (P = 0.55. CEPSS had a borderline correlation with BCTSAQ (P = 0.048. Conclusions: While the subjective symptoms of CTS are well correlated with psychological factors, their correlation with objective electrophysiological severity is weak. Hence, prompt treatment of psychological comorbidity is important in symptomatic management of CTS; decision about surgical intervention should be based on electrophysiological severity rather than symptom severity.

  20. Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers.

    Directory of Open Access Journals (Sweden)

    Muhammad Ahmad

    Full Text Available Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF, in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.g. SVM and KNN. The optimal samples are selected by first estimating the boundary of each class and then calculating the fuzziness-based distance between each sample and the estimated class boundaries. Those samples that are at smaller distances from the boundaries and have higher fuzziness are chosen as target candidates for the training set. Through detailed experimentation on three publically available datasets, we showed that when trained with the proposed sample selection framework, both classifiers achieved higher classification accuracy and lower processing time with the small amount of training data as opposed to the case where the training samples were selected randomly. Our experiments demonstrate the effectiveness of our proposed method, which equates favorably with the state-of-the-art methods.

  1. Fuzzy prototype classifier based on items and its application in recommender system

    Directory of Open Access Journals (Sweden)

    Mei Cai

    2017-01-01

    Full Text Available Currently, recommender systems (RS are incorporating implicit information from social circle of the Internet. The implicit social information in human mind is not easy to reflect in appropriate decision making techniques. This paper consists of 2 contributions. First, we develop an item-based prototype classifier (IPC in which a prototype represents a social circlers preferences as a pattern classification technique. We assume the social circle which distinguishes with others by the items their members like. The prototype structure of the classifier is defined by two2-dimensional matrices. We use information gain and OWA aggregator to construct a feature space. The item-based classifier assigns a new item to some prototypes with different prototypicalities. We reform a typical data setmIris data set in UCI Machine Learning Repository to verify our fuzzy prototype classifier. The second proposition of this paper is to give the application of IPC in recommender system to solve new item cold-start problems. We modify the dataset of MovieLens to perform experimental demonstrations of the proposed ideas.

  2. Classification of JERS-1 Image Mosaic of Central Africa Using A Supervised Multiscale Classifier of Texture Features

    Science.gov (United States)

    Saatchi, Sassan; DeGrandi, Franco; Simard, Marc; Podest, Erika

    1999-01-01

    In this paper, a multiscale approach is introduced to classify the Japanese Research Satellite-1 (JERS-1) mosaic image over the Central African rainforest. A series of texture maps are generated from the 100 m mosaic image at various scales. Using a quadtree model and relating classes at each scale by a Markovian relationship, the multiscale images are classified from course to finer scale. The results are verified at various scales and the evolution of classification is monitored by calculating the error at each stage.

  3. Classifying quantum entanglement through topological links

    Science.gov (United States)

    Quinta, Gonçalo M.; André, Rui

    2018-04-01

    We propose an alternative classification scheme for quantum entanglement based on topological links. This is done by identifying a nonrigid ring to a particle, attributing the act of cutting and removing a ring to the operation of tracing out the particle, and associating linked rings to entangled particles. This analogy naturally leads us to a classification of multipartite quantum entanglement based on all possible distinct links for a given number of rings. To determine all different possibilities, we develop a formalism that associates any link to a polynomial, with each polynomial thereby defining a distinct equivalence class. To demonstrate the use of this classification scheme, we choose qubit quantum states as our example of physical system. A possible procedure to obtain qubit states from the polynomials is also introduced, providing an example state for each link class. We apply the formalism for the quantum systems of three and four qubits and demonstrate the potential of these tools in a context of qubit networks.

  4. The sound of feelings: electrophysiological responses to emotional speech in alexithymia.

    Directory of Open Access Journals (Sweden)

    Katharina Sophia Goerlich

    Full Text Available Alexithymia is a personality trait characterized by difficulties in the cognitive processing of emotions (cognitive dimension and in the experience of emotions (affective dimension. Previous research focused mainly on visual emotional processing in the cognitive alexithymia dimension. We investigated the impact of both alexithymia dimensions on electrophysiological responses to emotional speech in 60 female subjects.During unattended processing, subjects watched a movie while an emotional prosody oddball paradigm was presented in the background. During attended processing, subjects detected deviants in emotional prosody. The cognitive alexithymia dimension was associated with a left-hemisphere bias during early stages of unattended emotional speech processing, and with generally reduced amplitudes of the late P3 component during attended processing. In contrast, the affective dimension did not modulate unattended emotional prosody perception, but was associated with reduced P3 amplitudes during attended processing particularly to emotional prosody spoken in high intensity.Our results provide evidence for a dissociable impact of the two alexithymia dimensions on electrophysiological responses during the attended and unattended processing of emotional prosody. The observed electrophysiological modulations are indicative of a reduced sensitivity to the emotional qualities of speech, which may be a contributing factor to problems in interpersonal communication associated with alexithymia.

  5. Slow and fast fatigable frog muscle fibres: electrophysiological and histochemical characteristics.

    Science.gov (United States)

    Vydevska-Chichova, M; Mileva, K; Todorova, R; Dimitrova, M; Radicheva, N

    2005-12-01

    Continuous activity of isolated frog gastrocnemius muscle fibres provoked by repetitive stimulation of 5 Hz was used as an experimental model for fatigue development in different fibre types. Parameter changes of the elicited intracellular action potentials and mechanical twitches during the period of uninterrupted activity were used as criteria for fatigue evaluation. Slow fatigable muscle fibre (SMF) and fast fatigable muscle fibre (FMF) types were distinguished depending on the duration of their uninterrupted activity, which was significantly longer in SMFs than in FMFs. The normalized changes of action potential amplitude and duration were significantly smaller in FMFs than in SMFs. The average twitch force and velocity of contraction and relaxation were significantly higher in FMFs than in SMFs. Myosin ATPase (mATPase) and succinate dehydrogenase activity were studied by histochemical assessment in order to validate the fibre type classification based on their electrophysiological characteristics. Based on the relative mATPase reactivity, the fibres of the studied muscle were classified as one of five different types (1-2, 2, 2-3, 3 and tonic). Smaller sized fibres (tonic and type 3) expressed higher succinate dehydrogenase activity than larger sized fibres (type 1-2, 2), which is related to the fatigue resistance. The differences between fatigue development in SMFs and FMFs during continuous activity were associated with fibre-type specific mATPase and succinate dehydrogenase activity.

  6. River recreation experience opportunities in two recreation opportunity spectrum (ROS) classes

    Science.gov (United States)

    Duane C. Wollmuth; John H. Schomaker; Lawrence C. Merriam

    1985-01-01

    The Recreation Opportunity Spectrum (ROS) system is used by the USDA Forest Service and USDI Bureau of Land Management for inventorying, classifying, and managing wildlands for recreation. Different ROS classes from the Colorado and Arkansas Rivers in Colorado were compared, using visitor survey data collected in 1979 and 1981, to see if the different classes offered...

  7. A Generalized Approach to Defining Item Discrimination for DCMs

    Science.gov (United States)

    Henson, Robert; DiBello, Lou; Stout, Bill

    2018-01-01

    Diagnostic classification models (DCMs, also known as cognitive diagnosis models) hold the promise of providing detailed classroom information about the skills a student has or has not mastered. Specifically, DCMs are special cases of constrained latent class models where classes are defined based on mastery/nonmastery of a set of attributes (or…

  8. Electrophysiological studies in healthy subjects involving caffeine.

    Science.gov (United States)

    de Carvalho, Mamede; Marcelino, Erica; de Mendonça, Alexandre

    2010-01-01

    We review the electrophysiological studies concerning the effects of caffeine on muscle, lower and upper motor neuron excitability and cognition. Several different methods have been used, such as electromyography, recruitment analysis, H-reflex, transcranial magnetic stimulation (TMS), electroencephalography and event-related potentials. The positive effect of caffeine on vigilance, attention, speed of reaction, information processing and arousal is supported by a number of electrophysiological studies. The evidence in favor of an increased muscle fiber resistance is not definitive, but higher or lower motor neuron excitability can occur as a consequence of a greater excitation of the descending input from the brainstem and upper motor neurons. TMS can address the influence of caffeine on the upper motor neuron. Previous studies showed that cortico-motor threshold and intracortical excitatory and inhibitory pathways are not influenced by caffeine. Nonetheless, our results indicate that cortical silent period (CSP) is reduced in resting muscles after caffeine consumption, when stimulating the motor cortex with intensities slightly above threshold. We present new data demonstrating that this effect is also observed in fatigued muscle. We conclude that CSP can be considered a surrogate marker of the effect of caffeine in the brain, in particular of its central ergogenic effect.

  9. Electrophysiological mechanisms of the SI SII SIII electrocardiographic morphology

    International Nuclear Information System (INIS)

    Bayes de Luna, A.; Carrio, I.; Subirana, M.T.; Torner, P.; Cosin, J.; Sagues, F.; Guindo, J.

    1987-01-01

    We studied three groups of individuals by means of spatial-velocity electrocardiograms and thallium-201 myocardial imaging to figure out the electrophysiological explanation of the SI SII SIII electrocardiographic morphology. We studied twelve healthy individuals without SI SII SIII, seven healthy individuals with SI SII SIII and fifteen patients with chronic obstructive pulmonary disease with SI SII SIII. The average values of the QRS-E and QRS-F intervals were higher in the second and third groups than in the first. One patient of the second group and thirteen of the third showed right ventricular enlargement. The slowing down of the right ventricular conduction explained the SI SII SIII morphology in normal individuals in more than half the cases. In patients with chronic obstructive pulmonary disease with SI SII SIII the conduction delay plays an important part in the electrogenesis of the right ventricular enlargement electrocardiographic morphology. We think that these observations can give further data about the electrophysiologic mechanism of the SI SII SIII morphology

  10. A fast learning method for large scale and multi-class samples of SVM

    Science.gov (United States)

    Fan, Yu; Guo, Huiming

    2017-06-01

    A multi-class classification SVM(Support Vector Machine) fast learning method based on binary tree is presented to solve its low learning efficiency when SVM processing large scale multi-class samples. This paper adopts bottom-up method to set up binary tree hierarchy structure, according to achieved hierarchy structure, sub-classifier learns from corresponding samples of each node. During the learning, several class clusters are generated after the first clustering of the training samples. Firstly, central points are extracted from those class clusters which just have one type of samples. For those which have two types of samples, cluster numbers of their positive and negative samples are set respectively according to their mixture degree, secondary clustering undertaken afterwards, after which, central points are extracted from achieved sub-class clusters. By learning from the reduced samples formed by the integration of extracted central points above, sub-classifiers are obtained. Simulation experiment shows that, this fast learning method, which is based on multi-level clustering, can guarantee higher classification accuracy, greatly reduce sample numbers and effectively improve learning efficiency.

  11. Novel Two-Step Classifier for Torsades de Pointes Risk Stratification from Direct Features

    Directory of Open Access Journals (Sweden)

    Jaimit Parikh

    2017-11-01

    Full Text Available While pre-clinical Torsades de Pointes (TdP risk classifiers had initially been based on drug-induced block of hERG potassium channels, it is now well established that improved risk prediction can be achieved by considering block of non-hERG ion channels. The current multi-channel TdP classifiers can be categorized into two classes. First, the classifiers that take as input the values of drug-induced block of ion channels (direct features. Second, the classifiers that are built on features extracted from output of the drug-induced multi-channel blockage simulations in the in-silico models (derived features. The classifiers built on derived features have thus far not consistently provided increased prediction accuracies, and hence casts doubt on the value of such approaches given the cost of including biophysical detail. Here, we propose a new two-step method for TdP risk classification, referred to as Multi-Channel Blockage at Early After Depolarization (MCB@EAD. In the first step, we classified the compound that produced insufficient hERG block as non-torsadogenic. In the second step, the role of non-hERG channels to modulate TdP risk are considered by constructing classifiers based on direct or derived features at critical hERG block concentrations that generates EADs in the computational cardiac cell models. MCB@EAD provides comparable or superior TdP risk classification of the drugs from the direct features in tests against published methods. TdP risk for the drugs highly correlated to the propensity to generate EADs in the model. However, the derived features of the biophysical models did not improve the predictive capability for TdP risk assessment.

  12. Electrophysiologic and cellular characteristics of cardiomyocytes after X-ray irradiation

    Energy Technology Data Exchange (ETDEWEB)

    Frieß, Johannes L., E-mail: johannes.friess@h-ab.de [University for Applied Sciences Aschaffenburg, biomems lab, Würzburger Straße 45, 63743 Aschaffenburg (Germany); Heselich, Anja [Technische Universität Darmstadt, Developmental Biology and Neurogenetics, Schnittspahnstraße 13, 64287 Darmstadt (Germany); Ritter, Sylvia [Helmholtz Institute for Heavy Ion Research (GSI), Biophysics Department, Planckstraße 1, 64291 Darmstadt (Germany); Haber, Angelina; Kaiser, Nicole; Layer, Paul G. [Technische Universität Darmstadt, Developmental Biology and Neurogenetics, Schnittspahnstraße 13, 64287 Darmstadt (Germany); Thielemann, Christiane [University for Applied Sciences Aschaffenburg, biomems lab, Würzburger Straße 45, 63743 Aschaffenburg (Germany)

    2015-07-15

    Highlights: • Electrophysiologic and cellular effects of X-rays on primary cardiac cell cultures. • X-ray doses between 0.5 and 7 Gy. • Higher beat rate at reduced field action potential durations 7 days after exposure. • More increased cell cycle checkpoint arrest in G2/M than in G1/S phase. • Induced DSBs were mostly repaired within 24 h after irradiation. - Abstract: The aim of this study was to investigate possible effects of ionizing irradiation on the electrophysiological functionality of cardiac myocytes in vitro. Primary chicken cardiomyocytes with spontaneous beating activity were irradiated with X-rays (dose range of 0.5–7 Gy). Functional alterations of cardiac cell cultures were evaluated up to 7 days after irradiation using microelectrode arrays. As examined endpoints, cell proliferation, apoptosis, reactive oxygen species (ROS) and DNA damage were evaluated. The beat rate of the cardiac networks increased in a dose-dependent manner over one week. The duration of single action potentials was slightly shortened. Additionally, we observed lower numbers of mitotic and S-phase cells at certain time points after irradiation. Also, the number of cells with γH2AX foci increased as a function of the dose. No significant changes in the level of ROS were detected. Induction of apoptosis was generally negligibly low. This is the first report to directly show alterations in cardiac electrophysiology caused by ionizing radiation, which were detectable up to one week after irradiation.

  13. Electrophysiologic and cellular characteristics of cardiomyocytes after X-ray irradiation

    International Nuclear Information System (INIS)

    Frieß, Johannes L.; Heselich, Anja; Ritter, Sylvia; Haber, Angelina; Kaiser, Nicole; Layer, Paul G.; Thielemann, Christiane

    2015-01-01

    Highlights: • Electrophysiologic and cellular effects of X-rays on primary cardiac cell cultures. • X-ray doses between 0.5 and 7 Gy. • Higher beat rate at reduced field action potential durations 7 days after exposure. • More increased cell cycle checkpoint arrest in G2/M than in G1/S phase. • Induced DSBs were mostly repaired within 24 h after irradiation. - Abstract: The aim of this study was to investigate possible effects of ionizing irradiation on the electrophysiological functionality of cardiac myocytes in vitro. Primary chicken cardiomyocytes with spontaneous beating activity were irradiated with X-rays (dose range of 0.5–7 Gy). Functional alterations of cardiac cell cultures were evaluated up to 7 days after irradiation using microelectrode arrays. As examined endpoints, cell proliferation, apoptosis, reactive oxygen species (ROS) and DNA damage were evaluated. The beat rate of the cardiac networks increased in a dose-dependent manner over one week. The duration of single action potentials was slightly shortened. Additionally, we observed lower numbers of mitotic and S-phase cells at certain time points after irradiation. Also, the number of cells with γH2AX foci increased as a function of the dose. No significant changes in the level of ROS were detected. Induction of apoptosis was generally negligibly low. This is the first report to directly show alterations in cardiac electrophysiology caused by ionizing radiation, which were detectable up to one week after irradiation

  14. The Social Psychology of Class and Classism

    Science.gov (United States)

    Lott, Bernice

    2012-01-01

    In the United States, one is born into a family that can be identified as working class, middle class, or affluent--divisions that denote status and power, as defined by access to resources. This article explores the relationships between social class membership and a wide array of personal and social daily life experiences. It concludes with a…

  15. Classifying Multi-Model Wheat Yield Impact Response Surfaces Showing Sensitivity to Temperature and Precipitation Change

    Science.gov (United States)

    Fronzek, Stefan; Pirttioja, Nina; Carter, Timothy R.; Bindi, Marco; Hoffmann, Holger; Palosuo, Taru; Ruiz-Ramos, Margarita; Tao, Fulu; Trnka, Miroslav; Acutis, Marco; hide

    2017-01-01

    Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (minus 2 to plus 9 degrees Centigrade) and precipitation (minus 50 to plus 50 percent). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the

  16. Reversible electrophysiological abnormalities in acute secondary hyperkalemic paralysis

    OpenAIRE

    Karkal R Naik; Aralikatte O Saroja; Mallikarjun S Khanpet

    2012-01-01

    Hyperkalemia manifests clinically with acute neuromuscular paralysis, which can simulate Guillain Barr? syndrome (GBS) and other causes of acute flaccid paralysis. Primary hyperkalemic paralysis occurs from genetic defects in the sodium channel, and secondary hyperkalemic paralysis (SHP) from diverse causes including renal dysfunction, potassium retaining drugs, Addison's disease, etc. Clinical characteristics of SHP have been addressed in a number of publications. However, electrophysiologic...

  17. Methods of generalizing and classifying layer structures of a special form

    Energy Technology Data Exchange (ETDEWEB)

    Viktorova, N P

    1981-09-01

    An examination is made of the problem of classifying structures represented by weighted multilayer graphs of special form with connections between the vertices of each layer. The classification of structures of such a form is based on the construction of resolving sets of graphs as a result of generalization of the elements of the training sample of each class and the testing of whether an input object is isomorphic (with allowance for the weights) to the structures of the resolving set or not. 4 references.

  18. Sustained oscillations, irregular firing and chaotic dynamics in hierarchical modular networks with mixtures of electrophysiological cell types

    Directory of Open Access Journals (Sweden)

    Petar eTomov

    2014-09-01

    Full Text Available The cerebral cortex exhibits neural activity even in the absence of externalstimuli. This self-sustained activity is characterized by irregular firing ofindividual neurons and population oscillations with a broad frequency range.Questions that arise in this context, are: What are the mechanismsresponsible for the existence of neuronal spiking activity in the cortexwithout external input? Do these mechanisms depend on the structural organization of the cortical connections? Do they depend onintrinsic characteristics of the cortical neurons? To approach the answers to these questions, we have used computer simulations of cortical network models. Our networks have hierarchical modular architecture and are composedof combinations of neuron models that reproduce the firing behavior of the five main cortical electrophysiological cell classes: regular spiking (RS, chattering (CH, intrinsically bursting (IB, low threshold spiking (LTS and fast spiking (FS. The population of excitatory neurons is built of RS cells(always present and either CH or IB cells. Inhibitoryneurons belong to the same class, either LTS or FS. Long-lived self-sustained activity states in our networksimulations display irregular single neuron firing and oscillatoryactivity similar to experimentally measured ones. The duration of self-sustained activity strongly depends on the initial conditions,suggesting a transient chaotic regime. Extensive analysis of the self-sustainedactivity states showed that their lifetime expectancy increases with the numberof network modules and is favored when the network is composed of excitatory neurons of the RS and CH classes combined with inhibitory neurons of the LTS class. These results indicate that the existence and properties of the self-sustained cortical activity states depend on both the topology of the network and the neuronal mixture that comprises the network.

  19. Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions

    Science.gov (United States)

    Tewarie, P.; Bright, M.G.; Hillebrand, A.; Robson, S.E.; Gascoyne, L.E.; Morris, P.G.; Meier, J.; Van Mieghem, P.; Brookes, M.J.

    2016-01-01

    Understanding the electrophysiological basis of resting state networks (RSNs) in the human brain is a critical step towards elucidating how inter-areal connectivity supports healthy brain function. In recent years, the relationship between RSNs (typically measured using haemodynamic signals) and electrophysiology has been explored using functional Magnetic Resonance Imaging (fMRI) and magnetoencephalography (MEG). Significant progress has been made, with similar spatial structure observable in both modalities. However, there is a pressing need to understand this relationship beyond simple visual similarity of RSN patterns. Here, we introduce a mathematical model to predict fMRI-based RSNs using MEG. Our unique model, based upon a multivariate Taylor series, incorporates both phase and amplitude based MEG connectivity metrics, as well as linear and non-linear interactions within and between neural oscillations measured in multiple frequency bands. We show that including non-linear interactions, multiple frequency bands and cross-frequency terms significantly improves fMRI network prediction. This shows that fMRI connectivity is not only the result of direct electrophysiological connections, but is also driven by the overlap of connectivity profiles between separate regions. Our results indicate that a complete understanding of the electrophysiological basis of RSNs goes beyond simple frequency-specific analysis, and further exploration of non-linear and cross-frequency interactions will shed new light on distributed network connectivity, and its perturbation in pathology. PMID:26827811

  20. Electrophysiological correlates of proactive interference in the 'Recent Probes' verbal working memory task.

    Science.gov (United States)

    Zhang, John X; Wu, Renhua; Kong, Lingyue; Weng, Xuchu; Du, Yingchun

    2010-06-01

    Using event-related potentials (ERPs), the present study examined the temporal dynamics of proactive interference in working memory using a recent probes task. Participants memorized and retained a target set of four letters over a short retention interval. They then responded to a recognition probe by judging whether it was from the memory set. ERP waveforms elicited by positive probes compared to those from negative probes showed positive shifts in a fronto-central early N2 component and a parietal late positive component (LPC). The LPC was identified as the electrophysiological signature of proactive interference, as it differentiated between two types of negative probes defined based on whether they were recently encountered. These results indicate that the proactive interference we observed arises from a mismatch between familiarity and contextual information during recognition memory. When considered together with related studies in the literature, the results also suggest that there are different forms of proactive interference associated with different neural correlates. Copyright 2010 Elsevier Ltd. All rights reserved.

  1. Oral health status in older adults with social security in Mexico City: Latent class analysis.

    Science.gov (United States)

    Sánchez-García, Sergio; Heredia-Ponce, Erika; Cruz-Hervert, Pablo; Juárez-Cedillo, Teresa; Cárdenas-Bahena, Angel; García-Peña, Carmen

    2014-02-01

    To explore the oral health status through a latent class analysis in elderly social security beneficiaries from Southwest Mexico City. Cross-sectional study of beneficiaries of the State Employee Social Security and Social Services Institute (ISSSTE, in Spanish) and the Mexican Institute of Social Security (IMSS, in Spanish) aged 60 years or older. Oral health conditions such as edentulism, coronal and root caries (DMFT and DFT ≥ 75 percentile), clinical attachment loss (≥ 4 mm), and healthy teeth (≤ 25 percentile) were determined. A latent class analysis (LCA) was performed to classify the oral health status of dentate patients. In total, 336 patients were included (47.9% from the ISSSTE and 52.1% from the IMSS), with an average age of 74.4 (SD = 7.1) years. The 75th percentile of the DMFT = 23 and of the DFT = 2. Of the patients, 77.9% had periodontal disease. The 25th percentile of healthy teeth = 4. A three class model is adequate, with a high classification quality (Entropy = 0.915). The patients were classified as "Edentulous" (15.2%), "Class 1 = Unfavorable" (13.7%), "Class 2 = Somewhat favorable" (10.4%), and "Class 3 = Favorable" (60.7%). Using "Class 3 = Favorable" as a reference, there was an association (OR = 3.4; 95% CI = 1.8-6.4) between being edentulous and being 75 years of age and over, compared with the 60- to 74-year age group. The oral health in elderly social security beneficiaries is not optimal. The probability of becoming edentulous increases with age. A three-class model appropriately classifies the oral health dimensions in the elderly population. Key words:Elderly, Latent class analysis (LCA), oral health, social security, Mexico.

  2. A Large Dimensional Analysis of Regularized Discriminant Analysis Classifiers

    KAUST Repository

    Elkhalil, Khalil

    2017-11-01

    This article carries out a large dimensional analysis of standard regularized discriminant analysis classifiers designed on the assumption that data arise from a Gaussian mixture model with different means and covariances. The analysis relies on fundamental results from random matrix theory (RMT) when both the number of features and the cardinality of the training data within each class grow large at the same pace. Under mild assumptions, we show that the asymptotic classification error approaches a deterministic quantity that depends only on the means and covariances associated with each class as well as the problem dimensions. Such a result permits a better understanding of the performance of regularized discriminant analsysis, in practical large but finite dimensions, and can be used to determine and pre-estimate the optimal regularization parameter that minimizes the misclassification error probability. Despite being theoretically valid only for Gaussian data, our findings are shown to yield a high accuracy in predicting the performances achieved with real data sets drawn from the popular USPS data base, thereby making an interesting connection between theory and practice.

  3. Behavioral and electrophysiological signatures of word translation processes.

    Science.gov (United States)

    Jost, Lea B; Radman, Narges; Buetler, Karin A; Annoni, Jean-Marie

    2018-01-31

    Translation is a demanding process during which a message is analyzed, translated and communicated from one language to another. Despite numerous studies on translation mechanisms, the electrophysiological processes underlying translation with overt production remain largely unexplored. Here, we investigated how behavioral response patterns and spatial-temporal brain dynamics differ in a translation compared to a control within-language word-generation task. We also investigated how forward and backward translation differs on the behavioral and electrophysiological level. To address these questions, healthy late bilingual subjects performed a translation and a within-language control task while a 128-channel EEG was recorded. Behavioral data showed faster responses for translation compared to within-language word generation and faster responses for backward than forward translation. The ERP-analysis revealed stronger early ( processes for between than within word generation. Later (424-630ms) differences were characterized by distinct engagement of domain-general control networks, namely self-monitoring and lexical access interference. Language asymmetry effects occurred at a later stage (600ms), reflecting differences in conceptual processing characterized by a larger involvement of areas implicated in attention, arousal and awareness for forward versus backward translation. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Large-scale electrophysiology: acquisition, compression, encryption, and storage of big data.

    Science.gov (United States)

    Brinkmann, Benjamin H; Bower, Mark R; Stengel, Keith A; Worrell, Gregory A; Stead, Matt

    2009-05-30

    The use of large-scale electrophysiology to obtain high spatiotemporal resolution brain recordings (>100 channels) capable of probing the range of neural activity from local field potential oscillations to single-neuron action potentials presents new challenges for data acquisition, storage, and analysis. Our group is currently performing continuous, long-term electrophysiological recordings in human subjects undergoing evaluation for epilepsy surgery using hybrid intracranial electrodes composed of up to 320 micro- and clinical macroelectrode arrays. DC-capable amplifiers, sampling at 32kHz per channel with 18-bits of A/D resolution are capable of resolving extracellular voltages spanning single-neuron action potentials, high frequency oscillations, and high amplitude ultra-slow activity, but this approach generates 3 terabytes of data per day (at 4 bytes per sample) using current data formats. Data compression can provide several practical benefits, but only if data can be compressed and appended to files in real-time in a format that allows random access to data segments of varying size. Here we describe a state-of-the-art, scalable, electrophysiology platform designed for acquisition, compression, encryption, and storage of large-scale data. Data are stored in a file format that incorporates lossless data compression using range-encoded differences, a 32-bit cyclically redundant checksum to ensure data integrity, and 128-bit encryption for protection of patient information.

  5. Operationalizing Max Weber's probability concept of class situation: the concept of social class.

    Science.gov (United States)

    Smith, Ken

    2007-03-01

    In this essay I take seriously Max Weber's astonishingly neglected claim that class situation may be defined, not in categorial terms, but probabilistically. I then apply this idea to another equally neglected claim made by Weber that the boundaries of social classes may be determined by the degree of social mobility within such classes. Taking these two ideas together I develop the idea of a non-categorial boundary 'surface' between classes and of a social class 'corridor' made up of all those people who are still to be found within the boundaries of the social class into which they were born. I call social mobility within a social class 'intra-class social mobility' and social mobility between classes 'inter-class social mobility'. I also claim that this distinction resolves the dispute between those sociologists who claim that late industrial societies are still highly class bound and those who think that this is no longer the case. Both schools are right I think, but one is referring to a high degree of intra-class social mobility and the other to an equally high degree of inter-class mobility. Finally I claim that this essay provides sociology with only one example among many other possible applications of how probability theory might usefully be used to overcome boundary problems generally in sociology.

  6. Mapping Woodland Cover in the Miombo Ecosystem: A Comparison of Machine Learning Classifiers

    Directory of Open Access Journals (Sweden)

    Courage Kamusoko

    2014-06-01

    Full Text Available Miombo woodlands in Southern Africa are experiencing accelerated changes due to natural and anthropogenic disturbances. In order to formulate sustainable woodland management strategies in the Miombo ecosystem, timely and up-to-date land cover information is required. Recent advances in remote sensing technology have improved land cover mapping in tropical evergreen ecosystems. However, woodland cover mapping remains a challenge in the Miombo ecosystem. The objective of the study was to evaluate the performance of decision trees (DT, random forests (RF, and support vector machines (SVM in the context of improving woodland and non-woodland cover mapping in the Miombo ecosystem in Zimbabwe. We used Multidate Landsat 8 spectral and spatial dependence (Moran’s I variables to map woodland and non-woodland cover. Results show that RF classifier outperformed the SVM and DT classifiers by 4% and 15%, respectively. The RF importance measures show that multidate Landsat 8 spectral and spatial variables had the greatest influence on class-separability in the study area. Therefore, the RF classifier has potential to improve woodland cover mapping in the Miombo ecosystem.

