WorldWideScience

Sample records for pattern classifier analysis

  1. Algorithms for Image Analysis and Combination of Pattern Classifiers with Application to Medical Diagnosis

    Georgiou, Harris

    2009-10-01

    Medical Informatics and the application of modern signal processing in the assistance of the diagnostic process in medical imaging is one of the more recent and active research areas today. This thesis addresses a variety of issues related to the general problem of medical image analysis, specifically in mammography, and presents a series of algorithms and design approaches for all the intermediate levels of a modern system for computer-aided diagnosis (CAD). The diagnostic problem is analyzed with a systematic approach, first defining the imaging characteristics and features that are relevant to probable pathology in mammo-grams. Next, these features are quantified and fused into new, integrated radio-logical systems that exhibit embedded digital signal processing, in order to improve the final result and minimize the radiological dose for the patient. In a higher level, special algorithms are designed for detecting and encoding these clinically interest-ing imaging features, in order to be used as input to advanced pattern classifiers and machine learning models. Finally, these approaches are extended in multi-classifier models under the scope of Game Theory and optimum collective deci-sion, in order to produce efficient solutions for combining classifiers with minimum computational costs for advanced diagnostic systems. The material covered in this thesis is related to a total of 18 published papers, 6 in scientific journals and 12 in international conferences.

  2. A Tool for Classifying Individuals with Chronic Back Pain: Using Multivariate Pattern Analysis with Functional Magnetic Resonance Imaging Data

    Callan, Daniel; Mills, Lloyd; Nott, Connie; England, Robert; England, Shaun

    2014-01-01

    Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two

  3. A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data.

    Daniel Callan

    Full Text Available Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1 chronic pain and 2 normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13 and 92.3% of the normal control group (N = 13 by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying

  4. Aggregation Operator Based Fuzzy Pattern Classifier Design

    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....

  5. Use of a machine learning algorithm to classify expertise: analysis of hand motion patterns during a simulated surgical task.

    Watson, Robert A

    2014-08-01

    To test the hypothesis that machine learning algorithms increase the predictive power to classify surgical expertise using surgeons' hand motion patterns. In 2012 at the University of North Carolina at Chapel Hill, 14 surgical attendings and 10 first- and second-year surgical residents each performed two bench model venous anastomoses. During the simulated tasks, the participants wore an inertial measurement unit on the dorsum of their dominant (right) hand to capture their hand motion patterns. The pattern from each bench model task performed was preprocessed into a symbolic time series and labeled as expert (attending) or novice (resident). The labeled hand motion patterns were processed and used to train a Support Vector Machine (SVM) classification algorithm. The trained algorithm was then tested for discriminative/predictive power against unlabeled (blinded) hand motion patterns from tasks not used in the training. The Lempel-Ziv (LZ) complexity metric was also measured from each hand motion pattern, with an optimal threshold calculated to separately classify the patterns. The LZ metric classified unlabeled (blinded) hand motion patterns into expert and novice groups with an accuracy of 70% (sensitivity 64%, specificity 80%). The SVM algorithm had an accuracy of 83% (sensitivity 86%, specificity 80%). The results confirmed the hypothesis. The SVM algorithm increased the predictive power to classify blinded surgical hand motion patterns into expert versus novice groups. With further development, the system used in this study could become a viable tool for low-cost, objective assessment of procedural proficiency in a competency-based curriculum.

  6. Investigation of using wavelet analysis for classifying pattern of cyclic voltammetry signals

    Jityen, Arthit; Juagwon, Teerasak; Jaisuthi, Rawat; Osotchan, Tanakorn

    2017-09-01

    Wavelet analysis is an excellent technique for data processing analysis based on linear vector algebra since it has an ability to perform local analysis and is able to analyze an unspecific localized area of a large signal. In this work, the wavelet analysis of cyclic waveform was investigated in order to find the distinguishable feature from the cyclic data. The analyzed wavelet coefficients were proposed to be used as selected cyclic feature parameters. The cyclic voltammogram (CV) of different electrodes consisting of carbon nanotube (CNT) and several types of metal phthalocyanine (MPc) including CoPc, FePc, ZnPc and MnPc powders was used as several sets of cyclic data for various types of coffee. The mixture powder was embedded in a hollow Teflon rod and used as working electrodes. Electrochemical response of the fabricated electrodes in Robusta, blend coffee I, blend coffee II, chocolate malt and cocoa at the same concentrations was measured with scanning rate of 0.05V/s from -1.5 to 1.5V respectively to Ag/AgCl electrode for five scanning loops. The CV of blended CNT electrode with some MPc electrodes indicated the ionic interaction which can be the effect of catalytic oxidation of saccharides and/or polyphenol on the sensor surface. The major information of CV response can be extracted by using several mother wavelet families viz. daubechies (dB1 to dB3), coiflets (coiflet1), biorthogonal (Bior1.1) and symlets (sym2) and then the discrimination of these wavelet coefficients of each data group can be separated by principal component analysis (PCA). The PCA results indicated the clearly separate groups with total contribution more than 62.37% representing from PC1 and PC2.

  7. Two channel EEG thought pattern classifier.

    Craig, D A; Nguyen, H T; Burchey, H A

    2006-01-01

    This paper presents a real-time electro-encephalogram (EEG) identification system with the goal of achieving hands free control. With two EEG electrodes placed on the scalp of the user, EEG signals are amplified and digitised directly using a ProComp+ encoder and transferred to the host computer through the RS232 interface. Using a real-time multilayer neural network, the actual classification for the control of a powered wheelchair has a very fast response. It can detect changes in the user's thought pattern in 1 second. Using only two EEG electrodes at positions O(1) and C(4) the system can classify three mental commands (forward, left and right) with an accuracy of more than 79 %

  8. Classifying sows' activity types from acceleration patterns

    Cornou, Cecile; Lundbye-Christensen, Søren

    2008-01-01

    An automated method of classifying sow activity using acceleration measurements would allow the individual sow's behavior to be monitored throughout the reproductive cycle; applications for detecting behaviors characteristic of estrus and farrowing or to monitor illness and welfare can be foreseen....... This article suggests a method of classifying five types of activity exhibited by group-housed sows. The method involves the measurement of acceleration in three dimensions. The five activities are: feeding, walking, rooting, lying laterally and lying sternally. Four time series of acceleration (the three...

  9. Consistency Analysis of Nearest Subspace Classifier

    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 ...

  10. Naive Bayesian classifiers for multinomial features: a theoretical analysis

    Van Dyk, E

    2007-11-01

    Full Text Available The authors investigate the use of naive Bayesian classifiers for multinomial feature spaces and derive error estimates for these classifiers. The error analysis is done by developing a mathematical model to estimate the probability density...

  11. Reducing variability in the output of pattern classifiers using histogram shaping

    Gupta, Shalini; Kan, Chih-Wen; Markey, Mia K.

    2010-01-01

    Purpose: The authors present a novel technique based on histogram shaping to reduce the variability in the output and (sensitivity, specificity) pairs of pattern classifiers with identical ROC curves, but differently distributed outputs. Methods: The authors identify different sources of variability in the output of linear pattern classifiers with identical ROC curves, which also result in classifiers with differently distributed outputs. They theoretically develop a novel technique based on the matching of the histograms of these differently distributed pattern classifier outputs to reduce the variability in their (sensitivity, specificity) pairs at fixed decision thresholds, and to reduce the variability in their actual output values. They empirically demonstrate the efficacy of the proposed technique by means of analyses on the simulated data and real world mammography data. Results: For the simulated data, with three different known sources of variability, and for the real world mammography data with unknown sources of variability, the proposed classifier output calibration technique significantly reduced the variability in the classifiers' (sensitivity, specificity) pairs at fixed decision thresholds. Furthermore, for classifiers with monotonically or approximately monotonically related output variables, the histogram shaping technique also significantly reduced the variability in their actual output values. Conclusions: Classifier output calibration based on histogram shaping can be successfully employed to reduce the variability in the output values and (sensitivity, specificity) pairs of pattern classifiers with identical ROC curves, but differently distributed outputs.

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

    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.

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

    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.

  14. Combining Biometric Fractal Pattern and Particle Swarm Optimization-Based Classifier for Fingerprint Recognition

    Chia-Hung Lin

    2010-01-01

    Full Text Available This paper proposes combining the biometric fractal pattern and particle swarm optimization (PSO-based classifier for fingerprint recognition. Fingerprints have arch, loop, whorl, and accidental morphologies, and embed singular points, resulting in the establishment of fingerprint individuality. An automatic fingerprint identification system consists of two stages: digital image processing (DIP and pattern recognition. DIP is used to convert to binary images, refine out noise, and locate the reference point. For binary images, Katz's algorithm is employed to estimate the fractal dimension (FD from a two-dimensional (2D image. Biometric features are extracted as fractal patterns using different FDs. Probabilistic neural network (PNN as a classifier performs to compare the fractal patterns among the small-scale database. A PSO algorithm is used to tune the optimal parameters and heighten the accuracy. For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition.

  15. A Large Dimensional Analysis of Regularized Discriminant Analysis Classifiers

    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.

  16. Comparison of Shallow and Deep Learning Methods on Classifying the Regional Pattern of Diffuse Lung Disease.

    Kim, Guk Bae; Jung, Kyu-Hwan; Lee, Yeha; Kim, Hyun-Jun; Kim, Namkug; Jun, Sanghoon; Seo, Joon Beom; Lynch, David A

    2017-10-17

    This study aimed to compare shallow and deep learning of classifying the patterns of interstitial lung diseases (ILDs). Using high-resolution computed tomography images, two experienced radiologists marked 1200 regions of interest (ROIs), in which 600 ROIs were each acquired using a GE or Siemens scanner and each group of 600 ROIs consisted of 100 ROIs for subregions that included normal and five regional pulmonary disease patterns (ground-glass opacity, consolidation, reticular opacity, emphysema, and honeycombing). We employed the convolution neural network (CNN) with six learnable layers that consisted of four convolution layers and two fully connected layers. The classification results were compared with the results classified by a shallow learning of a support vector machine (SVM). The CNN classifier showed significantly better performance for accuracy compared with that of the SVM classifier by 6-9%. As the convolution layer increases, the classification accuracy of the CNN showed better performance from 81.27 to 95.12%. Especially in the cases showing pathological ambiguity such as between normal and emphysema cases or between honeycombing and reticular opacity cases, the increment of the convolution layer greatly drops the misclassification rate between each case. Conclusively, the CNN classifier showed significantly greater accuracy than the SVM classifier, and the results implied structural characteristics that are inherent to the specific ILD patterns.

  17. Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiers.

    Bowd, Christopher; Weinreb, Robert N; Balasubramanian, Madhusudhanan; Lee, Intae; Jang, Giljin; Yousefi, Siamak; Zangwill, Linda M; Medeiros, Felipe A; Girkin, Christopher A; Liebmann, Jeffrey M; Goldbaum, Michael H

    2014-01-01

    The variational Bayesian independent component analysis-mixture model (VIM), an unsupervised machine-learning classifier, was used to automatically separate Matrix Frequency Doubling Technology (FDT) perimetry data into clusters of healthy and glaucomatous eyes, and to identify axes representing statistically independent patterns of defect in the glaucoma clusters. FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal FDT results from the UCSD-based Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES). For all eyes, VIM input was 52 threshold test points from the 24-2 test pattern, plus age. FDT mean deviation was -1.00 dB (S.D. = 2.80 dB) and -5.57 dB (S.D. = 5.09 dB) in FDT-normal eyes and FDT-abnormal eyes, respectively (p<0.001). VIM identified meaningful clusters of FDT data and positioned a set of statistically independent axes through the mean of each cluster. The optimal VIM model separated the FDT fields into 3 clusters. Cluster N contained primarily normal fields (1109/1190, specificity 93.1%) and clusters G1 and G2 combined, contained primarily abnormal fields (651/786, sensitivity 82.8%). For clusters G1 and G2 the optimal number of axes were 2 and 5, respectively. Patterns automatically generated along axes within the glaucoma clusters were similar to those known to be indicative of glaucoma. Fields located farther from the normal mean on each glaucoma axis showed increasing field defect severity. VIM successfully separated FDT fields from healthy and glaucoma eyes without a priori information about class membership, and identified familiar glaucomatous patterns of loss.

  18. Generalization in the XCSF classifier system: analysis, improvement, and extension.

    Lanzi, Pier Luca; Loiacono, Daniele; Wilson, Stewart W; Goldberg, David E

    2007-01-01

    We analyze generalization in XCSF and introduce three improvements. We begin by showing that the types of generalizations evolved by XCSF can be influenced by the input range. To explain these results we present a theoretical analysis of the convergence of classifier weights in XCSF which highlights a broader issue. In XCSF, because of the mathematical properties of the Widrow-Hoff update, the convergence of classifier weights in a given subspace can be slow when the spread of the eigenvalues of the autocorrelation matrix associated with each classifier is large. As a major consequence, the system's accuracy pressure may act before classifier weights are adequately updated, so that XCSF may evolve piecewise constant approximations, instead of the intended, and more efficient, piecewise linear ones. We propose three different ways to update classifier weights in XCSF so as to increase the generalization capabilities of XCSF: one based on a condition-based normalization of the inputs, one based on linear least squares, and one based on the recursive version of linear least squares. Through a series of experiments we show that while all three approaches significantly improve XCSF, least squares approaches appear to be best performing and most robust. Finally we show how XCSF can be extended to include polynomial approximations.

  19. Classifying Dementia Using Local Binary Patterns from Different Regions in Magnetic Resonance Images

    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. MAMMOGRAMS ANALYSIS USING SVM CLASSIFIER IN COMBINED TRANSFORMS DOMAIN

    B.N. Prathibha

    2011-02-01

    Full Text Available Breast cancer is a primary cause of mortality and morbidity in women. Reports reveal that earlier the detection of abnormalities, better the improvement in survival. Digital mammograms are one of the most effective means for detecting possible breast anomalies at early stages. Digital mammograms supported with Computer Aided Diagnostic (CAD systems help the radiologists in taking reliable decisions. The proposed CAD system extracts wavelet features and spectral features for the better classification of mammograms. The Support Vector Machines classifier is used to analyze 206 mammogram images from Mias database pertaining to the severity of abnormality, i.e., benign and malign. The proposed system gives 93.14% accuracy for discrimination between normal-malign and 87.25% accuracy for normal-benign samples and 89.22% accuracy for benign-malign samples. The study reveals that features extracted in hybrid transform domain with SVM classifier proves to be a promising tool for analysis of mammograms.

  1. Multivariate analysis of quantitative traits can effectively classify rapeseed germplasm

    Jankulovska Mirjana

    2014-01-01

    Full Text Available In this study, the use of different multivariate approaches to classify rapeseed genotypes based on quantitative traits has been presented. Tree regression analysis, PCA analysis and two-way cluster analysis were applied in order todescribe and understand the extent of genetic variability in spring rapeseed genotype by trait data. The traits which highly influenced seed and oil yield in rapeseed were successfully identified by the tree regression analysis. Principal predictor for both response variables was number of pods per plant (NP. NP and 1000 seed weight could help in the selection of high yielding genotypes. High values for both traits and oil content could lead to high oil yielding genotypes. These traits may serve as indirect selection criteria and can lead to improvement of seed and oil yield in rapeseed. Quantitative traits that explained most of the variability in the studied germplasm were classified using principal component analysis. In this data set, five PCs were identified, out of which the first three PCs explained 63% of the total variance. It helped in facilitating the choice of variables based on which the genotypes’ clustering could be performed. The two-way cluster analysissimultaneously clustered genotypes and quantitative traits. The final number of clusters was determined using bootstrapping technique. This approach provided clear overview on the variability of the analyzed genotypes. The genotypes that have similar performance regarding the traits included in this study can be easily detected on the heatmap. Genotypes grouped in the clusters 1 and 8 had high values for seed and oil yield, and relatively short vegetative growth duration period and those in cluster 9, combined moderate to low values for vegetative growth duration and moderate to high seed and oil yield. These genotypes should be further exploited and implemented in the rapeseed breeding program. The combined application of these multivariate methods

  2. Prescribing patterns of medicine classified as 'antidepressants' in South African children and adolescents

    Johanita R. Burger

    2009-07-01

    Full Text Available The main objective of this study was to characterise prescribing patterns of medicine classified as 'antidepressants' (hereafter simply referred to as antidepressants in children and adolescents in the private health care sector of South Africa. A retrospective drug utilisation design was used to identify patients aged 19 years and younger from a South African pharmaceutical benefit management company’s database, whom were issued at least one antidepressant between 1 January 2006 and 31 December 2006. Prescribed daily dosages (PDDs were calculated using the Statistical Analysis System® program. A total of 1 013 patients received a mean number of 2.88 (SD 3.04 prescriptions per patient. Females received more prescriptions than their male counterparts, with the highest prevalence in the 15 ≤ 19 years age group. The pharmacological groups most prescribed were the selective serotonin reuptake inhibitors (43.0% and the tricyclics (42.7%, with imipramine (22.04% and amitriptyline (19% as the most commonly prescribed drugs. Approximately 30% (n = 2 300 of all antidepressants in the study population were prescribed off-label. Amitriptyline and clomipramine were prescribed at daily dosages higher than recommended in children and adolescents aged 9 ≤ 15 years. Lithium, trimipramine, trazodone and sulpiride were prescribed at sub-therapeutic dosages in adolescents. This study provided insight in the prescribing patterns of medicine classified as antidepressants in South African children and adolescents. These drugs, however, have many indications. Further research is needed to determine reasons why specific drugs are prescribed in this population. Opsomming Die algemene doelstelling van hierdie studie was om die voorskrifpatrone van middels wat as 'antidepressante' geklassifiseer word (hierna verwys na as slegs antidepressante wat vir kinders en adolessente in die Suid-Afrikaanse private gesondheidsorgsektor voorgeskryf word, te beskryf. 'n

  3. Asymptotic performance of regularized quadratic discriminant analysis based classifiers

    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.

  4. Application of hierarchical clustering method to classify of space-time rainfall patterns

    Yu, Hwa-Lung; Chang, Tu-Je

    2010-05-01

    Understanding the local precipitation patterns is essential to the water resources management and flooding mitigation. The precipitation patterns can vary in space and time depending upon the factors from different spatial scales such as local topological changes and macroscopic atmospheric circulation. The spatiotemporal variation of precipitation in Taiwan is significant due to its complex terrain and its location at west pacific and subtropical area, where is the boundary between the pacific ocean and Asia continent with the complex interactions among the climatic processes. This study characterizes local-scale precipitation patterns by classifying the historical space-time precipitation records. We applied the hierarchical ascending clustering method to analyze the precipitation records from 1960 to 2008 at the six rainfall stations located in Lan-yang catchment at the northeast of the island. Our results identify the four primary space-time precipitation types which may result from distinct driving forces from the changes of atmospheric variables and topology at different space-time scales. This study also presents an important application of the statistical downscaling to combine large-scale upper-air circulation with local space-time precipitation patterns.

  5. Classifying Human Activity Patterns from Smartphone Collected GPS data: a Fuzzy Classification and Aggregation Approach.

    Wan, Neng; Lin, Ge

    2016-12-01

    Smartphones have emerged as a promising type of equipment for monitoring human activities in environmental health studies. However, degraded location accuracy and inconsistency of smartphone-measured GPS data have limited its effectiveness for classifying human activity patterns. This study proposes a fuzzy classification scheme for differentiating human activity patterns from smartphone-collected GPS data. Specifically, a fuzzy logic reasoning was adopted to overcome the influence of location uncertainty by estimating the probability of different activity types for single GPS points. Based on that approach, a segment aggregation method was developed to infer activity patterns, while adjusting for uncertainties of point attributes. Validations of the proposed methods were carried out based on a convenient sample of three subjects with different types of smartphones. The results indicate desirable accuracy (e.g., up to 96% in activity identification) with use of this method. Two examples were provided in the appendix to illustrate how the proposed methods could be applied in environmental health studies. Researchers could tailor this scheme to fit a variety of research topics.

  6. A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: Comparison to a Bayesian classifier

    Chang, Yongjun; Lim, Jonghyuck; Kim, Namkug; Seo, Joon Beom [Department of Radiology, University of Ulsan College of Medicine, 388-1 Pungnap2-dong, Songpa-gu, Seoul 138-736 (Korea, Republic of); Lynch, David A. [Department of Radiology, National Jewish Medical and Research Center, Denver, Colorado 80206 (United States)

    2013-05-15

    Purpose: To investigate the effect of using different computed tomography (CT) scanners on the accuracy of high-resolution CT (HRCT) images in classifying regional disease patterns in patients with diffuse lung disease, support vector machine (SVM) and Bayesian classifiers were applied to multicenter data. Methods: Two experienced radiologists marked sets of 600 rectangular 20 Multiplication-Sign 20 pixel regions of interest (ROIs) on HRCT images obtained from two scanners (GE and Siemens), including 100 ROIs for each of local patterns of lungs-normal lung and five of regional pulmonary disease patterns (ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). Each ROI was assessed using 22 quantitative features belonging to one of the following descriptors: histogram, gradient, run-length, gray level co-occurrence matrix, low-attenuation area cluster, and top-hat transform. For automatic classification, a Bayesian classifier and a SVM classifier were compared under three different conditions. First, classification accuracies were estimated using data from each scanner. Next, data from the GE and Siemens scanners were used for training and testing, respectively, and vice versa. Finally, all ROI data were integrated regardless of the scanner type and were then trained and tested together. All experiments were performed based on forward feature selection and fivefold cross-validation with 20 repetitions. Results: For each scanner, better classification accuracies were achieved with the SVM classifier than the Bayesian classifier (92% and 82%, respectively, for the GE scanner; and 92% and 86%, respectively, for the Siemens scanner). The classification accuracies were 82%/72% for training with GE data and testing with Siemens data, and 79%/72% for the reverse. The use of training and test data obtained from the HRCT images of different scanners lowered the classification accuracy compared to the use of HRCT images from the same scanner. For

  7. A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: Comparison to a Bayesian classifier

    Chang, Yongjun; Lim, Jonghyuck; Kim, Namkug; Seo, Joon Beom; Lynch, David A.

    2013-01-01

    Purpose: To investigate the effect of using different computed tomography (CT) scanners on the accuracy of high-resolution CT (HRCT) images in classifying regional disease patterns in patients with diffuse lung disease, support vector machine (SVM) and Bayesian classifiers were applied to multicenter data. Methods: Two experienced radiologists marked sets of 600 rectangular 20 × 20 pixel regions of interest (ROIs) on HRCT images obtained from two scanners (GE and Siemens), including 100 ROIs for each of local patterns of lungs—normal lung and five of regional pulmonary disease patterns (ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). Each ROI was assessed using 22 quantitative features belonging to one of the following descriptors: histogram, gradient, run-length, gray level co-occurrence matrix, low-attenuation area cluster, and top-hat transform. For automatic classification, a Bayesian classifier and a SVM classifier were compared under three different conditions. First, classification accuracies were estimated using data from each scanner. Next, data from the GE and Siemens scanners were used for training and testing, respectively, and vice versa. Finally, all ROI data were integrated regardless of the scanner type and were then trained and tested together. All experiments were performed based on forward feature selection and fivefold cross-validation with 20 repetitions. Results: For each scanner, better classification accuracies were achieved with the SVM classifier than the Bayesian classifier (92% and 82%, respectively, for the GE scanner; and 92% and 86%, respectively, for the Siemens scanner). The classification accuracies were 82%/72% for training with GE data and testing with Siemens data, and 79%/72% for the reverse. The use of training and test data obtained from the HRCT images of different scanners lowered the classification accuracy compared to the use of HRCT images from the same scanner. For integrated ROI

  8. Binary naive Bayesian classifiers for correlated Gaussian features: a theoretical analysis

    Van Dyk, E

    2008-11-01

    Full Text Available classifier with Gaussian features while using any quadratic decision boundary. Therefore, the analysis is not restricted to Naive Bayesian classifiers alone and can, for instance, be used to calculate the Bayes error performance. We compare the analytical...

  9. Performance analysis of a Principal Component Analysis ensemble classifier for Emotiv headset P300 spellers.

    Elsawy, Amr S; Eldawlatly, Seif; Taher, Mohamed; Aly, Gamal M

    2014-01-01

    The current trend to use Brain-Computer Interfaces (BCIs) with mobile devices mandates the development of efficient EEG data processing methods. In this paper, we demonstrate the performance of a Principal Component Analysis (PCA) ensemble classifier for P300-based spellers. We recorded EEG data from multiple subjects using the Emotiv neuroheadset in the context of a classical oddball P300 speller paradigm. We compare the performance of the proposed ensemble classifier to the performance of traditional feature extraction and classifier methods. Our results demonstrate the capability of the PCA ensemble classifier to classify P300 data recorded using the Emotiv neuroheadset with an average accuracy of 86.29% on cross-validation data. In addition, offline testing of the recorded data reveals an average classification accuracy of 73.3% that is significantly higher than that achieved using traditional methods. Finally, we demonstrate the effect of the parameters of the P300 speller paradigm on the performance of the method.

  10. Laban movement analysis to classify emotions from motion

    Dewan, Swati; Agarwal, Shubham; Singh, Navjyoti

    2018-04-01

    In this paper, we present the study of Laban Movement Analysis (LMA) to understand basic human emotions from nonverbal human behaviors. While there are a lot of studies on understanding behavioral patterns based on natural language processing and speech processing applications, understanding emotions or behavior from non-verbal human motion is still a very challenging and unexplored field. LMA provides a rich overview of the scope of movement possibilities. These basic elements can be used for generating movement or for describing movement. They provide an inroad to understanding movement and for developing movement efficiency and expressiveness. Each human being combines these movement factors in his/her own unique way and organizes them to create phrases and relationships which reveal personal, artistic, or cultural style. In this work, we build a motion descriptor based on a deep understanding of Laban theory. The proposed descriptor builds up on previous works and encodes experiential features by using temporal windows. We present a more conceptually elaborate formulation of Laban theory and test it in a relatively new domain of behavioral research with applications in human-machine interaction. The recognition of affective human communication may be used to provide developers with a rich source of information for creating systems that are capable of interacting well with humans. We test our algorithm on UCLIC dataset which consists of body motions of 13 non-professional actors portraying angry, fear, happy and sad emotions. We achieve an accuracy of 87.30% on this dataset.

  11. Immunohistochemical analysis of breast tissue microarray images using contextual classifiers

    Stephen J McKenna

    2013-01-01

    Full Text Available Background: Tissue microarrays (TMAs are an important tool in translational research for examining multiple cancers for molecular and protein markers. Automatic immunohistochemical (IHC scoring of breast TMA images remains a challenging problem. Methods: A two-stage approach that involves localization of regions of invasive and in-situ carcinoma followed by ordinal IHC scoring of nuclei in these regions is proposed. The localization stage classifies locations on a grid as tumor or non-tumor based on local image features. These classifications are then refined using an auto-context algorithm called spin-context. Spin-context uses a series of classifiers to integrate image feature information with spatial context information in the form of estimated class probabilities. This is achieved in a rotationally-invariant manner. The second stage estimates ordinal IHC scores in terms of the strength of staining and the proportion of nuclei stained. These estimates take the form of posterior probabilities, enabling images with uncertain scores to be referred for pathologist review. Results: The method was validated against manual pathologist scoring on two nuclear markers, progesterone receptor (PR and estrogen receptor (ER. Errors for PR data were consistently lower than those achieved with ER data. Scoring was in terms of estimated proportion of cells that were positively stained (scored on an ordinal scale of 0-6 and perceived strength of staining (scored on an ordinal scale of 0-3. Average absolute differences between predicted scores and pathologist-assigned scores were 0.74 for proportion of cells and 0.35 for strength of staining (PR. Conclusions: The use of context information via spin-context improved the precision and recall of tumor localization. The combination of the spin-context localization method with the automated scoring method resulted in reduced IHC scoring errors.

  12. Gene-expression patterns in peripheral blood classify familial breast cancer susceptibility.

    Piccolo, Stephen R; Andrulis, Irene L; Cohen, Adam L; Conner, Thomas; Moos, Philip J; Spira, Avrum E; Buys, Saundra S; Johnson, W Evan; Bild, Andrea H

    2015-11-04

    Women with a family history of breast cancer face considerable uncertainty about whether to pursue standard screening, intensive screening, or prophylactic surgery. Accurate and individualized risk-estimation approaches may help these women make more informed decisions. Although highly penetrant genetic variants have been associated with familial breast cancer (FBC) risk, many individuals do not carry these variants, and many carriers never develop breast cancer. Common risk variants have a relatively modest effect on risk and show limited potential for predicting FBC development. As an alternative, we hypothesized that additional genomic data types, such as gene-expression levels, which can reflect genetic and epigenetic variation, could contribute to classifying a person's risk status. Specifically, we aimed to identify common patterns in gene-expression levels across individuals who develop FBC. We profiled peripheral blood mononuclear cells from women with a family history of breast cancer (with or without a germline BRCA1/2 variant) and from controls. We used the support vector machines algorithm to differentiate between patients who developed FBC and those who did not. Our study used two independent datasets, a training set of 124 women from Utah (USA) and an external validation (test) set from Ontario (Canada) of 73 women (197 total). We controlled for expression variation associated with clinical, demographic, and treatment variables as well as lymphocyte markers. Our multigene biomarker provided accurate, individual-level estimates of FBC occurrence for the Utah cohort (AUC = 0.76 [0.67-84]) . Even at their lower confidence bounds, these accuracy estimates meet or exceed estimates from alternative approaches. Our Ontario cohort resulted in similarly high levels of accuracy (AUC = 0.73 [0.59-0.86]), thus providing external validation of our findings. Individuals deemed to have "high" risk by our model would have an estimated 2.4 times greater odds of

  13. Classifier utility modeling and analysis of hypersonic inlet start/unstart considering training data costs

    Chang, Juntao; Hu, Qinghua; Yu, Daren; Bao, Wen

    2011-11-01

    Start/unstart detection is one of the most important issues of hypersonic inlets and is also the foundation of protection control of scramjet. The inlet start/unstart detection can be attributed to a standard pattern classification problem, and the training sample costs have to be considered for the classifier modeling as the CFD numerical simulations and wind tunnel experiments of hypersonic inlets both cost time and money. To solve this problem, the CFD simulation of inlet is studied at first step, and the simulation results could provide the training data for pattern classification of hypersonic inlet start/unstart. Then the classifier modeling technology and maximum classifier utility theories are introduced to analyze the effect of training data cost on classifier utility. In conclusion, it is useful to introduce support vector machine algorithms to acquire the classifier model of hypersonic inlet start/unstart, and the minimum total cost of hypersonic inlet start/unstart classifier can be obtained by the maximum classifier utility theories.

  14. Exemplar-based optical neural net classifier for color pattern recognition

    Yu, Francis T. S.; Uang, Chii-Maw; Yang, Xiangyang

    1992-10-01

    We present a color exemplar-based neural network that can be used as an optimum image classifier or an associative memory. Color decomposition and composition technique is used for constructing the polychromatic interconnection weight matrix (IWM). The Hamming net algorithm is modified to relax the dynamic range requirement of the spatial light modulator and to reduce the number of iteration cycles in the winner-take-all layer. Computer simulation results demonstrated the feasibility of this approach

  15. Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers.

    Salimi-Khorshidi, Gholamreza; Douaud, Gwenaëlle; Beckmann, Christian F; Glasser, Matthew F; Griffanti, Ludovica; Smith, Stephen M

    2014-04-15

    Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject "at rest"). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing "signal" (brain activity) can be distinguished form the "noise" components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX ("FMRIB's ICA-based X-noiseifier"), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original

  16. Effects of cultural characteristics on building an emotion classifier through facial expression analysis

    da Silva, Flávio Altinier Maximiano; Pedrini, Helio

    2015-03-01

    Facial expressions are an important demonstration of humanity's humors and emotions. Algorithms capable of recognizing facial expressions and associating them with emotions were developed and employed to compare the expressions that different cultural groups use to show their emotions. Static pictures of predominantly occidental and oriental subjects from public datasets were used to train machine learning algorithms, whereas local binary patterns, histogram of oriented gradients (HOGs), and Gabor filters were employed to describe the facial expressions for six different basic emotions. The most consistent combination, formed by the association of HOG filter and support vector machines, was then used to classify the other cultural group: there was a strong drop in accuracy, meaning that the subtle differences of facial expressions of each culture affected the classifier performance. Finally, a classifier was trained with images from both occidental and oriental subjects and its accuracy was higher on multicultural data, evidencing the need of a multicultural training set to build an efficient classifier.