  7. Electrophysiological resting-state biomarker for diagnosing mesial temporal lobe epilepsy with hippocampal sclerosis.

    Science.gov (United States)

    Jin, Seung-Hyun; Chung, Chun Kee

    2017-01-01

    The main aim of the present study was to evaluate whether resting-state functional connectivity of magnetoencephalography (MEG) signals can differentiate patients with mesial temporal lobe epilepsy (MTLE) from healthy controls (HC) and can differentiate between right and left MTLE as a diagnostic biomarker. To this end, a support vector machine (SVM) method among various machine learning algorithms was employed. We compared resting-state functional networks between 46 MTLE (right MTLE=23; left MTLE=23) patients with histologically proven HS who were free of seizure after surgery, and 46 HC. The optimal SVM group classifier distinguished MTLE patients with a mean accuracy of 95.1% (sensitivity=95.8%; specificity=94.3%). Increased connectivity including the right posterior cingulate gyrus and decreased connectivity including at least one sensory-related resting-state network were key features reflecting the differences between MTLE patients and HC. The optimal SVM model distinguished between right and left MTLE patients with a mean accuracy of 76.2% (sensitivity=76.0%; specificity=76.5%). We showed the potential of electrophysiological resting-state functional connectivity, which reflects brain network reorganization in MTLE patients, as a possible diagnostic biomarker to differentiate MTLE patients from HC and differentiate between right and left MTLE patients. Copyright © 2016 Elsevier B.V. All rights reserved.

  8. Parametric embedding for class visualization.

    Science.gov (United States)

    Iwata, Tomoharu; Saito, Kazumi; Ueda, Naonori; Stromsten, Sean; Griffiths, Thomas L; Tenenbaum, Joshua B

    2007-09-01

    We propose a new method, parametric embedding (PE), that embeds objects with the class structure into a low-dimensional visualization space. PE takes as input a set of class conditional probabilities for given data points and tries to preserve the structure in an embedding space by minimizing a sum of Kullback-Leibler divergences, under the assumption that samples are generated by a gaussian mixture with equal covariances in the embedding space. PE has many potential uses depending on the source of the input data, providing insight into the classifier's behavior in supervised, semisupervised, and unsupervised settings. The PE algorithm has a computational advantage over conventional embedding methods based on pairwise object relations since its complexity scales with the product of the number of objects and the number of classes. We demonstrate PE by visualizing supervised categorization of Web pages, semisupervised categorization of digits, and the relations of words and latent topics found by an unsupervised algorithm, latent Dirichlet allocation.

  9. A naïve Bayes classifier for planning transfusion requirements in heart surgery.

    Science.gov (United States)

    Cevenini, Gabriele; Barbini, Emanuela; Massai, Maria R; Barbini, Paolo

    2013-02-01

    Transfusion of allogeneic blood products is a key issue in cardiac surgery. Although blood conservation and standard transfusion guidelines have been published by different medical groups, actual transfusion practices after cardiac surgery vary widely among institutions. Models can be a useful support for decision making and may reduce the total cost of care. The objective of this study was to propose and evaluate a procedure to develop a simple locally customized decision-support system. We analysed 3182 consecutive patients undergoing cardiac surgery at the University Hospital of Siena, Italy. Univariate statistical tests were performed to identify a set of preoperative and intraoperative variables as likely independent features for planning transfusion quantities. These features were utilized to design a naïve Bayes classifier. Model performance was evaluated using the leave-one-out cross-validation approach. All computations were done using spss and matlab code. The overall correct classification percentage was not particularly high if several classes of patients were to be identified. Model performance improved appreciably when the patient sample was divided into two classes (transfused and non-transfused patients). In this case the naïve Bayes model correctly classified about three quarters of patients with 71.2% sensitivity and 78.4% specificity, thus providing useful information for recognizing patients with transfusion requirements in the specific scenario considered. Although the classifier is customized to a particular setting and cannot be generalized to other scenarios, the simplicity of its development and the results obtained make it a promising approach for designing a simple model for different heart surgery centres needing a customized decision-support system for planning transfusion requirements in intensive care unit. © 2011 Blackwell Publishing Ltd.

  10. Development of a skateboarding trick classifier using accelerometry and machine learning

    Directory of Open Access Journals (Sweden)

    Nicholas Kluge Corrêa

    Full Text Available Abstract Introduction Skateboarding is one of the most popular cultures in Brazil, with more than 8.5 million skateboarders. Nowadays, the discipline of street skating has gained recognition among other more classical sports and awaits its debut at the Tokyo 2020 Summer Olympic Games. This study aimed to explore the state-of-the-art for inertial measurement unit (IMU use in skateboarding trick detection, and to develop new classification methods using supervised machine learning and artificial neural networks (ANN. Methods State-of-the-art knowledge regarding motion detection in skateboarding was used to generate 543 artificial acceleration signals through signal modeling, corresponding to 181 flat ground tricks divided into five classes (NOLLIE, NSHOV, FLIP, SHOV, OLLIE. The classifier consisted of a multilayer feed-forward neural network created with three layers and a supervised learning algorithm (backpropagation. Results The use of ANNs trained specifically for each measured axis of acceleration resulted in error percentages inferior to 0.05%, with a computational efficiency that makes real-time application possible. Conclusion Machine learning can be a useful technique for classifying skateboarding flat ground tricks, assuming that the classifiers are properly constructed and trained, and the acceleration signals are preprocessed correctly.

  11. An animal model (guinea pig) of ocular siderosis: histopathology, pharmacology, and electrophysiology.

    Science.gov (United States)

    Mumcuoglu, Tarkan; Ozge, Gokhan; Soykut, Bugra; Erdem, Onur; Gunal, Armagan; Acikel, Cengizhan

    2015-03-01

    Ocular siderosis is a rare sight-threatening complication that occurs after a penetrating ocular injury by an iron-containing foreign body. The purposes of this study were to (i) investigate the histopathology, electrophysiology and iron levels/accumulation in ocular siderosis using an animal (Guinea pig) model and (ii) determine the appropriate timing for follow-up foreign body-removal surgery. Thirty guinea pigs were divided into five groups (n = 6 animals/group). On day-1, an iron body was inserted into the vitreous of the right eye of all animals; the left eyes were left undisturbed and were used as controls. At the end of each week during the 5-week study period, electroretinography (ERG) was performed on all animals in one of the five groups. Each animal in that group was sacrificed, after which both eyes were enucleated for histopathological and pharmacological evaluation of intraocular iron. Accumulated iron levels of study eyes were significantly higher than those of control eyes (135.13 and 13.55 μg/g, respectively, p < 0.01). In addition, there was a significant decrease in electrophysiological responses of study eyes. During the first week, iron levels were higher in study eyes than control eyes, but neither histological iron accumulation nor decreased electrophysiological responses could be detected. By the end of the second week, increased iron accumulation was observed histologically in intraocular tissues, along with signs of retinal toxicity, as verified by decreased electrophysiological responses. The present study indicates that the 14th day after a penetrating eye injury by an iron-containing intraocular foreign body represents a clinically critical threshold, after which structural damage to and functional alterations in ocular tissues occur.

  12. Identification of the dynamic operating envelope of HCCI engines using class imbalance learning.

    Science.gov (United States)

    Janakiraman, Vijay Manikandan; Nguyen, XuanLong; Sterniak, Jeff; Assanis, Dennis

    2015-01-01

    Homogeneous charge compression ignition (HCCI) is a futuristic automotive engine technology that can significantly improve fuel economy and reduce emissions. HCCI engine operation is constrained by combustion instabilities, such as knock, ringing, misfires, high-variability combustion, and so on, and it becomes important to identify the operating envelope defined by these constraints for use in engine diagnostics and controller design. HCCI combustion is dominated by complex nonlinear dynamics, and a first-principle-based dynamic modeling of the operating envelope becomes intractable. In this paper, a machine learning approach is presented to identify the stable operating envelope of HCCI combustion, by learning directly from the experimental data. Stability is defined using thresholds on combustion features obtained from engine in-cylinder pressure measurements. This paper considers instabilities arising from engine misfire and high-variability combustion. A gasoline HCCI engine is used for generating stable and unstable data observations. Owing to an imbalance in class proportions in the data set, the models are developed both based on resampling the data set (by undersampling and oversampling) and based on a cost-sensitive learning method (by overweighting the minority class relative to the majority class observations). Support vector machines (SVMs) and recently developed extreme learning machines (ELM) are utilized for developing dynamic classifiers. The results compared against linear classification methods show that cost-sensitive nonlinear ELM and SVM classification algorithms are well suited for the problem. However, the SVM envelope model requires about 80% more parameters for an accuracy improvement of 3% compared with the ELM envelope model indicating that ELM models may be computationally suitable for the engine application. The proposed modeling approach shows that HCCI engine misfires and high-variability combustion can be predicted ahead of time

  13. Pelvic floor electrophysiology in spinal cord injury.

    Science.gov (United States)

    Tankisi, H; Pugdahl, K; Rasmussen, M M; Clemmensen, D; Rawashdeh, Y F; Christensen, P; Krogh, K; Fuglsang-Frederiksen, A

    2016-05-01

    The study aimed to investigate sacral peripheral nerve function and continuity of pudendal nerve in patients with chronic spinal cord injury (SCI) using pelvic floor electrophysiological tests. Twelve patients with low cervical or thoracic SCI were prospectively included. Quantitative external anal sphincter (EAS) muscle electromyography (EMG), pudendal nerve terminal motor latency (PNTML) testing, bulbocavernosus reflex (BCR) testing and pudendal short-latency somatosensory-evoked potential (SEP) measurement were performed. In EAS muscle EMG, two patients had abnormal increased spontaneous activity and seven prolonged motor unit potential duration. PNTML was normal in 10 patients. BCR was present with normal latency in 11 patients and with prolonged latency in one. The second component of BCR could be recorded in four patients. SEPs showed absent cortical responses in 11 patients and normal latency in one. Pudendal nerve and sacral lower motor neuron involvement are significantly associated with chronic SCI, most prominently in EAS muscle EMG. The frequent finding of normal PNTML latencies supports earlier concerns on the utility of this test; however, BCR and pudendal SEPs may have clinical relevance. As intact peripheral nerves including pudendal nerve are essential for efficient supportive therapies, pelvic floor electrophysiological testing prior to these interventions is highly recommended. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  14. Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction

    Directory of Open Access Journals (Sweden)

    Boulesteix Anne-Laure

    2009-12-01

    Full Text Available Abstract Background In biometric practice, researchers often apply a large number of different methods in a "trial-and-error" strategy to get as much as possible out of their data and, due to publication pressure or pressure from the consulting customer, present only the most favorable results. This strategy may induce a substantial optimistic bias in prediction error estimation, which is quantitatively assessed in the present manuscript. The focus of our work is on class prediction based on high-dimensional data (e.g. microarray data, since such analyses are particularly exposed to this kind of bias. Methods In our study we consider a total of 124 variants of classifiers (possibly including variable selection or tuning steps within a cross-validation evaluation scheme. The classifiers are applied to original and modified real microarray data sets, some of which are obtained by randomly permuting the class labels to mimic non-informative predictors while preserving their correlation structure. Results We assess the minimal misclassification rate over the different variants of classifiers in order to quantify the bias arising when the optimal classifier is selected a posteriori in a data-driven manner. The bias resulting from the parameter tuning (including gene selection parameters as a special case and the bias resulting from the choice of the classification method are examined both separately and jointly. Conclusions The median minimal error rate over the investigated classifiers was as low as 31% and 41% based on permuted uninformative predictors from studies on colon cancer and prostate cancer, respectively. We conclude that the strategy to present only the optimal result is not acceptable because it yields a substantial bias in error rate estimation, and suggest alternative approaches for properly reporting classification accuracy.

  15. A latent class distance association model for cross-classified data with a categorical response variable.

    Science.gov (United States)

    Vera, José Fernando; de Rooij, Mark; Heiser, Willem J

    2014-11-01

    In this paper we propose a latent class distance association model for clustering in the predictor space of large contingency tables with a categorical response variable. The rows of such a table are characterized as profiles of a set of explanatory variables, while the columns represent a single outcome variable. In many cases such tables are sparse, with many zero entries, which makes traditional models problematic. By clustering the row profiles into a few specific classes and representing these together with the categories of the response variable in a low-dimensional Euclidean space using a distance association model, a parsimonious prediction model can be obtained. A generalized EM algorithm is proposed to estimate the model parameters and the adjusted Bayesian information criterion statistic is employed to test the number of mixture components and the dimensionality of the representation. An empirical example highlighting the advantages of the new approach and comparing it with traditional approaches is presented. © 2014 The British Psychological Society.

  16. SpectraClassifier 1.0: a user friendly, automated MRS-based classifier-development system

    Directory of Open Access Journals (Sweden)

    Julià-Sapé Margarida

    2010-02-01

    Full Text Available Abstract Background SpectraClassifier (SC is a Java solution for designing and implementing Magnetic Resonance Spectroscopy (MRS-based classifiers. The main goal of SC is to allow users with minimum background knowledge of multivariate statistics to perform a fully automated pattern recognition analysis. SC incorporates feature selection (greedy stepwise approach, either forward or backward, and feature extraction (PCA. Fisher Linear Discriminant Analysis is the method of choice for classification. Classifier evaluation is performed through various methods: display of the confusion matrix of the training and testing datasets; K-fold cross-validation, leave-one-out and bootstrapping as well as Receiver Operating Characteristic (ROC curves. Results SC is composed of the following modules: Classifier design, Data exploration, Data visualisation, Classifier evaluation, Reports, and Classifier history. It is able to read low resolution in-vivo MRS (single-voxel and multi-voxel and high resolution tissue MRS (HRMAS, processed with existing tools (jMRUI, INTERPRET, 3DiCSI or TopSpin. In addition, to facilitate exchanging data between applications, a standard format capable of storing all the information needed for a dataset was developed. Each functionality of SC has been specifically validated with real data with the purpose of bug-testing and methods validation. Data from the INTERPRET project was used. Conclusions SC is a user-friendly software designed to fulfil the needs of potential users in the MRS community. It accepts all kinds of pre-processed MRS data types and classifies them semi-automatically, allowing spectroscopists to concentrate on interpretation of results with the use of its visualisation tools.

  17. Classified installations for environmental protection subject to declaration. Tome 2

    International Nuclear Information System (INIS)

    Anon.

    1992-01-01

    Legislation concerning classified installations govern most of industries or dangerous or pollutant activities. This legislation aims to prevent risks and harmful effects coming from an installation, air pollution, water pollution, noise, wastes produced by installations, even aesthetic bad effects. Pollutant or dangerous activities are defined in a list called nomenclature which obliged installations to a rule of declaration or authorization. Technical regulations ordered by the secretary of state for the environment are listed in tome 2

  18. A time course analysis of the electrophysiological properties of neurons differentiated from human induced pluripotent stem cells (iPSCs.

    Directory of Open Access Journals (Sweden)

    Deborah Prè

    Full Text Available Many protocols have been designed to differentiate human embryonic stem cells (ESCs and human induced pluripotent stem cells (iPSCs into neurons. Despite the relevance of electrophysiological properties for proper neuronal function, little is known about the evolution over time of important neuronal electrophysiological parameters in iPSC-derived neurons. Yet, understanding the development of basic electrophysiological characteristics of iPSC-derived neurons is critical for evaluating their usefulness in basic and translational research. Therefore, we analyzed the basic electrophysiological parameters of forebrain neurons differentiated from human iPSCs, from day 31 to day 55 after the initiation of neuronal differentiation. We assayed the developmental progression of various properties, including resting membrane potential, action potential, sodium and potassium channel currents, somatic calcium transients and synaptic activity. During the maturation of iPSC-derived neurons, the resting membrane potential became more negative, the expression of voltage-gated sodium channels increased, the membrane became capable of generating action potentials following adequate depolarization and, at day 48-55, 50% of the cells were capable of firing action potentials in response to a prolonged depolarizing current step, of which 30% produced multiple action potentials. The percentage of cells exhibiting miniature excitatory post-synaptic currents increased over time with a significant increase in their frequency and amplitude. These changes were associated with an increase of Ca2+ transient frequency. Co-culturing iPSC-derived neurons with mouse glial cells enhanced the development of electrophysiological parameters as compared to pure iPSC-derived neuronal cultures. This study demonstrates the importance of properly evaluating the electrophysiological status of the newly generated neurons when using stem cell technology, as electrophysiological properties of

  19. Time course of electrophysiologic effects induced by di-n-butyl-2,2-dichlorovinyl phosphate (DBCV) in the adult hen.

    Science.gov (United States)

    Robertson, D G; Mattson, A M; Bestervelt, L L; Richardson, R J; Anderson, R J

    1988-01-01

    Previous work in our laboratory indicated that di-n-butyl-2,2-dichlorovinyl phosphate (DBCV) produced electrophysiologic changes in hen peripheral nerve that coincided with the development of histopathologic changes and neurologic signs of peripheral neuropathy. The purpose of the present study was to follow the time course for the development of the electrophysiologic changes and to determine whether pretreatment with the phosphinate analog of DBCV (DBCV-P), a nonageable organophosphorus compound, prevented these effects. Although significant electrophysiologic deficits occurred in the tibial and sciatic nerve 24 h after DBCV treatment, the most marked changes coincided with the onset of clinical signs of organophosphorus-induced delayed neuropathy (14-21 d). The sciatic and tibial nerves were equally susceptible to DBCV in producing deficits characterized by changes in the relative refractory period and an increased strength-duration threshold. Pretreatment with DBCV-P prevented the clinical signs and also attenuated the electrophysiologic deficits induced by DBCV treatment. These data suggest that electrophysiologic deficits occur before clinical signs of organophosphorus-induced delayed neuropathy (OPIDN) and may be indicative of a link between neurotoxic esterase (NTE) inhibition and onset of overt clinical toxicity.

  20. Mapping the social class structure: From occupational mobility to social class categories using network analysis

    DEFF Research Database (Denmark)

    Toubøl, Jonas; Larsen, Anton Grau

    2017-01-01

    This article develops a new explorative method for deriving social class categories from patterns of occupational mobility. In line with Max Weber, our research is based on the notion that, if class boundaries do not inhibit social mobility then the class categories are of little value. Thus......, unlike dominant, theoretically defined class schemes, this article derives social class categories from observed patterns in a mobility network covering intra-generational mobility. The network is based on a mobility table of 109 occupational categories tied together by 1,590,834 job shifts on the Danish...... labour market 2001–2007. The number of categories are reduced from 109 to 34 by applying a new clustering algorithm specifically designed for the study of mobility tables (MONECA). These intra-generational social class categories are related to the central discussions of gender, income, education...

  1. Anomaly detection for medical images based on a one-class classification

    Science.gov (United States)

    Wei, Qi; Ren, Yinhao; Hou, Rui; Shi, Bibo; Lo, Joseph Y.; Carin, Lawrence

    2018-02-01

    Detecting an anomaly such as a malignant tumor or a nodule from medical images including mammogram, CT or PET images is still an ongoing research problem drawing a lot of attention with applications in medical diagnosis. A conventional way to address this is to learn a discriminative model using training datasets of negative and positive samples. The learned model can be used to classify a testing sample into a positive or negative class. However, in medical applications, the high unbalance between negative and positive samples poses a difficulty for learning algorithms, as they will be biased towards the majority group, i.e., the negative one. To address this imbalanced data issue as well as leverage the huge amount of negative samples, i.e., normal medical images, we propose to learn an unsupervised model to characterize the negative class. To make the learned model more flexible and extendable for medical images of different scales, we have designed an autoencoder based on a deep neural network to characterize the negative patches decomposed from large medical images. A testing image is decomposed into patches and then fed into the learned autoencoder to reconstruct these patches themselves. The reconstruction error of one patch is used to classify this patch into a binary class, i.e., a positive or a negative one, leading to a one-class classifier. The positive patches highlight the suspicious areas containing anomalies in a large medical image. The proposed method has been tested on InBreast dataset and achieves an AUC of 0.84. The main contribution of our work can be summarized as follows. 1) The proposed one-class learning requires only data from one class, i.e., the negative data; 2) The patch-based learning makes the proposed method scalable to images of different sizes and helps avoid the large scale problem for medical images; 3) The training of the proposed deep convolutional neural network (DCNN) based auto-encoder is fast and stable.

  2. [Electrophysiological study on rat conduit pulmonary artery smooth muscle cells under normoxia and acute hypoxia].

    Science.gov (United States)

    Hu, Ying; Zou, Fei; Cai, Chun-Qing; Wu, Hang-Yu; Yun, Hai-Xia; Chen, Yun-Tian; Jin, Guo-En; Ge, Ri-Li

    2006-10-25

    The present study was designed to investigate the electrophysiological characteristics of rat conduit pulmonary artery smooth muscle cells (PASMCs) and the response to acute hypoxia. PASMCs of the 1st to 2nd order branches in the conduit pulmonary arteries were obtained by enzymatic isolation. The PASMCs were divided into acute hypoxia preconditioned group and normoxia group. Hypoxia solutions were achieved by bubbling with 5% CO2 plus 95% N2 for at least 30 min before cell perfusion. Potassium currents were compared between these two groups using whole-cell patch clamp technique. The total outward current of PASMCs was measured under normoxia condition when iBTX [specific blocking agent of large conductance Ca-activated K(+) (BK(Ca)) channel] and 4-AP [specific blocking agent of delayed rectifier K(+) (K(DR)) channel] were added consequently into bath solution. PASMCs were classified into three types according to their size, shape and electrophysiological characteristics. Type I cells are the smallest with spindle shape, smooth surface and discrete perinuclear bulge. Type II cells show the biggest size with banana-like appearance. Type III cells have the similar size with type I, and present intermediary shape between type I and type II. iBTX had little effect on the total outward current in type I cells, while 4-AP almost completely blocked it. Most of the total outward current in type II cells was inhibited by iBTX, and the remaining was sensitive to 4-AP. In type III cells, the total outward current was sensitive to both iBTX and 4-AP. Acute hypoxia reduced the current in all three types of cells: (1614.8+/-62.5) pA to (892.4+/-33.6) pA for type I cells (Ppotassium current and improves the E(m) in PASMCs. These effects may be involved in the modulation of constriction/relaxation of conduit artery under acute hypoxia. Different distribution of K(DR) and BK(Ca) channels in these three types of PASMCs might account for their different constriction

  3. Automated characterization of nerve fibers labeled fluorescently: determination of size, class and spatial distribution.

    Science.gov (United States)

    Prodanov, Dimiter; Feirabend, Hans K P

    2008-10-03

    Morphological classification of nerve fibers could help interpret the assessment of neural regeneration and the understanding of selectivity of nerve stimulation. Specific populations of myelinated nerve fibers can be investigated by retrograde tracing from a muscle followed by microscopic measurements of the labeled fibers at different anatomical levels. Gastrocnemius muscles of adult rats were injected with the retrograde tracer Fluoro-Gold. After a survival period of 3 days, cross-sections of spinal cords, ventral roots, sciatic, and tibial nerves were collected and imaged on a fluorescence microscope. Nerve fibers were classified using a variation-based criterion acting on the distribution of their equivalent diameters. The same criterion was used to classify the labeled axons using the size of the fluorescent marker. Measurements of the axons were paired to those of the entire fibers (axons+myelin sheaths) in order to establish the correspondence between so-established axonal and fiber classifications. It was found that nerve fibers in L6 ventral roots could be classified into four populations comprising two classes of Aalpha (denoted Aalpha1 and Aalpha2), Agamma, and an additional class of Agammaalpha fibers. Cut-off borders between Agamma and Agammaalpha fiber classes were estimated to be 5.00+/-0.09 microm (SEM); between Agammaalpha and Aalpha1 fiber classes to be 6.86+/-0.11 microm (SEM); and between Aalpha1 and Aalpha2 fiber classes to be 8.66+/-0.16 microm (SEM). Topographical maps of the nerve fibers that innervate the gastrocnemius muscles were constructed per fiber class for the spinal root L6. The major advantage of the presented approach consists of the combined indirect classification of nerve fiber types and the construction of topographical maps of so-identified fiber classes.

  4. EXAMINATION OF ELECTROPHYSIOLOGICAL PARAMETERS OF THE ATRIUMS IN PATIENTS WITH LONG-TERM PERSISTENT FORM OF ATRIAL FIBRILLATION AND VALVULAR HEART DISEASE

    Directory of Open Access Journals (Sweden)

    A. A. Kulikov

    2017-01-01

    Full Text Available The study objective is to examine electrophysiological parameters of atrial myocardium, characteristics of atrioventricular conduction, and potential factors affecting recurrent atrial fibrillation (AF in patients with persistent and long-term persistent forms of AF prior to the Labirynth IIIB surgery with single-step correction of valvular heart disease.Materials and methods. The study included 100 adults (48 men, 52 women with persistent and long-term persistent forms of AF and different valvular heart diseases. Mean patient age was 59 years. Mean AF duration was 4 years. All patients were prescribed antiarrhythmic therapy but it proved ineffective. In 15 % of patients, restoration of the sinus rhythm was attempted through electrical cardioversion but long-term control of the sinus rhythm wasn’t achieved. All patients were diagnosed with organic pathology of the mitral valve. Also, in 80 % of patients, relative insufficiency of the tricuspid valve was detected. Chronic heart failure functional class per NYHA was III. Size of the left atrium was 5 cm, mean left ventricular ejection fraction was 61 %. All patients underwent electrical cardioversion. After successful restoration of the sinus rhythm, endocardial electrophysiology study (EES of the heart was performed. Then, correction of valvular pathologies and the Labyrinth IIIB surgery were performed. Results. Examination of refractoriness of different parts of the atriums has shown that effective refractory period (ERP of the atrioventricular node was minimal compared to other parts of the atriums. Maximal ERP duration was observed in the upper part of the right atrium. Therefore, in patients with long history of AF, heterogeneity of atrial myocardium ERP duration is observed. In 17 % of patients, atrial vulnerability was detected. The area of atrial vulnerability was always associated with ERP. Its duration in patients with atrial vulnerability was significantly higher.Conclusion. Long

  5. Clean-up progress at the SNL/NM Classified Waste Landfill

    International Nuclear Information System (INIS)

    Slavin, P.J.; Galloway, R.B.

    1999-01-01

    The Sandia National Laboratories/New Mexico (SNL/NM)Environmental Restoration Project is currently excavating the Classified Waste Landfill in Technical Area II, a disposal area for weapon components for approximately 40 years until it closed in 1987. Many different types of classified parts were disposed in unlined trenches and pits throughout the course of the landfill's history. A percentage of the parts contain explosives and/or radioactive components or contamination. The excavation has progressed backward chronologically from the last trenches filled through to the earlier pits. Excavation commenced in March 1998, and approximately 75 percent of the site (as defined by geophysical anomalies) has been completed as of November 1999. The material excavated consists primarily of classified weapon assemblies and related components, so disposition must include demilitarization and sanitization. This has resulted in substantial waste minimization and cost avoidance for the project as upwards of 90 percent of the classified materials are being demilitarized and recycled. The project is using field screening and lab analysis in conjunction with preliminary and in-process risk assessments to characterize soil and make waste determinations in a timely a fashion as possible. Challenges in waste management have prompted the adoption of innovative solutions. The hand-picked crew (both management and field staff) and the ability to quickly adapt to changing conditions has ensured the success of the project. The current schedule is to complete excavation in July 2000, with follow-on verification sampling, demilitarization, and waste management activities following

  6. Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load.

    Science.gov (United States)

    Zhang, Jianhua; Yin, Zhong; Wang, Rubin

    2017-01-01

    This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few CTL classes. The least-squares support vector machine (LSSVM) is used as dynamic pattern classifier. A series of electrophysiological and performance data acquisition experiments were performed on seven volunteer participants under a simulated process control task environment. The participant-specific dynamic LSSVM model is constructed to classify the instantaneous CTL into five classes at each time instant. The initial feature set, comprising 56 EEG and ECG related features, is reduced to a set of 12 salient features (including 11 EEG-related features) by using the locality preserving projection (LPP) technique. An overall correct classification rate of about 80% is achieved for the 5-class CTL classification problem. Then the predicted CTL is used to adaptively allocate the number of process control tasks between operator and computer-based controller. Simulation results showed that the overall performance of the human-machine system can be improved by using the adaptive automation strategy proposed.

  7. Prediction of small molecule binding property of protein domains with Bayesian classifiers based on Markov chains.

    Science.gov (United States)

    Bulashevska, Alla; Stein, Martin; Jackson, David; Eils, Roland

    2009-12-01

    Accurate computational methods that can help to predict biological function of a protein from its sequence are of great interest to research biologists and pharmaceutical companies. One approach to assume the function of proteins is to predict the interactions between proteins and other molecules. In this work, we propose a machine learning method that uses a primary sequence of a domain to predict its propensity for interaction with small molecules. By curating the Pfam database with respect to the small molecule binding ability of its component domains, we have constructed a dataset of small molecule binding and non-binding domains. This dataset was then used as training set to learn a Bayesian classifier, which should distinguish members of each class. The domain sequences of both classes are modelled with Markov chains. In a Jack-knife test, our classification procedure achieved the predictive accuracies of 77.2% and 66.7% for binding and non-binding classes respectively. We demonstrate the applicability of our classifier by using it to identify previously unknown small molecule binding domains. Our predictions are available as supplementary material and can provide very useful information to drug discovery specialists. Given the ubiquitous and essential role small molecules play in biological processes, our method is important for identifying pharmaceutically relevant components of complete proteomes. The software is available from the author upon request.