  17. How to Diagnose and Classify Tattoo Complications in the Clinic: A System of Distinctive Patterns.

    Serup, Jørgen

    2017-01-01

    Tattoo complications represent a broad spectrum of clinical entities and disease mechanisms. Infections are known, but chronic inflammatory reactions have hitherto been inconsistently reported and given many interpretations and terms. A clinical classification system of distinct patterns with emphasis on inflammatory tattoo reactions is introduced. Allergic reactions prevalent in red tattoos and often associated with azo pigments are manifested as the 'plaque elevation', 'excessive hyperkeratosis', and 'ulceronecrotic' patterns. The allergen is a hapten. Nonallergic reactions prevalent in black tattoos and associated with carbon black are manifested as the 'papulonodular' pattern. Carbon black nanoparticles agglomerate in the dermis over time forming foreign bodies that elicit reactions. Many black tattoos even develop sarcoid granuloma, and the 'papulonodular' pattern is strongly associated with sarcoidosis affecting other organs. Tattoo complications include a large group of less frequent but nevertheless specific entities, i.e. irritant and toxic local events, photosensitivity, urticaria, eczematous rash due to soluble allergen, neurosensitivity and pain syndrome, lymphopathies, pigment diffusion or fan, scars, and other sequels of tattooing or tattoo removal. Keratoacanthoma occurs in tattoos. Carcinoma and melanoma are rare and occur by coincidence only. Different tattoo complications require different therapeutic approaches, and precise diagnosis is thus important as a key to therapy. The proposed new classification with characteristic patterns relies on simple tools, namely patient history, objective findings, and supplementary punch biopsy. By virtue of simplicity and broad access, these methods make the proposed classification widely applicable in clinics and hospitals. The system is reported to the 11th revision of the WHO diagnosis classification used as international standard. © 2017 S. Karger AG, Basel.

  18. Analysis and minimization of overtraining effect in rule-based classifiers for computer-aided diagnosis

    Li Qiang; Doi Kunio

    2006-01-01

    Computer-aided diagnostic (CAD) schemes have been developed to assist radiologists detect various lesions in medical images. In CAD schemes, classifiers play a key role in achieving a high lesion detection rate and a low false-positive rate. Although many popular classifiers such as linear discriminant analysis and artificial neural networks have been employed in CAD schemes for reduction of false positives, a rule-based classifier has probably been the simplest and most frequently used one since the early days of development of various CAD schemes. However, with existing rule-based classifiers, there are major disadvantages that significantly reduce their practicality and credibility. The disadvantages include manual design, poor reproducibility, poor evaluation methods such as resubstitution, and a large overtraining effect. An automated rule-based classifier with a minimized overtraining effect can overcome or significantly reduce the extent of the above-mentioned disadvantages. In this study, we developed an 'optimal' method for the selection of cutoff thresholds and a fully automated rule-based classifier. Experimental results performed with Monte Carlo simulation and a real lung nodule CT data set demonstrated that the automated threshold selection method can completely eliminate overtraining effect in the procedure of cutoff threshold selection, and thus can minimize overall overtraining effect in the constructed rule-based classifier. We believe that this threshold selection method is very useful in the construction of automated rule-based classifiers with minimized overtraining effect

  19. Comparative analysis of instance selection algorithms for instance-based classifiers in the context of medical decision support

    Mazurowski, Maciej A; Tourassi, Georgia D; Malof, Jordan M

    2011-01-01

    When constructing a pattern classifier, it is important to make best use of the instances (a.k.a. cases, examples, patterns or prototypes) available for its development. In this paper we present an extensive comparative analysis of algorithms that, given a pool of previously acquired instances, attempt to select those that will be the most effective to construct an instance-based classifier in terms of classification performance, time efficiency and storage requirements. We evaluate seven previously proposed instance selection algorithms and compare their performance to simple random selection of instances. We perform the evaluation using k-nearest neighbor classifier and three classification problems: one with simulated Gaussian data and two based on clinical databases for breast cancer detection and diagnosis, respectively. Finally, we evaluate the impact of the number of instances available for selection on the performance of the selection algorithms and conduct initial analysis of the selected instances. The experiments show that for all investigated classification problems, it was possible to reduce the size of the original development dataset to less than 3% of its initial size while maintaining or improving the classification performance. Random mutation hill climbing emerges as the superior selection algorithm. Furthermore, we show that some previously proposed algorithms perform worse than random selection. Regarding the impact of the number of instances available for the classifier development on the performance of the selection algorithms, we confirm that the selection algorithms are generally more effective as the pool of available instances increases. In conclusion, instance selection is generally beneficial for instance-based classifiers as it can improve their performance, reduce their storage requirements and improve their response time. However, choosing the right selection algorithm is crucial.

  20. New approach to detect and classify stroke in skull CT images via analysis of brain tissue densities.

    Rebouças Filho, Pedro P; Sarmento, Róger Moura; Holanda, Gabriel Bandeira; de Alencar Lima, Daniel

    2017-09-01

    Cerebral vascular accident (CVA), also known as stroke, is an important health problem worldwide and it affects 16 million people worldwide every year. About 30% of those that have a stroke die and 40% remain with serious physical limitations. However, recovery in the damaged region is possible if treatment is performed immediately. In the case of a stroke, Computed Tomography (CT) is the most appropriate technique to confirm the occurrence and to investigate its extent and severity. Stroke is an emergency problem for which early identification and measures are difficult; however, computer-aided diagnoses (CAD) can play an important role in obtaining information imperceptible to the human eye. Thus, this work proposes a new method for extracting features based on radiological density patterns of the brain, called Analysis of Brain Tissue Density (ABTD). The proposed method is a specific approach applied to CT images to identify and classify the occurrence of stroke diseases. The evaluation of the results of the ABTD extractor proposed in this paper were compared with extractors already established in the literature, such as features from Gray-Level Co-Occurrence Matrix (GLCM), Local binary patterns (LBP), Central Moments (CM), Statistical Moments (SM), Hu's Moment (HM) and Zernike's Moments (ZM). Using a database of 420 CT images of the skull, each extractor was applied with the classifiers such as MLP, SVM, kNN, OPF and Bayesian to classify if a CT image represented a healthy brain or one with an ischemic or hemorrhagic stroke. ABTD had the shortest extraction time and the highest average accuracy (99.30%) when combined with OPF using the Euclidean distance. Also, the average accuracy values for all classifiers were higher than 95%. The relevance of the results demonstrated that the ABTD method is a useful algorithm to extract features that can potentially be integrated with CAD systems to assist in stroke diagnosis. Copyright © 2017 Elsevier B.V. All rights

  1. Two Classifiers Based on Serum Peptide Pattern for Prediction of HBV-Induced Liver Cirrhosis Using MALDI-TOF MS

    Yuan Cao

    2013-01-01

    Full Text Available Chronic infection with hepatitis B virus (HBV is associated with the majority of cases of liver cirrhosis (LC in China. Although liver biopsy is the reference method for evaluation of cirrhosis, it is an invasive procedure with inherent risk. The aim of this study is to discover novel noninvasive specific serum biomarkers for the diagnosis of HBV-induced LC. We performed bead fractionation/MALDI-TOF MS analysis on sera from patients with LC. Thirteen feature peaks which had optimal discriminatory performance were obtained by using support-vector-machine-(SVM- based strategy. Based on the previous results, five supervised machine learning methods were employed to construct classifiers that discriminated proteomic spectra of patients with HBV-induced LC from those of controls. Here, we describe two novel methods for prediction of HBV-induced LC, termed LC-NB and LC-MLP, respectively. We obtained a sensitivity of 90.9%, a specificity of 94.9%, and overall accuracy of 93.8% on an independent test set. Comparisons with the existing methods showed that LC-NB and LC-MLP held better accuracy. Our study suggests that potential serum biomarkers can be determined for discriminating LC and non-LC cohorts by using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. These two classifiers could be used for clinical practice in HBV-induced LC assessment.

  2. Using Neural Pattern Classifiers to Quantify the Modularity of Conflict–Control Mechanisms in the Human Brain

    Jiang, Jiefeng; Egner, Tobias

    2014-01-01

    Resolving conflicting sensory and motor representations is a core function of cognitive control, but it remains uncertain to what degree control over different sources of conflict is implemented by shared (domain general) or distinct (domain specific) neural resources. Behavioral data suggest conflict–control to be domain specific, but results from neuroimaging studies have been ambivalent. Here, we employed multivoxel pattern analyses that can decode a brain region's informational content, allowing us to distinguish incidental activation overlap from actual shared information processing. We trained independent sets of “searchlight” classifiers on functional magnetic resonance imaging data to decode control processes associated with stimulus-conflict (Stroop task) and ideomotor-conflict (Simon task). Quantifying the proportion of domain-specific searchlights (capable of decoding only one type of conflict) and domain-general searchlights (capable of decoding both conflict types) in each subject, we found both domain-specific and domain-general searchlights, though the former were more common. When mapping anatomical loci of these searchlights across subjects, neural substrates of stimulus- and ideomotor-specific conflict–control were found to be anatomically consistent across subjects, whereas the substrates of domain-general conflict–control were not. Overall, these findings suggest a hybrid neural architecture of conflict–control that entails both modular (domain specific) and global (domain general) components. PMID:23402762

  3. Using neural pattern classifiers to quantify the modularity of conflict-control mechanisms in the human brain.

    Jiang, Jiefeng; Egner, Tobias

    2014-07-01

    Resolving conflicting sensory and motor representations is a core function of cognitive control, but it remains uncertain to what degree control over different sources of conflict is implemented by shared (domain general) or distinct (domain specific) neural resources. Behavioral data suggest conflict-control to be domain specific, but results from neuroimaging studies have been ambivalent. Here, we employed multivoxel pattern analyses that can decode a brain region's informational content, allowing us to distinguish incidental activation overlap from actual shared information processing. We trained independent sets of "searchlight" classifiers on functional magnetic resonance imaging data to decode control processes associated with stimulus-conflict (Stroop task) and ideomotor-conflict (Simon task). Quantifying the proportion of domain-specific searchlights (capable of decoding only one type of conflict) and domain-general searchlights (capable of decoding both conflict types) in each subject, we found both domain-specific and domain-general searchlights, though the former were more common. When mapping anatomical loci of these searchlights across subjects, neural substrates of stimulus- and ideomotor-specific conflict-control were found to be anatomically consistent across subjects, whereas the substrates of domain-general conflict-control were not. Overall, these findings suggest a hybrid neural architecture of conflict-control that entails both modular (domain specific) and global (domain general) components. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  4. Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT

    Lee, Juhun; Nishikawa, Robert M.; Reiser, Ingrid; Boone, John M.; Lindfors, Karen K.

    2015-01-01

    Purpose: The purpose of this study is to measure the effectiveness of local curvature measures as novel image features for classifying breast tumors. Methods: A total of 119 breast lesions from 104 noncontrast dedicated breast computed tomography images of women were used in this study. Volumetric segmentation was done using a seed-based segmentation algorithm and then a triangulated surface was extracted from the resulting segmentation. Total, mean, and Gaussian curvatures were then computed. Normalized curvatures were used as classification features. In addition, traditional image features were also extracted and a forward feature selection scheme was used to select the optimal feature set. Logistic regression was used as a classifier and leave-one-out cross-validation was utilized to evaluate the classification performances of the features. The area under the receiver operating characteristic curve (AUC, area under curve) was used as a figure of merit. Results: Among curvature measures, the normalized total curvature (C_T) showed the best classification performance (AUC of 0.74), while the others showed no classification power individually. Five traditional image features (two shape, two margin, and one texture descriptors) were selected via the feature selection scheme and its resulting classifier achieved an AUC of 0.83. Among those five features, the radial gradient index (RGI), which is a margin descriptor, showed the best classification performance (AUC of 0.73). A classifier combining RGI and C_T yielded an AUC of 0.81, which showed similar performance (i.e., no statistically significant difference) to the classifier with the above five traditional image features. Additional comparisons in AUC values between classifiers using different combinations of traditional image features and C_T were conducted. The results showed that C_T was able to replace the other four image features for the classification task. Conclusions: The normalized curvature measure

  5. Knee Joint Vibration Signal Analysis with Matching Pursuit Decomposition and Dynamic Weighted Classifier Fusion

    Suxian Cai

    2013-01-01

    detected with the fixed threshold in the time domain. To perform a better classification over the data set of 89 VAG signals, we applied a novel classifier fusion system based on the dynamic weighted fusion (DWF method to ameliorate the classification performance. For comparison, a single leastsquares support vector machine (LS-SVM and the Bagging ensemble were used for the classification task as well. The results in terms of overall accuracy in percentage and area under the receiver operating characteristic curve obtained with the DWF-based classifier fusion method reached 88.76% and 0.9515, respectively, which demonstrated the effectiveness and superiority of the DWF method with two distinct features for the VAG signal analysis.

  6. Angiographic patterns of in-stent restenosis classified by computed tomography in patients with drug-eluting stents: correlation with invasive coronary angiography

    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.)

  7. Spatial analysis of weed patterns

    Heijting, S.

    2007-01-01

    Keywords: Spatial analysis, weed patterns, Mead’s test, space-time correlograms, 2-D correlograms, dispersal, Generalized Linear Models, heterogeneity, soil, Taylor’s power law. Weeds in agriculture occur in patches. This thesis is a contribution to the characterization of this patchiness, to its

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

    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…

  9. Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers

    Esperanza García-Gonzalo

    2016-06-01

    Full Text Available The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine. The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine.

  10. Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers.

    García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

    2016-06-29

    The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine.

  11. Splicing analysis of 14 BRCA1 missense variants classifies nine variants as pathogenic

    Ahlborn, Lise B; Dandanell, Mette; Steffensen, Ane Y

    2015-01-01

    by functional analysis at the protein level. Results from a validated mini-gene splicing assay indicated that nine BRCA1 variants resulted in splicing aberrations leading to truncated transcripts and thus can be considered pathogenic (c.4987A>T/p.Met1663Leu, c.4988T>A/p.Met1663Lys, c.5072C>T/p.Thr1691Ile, c......Pathogenic germline mutations in the BRCA1 gene predispose carriers to early onset breast and ovarian cancer. Clinical genetic screening of BRCA1 often reveals variants with uncertain clinical significance, complicating patient and family management. Therefore, functional examinations are urgently...... needed to classify whether these uncertain variants are pathogenic or benign. In this study, we investigated 14 BRCA1 variants by in silico splicing analysis and mini-gene splicing assay. All 14 alterations were missense variants located within the BRCT domain of BRCA1 and had previously been examined...

  12. Performance Analysis and Optimization for Cognitive Radio Networks with Classified Secondary Users and Impatient Packets

    Yuan Zhao

    2017-01-01

    Full Text Available A cognitive radio network with classified Secondary Users (SUs is considered. There are two types of SU packets, namely, SU1 packets and SU2 packets, in the system. The SU1 packets have higher priority than the SU2 packets. Considering the diversity of the SU packets and the real-time need of the interrupted SU packets, a novel spectrum allocation strategy with classified SUs and impatient packets is proposed. Based on the number of PU packets, SU1 packets, and SU2 packets in the system, by modeling the queue dynamics of the networks users as a three-dimensional discrete-time Markov chain, the transition probability matrix of the Markov chain is given. Then with the steady-state analysis, some important performance measures of the SU2 packets are derived to show the system performance with numerical results. Specially, in order to optimize the system actions of the SU2 packets, the individually optimal strategy and the socially optimal strategy for the SU2 packets are demonstrated. Finally, a pricing mechanism is provided to oblige the SU2 packets to follow the socially optimal strategy.