  8. A novel radiation protection drape reduces radiation exposure during fluoroscopy guided electrophysiology procedures.

    Science.gov (United States)

    Germano, Joseph J; Day, Gina; Gregorious, David; Natarajan, Venkataraman; Cohen, Todd

    2005-09-01

    The purpose of this study was to evaluate a novel disposable lead-free radiation protection drape for decreasing radiation scatter during electrophysiology procedures. In recent years, there has been an exponential increase in the number of electrophysiology (EP) procedures exposing patients, operators and laboratory staff to higher radiation doses. The RADPAD was positioned slightly lateral to the incision site for pectoral device implants and superior to the femoral vein during electrophysiology studies. Each patient served as their own control and dosimetric measurements were obtained at the examiner's elbow and hand. Radiation badge readings for the operator were obtained three months prior to RADPAD use and three months after introduction. Radiation dosimetry was obtained in twenty patients: 7 electrophysiology studies, 6 pacemakers, 5 catheter ablations, and 2 implantable cardioverter-defibrillators. Eleven women and nine men with a mean age of 63 +/- 4 years had an average fluoroscopy time of 2.5 +/- 0.42 minutes per case. Mean dosimetric measurements at the hand were reduced from 141.38 +/- 24.67 to 48.63 +/- 9.02 milliroentgen (mR) per hour using the protective drape (63% reduction; p < 0.0001). Measurements at the elbow were reduced from 78.78 +/- 7.95 mR per hour to 34.50 +/- 4.18 mR per hour using the drape (55% reduction; p < 0.0001). Badge readings for three months prior to drape introduction averaged 2.45 mR per procedure versus 1.54 mR per procedure for 3 months post-initiation (37% reduction). The use of a novel radiation protection surgical drape can significantly reduce scatter radiation exposure to staff and operators during a variety of EP procedures.

  9. Quantum ensembles of quantum classifiers.

    Science.gov (United States)

    Schuld, Maria; Petruccione, Francesco

    2018-02-09

    Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which - similar to Bayesian learning - the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.

  10. A linear-RBF multikernel SVM to classify big text corpora.

    Science.gov (United States)

    Romero, R; Iglesias, E L; Borrajo, L

    2015-01-01

    Support vector machine (SVM) is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility. This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search. The model consists in spreading the dataset into cohesive term slices (clusters) to construct a defined structure (multikernel). The new approach is tested on different text corpora. Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers.

  11. Serum cytokine contents in schizophrenia patient with metabolic syndrome and their correlation with nerve electrophysiology

    Directory of Open Access Journals (Sweden)

    Li-Yong Chen

    2016-07-01

    Full Text Available Objective: To analyze serum cytokine contents in schizophrenia patient with metabolic syndrome (MS and their correlation with nerve electrophysiology. Methods: A total of 90 chizophrenia patient with MS, including 41 cases with simple schizophrenia and 39 cases with simple metabolic syndrome were included for study. The values of nerve electrophysiology indexes and serum illness-related indexes were compared among included patients, and the correlation between the two was further analyzed. Results: Compared with simple schizophrenia group and simple MS group, P300 latency of schizophrenia with MS group was longer, and the amplitude was shorter; N2-P3 latency and amplitude were shorter (P<0.05; serum SOD, S100b, BDNF, ABAb, PAI-1, 毩-HBDH, AST, cystatin c, TG, FBG and 2hPG values of schizophrenia with MS group were higher, IGF1, HMW-APN and HDL-C levels were lower, and compared with simple schizophrenia group and simple MS group, differences were significant (P<0.05; P300 latency, P300 amplitude, N2-P3 latency and N2- P3 amplitude of schizophrenia with MS group were directly correlated with serum cytokine contents (P<0.05. Conclusions: There are significantly abnormal serum cytokines and nerve electrophysiology indexes in schizophrenia patient with MS, and nerve electrophysiology detection can be used as the means to judge disease and guide treatment.

  12. 47 CFR 36.331 - Information origination/termination expenses-Account 6310 (Class B telephone companies); Accounts...

    Science.gov (United States)

    2010-10-01

    ... telephone companies). (a) The expenses in this account are classified as follows: (1) Other Information... 47 Telecommunication 2 2010-10-01 2010-10-01 false Information origination/termination expenses-Account 6310 (Class B telephone companies); Accounts 6311, 6341, 6351, and 6362 (Class A telephone...

  13. Robotic Automation of In Vivo Two-Photon Targeted Whole-Cell Patch-Clamp Electrophysiology.

    Science.gov (United States)

    Annecchino, Luca A; Morris, Alexander R; Copeland, Caroline S; Agabi, Oshiorenoya E; Chadderton, Paul; Schultz, Simon R

    2017-08-30

    Whole-cell patch-clamp electrophysiological recording is a powerful technique for studying cellular function. While in vivo patch-clamp recording has recently benefited from automation, it is normally performed "blind," meaning that throughput for sampling some genetically or morphologically defined cell types is unacceptably low. One solution to this problem is to use two-photon microscopy to target fluorescently labeled neurons. Combining this with robotic automation is difficult, however, as micropipette penetration induces tissue deformation, moving target cells from their initial location. Here we describe a platform for automated two-photon targeted patch-clamp recording, which solves this problem by making use of a closed loop visual servo algorithm. Our system keeps the target cell in focus while iteratively adjusting the pipette approach trajectory to compensate for tissue motion. We demonstrate platform validation with patch-clamp recordings from a variety of cells in the mouse neocortex and cerebellum. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  14. Rapid Land Cover Map Updates Using Change Detection and Robust Random Forest Classifiers

    Directory of Open Access Journals (Sweden)

    Konrad J. Wessels

    2016-10-01

    Full Text Available The paper evaluated the Landsat Automated Land Cover Update Mapping (LALCUM system designed to rapidly update a land cover map to a desired nominal year using a pre-existing reference land cover map. The system uses the Iteratively Reweighted Multivariate Alteration Detection (IRMAD to identify areas of change and no change. The system then automatically generates large amounts of training samples (n > 1 million in the no-change areas as input to an optimized Random Forest classifier. Experiments were conducted in the KwaZulu-Natal Province of South Africa using a reference land cover map from 2008, a change mask between 2008 and 2011 and Landsat ETM+ data for 2011. The entire system took 9.5 h to process. We expected that the use of the change mask would improve classification accuracy by reducing the number of mislabeled training data caused by land cover change between 2008 and 2011. However, this was not the case due to exceptional robustness of Random Forest classifier to mislabeled training samples. The system achieved an overall accuracy of 65%–67% using 22 detailed classes and 72%–74% using 12 aggregated national classes. “Water”, “Plantations”, “Plantations—clearfelled”, “Orchards—trees”, “Sugarcane”, “Built-up/dense settlement”, “Cultivation—Irrigated” and “Forest (indigenous” had user’s accuracies above 70%. Other detailed classes (e.g., “Low density settlements”, “Mines and Quarries”, and “Cultivation, subsistence, drylands” which are required for operational, provincial-scale land use planning and are usually mapped using manual image interpretation, could not be mapped using Landsat spectral data alone. However, the system was able to map the 12 national classes, at a sufficiently high level of accuracy for national scale land cover monitoring. This update approach and the highly automated, scalable LALCUM system can improve the efficiency and update rate of regional land

  15. Evaluation of classifier topologies for the real-time classification of simultaneous limb motions.

    Science.gov (United States)

    Ortiz-Catalan, Max; Branemark, Rickard; Hakansson, Bo

    2013-01-01

    The prediction of motion intent through the decoding of myoelectric signals has the potential to improve the functionally of limb prostheses. Considerable research on individual motion classifiers has been done to exploit this idea. A drawback with the individual prediction approach, however, is its limitation to serial control, which is slow, cumbersome, and unnatural. In this work, different classifier topologies suitable for the decoding of mixed classes, and thus capable of predicting simultaneous motions, were investigated in real-time. These topologies resulted in higher offline accuracies than previously achieved, but more importantly, positive indications of their suitability for real-time systems were found. Furthermore, in order to facilitate further development, benchmarking, and cooperation, the algorithms and data generated in this study are freely available as part of BioPatRec, an open source framework for the development of advanced prosthetic control strategies.

  16. To4, the first Tityus obscurus β-toxin fully electrophysiologically characterized on human sodium channel isoforms.

    Science.gov (United States)

    Duque, Harry Morales; Mourão, Caroline Barbosa Farias; Tibery, Diogo Vieira; Barbosa, Eder Alves; Campos, Leandro Ambrósio; Schwartz, Elisabeth Ferroni

    2017-09-01

    Many scorpion toxins that act on sodium channels (NaScTxs) have been characterized till date. These toxins may act modulating the inactivation or the activation of sodium channels and are named α- or β-types, respectively. Some venom toxins from Tityus obscurus (Buthidae), a scorpion widely distributed in the Brazilian Amazon, have been partially characterized in previous studies; however, little information about their electrophysiological role on sodium ion channels has been published. In the present study, we describe the purification, identification and electrophysiological characterization of a NaScTx, which was first described as Tc54 and further fully sequenced and renamed To4. This toxin shows a marked β-type effect on different sodium channel subtypes (hNa v 1.1-hNa v 1.7) at low concentrations, and has more pronounced activity on hNa v 1.1, hNa v 1.2 and hNa v 1.4. By comparing To4 primary structure with other Tityus β-toxins which have already been electrophysiologically tested, it is possible to establish some key amino acid residues for the sodium channel activity. Thus, To4 is the first toxin from T. obscurus fully electrophysiologically characterized on different human sodium channel isoforms. Copyright © 2017 Elsevier Inc. All rights reserved.

  17. Application of a naive Bayesians classifiers in assessing the supplier

    Directory of Open Access Journals (Sweden)

    Mijailović Snežana

    2017-01-01

    Full Text Available The paper considers the class of interactive knowledge based systems whose main purpose of making proposals and assisting customers in making decisions. The mathematical model provides a set of examples of learning about the delivered series of outflows from three suppliers, as well as an analysis of an illustrative example for assessing the supplier using a naive Bayesian classifier. The model was developed on the basis of the analysis of subjective probabilities, which are later revised with the help of new empirical information and Bayesian theorem on a posterior probability, i.e. combining of subjective and objective conditional probabilities in the choice of a reliable supplier.

  18. Vision based nutrient deficiency classification in maize plants using multi class support vector machines

    Science.gov (United States)

    Leena, N.; Saju, K. K.

    2018-04-01

    Nutritional deficiencies in plants are a major concern for farmers as it affects productivity and thus profit. The work aims to classify nutritional deficiencies in maize plant in a non-destructive mannerusing image processing and machine learning techniques. The colored images of the leaves are analyzed and classified with multi-class support vector machine (SVM) method. Several images of maize leaves with known deficiencies like nitrogen, phosphorous and potassium (NPK) are used to train the SVM classifier prior to the classification of test images. The results show that the method was able to classify and identify nutritional deficiencies.

  19. Classifier-guided sampling for discrete variable, discontinuous design space exploration: Convergence and computational performance

    Energy Technology Data Exchange (ETDEWEB)

    Backlund, Peter B. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Shahan, David W. [HRL Labs., LLC, Malibu, CA (United States); Seepersad, Carolyn Conner [Univ. of Texas, Austin, TX (United States)

    2014-04-22

    A classifier-guided sampling (CGS) method is introduced for solving engineering design optimization problems with discrete and/or continuous variables and continuous and/or discontinuous responses. The method merges concepts from metamodel-guided sampling and population-based optimization algorithms. The CGS method uses a Bayesian network classifier for predicting the performance of new designs based on a set of known observations or training points. Unlike most metamodeling techniques, however, the classifier assigns a categorical class label to a new design, rather than predicting the resulting response in continuous space, and thereby accommodates nondifferentiable and discontinuous functions of discrete or categorical variables. The CGS method uses these classifiers to guide a population-based sampling process towards combinations of discrete and/or continuous variable values with a high probability of yielding preferred performance. Accordingly, the CGS method is appropriate for discrete/discontinuous design problems that are ill-suited for conventional metamodeling techniques and too computationally expensive to be solved by population-based algorithms alone. In addition, the rates of convergence and computational properties of the CGS method are investigated when applied to a set of discrete variable optimization problems. Results show that the CGS method significantly improves the rate of convergence towards known global optima, on average, when compared to genetic algorithms.

  20. Computational Intelligence Techniques for Electro-Physiological Data Analysis

    OpenAIRE

    Riera Sardà, Alexandre

    2012-01-01

    This work contains the efforts I have made in the last years in the field of Electrophysiological data analysis. Most of the work has been done at Starlab Barcelona S.L. and part of it at the Neurodynamics Laboratory of the Department of Psychiatry and Clinical Psychobiology of the University of Barcelona. The main work deals with the analysis of electroencephalography (EEG) signals, although other signals, such as electrocardiography (ECG), electroculography (EOG) and electromiography (EMG) ...

  1. Electrophysiological safety of sertindole in dogs with normal and remodeled hearts

    DEFF Research Database (Denmark)

    Thomsen, Morten Bækgaard; Volders, Paul G A; Stengl, Milan

    2003-01-01

    Inhibition of the potassium current IKr and QT prolongation are associated with drug-induced torsades de pointes arrhythmias (TdP) and sudden cardiac death. We investigated the cardiac electrophysiological effects of sertindole, an antipsychotic drug reported to prolong the QT interval...

  2. Gender Performativity in the Community College: A Case Study of Female Backline Classified Staff

    Science.gov (United States)

    Powers, Samantha Rose

    2012-01-01

    This case study explored the gendered performances of five female backline classified staff members who work in non-traditional fields within a community college. More specifically, this study defined gendered behaviors at a community college, and explored how these behaviors have affected the identities of women working in non-traditional fields…

  3. Intraoperative high-field magnetic resonance imaging, multimodal neuronavigation, and intraoperative electrophysiological monitoring-guided surgery for treating supratentorial cavernomas.

    Science.gov (United States)

    Li, Fang-Ye; Chen, Xiao-Lei; Xu, Bai-Nan

    2016-09-01

    To determine the beneficial effects of intraoperative high-field magnetic resonance imaging (MRI), multimodal neuronavigation, and intraoperative electrophysiological monitoring-guided surgery for treating supratentorial cavernomas. Twelve patients with 13 supratentorial cavernomas were prospectively enrolled and operated while using a 1.5 T intraoperative MRI, multimodal neuronavigation, and intraoperative electrophysiological monitoring. All cavernomas were deeply located in subcortical areas or involved critical areas. Intraoperative high-field MRIs were obtained for the intraoperative "visualization" of surrounding eloquent structures, "brain shift" corrections, and navigational plan updates. All cavernomas were successfully resected with guidance from intraoperative MRI, multimodal neuronavigation, and intraoperative electrophysiological monitoring. In 5 cases with supratentorial cavernomas, intraoperative "brain shift" severely deterred locating of the lesions; however, intraoperative MRI facilitated precise locating of these lesions. During long-term (>3 months) follow-up, some or all presenting signs and symptoms improved or resolved in 4 cases, but were unchanged in 7 patients. Intraoperative high-field MRI, multimodal neuronavigation, and intraoperative electrophysiological monitoring are helpful in surgeries for the treatment of small deeply seated subcortical cavernomas.

  4. Hand-arm vibration syndrome: clinical characteristics, conventional electrophysiology and quantitative sensory testing.

    Science.gov (United States)

    Rolke, Roman; Rolke, Silke; Vogt, Thomas; Birklein, Frank; Geber, Christian; Treede, Rolf-Detlef; Letzel, Stephan; Voelter-Mahlknecht, Susanne

    2013-08-01

    Workers exposed to vibrating tools may develop hand-arm vibration syndrome (HAVS). We assessed the somatosensory phenotype using quantitative sensory testing (QST) in comparison to electrophysiology to characterize (1) the most sensitive QST parameter for detecting sensory loss, (2) the correlation of QST and electrophysiology, and (3) the frequency of a carpal tunnel syndrome (CTS) in HAVS. QST, cold provocation tests, fine motor skills, and median nerve neurography were used. QST included thermal and mechanical detection and pain thresholds. Thirty-two patients were examined (54 ± 11 years, 91% men) at the more affected hand compared to 16 matched controls. Vibration detection threshold was the most sensitive parameter to detect sensory loss that was more pronounced in the sensitivity range of Pacinian (150 Hz, x12) than Meissner's corpuscles (20 Hz, x3). QST (84% abnormal) was more sensitive to detect neural dysfunction than conventional electrophysiology (37% abnormal). Motor (34%) and sensory neurography (25%) were abnormal in HAVS. CTS frequency was not increased (9.4%). Findings are consistent with a mechanically-induced, distally pronounced motor and sensory neuropathy independent of CTS. HAVS involves a neuropathy predominantly affecting large fibers with a sensory damage related to resonance frequencies of vibrating tools. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  5. Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants.

    Science.gov (United States)

    Yousef, Malik; Saçar Demirci, Müşerref Duygu; Khalifa, Waleed; Allmer, Jens

    2016-01-01

    MicroRNAs (miRNAs) are short RNA sequences involved in posttranscriptional gene regulation. Their experimental analysis is complicated and, therefore, needs to be supplemented with computational miRNA detection. Currently computational miRNA detection is mainly performed using machine learning and in particular two-class classification. For machine learning, the miRNAs need to be parametrized and more than 700 features have been described. Positive training examples for machine learning are readily available, but negative data is hard to come by. Therefore, it seems prerogative to use one-class classification instead of two-class classification. Previously, we were able to almost reach two-class classification accuracy using one-class classifiers. In this work, we employ feature selection procedures in conjunction with one-class classification and show that there is up to 36% difference in accuracy among these feature selection methods. The best feature set allowed the training of a one-class classifier which achieved an average accuracy of ~95.6% thereby outperforming previous two-class-based plant miRNA detection approaches by about 0.5%. We believe that this can be improved upon in the future by rigorous filtering of the positive training examples and by improving current feature clustering algorithms to better target pre-miRNA feature selection.

  6. Hierarchical Learning of Tree Classifiers for Large-Scale Plant Species Identification.

    Science.gov (United States)

    Fan, Jianping; Zhou, Ning; Peng, Jinye; Gao, Ling

    2015-11-01

    In this paper, a hierarchical multi-task structural learning algorithm is developed to support large-scale plant species identification, where a visual tree is constructed for organizing large numbers of plant species in a coarse-to-fine fashion and determining the inter-related learning tasks automatically. For a given parent node on the visual tree, it contains a set of sibling coarse-grained categories of plant species or sibling fine-grained plant species, and a multi-task structural learning algorithm is developed to train their inter-related classifiers jointly for enhancing their discrimination power. The inter-level relationship constraint, e.g., a plant image must first be assigned to a parent node (high-level non-leaf node) correctly if it can further be assigned to the most relevant child node (low-level non-leaf node or leaf node) on the visual tree, is formally defined and leveraged to learn more discriminative tree classifiers over the visual tree. Our experimental results have demonstrated the effectiveness of our hierarchical multi-task structural learning algorithm on training more discriminative tree classifiers for large-scale plant species identification.

  7. Electrophysiological evidence for enhanced representation of food stimuli in working memory

    NARCIS (Netherlands)

    Rutters, F.; Kumar, S.; Higgs, S.; Humphreys, G.W.

    2015-01-01

    Studies from our laboratory have shown that, relative to neutral objects, food-related objects kept in working memory (WM) are particularly effective in guiding attention to food stimuli (Higgs et al. in Appetite, 2012). Here, we used electrophysiological measurements to investigate the neural

  8. Combining non-invasive transcranial brain stimulation with neuroimaging and electrophysiology: Current approaches and future perspectives.

    Science.gov (United States)

    Bergmann, Til Ole; Karabanov, Anke; Hartwigsen, Gesa; Thielscher, Axel; Siebner, Hartwig Roman

    2016-10-15

    Non-invasive transcranial brain stimulation (NTBS) techniques such as transcranial magnetic stimulation (TMS) and transcranial current stimulation (TCS) are important tools in human systems and cognitive neuroscience because they are able to reveal the relevance of certain brain structures or neuronal activity patterns for a given brain function. It is nowadays feasible to combine NTBS, either consecutively or concurrently, with a variety of neuroimaging and electrophysiological techniques. Here we discuss what kind of information can be gained from combined approaches, which often are technically demanding. We argue that the benefit from this combination is twofold. Firstly, neuroimaging and electrophysiology can inform subsequent NTBS, providing the required information to optimize where, when, and how to stimulate the brain. Information can be achieved both before and during the NTBS experiment, requiring consecutive and concurrent applications, respectively. Secondly, neuroimaging and electrophysiology can provide the readout for neural changes induced by NTBS. Again, using either concurrent or consecutive applications, both "online" NTBS effects immediately following the stimulation and "offline" NTBS effects outlasting plasticity-inducing NTBS protocols can be assessed. Finally, both strategies can be combined to close the loop between measuring and modulating brain activity by means of closed-loop brain state-dependent NTBS. In this paper, we will provide a conceptual framework, emphasizing principal strategies and highlighting promising future directions to exploit the benefits of combining NTBS with neuroimaging or electrophysiology. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  9. Multiple meanings of the middle class in Soweto, South Africa ...

    African Journals Online (AJOL)

    The study revealed that a diverse number of people call themselves middle class and defined class in terms of ability to afford basic goods. The label middle class seems also to denote self-sufficiency, responsibility and social mobility The paper concludes that studies of the middle class does not seen to focus on how social ...

  10. Electrophysiology of Axonal Constrictions

    Science.gov (United States)

    Johnson, Christopher; Jung, Peter; Brown, Anthony

    2013-03-01

    Axons of myelinated neurons are constricted at the nodes of Ranvier, where they are directly exposed to the extracellular space and where the vast majority of the ion channels are located. These constrictions are generated by local regulation of the kinetics of neurofilaments the most important cytoskeletal elements of the axon. In this paper we discuss how this shape affects the electrophysiological function of the neuron. Specifically, although the nodes are short (about 1 μm) in comparison to the distance between nodes (hundreds of μm) they have a substantial influence on the conduction velocity of neurons. We show through computational modeling that nodal constrictions (all other features such as numbers of ion channels left constant) reduce the required fiber diameter for a given target conduction velocity by up to 50% in comparison to an unconstricted axon. We further show that the predicted optimal fiber morphologies closely match reported fiber morphologies. Supported by The National Science Foundation (IOS 1146789)

  11. Electrophysiological Evaluation of Dysphagia in the Mild or Moderate Patients with Multiple Sclerosis: A Concept of Subclinical Dysphagia.

    Science.gov (United States)

    Beckmann, Yesim; Gürgör, Nevin; Çakır, Ahmet; Arıcı, Şehnaz; İncesu, Tülay Kurt; Seçil, Yaprak; Ertekin, Cumhur

    2015-06-01

    Swallowing mechanism and neurogenic dysphagia in MS have been rarely studied by electromyographical (EMG) methods. This study aims to evaluate the presence of subclinical dysphagia in patients with mild multiple sclerosis (MS) using electrophysiological methods. A prospective study of 51 patients with relapsing remitting multiple sclerosis and 18 age-matched healthy adults was investigated. We used electromyography to measure the activity of the submental muscles during swallowing. Electrophysiological recordings of patients were obtained during relapse, after relapse, and at any time in remission period. Clinical dysphagia was found in 12% of MS patients, while electrophysiological swallowing abnormalities were encountered in 33% of patients. Subclinical dysphagia was determined in 35% of patients during an MS relapse, in 20% of patients after a relapse, and in 25% of all 51 patients in the remission period based on EMG findings. Duration of swallowing signal of submental muscles in all MS patients was found to be longer than in normal subjects (p = 0.001). During swallowing of 50 ml of sequential water, the compensatory respiratory cycles occurred more often in MS patients than normal subjects, especially during a relapse (p = 0.005). This is the first study investigating swallowing abnormalities and subclinical dysphagia from the electrophysiological aspect in MS patients with mild disability. The electrophysiological tests described in this study are useful to uncover subclinical dysphagia since they have the advantage of being rapid, easy to apply, non-invasive, and without risk for the patients.

  12. Electrophysiological Monitoring in Patients With Tumors of the Skull Base Treated by Carbon-12 Radiation Therapy

    Energy Technology Data Exchange (ETDEWEB)

    Carozzo, Simone [Department of Neuroscience, Ophthalmology, and Genetics, University of Genova, Genova (Italy); Schardt, Dieter [Department of Biophysics, GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt (Germany); Narici, Livio [Department of Physics, University of Rome Tor Vergata, Rome (Italy); Combs, Stephanie E.; Debus, Jürgen [Department of Radiation Oncology, University of Heidelberg, Heidelberg (Germany); Sannita, Walter G., E-mail: wgs@dism.unige.it [Department of Neuroscience, Ophthalmology, and Genetics, University of Genova, Genova (Italy); Department of Psychiatry, State University of New York, Stony Brook, New York (United States)

    2013-03-15

    Purpose: To report the results of short-term electrophysiologic monitoring of patients undergoing {sup 12}C therapy for the treatment of skull chordomas and chondrosarcomas unsuitable for radical surgery. Methods and Materials: Conventional electroencephalogram (EEG) and retinal and cortical electrophysiologic responses to contrast stimuli were recorded from 30 patients undergoing carbon ion radiation therapy, within a few hours before the first treatment and after completion of therapy. Methodologies and procedures were compliant with the guidelines of the International Federation for Clinical Neurophysiology and International Society for Clinical Electrophysiology of Vision. Results: At baseline, clinical signs were reported in 56.6% of subjects. Electrophysiologic test results were abnormal in 76.7% (EEG), 78.6% (cortical evoked potentials), and 92.8% (electroretinogram) of cases, without correlation with neurologic signs, tumor location, or therapy plan. Results on EEG, but not electroretinograms and cortical responses, were more often abnormal in patients with reported clinical signs. Abnormal EEG results and retinal/cortical responses improved after therapy in 40% (EEG), 62.5% (cortical potentials), and 70% (electroretinogram) of cases. Results on EEG worsened after therapy in one-third of patients whose recordings were normal at baseline. Conclusions: The percentages of subjects whose EEG results improved or worsened after therapy and the improvement of retinal/cortical responses in the majority of patients are indicative of a limited or negligible (and possibly transient) acute central nervous system toxicity of carbon ion therapy, with a significant beneficial effect on the visual pathways. Research on large samples would validate electrophysiologic procedures as a possible independent test for central nervous system toxicity and allow investigation of the correlation with clinical signs; repeated testing over time after therapy would demonstrate, and may

  13. IMPROVING QUALITY OF WORK LIFE THROUGH ELECTROPHYSIOLOGY: AN IDEA ACCEPTED BY INDUSTRY

    Directory of Open Access Journals (Sweden)

    Evanthia Giagloglou

    2015-12-01

    Full Text Available Quality of Work Life (QWL and Occupational Health and Safety (OHS are two interconnected and important human needs. Modern industry shows a clear will for improving QWL and OHS, nevertheless, existent automatization and technological advances may negatively influence employees' wellbeing and result as triggers to their health deterioration. Subjective measures of employees workload can help, however, the lack of objectivity may be an issue. Improvement of working life needs objective measures. There is technology for measuring objectively employees' psychophysiology, but is considered to interfere with the flexibility needed for performing working tasks. Today electrophysiological methods require minimal dimensions, are wireless connected, allow movement and are proved to be useful in capturing psychophysical wellbeing. This study shows that the industry is ready to accept electrophysiological measures for monitoring and improving the employees' wellbeing.

  14. EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine

    Science.gov (United States)

    Gao, Lin; Cheng, Wei; Zhang, Jinhua; Wang, Jue

    2016-08-01

    Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, the feature extraction and classification approach should differentiate the relative unusual state of motion intention from a common resting state. In this paper, we sought a novel approach for multi-class classification in BCI applications. We collected electroencephalographic (EEG) signals registered by electrodes placed over the scalp during left hand motor imagery, right hand motor imagery, and resting state for ten healthy human subjects. We proposed using the Kolmogorov complexity (Kc) for feature extraction and a multi-class Adaboost classifier with extreme learning machine as base classifier for classification, in order to classify the three-class EEG samples. An average classification accuracy of 79.5% was obtained for ten subjects, which greatly outperformed commonly used approaches. Thus, it is concluded that the proposed method could improve the performance for classification of motor imagery tasks for multi-class samples. It could be applied in further studies to generate the control commands to initiate the movement of a robotic exoskeleton or orthosis, which finally facilitates the rehabilitation of disabled people.

  15. Automated Electrophysiology Makes the Pace for Cardiac Ion Channel Safety Screening

    Directory of Open Access Journals (Sweden)

    Clemens eMoeller

    2011-11-01

    Full Text Available The field of automated patch-clamp electrophysiology has emerged from the tension between the pharmaceutical industry’s need for high-throughput compound screening versus its need to be conservative due to regulatory requirements. On the one hand, hERG channel screening was increasingly requested for new chemical entities, as the correlation between blockade of the ion channel coded by hERG and Torsades de Pointes cardiac arrhythmia gained increasing attention. On the other hand, manual patch-clamping, typically quoted as the gold-standard for understanding ion channel function and modulation, was far too slow (and, consequently, too expensive for keeping pace with the numbers of compounds submitted for hERG channel investigations from pharmaceutical R&D departments. In consequence it became more common for some pharmaceutical companies to outsource safety pharmacological investigations, with a focus on hERG channel interactions. This outsourcing has allowed those pharmaceutical companies to build up operational flexibility and greater independence from internal resources, and allowed them to obtain access to the latest technological developments that emerged in automated patch-clamp electrophysiology – much of which arose in specialized biotech companies. Assays for nearly all major cardiac ion channels are now available by automated patch-clamping using heterologous expression systems, and recently, automated action potential recordings from stem-cell derived cardiomyocytes have been demonstrated. Today, most of the large pharmaceutical companies have acquired automated electrophysiology robots and have established various automated cardiac ion channel safety screening assays on these, in addition to outsourcing parts of their needs for safety screening.

  16. Feature selection and classification of mechanical fault of an induction motor using random forest classifier

    OpenAIRE

    Patel, Raj Kumar; Giri, V.K.

    2016-01-01

    Fault detection and diagnosis is the most important technology in condition-based maintenance (CBM) system for rotating machinery. This paper experimentally explores the development of a random forest (RF) classifier, a recently emerged machine learning technique, for multi-class mechanical fault diagnosis in bearing of an induction motor. Firstly, the vibration signals are collected from the bearing using accelerometer sensor. Parameters from the vibration signal are extracted in the form of...