  13. Role of BRAFV600E Mutation Analysis for Thyroid Nodules Classified as Indeterminate on Ultrasonography

    Nam, Sang Yu; Shin, Jung Hee; Han, Boo Kyung; Ko, Eun Young; Kang, Seok Seon; Hahn, Soo Yeon; Hwang, Ji Young; Nam, Mee Young; Kim, Jong Won; Chung, Jae Hoon

    2010-01-01

    We aimed to evaluate a possible role for BRAFV600E mutation analysis of aspiration specimens in the work up of thyroid nodules classified as indeterminate on US. A total of 122 nodules from 122 patients were prospectively classified as indeterminate nodules based on US findings. US-guided fine needle aspiration (FNA) was done for all 122 nodules. The presence of a BRAFV600E mutation in FNA specimens was determined by allele-specific PCR. US-indeterminate nodules were confirmed as malignant in 20.5% (25/122) of cases and benign in 76.2% (93/122) after FNA or surgery. A few (3.3% (4/122), remained indeterminate. A BRAFV600E mutation was identified in 14.8% (18/122) of US indeterminate nodules. Of those 18 nodules, three were benign and 13 were malignant after the initial FNA. One (0.8%, 1/122) with an initially benign cytology and a BRAFV600E mutation was confirmed to be malignant after surgery. The remaining two benign nodules with a mutation were not followed-up. All 9 initial FNA-nondiagnostic nodules were mutation negative but 2 (11.8%) of 17 indeterminate nodules on initial FNAs were mutation positive. BRAFV600E mutation analysis prevents false negative cytology for only 0.8% of cases and reduces ambiguous diagnoses for 1.6% of all US-indeterminate thyroid nodules. Therefore, adding BRAFV600E mutation analysis to FNA for US-indeterminate nodules is of limited usefulness

  14. Correspondence analysis: a method for classifying similar patterns of violence against women Análise de correspondência: um método para classificação de mulheres com perfil semelhante de vitimização

    Jurema Corrêa da Mota

    2008-06-01

    Full Text Available Violence against woman has received relatively little debate in society. It includes physical, psychological, and sexual abuse that jeopardizes the victim's health. Multivariate correspondence analysis and cluster analysis were applied to crimes reported to the Integrated Women's Aid Center in Rio de Janeiro, Brazil, to investigate associations between injury and define criteria for classifying the aggressions. Three groups of abuse were identified, differing according to the nature (physical, psychological, or sexual and severity of the crimes. Less serious crimes consisted of threats and moderate physical injuries. The intermediate severity group included serious physical assault and threats. More serious crimes included death threats, rape, and sexual assault. The method thus allowed classification of the crimes in three groups according to severity.A violência contra a mulher é uma questão ainda pouco debatida na sociedade, abrangendo um conjunto de agressões físicas, psicológicas e sexuais que contribuem para a depreciação da saúde da vítima. Aplicou-se a técnica de análise de correspondência multivariada, seguida da técnica de análise de cluster aos crimes registrados no Centro Integrado de Atendimento à Mulher, Rio de Janeiro, Brasil, com o objetivo de investigar o padrão de associação entre os agravos e estabelecer critérios para a classificação das agressões. Identificaram-se três grupos que se distinguem pela natureza do crime (físico, psicológico e sexual e pelos níveis de gravidade. O menos grave é formado pelos crimes de lesão corporal leve e ameaça. O de gravidade intermediária reúne crimes de lesão corporal grave e ameaça. No de maior gravidade estão os crimes de ameaça de morte, estupro e abuso sexual. O método permitiu a classificação dos crimes em três grupos, que podem ser ordenados de acordo com o grau de severidade que guardam entre si.

  15. A Learning Outcome-Oriented Approach towards Classifying Pervasive Games for Learning Using Game Design Patterns and Contextual Information

    Schmitz, Birgit; Klemke, Roland; Specht, Marcus

    2013-01-01

    Mobile and in particular pervasive games are a strong component of future scenarios for teaching and learning. Based on results from a previous review of practical papers, this work explores the educational potential of pervasive games for learning by analysing underlying game mechanisms. In order to determine and classify cognitive and affective…

  16. Classifying oxidative stress by F2-isoprostane levels across human diseases: A meta-analysis.

    van 't Erve, Thomas J; Kadiiska, Maria B; London, Stephanie J; Mason, Ronald P

    2017-08-01

    The notion that oxidative stress plays a role in virtually every human disease and environmental exposure has become ingrained in everyday knowledge. However, mounting evidence regarding the lack of specificity of biomarkers traditionally used as indicators of oxidative stress in human disease and exposures now necessitates re-evaluation. To prioritize these re-evaluations, published literature was comprehensively analyzed in a meta-analysis to quantitatively classify the levels of systemic oxidative damage across human disease and in response to environmental exposures. In this meta-analysis, the F 2 -isoprostane, 8-iso-PGF 2α , was specifically chosen as the representative marker of oxidative damage. To combine published values across measurement methods and specimens, the standardized mean differences (Hedges' g) in 8-iso-PGF 2α levels between affected and control populations were calculated. The meta-analysis resulted in a classification of oxidative damage levels as measured by 8-iso-PGF 2α across 50 human health outcomes and exposures from 242 distinct publications. Relatively small increases in 8-iso-PGF 2α levels (ganalysis of published data. This analysis provides knowledge on the true involvement of oxidative damage across human health outcomes as well as utilizes past research to prioritize those conditions requiring further scrutiny on the mechanisms of biomarker generation. Copyright © 2017. Published by Elsevier B.V.

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

    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

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

    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

  19. WAVELET ANALYSIS AND NEURAL NETWORK CLASSIFIERS TO DETECT MID-SAGITTAL SECTIONS FOR NUCHAL TRANSLUCENCY MEASUREMENT

    Giuseppa Sciortino

    2016-04-01

    Full Text Available We propose a methodology to support the physician in the automatic identification of mid-sagittal sections of the fetus in ultrasound videos acquired during the first trimester of pregnancy. A good mid-sagittal section is a key requirement to make the correct measurement of nuchal translucency which is one of the main marker for screening of chromosomal defects such as trisomy 13, 18 and 21. NT measurement is beyond the scope of this article. The proposed methodology is mainly based on wavelet analysis and neural network classifiers to detect the jawbone and on radial symmetry analysis to detect the choroid plexus. Those steps allow to identify the frames which represent correct mid-sagittal sections to be processed. The performance of the proposed methodology was analyzed on 3000 random frames uniformly extracted from 10 real clinical ultrasound videos. With respect to a ground-truth provided by an expert physician, we obtained a true positive, a true negative and a balanced accuracy equal to 87.26%, 94.98% and 91.12% respectively.

  20. Analysis of unintended events in hospitals: inter-rater reliability of constructing causal trees and classifying root causes

    Smits, M.; Janssen, J.; Vet, de H.C.W.; Zwaan, L.; Timmermans, D.R.M.; Groenewegen, P.P.; Wagner, C.

    2009-01-01

    BACKGROUND: Root cause analysis is a method to examine causes of unintended events. PRISMA (Prevention and Recovery Information System for Monitoring and Analysis: is a root cause analysis tool. With PRISMA, events are described in causal trees and root causes are subsequently classified with the

  1. Analysis of unintended events in hospitals : inter-rater reliability of constructing causal trees and classifying root causes

    Smits, M.; Janssen, J.; Vet, R. de; Zwaan, L.; Groenewegen, P.P.; Timmermans, D.

    2009-01-01

    Background. Root cause analysis is a method to examine causes of unintended events. PRISMA (Prevention and Recovery Information System for Monitoring and Analysis) is a root cause analysis tool. With PRISMA, events are described in causal trees and root causes are subsequently classified with the

  2. Analysis of unintended events in hospitals: inter-rater reliability of constructing causal trees and classifying root causes.

    Smits, M.; Janssen, J.; Vet, R. de; Zwaan, L.; Timmermans, D.; Groenewegen, P.; Wagner, C.

    2009-01-01

    Background: Root cause analysis is a method to examine causes of unintended events. PRISMA (Prevention and Recovery Information System for Monitoring and Analysis) is a root cause analysis tool. With PRISMA, events are described in causal trees and root causes are subsequently classified with the

  3. Association of Dietary Patterns With Risk of Colorectal Cancer Subtypes Classified by Fusobacterium nucleatum in Tumor Tissue.

    Mehta, Raaj S; Nishihara, Reiko; Cao, Yin; Song, Mingyang; Mima, Kosuke; Qian, Zhi Rong; Nowak, Jonathan A; Kosumi, Keisuke; Hamada, Tsuyoshi; Masugi, Yohei; Bullman, Susan; Drew, David A; Kostic, Aleksandar D; Fung, Teresa T; Garrett, Wendy S; Huttenhower, Curtis; Wu, Kana; Meyerhardt, Jeffrey A; Zhang, Xuehong; Willett, Walter C; Giovannucci, Edward L; Fuchs, Charles S; Chan, Andrew T; Ogino, Shuji

    2017-07-01

    Fusobacterium nucleatum appears to play a role in colorectal carcinogenesis through suppression of the hosts' immune response to tumor. Evidence also suggests that diet influences intestinal F nucleatum. However, the role of F nucleatum in mediating the relationship between diet and the risk of colorectal cancer is unknown. To test the hypothesis that the associations of prudent diets (rich in whole grains and dietary fiber) and Western diets (rich in red and processed meat, refined grains, and desserts) with colorectal cancer risk may differ according to the presence of F nucleatum in tumor tissue. A prospective cohort study was conducted using data from the Nurses' Health Study (June 1, 1980, to June 1, 2012) and the Health Professionals Follow-up Study (June 1, 1986, to June 1, 2012) on a total of 121 700 US female nurses and 51 529 US male health professionals aged 30 to 55 years and 40 to 75 years, respectively (both predominantly white individuals), at enrollment. Data analysis was performed from March 15, 2015, to August 10, 2016. Prudent and Western diets. Incidence of colorectal carcinoma subclassified by F nucleatum status in tumor tissue, determined by quantitative polymerase chain reaction. Of the 173 229 individuals considered for the study, 137 217 were included in the analysis, 47 449 were male (34.6%), and mean (SD) baseline age for men was 54.0 (9.8) years and for women, 46.3 (7.2) years. A total of 1019 incident colon and rectal cancer cases with available F nucleatum data were documented over 26 to 32 years of follow-up, encompassing 3 643 562 person-years. The association of prudent diet with colorectal cancer significantly differed by tissue F nucleatum status (P = .01 for heterogeneity); prudent diet score was associated with a lower risk of F nucleatum-positive cancers (P = .003 for trend; multivariable hazard ratio of 0.43; 95% CI, 0.25-0.72, for the highest vs the lowest prudent score quartile) but not with F nucleatum

  4. Classifying Microorganisms

    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...

  5. Comparison of objective methods to classify the pattern of respiratory sinus arrhythmia during mechanical ventilation and paced spontaneous breathing

    Carvalho, N C; Beda, A; Granja-Filho, P; Jandre, F C; Giannella-Neto, A; De Abreu, M G; Spieth, P M

    2009-01-01

    Respiratory sinus arrhythmia (RSA) is a fluctuation of heart period that occurs during a respiratory cycle. It has been suggested that inspiratory heart period acceleration and expiratory deceleration during spontaneous ventilation (henceforth named positive RSA) improve the efficiency of gas exchange compared to the absence or the inversion of such a pattern (negative RSA). During mechanical ventilation (MV), for which maximizing the efficiency of gas exchange is of critical importance, the pattern of RSA is still the object of debate. In order to gain a better insight into this matter, we compared five different methods of RSA classification using the data of five mechanically ventilated piglets. The comparison was repeated using the data of 15 volunteers undergoing a protocol of paced spontaneous breathing, which is expected to result in a positive RSA pattern. The results showed that the agreement between the employed methods is limited, suggesting that the lack of a consensus about the RSA pattern during MV is, at least in part, of methodological origin. However, independently of the method used, the pattern of RSA within the respiratory cycle was not consistent among the subjects and conditions of MV considered. Also, the outcomes showed that even during paced spontaneous breathing a negative RSA pattern might be present, when a low respiratory frequency is imposed

  6. PATTER, Pattern Recognition Data Analysis

    Cox, L.C. Jr.; Bender, C.F.

    1986-01-01

    1 - Description of program or function: PATTER is an interactive program with extensive facilities for modeling analytical processes and solving complex data analysis problems using statistical methods, spectral analysis, and pattern recognition techniques. PATTER addresses the type of problem generally stated as follows: given a set of objects and a list of measurements made on these objects, is it possible to find or predict a property of the objects which is not directly measurable but is known to define some unknown relationship? When employed intelligently, PATTER will act upon a data set in such a way it becomes apparent if useful information, beyond that already discerned, is contained in the data. 2 - Method of solution: In order to solve the general problem, PATTER contains preprocessing techniques to produce new variables that are related to the values of the measurements which may reduce the number of variables and/or reveal useful information about the 'obscure' property; display techniques to represent the variable space in some way that can be easily projected onto a two- or three-dimensional plot for human observation to see if any significant clustering of points occurs; and learning techniques based on both unsupervised and supervised methods, to extract as much information from the data as possible so that the optimum solution can be found

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

    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.

  8. Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations.

    Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

    2015-01-01

    Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

  9. Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations

    Yi Zhang

    2015-01-01

    Full Text Available Maximum likelihood classifier (MLC and support vector machines (SVM are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

  10. Association Between Inflammatory Diet Pattern and Risk of Colorectal Carcinoma Subtypes Classified by Immune Responses to Tumor.

    Liu, Li; Nishihara, Reiko; Qian, Zhi Rong; Tabung, Fred K; Nevo, Daniel; Zhang, Xuehong; Song, Mingyang; Cao, Yin; Mima, Kosuke; Masugi, Yohei; Shi, Yan; da Silva, Annacarolina; Twombly, Tyler; Gu, Mancang; Li, Wanwan; Hamada, Tsuyoshi; Kosumi, Keisuke; Inamura, Kentaro; Nowak, Jonathan A; Drew, David A; Lochhead, Paul; Nosho, Katsuhiko; Wu, Kana; Wang, Molin; Garrett, Wendy S; Chan, Andrew T; Fuchs, Charles S; Giovannucci, Edward L; Ogino, Shuji

    2017-12-01

    Dietary patterns affect systemic and local intestinal inflammation, which have been linked to colorectal carcinogenesis. Chronic inflammation can interfere with the adaptive immune response. We investigated whether the association of a diet that promotes intestinal inflammation with risk of colorectal carcinoma was stronger for tumors with lower lymphocytic reactions than tumors with higher lymphocytic reactions. We collected data from the molecular pathological epidemiology databases of 2 prospective cohort studies: the Nurses' Health Study (since 1976) and the Health Professionals Follow-Up Study (since 1986). We used duplication-method time-varying Cox proportional cause-specific hazards regression to assess the association of empirical dietary inflammatory pattern (EDIP) score (derived from food frequency questionnaire data) with colorectal carcinoma subtype. Foods that contribute to high EDIP scores include red and processed meats, refined grains, carbonated beverages, and some vegetables; foods that contribute to low EDIP scores include beer, wine, coffee, tea, yellow and leafy vegetables, and fruit juice. Colorectal tissue samples were analyzed histologically for patterns of lymphocytic reactions (Crohn's-like lymphoid reaction, peritumoral lymphocytic reaction, intratumoral periglandular reaction, and tumor-infiltrating lymphocytes). During follow-up of 124,433 participants, we documented 1311 incident colon and rectal cancer cases with available tissue data. The association between the EDIP and colorectal cancer risk was significant (P trend  = .02), and varied with degree of peritumoral lymphocytic reaction (P heterogeneity colorectal cancer with an absent or low peritumoral lymphocytic reaction (highest vs lowest EDIP score quintile hazard ratio, 2.60; 95% confidence interval, 1.60-4.23; P trend .80). In 2 prospective cohort studies, we associated inflammatory diets with a higher risk of colorectal cancer subtype that contains little or no peritumoral

  11. Computer aided fringe pattern analysis

    Sciammarella, Cesar A.

    The paper reviews the basic laws of fringe pattern interpretation. The different techniques that are currently utilized are presented using a common frame of reference stressing the fact that these techniques are different variations of the same basic principle. Digital and analog techniques are discussed. Currently available hardware is presented and the relationships between hardware and the operations of pattern fringe processing are pointed out. Examples are given to illustrate the ideas discussed in the paper.

  12. A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents.

    Colomer Granero, Adrián; Fuentes-Hurtado, Félix; Naranjo Ornedo, Valery; Guixeres Provinciale, Jaime; Ausín, Jose M; Alcañiz Raya, Mariano

    2016-01-01

    This work focuses on finding the most discriminatory or representative features that allow to classify commercials according to negative, neutral and positive effectiveness based on the Ace Score index. For this purpose, an experiment involving forty-seven participants was carried out. In this experiment electroencephalography (EEG), electrocardiography (ECG), Galvanic Skin Response (GSR) and respiration data were acquired while subjects were watching a 30-min audiovisual content. This content was composed by a submarine documentary and nine commercials (one of them the ad under evaluation). After the signal pre-processing, four sets of features were extracted from the physiological signals using different state-of-the-art metrics. These features computed in time and frequency domains are the inputs to several basic and advanced classifiers. An average of 89.76% of the instances was correctly classified according to the Ace Score index. The best results were obtained by a classifier consisting of a combination between AdaBoost and Random Forest with automatic selection of features. The selected features were those extracted from GSR and HRV signals. These results are promising in the audiovisual content evaluation field by means of physiological signal processing.

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

    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…

  14. Tabular data base construction and analysis from thematic classified Landsat imagery of Portland, Oregon

    Bryant, N. A.; George, A. J., Jr.; Hegdahl, R.

    1977-01-01

    A systematic verification of Landsat data classifications of the Portland, Oregon metropolitan area has been undertaken on the basis of census tract data. The degree of systematic misclassification due to the Bayesian classifier used to process the Landsat data was noted for the various suburban, industrialized and central business districts of the metropolitan area. The Landsat determinations of residential land use were employed to estimate the number of automobile trips generated in the region and to model air pollution hazards.

  15. Pinpointing the classifiers of English language writing ability: A discriminant function analysis approach

    Mohammad Ali Shams

    2013-02-01

    Full Text Available     The major aim of this paper was to investigate the validity of language and intelligence factors for classifying Iranian English learners` writing performance. Iranian participants of the study took three tests for grammar, breadth, and depth of vocabulary, and two tests for verbal and narrative intelligence. They also produced a corpus of argumentative writings in answer to IELTS specimen. Several runs of discriminant function analyses were used to examine the classifying power of the five variables for discriminating between low and high ability L2 writers. The results revealed that among language factors, depth of vocabulary (collocational knowledge produces the best discriminant function. In general, narrative intelligence was found to be the most reliable predictor for membership in low or high groups. It was also found that, among the five sub-abilities of narrative intelligence, emplotment carries the highest classifying value. Finally, the applications and implications of the results for second language researchers, cognitive scientists, and applied linguists were discussed.Â

  16. Carbon classified?

    Lippert, Ingmar

    2012-01-01

    . Using an actor- network theory (ANT) framework, the aim is to investigate the actors who bring together the elements needed to classify their carbon emission sources and unpack the heterogeneous relations drawn on. Based on an ethnographic study of corporate agents of ecological modernisation over...... a period of 13 months, this paper provides an exploration of three cases of enacting classification. Drawing on ANT, we problematise the silencing of a range of possible modalities of consumption facts and point to the ontological ethics involved in such performances. In a context of global warming...

  17. General aviation air traffic pattern safety analysis

    Parker, L. C.

    1973-01-01

    A concept is described for evaluating the general aviation mid-air collision hazard in uncontrolled terminal airspace. Three-dimensional traffic pattern measurements were conducted at uncontrolled and controlled airports. Computer programs for data reduction, storage retrieval and statistical analysis have been developed. Initial general aviation air traffic pattern characteristics are presented. These preliminary results indicate that patterns are highly divergent from the expected standard pattern, and that pattern procedures observed can affect the ability of pilots to see and avoid each other.

  18. Analysis of Web Spam for Non-English Content: Toward More Effective Language-Based Classifiers.

    Mansour Alsaleh

    Full Text Available Web spammers aim to obtain higher ranks for their web pages by including spam contents that deceive search engines in order to include their pages in search results even when they are not related to the search terms. Search engines continue to develop new web spam detection mechanisms, but spammers also aim to improve their tools to evade detection. In this study, we first explore the effect of the page language on spam detection features and we demonstrate how the best set of detection features varies according to the page language. We also study the performance of Google Penguin, a newly developed anti-web spamming technique for their search engine. Using spam pages in Arabic as a case study, we show that unlike similar English pages, Google anti-spamming techniques are ineffective against a high proportion of Arabic spam pages. We then explore multiple detection features for spam pages to identify an appropriate set of features that yields a high detection accuracy compared with the integrated Google Penguin technique. In order to build and evaluate our classifier, as well as to help researchers to conduct consistent measurement studies, we collected and manually labeled a corpus of Arabic web pages, including both benign and spam pages. Furthermore, we developed a browser plug-in that utilizes our classifier to warn users about spam pages after clicking on a URL and by filtering out search engine results. Using Google Penguin as a benchmark, we provide an illustrative example to show that language-based web spam classifiers are more effective for capturing spam contents.

  19. Energy-aware embedded classifier design for real-time emotion analysis.

    Padmanabhan, Manoj; Murali, Srinivasan; Rincon, Francisco; Atienza, David

    2015-01-01

    Detection and classification of human emotions from multiple bio-signals has a wide variety of applications. Though electronic devices are available in the market today that acquire multiple body signals, the classification of human emotions in real-time, adapted to the tight energy budgets of wearable embedded systems is a big challenge. In this paper we present an embedded classifier for real-time emotion classification. We propose a system that operates at different energy budgeted modes, depending on the available energy, where each mode is constrained by an operating energy bound. The classifier has an offline training phase where feature selection is performed for each operating mode, with an energy-budget aware algorithm that we propose. Across the different operating modes, the classification accuracy ranges from 95% - 75% and 89% - 70% for arousal and valence respectively. The accuracy is traded off for less power consumption, which results in an increased battery life of up to 7.7 times (from 146.1 to 1126.9 hours).

  20. Pattern Analysis On Banking Dataset

    Amritpal Singh

    2015-06-01

    Full Text Available Abstract Everyday refinement and development of technology has led to an increase in the competition between the Tech companies and their going out of way to crack the system andbreak down. Thus providing Data mining a strategically and security-wise important area for many business organizations including banking sector. It allows the analyzes of important information in the data warehouse and assists the banks to look for obscure patterns in a group and discover unknown relationship in the data.Banking systems needs to process ample amount of data on daily basis related to customer information their credit card details limit and collateral details transaction details risk profiles Anti Money Laundering related information trade finance data. Thousands of decisionsbased on the related data are taken in a bank daily. This paper analyzes the banking dataset in the weka environment for the detection of interesting patterns based on its applications ofcustomer acquisition customer retention management and marketing and management of risk fraudulence detections.

  1. What makes a pattern? Matching decoding methods to data in multivariate pattern analysis

    Philip A Kragel

    2012-11-01

    Full Text Available Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. A promising technique for harnessing information at a range of spatial scales is multivariate pattern analysis (MVPA of functional magnetic resonance imaging (fMRI data. While the prevalence of MVPA has increased dramatically in recent years, its typical implementations for classification of mental states utilize only a subset of the information encoded in local fMRI signals. We review published studies employing multivariate pattern classification since the technique’s introduction, which reveal an extensive focus on the improved detection power that linear classifiers provide over traditional analysis techniques. We demonstrate using simulations and a searchlight approach, however, that nonlinear classifiers are capable of extracting distinct information about interactions within a local region. We conclude that for spatially localized analyses, such as searchlight and region of interest, multiple classification approaches should be compared in order to match fMRI analyses to the properties of local circuits.

  2. Multivoxel Pattern Analysis for fMRI Data: A Review

    Abdelhak Mahmoudi

    2012-01-01

    Full Text Available Functional magnetic resonance imaging (fMRI exploits blood-oxygen-level-dependent (BOLD contrasts to map neural activity associated with a variety of brain functions including sensory processing, motor control, and cognitive and emotional functions. The general linear model (GLM approach is used to reveal task-related brain areas by searching for linear correlations between the fMRI time course and a reference model. One of the limitations of the GLM approach is the assumption that the covariance across neighbouring voxels is not informative about the cognitive function under examination. Multivoxel pattern analysis (MVPA represents a promising technique that is currently exploited to investigate the information contained in distributed patterns of neural activity to infer the functional role of brain areas and networks. MVPA is considered as a supervised classification problem where a classifier attempts to capture the relationships between spatial pattern of fMRI activity and experimental conditions. In this paper , we review MVPA and describe the mathematical basis of the classification algorithms used for decoding fMRI signals, such as support vector machines (SVMs. In addition, we describe the workflow of processing steps required for MVPA such as feature selection, dimensionality reduction, cross-validation, and classifier performance estimation based on receiver operating characteristic (ROC curves.

  3. Multivoxel Pattern Analysis for fMRI Data: A Review

    Takerkart, Sylvain; Regragui, Fakhita; Boussaoud, Driss; Brovelli, Andrea

    2012-01-01

    Functional magnetic resonance imaging (fMRI) exploits blood-oxygen-level-dependent (BOLD) contrasts to map neural activity associated with a variety of brain functions including sensory processing, motor control, and cognitive and emotional functions. The general linear model (GLM) approach is used to reveal task-related brain areas by searching for linear correlations between the fMRI time course and a reference model. One of the limitations of the GLM approach is the assumption that the covariance across neighbouring voxels is not informative about the cognitive function under examination. Multivoxel pattern analysis (MVPA) represents a promising technique that is currently exploited to investigate the information contained in distributed patterns of neural activity to infer the functional role of brain areas and networks. MVPA is considered as a supervised classification problem where a classifier attempts to capture the relationships between spatial pattern of fMRI activity and experimental conditions. In this paper , we review MVPA and describe the mathematical basis of the classification algorithms used for decoding fMRI signals, such as support vector machines (SVMs). In addition, we describe the workflow of processing steps required for MVPA such as feature selection, dimensionality reduction, cross-validation, and classifier performance estimation based on receiver operating characteristic (ROC) curves. PMID:23401720

  4. Students classified as LD and the college foreign language requirement: a quantitative analysis.

    Sparks, R L; Philips, L; Ganschow, L; Javorsky, J

    1999-01-01

    This study was conducted to determine whether students classified as learning disabled (LD) who were permitted to substitute courses for the college foreign language (FL) requirement at one university would display significant cognitive and academic achievement differences when grouped by level of discrepancy between IQ and achievement, by discrepancy between achievement according to different measures, and by level of performance on phonological-orthographic processing measures, on the Modern Language Aptitude Test (MLAT), and in FL courses. Results showed that there were no differences among students with different levels of discrepancy (i.e., 1.50 SD) on MLAT and American College Testing (ACT) scores, graduating grade point average (GPA) or college FL GPA. Results also showed that among students who scored below versus at or above the 25th percentile on phonological-orthographic processing measures, there were no differences on measures of IQ, ACT, MLAT, and GPA, as well as most measures of academic achievement. Implications for the use of the LD label to grant FL course substitutions or waivers, use of the MLAT in the diagnostic and course substitution/waiver process, and the validity and reliability of traditional criteria for the classification as LD are discussed.

  5. Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation.

    Lee, Jin San; Kim, Changsoo; Shin, Jeong-Hyeon; Cho, Hanna; Shin, Dae-Seock; Kim, Nakyoung; Kim, Hee Jin; Kim, Yeshin; Lockhart, Samuel N; Na, Duk L; Seo, Sang Won; Seong, Joon-Kyung

    2018-03-07

    To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. A total of 869 cognitively normal (CN) individuals and 473 patients with probable AD dementia who underwent high-resolution 3T brain MRI were included. We propose a machine learning-based method for measuring the similarity of an individual subject's cortical atrophy pattern with that of a representative AD patient cohort. In addition, we validated this similarity measure in two longitudinal cohorts consisting of 79 patients with amnestic-mild cognitive impairment (aMCI) and 27 patients with probable AD dementia. Surface-based morphometry classifier for discriminating AD from CN showed sensitivity and specificity values of 87.1% and 93.3%, respectively. In the longitudinal validation study, aMCI-converts had higher atrophy similarity at both baseline (p < 0.001) and first year visits (p < 0.001) relative to non-converters. Similarly, AD patients with faster decline had higher atrophy similarity than slower decliners at baseline (p = 0.042), first year (p = 0.028), and third year visits (p = 0.027). The AD-specific atrophy similarity measure is a novel approach for the prediction of dementia risk and for the evaluation of AD trajectories on an individual subject level.

  6. A DNA-based pattern classifier with in vitro learning and associative recall for genomic characterization and biosensing without explicit sequence knowledge.

    Lee, Ju Seok; Chen, Junghuei; Deaton, Russell; Kim, Jin-Woo

    2014-01-01

    Genetic material extracted from in situ microbial communities has high promise as an indicator of biological system status. However, the challenge is to access genomic information from all organisms at the population or community scale to monitor the biosystem's state. Hence, there is a need for a better diagnostic tool that provides a holistic view of a biosystem's genomic status. Here, we introduce an in vitro methodology for genomic pattern classification of biological samples that taps large amounts of genetic information from all genes present and uses that information to detect changes in genomic patterns and classify them. We developed a biosensing protocol, termed Biological Memory, that has in vitro computational capabilities to "learn" and "store" genomic sequence information directly from genomic samples without knowledge of their explicit sequences, and that discovers differences in vitro between previously unknown inputs and learned memory molecules. The Memory protocol was designed and optimized based upon (1) common in vitro recombinant DNA operations using 20-base random probes, including polymerization, nuclease digestion, and magnetic bead separation, to capture a snapshot of the genomic state of a biological sample as a DNA memory and (2) the thermal stability of DNA duplexes between new input and the memory to detect similarities and differences. For efficient read out, a microarray was used as an output method. When the microarray-based Memory protocol was implemented to test its capability and sensitivity using genomic DNA from two model bacterial strains, i.e., Escherichia coli K12 and Bacillus subtilis, results indicate that the Memory protocol can "learn" input DNA, "recall" similar DNA, differentiate between dissimilar DNA, and detect relatively small concentration differences in samples. This study demonstrated not only the in vitro information processing capabilities of DNA, but also its promise as a genomic pattern classifier that could

  7. Using quantitative image analysis to classify axillary lymph nodes on breast MRI: A new application for the Z 0011 Era

    Schacht, David V., E-mail: dschacht@radiology.bsd.uchicago.edu; Drukker, Karen, E-mail: kdrukker@uchicago.edu; Pak, Iris, E-mail: irisgpak@gmail.com; Abe, Hiroyuki, E-mail: habe@radiology.bsd.uchicago.edu; Giger, Maryellen L., E-mail: m-giger@uchicago.edu

    2015-03-15

    Highlights: •Quantitative image analysis showed promise in evaluating axillary lymph nodes. •13 of 28 features performed better than guessing at metastatic status. •When all features were used in together, a considerably higher AUC was obtained. -- Abstract: Purpose: To assess the performance of computer extracted feature analysis of dynamic contrast enhanced (DCE) magnetic resonance images (MRI) of axillary lymph nodes. To determine which quantitative features best predict nodal metastasis. Methods: This institutional board-approved HIPAA compliant study, in which informed patient consent was waived, collected enhanced T1 images of the axilla from patients with breast cancer. Lesion segmentation and feature analysis were performed on 192 nodes using a laboratory-developed quantitative image analysis (QIA) workstation. The importance of 28 features were assessed. Classification used the features as input to a neural net classifier in a leave-one-case-out cross-validation and evaluated with receiver operating characteristic (ROC) analysis. Results: The area under the ROC curve (AUC) values for features in the task of distinguishing between positive and negative nodes ranged from just over 0.50 to 0.70. Five features yielded AUCs greater than 0.65: two morphological and three textural features. In cross-validation, the neural net classifier obtained an AUC of 0.88 (SE 0.03) for the task of distinguishing between positive and negative nodes. Conclusion: QIA of DCE MRI demonstrated promising performance in discriminating between positive and negative axillary nodes.