  17. Evidence for Acute Electrophysiological and Cognitive Changes Following Routine Soccer Heading

    Directory of Open Access Journals (Sweden)

    Thomas G. Di Virgilio

    2016-11-01

    Discussion: Sub-concussive head impacts routine in soccer heading are associated with immediate, measurable electrophysiological and cognitive impairments. Although these changes in brain function were transient, these effects may signal direct consequences of routine soccer heading on (long-term brain health which requires further study.

  18. Implementation of Naive Bayes Classifier Algorithm to Evaluation in Utilizing Online Hotel Tax Reporting Application

    Directory of Open Access Journals (Sweden)

    R. Dimas Adityo

    2017-10-01

    Full Text Available The current implementation of tax reporting regional Pasuruan hotels have used online (Web-based, with the aim of reporting systems can run effectively and efficiently in receiving the financial statements especially from taxpayer property. Pasuruan as one small town quite rapidly in East Java, have implemented role models online tax filing system starting in 2015, with the amount of 6 hotels, there are several classes of hotels ranging from the budget class up to class three stars. After the application of the system running for 18 months (2015-2016, from existing data, conducted research on the analysis of the level of compliance of taxpayers reporting incomes in a hotel. On the research was designed and built a system to evaluate the level of compliance with the performance from the taxpayer (WP in the 2nd year (2016 and are classified in categories (1 the taxpayer (WP very obedient (ST, (2 the taxpayer (WP is quite obedient (CT, (3 Taxpayers (WP less obedient (KT. Input data will be processed using the technique of data mining algorithms Naive Bayes Classifier (NBC to form the table of probability as a basis for the process of classification levels of taxpayer compliance. Based on the results of the measurement, the test results show with an accuracy of 50% i.e. 3 taxpayers is the very obedient (ST to pay taxes. Then from the classification, the study could be made of recommendation solutions to guide the taxpayer in reporting revenues well and true.

  19. Somatomotor and oculomotor inferior olivary neurons have distinct electrophysiological phenotypes

    Science.gov (United States)

    Urbano, Francisco J.; Simpson, John I.; Llinás, Rodolfo R.

    2006-01-01

    The electrophysiological properties of rat inferior olive (IO) neurons in the dorsal cap of Kooy (DCK) and the adjacent ventrolateral outgrowth (VLO) were compared with those of IO neurons in the principal olive (PO). Whereas DCK/VLO neurons are involved in eye movement control via their climbing fiber projection to the cerebellar flocculus, PO neurons control limb and digit movements via their climbing fiber projection to the lateral cerebellar hemisphere. In vitro patch recordings from DCK/VLO neurons revealed that low threshold calcium currents, Ih currents, and subthreshold oscillations are lacking in this subset of IO neurons. The recordings of activity in DCK neurons obtained by using voltage-sensitive dye imaging showed that activity is not limited to a single neuron, but rather that clusters of DCK neurons can be active in unison. These electrophysiological results show that the DCK/VLO neurons have unique properties that set them apart from the neurons in the PO nucleus. This finding indicates that motor control, from the perspective of the olivocerebellar system, is fundamentally different for the oculomotor and the somatomotor systems. PMID:17050678

  20. Analyzing the electrophysiological effects of local epicardial temperature in experimental studies with isolated hearts

    International Nuclear Information System (INIS)

    Tormos, Alvaro; Millet, José; Guill, Antonio; Chorro, Francisco J; Cánoves, Joaquín; Mainar, Luis; Such, Luis; Alberola, Antonio; Trapero, Isabel; Such-Miquel, Luis

    2008-01-01

    As a result of their modulating effects upon myocardial electrophysiology, both hypo- and hyperthermia can be used to study the mechanisms that generate or sustain cardiac arrhythmias. The present study describes an original electrode developed with thick-film technology and capable of controlling regional temperature variations in the epicardium while simultaneously registering its electrical activity. In this way, it is possible to measure electrophysiological parameters of the heart at different temperatures. The results obtained with this device in a study with isolated and perfused rabbit hearts are reported. An exploration has been made of the effects of local temperature changes upon the electrophysiological parameters implicated in myocardial conduction. Likewise, an analysis has been made of the influence of local temperature upon ventricular fibrillation activation frequency. It is concluded that both regional hypo- and hyperthermia exert reversible and opposite effects upon myocardial refractoriness and conduction velocity in the altered zone. The ventricular activation wavelength determined during constant pacing at 250 ms cycles is not significantly modified, however. During ventricular fibrillation, the changes in the fibrillatory frequency do not seem to be transmitted to normal temperature zones

  1. Effects of emotion on different phoneme classes

    Science.gov (United States)

    Lee, Chul Min; Yildirim, Serdar; Bulut, Murtaza; Busso, Carlos; Kazemzadeh, Abe; Lee, Sungbok; Narayanan, Shrikanth

    2004-10-01

    This study investigates the effects of emotion on different phoneme classes using short-term spectral features. In the research on emotion in speech, most studies have focused on prosodic features of speech. In this study, based on the hypothesis that different emotions have varying effects on the properties of the different speech sounds, we investigate the usefulness of phoneme-class level acoustic modeling for automatic emotion classification. Hidden Markov models (HMM) based on short-term spectral features for five broad phonetic classes are used for this purpose using data obtained from recordings of two actresses. Each speaker produces 211 sentences with four different emotions (neutral, sad, angry, happy). Using the speech material we trained and compared the performances of two sets of HMM classifiers: a generic set of ``emotional speech'' HMMs (one for each emotion) and a set of broad phonetic-class based HMMs (vowel, glide, nasal, stop, fricative) for each emotion type considered. Comparison of classification results indicates that different phoneme classes were affected differently by emotional change and that the vowel sounds are the most important indicator of emotions in speech. Detailed results and their implications on the underlying speech articulation will be discussed.

  2. Novel Mahalanobis-based feature selection improves one-class classification of early hepatocellular carcinoma.

    Science.gov (United States)

    Thomaz, Ricardo de Lima; Carneiro, Pedro Cunha; Bonin, João Eliton; Macedo, Túlio Augusto Alves; Patrocinio, Ana Claudia; Soares, Alcimar Barbosa

    2018-05-01

    Detection of early hepatocellular carcinoma (HCC) is responsible for increasing survival rates in up to 40%. One-class classifiers can be used for modeling early HCC in multidetector computed tomography (MDCT), but demand the specific knowledge pertaining to the set of features that best describes the target class. Although the literature outlines several features for characterizing liver lesions, it is unclear which is most relevant for describing early HCC. In this paper, we introduce an unconstrained GA feature selection algorithm based on a multi-objective Mahalanobis fitness function to improve the classification performance for early HCC. We compared our approach to a constrained Mahalanobis function and two other unconstrained functions using Welch's t-test and Gaussian Data Descriptors. The performance of each fitness function was evaluated by cross-validating a one-class SVM. The results show that the proposed multi-objective Mahalanobis fitness function is capable of significantly reducing data dimensionality (96.4%) and improving one-class classification of early HCC (0.84 AUC). Furthermore, the results provide strong evidence that intensity features extracted at the arterial to portal and arterial to equilibrium phases are important for classifying early HCC.

  3. IAEA safeguards and classified materials

    International Nuclear Information System (INIS)

    Pilat, J.F.; Eccleston, G.W.; Fearey, B.L.; Nicholas, N.J.; Tape, J.W.; Kratzer, M.

    1997-01-01

    The international community in the post-Cold War period has suggested that the International Atomic Energy Agency (IAEA) utilize its expertise in support of the arms control and disarmament process in unprecedented ways. The pledges of the US and Russian presidents to place excess defense materials, some of which are classified, under some type of international inspections raises the prospect of using IAEA safeguards approaches for monitoring classified materials. A traditional safeguards approach, based on nuclear material accountancy, would seem unavoidably to reveal classified information. However, further analysis of the IAEA's safeguards approaches is warranted in order to understand fully the scope and nature of any problems. The issues are complex and difficult, and it is expected that common technical understandings will be essential for their resolution. Accordingly, this paper examines and compares traditional safeguards item accounting of fuel at a nuclear power station (especially spent fuel) with the challenges presented by inspections of classified materials. This analysis is intended to delineate more clearly the problems as well as reveal possible approaches, techniques, and technologies that could allow the adaptation of safeguards to the unprecedented task of inspecting classified materials. It is also hoped that a discussion of these issues can advance ongoing political-technical debates on international inspections of excess classified materials

  4. The structural dynamics of social class.

    Science.gov (United States)

    Kraus, Michael W; Park, Jun Won

    2017-12-01

    Individual agency accounts of social class persist in society and even in psychological science despite clear evidence for the role of social structures. This article argues that social class is defined by the structural dynamics of society. Specifically, access to powerful networks, groups, and institutions, and inequalities in wealth and other economic resources shape proximal social environments that influence how individuals express their internal states and motivations. An account of social class that highlights the means by which structures shape and are shaped by individuals guides our understanding of how people move up or down in the social class hierarchy, and provides a framework for interpreting neuroscience studies, experimental paradigms, and approaches that attempt to intervene on social class disparities. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. A space-fractional Monodomain model for cardiac electrophysiology combining anisotropy and heterogeneity on realistic geometries

    Science.gov (United States)

    Cusimano, N.; Gerardo-Giorda, L.

    2018-06-01

    Classical models of electrophysiology do not typically account for the effects of high structural heterogeneity in the spatio-temporal description of excitation waves propagation. We consider a modification of the Monodomain model obtained by replacing the diffusive term of the classical formulation with a fractional power of the operator, defined in the spectral sense. The resulting nonlocal model describes different levels of tissue heterogeneity as the fractional exponent is varied. The numerical method for the solution of the fractional Monodomain relies on an integral representation of the nonlocal operator combined with a finite element discretisation in space, allowing to handle in a natural way bounded domains in more than one spatial dimension. Numerical tests in two spatial dimensions illustrate the features of the model. Activation times, action potential duration and its dispersion throughout the domain are studied as a function of the fractional parameter: the expected peculiar behaviour driven by tissue heterogeneities is recovered.

  6. Biofeedback in psychomotor training. Electrophysiological basis.

    Science.gov (United States)

    Bazanova, O M; Mernaya, E M; Shtark, M B

    2009-06-01

    The influences of individual musical practice and the same practice supplemented with biofeedback using electrophysiological markers for optimum music-performing activity were studied in 39 music students. Traditional technical practice produced increases in integral EMG power and decreases in alpha activity in most of the students with initially low maximum alpha activity peak frequencies. Similar practice but combined with individual sessions of alpha-EEG/EMG biofeedback were accompanied by increases in the frequency, bandwidth, and activation responses of EEG alpha rhythms in all subjects, along with decreases in EEG integral power. The efficacy of training with biofeedback and the ability to experience psychomotor learning depended on the initial individual characteristics of EEG alpha activity.

  7. Zooniverse: Combining Human and Machine Classifiers for the Big Survey Era

    Science.gov (United States)

    Fortson, Lucy; Wright, Darryl; Beck, Melanie; Lintott, Chris; Scarlata, Claudia; Dickinson, Hugh; Trouille, Laura; Willi, Marco; Laraia, Michael; Boyer, Amy; Veldhuis, Marten; Zooniverse

    2018-01-01

    Many analyses of astronomical data sets, ranging from morphological classification of galaxies to identification of supernova candidates, have relied on humans to classify data into distinct categories. Crowdsourced galaxy classifications via the Galaxy Zoo project provided a solution that scaled visual classification for extant surveys by harnessing the combined power of thousands of volunteers. However, the much larger data sets anticipated from upcoming surveys will require a different approach. Automated classifiers using supervised machine learning have improved considerably over the past decade but their increasing sophistication comes at the expense of needing ever more training data. Crowdsourced classification by human volunteers is a critical technique for obtaining these training data. But several improvements can be made on this zeroth order solution. Efficiency gains can be achieved by implementing a “cascade filtering” approach whereby the task structure is reduced to a set of binary questions that are more suited to simpler machines while demanding lower cognitive loads for humans.Intelligent subject retirement based on quantitative metrics of volunteer skill and subject label reliability also leads to dramatic improvements in efficiency. We note that human and machine classifiers may retire subjects differently leading to trade-offs in performance space. Drawing on work with several Zooniverse projects including Galaxy Zoo and Supernova Hunter, we will present recent findings from experiments that combine cohorts of human and machine classifiers. We show that the most efficient system results when appropriate subsets of the data are intelligently assigned to each group according to their particular capabilities.With sufficient online training, simple machines can quickly classify “easy” subjects, leaving more difficult (and discovery-oriented) tasks for volunteers. We also find humans achieve higher classification purity while samples

  8. Gene expression-based classifiers identify Staphylococcus aureus infection in mice and humans.

    Directory of Open Access Journals (Sweden)

    Sun Hee Ahn

    Full Text Available Staphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. A growing literature suggests the host's inflammatory response to the pathogen represents a potential tool to improve upon current diagnostics. The hypothesis of this study is that the host responds differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. This study uses Bayesian sparse factor modeling and penalized binary regression to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli-infected mice across a range of conditions (mouse and bacterial strain, time post infection and was validated in outbred mice (AUC>0.97. A S. aureus classifier derived from a cohort of 94 human subjects distinguished S. aureus blood stream infection (BSI from healthy subjects (AUC 0.99 and E. coli BSI (AUC 0.84. Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84. Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.92, respectively. The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances our understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues.

  9. Representations of Urbanik's classes and multiparameter Ornstein-Uhlenbeck processes

    DEFF Research Database (Denmark)

    Graversen, Svend-Erik; Pedersen, Jan

    2011-01-01

    A class of integrals with respect to homogeneous Lévy bases on Rk is considered. In the one-dimensional case k=1 this class corresponds to the selfdecomposable distributions. Necessary and sufficient conditions for existence as well as some representations of the integrals are given. Generalizing...... the one-dimensional case it is shown that the class of integrals corresponds to Urbanik's class Lk-1(R). Finally, multiparameter Ornstein-Uhlenbeck processes are defined and studied....

  10. Role of Electrophysiological Study and Catheter Ablation for Recurrent Ventricular Tachycardia Complicating Myocarditis

    Directory of Open Access Journals (Sweden)

    Emanuele Cecchi

    2012-01-01

    Full Text Available Here we report the case of a 31-year-old man admitted to our hospital with echocardiografic and Cardiac Magnetic Resonance signs of myocarditis complicated by ventricular tachycardia, initially resolved with direct current shock. After the recurrence of ventricular tachycardia the patient was submitted to electrophysiological study revealing a re-entrant circuit at the level of the medium segment of interventricular septum, successfully treated with transcatheter ablation. This case highlights how the presence of recurrent ventricular arrhythmias at the onset of acute myocarditis, suspected or proven, could be associated with a pre-existing arrhythmogenic substrate, therefore these patients should be submitted to electrophysiological study in order to rule out the presence of arrhythmogenic focuses that can be treated with transcatheter ablation.

  11. Painting with the Multiple Intelligences: Defining Student Success and Permanence in Art Class

    Science.gov (United States)

    Taspinar, Seyda Eraslan; Kaya, Ali

    2016-01-01

    Objectives of the study are to determine the effect of teaching based on multiple intelligence theory (TBMIT) in visual arts class on student success and permanence. Experimental design is used in the study. Study group is composed of students at 8th grade in 2012-2013 educational term at Atatürk Secondary School in Igdir city centre. Experimental…

  12. A low-energy x-ray irradiator for electrophysiological studies

    International Nuclear Information System (INIS)

    Schauer, D.A.; Zeman, G.H.; Pellmar, T.C.

    1989-01-01

    A 50 kVp molybdenum target/filter x-ray tube has been installed inside a lead-shielded Faraday cage. High-dose rates of up to 1.54 Gy min -1 (17.4 keV weighted average photons) have been used to conduct local in vitro irradiations of the hippocampal region of guinea pig brains. Electrophysiological recordings of subtle changes in neuronal activity indicate this system is suitable for this application. (author)

  13. Anatomical and electrophysiological changes in striatal TH interneurons after loss of the nigrostriatal dopaminergic pathway.

    Science.gov (United States)

    Ünal, Bengi; Shah, Fulva; Kothari, Janish; Tepper, James M

    2015-01-01

    Using transgenic mice that express enhanced green fluorescent protein (EGFP) under the control of the tyrosine hydroxylase (TH) promoter, we have previously shown that there are approximately 3,000 striatal EGFP-TH interneurons per hemisphere in mice. Here, we report that striatal TH-EGFP interneurons exhibit a small, transient but significant increase in number after unilateral destruction of the nigrostriatal dopaminergic pathway. The increase in cell number is accompanied by electrophysiological and morphological changes. The intrinsic electrophysiological properties of EGFP-TH interneurons ipsilateral to 6-OHDA lesion were similar to those originally reported in intact mice except for a significant reduction in the duration of a characteristic depolarization induced plateau potential. There was a significant change in the distribution of the four previously described electrophysiologically distinct subtypes of striatal TH interneurons. There was a concomitant increase in the frequency of both spontaneous excitatory and inhibitory post-synaptic currents, while their amplitudes did not change. Nigrostriatal lesions did not affect somatic size or dendritic length or branching, but resulted in an increase in the density of proximal dendritic spines and spine-like appendages in EGFP-TH interneurons. The changes indicate that electrophysiology properties and morphology of striatal EGFP-TH interneurons depend on endogenous levels of dopamine arising from the nigrostriatal pathway. Furthermore, these changes may serve to help compensate for the changes in activity of spiny projection neurons that occur following loss of the nigrostriatal innervation in experimental or in early idiopathic Parkinson's disease by increasing feedforward GABAergic inhibition exerted by these interneurons.

  14. A relationship between bruxism and orofacial-dystonia? A trigeminal electrophysiological approach in a case report of pineal cavernoma

    OpenAIRE

    Frisardi, Gianni; Iani, Cesare; Sau, Gianfranco; Frisardi, Flavio; Leornadis, Carlo; Lumbau, Aurea; Enrico, Paolo; Sirca, Donatella; Staderini, Enrico Maria; Chessa, Giacomo

    2013-01-01

    Background: In some clinical cases, bruxism may be correlated to central nervous system hyperexcitability, suggesting that bruxism may represent a subclinical form of dystonia. To examine this hypothesis, we performed an electrophysiological evaluation of the excitability of the trigeminal nervous system in a patient affected by pineal cavernoma with pain symptoms in the orofacial region and pronounced bruxism. Methods: Electrophysiological studies included bilateral electrical transcrania...

  15. Classifying features in CT imagery: accuracy for some single- and multiple-species classifiers

    Science.gov (United States)

    Daniel L. Schmoldt; Jing He; A. Lynn Abbott

    1998-01-01

    Our current approach to automatically label features in CT images of hardwood logs classifies each pixel of an image individually. These feature classifiers use a back-propagation artificial neural network (ANN) and feature vectors that include a small, local neighborhood of pixels and the distance of the target pixel to the center of the log. Initially, this type of...

  16. Intrusion detection model using fusion of chi-square feature selection and multi class SVM

    Directory of Open Access Journals (Sweden)

    Ikram Sumaiya Thaseen

    2017-10-01

    Full Text Available Intrusion detection is a promising area of research in the domain of security with the rapid development of internet in everyday life. Many intrusion detection systems (IDS employ a sole classifier algorithm for classifying network traffic as normal or abnormal. Due to the large amount of data, these sole classifier models fail to achieve a high attack detection rate with reduced false alarm rate. However by applying dimensionality reduction, data can be efficiently reduced to an optimal set of attributes without loss of information and then classified accurately using a multi class modeling technique for identifying the different network attacks. In this paper, we propose an intrusion detection model using chi-square feature selection and multi class support vector machine (SVM. A parameter tuning technique is adopted for optimization of Radial Basis Function kernel parameter namely gamma represented by ‘ϒ’ and over fitting constant ‘C’. These are the two important parameters required for the SVM model. The main idea behind this model is to construct a multi class SVM which has not been adopted for IDS so far to decrease the training and testing time and increase the individual classification accuracy of the network attacks. The investigational results on NSL-KDD dataset which is an enhanced version of KDDCup 1999 dataset shows that our proposed approach results in a better detection rate and reduced false alarm rate. An experimentation on the computational time required for training and testing is also carried out for usage in time critical applications.

  17. Electrophysiological correlates of the BOLD signal for EEG-informed fMRI

    Science.gov (United States)

    Murta, Teresa; Leite, Marco; Carmichael, David W; Figueiredo, Patrícia; Lemieux, Louis

    2015-01-01

    Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are important tools in cognitive and clinical neuroscience. Combined EEG–fMRI has been shown to help to characterise brain networks involved in epileptic activity, as well as in different sensory, motor and cognitive functions. A good understanding of the electrophysiological correlates of the blood oxygen level-dependent (BOLD) signal is necessary to interpret fMRI maps, particularly when obtained in combination with EEG. We review the current understanding of electrophysiological–haemodynamic correlates, during different types of brain activity. We start by describing the basic mechanisms underlying EEG and BOLD signals and proceed by reviewing EEG-informed fMRI studies using fMRI to map specific EEG phenomena over the entire brain (EEG–fMRI mapping), or exploring a range of EEG-derived quantities to determine which best explain colocalised BOLD fluctuations (local EEG–fMRI coupling). While reviewing studies of different forms of brain activity (epileptic and nonepileptic spontaneous activity; cognitive, sensory and motor functions), a significant attention is given to epilepsy because the investigation of its haemodynamic correlates is the most common application of EEG-informed fMRI. Our review is focused on EEG-informed fMRI, an asymmetric approach of data integration. We give special attention to the invasiveness of electrophysiological measurements and the simultaneity of multimodal acquisitions because these methodological aspects determine the nature of the conclusions that can be drawn from EEG-informed fMRI studies. We emphasise the advantages of, and need for, simultaneous intracranial EEG–fMRI studies in humans, which recently became available and hold great potential to improve our understanding of the electrophysiological correlates of BOLD fluctuations. PMID:25277370

  18. Angiographic patterns of in-stent restenosis classified by computed tomography in patients with drug-eluting stents: correlation with invasive coronary angiography

    International Nuclear Information System (INIS)

    Pan, Jingwei; Lu, Zhigang; Wei, Meng; Zhang, Jiayin; Li, Minghua

    2013-01-01

    To evaluate the diagnostic accuracy of Mehran's in-stent restenosis (ISR) classification by coronary computed angiography (CCTA), with reference to invasive coronary angiography (ICA). Consecutive symptomatic patients, who had clinically suspected ISR and implanted stent diameter ≥ 3 mm, were prospectively enrolled in our study. Mehran's classification was employed by CCTA and ICA to classify ISR lesions into four subtypes: focal, diffuse intrastent, diffuse proliferative and total occlusion. CCTA and ICA measurement of lesion length was further compared. Sixty-one patients with 101 implanted stents were included in our study. The overall sensitivity, specificity, PPV and NPV of CCTA diagnosis of binary ISR, as shown by patient-based analysis (n = 61), were 100 % (49/49), 75 % (8/12), 92.45 % (49/53) and 100 % (8/8) respectively. Mehran's classification of CCTA correlated well with ICA findings. The diagnostic accuracy of CCTA for class I, class II, class III and class IV lesions was 92.5 %, 91.67 %, 100 % and 100 % respectively. Lesion length was assessed to be significantly longer with CCTA than with ICA (11.03 ± 5.89 mm versus 8.56 ± 4.99 mm, P < 0.001). Angiographic patterns of in-stent restenosis can be accurately classified by coronary computed angiography. The lesion length measured by CCTA is longer than that assessed by invasive coronary angiography. (orig.)

  19. Content Themes of Alcohol Advertising in U.S. Television-Latent Class Analysis.

    Science.gov (United States)

    Morgenstern, Matthis; Schoeppe, Franziska; Campbell, Julie; Braam, Marloes W G; Stoolmiller, Michael; Sargent, James D

    2015-09-01

    There is little alcohol research that reports on the thematic contents of contemporary alcohol advertisements in U.S. television. Studies of alcohol ads from 2 decades ago did not identify "Partying" as a social theme. Aim of this study was to describe and classify alcohol advertisements aired in national television in terms of contents, airing times, and channel placements and to identify different marketing strategies of alcohol brands. Content analysis of all ads from the top 20 U.S. beer and spirit brands aired between July 2009 and June 2011. These were 581 unique alcohol ads accounting for 272,828 (78%) national television airings. Ads were coded according to predefined definitions of 13 content areas. A latent class analysis (LCA) was conducted to define content cluster themes and determine alcoholic brands that were more likely to exploit these themes. About half of the advertisements (46%) were aired between 3 am and 8 pm, and the majority were placed either in Entertainment (40%) and Sports (38%) channels. Beer ads comprised 64% of the sample, with significant variation in airing times and channels between types of products and brands. LCA revealed 5 content classes that exploited the "Partying," "Quality," "Sports," "Manly," and "Relax" themes. The partying class, indicative of ad messages surrounding partying, love, and sex, was the dominant theme comprising 42% of all advertisements. Ads for alcopops, flavored spirits, and liqueur were more likely to belong to the party class, but there were also some beer brands (Corona, Heineken) where more than 67% of ads exploited this theme. This is the first analysis to identify a partying theme to contemporary alcohol advertising. Future analyses can now determine whether exposure to that or other themes predicts alcohol misuse among youth audiences. Copyright © 2015 by the Research Society on Alcoholism.

  20. Content Themes of Alcohol Advertising in US Television — Latent Class Analysis

    Science.gov (United States)

    Morgenstern, Matthis; Schoeppe, Franziska; Campbell, Julie; Braam, Marloes W.G.; Stoolmiller, Michael; Sargent, James D.

    2015-01-01

    Background There is little alcohol research that reports on the thematic contents of contemporary alcohol advertisements in US television. Studies of alcohol ads from two decades ago did not identify “partying” as a social theme. Aim of the present study was to describe and classify alcohol advertisements aired in national television in terms of contents, airing times, and channel placements and to identify different marketing strategies of alcohol brands. Methods Content analysis of all ads from the top 20 US beer and spirit brands aired between July 2009 and June 2011. These were 581 unique alcohol ads accounting for 272,828 (78%) national television airings. Ads were coded according to predefined definitions of 13 content areas. A latent class analysis (LCA) was conducted to define content cluster themes and determine alcoholic brands that were more likely to exploit these themes. Results About half of the advertisements (46%) were aired between 3am and 8pm, and the majority were placed either in Entertainment (40%) and Sports (38%) channels. Beer ads comprised 64% of the sample, with significant variation in airing times and channels between types of products and brands. LCA revealed five content classes that exploited the “Partying”, “Quality”, “Sports”, “Manly”, and “Relax” themes. The partying class, indicative of ad messages surrounding partying, love and sex, was the dominant theme, comprising 42% of all advertisements. Ads for alcopops, flavored spirits, and liqueur were more likely to belong to the party class, but there were also some beer brands (Corona, Heineken) where more than 67% of ads exploited this theme. Conclusions This is the first analysis to identify a partying theme to contemporary alcohol advertising. Future analyses can now determine whether exposure to that or other themes predicts alcohol misuse among youth audiences. PMID:26207317

  1. LCC: Light Curves Classifier

    Science.gov (United States)

    Vo, Martin

    2017-08-01

    Light Curves Classifier uses data mining and machine learning to obtain and classify desired objects. This task can be accomplished by attributes of light curves or any time series, including shapes, histograms, or variograms, or by other available information about the inspected objects, such as color indices, temperatures, and abundances. After specifying features which describe the objects to be searched, the software trains on a given training sample, and can then be used for unsupervised clustering for visualizing the natural separation of the sample. The package can be also used for automatic tuning parameters of used methods (for example, number of hidden neurons or binning ratio). Trained classifiers can be used for filtering outputs from astronomical databases or data stored locally. The Light Curve Classifier can also be used for simple downloading of light curves and all available information of queried stars. It natively can connect to OgleII, OgleIII, ASAS, CoRoT, Kepler, Catalina and MACHO, and new connectors or descriptors can be implemented. In addition to direct usage of the package and command line UI, the program can be used through a web interface. Users can create jobs for ”training” methods on given objects, querying databases and filtering outputs by trained filters. Preimplemented descriptors, classifier and connectors can be picked by simple clicks and their parameters can be tuned by giving ranges of these values. All combinations are then calculated and the best one is used for creating the filter. Natural separation of the data can be visualized by unsupervised clustering.

  2. 15 CFR 4.8 - Classified Information.

    Science.gov (United States)

    2010-01-01

    ... 15 Commerce and Foreign Trade 1 2010-01-01 2010-01-01 false Classified Information. 4.8 Section 4... INFORMATION Freedom of Information Act § 4.8 Classified Information. In processing a request for information..., the information shall be reviewed to determine whether it should remain classified. Ordinarily the...

  3. NeoAnalysis: a Python-based toolbox for quick electrophysiological data processing and analysis.

    Science.gov (United States)

    Zhang, Bo; Dai, Ji; Zhang, Tao

    2017-11-13

    In a typical electrophysiological experiment, especially one that includes studying animal behavior, the data collected normally contain spikes, local field potentials, behavioral responses and other associated data. In order to obtain informative results, the data must be analyzed simultaneously with the experimental settings. However, most open-source toolboxes currently available for data analysis were developed to handle only a portion of the data and did not take into account the sorting of experimental conditions. Additionally, these toolboxes require that the input data be in a specific format, which can be inconvenient to users. Therefore, the development of a highly integrated toolbox that can process multiple types of data regardless of input data format and perform basic analysis for general electrophysiological experiments is incredibly useful. Here, we report the development of a Python based open-source toolbox, referred to as NeoAnalysis, to be used for quick electrophysiological data processing and analysis. The toolbox can import data from different data acquisition systems regardless of their formats and automatically combine different types of data into a single file with a standardized format. In cases where additional spike sorting is needed, NeoAnalysis provides a module to perform efficient offline sorting with a user-friendly interface. Then, NeoAnalysis can perform regular analog signal processing, spike train, and local field potentials analysis, behavioral response (e.g. saccade) detection and extraction, with several options available for data plotting and statistics. Particularly, it can automatically generate sorted results without requiring users to manually sort data beforehand. In addition, NeoAnalysis can organize all of the relevant data into an informative table on a trial-by-trial basis for data visualization. Finally, NeoAnalysis supports analysis at the population level. With the multitude of general-purpose functions provided

  4. Breakdown evaluation of corneal epithelial barrier caused by antiallergic eyedrops using an electrophysiologic method.