  8. Pattern recognition methods for acoustic emission analysis

    Doctor, P.G.; Harrington, T.P.; Hutton, P.H.

    1979-07-01

    Models have been developed that relate the rate of acoustic emissions to structural integrity. The implementation of these techniques in the field has been hindered by the noisy environment in which the data must be taken. Acoustic emissions from noncritical sources are recorded in addition to those produced by critical sources, such as flaws. A technique is discussed for prescreening acoustic events and filtering out those that are produced by noncritical sources. The methodology that was investigated is pattern recognition. Three different pattern recognition techniques were applied to a data set that consisted of acoustic emissions caused by crack growth and acoustic signals caused by extraneous noise sources. Examination of the acoustic emission data presented has uncovered several features of the data that can provide a reasonable filter. Two of the most valuable features are the frequency of maximum response and the autocorrelation coefficient at Lag 13. When these two features and several others were combined with a least squares decision algorithm, 90% of the acoustic emissions in the data set were correctly classified. It appears possible to design filters that eliminate extraneous noise sources from flaw-growth acoustic emissions using pattern recognition techniques

  9. Classifying Classifications

    Debus, Michael S.

    2017-01-01

    This paper critically analyzes seventeen game classifications. The classifications were chosen on the basis of diversity, ranging from pre-digital classification (e.g. Murray 1952), over game studies classifications (e.g. Elverdam & Aarseth 2007) to classifications of drinking games (e.g. LaBrie et...... al. 2013). The analysis aims at three goals: The classifications’ internal consistency, the abstraction of classification criteria and the identification of differences in classification across fields and/or time. Especially the abstraction of classification criteria can be used in future endeavors...... into the topic of game classifications....

  10. Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier

    Gang Li

    2013-12-01

    Full Text Available Driving while fatigued is just as dangerous as drunk driving and may result in car accidents. Heart rate variability (HRV analysis has been studied recently for the detection of driver drowsiness. However, the detection reliability has been lower than anticipated, because the HRV signals of drivers were always regarded as stationary signals. The wavelet transform method is a method for analyzing non-stationary signals. The aim of this study is to classify alert and drowsy driving events using the wavelet transform of HRV signals over short time periods and to compare the classification performance of this method with the conventional method that uses fast Fourier transform (FFT-based features. Based on the standard shortest duration for FFT-based short-term HRV evaluation, the wavelet decomposition is performed on 2-min HRV samples, as well as 1-min and 3-min samples for reference purposes. A receiver operation curve (ROC analysis and a support vector machine (SVM classifier are used for feature selection and classification, respectively. The ROC analysis results show that the wavelet-based method performs better than the FFT-based method regardless of the duration of the HRV sample that is used. Finally, based on the real-time requirements for driver drowsiness detection, the SVM classifier is trained using eighty FFT and wavelet-based features that are extracted from 1-min HRV signals from four subjects. The averaged leave-one-out (LOO classification performance using wavelet-based feature is 95% accuracy, 95% sensitivity, and 95% specificity. This is better than the FFT-based results that have 68.8% accuracy, 62.5% sensitivity, and 75% specificity. In addition, the proposed hardware platform is inexpensive and easy-to-use.

  11. An analysis of correlation between occlusion classification and skeletal pattern

    Lu Xinhua; Cai Bin; Wang Dawei; Wu Liping

    2003-01-01

    Objective: To study the correlation between dental relationship and skeletal pattern of individuals. Methods: 194 cases were selected and classified by angle classification, incisor relationship and skeletal pattern respectively. The correlation of angle classification and incisor relationship to skeletal pattern was analyzed with SPSS 10.0. Results: The values of correlation index (Kappa) were 0.379 and 0.494 respectively. Conclusion: The incisor relationship is more consistent with skeletal pattern than angle classification

  12. Costochondral ossification pattern. Analysis by 3-dimensional CT image

    Ma, Hailong; Nakatani, Kimiko

    2005-01-01

    We reviewed about an ossification pattern of costal cartilage with using three dimensional images made from computed tomography. We analyzed ossification of 16 costal cartilages in each case. We classified ossification pattern into eight groups by its configuration in one hundred cases. The sexual specificity of ossification pattern was revealed, and we can determinate sex in 82%. It was also revealed that ossification grows with increasing age. Finally, the knowledge of costochondral ossification pattern must help in case of reading chest radiographs. (author)

  13. Temporal fringe pattern analysis with parallel computing

    Tuck Wah Ng; Kar Tien Ang; Argentini, Gianluca

    2005-01-01

    Temporal fringe pattern analysis is invaluable in transient phenomena studies but necessitates long processing times. Here we describe a parallel computing strategy based on the single-program multiple-data model and hyperthreading processor technology to reduce the execution time. In a two-node cluster workstation configuration we found that execution periods were reduced by 1.6 times when four virtual processors were used. To allow even lower execution times with an increasing number of processors, the time allocated for data transfer, data read, and waiting should be minimized. Parallel computing is found here to present a feasible approach to reduce execution times in temporal fringe pattern analysis

  14. Automatic progressive damage detection of rotor bar in induction motor using vibration analysis and multiple classifiers

    Cruz-Vega, Israel; Rangel-Magdaleno, Jose; Ramirez-Cortes, Juan; Peregrina-Barreto, Hayde

    2017-01-01

    There is an increased interest in developing reliable condition monitoring and fault diagnosis systems of machines like induction motors; such interest is not only in the final phase of the failure but also at early stages. In this paper, several levels of damage of rotor bars under different load conditions are identified by means of vibration signals. The importance of this work relies on a simple but effective automatic detection algorithm of the damage before a break occurs. The feature extraction is based on discrete wavelet analysis and auto- correlation process. Then, the automatic classification of the fault degree is carried out by a binary classification tree. In each node, com- paring the learned levels of the breaking off correctly identifies the fault degree. The best results of classification are obtained employing computational intelligence techniques like support vector machines, multilayer perceptron, and the k-NN algorithm, with a proper selection of their optimal parameters.

  15. Automatic progressive damage detection of rotor bar in induction motor using vibration analysis and multiple classifiers

    Cruz-Vega, Israel; Rangel-Magdaleno, Jose; Ramirez-Cortes, Juan; Peregrina-Barreto, Hayde [Santa María Tonantzintla, Puebla (Mexico)

    2017-06-15

    There is an increased interest in developing reliable condition monitoring and fault diagnosis systems of machines like induction motors; such interest is not only in the final phase of the failure but also at early stages. In this paper, several levels of damage of rotor bars under different load conditions are identified by means of vibration signals. The importance of this work relies on a simple but effective automatic detection algorithm of the damage before a break occurs. The feature extraction is based on discrete wavelet analysis and auto- correlation process. Then, the automatic classification of the fault degree is carried out by a binary classification tree. In each node, com- paring the learned levels of the breaking off correctly identifies the fault degree. The best results of classification are obtained employing computational intelligence techniques like support vector machines, multilayer perceptron, and the k-NN algorithm, with a proper selection of their optimal parameters.

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

    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

  17. Quantitative texture analysis of electrodeposited line patterns

    Pantleon, Karen; Somers, Marcel A.J.

    2005-01-01

    Free-standing line patterns of Cu and Ni were manufactured by electrochemical deposition into lithographically prepared patterns. Electrodeposition was carried out on top of a highly oriented Au-layer physically vapor deposited on glass. Quantitative texture analysis carried out by means of x......-ray diffraction for both the substrate layer and the electrodeposits yielded experimental evidence for epitaxy between Cu and Au. An orientation relation between film and substrate was discussed with respect to various concepts of epitaxy. While the conventional mode of epitaxy fails for the Cu...

  18. RECOG-ORNL, Pattern Recognition Data Analysis

    Begovich, C.L.; Larson, N.M.

    2000-01-01

    Description of program or function: RECOG-ORNL, a general-purpose pattern recognition code, is a modification of the RECOG program, written at Lawrence Livermore National Laboratory. RECOG-ORNL contains techniques for preprocessing, analyzing, and displaying data, and for unsupervised and supervised learning. Data preprocessing routines transform the data into useful representations by auto-calling, selecting important variables, and/or adding products or transformations of the variables of the data set. Data analysis routines use correlations to evaluate the data and interrelationships among the data. Display routines plot the multidimensional patterns in two dimensions or plot histograms, patterns, or one variable versus another. Unsupervised learning techniques search for classes contained inherently in the data. Supervised learning techniques use known information about some of the data to generate predicted properties for an unknown set

  19. Spiking Neural Classifier with Lumped Dendritic Nonlinearity and Binary Synapses: A Current Mode VLSI Implementation and Analysis.

    Bhaduri, Aritra; Banerjee, Amitava; Roy, Subhrajit; Kar, Sougata; Basu, Arindam

    2018-03-01

    We present a neuromorphic current mode implementation of a spiking neural classifier with lumped square law dendritic nonlinearity. It has been shown previously in software simulations that such a system with binary synapses can be trained with structural plasticity algorithms to achieve comparable classification accuracy with fewer synaptic resources than conventional algorithms. We show that even in real analog systems with manufacturing imperfections (CV of 23.5% and 14.4% for dendritic branch gains and leaks respectively), this network is able to produce comparable results with fewer synaptic resources. The chip fabricated in [Formula: see text]m complementary metal oxide semiconductor has eight dendrites per cell and uses two opposing cells per class to cancel common-mode inputs. The chip can operate down to a [Formula: see text] V and dissipates 19 nW of static power per neuronal cell and [Formula: see text] 125 pJ/spike. For two-class classification problems of high-dimensional rate encoded binary patterns, the hardware achieves comparable performance as software implementation of the same with only about a 0.5% reduction in accuracy. On two UCI data sets, the IC integrated circuit has classification accuracy comparable to standard machine learners like support vector machines and extreme learning machines while using two to five times binary synapses. We also show that the system can operate on mean rate encoded spike patterns, as well as short bursts of spikes. To the best of our knowledge, this is the first attempt in hardware to perform classification exploiting dendritic properties and binary synapses.

  20. Hazards study of environmental protection classified facilities. Scenarios analysis; Etude de dangers des ICPE. Analyse des scenarios

    Seveque, J.L. [Cour d' Appel d' Amiens, 80 (France)

    2006-04-15

    This article describes the analysis and study of the possible impacts of accidents occurring at industrial facilities classified with respect to the environment protection. The operators of such facilities have to describe the possible risks and their consequences, the measures taken to prevent them and the level of residual risk. Therefore, it consists in calculating the consequences of all possible aggressions that a facility can undergo. The receptors are of 2 type: the human body (burns, asphyxia, intoxication, shock wave, projectile), and the surrounding equipments (fire, unconfined vapour cloud explosion (UVCE), boiling liquid expanding vapour explosion (BLEVE), fireball, dispersion of toxic gases). Content: 1 - fire-type scenario: description, modeling of thermal effects, conclusion; 2 - UVCE-type scenario: description, Lannoy method (TNT equivalent), multi-energy method, conclusion; 3 - BLEVE-type scenario: description, modeling of overpressure effects, thermal effects of the fireball; 4 - toxic cloud scenario: modeling of a toxic cloud dispersion, effects and consequences; 5 - conclusions. (J.S.)

  1. Gravity models to classify commuting vs. resident workers. An application to the analysis of residential risk in a contaminated area

    2011-01-01

    Background The analysis of risk for the population residing and/or working in contaminated areas raises the topic of commuting. In fact, especially in contaminated areas, commuting groups are likely to be subject to lower exposure than residents. Only very recently environmental epidemiology has started considering the role of commuting as a differential source of exposure in contaminated areas. In order to improve the categorization of groups, this paper applies a gravitational model to the analysis of residential risk for workers in the Gela petrochemical complex, which began life in the early 60s in the municipality of Gela (Sicily, Italy) and is the main source of industrial pollution in the local area. Results A logistic regression model is implemented to measure the capacity of Gela "central location" to attract commuting flows from other sites. Drawing from gravity models, the proposed methodology: a) defines the probability of finding commuters from municipalities outside Gela as a function of the origin's "economic mass" and of its distance from each destination; b) establishes "commuting thresholds" relative to the origin's mass. The analysis includes 367 out of the 390 Sicilian municipalities. Results are applied to define "commuters" and "residents" within the cohort of petrochemical workers. The study population is composed of 5,627 workers. Different categories of residence in Gela are compared calculating Mortality Rate Ratios for lung cancer through a Poisson regression model, controlling for age and calendar period. The mobility model correctly classifies almost 90% of observations. Its application to the mortality analysis confirms a major risk for lung cancer associated with residence in Gela. Conclusions Commuting is a critical aspect of the health-environment relationship in contaminated areas. The proposed methodology can be replicated to different contexts when residential information is lacking or unreliable; however, a careful consideration

  2. Gravity models to classify commuting vs. resident workers. An application to the analysis of residential risk in a contaminated area

    La Rocca Marina

    2011-01-01

    Full Text Available Abstract Background The analysis of risk for the population residing and/or working in contaminated areas raises the topic of commuting. In fact, especially in contaminated areas, commuting groups are likely to be subject to lower exposure than residents. Only very recently environmental epidemiology has started considering the role of commuting as a differential source of exposure in contaminated areas. In order to improve the categorization of groups, this paper applies a gravitational model to the analysis of residential risk for workers in the Gela petrochemical complex, which began life in the early 60s in the municipality of Gela (Sicily, Italy and is the main source of industrial pollution in the local area. Results A logistic regression model is implemented to measure the capacity of Gela "central location" to attract commuting flows from other sites. Drawing from gravity models, the proposed methodology: a defines the probability of finding commuters from municipalities outside Gela as a function of the origin's "economic mass" and of its distance from each destination; b establishes "commuting thresholds" relative to the origin's mass. The analysis includes 367 out of the 390 Sicilian municipalities. Results are applied to define "commuters" and "residents" within the cohort of petrochemical workers. The study population is composed of 5,627 workers. Different categories of residence in Gela are compared calculating Mortality Rate Ratios for lung cancer through a Poisson regression model, controlling for age and calendar period. The mobility model correctly classifies almost 90% of observations. Its application to the mortality analysis confirms a major risk for lung cancer associated with residence in Gela. Conclusions Commuting is a critical aspect of the health-environment relationship in contaminated areas. The proposed methodology can be replicated to different contexts when residential information is lacking or unreliable

  3. Laban Movement Analysis towards Behavior Patterns

    Santos, Luís; Dias, Jorge

    This work presents a study about the use of Laban Movement Analysis (LMA) as a robust tool to describe human basic behavior patterns, to be applied in human-machine interaction. LMA is a language used to describe and annotate dancing movements and is divided in components [1]: Body, Space, Shape and Effort. Despite its general framework is widely used in physical and mental therapy [2], it has found little application in the engineering domain. Rett J. [3] proposed to implement LMA using Bayesian Networks. However LMA component models have not yet been fully implemented. A study on how to approach behavior using LMA is presented. Behavior is a complex feature and movement chain, but we believe that most basic behavior primitives can be discretized in simple features. Correctly identifying Laban parameters and the movements the authors feel that good patterns can be found within a specific set of basic behavior semantics.

  4. Stack filter classifiers

    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.

  5. Movement Pattern Analysis Based on Sequence Signatures

    Seyed Hossein Chavoshi

    2015-09-01

    Full Text Available Increased affordability and deployment of advanced tracking technologies have led researchers from various domains to analyze the resulting spatio-temporal movement data sets for the purpose of knowledge discovery. Two different approaches can be considered in the analysis of moving objects: quantitative analysis and qualitative analysis. This research focuses on the latter and uses the qualitative trajectory calculus (QTC, a type of calculus that represents qualitative data on moving point objects (MPOs, and establishes a framework to analyze the relative movement of multiple MPOs. A visualization technique called sequence signature (SESI is used, which enables to map QTC patterns in a 2D indexed rasterized space in order to evaluate the similarity of relative movement patterns of multiple MPOs. The applicability of the proposed methodology is illustrated by means of two practical examples of interacting MPOs: cars on a highway and body parts of a samba dancer. The results show that the proposed method can be effectively used to analyze interactions of multiple MPOs in different domains.

  6. Similarity-based pattern analysis and recognition

    Pelillo, Marcello

    2013-01-01

    This accessible text/reference presents a coherent overview of the emerging field of non-Euclidean similarity learning. The book presents a broad range of perspectives on similarity-based pattern analysis and recognition methods, from purely theoretical challenges to practical, real-world applications. The coverage includes both supervised and unsupervised learning paradigms, as well as generative and discriminative models. Topics and features: explores the origination and causes of non-Euclidean (dis)similarity measures, and how they influence the performance of traditional classification alg

  7. Analysis of Long Bone and Vertebral Failure Patterns

    1985-02-14

    and alter the injury pattern. Classified on an anatomical, kinesiologic , £s and pathologic basis, the vertebral body fracture patterns may...814. Boyde, A. (1972) Scanning electron microscope studies of bone. In Bourne, G.H. (ed): The Biochemistry and Physiology of Bone. New York...Eyring, E.J. (1969) The biochemistry and physiology of intervertebral disk. Clin. Orthop. Rel, Res. 67: 16-18. Fick, R. (1904) Handbuch der Anatomie

  8. Speciation analysis of antimony in extracts of size-classified volcanic ash by HPLC-ICP-MS.

    Miravet, R; López-Sánchez, J F; Rubio, R; Smichowski, P; Polla, G

    2007-03-01

    Although there is concern about the presence of toxic elements and their species in environmental matrices, for example water, sediment, and soil, speciation analysis of volcanic ash has received little attention. Antimony, in particular, an emerging element of environmental concern, has been less studied than other potentially toxic trace elements. In this context, a study was undertaken to assess the presence of inorganic Sb species in ash emitted from the Copahue volcano (Argentina). Antimony species were extracted from size-classified volcanic ash (<36 microm, 35-45 microm, 45-150 microm, and 150-300 microm) by use of 1 mol L(-1) citrate buffer at pH 5. Antimony(III) and (V) in the extracts were separated and quantified by high-performance liquid chromatography combined on-line with inductively coupled plasma mass spectrometry (HPLC-ICP-MS). Antimony species concentrations (microg g(-1)) in the four fractions varied from 0.14 to 0.67 for Sb(III) and from 0.02 to 0.03 for Sb(V). The results reveal, for the first time, the occurrence of both inorganic Sb species in the extractable portion of volcanic ash. Sb(III) was always the predominant species.

  9. A NEW FRAMEWORK FOR OBJECT-BASED IMAGE ANALYSIS BASED ON SEGMENTATION SCALE SPACE AND RANDOM FOREST CLASSIFIER

    A. Hadavand

    2015-12-01

    Full Text Available In this paper a new object-based framework is developed for automate scale selection in image segmentation. The quality of image objects have an important impact on further analyses. Due to the strong dependency of segmentation results to the scale parameter, choosing the best value for this parameter, for each class, becomes a main challenge in object-based image analysis. We propose a new framework which employs pixel-based land cover map to estimate the initial scale dedicated to each class. These scales are used to build segmentation scale space (SSS, a hierarchy of image objects. Optimization of SSS, respect to NDVI and DSM values in each super object is used to get the best scale in local regions of image scene. Optimized SSS segmentations are finally classified to produce the final land cover map. Very high resolution aerial image and digital surface model provided by ISPRS 2D semantic labelling dataset is used in our experiments. The result of our proposed method is comparable to those of ESP tool, a well-known method to estimate the scale of segmentation, and marginally improved the overall accuracy of classification from 79% to 80%.

  10. A morphometric analysis of vegetation patterns in dryland ecosystems

    Mander, Luke; Dekker, Stefan C.; Li, Mao; Mio, Washington; Punyasena, Surangi W.; Lenton, Timothy M.

    2017-02-01

    Vegetation in dryland ecosystems often forms remarkable spatial patterns. These range from regular bands of vegetation alternating with bare ground, to vegetated spots and labyrinths, to regular gaps of bare ground within an otherwise continuous expanse of vegetation. It has been suggested that spotted vegetation patterns could indicate that collapse into a bare ground state is imminent, and the morphology of spatial vegetation patterns, therefore, represents a potentially valuable source of information on the proximity of regime shifts in dryland ecosystems. In this paper, we have developed quantitative methods to characterize the morphology of spatial patterns in dryland vegetation. Our approach is based on algorithmic techniques that have been used to classify pollen grains on the basis of textural patterning, and involves constructing feature vectors to quantify the shapes formed by vegetation patterns. We have analysed images of patterned vegetation produced by a computational model and a small set of satellite images from South Kordofan (South Sudan), which illustrates that our methods are applicable to both simulated and real-world data. Our approach provides a means of quantifying patterns that are frequently described using qualitative terminology, and could be used to classify vegetation patterns in large-scale satellite surveys of dryland ecosystems.

  11. Recurrence analysis of ant activity patterns.

    Felipe Marcel Neves

    Full Text Available In this study, we used recurrence quantification analysis (RQA and recurrence plots (RPs to compare the movement activity of individual workers of three ant species, as well as a gregarious beetle species. RQA and RPs quantify the number and duration of recurrences of a dynamical system, including a detailed quantification of signals that could be stochastic, deterministic, or both. First, we found substantial differences between the activity dynamics of beetles and ants, with the results suggesting that the beetles have quasi-periodic dynamics and the ants do not. Second, workers from different ant species varied with respect to their dynamics, presenting degrees of predictability as well as stochastic signals. Finally, differences were found among minor and major caste of the same (dimorphic ant species. Our results underscore the potential of RQA and RPs in the analysis of complex behavioral patterns, as well as in general inferences on animal behavior and other biological phenomena.

  12. Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study.

    Ortiz-Ramón, Rafael; Larroza, Andrés; Ruiz-España, Silvia; Arana, Estanislao; Moratal, David

    2018-05-14

    To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach. Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one. In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 ± 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 ± 0.054) and melanoma BM (eight features, AUC = 0.936 ± 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 ± 0.180). Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels. • Texture analysis is a promising source of biomarkers for classifying brain neoplasms. • MRI texture features of brain metastases could help identifying the primary cancer. • Volumetric texture features are more discriminative than traditional 2D texture features.

  13. Melodic pattern discovery by structural analysis via wavelets and clustering techniques

    Velarde, Gissel; Meredith, David

    We present an automatic method to support melodic pattern discovery by structural analysis of symbolic representations by means of wavelet analysis and clustering techniques. In previous work, we used the method to recognize the parent works of melodic segments, or to classify tunes into tune......-means to cluster melodic segments into groups of measured similarity and obtain a raking of the most prototypical melodic segments or patterns and their occurrences. We test the method on the JKU Patterns Development Database and evaluate it based on the ground truth defined by the MIREX 2013 Discovery of Repeated...... Themes & Sections task. We compare the results of our method to the output of geometric approaches. Finally, we discuss about the relevance of our wavelet-based analysis in relation to structure, pattern discovery, similarity and variation, and comment about the considerations of the method when used...

  14. Cross-View Neuroimage Pattern Analysis for Alzheimer's Disease Staging

    Sidong eLiu

    2016-02-01

    Full Text Available The research on staging of pre-symptomatic and prodromal phase of neurological disorders, e.g., Alzheimer's disease (AD, is essential for prevention of dementia. New strategies for AD staging with a focus on early detection, are demanded to optimize potential efficacy of disease-modifying therapies that can halt or slow the disease progression. Recently, neuroimaging are increasingly used as additional research-based markers to detect AD onset and predict conversion of MCI and normal control (NC to AD. Researchers have proposed a variety of neuroimaging biomarkers to characterize the patterns of the pathology of AD and MCI, and suggested that multi-view neuroimaging biomarkers could lead to better performance than single-view biomarkers in AD staging. However, it is still unclear what leads to such synergy and how to preserve or maximize. In an attempt to answer these questions, we proposed a cross-view pattern analysis framework for investigating the synergy between different neuroimaging biomarkers. We quantitatively analyzed 9 types of biomarkers derived from FDG-PET and T1-MRI, and evaluated their performance in a task of classifying AD, MCI and NC subjects obtained from the ADNI baseline cohort. The experiment results showed that these biomarkers could depict the pathology of AD from different perspectives, and output distinct patterns that are significantly associated with the disease progression. Most importantly, we found that these features could be separated into clusters, each depicting a particular aspect; and the inter-cluster features could always achieve better performance than the intra-cluster features in AD staging.

  15. Analysis of error patterns in clinical radiotherapy

    Macklis, Roger; Meier, Tim; Barrett, Patricia; Weinhous, Martin

    1996-01-01

    Purpose: Until very recently, prescription errors and adverse treatment events have rarely been studied or reported systematically in oncology. We wished to understand the spectrum and severity of radiotherapy errors that take place on a day-to-day basis in a high-volume academic practice and to understand the resource needs and quality assurance challenges placed on a department by rapid upswings in contract-based clinical volumes requiring additional operating hours, procedures, and personnel. The goal was to define clinical benchmarks for operating safety and to detect error-prone treatment processes that might function as 'early warning' signs. Methods: A multi-tiered prospective and retrospective system for clinical error detection and classification was developed, with formal analysis of the antecedents and consequences of all deviations from prescribed treatment delivery, no matter how trivial. A department-wide record-and-verify system was operational during this period and was used as one method of treatment verification and error detection. Brachytherapy discrepancies were analyzed separately. Results: During the analysis year, over 2000 patients were treated with over 93,000 individual fields. A total of 59 errors affecting a total of 170 individual treated fields were reported or detected during this period. After review, all of these errors were classified as Level 1 (minor discrepancy with essentially no potential for negative clinical implications). This total treatment delivery error rate (170/93, 332 or 0.18%) is significantly better than corresponding error rates reported for other hospital and oncology treatment services, perhaps reflecting the relatively sophisticated error avoidance and detection procedures used in modern clinical radiation oncology. Error rates were independent of linac model and manufacturer, time of day (normal operating hours versus late evening or early morning) or clinical machine volumes. There was some relationship to

  16. Gradient pattern analysis applied to galaxy morphology

    Rosa, R. R.; de Carvalho, R. R.; Sautter, R. A.; Barchi, P. H.; Stalder, D. H.; Moura, T. C.; Rembold, S. B.; Morell, D. R. F.; Ferreira, N. C.

    2018-06-01

    Gradient pattern analysis (GPA) is a well-established technique for measuring gradient bilateral asymmetries of a square numerical lattice. This paper introduces an improved version of GPA designed for galaxy morphometry. We show the performance of the new method on a selected sample of 54 896 objects from the SDSS-DR7 in common with Galaxy Zoo 1 catalogue. The results suggest that the second gradient moment, G2, has the potential to dramatically improve over more conventional morphometric parameters. It separates early- from late-type galaxies better (˜ 90 per cent) than the CAS system (C˜ 79 per cent, A˜ 50 per cent, S˜ 43 per cent) and a benchmark test shows that it is applicable to hundreds of thousands of galaxies using typical processing systems.

  17. System of pattern analysis of PIXE spectra

    Murozono, K; Iwasaki, S; Inoue, J; Ishii, K; Kitamura, M [Tohoku Univ., Sendai (Japan). Faculty of Engineering; Sera, K; Futatsugawa, S

    1996-07-01

    We have developed an analysis system based on the pattern analysis method. By testing the system, several difficulties of the present method have been identified. We found the following solutions for them: pre-selection of candidate elements in a sample and the use of a proper absorber. The pre-selection of the candidate elements will not be a serious drawback in the industrial PIXE, because it will be easy to pre-process the spectra for a few samples in the beginning of the mass processing of samples of the same kind. On the other hand, reduction of the efficiency due to the use of funny filter is significant only in the lower energy region, where we usually do not suffer from insufficient yields of lighter elements in common samples. The selection of the most suitable filter requires PIXE user to be deeply experienced. In particular, it is not easy to choose the best filter to suppress the yield of peak of an abundant element as the absorption edge filter. It will be important task to find a set of suitable combination of representative samples and corresponding filters. Furthermore, the peak profile model should be improved from the simple Gaussian approximation to more realistic ones with exponential tail, flat component below the peak and escape peaks, etc. It is also necessary to develop a theoretical approach for the background shape of the bremsstrahlung. (J.P.N.)

  18. Geospatial Analysis of Grey Wolf Movement Patterns

    Sur, D.

    2017-12-01

    The grey wolf is a top predator that lives across a diverse habitat, ranging from Europe to North America. They often hunt in packs, preferring caribou, deer and elk as prey. Currently, many gray wolves live in Denali National Park and Preserve. In this study, several wolf packs were studied in three distinct regions of Denali. The purpose of my research was to investigate the links between wolf habitat, movement patterns, and prey thresholds. These are needed for projecting future population, growth and distribution of wolves in the studied region. I also investigated the effect wolves have on the ecological structure of the communities they inhabit. In the study I carried out a quantitative analysis of wolf population trends and daily distance movement by utilizing an analysis of variance (ANOVA) in the program JmpPro12 (SAS Institute, Crary, NC) to assess regional differences in pack size, wolf density, average daily distance moved. I found a clear link between the wolf habitat and prey thresholds; the habitat directly influences the types of prey available. However there was no link between the daily distance movement, the wolf habitat and prey density.

  19. An ensemble of dissimilarity based classifiers for Mackerel gender determination

    Blanco, A; Rodriguez, R; Martinez-Maranon, I

    2014-01-01

    Mackerel is an infravalored fish captured by European fishing vessels. A manner to add value to this specie can be achieved by trying to classify it attending to its sex. Colour measurements were performed on Mackerel females and males (fresh and defrozen) extracted gonads to obtain differences between sexes. Several linear and non linear classifiers such as Support Vector Machines (SVM), k Nearest Neighbors (k-NN) or Diagonal Linear Discriminant Analysis (DLDA) can been applied to this problem. However, theyare usually based on Euclidean distances that fail to reflect accurately the sample proximities. Classifiers based on non-Euclidean dissimilarities misclassify a different set of patterns. We combine different kind of dissimilarity based classifiers. The diversity is induced considering a set of complementary dissimilarities for each model. The experimental results suggest that our algorithm helps to improve classifiers based on a single dissimilarity

  20. An ensemble of dissimilarity based classifiers for Mackerel gender determination

    Blanco, A.; Rodriguez, R.; Martinez-Maranon, I.

    2014-03-01

    Mackerel is an infravalored fish captured by European fishing vessels. A manner to add value to this specie can be achieved by trying to classify it attending to its sex. Colour measurements were performed on Mackerel females and males (fresh and defrozen) extracted gonads to obtain differences between sexes. Several linear and non linear classifiers such as Support Vector Machines (SVM), k Nearest Neighbors (k-NN) or Diagonal Linear Discriminant Analysis (DLDA) can been applied to this problem. However, theyare usually based on Euclidean distances that fail to reflect accurately the sample proximities. Classifiers based on non-Euclidean dissimilarities misclassify a different set of patterns. We combine different kind of dissimilarity based classifiers. The diversity is induced considering a set of complementary dissimilarities for each model. The experimental results suggest that our algorithm helps to improve classifiers based on a single dissimilarity.

  1. Pattern Recognition-Based Analysis of COPD in CT

    Sørensen, Lauge Emil Borch Laurs

    recognition part is used to turn the texture measures, measured in a CT image of the lungs, into a quantitative measure of disease. This is done by applying a classifier that is trained on a training set of data examples with known lung tissue patterns. Different classification systems are considered, and we...... will in particular use the pattern recognition concepts of supervised learning, multiple instance learning, and dissimilarity representation-based classification. The proposed texture-based measures are applied to CT data from two different sources, one comprising low dose CT slices from subjects with manually...... annotated regions of emphysema and healthy tissue, and one comprising volumetric low dose CT images from subjects that are either healthy or suffer from COPD. Several experiments demonstrate that it is clearly beneficial to take the lung tissue texture into account when classifying or quantifying emphysema...