    Science.gov (United States)

    Nakashima, Mikiro; Nakamura, Tadahiro; Teshima, Mugen; To, Hideto; Uematsu, Masafumi; Kitaoka, Takashi; Taniyama, Kotaro; Nishida, Koyo; Nakamura, Junzo; Sasaki, Hitoshi

    2008-02-01

    The aim of this study was to examine the usefulness of an electrophysiologic method for predicting corneal epithelial breakdown by antiallergic eyedrops and comparing the results with those in other appraisal methods. Six kinds of antiallergic eyedrops, including benzalkonium chloride (BK) as an ophthalmic preservative and two kinds of BK-free antiallergic eyedrops, were used in this study. Eyedrops were applied to excise rabbit corneas and monitoring was performed according to an electrophysiologic method, using a commercially available chamber system to mimic human tear turnover. Changes in transepithelial electrical resistance (TEER) in the corneal surface were recorded. The cytotoxicity of each kind of eyedrops in a normal rabbit corneal epithelial (NRCE) cell line and a human endothelial cell line EA.hy926 was also examined. The extent of decrease in the corneal TEER after applying antiallergic eyedrops was dependent on the concentration of the BK included as a preservative, but it was also affected by the different kinds of drugs when the BK concentration was low. Higher cytotoxicity of the eyedrops against the NRCE and EA.hy926 cell lines was observed with a reduction of TEER. Monitoring changes in the corneal TEER, according to the electrophysiologic method with the application of antiallergic eyedrops, is useful for predicting corneal epithelial breakdown caused by their instillation.

  5. 3D stereotaxis for epileptic foci through integrating MR imaging with neurological electrophysiology data

    International Nuclear Information System (INIS)

    Luo Min; Peng Chenglin; Wang Kang; Lei Wenyong; Luo Song; Wang Xiaolin; Wang Xuejian; Wu Ruoqiu; Wu Guofeng

    2005-01-01

    Objective: To improve the accuracy of the epilepsy diagnoses by integrating MR image from PACS with data from neurological electrophysiology. The integration is also very important for transmiting diagnostic information to 3D TPS of radiotherapy. Methods: The electroencephalogram was redisplayed by EEG workstation, while MR image was reconstructed by Brainvoyager software. 3D model of patient brain was built up by combining reconstructed images with electroencephalogram data in Base 2000. 30 epileptic patients (18 males and 12 females) with their age ranged from 12 to 54 years were confirmed by using the integrated MR images and the data from neurological electrophysiology and their 3D stereolocating. Results: The corresponding data in 3D model could show the real situation of patients' brain and visually locate the precise position of the focus. The suddessful rate of 3D guided operation was greatly improved, and the number of epileptic onset was markedly decreased. The epilepsy was stopped for 6 months in 8 of the 30 patients. Conclusion: The integration of MR image and information of neurological electrophysiology can improve the diagnostic level for epilepsy, and it is crucial for imp roving the successful rate of manipulations and the epilepsy analysis. (authors)

  6. Clinical and electrophysiological aspects of tics in children.

    Science.gov (United States)

    Safiullina, G I; Safiullina, A A

    2015-01-01

    Tics are diverse in nature inappropriate movements or vocalizations. They significantly degrade patients' quality of life, lead to social difficulties, and disturbance of learning especially during exacerbations. The prevalence of tics among children ranges from 4% to 24%, thus emphasizing the relevance of the problem. To study clinical and electrophysiological features of tics in children with development of new treatment methods. We conducted a comprehensive clinical and electrophysiological examination of 50 patients with tics, aged 5 to 15 years. The control group consisted of 20 healthy children. The research included a thorough study of the history, neurological examination, manual testing of skeletal muscles, psychological testing. Electrophysiological examination included a review of the functional state of corticospinal tract (CST) by the method of magnetic stimulation (MS), study of polysynaptic reflex excitability (PRE) according to a late component of the blink reflex (BR). Statistical analysis included parametric and nonparametric methods of data processing. All children of the study group showed signs of minimal brain dysfunction (MBD), they had complicated antenatal and postnatal history (trauma, disease, occurring with intoxication). There was a trend towards the increase of MBD signs with worsening of tics. Manual diagnosis in patients identified functional blockade at different levels of the vertebral column, sacroiliac joints, we identified latent myofascial trigger points (MFTP) mainly in the cervical-collar zone, in the area of the paravertebral muscles, periosteal triggers in the area of the sacroiliac joints.The research allowed determining decrease in propagation velocity of excitation (PVE) throughout CST in patients with tics. Correlation analysis revealed a negative correlation between the severity of tics and PVE (r = -0.38; p tics: I - low and moderate type of reflex responses; and II - high type of reflex responses. Collation of data

  7. Lipidomic approach to identify patterns in phospholipid profiles and define class differences in mammary epithelial and breast cancer cells.

    Science.gov (United States)

    Dória, M Luísa; Cotrim, Zita; Macedo, Bárbara; Simões, Cláudia; Domingues, Pedro; Helguero, Luisa; Domingues, M Rosário

    2012-06-01

    Breast cancer is the leading cause of cancer-related deaths in women. Altered cellular functions of cancer cells lead to uncontrolled cellular growth and morphological changes. Cellular biomembranes are intimately involved in the regulation of cell signaling; however, they remain largely understudied. Phospholipids (PLs) are the main constituents of biological membranes and play important functional, structural and metabolic roles. The aim of this study was to establish if patterns in the PL profiles of mammary epithelial cells and breast cancer cells differ in relation to degree of differentiation and metastatic potential. For this purpose, PLs were analyzed using a lipidomic approach. In brief, PLs were extracted using Bligh and Dyer method, followed by a separation of PL classes by thin layer chromatography, and subsequent analysis by mass spectrometry (MS). Differences and similarities were found in the relative levels of PL content between mammary epithelial and breast cancer cells and between breast cancer cells with different levels of aggressiveness. When compared to the total PL content, phosphatidylcholine levels were reduced and lysophosphatydilcholines increased in the more aggressive cancer cells; while phosphatidylserine levels remained unchanged. MS analysis showed alterations in the classes of phosphatidylcholine, lysophosphatidylcholine, sphingomyelin, and phosphatidylinositides. In particular, the phosphatidylinositides, which are signaling molecules that affect proliferation, survival, and migration, showed dramatic alterations in their profile, where an increase of phosphatdylinositides saturated fatty acids chains and a decrease in C20 fatty acids in cancer cells compared with mammary epithelial cells was observed. At present, information about PL changes in cancer progression is lacking. Therefore, these data will be useful as a starting point to define possible PLs with prospective as biomarkers and disclose metabolic pathways with potential

  8. Fingerprint prediction using classifier ensembles

    CSIR Research Space (South Africa)

    Molale, P

    2011-11-01

    Full Text Available ); logistic discrimination (LgD), k-nearest neighbour (k-NN), artificial neural network (ANN), association rules (AR) decision tree (DT), naive Bayes classifier (NBC) and the support vector machine (SVM). The performance of several multiple classifier systems...

  9. Torsionfree Sheaves over a Nodal Curve of Arithmetic Genus One

    Indian Academy of Sciences (India)

    We classify all isomorphism classes of stable torsionfree sheaves on an irreducible nodal curve of arithmetic genus one defined over C C . Let be a nodal curve of arithmetic genus one defined over R R , with exactly one node, such that does not have any real points apart from the node. We classify all isomorphism ...

  10. Zero-Shot Learning via Attribute Regression and Class Prototype Rectification.

    Science.gov (United States)

    Luo, Changzhi; Li, Zhetao; Huang, Kaizhu; Feng, Jiashi; Wang, Meng

    2018-02-01

    Zero-shot learning (ZSL) aims at classifying examples for unseen classes (with no training examples) given some other seen classes (with training examples). Most existing approaches exploit intermedia-level information (e.g., attributes) to transfer knowledge from seen classes to unseen classes. A common practice is to first learn projections from samples to attributes on seen classes via a regression method, and then apply such projections to unseen classes directly. However, it turns out that such a manner of learning strategy easily causes projection domain shift problem and hubness problem, which hinder the performance of ZSL task. In this paper, we also formulate ZSL as an attribute regression problem. However, different from general regression-based solutions, the proposed approach is novel in three aspects. First, a class prototype rectification method is proposed to connect the unseen classes to the seen classes. Here, a class prototype refers to a vector representation of a class, and it is also known as a class center, class signature, or class exemplar. Second, an alternating learning scheme is proposed for jointly performing attribute regression and rectifying the class prototypes. Finally, a new objective function which takes into consideration both the attribute regression accuracy and the class prototype discrimination is proposed. By introducing such a solution, domain shift problem and hubness problem can be mitigated. Experimental results on three public datasets (i.e., CUB200-2011, SUN Attribute, and aPaY) well demonstrate the effectiveness of our approach.

  11. Usefulness of StereoEEG-based tailored surgery for medial temporal lobe epilepsy. Preliminary results in 11 patients.

    Science.gov (United States)

    Kubota, Yuichi; Ochiai, Taku; Hori, Tomokatsu; Kawamata, Takakazu

    2017-07-01

    Surgical options for medial temporal lobe epilepsy (MTLE) include anterior temporal lobectomy (ATL) and selective amygdalohippocampectomy (SAH). Optimal criteria for choosing the appropriate surgical approach remain uncertain. This article reports 11 consecutive cases in which electrophysiological findings of stereoelectroencephalography (SEEG) were used to determine the optimal surgical approach. Eleven consecutive patients with MTLE underwent SEEG evaluation and were placed in either the medial or the medial+lateral group based on the findings. Patients in the medial group underwent SAH using the subtemporal approach, and patients in the medial+lateral group underwent SEEG-guided anterior temporal lobectomy. SEEG findings were also compared with other examinations including flumazenil (FMZ)-positron emission tomography (PET), fluorine-18 labeled fluorodeoxyglucose (FDG)-PET, and magnetoencephalography (MEG). Results were evaluated to determine which examinations most consistently identified the epileptogenic zone. Of the 11 cases, 4 patients were placed in the medial group, and 7 patients in the medial+lateral group. Of patients, 90.9% were classified in class I of the Engel Epilepsy Surgery Outcome Scale, while 72.7% were classified in class I by the International League Against Epilepsy (ILAE) system. Analyzed by group, 100% of the medial group experienced an Engel class I outcome in the medial group, compared to 85.7% in the medial+lateral group. SEEG findings were comparable with FDG-PET results (10 of 11, 91%). Tailored surgery guided by SEEG is an electrophysiologically feasible treatment for MTLE that can result in favorable outcomes. Although seizures are thought to originate in the medial temporal lobe in MTLE, it is important for involvement of the lateral temporal cortex to be also considered in some cases. Copyright © 2017. Published by Elsevier B.V.

  12. Nucleus accumbens core medium spiny neuron electrophysiological properties and partner preference behavior in the adult male prairie vole, Microtus ochrogaster.

    Science.gov (United States)

    Willett, Jaime A; Johnson, Ashlyn G; Vogel, Andrea R; Patisaul, Heather B; McGraw, Lisa A; Meitzen, John

    2018-04-01

    Medium spiny neurons (MSNs) in the nucleus accumbens have long been implicated in the neurobiological mechanisms that underlie numerous social and motivated behaviors as studied in rodents such as rats. Recently, the prairie vole has emerged as an important model animal for studying social behaviors, particularly regarding monogamy because of its ability to form pair bonds. However, to our knowledge, no study has assessed intrinsic vole MSN electrophysiological properties or tested how these properties vary with the strength of the pair bond between partnered voles. Here we performed whole cell patch-clamp recordings of MSNs in acute brain slices of the nucleus accumbens core (NAc) of adult male voles exhibiting strong and weak preferences for their respective partnered females. We first document vole MSN electrophysiological properties and provide comparison to rat MSNs. Vole MSNs demonstrated many canonical electrophysiological attributes shared across species but exhibited notable differences in excitability compared with rat MSNs. Second, we assessed male vole partner preference behavior and tested whether MSN electrophysiological properties varied with partner preference strength. Male vole partner preference showed extensive variability. We found that decreases in miniature excitatory postsynaptic current amplitude and the slope of the evoked action potential firing rate to depolarizing current injection weakly associated with increased preference for the partnered female. This suggests that excitatory synaptic strength and neuronal excitability may be decreased in MSNs in males exhibiting stronger preference for a partnered female. Overall, these data provide extensive documentation of MSN electrophysiological characteristics and their relationship to social behavior in the prairie vole. NEW & NOTEWORTHY This research represents the first assessment of prairie vole nucleus accumbens core medium spiny neuron intrinsic electrophysiological properties and

  13. Latent class models for classification

    NARCIS (Netherlands)

    Vermunt, J.K.; Magidson, J.

    2003-01-01

    An overview is provided of recent developments in the use of latent class (LC) and other types of finite mixture models for classification purposes. Several extensions of existing models are presented. Two basic types of LC models for classification are defined: supervised and unsupervised

  14. The Acoustic Lens Design and in Vivo Use of a Multifunctional Catheter Combining Intracardiac Ultrasound Imaging and Electrophysiology Sensing

    Science.gov (United States)

    Stephens, Douglas N.; Cannata, Jonathan; Liu, Ruibin; Zhao, Jian Zhong; Shung, K. Kirk; Nguyen, Hien; Chia, Raymond; Dentinger, Aaron; Wildes, Douglas; Thomenius, Kai E.; Mahajan, Aman; Shivkumar, Kalyanam; Kim, Kang; O’Donnell, Matthew; Sahn, David

    2009-01-01

    A multifunctional 9F intracardiac imaging and electrophysiology mapping catheter was developed and tested to help guide diagnostic and therapeutic intracardiac electrophysiology (EP) procedures. The catheter tip includes a 7.25-MHz, 64-element, side-looking phased array for high resolution sector scanning. Multiple electrophysiology mapping sensors were mounted as ring electrodes near the array for electrocardiographic synchronization of ultrasound images. The catheter array elevation beam performance in particular was investigated. An acoustic lens for the distal tip array designed with a round cross section can produce an acceptable elevation beam shape; however, the velocity of sound in the lens material should be approximately 155 m/s slower than in tissue for the best beam shape and wide bandwidth performance. To help establish the catheter’s unique ability for integration with electrophysiology interventional procedures, it was used in vivo in a porcine animal model, and demonstrated both useful intracardiac echocardiographic visualization and simultaneous 3-D positional information using integrated electroanatomical mapping techniques. The catheter also performed well in high frame rate imaging, color flow imaging, and strain rate imaging of atrial and ventricular structures. PMID:18407850

  15. Defining and treating the spectrum of intermediate risk nonmuscle invasive bladder cancer

    NARCIS (Netherlands)

    Kamat, A.M.; Witjes, J.A.; Brausi, M.; Soloway, M.; Lamm, D.; Persad, R.; Buckley, R.; Bohle, A.; Colombel, M.; Palou, J.

    2014-01-01

    PURPOSE: Low, intermediate and high risk categories have been defined to help guide the treatment of patients with nonmuscle invasive bladder cancer (Ta, T1, CIS). However, while low and high risk disease has been well classified, the intermediate risk category has traditionally comprised a

  16. Pain patterns during adolescence can be grouped into four pain classes with distinct profiles

    DEFF Research Database (Denmark)

    Holden, Sinead; Rathleff, Michael Skovdal; Roos, E. M.

    2018-01-01

    L (assessed by Euro-QoL 5D-3L). Latent class analysis was used to classify spatial pain patterns, based on the pain sites. The analysis included 2953 adolescents. RESULTS: Four classes were identified as follows: (1) little or no pain (63% of adolescents), (2) majority lower extremity pain (10%), (3) multi......-site bodily pain (22%) and (4) head and stomach pain (3%). The lower extremity multi-site pain group reported highest weekly sports participation (p ....001). Males were more likely to belong to the little or no pain class, whereas females were more likely to belong to the multi-site bodily pain class. CONCLUSIONS: Latent class analysis identified distinct classes of pain patterns in adolescents, characterized by sex, differences in HRQoL and sports...

  17. Classes of feedforward neural networks and their circuit complexity

    NARCIS (Netherlands)

    Shawe-Taylor, John S.; Anthony, Martin H.G.; Kern, Walter

    1992-01-01

    This paper aims to place neural networks in the context of boolean circuit complexity. We define appropriate classes of feedforward neural networks with specified fan-in, accuracy of computation and depth and using techniques of communication complexity proceed to show that the classes fit into a

  18. Assessment of cortical and sub-cortical function in neonates by electrophysiological monitoring

    NARCIS (Netherlands)

    Jennekens, W.

    2012-01-01

    The aim of this thesis was the assessment of cortical and sub-cortical function in neonates by electrophysiological monitoring, i.e. to evaluate the function of the neonatal cortex and brainstem through quantitative analysis of signals readily available in the NICU. These signals include

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

    International Nuclear Information System (INIS)

    Wardaya, P D

    2014-01-01

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

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

    Science.gov (United States)

    Wardaya, P. D.

    2014-02-01

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

  1. Five-class height-weight mean and SD system applying Estonian reference values of height-weight mean and SD for systematization of seventeen-year-old conscripts' anthropometric data.

    Science.gov (United States)

    Lintsi, Mart; Kaarma, Helje; Aunapuu, Marina; Arend, Andres

    2007-03-01

    A study of 739 conscripts aged 17 years from the town of Tartu and from the Tartu county was performed. Height, weight, 33 anthropometric measurements and 12 skinfolds were measured. The data were classified into five height-weight mean and SD-classes applying the Estonian reference values for this age and sex (Grünberg et al. 1998). There were 3 classes with conformity between height and weight class: 1--small (small height and small weight), 2--medium (medium height and medium weight), 3--large (large height and large weight), 4--weight class dominating (pyknomorphic) and 5--height class dominating (leptomorphic). It was found, that in classes 1, 2 and 3 the height and weight increase was in accordance with the increase in all heights, breadths and depths, circumferences, skinfolds, body fat, muscle and bone mass. In class 4 circumferences, skinfolds, body fat and muscle mass were bigger. In class 5 all heights and the relative bone mass were bigger. The present investigation confirms the assumption that the five height-weight mean and SD five-class system applying the Estonian reference values for classifying the anthropometric variables is suitable for seventeen-year-old conscripts. As well the border values of 5%, 50% and 95% for every anthropometrical variable in the five-classes were calculated, which may be helpful for practical classifying.

  2. The entropy function for the black holes of Nariai class

    International Nuclear Information System (INIS)

    Cho, Jin-Ho; Nam, Soonkeon

    2008-01-01

    Based on the fact that the near horizon geometry of the extremal Schwarzschild-de Sitter black holes is Nariai geometry, we define the black holes of Nariai class as the configuration whose near-horizon geometry is factorized as two dimensional de Sitter space-time and some compact topology, that is Nariai geometry. We extend the entropy function formalism to the case of the black holes of Nariai class. The conventional entropy function (for the extremal black holes) is defined as Legendre transformation of Lagrangian density, thus the 'Routhian density', over two dimensional anti-de Sitter. As for the black holes of Nariai class, it is defined as minus 'Routhian density' over two dimensional de Sitter space-time. We found an exact agreement of the result with Bekenstein-Hawking entropy. The higher order corrections are nontrivial only when the space-time dimension is over four, that is, d>4. There is a subtlety as regards the temperature of the black holes of Nariai class. We show that in order to be consistent with the near horizon geometry, the temperature should be non-vanishing despite the extremality of the black holes

  3. High throughput electrophysiology: new perspectives for ion channel drug discovery

    DEFF Research Database (Denmark)

    Willumsen, Niels J; Bech, Morten; Olesen, Søren-Peter

    2003-01-01

    . A cornerstone in current drug discovery is high throughput screening assays which allow examination of the activity of specific ion channels though only to a limited extent. Conventional patch clamp remains the sole technique with sufficiently high time resolution and sensitivity required for precise and direct....... The introduction of new powerful HTS electrophysiological techniques is predicted to cause a revolution in ion channel drug discovery....

  4. Pediatric Electrophysiology in India: A Sub-speciality Come of Age

    Directory of Open Access Journals (Sweden)

    Johnson Francis

    2008-05-01

    Full Text Available Electrophysiology started in India in the early 70's with the earliest published diagnostic His bundle studies coming from the All India Institute of Medical Sciences by Bhatia ML et al and the GB Pant Hospital by Khalilullah et al . That era was remarkable with the first indigenously made temporary pacemaker being used to treat complete heart block as early as in 1970

  5. Evidence-based medicine evaluation of electrophysiological studies of the anxiety disorders.

    Science.gov (United States)

    Clark, C Richard; Galletly, Cherrie A; Ash, David J; Moores, Kathryn A; Penrose, Rebecca A; McFarlane, Alexander C

    2009-04-01

    We provide a systematic, evidence-based medicine (EBM) review of the field of electrophysiology in the anxiety disorders. Presently, electrophysiological studies of anxiety focus primarily on etiological aspects of brain dysfunction. The review highlights many functional similarities across studies, but also identifies patterns that clearly differentiate disorder classifications. Such measures offer clinical utility as reliable and objective indicators of brain dysfunction in individuals and indicate potential as biomarkers for the improvement of diagnostic specificity and for informing treatment decisions and prognostic assessments. Common to most of the anxiety disorders is basal instability in cortical arousal, as reflected in measures of quantitative electroencephalography (qEEG). Resting electroencephalographic (EEG) measures tend to correlate with symptom sub-patterns and be exacerbated by condition-specific stimulation. Also common to most of the anxiety disorders are condition-specific difficulties with sensory gating and the allocation and deployment of attention. These are clearly evident from evoked potential (EP) and event-related potential (ERP) electrical measures of information processing in obsessive compulsive disorder (OCD), post-traumatic stress disorder (PTSD), panic disorder (PD), generalized anxiety disorder (GAD) and the phobias. Other'ERP measures clearly differentiate the disorders. However, there is considerable variation across studies, with inclusion and exclusion criteria, medication status and control group selection not standardized within condition or across studies. Study numbers generally preclude analysis for confound removal or for the derivation of diagnostic biomarker patterns at this time. The current trend towards development of databases of brain and cognitive function is likely to obviate these difficulties. In particular, electrophysiological measures of function are likely to play a significant role in the development and

  6. Neo: an object model for handling electrophysiology data in multiple formats

    Directory of Open Access Journals (Sweden)

    Samuel eGarcia

    2014-02-01

    Full Text Available Neuroscientists use many different software tools to acquire, analyse and visualise electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs.A common representation of the core data would improve interoperability and facilitate data-sharing.To that end, we propose here a language-independent object model, named Neo, suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language.In addition to representing electrophysiology data in memory for the purposes of analysis and visualisation, the Python implementation provides a set of input/output (IO modules for reading/writing the data from/to a variety of commonly used file formats.Support is included for formats produced by most of the major manufacturers of electrophysiology recording equipment and also for more generic formats such as MATLAB.Data representation and data analysis are conceptually separate: it is easier to write robust analysis code if it is focused on analysis and relies on an underlying package to handle data representation.For that reason, and also to be as lightweight as possible, the Neo object model and the associated Python package are deliberately limited to representation of data, with no functions for data analysis or visualisation.Software for neurophysiology data analysis and visualisation built on top of Neo automatically gains the benefits of interoperability, easier data sharing and automatic format conversion; there is already a burgeoning ecosystem of such tools. We intend that Neo should become the standard basis for Python tools in

  7. Electrophysiological assessment of water stress in fruit-bearing woody plants.

    Science.gov (United States)

    Ríos-Rojas, Liliana; Tapia, Franco; Gurovich, Luis A

    2014-06-15

    Development and evaluation of a real-time plant water stress sensor, based on the electrophysiological behavior of fruit-bearing woody plants is presented. Continuous electric potentials are measured in tree trunks for different irrigation schedules, inducing variable water stress conditions; results are discussed in relation to soil water content and micro-atmospheric evaporative demand, determined continuously by conventional sensors, correlating this information with tree electric potential measurements. Systematic and differentiable patterns of electric potentials for water-stressed and no-stressed trees in 2 fruit species are presented. Early detection and recovery dynamics of water stress conditions can also be monitored with these electrophysiology sensors, which enable continuous and non-destructive measurements for efficient irrigation scheduling throughout the year. The experiment is developed under controlled conditions, in Faraday cages located at a greenhouse area, both in Persea americana and Prunus domestica plants. Soil moisture evolution is controlled using capacitance sensors and solar radiation, temperature, relative humidity, wind intensity and direction are continuously registered with accurate weather sensors, in a micro-agrometeorological automatic station located at the experimental site. The electrophysiological sensor has two stainless steel electrodes (measuring/reference), inserted on the stem; a high precision Keithley 2701 digital multimeter is used to measure plant electrical signals; an algorithm written in MatLab(®), allows correlating the signal to environmental variables. An electric cyclic behavior is observed (circadian cycle) in the experimental plants. For non-irrigated plants, the electrical signal shows a time positive slope and then, a negative slope after restarting irrigation throughout a rather extended recovery process, before reaching a stable electrical signal with zero slope. Well-watered plants presented a

  8. Classifying Sluice Occurrences in Dialogue

    DEFF Research Database (Denmark)

    Baird, Austin; Hamza, Anissa; Hardt, Daniel

    2018-01-01

    perform manual annotation with acceptable inter-coder agreement. We build classifier models with Decision Trees and Naive Bayes, with accuracy of 67%. We deploy a classifier to automatically classify sluice occurrences in OpenSubtitles, resulting in a corpus with 1.7 million occurrences. This will support....... Despite this, the corpus can be of great use in research on sluicing and development of systems, and we are making the corpus freely available on request. Furthermore, we are in the process of improving the accuracy of sluice identification and annotation for the purpose of created a subsequent version...

  9. Single classifier, OvO, OvA and RCC multiclass classification method in handheld based smartphone gait identification

    Science.gov (United States)

    Raziff, Abdul Rafiez Abdul; Sulaiman, Md Nasir; Mustapha, Norwati; Perumal, Thinagaran

    2017-10-01

    Gait recognition is widely used in many applications. In the application of the gait identification especially in people, the number of classes (people) is many which may comprise to more than 20. Due to the large amount of classes, the usage of single classification mapping (direct classification) may not be suitable as most of the existing algorithms are mostly designed for the binary classification. Furthermore, having many classes in a dataset may result in the possibility of having a high degree of overlapped class boundary. This paper discusses the application of multiclass classifier mappings such as one-vs-all (OvA), one-vs-one (OvO) and random correction code (RCC) on handheld based smartphone gait signal for person identification. The results is then compared with a single J48 decision tree for benchmark. From the result, it can be said that using multiclass classification mapping method thus partially improved the overall accuracy especially on OvO and RCC with width factor more than 4. For OvA, the accuracy result is worse than a single J48 due to a high number of classes.

  10. Electrophysiologic characteristics of tremor in Parkinson?s disease and essential tremor

    Directory of Open Access Journals (Sweden)

    Ederson Cichaczewski

    2014-04-01

    Full Text Available Tremor in essential tremor (ET and Parkinson’s disease (PD usually present specific electrophysiologic profiles, however amplitude and frequency may have wide variations. Objective: To present the electrophysiologic findings in PD and ET. Method: Patients were assessed at rest, with posture and action. Seventeen patients with ET and 62 with PD were included. PD cases were clustered into three groups: predominant rest tremor; tremor with similar intensity at rest, posture and during kinetic task; and predominant kinetic tremor. Results: Patients with PD presented tremors with average frequency of 5.29±1.18 Hz at rest, 5.79±1.39 Hz with posture and 6.48±1.34 Hz with the kinetic task. Tremor in ET presented with an average frequency of 5.97±1.1 Hz at rest, 6.18±1 Hz with posture and 6.53±1.2 Hz with kinetic task. Seven (41.2% also showed rest tremor. Conclusion: The tremor analysis alone using the methodology described here, is not sufficient to differentiate tremor in ET and PD.

  11. Defining immune engagement thresholds for in vivo control of virus-driven lymphoproliferation.

    Directory of Open Access Journals (Sweden)

    Cristina Godinho-Silva

    2014-06-01

    Full Text Available Persistent infections are subject to constant surveillance by CD8+ cytotoxic T cells (CTL. Their control should therefore depend on MHC class I-restricted epitope presentation. Many epitopes are described for γ-herpesviruses and form a basis for prospective immunotherapies and vaccines. However the quantitative requirements of in vivo immune control for epitope presentation and recognition remain poorly defined. We used Murid Herpesvirus-4 (MuHV-4 to determine for a latently expressed viral epitope how MHC class-I binding and CTL functional avidity impact on host colonization. Tracking MuHV-4 recombinants that differed only in epitope presentation, we found little latitude for sub-optimal MHC class I binding before immune control failed. By contrast, control remained effective across a wide range of T cell functional avidities. Thus, we could define critical engagement thresholds for the in vivo immune control of virus-driven B cell proliferation.

  12. Prognostic significance of electrophysiological tests for facial nerve outcome in vestibular schwannoma surgery.

    Science.gov (United States)

    van Dinther, J J S; Van Rompaey, V; Somers, T; Zarowski, A; Offeciers, F E

    2011-01-01

    To assess the prognostic significance of pre-operative electrophysiological tests for facial nerve outcome in vestibular schwannoma surgery. Retrospective study design in a tertiary referral neurology unit. We studied a total of 123 patients with unilateral vestibular schwannoma who underwent microsurgical removal of the lesion. Nine patients were excluded because they had clinically abnormal pre-operative facial function. Pre-operative electrophysiological facial nerve function testing (EPhT) was performed. Short-term (1 month) and long-term (1 year) post-operative clinical facial nerve function were assessed. When pre-operative facial nerve function, evaluated by EPhT, was normal, the outcome from clinical follow-up at 1-month post-operatively was excellent in 78% (i.e. HB I-II) of patients, moderate in 11% (i.e. HB III-IV), and bad in 11% (i.e. HB V-VI). After 1 year, 86% had excellent outcomes, 13% had moderate outcomes, and 1% had bad outcomes. Of all patients with normal clinical facial nerve function, 22% had an abnormal EPhT result and 78% had a normal result. No statistically significant differences could be observed in short-term and long-term post-operative facial function between the groups. In this study, electrophysiological tests were not able to predict facial nerve outcome after vestibular schwannoma surgery. Tumour size remains the best pre-operative prognostic indicator of facial nerve function outcome, i.e. a better outcome in smaller lesions.

  13. Electrophysiological Evidence of Developmental Changes in the Duration of Auditory Sensory Memory.

    Science.gov (United States)

    Gomes, Hilary; And Others

    1999-01-01

    Investigated developmental change in duration of auditory sensory memory for tonal frequency by measuring mismatch negativity, an electrophysiological component of the auditory event-related potential that is relatively insensitive to attention and does not require a behavioral response. Findings among children and adults suggest that there are…

  14. Viewing the dynamics and control of visual attention through the lens of electrophysiology

    Science.gov (United States)

    Woodman, Geoffrey F.

    2013-01-01

    How we find what we are looking for in complex visual scenes is a seemingly simple ability that has taken half a century to unravel. The first study to use the term visual search showed that as the number of objects in a complex scene increases, observers’ reaction times increase proportionally (Green and Anderson, 1956). This observation suggests that our ability to process the objects in the scenes is limited in capacity. However, if it is known that the target will have a certain feature attribute, for example, that it will be red, then only an increase in the number of red items increases reaction time. This observation suggests that we can control which visual inputs receive the benefit of our limited capacity to recognize the objects, such as those defined by the color red, as the items we seek. The nature of the mechanisms that underlie these basic phenomena in the literature on visual search have been more difficult to definitively determine. In this paper, I discuss how electrophysiological methods have provided us with the necessary tools to understand the nature of the mechanisms that give rise to the effects observed in the first visual search paper. I begin by describing how recordings of event-related potentials from humans and nonhuman primates have shown us how attention is deployed to possible target items in complex visual scenes. Then, I will discuss how event-related potential experiments have allowed us to directly measure the memory representations that are used to guide these deployments of attention to items with target-defining features. PMID:23357579

  15. Wearable Multi-Channel Microelectrode Membranes for Elucidating Electrophysiological Phenotypes of Injured Myocardium

    Science.gov (United States)

    Cao, Hung; Yu, Fei; Zhao, Yu; Zhang, Xiaoxiao; Tai, Joyce; Lee, Juhyun; Darehzereshki, Ali; Bersohn, Malcolm; Lien, Ching-Ling; Chi, Neil C.; Tai, Yu-Chong; Hsiai, Tzung K.

    2014-01-01

    Understanding the regenerative capacity of small vertebrate models has provided new insights into the plasticity of injured myocardium. Here, we demonstrated the application of flexible microelectrode arrays (MEAs) in elucidating electrophysiological phenotypes of zebrafish and neonatal mouse models of heart regeneration. The 4-electrode MEA membranes were designed to detect electrical signals in the aquatic environment. They were micro-fabricated to adhere to the non-planar body surface of zebrafish and neonatal mice. The acquired signals were processed to display electrocardiogram (ECG) with high signal-to-noise-ratios, and were validated via the use of conventional micro-needle electrodes. The 4-channel MEA provided signal stability and spatial resolution, revealing the site-specific electrical injury currents such as ST-depression in response to ventricular cryo-injury. Thus, our polymer-based and wearable MEA membranes provided electrophysiological insights in long-term conduction phenotypes for small vertebral models of heart injury and regeneration with a translational implication for monitoring cardiac patients. PMID:24945366

  16. Multi-class Mode of Action Classification of Toxic Compounds Using Logic Based Kernel Methods.

    Science.gov (United States)

    Lodhi, Huma; Muggleton, Stephen; Sternberg, Mike J E

    2010-09-17

    Toxicity prediction is essential for drug design and development of effective therapeutics. In this paper we present an in silico strategy, to identify the mode of action of toxic compounds, that is based on the use of a novel logic based kernel method. The technique uses support vector machines in conjunction with the kernels constructed from first order rules induced by an Inductive Logic Programming system. It constructs multi-class models by using a divide and conquer reduction strategy that splits multi-classes into binary groups and solves each individual problem recursively hence generating an underlying decision list structure. In order to evaluate the effectiveness of the approach for chemoinformatics problems like predictive toxicology, we apply it to toxicity classification in aquatic systems. The method is used to identify and classify 442 compounds with respect to the mode of action. The experimental results show that the technique successfully classifies toxic compounds and can be useful in assessing environmental risks. Experimental comparison of the performance of the proposed multi-class scheme with the standard multi-class Inductive Logic Programming algorithm and multi-class Support Vector Machine yields statistically significant results and demonstrates the potential power and benefits of the approach in identifying compounds of various toxic mechanisms. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  17. Classifiers based on optimal decision rules

    KAUST Repository

    Amin, Talha

    2013-11-25

    Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

  18. Classifiers based on optimal decision rules

    KAUST Repository

    Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

    2013-01-01

    Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

  19. Obesity, Cardiovascular Fitness, and Inhibition Function: An Electrophysiological Study

    Directory of Open Access Journals (Sweden)

    Tai-Fen Song

    2016-07-01

    Full Text Available The purpose of the present study was to examine how obesity and cardiovascular fitness are associated with the inhibition aspect of executive function from behavioral and electrophysiological perspectives. One hundred college students, aged 18 to 25 years, were categorized into four groups of equal size on the basis of body mass index and cardiovascular fitness: a normal-weight and high-fitness (NH group, an obese-weight and high-fitness (OH group, a normal-weight and low-fitness (NL group, and an obese-weight and low-fitness (OL group. Behavioral measures of response time and number of errors, as well as event-related potential (ERP measures of P3 and N1, were assessed during the Stroop Task. The results revealed that, in general, the NH group exhibited shorter response times and larger P3 amplitudes relative to the OH, NL, and OL groups, wherein the OL group exhibited the longest response time in the incongruent condition. No group differences in N1 indices were also revealed. These findings suggest that the status of being both normal weight and having high cardiovascular fitness is associated with better behavioral and later stages of electrophysiological indices of inhibition. However, these benefits in inhibition function would be lost in an individual who is obese or has low cardiovascular fitness, reflecting the importance keeping both normal weight and having high cardiovascular fitness.

  20. The Pearson diffusions: A class of statistically tractable diffusion processes

    DEFF Research Database (Denmark)

    Forman, Julie Lyng; Sørensen, Michael

    The Pearson diffusions is a flexible class of diffusions defined by having linear drift and quadratic squared diffusion coefficient. It is demonstrated that for this class explicit statistical inference is feasible. Explicit optimal martingale estimating func- tions are found, and the corresponding...

  1. Heterogeneity of Monosymptomatic Resting Tremor in a Prospective Study: Clinical Features, Electrophysiological Test, and Dopamine Transporter Positron Emission Tomography

    Institute of Scientific and Technical Information of China (English)

    Hua-Guang Zheng; Rong Zhang; Xin Li; Fang-Fei Li; Ya-Chen Wang; Xue-Mei Wang; Ling-Long Lu

    2015-01-01

    Background:The relationship between monosymptomatic resting tremor (mRT) and Parkinson's disease (PD) remains controversial.In this study,we aimed to assess the function ofpresynaptic dopaminergic neurons in patients with mRT by dopamine transporter positron emission tomography (DAT-PET) and to evaluate the utility of clinical features or electrophysiological studies in differential diagnosis.Methods:Thirty-three consecutive patients with mRT were enrolled prospectively.The Unified Parkinson's Disease Rating Scale and electromyography were tested before DAT-PET.Striatal asymmetry index (SAI) was calculated,and a normal DAT-PET was defined as a SAI of <15%.Scans without evidence of dopaminergic deficits (SWEDDs) were diagnosed in patients with a subsequent normal DAT-PET and structural magnetic resonance imaging.Results:Twenty-eight mRT patients with a significant reduction in uptake of DAT binding in the striatum were diagnosed with PD,while the remained 5 with a normal DAT-PET scan were SWEDDs.As for UPRDS,the dressing and hygiene score,walking in motor experiences of daily living (Part Ⅱ) and motor examination (Part Ⅲ) were significant different between two groups (P < 0.05 andP< 0.01,respectively).Bilateral tremor was more frequent in the SWEDDs group (P < 0.05).The frequency of resting tremor and the amplitude of postural tremor tend to be higher in the SWEDDs group (P =0.08 and P =0.05,respectively).Conclusions:mRT is heterogeneous in presynaptic nigrostriatal dopaminergic degeneration,which can be determined by DAT-PET brain imaging.Clinical and electrophysiological features may provide clues to distinguish PD from SWEDDs.

  2. SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier.

    Science.gov (United States)

    Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, W M; Li, R K; Jiang, Bo-Ru

    2014-01-01

    Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.

  3. DIVERGÊNCIA MORFOMÉTRICA EM BOVINOS NELORE EM CRESCIMENTO CLASSIFICADOS PARA DIFERENTES CLASSES DE FRAME SIZE

    Directory of Open Access Journals (Sweden)

    LÚCIO FLÁVIO MACEDO MOTA

    2015-01-01

    Full Text Available This study aimed at evaluating the performance of Nelore cattle during growth classified for different classes of frame size regarding body weights and morphometric measures at different ages. Weights and morphometric measures Nelore bulls up to 1 year of age were monthly recorded. The characteristics evalu-ated were birth weight, 120, 205, 240 and 365 days of age, withers height and rump height, thoracic perimeter, distance between pin bones, distance between hip bones and chest width, depth of chest, space under sternal and hip length. Frame size scores classified as medium, large and extreme, were estimated using equations and tables according to Beef Improvement Federation (BIF. Data were subjected to analysis of variance and Tukey-Kramer test at 5% probability and analyses were performed by canonical variables and the grouping analyses of genotype by method of Tocher. The animals with larger class of frame size were heavier and morphometric measurements as well, when compared with animals classified for smaller class. The correlation between weight at different ages were higher. The weight correlates with body features positively, indicating that the weight gain of the animals increased their influence on the frame size. Cluster analysis resulted in three distinct genetic groups that have similar within the group and genetic divergence between them.

  4. Regulation of MIR165/166 by class II and class III homeodomain leucine zipper proteins establishes leaf polarity

    DEFF Research Database (Denmark)

    Merelo, Paz; Ram, Hathi; Caggiano, Monica Pia

    2016-01-01

    A defining feature of plant leaves is their flattened shape. This shape depends on an antagonism between the genes that specify adaxial (top) and abaxial (bottom) tissue identity; however, the molecular nature of this antagonism remains poorly understood. Class III homeodomain leucine zipper (HD-...... show that class III and class II HD-ZIP proteins act together to repress MIR165/166 via a conserved cis-element in their promoters. Organ morphology and tissue patterning in plants, therefore, depend on a bidirectional repressive circuit involving a set of miRNAs and its targets....

  5. Positive Behavioral and Electrophysiological Changes following Neurofeedback Training in Children with Autism

    Science.gov (United States)

    Pineda, J. A.; Brang, D.; Hecht, E.; Edwards, L.; Carey, S.; Bacon, M.; Futagaki, C.; Suk, D.; Tom, J.; Birnbaum, C.; Rork, A.

    2008-01-01

    Two electrophysiological studies tested the hypothesis that operant conditioning of mu rhythms via neurofeedback training can renormalize mu suppression, an index of mirror neuron activity, and improve behavior in children diagnosed with autism spectrum disorders (ASD). In Study 1, eight high-functioning ASD participants were assigned to placebo…

  6. A hybrid three-class brain-computer interface system utilizing SSSEPs and transient ERPs

    Science.gov (United States)

    Breitwieser, Christian; Pokorny, Christoph; Müller-Putz, Gernot R.

    2016-12-01

    Objective. This paper investigates the fusion of steady-state somatosensory evoked potentials (SSSEPs) and transient event-related potentials (tERPs), evoked through tactile simulation on the left and right-hand fingertips, in a three-class EEG based hybrid brain-computer interface. It was hypothesized, that fusing the input signals leads to higher classification rates than classifying tERP and SSSEP individually. Approach. Fourteen subjects participated in the studies, consisting of a screening paradigm to determine person dependent resonance-like frequencies and a subsequent online paradigm. The whole setup of the BCI system was based on open interfaces, following suggestions for a common implementation platform. During the online experiment, subjects were instructed to focus their attention on the stimulated fingertips as indicated by a visual cue. The recorded data were classified during runtime using a multi-class shrinkage LDA classifier and the outputs were fused together applying a posterior probability based fusion. Data were further analyzed offline, involving a combined classification of SSSEP and tERP features as a second fusion principle. The final results were tested for statistical significance applying a repeated measures ANOVA. Main results. A significant classification increase was achieved when fusing the results with a combined classification compared to performing an individual classification. Furthermore, the SSSEP classifier was significantly better in detecting a non-control state, whereas the tERP classifier was significantly better in detecting control states. Subjects who had a higher relative band power increase during the screening session also achieved significantly higher classification results than subjects with lower relative band power increase. Significance. It could be shown that utilizing SSSEP and tERP for hBCIs increases the classification accuracy and also that tERP and SSSEP are not classifying control- and non

  7. The Changing Pattern of Nutrition Intake by Social Class in Contemporary China, 1991-2011.

    Science.gov (United States)

    Xu, Zhun; Zhang, Wei

    2017-11-01

    To explore the changing pattern of nutrition intake by social class in contemporary China. We defined social class in 2 ways. The first definition was based on employment, and the second definition was based on per capita household income levels. We used China Health and Nutrition Survey data from 1991 to 2011 to show the changes in the relation between social class and nutrition intake. The relation between social class and nutrition intake in China changed significantly within the 2 decades. For example, in the early 1990s, the lowest social class (defined by employment or income) had more caloric intake than did the highest social class; 20 years later, however, the relation reversed, and the lowest social class consumed significantly fewer calories. China has seen a great reversal in its social class-nutrition relationship since the early 1990s. Our study calls for wider recognition that insufficient consumption of food and nutrition is increasingly an issue for people in the lower social classes in China.

  8. DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

    KAUST Repository

    Kulmanov, Maxat

    2017-09-27

    Motivation A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. Results We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein–protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations.

  9. Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

    Science.gov (United States)

    Özdemir, Merve Erkınay; Telatar, Ziya; Eroğul, Osman; Tunca, Yusuf

    2018-05-01

    Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points' distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient's age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.

  10. Safety class methodology

    International Nuclear Information System (INIS)

    Donner, E.B.; Low, J.M.; Lux, C.R.

    1992-01-01

    DOE Order 6430.1A, General Design Criteria (GDC), requires that DOE facilities be evaluated with respect to ''safety class items.'' Although the GDC defines safety class items, it does not provide a methodology for selecting safety class items. The methodology described in this paper was developed to assure that Safety Class Items at the Savannah River Site (SRS) are selected in a consistent and technically defensible manner. Safety class items are those in the highest of four categories determined to be of special importance to nuclear safety and, merit appropriately higher-quality design, fabrication, and industrial test standards and codes. The identification of safety class items is approached using a cascading strategy that begins at the 'safety function' level (i.e., a cooling function, ventilation function, etc.) and proceeds down to the system, component, or structure level. Thus, the items that are required to support a safety function are SCls. The basic steps in this procedure apply to the determination of SCls for both new project activities, and for operating facilities. The GDC lists six characteristics of SCls to be considered as a starting point for safety item classification. They are as follows: 1. Those items whose failure would produce exposure consequences that would exceed the guidelines in Section 1300-1.4, ''Guidance on Limiting Exposure of the Public,'' at the site boundary or nearest point of public access 2. Those items required to maintain operating parameters within the safety limits specified in the Operational Safety Requirements during normal operations and anticipated operational occurrences. 3. Those items required for nuclear criticality safety. 4. Those items required to monitor the release of radioactive material to the environment during and after a Design Basis Accident. Those items required to achieve, and maintain the facility in a safe shutdown condition 6. Those items that control Safety Class Item listed above

  11. Electrophysiological Properties of Melanin-Concentrating Hormone and Orexin Neurons in Adolescent Rats

    Directory of Open Access Journals (Sweden)

    Victoria Linehan

    2018-03-01

    Full Text Available Orexin and melanin-concentrating hormone (MCH neurons have complementary roles in various physiological functions including energy balance and the sleep/wake cycle. in vitro electrophysiological studies investigating these cells typically use post-weaning rodents, corresponding to adolescence. However, it is unclear whether these neurons are functionally mature at this period and whether these studies can be generalized to adult cells. Therefore, we examined the electrophysiological properties of orexin and MCH neurons in brain slices from post-weaning rats and found that MCH neurons undergo an age-dependent reduction in excitability, but not orexin neurons. Specifically, MCH neurons displayed an age-dependent hyperpolarization of the resting membrane potential (RMP, depolarizing shift of the threshold, and decrease in excitatory transmission, which reach the adult level by 7 weeks of age. In contrast, basic properties of orexin neurons were stable from 4 weeks to 14 weeks of age. Furthermore, a robust short-term facilitation of excitatory synapses was found in MCH neurons, which showed age-dependent changes during the post-weaning period. On the other hand, a strong short-term depression was observed in orexin neurons, which was similar throughout the same period. These differences in synaptic responses and age dependence likely differentially affect the network activity within the lateral hypothalamus where these cells co-exist. In summary, our study suggests that orexin neurons are electrophysiologically mature before adolescence whereas MCH neurons continue to develop until late adolescence. These changes in MCH neurons may contribute to growth spurts or consolidation of adult sleep patterns associated with adolescence. Furthermore, these results highlight the importance of considering the age of animals in studies involving MCH neurons.

  12. A Pareto-based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets.

    Science.gov (United States)

    Fernández, Alberto; Carmona, Cristobal José; José Del Jesus, María; Herrera, Francisco

    2017-09-01

    Imbalanced classification is related to those problems that have an uneven distribution among classes. In addition to the former, when instances are located into the overlapped areas, the correct modeling of the problem becomes harder. Current solutions for both issues are often focused on the binary case study, as multi-class datasets require an additional effort to be addressed. In this research, we overcome these problems by carrying out a combination between feature and instance selections. Feature selection will allow simplifying the overlapping areas easing the generation of rules to distinguish among the classes. Selection of instances from all classes will address the imbalance itself by finding the most appropriate class distribution for the learning task, as well as possibly removing noise and difficult borderline examples. For the sake of obtaining an optimal joint set of features and instances, we embedded the searching for both parameters in a Multi-Objective Evolutionary Algorithm, using the C4.5 decision tree as baseline classifier in this wrapper approach. The multi-objective scheme allows taking a double advantage: the search space becomes broader, and we may provide a set of different solutions in order to build an ensemble of classifiers. This proposal has been contrasted versus several state-of-the-art solutions on imbalanced classification showing excellent results in both binary and multi-class problems.

  13. A Quantitative Electrophysiological Biomarker of Duplication 15q11.2-q13.1 Syndrome.

    Directory of Open Access Journals (Sweden)

    Joel Frohlich

    Full Text Available Duplications of 15q11.2-q13.1 (Dup15q syndrome are highly penetrant for autism spectrum disorder (ASD. A distinct electrophysiological (EEG pattern characterized by excessive activity in the beta band has been noted in clinical reports. We asked whether EEG power in the beta band, as well as in other frequency bands, distinguished children with Dup15q syndrome from those with non-syndromic ASD and then examined the clinical correlates of this electrophysiological biomarker in Dup15q syndrome.In the first study, we recorded spontaneous EEG from children with Dup15q syndrome (n = 11, age-and-IQ-matched children with ASD (n = 10 and age-matched typically developing (TD children (n = 9 and computed relative power in 6 frequency bands for 9 regions of interest (ROIs. Group comparisons were made using a repeated measures analysis of variance. In the second study, we recorded spontaneous EEG from a larger cohort of individuals with Dup15q syndrome (n = 27 across two sites and examined age, epilepsy, and duplication type as predictors of beta power using simple linear regressions.In the first study, spontaneous beta1 (12-20 Hz and beta2 (20-30 Hz power were significantly higher in Dup15q syndrome compared with both comparison groups, while delta (1-4 Hz was significantly lower than both comparison groups. Effect sizes in all three frequency bands were large (|d| > 1. In the second study, we found that beta2 power was significantly related to epilepsy diagnosis in Dup15q syndrome.Here, we have identified an electrophysiological biomarker of Dup15q syndrome that may facilitate clinical stratification, treatment monitoring, and measurement of target engagement for future clinical trials. Future work will investigate the genetic and neural underpinnings of this electrophysiological signature as well as the functional consequences of excessive beta oscillations in Dup15q syndrome.

  14. 76 FR 34761 - Classified National Security Information

    Science.gov (United States)

    2011-06-14

    ... MARINE MAMMAL COMMISSION Classified National Security Information [Directive 11-01] AGENCY: Marine... Commission's (MMC) policy on classified information, as directed by Information Security Oversight Office... of Executive Order 13526, ``Classified National Security Information,'' and 32 CFR part 2001...

  15. Computational consideration for selection of social classes in Romania

    Directory of Open Access Journals (Sweden)

    Andoria Ioniţă

    2015-09-01

    Full Text Available Middle class is a subject discussed by almost everyone, judging it in most cases from the visible living standard’s point of view: having the ownership of the dwelling, a car, making trips inside country or abroad, buying good quality and expensive goods or services and so on. But, at least in the case of our country, very often there is not a quantitative measurement of middle class, due to the fact that defining correct and reliable criteria to separate this social class from the others isn’t an easy task. Which are the “latent” factors which ensure each person’s capability to belong to the middle class? How much this affiliation depends on the individual characteristics and how much it depends on external factors like the characteristics of the society in which the persons are living in? A subtle definition of the middle class has to take into consideration several aspects, some of them more easily or more difficult to measure from the quantitative point of view. We are taking about some quantitative criteria like incomes or the number of endowment goods owned by a person, which are criteria relatively easy to estimate thought statistical methods, but also about aspects like wellbeing or social prestige, variables with a strong subjective specificity, on which there is very difficult to find an accord regarding methods of measurement between different specialists. This paper presents the results of an attempt to define social classes for Romania, in order to highlight the dimensions and the social importance of the middle class in our country. The elaboration of the methodology to build the social classes starts from the definition of 11 professional categories, based on the Classification of Occupation in Romania. By using the professional categories defined, which can be considered a first instrument (or a first step for the separation of middle class from the other ones, the present paper presents a first image of the middle

  16. Automatic Parameterization Strategy for Cardiac Electrophysiology Simulations.

    Science.gov (United States)

    Costa, Caroline Mendonca; Hoetzl, Elena; Rocha, Bernardo Martins; Prassl, Anton J; Plank, Gernot

    2013-10-01

    Driven by recent advances in medical imaging, image segmentation and numerical techniques, computer models of ventricular electrophysiology account for increasingly finer levels of anatomical and biophysical detail. However, considering the large number of model parameters involved parameterization poses a major challenge. A minimum requirement in combined experimental and modeling studies is to achieve good agreement in activation and repolarization sequences between model and experiment or patient data. In this study, we propose basic techniques which aid in determining bidomain parameters to match activation sequences. An iterative parameterization algorithm is implemented which determines appropriate bulk conductivities which yield prescribed velocities. In addition, a method is proposed for splitting the computed bulk conductivities into individual bidomain conductivities by prescribing anisotropy ratios.

  17. RTIME: a program for time-series measurements and evaluation in electrophysiology with the AT-PC.

    Science.gov (United States)

    Gras, H; Hackenberg, K

    1992-02-01

    This work describes a program which uses a standard RS232-serial interface of an IBM-AT-compatible microcomputer to receive via four data lines input from any laboratory equipment (spike discriminators, stimulus equipment, etc.) generating pulses between 0 to -15 V (low) and +5 to +15 V (high). Therefore, program operation is independent from any hardware extensions of the computer. The time of occurrence of input pulses is recorded with a resolution of 10 microseconds. Depending on processor speed and optional on-line display of interval histograms, a maximum sampling rate of 1.3-6 kHz is attained. Designed primarily for electrophysiological applications, the program comprises an extensive set of functions for off-line analysis of data either in the time- or in the phase-domain. The program is controlled by menu-selectable commands with detailed on-line explanation and is therefore suited for use even by operators without computer experience, e.g., students on courses in experimental physiology. Elaborate schemes of evaluation can be defined as macro commands to speed up and simplify complex data analysis in actual research.

  18. Aggregation Operator Based Fuzzy Pattern Classifier Design

    DEFF Research Database (Denmark)

    Mönks, Uwe; Larsen, Henrik Legind; Lohweg, Volker

    2009-01-01

    This paper presents a novel modular fuzzy pattern classifier design framework for intelligent automation systems, developed on the base of the established Modified Fuzzy Pattern Classifier (MFPC) and allows designing novel classifier models which are hardware-efficiently implementable....... The performances of novel classifiers using substitutes of MFPC's geometric mean aggregator are benchmarked in the scope of an image processing application against the MFPC to reveal classification improvement potentials for obtaining higher classification rates....

  19. Electrophysiological Markers of Categorical Perception of Color in 7-Month Old Infants

    Science.gov (United States)

    Clifford, Alexandra; Franklin, Anna; Davies, Ian R. L.; Holmes, Amanda

    2009-01-01

    The origin of color categories has been debated by psychologists, linguists and cognitive scientists for many decades. Here, we present the first electrophysiological evidence for categorical responding to color before color terms are acquired. Event-related potentials were recorded on a visual oddball task in 7-month old infants. Infants were…

  20. Cardiac autonomic modulation by estrogen in female mice undergoing ambulatory monitoring and in vivo electrophysiologic testing.

    Science.gov (United States)

    Saba, Samir; Shusterman, Vladimir; Usiene, Irmute; London, Barry

    2004-04-01

    Estrogen is an important modulator of cardiovascular risk, but its mechanism of action is not fully understood. We investigated the effect of ovariectomy and its timing on the cardiac electrophysiology in mice. Thirty female mice (age 18.8 +/- 3.1 weeks) underwent in vivo electrophysiologic testing before and after autonomic blockade. Fifteen mice were ovariectomized prepuberty (PRE) and ten postpuberty (POST), 2 weeks prior to electrophysiologic testing. Five age-matched sham-operated female mice (Control) served as controls. A subset of 13 mice (5 PRE, 3 POST, and 5 Controls) underwent 24-hour ambulatory monitoring. With ambulatory monitoring, the average (668 +/- 28 vs 769 +/- 52 b/min, P = 0.008) and minimum (485 +/- 47 vs 587 +/- 53 b/min, P = 0.02) heart rates were significantly slower in the ovariectomized mice (PRE and POST groups) compared to the Control group. At baseline electrophysiologic testing, there were no significant differences among the ovariectomized and intact mice in any of the measured parameters. With autonomic blockade, the Control group had a significantly larger change (delta) in the atrioventricular (AV) nodal Wenckebach (AVW) periodicity (deltaAVW = 11.3 +/- 2.9 vs 2.1 +/- 7.3 ms, P = 0.05) and functional refractory period (deltaFRP = 11.3 +/- 2.1 vs 1.25 +/- 6.8 ms, P = 0.02) compared to the ovariectomized mice. These results were not altered by the time of ovariectomy (PRE vs POST groups). Our results suggest that estrogen modulates the autonomic inputs into the murine sinus and AV nodes. These findings, if replicated in humans, might underlie the observed clustering of certain arrhythmias around menstruation and explain the higher incidence of arrhythmias in men and postmenopausal women.

  1. The Enhancement of Students’ Skill in Classifying and Concept Mastery in Salt Hydrolysis Material Through Problem Solving Learning Model

    OpenAIRE

    Safitri, Esty Indriyani; Rosilawati, Ila; Efkar, Tasviri

    2012-01-01

    The purpose of this research is to find out effectiveness of problem solving learning model on salt hydrolysis material in improve the skill of classifying and concept mastery. The population of the research was all students in XI science class in SMAN I Way Jepara number in 120 students. The samples were 30 students in XI science 3 class and 30 students in XI science 4 that have equal academic abilities. This research was a quasi experiment using non equivalent (pretest-postest) control grou...

  2. Classified installations for environmental protection: Tome 3. Departmental orders, memorandum, technical instructions

    International Nuclear Information System (INIS)

    Anon.

    1993-06-01

    Legislation about classified installations or plants govern most of industries or dangerous or pollutant activities. This legislation aims to prevent risks and harmful effects coming from an installation or equipments : air pollution, water pollution, noise, wastes produced by installation, even aesthetic bad effects are taken in account. Pollutant or dangerous activities are defined in a list called nomenclature which obliged installations to a rule of declaration or authorization. Technical regulations ordered by the prefect (administrator of a french department) are listed in tome 3

  3. Simulation Methods and Validation Criteria for Modeling Cardiac Ventricular Electrophysiology.

    Directory of Open Access Journals (Sweden)

    Shankarjee Krishnamoorthi

    Full Text Available We describe a sequence of methods to produce a partial differential equation model of the electrical activation of the ventricles. In our framework, we incorporate the anatomy and cardiac microstructure obtained from magnetic resonance imaging and diffusion tensor imaging of a New Zealand White rabbit, the Purkinje structure and the Purkinje-muscle junctions, and an electrophysiologically accurate model of the ventricular myocytes and tissue, which includes transmural and apex-to-base gradients of action potential characteristics. We solve the electrophysiology governing equations using the finite element method and compute both a 6-lead precordial electrocardiogram (ECG and the activation wavefronts over time. We are particularly concerned with the validation of the various methods used in our model and, in this regard, propose a series of validation criteria that we consider essential. These include producing a physiologically accurate ECG, a correct ventricular activation sequence, and the inducibility of ventricular fibrillation. Among other components, we conclude that a Purkinje geometry with a high density of Purkinje muscle junctions covering the right and left ventricular endocardial surfaces as well as transmural and apex-to-base gradients in action potential characteristics are necessary to produce ECGs and time activation plots that agree with physiological observations.

  4. Simulation Methods and Validation Criteria for Modeling Cardiac Ventricular Electrophysiology.

    Science.gov (United States)

    Krishnamoorthi, Shankarjee; Perotti, Luigi E; Borgstrom, Nils P; Ajijola, Olujimi A; Frid, Anna; Ponnaluri, Aditya V; Weiss, James N; Qu, Zhilin; Klug, William S; Ennis, Daniel B; Garfinkel, Alan

    2014-01-01

    We describe a sequence of methods to produce a partial differential equation model of the electrical activation of the ventricles. In our framework, we incorporate the anatomy and cardiac microstructure obtained from magnetic resonance imaging and diffusion tensor imaging of a New Zealand White rabbit, the Purkinje structure and the Purkinje-muscle junctions, and an electrophysiologically accurate model of the ventricular myocytes and tissue, which includes transmural and apex-to-base gradients of action potential characteristics. We solve the electrophysiology governing equations using the finite element method and compute both a 6-lead precordial electrocardiogram (ECG) and the activation wavefronts over time. We are particularly concerned with the validation of the various methods used in our model and, in this regard, propose a series of validation criteria that we consider essential. These include producing a physiologically accurate ECG, a correct ventricular activation sequence, and the inducibility of ventricular fibrillation. Among other components, we conclude that a Purkinje geometry with a high density of Purkinje muscle junctions covering the right and left ventricular endocardial surfaces as well as transmural and apex-to-base gradients in action potential characteristics are necessary to produce ECGs and time activation plots that agree with physiological observations.

  5. Female preponderance in atrioventricular node reentrant tachycardia, but no sex related electrophysiological differences

    Directory of Open Access Journals (Sweden)

    Claes Williamsson

    2014-01-01

    Full Text Available The mechanism behind the female preponderance for atrio-ventricular node reentrant tachycardia (AVNRT is not clear. We compared baseline electrophysiological measurements and clinical data in 141 consecutive patients (96 women who underwent successful AVNRT ablation at their fi rst therapeutic procedure. Women had on average 9% higher resting heart rate than men (p<0.05, but were similar in all measures of AV node function. Isoproterenol infusion was required for AVNRT induction in 69 cases (49%, and the need for isoproterenol was associated with lower resting heart rate and longer anterograde and retrograde AV node refractory periods (p<0.05 for comparisons, but not with sex. We conclude that the spectrum of baseline AV node physiology in AVNRT patients is wide, and is similar in men and women. The female preponderance for AVNRT cannot be explained from comparisons of baseline AV node electrophysiological properties.

  6. Proposal for the classification of scenarios for deep geological repositories in probability classes

    International Nuclear Information System (INIS)

    Beuth, Thomas

    2013-03-01

    The provided report was elaborated in the framework of the project 3609R03210 ''Research and Development for Proof of the long-term Safety of Deep Geological Repositories''. It contains a proposal for a methodology that enables the assignment of developed scenarios in the frame of Safety Cases to defined probability classes. The assignment takes place indirectly through the categorization of the defining relevant factors (so-called FEP: Features, Events and Processes) of the respective scenarios also in probability classes. Therefore, decision trees and criteria were developed for the categorization of relevant factors in classes. Besides the description of the methodology another focal point of the work was the application of the method taking into account a defined scenario. By means of the scenario the different steps of the method and the decision criteria were documented, respectively. In addition, potential subjective influences along the path of decisions regarding the assignment of scenarios in probability classes were identified.

  7. Highly Accurate Prediction of Jobs Runtime Classes

    OpenAIRE

    Reiner-Benaim, Anat; Grabarnick, Anna; Shmueli, Edi

    2016-01-01

    Separating the short jobs from the long is a known technique to improve scheduling performance. In this paper we describe a method we developed for accurately predicting the runtimes classes of the jobs to enable this separation. Our method uses the fact that the runtimes can be represented as a mixture of overlapping Gaussian distributions, in order to train a CART classifier to provide the prediction. The threshold that separates the short jobs from the long jobs is determined during the ev...

  8. Social power and social class: conceptualization, consequences, and current challenges.

    Science.gov (United States)

    Rucker, Derek D; Galinsky, Adam D

    2017-12-01

    This article offers a primer on social power and social class with respect to their theoretical importance, conceptual distinction, and empirical relationship. We introduce and define the constructs of social power, social class, and one's psychological sense of power. We next explore the complex relationship between social power and social class. Because social class can produce a sense of power within an individual, studies on social power can inform theory and research on social class. We conclude with a discussion of the current challenges and future opportunities for the study of social power and social class. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Poverty and Depression among Men: The Social Class Worldview Model and Counseling Implications.

    Science.gov (United States)

    Liu, William M.

    This paper outlines a theory for understanding social class in men's lives, and argues that poverty and depression are a function of social class and internalized classism. It begins by defining poverty, then explains the Social Class Worldview Model, which is a subjective social class model, and the Modern Classism Theory, which allows clinicians…

  10. Electrophysiological effects of trace amines on mesencephalic dopaminergic neurons

    Directory of Open Access Journals (Sweden)

    Ada eLedonne

    2011-07-01

    Full Text Available Trace amines (TAs are a class of endogenous compounds strictly related to classic monoamine neurotransmitters with regard to their structure, metabolism and tissue distribution. Although the presence of TAs in mammalian brain has been recognized for decades, until recently they were considered to be by-products of amino acid metabolism or as ‘false’ neurotransmitters. The discovery in 2001 of a new family of G protein-coupled receptors (GPCRs, namely trace amines receptors, has re-ignited interest in TAs. In particular, two members of the family, trace amine receptor 1 (TA1 and trace amine receptor 2 (TA2, were shown to be highly sensitive to these endogenous compounds. Experimental evidence suggests that TAs modulate the activity of catecholaminergic neurons and that TA dysregulation may contribute to neuropsychiatric disorders, including schizophrenia, attention deficit hyperactivity disorder, depression and Parkinson’s disease, all of which are characterised by altered monoaminergic networks. Here we review recent data concerning the electrophysiological effects of TAs on the activity of mesencephalic dopaminergic neurons. In the context of recent data obtained with TA1 receptor knockout mice, we also discuss the mechanisms by which the activation of these receptors modulates the activity of these neurons. Three important new aspects of TAs action have recently emerged: (a inhibition of firing due to increased release of dopamine; (b reduction of D2 and GABAB receptor-mediated inhibitory responses (excitatory effects due to dysinhibition; and (c a direct TA1 receptor-mediated activation of GIRK channels which produce cell membrane hyperpolarization. While the first two effects have been well documented in our laboratory, the direct activation of GIRK channels by TA1 receptors has been reported by others, but has not been seen in our laboratory (Geracitano et al., 2004. Further research is needed to address this point, and to further

  11. Using hierarchical clustering methods to classify motor activities of COPD patients from wearable sensor data

    Directory of Open Access Journals (Sweden)

    Reilly John J

    2005-06-01

    Full Text Available Abstract Background Advances in miniature sensor technology have led to the development of wearable systems that allow one to monitor motor activities in the field. A variety of classifiers have been proposed in the past, but little has been done toward developing systematic approaches to assess the feasibility of discriminating the motor tasks of interest and to guide the choice of the classifier architecture. Methods A technique is introduced to address this problem according to a hierarchical framework and its use is demonstrated for the application of detecting motor activities in patients with chronic obstructive pulmonary disease (COPD undergoing pulmonary rehabilitation. Accelerometers were used to collect data for 10 different classes of activity. Features were extracted to capture essential properties of the data set and reduce the dimensionality of the problem at hand. Cluster measures were utilized to find natural groupings in the data set and then construct a hierarchy of the relationships between clusters to guide the process of merging clusters that are too similar to distinguish reliably. It provides a means to assess whether the benefits of merging for performance of a classifier outweigh the loss of resolution incurred through merging. Results Analysis of the COPD data set demonstrated that motor tasks related to ambulation can be reliably discriminated from tasks performed in a seated position with the legs in motion or stationary using two features derived from one accelerometer. Classifying motor tasks within the category of activities related to ambulation requires more advanced techniques. While in certain cases all the tasks could be accurately classified, in others merging clusters associated with different motor tasks was necessary. When merging clusters, it was found that the proposed method could lead to more than 12% improvement in classifier accuracy while retaining resolution of 4 tasks. Conclusion Hierarchical

  12. Detection Performance of Signals in Dependent Noise From a Gaussian Mixture Uncertainty Class

    National Research Council Canada - National Science Library

    Gerlach, K

    1998-01-01

    ... (correlated) multivariate noise from a Gaussian mixture uncertainty class. This uncertainty class is defined using upper and lower bounding functions on the univariate Gaussian mixing distribution function...

  13. Composite Classifiers for Automatic Target Recognition

    National Research Council Canada - National Science Library

    Wang, Lin-Cheng

    1998-01-01

    ...) using forward-looking infrared (FLIR) imagery. Two existing classifiers, one based on learning vector quantization and the other on modular neural networks, are used as the building blocks for our composite classifiers...

  14. Evolution of Class III treatment in orthodontics.

    Science.gov (United States)

    Ngan, Peter; Moon, Won

    2015-07-01

    Angle, Tweed, and Moyers classified Class III malocclusions into 3 types: pseudo, dentoalveolar, and skeletal. Clinicians have been trying to identify the best timing to intercept a Class III malocclusion that develops as early as the deciduous dentition. With microimplants as skeletal anchorage, orthopedic growth modification became more effective, and it also increased the scope of camouflage orthodontic treatment for patients who were not eligible for orthognathic surgery. However, orthodontic treatment combined with orthognathic surgery remains the only option for patients with a severe skeletal Class III malocclusion or a craniofacial anomaly. Distraction osteogenesis can now be performed intraorally at an earlier age. The surgery-first approach can minimize the length of time that the malocclusion needs to worsen before orthognathic surgery. Finally, the use of computed tomography scans for 3-dimensional diagnosis and treatment planning together with advances in imaging technology can improve the accuracy of surgical movements and the esthetic outcomes for these patients. Copyright © 2015 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

  15. Defining the HLA class I-associated viral antigen repertoire from HIV-1-infected human cells

    DEFF Research Database (Denmark)

    Ternette, Nicola; Yang, Hongbing; Partridge, Thomas

    2016-01-01

    Recognition and eradication of infected cells by cytotoxic T lymphocytes is a key defense mechanism against intracellular pathogens. High-throughput definition of HLA class I-associated immunopeptidomes by mass spectrometry is an increasingly important analytical tool to advance our understanding...

  16. Electrophysiological signal analysis and visualization using Cloudwave for epilepsy clinical research.

    Science.gov (United States)

    Jayapandian, Catherine P; Chen, Chien-Hung; Bozorgi, Alireza; Lhatoo, Samden D; Zhang, Guo-Qiang; Sahoo, Satya S

    2013-01-01

    Epilepsy is the most common serious neurological disorder affecting 50-60 million persons worldwide. Electrophysiological data recordings, such as electroencephalogram (EEG), are the gold standard for diagnosis and pre-surgical evaluation in epilepsy patients. The increasing trend towards multi-center clinical studies require signal visualization and analysis tools to support real time interaction with signal data in a collaborative environment, which cannot be supported by traditional desktop-based standalone applications. As part of the Prevention and Risk Identification of SUDEP Mortality (PRISM) project, we have developed a Web-based electrophysiology data visualization and analysis platform called Cloudwave using highly scalable open source cloud computing infrastructure. Cloudwave is integrated with the PRISM patient cohort identification tool called MEDCIS (Multi-modality Epilepsy Data Capture and Integration System). The Epilepsy and Seizure Ontology (EpSO) underpins both Cloudwave and MEDCIS to support query composition and result retrieval. Cloudwave is being used by clinicians and research staff at the University Hospital - Case Medical Center (UH-CMC) Epilepsy Monitoring Unit (EMU) and will be progressively deployed at four EMUs in the United States and the United Kingdomas part of the PRISM project.

  17. Hybrid Neuro-Fuzzy Classifier Based On Nefclass Model

    Directory of Open Access Journals (Sweden)

    Bogdan Gliwa

    2011-01-01

    Full Text Available The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods. Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented.

  18. Wolff-Parkinson-White syndrome type B and left bundle-branch block: electrophysiologic and radionuclide study

    Energy Technology Data Exchange (ETDEWEB)

    Rakovec, P.; Kranjec, I.; Fettich, J.J.; Jakopin, J.; Fidler, V.; Turk, J.

    1985-01-01

    Coinciding left bundle-branch block and Wolff-Parkinson-White syndrome type B, a very rare electrocardiographic occurrence, was found in a patient with dilated cardiomyopathy. Electrophysiologic study revealed eccentric retrograde atrial activation during ventricular pacing, suggesting right-sided accessory pathway. At programmed atrial pacing, effective refractory period of the accessory pathway was 310 ms; at shorter pacing coupling intervals, normal atrioventricular conduction with left bundle-branch block was seen. Left bundle-branch block was seen also with His bundle pacing. Radionuclide phase imaging demonstrated right ventricular phase advance and left ventricular phase delay; both right and left ventricular phase images revealed broad phase distribution histograms. Combined electrophysiologic and radionuclide investigations are useful to disclose complex conduction abnormalities and their mechanical correlates.

  19. Wolff-Parkinson-White syndrome type B and left bundle-branch block: electrophysiologic and radionuclide study

    International Nuclear Information System (INIS)

    Rakovec, P.; Kranjec, I.; Fettich, J.J.; Jakopin, J.; Fidler, V.; Turk, J.

    1985-01-01

    Coinciding left bundle-branch block and Wolff-Parkinson-White syndrome type B, a very rare electrocardiographic occurrence, was found in a patient with dilated cardiomyopathy. Electrophysiologic study revealed eccentric retrograde atrial activation during ventricular pacing, suggesting right-sided accessory pathway. At programmed atrial pacing, effective refractory period of the accessory pathway was 310 ms; at shorter pacing coupling intervals, normal atrioventricular conduction with left bundle-branch block was seen. Left bundle-branch block was seen also with His bundle pacing. Radionuclide phase imaging demonstrated right ventricular phase advance and left ventricular phase delay; both right and left ventricular phase images revealed broad phase distribution histograms. Combined electrophysiologic and radionuclide investigations are useful to disclose complex conduction abnormalities and their mechanical correlates

  20. Combining non-invasive transcranial brain stimulation with neuroimaging and electrophysiology: Current approaches and future perspectives

    DEFF Research Database (Denmark)

    Bergmann, Til Ole; Karabanov, Anke; Hartwigsen, Gesa

    2016-01-01

    Non-invasive transcranial brain stimulation (NTBS) techniques such as transcranial magnetic stimulation (TMS) and transcranial current stimulation (TCS) are important tools in human systems and cognitive neuroscience because they are able to reveal the relevance of certain brain structures...... are technically demanding. We argue that the benefit from this combination is twofold. Firstly, neuroimaging and electrophysiology can inform subsequent NTBS, providing the required information to optimize where, when, and how to stimulate the brain. Information can be achieved both before and during the NTBS...... experiment, requiring consecutive and concurrent applications, respectively. Secondly, neuroimaging and electrophysiology can provide the readout for neural changes induced by NTBS. Again, using either concurrent or consecutive applications, both "online" NTBS effects immediately following the stimulation...

  1. Electrophysiological Correlates of Reading the Single- and Interactive-Mind

    Science.gov (United States)

    Wang, Yi-Wen; Zheng, Yu-Wei; Lin, Chong-De; Wu, Jie; Shen, De-Li

    2011-01-01

    Understanding minds is the cognitive basis of successful social interaction. In everyday life, human mental activity often happens at the moment of social interaction among two or multiple persons instead of only one-person. Understanding the interactive mind of two- or multi-person is more complex and higher than understanding the single-person mind in the hierarchical structure of theory of mind. Understanding the interactive mind maybe differentiate from understanding the single mind. In order to examine the dissociative electrophysiological correlates of reading the single mind and reading the interactive mind, the 64 channels event-related potentials were recorded while 16 normal adults were observing three kinds of Chinese idioms depicted physical scenes, one-person with mental activity, and two- or multi-person with mental interaction. After the equivalent N400, in the 500- to 700-ms epoch, the mean amplitudes of late positive component (LPC) over frontal for reading the single mind and reading the interactive mind were significantly more positive than for physical representation, while there was no difference between the former two. In the 700- to 800-ms epoch, the mean amplitudes of LPC over frontal–central for reading the interactive mind were more positive than for reading the single mind and physical representation, while there was no difference between the latter two. The present study provides electrophysiological signature of the dissociations between reading the single mind and reading the interactive mind. PMID:21845178

  2. Electrophysiological correlates of reading the single- and interactive-mind

    Directory of Open Access Journals (Sweden)

    Yi-Wen eWang

    2011-07-01

    Full Text Available Understanding minds is the cognitive basis of successful social interaction. In everyday life, human mental activity often happens at the moment of social interaction among two or multiple persons instead of only one person. Understanding the interactive mind of two- or multi-person is more complex and higher than understanding the single-person mind in the hierarchical structure of theory-of-mind. Understanding the interactive mind maybe differentiate from understanding the single mind. In order to examine the dissociative electrophysiological correlates of reading the single mind and reading the interactive mind, the 64 channels event-related potentials (ERP were recorded while 16 normal adults were observing three kinds of Chinese idioms depicted physical scenes, one-person with mental activity and two- or multi-person with mental interaction. After the equivalent N400, in the 500- to 700-ms epoch, the mean amplitudes of late positive component (LPC over frontal for reading the single mind and reading the interactive mind were significantly more positive than for physical representation, while there was no difference between the former two. In the 700-to 800-ms epoch, the mean amplitudes of LPC over frontal-central for reading the interactive mind were more positive than for reading the single mind and physical representation, while there was no difference between the latter two. The present study provides electrophysiological signature of the dissociations between reading the single mind and reading the interactive mind.

  3. Conduction disturbances after TAVR: Electrophysiological studies and pacemaker dependency.

    Science.gov (United States)

    Makki, Nader; Dollery, Jenn; Jones, Danielle; Crestanello, Juan; Lilly, Scott

    Permanent pacemaker (PPM) placement occurs in 5-20% of patients after transcatheter aortic valve replacement (TAVR). Although predictors of pacemaker implantation have been established, features that predispose patients to pacemaker utilization on follow up have not been widely reported. We performed a retrospective review of patients undergoing commercial TAVR between 2011 and 2016. We collated patients that underwent in-hospital PPM implantation and had a follow up of at least 3months. Data abstraction was performed for electrophysiological studies (EPS), pacemaker indication, timing, and device interrogation for pacemaker dependency on follow up. A total of 24 patients received in-hospital PPM post-TAVR (14% of total cohort), and mean follow up was 22months. Indications for PPM included resting complete heart block (CHB; 15/24, 63%), left bundle branch block and abnormal electrophysiological study (EPS; 7/24, 29%), alternating bundle branch block (1/24, 4%) and tachy-brady syndrome (1/24, 4%). Pacemaker dependency (underlying ventricular asystole, complete heart block, or >50% pacing) occurred in 8/24 patients (33%) during follow-up, 7 of whom had resting CHB, and one with CHB invoked during EPS. Pacemaker dependency after TAVR is common among those that exhibited CHB, but not among those with a prolonged HV delay during EPS. Although preliminary, these observations are relevant to management of rhythm disturbances after TAVR, and may inform the practice of EPS-based PPM implantation. Copyright © 2017 Elsevier Inc. All rights reserved.

  4. Electrical Stimulation of Artificial Heart Muscle: a look into the electrophysiological and genetic implications

    Science.gov (United States)

    Mohamed, Mohamed A; Islas, Jose F; Schwartz, Robert J; Birla, Ravi K

    2016-01-01

    Development of tissue-engineered hearts for treatment of myocardial infarction or biological pacemakers has been hindered by the production of mostly arrhythmic or in-synergistic constructs. Electrical stimulation (ES) of these constructs has been shown to produce tissues with greater twitch force and better adrenergic response. In order to further our understanding of the mechanisms underlying the effect of ES, we fabricated a bioreactor capable of delivering continuous or intermittent waveforms of various types to multiple constructs simultaneously. In this study, we examined the effect of an intermittent biphasic square wave on our artificial heart muscle (AHM) composed of neonatal rat cardiac cells and fibrin gel. Twitch forces, spontaneous contraction rates, biopotentials, gene expression profiles, and histological observations were examined for the ES protocol over a 12 day culture period. We demonstrate improved consistency between samples for twitch force and contraction rate, and higher normalized twitch force amplitudes for electrically stimulated AHM. Improvements in electrophysiology within the AHM was noted by higher conduction velocities and lower latency in electrical response for electrically stimulated AHM. Genes expressing key electrophysiological and structural markers peaked at days 6 and 8 of culture, only a few days after the initiation of ES. These results may be used for optimization strategies to establish protocols for producing AHM capable of replacing damaged heart tissue in either a contractile or electrophysiological capacity. Optimized AHM can lead to alternative treatments to heart failure and alleviate the limited donor supply crisis. PMID:28459744

  5. Electrical Stimulation of Artificial Heart Muscle: A Look Into the Electrophysiologic and Genetic Implications.

    Science.gov (United States)

    Mohamed, Mohamed A; Islas, Jose F; Schwartz, Robert J; Birla, Ravi K

    Development of tissue-engineered hearts for treatment of myocardial infarction or biologic pacemakers has been hindered by the production of mostly arrhythmic or in-synergistic constructs. Electrical stimulation (ES) of these constructs has been shown to produce tissues with greater twitch force and better adrenergic response. To further our understanding of the mechanisms underlying the effect of ES, we fabricated a bioreactor capable of delivering continuous or intermittent waveforms of various types to multiple constructs simultaneously. In this study, we examined the effect of an intermittent biphasic square wave on our artificial heart muscle (AHM) composed of neonatal rat cardiac cells and fibrin gel. Twitch forces, spontaneous contraction rates, biopotentials, gene expression profiles, and histologic observations were examined for the ES protocol over a 12 day culture period. We demonstrate improved consistency between samples for twitch force and contraction rate, and higher normalized twitch force amplitudes for electrically stimulated AHMs. Improvements in electrophysiology within the AHM were noted by higher conduction velocities and lower latency in electrical response for electrically stimulated AHMs. Genes expressing key electrophysiologic and structural markers peaked at days 6 and 8 of culture, only a few days after the initiation of ES. These results may be used for optimization strategies to establish protocols for producing AHMs capable of replacing damaged heart tissue in either a contractile or electrophysiologic capacity. Optimized AHMs can lead to alternative treatments to heart failure and alleviate the limited donor supply crisis.

  6. Scalable Electrophysiology in Intact Small Animals with Nanoscale Suspended Electrode Arrays

    OpenAIRE

    Gonzales, Daniel L.; Badhiwala, Krishna N.; Vercosa, Daniel G.; Avants, Ben W.; Liu, Zheng; Zhong, Weiwei; Robinson, Jacob T.

    2017-01-01

    Electrical measurements from large populations of animals would help reveal fundamental properties of the nervous system and neurological diseases. Small invertebrates are ideal for these large-scale studies; however, patch-clamp electrophysiology in microscopic animals typically requires low-throughput and invasive dissections. To overcome these limitations, we present nano-SPEARs: suspended electrodes integrated into a scalable microfluidic device. Using this technology, we have made the fi...

  7. Neural ensemble communities: Open-source approaches to hardware for large-scale electrophysiology

    Science.gov (United States)

    Siegle, Joshua H.; Hale, Gregory J.; Newman, Jonathan P.; Voigts, Jakob

    2014-01-01

    One often-overlooked factor when selecting a platform for large-scale electrophysiology is whether or not a particular data acquisition system is “open” or “closed”: that is, whether or not the system’s schematics and source code are available to end users. Open systems have a reputation for being difficult to acquire, poorly documented, and hard to maintain. With the arrival of more powerful and compact integrated circuits, rapid prototyping services, and web-based tools for collaborative development, these stereotypes must be reconsidered. We discuss some of the reasons why multichannel extracellular electrophysiology could benefit from open-source approaches and describe examples of successful community-driven tool development within this field. In order to promote the adoption of open-source hardware and to reduce the need for redundant development efforts, we advocate a move toward standardized interfaces that connect each element of the data processing pipeline. This will give researchers the flexibility to modify their tools when necessary, while allowing them to continue to benefit from the high-quality products and expertise provided by commercial vendors. PMID:25528614

  8. 36 CFR 1256.46 - National security-classified information.

    Science.gov (United States)

    2010-07-01

    ... 36 Parks, Forests, and Public Property 3 2010-07-01 2010-07-01 false National security-classified... Restrictions § 1256.46 National security-classified information. In accordance with 5 U.S.C. 552(b)(1), NARA... properly classified under the provisions of the pertinent Executive Order on Classified National Security...

  9. CKD273, a new proteomics classifier assessing CKD and its prognosis.

    Directory of Open Access Journals (Sweden)

    Ángel Argilés

    Full Text Available National Kidney Foundation CKD staging has allowed uniformity in studies on CKD. However, early diagnosis and predicting progression to end stage renal disease are yet to be improved. Seventy six patients with different levels of CKD, including outpatients and dialysed patients were studied for transcriptome, metabolome and proteome description. High resolution urinary proteome analysis was blindly performed in the 53 non-anuric out of the 76 CKD patients. In addition to routine clinical parameters, CKD273, a urinary proteomics-based classifier and its peptides were quantified. The baseline values were analyzed with regard to the clinical parameters and the occurrence of death or renal death during follow-up (3.6 years as the main outcome measurements. None of the patients with CKD2730.55. Unsupervised clustering analysis of the CKD273 peptides separated the patients into two main groups differing in CKD associated parameters. Among the 273 biomarkers, peptides derived from serum proteins were relatively increased in patients with lower glomerular filtration rate, while collagen-derived peptides were relatively decreased (p<0.05; Spearman. CKD273 was different in the groups with different renal function (p<0.003. The CKD273 classifier separated CKD patients according to their renal function and informed on the likelihood of experiencing adverse outcome. Recently defined in a large population, CKD273 is the first proteomic-based classifier successfully tested for prognosis of CKD progression in an independent cohort.

  10. The Use of BDDCS in Classifying the Permeability of Marketed Drugs1

    Science.gov (United States)

    Benet, Leslie Z.; Amidon, Gordon L.; Barends, Dirk M.; Lennernäs, Hans; Polli, James E.; Shah, Vinod P.; Stavchansky, Salomon A.; Yu, Lawrence X.

    2013-01-01

    We recommend that regulatory agencies add the extent of drug metabolism (i.e., ≥90% metabolized) as an alternate method in defining Class 1 marketed drugs suitable for a waiver of in vivo studies of bioequivalence. That is, ≥90% metabolized is an additional methodology that may be substituted for ≥90% absorbed. We propose that the following criteria be used to define ≥ 90% metabolized for marketed drugs: Following a single oral dose to humans, administered at the highest dose strength, mass balance of the Phase 1 oxidative and Phase 2 conjugative drug metabolites in the urine and feces, measured either as unlabeled, radioactive labeled or nonradioactive labeled substances, account for ≥ 90% of the drug dosed. This is the strictest definition for a waiver based on metabolism. For an orally administered drug to be ≥ 90% metabolized by Phase 1 oxidative and Phase 2 conjugative processes, it is obvious that the drug must be absorbed. This proposal, which strictly conforms to the present ≥90% criteria, is a suggested modification to facilitate a number of marketed drugs being appropriately assigned to Class 1. PMID:18236138

  11. THE Onfp CLASS IN THE MAGELLANIC CLOUDS

    International Nuclear Information System (INIS)

    Walborn, Nolan R.; Howarth, Ian D.; Evans, Christopher J.

    2010-01-01

    The Onfp class of rotationally broadened, hot spectra was defined some time ago in the Galaxy, where its membership to date numbers only eight. The principal defining characteristic is a broad, centrally reversed He II λ 4686 emission profile; other emission and absorption lines are also rotationally broadened. Recent surveys in the Magellanic Clouds (MCs) have brought the class membership there, including some related spectra, to 28. We present a survey of the spectral morphology and rotational velocities, as a first step toward elucidating the nature of this class. Evolved, rapidly rotating hot stars are not expected theoretically, because the stellar winds should brake the rotation. Luminosity classification of these spectra is not possible, because the principal criterion (He II λ4686) is peculiar; however, the MCs provide reliable absolute magnitudes, which show that they span the entire range from dwarfs to supergiants. The Onfp line-broadening distribution is distinct and shifted toward larger values from those of normal O dwarfs and supergiants with >99.99% confidence. All cases with multiple observations show line-profile variations, which even remove some objects from the class temporarily. Some of them are spectroscopic binaries; it is possible that the peculiar profiles may have multiple causes among different objects. The origin and future of these stars are intriguing; for instance, they could be stellar mergers and/or gamma-ray-burst progenitors.

  12. BDDCS Class Prediction for New Molecular Entities

    DEFF Research Database (Denmark)

    Broccatelli, Fabio; Cruciani, Gabriele; Benet, Leslie Z.

    2012-01-01

    M) predicts high versus low intestinal permeability rate, and vice versa, at least when uptake transporters or paracellular transport is not involved. We recently published a collection of over 900 marketed drugs classified for BDDCS. We suggest that a reliable model for predicting BDDCS class, integrated...... chemistry compounds (over 30,000 chemicals). Based on this application, we suggest that solubility, and not permeability, is the major difference between NMEs and drugs. We anticipate that the forecast of BDDCS categories in early drug discovery may lead to a significant R&D cost reduction....... descriptors calculated or derived from the VolSurf+ software. For each molecule, a probability of BDDCS class membership was given, based on predicted EoM, FDA solubility (FDAS) and their confidence scores. The accuracy in predicting FDAS was 78% in training and 77% in validation, while for EoM prediction...

  13. Deconvolution When Classifying Noisy Data Involving Transformations

    KAUST Repository

    Carroll, Raymond

    2012-09-01

    In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.

  14. Deconvolution When Classifying Noisy Data Involving Transformations.

    Science.gov (United States)

    Carroll, Raymond; Delaigle, Aurore; Hall, Peter

    2012-09-01

    In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.

  15. Deconvolution When Classifying Noisy Data Involving Transformations

    KAUST Repository

    Carroll, Raymond; Delaigle, Aurore; Hall, Peter

    2012-01-01

    In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.

  16. Is there a prognostic relevance of electrophysiological studies in bundle branch block patients?

    Science.gov (United States)

    Bogossian, Harilaos; Frommeyer, Gerrit; Göbbert, Kornelius; Hasan, Fuad; Nguyen, Quy Suu; Ninios, Ilias; Mijic, Dejan; Bandorski, Dirk; Hoeltgen, Reinhard; Seyfarth, Melchior; Lemke, Bernd; Eckardt, Lars; Zarse, Markus

    2017-08-01

    The present European guidelines suggest a diagnostic electrophysiological (EP) study to determine indication for cardiac pacing in patients with bundle branch block and unexplained syncope. We evaluated the prognostic relevance of an EP study for mortality and the development of permanent complete atrioventricular (AV) block in patients with symptomatic bifascicular block and first-degree AV block. The HV interval is a poor prognostic marker to predict the development of permanent AV block in patients with symptomatic bifascicular block (BFB) and AV block I°. Thirty consecutive patients (mean age, 74.8 ± 8.6 years; 25 males) with symptomatic BFB and first-degree AV block underwent an EP study before device implantation, according to current guidelines. For 53 ± 31 months, patients underwent yearly follow-up screening for syncope or higher-degree AV block. Thirty patients presented with prolonged HV interval during the EP study (mean, 82.2 ± 20.1 ms; range, 57-142 ms), classified into 3 groups: group 1, 70 to ≤100 ms (mean, 80 ± 8 ms; range, 70-97 ms; n = 18), and group 3, >100 ms (mean, 119 ± 14 ms; range, 107-142 ms; n = 5). According to the guidelines, patients in groups 2 and 3 received a pacemaker. The length of the HV interval was not associated with the later development of third-degree AV block or with increased mortality. Our present study suggests that an indication for pacemaker implantation based solely on a diagnostic EP study with prolongation of the HV interval is not justified. © 2017 Wiley Periodicals, Inc.

  17. Authentication of bee pollen grains in bright-field microscopy by combining one-class classification techniques and image processing.

    Science.gov (United States)

    Chica, Manuel

    2012-11-01

    A novel method for authenticating pollen grains in bright-field microscopic images is presented in this work. The usage of this new method is clear in many application fields such as bee-keeping sector, where laboratory experts need to identify fraudulent bee pollen samples against local known pollen types. Our system is based on image processing and one-class classification to reject unknown pollen grain objects. The latter classification technique allows us to tackle the major difficulty of the problem, the existence of many possible fraudulent pollen types, and the impossibility of modeling all of them. Different one-class classification paradigms are compared to study the most suitable technique for solving the problem. In addition, feature selection algorithms are applied to reduce the complexity and increase the accuracy of the models. For each local pollen type, a one-class classifier is trained and aggregated into a multiclassifier model. This multiclassification scheme combines the output of all the one-class classifiers in a unique final response. The proposed method is validated by authenticating pollen grains belonging to different Spanish bee pollen types. The overall accuracy of the system on classifying fraudulent microscopic pollen grain objects is 92.3%. The system is able to rapidly reject pollen grains, which belong to nonlocal pollen types, reducing the laboratory work and effort. The number of possible applications of this authentication method in the microscopy research field is unlimited. Copyright © 2012 Wiley Periodicals, Inc.

  18. Latent classes of polydrug and polyroute use and associations with human immunodeficiency virus risk behaviours and overdose among people who inject drugs in Tijuana, Baja California, Mexico.

    Science.gov (United States)

    Meacham, Meredith C; Roesch, Scott C; Strathdee, Steffanie A; Lindsay, Suzanne; Gonzalez-Zuniga, Patricia; Gaines, Tommi L

    2018-01-01

    Patterns of polydrug use among people who inject drugs (PWID) may be differentially associated with overdose and unique human immunodeficiency virus (HIV) risk factors. Subgroups of PWID in Tijuana, Mexico, were identified based on substances used, route of administration, frequency of use and co-injection indicators. Participants were PWID residing in Tijuana age ≥18 years sampled from 2011 to 2012 who reported injecting an illicit substance in the past month (n = 735). Latent class analysis identified discrete classes of polydrug use characterised by 11 indicators of past 6 months substance use. Multinomial logistic regression examined class membership association with HIV risk behaviours, overdose and other covariates using an automated three-step procedure in mplus to account for classification error. Participants were classified into five subgroups. Two polydrug and polyroute classes were defined by use of multiple substances through several routes of administration and were primarily distinguished from each other by cocaine use (class 1: 5%) or no cocaine use (class 2: 29%). The other classes consisted primarily of injectors: cocaine, methamphetamine and heroin injection (class 3: 4%); methamphetamine and heroin injection (class 4: 10%); and heroin injection (class 5: 52%). Compared with the heroin-only injection class, memberships in the two polydrug and polyroute use classes were independently associated with both HIV injection and sexual risk behaviours. Substance use patterns among PWID in Tijuana are highly heterogeneous, and polydrug and polyroute users are a high-risk subgroup who may require more tailored prevention and treatment interventions. [Meacham MC, Roesch SC, Strathdee SA, Lindsay S, Gonzalez-Zuniga P, Gaines TL. Latent classes of polydrug and polyroute use and associations with human immunodeficiency virus risk behaviours and overdose among people who inject drugs in Tijuana, Baja California, Mexico. Drug Alcohol Rev 2018;37:128-136].

  19. How well Can We Classify SWOT-derived Water Surface Profiles?

    Science.gov (United States)

    Frasson, R. P. M.; Wei, R.; Picamilh, C.; Durand, M. T.

    2015-12-01

    The upcoming Surface Water Ocean Topography (SWOT) mission will detect water bodies and measure water surface elevation throughout the globe. Within its continental high resolution mask, SWOT is expected to deliver measurements of river width, water elevation and slope of rivers wider than ~50 m. The definition of river reaches is an integral step of the computation of discharge based on SWOT's observables. As poorly defined reaches can negatively affect the accuracy of discharge estimations, we seek strategies to break up rivers into physically meaningful sections. In the present work, we investigate how accurately we can classify water surface profiles based on simulated SWOT observations. We assume that most river sections can be classified as either M1 (mild slope, with depth larger than the normal depth), or A1 (adverse slope with depth larger than the critical depth). This assumption allows the classification to be based solely on the second derivative of water surface profiles, with convex profiles being classified as A1 and concave profiles as M1. We consider a HEC-RAS model of the Sacramento River as a representation of the true state of the river. We employ the SWOT instrument simulator to generate a synthetic pass of the river, which includes our best estimates of height measurement noise and geolocation errors. We process the resulting point cloud of water surface heights with the RiverObs package, which delineates the river center line and draws the water surface profile. Next, we identify inflection points in the water surface profile and classify the sections between the inflection points. Finally, we compare our limited classification of simulated SWOT-derived water surface profile to the "exact" classification of the modeled Sacramento River. With this exercise, we expect to determine if SWOT observations can be used to find inflection points in water surface profiles, which would bring knowledge of flow regimes into the definition of river reaches.

  20. Zika virus infection and Guillain-Barré syndrome: a review focused on clinical and electrophysiological subtypes.

    Science.gov (United States)

    Uncini, Antonino; Shahrizaila, Nortina; Kuwabara, Satoshi

    2017-03-01

    In 2016, we have seen a rapid emergence of Zika virus-associated Guillain-Barré syndrome (GBS) since its first description in a French-Polynesian patient in 2014. Current evidence estimates the incidence of GBS at 24 cases per 100 000 persons infected by Zika virus. This will result in a sharp rise in the number of GBS cases worldwide with the anticipated global spread of Zika virus. A better understanding of the pathogenesis of Zika-associated GBS is crucial to prepare us for the current epidemic. In this review, we evaluate the existing literature on GBS in association with Zika and other flavivirus to better define its clinical subtypes and electrophysiological characteristics, demonstrating a demyelinating subtype of GBS in most cases. We also recommend measures that will help reduce the gaps in knowledge that currently exist. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  1. OptoDyCE: Automated system for high-throughput all-optical dynamic cardiac electrophysiology

    Science.gov (United States)

    Klimas, Aleksandra; Yu, Jinzhu; Ambrosi, Christina M.; Williams, John C.; Bien, Harold; Entcheva, Emilia

    2016-02-01

    In the last two decades, market were due to cardiac toxicity, where unintended interactions with ion channels disrupt the heart's normal electrical function. Consequently, all new drugs must undergo preclinical testing for cardiac liability, adding to an already expensive and lengthy process. Recognition that proarrhythmic effects often result from drug action on multiple ion channels demonstrates a need for integrative and comprehensive measurements. Additionally, patient-specific therapies relying on emerging technologies employing stem-cell derived cardiomyocytes (e.g. induced pluripotent stem-cell-derived cardiomyocytes, iPSC-CMs) require better screening methods to become practical. However, a high-throughput, cost-effective approach for cellular cardiac electrophysiology has not been feasible. Optical techniques for manipulation and recording provide a contactless means of dynamic, high-throughput testing of cells and tissues. Here, we consider the requirements for all-optical electrophysiology for drug testing, and we implement and validate OptoDyCE, a fully automated system for all-optical cardiac electrophysiology. We demonstrate the high-throughput capabilities using multicellular samples in 96-well format by combining optogenetic actuation with simultaneous fast high-resolution optical sensing of voltage or intracellular calcium. The system can also be implemented using iPSC-CMs and other cell-types by delivery of optogenetic drivers, or through the modular use of dedicated light-sensitive somatic cells in conjunction with non-modified cells. OptoDyCE provides a truly modular and dynamic screening system, capable of fully-automated acquisition of high-content information integral for improved discovery and development of new drugs and biologics, as well as providing a means of better understanding of electrical disturbances in the heart.

  2. Comparing classifiers for pronunciation error detection

    NARCIS (Netherlands)

    Strik, H.; Truong, K.; Wet, F. de; Cucchiarini, C.

    2007-01-01

    Providing feedback on pronunciation errors in computer assisted language learning systems requires that pronunciation errors be detected automatically. In the present study we compare four types of classifiers that can be used for this purpose: two acoustic-phonetic classifiers (one of which employs

  3. Right brain, left brain in depressive disorders: Clinical and theoretical implications of behavioral, electrophysiological and neuroimaging findings.

    Science.gov (United States)

    Bruder, Gerard E; Stewart, Jonathan W; McGrath, Patrick J

    2017-07-01

    The right and left side of the brain are asymmetric in anatomy and function. We review electrophysiological (EEG and event-related potential), behavioral (dichotic and visual perceptual asymmetry), and neuroimaging (PET, MRI, NIRS) evidence of right-left asymmetry in depressive disorders. Recent electrophysiological and fMRI studies of emotional processing have provided new evidence of altered laterality in depressive disorders. EEG alpha asymmetry and neuroimaging findings at rest and during cognitive or emotional tasks are consistent with reduced left prefrontal activity in depressed patients, which may impair downregulation of amygdala response to negative emotional information. Dichotic listening and visual hemifield findings for non-verbal or emotional processing have revealed abnormal perceptual asymmetry in depressive disorders, and electrophysiological findings have shown reduced right-lateralized responsivity to emotional stimuli in occipitotemporal or parietotemporal cortex. We discuss models of neural networks underlying these alterations. Of clinical relevance, individual differences among depressed patients on measures of right-left brain function are related to diagnostic subtype of depression, comorbidity with anxiety disorders, and clinical response to antidepressants or cognitive behavioral therapy. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. [Biofeedback in psychomotor training. Electrophysiological bases].

    Science.gov (United States)

    Bazanova, O M; Mernaia, E M; Shtark, M B

    2008-05-01

    Comparison of influence of usual musical practice and the same trainings but using biofeedback on electrophysiological and psychological markers of optimal psychomotor functioning in 39 students-musicians revealed that the obvious musical practice caused psychomotor pressure in most students (with initially low individual alpha peak frequency), whereas similar practice combined with an individualized session of alpha-EEG/EMG biofeedback was accompanied by increase of alpha-activity in all examinees and a decrease (reduction) of integrated EMG that indicated reaching of optimal psychomotor functioning. It appears that the psychomotor learning ability depends on the baseline individual alpha-activity. Individual alpha peak frequency was associated with fluency and efficiency of psychomotor performance, individual alpha band width--with plasticity and creativity, individual amount of alpha suppression in response to opening eyes--with the level of selfactualization. These alpha activity EEG indices correlated with efficiency of the biofeedback training.

  5. Three-class classification in computer-aided diagnosis of breast cancer by support vector machine

    Science.gov (United States)

    Sun, Xuejun; Qian, Wei; Song, Dansheng

    2004-05-01

    Design of classifier in computer-aided diagnosis (CAD) scheme of breast cancer plays important role to its overall performance in sensitivity and specificity. Classification of a detected object as malignant lesion, benign lesion, or normal tissue on mammogram is a typical three-class pattern recognition problem. This paper presents a three-class classification approach by using two-stage classifier combined with support vector machine (SVM) learning algorithm for classification of breast cancer on mammograms. The first classification stage is used to detect abnormal areas and normal breast tissues, and the second stage is for classification of malignant or benign in detected abnormal objects. A series of spatial, morphology and texture features have been extracted on detected objects areas. By using genetic algorithm (GA), different feature groups for different stage classification have been investigated. Computerized free-response receiver operating characteristic (FROC) and receiver operating characteristic (ROC) analyses have been employed in different classification stages. Results have shown that obvious performance improvement in both sensitivity and specificity was observed through proposed classification approach compared with conventional two-class classification approaches, indicating its effectiveness in classification of breast cancer on mammograms.

  6. Stress fracture development classified by bone scintigraphy

    International Nuclear Information System (INIS)

    Zwas, S.T.; Elkanovich, R.; Frank, G.; Aharonson, Z.

    1985-01-01

    There is no consensus on classifying stress fractures (SF) appearing on bone scans. The authors present a system of classification based on grading the severity and development of bone lesions by visual inspection, according to three main scintigraphic criteria: focality and size, intensity of uptake compare to adjacent bone, and local medular extension. Four grades of development (I-IV) were ranked, ranging from ill defined slightly increased cortical uptake to well defined regions with markedly increased uptake extending transversely bicortically. 310 male subjects aged 19-2, suffering several weeks from leg pains occurring during intensive physical training underwent bone scans of the pelvis and lower extremities using Tc-99-m-MDP. 76% of the scans were positive with 354 lesions, of which 88% were in th4e mild (I-II) grades and 12% in the moderate (III) and severe (IV) grades. Post-treatment scans were obtained in 65 cases having 78 lesions during 1- to 6-month intervals. Complete resolution was found after 1-2 months in 36% of the mild lesions but in only 12% of the moderate and severe ones, and after 3-6 months in 55% of the mild lesions and 15% of the severe ones. 75% of the moderate and severe lesions showed residual uptake in various stages throughout the follow-up period. Early recognition and treatment of mild SF lesions in this study prevented protracted disability and progression of the lesions and facilitated complete healing

  7. Examining the Capability of Supervised Machine Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study from a Mediterranean Flood

    Directory of Open Access Journals (Sweden)

    Gareth Ireland

    2015-03-01

    Full Text Available This study explored the capability of Support Vector Machines (SVMs and regularised kernel Fisher’s discriminant analysis (rkFDA machine learning supervised classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a riverine flood event in 2010 in a heterogeneous Mediterranean region, for which TM imagery acquired shortly after the flood event was available. For the two classifiers, both linear and non-linear (kernel versions were utilised in their implementation. The ability of the different classifiers to map the flooded area extent was assessed on the basis of classification accuracy assessment metrics. Results showed that rkFDA outperformed SVMs in terms of accurate flooded pixels detection, also producing fewer missed detections of the flooded area. Yet, SVMs showed less false flooded area detections. Overall, the non-linear rkFDA classification method was the more accurate of the two techniques (OA = 96.23%, K = 0.877. Both methods outperformed the standard Normalized Difference Water Index (NDWI thresholding (OA = 94.63, K = 0.818 by roughly 0.06 K points. Although overall accuracy results for the rkFDA and SVMs classifications only showed a somewhat minor improvement on the overall accuracy exhibited by the NDWI thresholding, notably both classifiers considerably outperformed the thresholding algorithm in other specific accuracy measures (e.g. producer accuracy for the “not flooded” class was ~10.5% less accurate for the NDWI thresholding algorithm in comparison to the classifiers, and average per-class accuracy was ~5% less accurate than the machine learning models. This study provides evidence of the successful application of supervised machine learning for classifying flooded areas in Landsat imagery, where few studies so far exist in this direction. Considering that Landsat data is open access and has global coverage, the results of this study

  8. "Always in My Face": An Exploration of Social Class Consciousness, Salience, and Values

    Science.gov (United States)

    Martin, Georgianna L.

    2015-01-01

    This qualitative study explores social class consciousness, salience, and values of White, low-income, first-generation college students. Overall, participants minimized the salience of social class as an aspect of their identity with many of them expressing that they did not want their social class to define them. Although participants largely…

  9. Photoreceptor processing speed and input resistance changes during light adaptation correlate with spectral class in the bumblebee, Bombus impatiens.

    Directory of Open Access Journals (Sweden)

    Peter Skorupski

    Full Text Available Colour vision depends on comparison of signals from photoreceptors with different spectral sensitivities. However, response properties of photoreceptor cells may differ in ways other than spectral tuning. In insects, for example, broadband photoreceptors, with a major sensitivity peak in the green region of the spectrum (>500 nm, drive fast visual processes, which are largely blind to chromatic signals from more narrowly-tuned photoreceptors with peak sensitivities in the blue and UV regions of the spectrum. In addition, electrophysiological properties of the photoreceptor membrane may result in differences in response dynamics of photoreceptors of similar spectral class between species, and different spectral classes within a species. We used intracellular electrophysiological techniques to investigate response dynamics of the three spectral classes of photoreceptor underlying trichromatic colour vision in the bumblebee, Bombus impatiens, and we compare these with previously published data from a related species, Bombus terrestris. In both species, we found significantly faster responses in green, compared with blue- or UV-sensitive photoreceptors, although all 3 photoreceptor types are slower in B. impatiens than in B. terrestris. Integration times for light-adapted B. impatiens photoreceptors (estimated from impulse response half-width were 11.3 ± 1.6 ms for green photoreceptors compared with 18.6 ± 4.4 ms and 15.6 ± 4.4 for blue and UV, respectively. We also measured photoreceptor input resistance in dark- and light-adapted conditions. All photoreceptors showed a decrease in input resistance during light adaptation, but this decrease was considerably larger (declining to about 22% of the dark value in green photoreceptors, compared to blue and UV (41% and 49%, respectively. Our results suggest that the conductances associated with light adaptation are largest in green photoreceptors, contributing to their greater temporal processing speed

  10. Hierarchical mixtures of naive Bayes classifiers

    NARCIS (Netherlands)

    Wiering, M.A.

    2002-01-01

    Naive Bayes classifiers tend to perform very well on a large number of problem domains, although their representation power is quite limited compared to more sophisticated machine learning algorithms. In this pa- per we study combining multiple naive Bayes classifiers by using the hierar- chical

  11. A class of convergent neural network dynamics

    Science.gov (United States)

    Fiedler, Bernold; Gedeon, Tomáš

    1998-01-01

    We consider a class of systems of differential equations in Rn which exhibits convergent dynamics. We find a Lyapunov function and show that every bounded trajectory converges to the set of equilibria. Our result generalizes the results of Cohen and Grossberg (1983) for convergent neural networks. It replaces the symmetry assumption on the matrix of weights by the assumption on the structure of the connections in the neural network. We prove the convergence result also for a large class of Lotka-Volterra systems. These are naturally defined on the closed positive orthant. We show that there are no heteroclinic cycles on the boundary of the positive orthant for the systems in this class.

  12. Electrophysiological and olfactometer responses of two histerid predators to three pine bark beetle pheromones

    Science.gov (United States)

    William P. Shepherd; Brian T. Sullivan; Richard A. Goyer; Kier D. Klepzig

    2005-01-01

    We measured electrophysiological responses in the antennae of two predaceous hister beetles, Platysoma parallelum and Plegaderus transversus, exposes to racemic mixtures of primary aggregation pheromones of scolytid bark beetle prey, ipsenol, ipsdienol, and frontalin. No significant differences were found for either histerid...

  13. A Supervised Multiclass Classifier for an Autocoding System

    Directory of Open Access Journals (Sweden)

    Yukako Toko

    2017-11-01

    Full Text Available Classification is often required in various contexts, including in the field of official statistics. In the previous study, we have developed a multiclass classifier that can classify short text descriptions with high accuracy. The algorithm borrows the concept of the naïve Bayes classifier and is so simple that its structure is easily understandable. The proposed classifier has the following two advantages. First, the processing times for both learning and classifying are extremely practical. Second, the proposed classifier yields high-accuracy results for a large portion of a dataset. We have previously developed an autocoding system for the Family Income and Expenditure Survey in Japan that has a better performing classifier. While the original system was developed in Perl in order to improve the efficiency of the coding process of short Japanese texts, the proposed system is implemented in the R programming language in order to explore versatility and is modified to make the system easily applicable to English text descriptions, in consideration of the increasing number of R users in the field of official statistics. We are planning to publish the proposed classifier as an R-package. The proposed classifier would be generally applicable to other classification tasks including coding activities in the field of official statistics, and it would contribute greatly to improving their efficiency.

  14. Electrophysiological Evidence of Heterogeneity in Visual Statistical Learning in Young Children with ASD

    Science.gov (United States)

    Jeste, Shafali S.; Kirkham, Natasha; Senturk, Damla; Hasenstab, Kyle; Sugar, Catherine; Kupelian, Chloe; Baker, Elizabeth; Sanders, Andrew J.; Shimizu, Christina; Norona, Amanda; Paparella, Tanya; Freeman, Stephanny F. N.; Johnson, Scott P.

    2015-01-01

    Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism…

  15. Defining the mobilome.

    Science.gov (United States)

    Siefert, Janet L

    2009-01-01

    This chapter defines the agents that provide for the movement of genetic material which fuels the adaptive potential of life on our planet. The chapter has been structured to be broadly comprehensive, arbitrarily categorizing the mobilome into four classes: (1) transposons, (2) plasmids, (3) bacteriophage, and (4) self-splicing molecular parasites.Our increasing understanding of the mobilome is as dynamic as the mobilome itself. With continuing discovery, it is clear that nature has not confined these genomic agents of change to neat categories, but rather the classification categories overlap and intertwine. Massive sequencing efforts and their published analyses are continuing to refine our understanding of the extent of the mobilome. This chapter provides a framework to describe our current understanding of the mobilome and a foundation on which appreciation of its impact on genome evolution can be understood.

  16. Latent classes of resilience and psychological response among only-child loss parents in China.

    Science.gov (United States)

    Wang, An-Ni; Zhang, Wen; Zhang, Jing-Ping; Huang, Fei-Fei; Ye, Man; Yao, Shu-Yu; Luo, Yuan-Hui; Li, Zhi-Hua; Zhang, Jie; Su, Pan

    2017-10-01

    Only-child loss parents in China recently gained extensive attention as a newly defined social group. Resilience could be a probable solution out of the psychological dilemma. Using a sample of 185 only-child loss people, this study employed latent class analysis (a) to explore whether different classes of resilience could be identified, (b) to determine socio-demographic characteristics of each class, and (c) to compare the depression and the subjective well-being of each class. The results supported a three-class solution, defined as 'high tenacity-strength but moderate optimism class', 'moderate resilience but low self-efficacy class' and 'low tenacity but moderate adaption-dependence class'. Parents with low income and medical insurance of low reimbursement type and without endowment insurance occupied more proportions in the latter two classes. The latter two classes also had a significant higher depression scores and lower subjective well-being scores than high tenacity-strength but moderate optimism class. Future work should care those socio-economically vulnerable bereaved parents, and an elastic economic assistance policy was needed. To develop targeted resilience interventions, the emphasis of high tenacity-strength but moderate optimism class should be the optimism. Moderate resilience but low self-efficacy class should be self-efficacy, and low tenacity but moderate adaption-dependence class should be tenacity. Copyright © 2016 John Wiley & Sons, Ltd.

  17. Identification of children with mathematics learning disabilities (MLDs) using latent class growth analysis.

    Science.gov (United States)

    Wong, Terry T-Y; Ho, Connie S-H; Tang, Joey

    2014-11-01

    The traditional way of identifying children with mathematics learning disabilities (MLDs) using the low-achievement method with one-off assessment suffers from several limitations (e.g., arbitrary cutoff, measurement error, lacking consideration of growth). The present study attempted to identify children with MLD using the latent growth modelling approach, which minimizes the above potential problems. Two hundred and ten Chinese-speaking children were classified into five classes based on their arithmetic performance over 3 years. Their performance on various number-related cognitive measures was also assessed. A potential MLD class was identified, which demonstrated poor achievement over the 3 years and showed smaller improvement over time compared with the average-achieving class. This class had deficits in all number-related cognitive skills, hence supporting the number sense deficit hypothesis. On the other hand, another low-achieving class, which showed little improvement in arithmetic skills over time, was also identified. This class had an average cognitive profile but a low SES. Interventions should be provided to both low-achieving classes according to their needs. Copyright © 2014 Elsevier Ltd. All rights reserved.

  18. A new method for class prediction based on signed-rank algorithms applied to Affymetrix® microarray experiments

    Directory of Open Access Journals (Sweden)

    Vassal Aurélien

    2008-01-01

    Full Text Available Abstract Background The huge amount of data generated by DNA chips is a powerful basis to classify various pathologies. However, constant evolution of microarray technology makes it difficult to mix data from different chip types for class prediction of limited sample populations. Affymetrix® technology provides both a quantitative fluorescence signal and a decision (detection call: absent or present based on signed-rank algorithms applied to several hybridization repeats of each gene, with a per-chip normalization. We developed a new prediction method for class belonging based on the detection call only from recent Affymetrix chip type. Biological data were obtained by hybridization on U133A, U133B and U133Plus 2.0 microarrays of purified normal B cells and cells from three independent groups of multiple myeloma (MM patients. Results After a call-based data reduction step to filter out non class-discriminative probe sets, the gene list obtained was reduced to a predictor with correction for multiple testing by iterative deletion of probe sets that sequentially improve inter-class comparisons and their significance. The error rate of the method was determined using leave-one-out and 5-fold cross-validation. It was successfully applied to (i determine a sex predictor with the normal donor group classifying gender with no error in all patient groups except for male MM samples with a Y chromosome deletion, (ii predict the immunoglobulin light and heavy chains expressed by the malignant myeloma clones of the validation group and (iii predict sex, light and heavy chain nature for every new patient. Finally, this method was shown powerful when compared to the popular classification method Prediction Analysis of Microarray (PAM. Conclusion This normalization-free method is routinely used for quality control and correction of collection errors in patient reports to clinicians. It can be easily extended to multiple class prediction suitable with

  19. Classifying Dementia Using Local Binary Patterns from Different Regions in Magnetic Resonance Images

    Directory of Open Access Journals (Sweden)

    Ketil Oppedal

    2015-01-01

    Full Text Available Dementia is an evolving challenge in society, and no disease-modifying treatment exists. Diagnosis can be demanding and MR imaging may aid as a noninvasive method to increase prediction accuracy. We explored the use of 2D local binary pattern (LBP extracted from FLAIR and T1 MR images of the brain combined with a Random Forest classifier in an attempt to discern patients with Alzheimer's disease (AD, Lewy body dementia (LBD, and normal controls (NC. Analysis was conducted in areas with white matter lesions (WML and all of white matter (WM. Results from 10-fold nested cross validation are reported as mean accuracy, precision, and recall with standard deviation in brackets. The best result we achieved was in the two-class problem NC versus AD + LBD with total accuracy of 0.98 (0.04. In the three-class problem AD versus LBD versus NC and the two-class problem AD versus LBD, we achieved 0.87 (0.08 and 0.74 (0.16, respectively. The performance using 3DT1 images was notably better than when using FLAIR images. The results from the WM region gave similar results as in the WML region. Our study demonstrates that LBP texture analysis in brain MR images can be successfully used for computer based dementia diagnosis.

  20. Electrophysiological mechanisms of sophocarpine as a potential antiarrhythmic agent.

    Science.gov (United States)

    Yang, Zhi-fang; Li, Ci-zhen; Wang, Wei; Chen, Ying-min; Zhang, Ying; Liu, Yuan-mou; Wang, Hong-wei

    2011-03-01

    To examine the electrophysiological effects of sophocarpine on action potentials (AP) and ionic currents of cardiac myocytes and to compare some of these effects with those of amiodarone. Langendorff perfusion set-up was used in isolated guinea pig heart, and responses to sophocarpine were monitored using electrocardiograph. Conventional microelectrode, voltage clamp technique and perforated patch were employed to record fast response AP (fAP), slow response AP (sAP) and ionic currents in guinea pig papillary muscle or rabbit sinus node cells. Tachyarrhythmia produced by isoprenaline (15 μmol/L) could be reversed by sophocarpine (300 μmol/L). Sophocarpine (10 μmol/L) decreased the amplitude by 4.0%, maximal depolarization velocity (V(max)) of the fAP by 24.4%, and Na(+) current (I(Na)) by 18.0%, while it prolonged the effective refractory period (ERP) by 21.1%. The same concentration of sophocarpine could also decrease the amplitude and V(max) of the sAP, by 26.8% and 25.7%, respectively, and attenuated the Ca(2+) current (I(CaL)) and the K(+) tail current substantially. Comparison of sophocarpine with amiodarone demonstrated that both prolonged the duration and the ERP of fAP and sAP, both decreased the amplitude and V(max) of the fAP and sAP, and both slowed the automatic heart rate. Sophocarpine could reverse isoprenaline-induced arrhythmia and inhibit I(Na), I(CaL), and I(Kr) currents. The electrophysiological effects of sophocarpine are similar to those of amiodarone, which might be regarded as a prospective antiarrhythmic agent.