  2. A study of grout flow pattern analysis

    Lee, S. Y.; Hyun, S.

    2013-01-01

    A new disposal unit, designated as Salt Disposal Unit no. 6 (SDU6), is being designed for support of site accelerated closure goals and salt nuclear waste projections identified in the new Liquid Waste System plan. The unit is cylindrical disposal vault of 380 ft diameter and 43 ft in height, and it has about 30 million gallons of capacity. Primary objective was to develop the computational model and to perform the evaluations for the flow patterns of grout material in SDU6 as function of elevation of grout discharge port, and slurry rheology. A Bingham plastic model was basically used to represent the grout flow behavior. A two-phase modeling approach was taken to achieve the objective. This approach assumes that the air-grout interface determines the shape of the accumulation mound. The results of this study were used to develop the design guidelines for the discharge ports of the Saltstone feed materials in the SDU6 facility. The focusing areas of the modeling study are to estimate the domain size of the grout materials radially spread on the facility floor under the baseline modeling conditions, to perform the sensitivity analysis with respect to the baseline design and operating conditions such as elevation of discharge port, discharge pipe diameter, and grout properties, and to determine the changes in grout density as it is related to grout drop height. An axi-symmetric two-phase modeling method was used for computational efficiency. Based on the nominal design and operating conditions, a transient computational approach was taken to compute flow fields mainly driven by pumping inertia and natural gravity. Detailed solution methodology and analysis results are discussed here

  3. Diagnostics of synchronous motor based on analysis of acoustic signals with application of MFCC and Nearest Mean classifier

    Adam Głowacz; Witold Głowacz; Andrzej Głowacz

    2010-01-01

    The paper presents method of diagnostics of imminent failure conditions of synchronous motor. This method is based on a study ofacoustic signals generated by synchronous motor. Sound recognition system is based on algorithms of data processing, such as MFCC andNearest Mean classifier with cosine distance. Software to recognize the sounds of synchronous motor was implemented. The studies werecarried out for four imminent failure conditions of synchronous motor. The results confirm that the sys...

  4. Analysis of the Variability of Classified and Unclassified Radiological Source term Inventories in the Frenchman Flat Area, Nevada test Site

    Zhao, P.; Zavarin, M.

    2008-01-01

    It has been proposed that unclassified source terms used in the reactive transport modeling investigations at NTS CAUs should be based on yield-weighted source terms calculated using the average source term from Bowen et al. (2001) and the unclassified announced yields reported in DOE/NV-209. This unclassified inventory is likely to be used in unclassified contaminant boundary calculations and is, thus, relevant to compare to the classified inventory. They have examined the classified radionuclide inventory produced by 10 underground nuclear tests conducted in the Frenchman Flat (FF) area of the Nevada Test Site. The goals were to (1) evaluate the variability in classified radiological source terms among the 10 tests and (2) compare that variability and inventory uncertainties to an average unclassified inventory (e.g. Bowen 2001). To evaluate source term variability among the 10 tests, radiological inventories were compared on two relative scales: geometric mean and yield-weighted geometric mean. Furthermore, radiological inventories were either decay corrected to a common date (9/23/1992) or the time zero (t 0 ) of each test. Thus, a total of four data sets were produced. The date of 9/23/1992 was chosen based on the date of the last underground nuclear test at the Nevada Test Site

  5. Relevance Vector Machine and Support Vector Machine Classifier Analysis of Scanning Laser Polarimetry Retinal Nerve Fiber Layer Measurements

    Bowd, Christopher; Medeiros, Felipe A.; Zhang, Zuohua; Zangwill, Linda M.; Hao, Jiucang; Lee, Te-Won; Sejnowski, Terrence J.; Weinreb, Robert N.; Goldbaum, Michael H.

    2010-01-01

    Purpose To classify healthy and glaucomatous eyes using relevance vector machine (RVM) and support vector machine (SVM) learning classifiers trained on retinal nerve fiber layer (RNFL) thickness measurements obtained by scanning laser polarimetry (SLP). Methods Seventy-two eyes of 72 healthy control subjects (average age = 64.3 ± 8.8 years, visual field mean deviation =−0.71 ± 1.2 dB) and 92 eyes of 92 patients with glaucoma (average age = 66.9 ± 8.9 years, visual field mean deviation =−5.32 ± 4.0 dB) were imaged with SLP with variable corneal compensation (GDx VCC; Laser Diagnostic Technologies, San Diego, CA). RVM and SVM learning classifiers were trained and tested on SLP-determined RNFL thickness measurements from 14 standard parameters and 64 sectors (approximately 5.6° each) obtained in the circumpapillary area under the instrument-defined measurement ellipse (total 78 parameters). Tenfold cross-validation was used to train and test RVM and SVM classifiers on unique subsets of the full 164-eye data set and areas under the receiver operating characteristic (AUROC) curve for the classification of eyes in the test set were generated. AUROC curve results from RVM and SVM were compared to those for 14 SLP software-generated global and regional RNFL thickness parameters. Also reported was the AUROC curve for the GDx VCC software-generated nerve fiber indicator (NFI). Results The AUROC curves for RVM and SVM were 0.90 and 0.91, respectively, and increased to 0.93 and 0.94 when the training sets were optimized with sequential forward and backward selection (resulting in reduced dimensional data sets). AUROC curves for optimized RVM and SVM were significantly larger than those for all individual SLP parameters. The AUROC curve for the NFI was 0.87. Conclusions Results from RVM and SVM trained on SLP RNFL thickness measurements are similar and provide accurate classification of glaucomatous and healthy eyes. RVM may be preferable to SVM, because it provides a

  6. Spiking Neurons for Analysis of Patterns

    Huntsberger, Terrance

    2008-01-01

    Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological

  7. A Comparison of Spectral Angle Mapper and Artificial Neural Network Classifiers Combined with Landsat TM Imagery Analysis for Obtaining Burnt Area Mapping

    Marko Scholze

    2010-03-01

    Full Text Available Satellite remote sensing, with its unique synoptic coverage capabilities, can provide accurate and immediately valuable information on fire analysis and post-fire assessment, including estimation of burnt areas. In this study the potential for burnt area mapping of the combined use of Artificial Neural Network (ANN and Spectral Angle Mapper (SAM classifiers with Landsat TM satellite imagery was evaluated in a Mediterranean setting. As a case study one of the most catastrophic forest fires, which occurred near the capital of Greece during the summer of 2007, was used. The accuracy of the two algorithms in delineating the burnt area from the Landsat TM imagery, acquired shortly after the fire suppression, was determined by the classification accuracy results of the produced thematic maps. In addition, the derived burnt area estimates from the two classifiers were compared with independent estimates available for the study region, obtained from the analysis of higher spatial resolution satellite data. In terms of the overall classification accuracy, ANN outperformed (overall accuracy 90.29%, Kappa coefficient 0.878 the SAM classifier (overall accuracy 83.82%, Kappa coefficient 0.795. Total burnt area estimates from the two classifiers were found also to be in close agreement with the other available estimates for the study region, with a mean absolute percentage difference of ~1% for ANN and ~6.5% for SAM. The study demonstrates the potential of the examined here algorithms in detecting burnt areas in a typical Mediterranean setting.

  8. Comparison of several chemometric methods of libraries and classifiers for the analysis of expired drugs based on Raman spectra.

    Gao, Qun; Liu, Yan; Li, Hao; Chen, Hui; Chai, Yifeng; Lu, Feng

    2014-06-01

    Some expired drugs are difficult to detect by conventional means. If they are repackaged and sold back into market, they will constitute a new public health challenge. For the detection of repackaged expired drugs within specification, paracetamol tablet from a manufacturer was used as a model drug in this study for comparison of Raman spectra-based library verification and classification methods. Raman spectra of different batches of paracetamol tablets were collected and a library including standard spectra of unexpired batches of tablets was established. The Raman spectrum of each sample was identified by cosine and correlation with the standard spectrum. The average HQI of the suspicious samples and the standard spectrum were calculated. The optimum threshold values were 0.997 and 0.998 respectively as a result of ROC and four evaluations, for which the accuracy was up to 97%. Three supervised classifiers, PLS-DA, SVM and k-NN, were chosen to establish two-class classification models and compared subsequently. They were used to establish a classification of expired batches and an unexpired batch, and predict the suspect samples. The average accuracy was 90.12%, 96.80% and 89.37% respectively. Different pre-processing techniques were tried to find that first derivative was optimal for methods of libraries and max-min normalization was optimal for that of classifiers. The results obtained from these studies indicated both libraries and classifier methods could detect the expired drugs effectively, and they should be used complementarily in the fast-screening. Copyright © 2014 Elsevier B.V. All rights reserved.

  9. Analysis of cervical kyphosis and spinal balance in young idiopathic scoliosis patients classified by the apex of thoracic kyphosis.

    Ito, Kenyu; Imagama, Shiro; Ito, Zenya; Ando, Kei; Kobayashi, Kazuyoshi; Hida, Tetsuro; Tsushima, Mikito; Ishikawa, Yoshimoto; Matsumoto, Akiyuki; Nishida, Yoshihiro; Ishiguro, Naoki

    2016-10-01

    Sagittal balance has recently been the focus of studies aimed at understanding the correction force required for both coronal and sagittal malalignment. However, the correlation between cervical kyphosis and sagittal balance in AIS patients has yet to be thoroughly investigated. This study aimed to clarify the correlation between cervical alignment and spinal balance in patients with adolescent idiopathic scoliosis (AIS). Here, we hypothesized that cervical kyphosis patients can be classified into groups by the apex of thoracic kyphosis. This study included 92 AIS patients (84 females, 8 males; mean age, 15.1 years). Patients were divided into the cervical lordosis (CL), cervical sigmoid (CS), or cervical kyphosis (CK) groups and further classified according to the apex of thoracic kyphosis into High (above T3), Middle (T4-T9), and Low (below T10) groups. There were 17 (18.5 %), 22 (23.9 %), and 53 (57.6 %) patients with CL, CS, and CK, respectively. In the CK group, 13 had CK-High, 35 had CK-Middle, and 5 had CK-Low. The C7 sagittal vertical axis (C7SVA) measurements were most backward in CK-High and most forward in CK-Low. The T5-12 kyphosis (TK) measurement was significantly lower in CK-High. Most AIS patients had kyphotic cervical alignment. Patients with CK can be classified as having CK-High, CK-Middle, or CK-Low according to the apex of thoracic kyphosis. CK-High is due to thoracic hypokyphosis with a backward balanced C7SVA. CK-Middle is well-balanced cervical kyphosis. CK-Low has forward-bent global kyphosis of the cervicothoracic spine that positioned the C7SVA forward.

  10. An optoelectronic system for fringe pattern analysis

    Sciammarella, C. A.; Ahmadshahi, M.

    A system capable of retrieving and processing information recorded in fringe patterns is reported. The principal components are described as well as the architecture in which they are assembled. An example of application is given.

  11. Software patterns, knowledge maps, and domain analysis

    Fayad, Mohamed E; Hegde, Srikanth GK; Basia, Anshu; Vakil, Ashka

    2014-01-01

    Preface AcknowledgmentsAuthors INTRODUCTIONAn Overview of Knowledge MapsIntroduction: Key Concepts-Software Stable Models, Knowledge Maps, Pattern Language, Goals, Capabilities (Enduring Business Themes + Business Objects) The Motivation The Problem The Objectives Overview of Software Stability Concepts Overview of Knowledge Maps Pattern Languages versus Knowledge Maps: A Brief ComparisonThe Solution Knowledge Maps Methodology or Concurrent Software Development ModelWhy Knowledge Maps? Research Methodology Undertaken Research Verification and Validation The Stratification of This Book Summary

  12. Unsaturated Zone Flow Patterns and Analysis

    C. Ahlers

    2001-10-17

    This Analysis/Model Report (AMR) documents the development of an expected-case model for unsaturated zone (UZ) flow and transport that will be described in terms of the representativeness of models of the natural system. The expected-case model will provide an evaluation of the effectiveness of the natural barriers, assess the impact of conservatism in the Total System Performance Assessment (TSPA), and support the development of further models and analyses for public confidence building. The present models used in ''Total System Performance Assessment for the Site Recommendation'' (Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M&O) 2000 [1532461]) underestimate the natural-barrier performance because of conservative assumptions and parameters and do not adequately address uncertainty and alternative models. The development of an expected case model for the UZ natural barrier addresses issues regarding flow-pattern analysis and modeling that had previously been treated conservatively. This is in line with the Repository Safety Strategy (RSS) philosophy of treating conservatively those aspects of the UZ flow and transport system that are not important for achieving regulatory dose (CRWMS M&O 2000 [153246], Section 1.1.1). The development of an expected case model for the UZ also provides defense-in-depth in areas requiring further analysis of uncertainty and alternative models. In general, the value of the conservative case is to provide a more easily defensible TSPA for behavior of UZ flow and transport processes at Yucca Mountain. This AMR has been prepared in accordance with the ''Technical Work Plan for Unsaturated Zone (UZ) Flow and Transport Process Model Report'' (Bechtel SAIC Company (BSC) 2001 [155051], Section 1.3 - Work Package 4301213UMG). The work scope is to examine the data and current models of flow and transport in the Yucca Mountain UZ to identify models and analyses

  13. Unsaturated Zone Flow Patterns and Analysis

    Ahlers, C.

    2001-01-01

    This Analysis/Model Report (AMR) documents the development of an expected-case model for unsaturated zone (UZ) flow and transport that will be described in terms of the representativeness of models of the natural system. The expected-case model will provide an evaluation of the effectiveness of the natural barriers, assess the impact of conservatism in the Total System Performance Assessment (TSPA), and support the development of further models and analyses for public confidence building. The present models used in ''Total System Performance Assessment for the Site Recommendation'' (Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M and O) 2000 [1532461]) underestimate the natural-barrier performance because of conservative assumptions and parameters and do not adequately address uncertainty and alternative models. The development of an expected case model for the UZ natural barrier addresses issues regarding flow-pattern analysis and modeling that had previously been treated conservatively. This is in line with the Repository Safety Strategy (RSS) philosophy of treating conservatively those aspects of the UZ flow and transport system that are not important for achieving regulatory dose (CRWMS M and O 2000 [153246], Section 1.1.1). The development of an expected case model for the UZ also provides defense-in-depth in areas requiring further analysis of uncertainty and alternative models. In general, the value of the conservative case is to provide a more easily defensible TSPA for behavior of UZ flow and transport processes at Yucca Mountain. This AMR has been prepared in accordance with the ''Technical Work Plan for Unsaturated Zone (UZ) Flow and Transport Process Model Report'' (Bechtel SAIC Company (BSC) 2001 [155051], Section 1.3 - Work Package 4301213UMG). The work scope is to examine the data and current models of flow and transport in the Yucca Mountain UZ to identify models and analyses where conservatism may be

  14. Development of a simple method for classifying the degree of importance of components in nuclear power plants using probabilistic analysis technique

    Shimada, Yoshio; Miyazaki, Takamasa

    2006-01-01

    In order to analyze large amounts of trouble information of overseas nuclear power plants, it is necessary to select information that is significant in terms of both safety and reliability. In this research, a method of efficiently and simply classifying degrees of importance of components in terms of safety and reliability while paying attention to root-cause components appearing in the information was developed. Regarding safety, the reactor core damage frequency (CDF), which is used in the probabilistic analysis of a reactor, was used. Regarding reliability, the automatic plant trip probability (APTP), which is used in the probabilistic analysis of automatic reactor trips, was used. These two aspects were reflected in the development of criteria for classifying degrees of importance of components. By applying these criteria, a method of quantitatively and simply judging the significance of trouble information of overseas nuclear power plants was developed. (author)

  15. Using pattern analysis methods to do fast detection of manufacturing pattern failures

    Zhao, Evan; Wang, Jessie; Sun, Mason; Wang, Jeff; Zhang, Yifan; Sweis, Jason; Lai, Ya-Chieh; Ding, Hua

    2016-03-01

    At the advanced technology node, logic design has become extremely complex and is getting more challenging as the pattern geometry size decreases. The small sizes of layout patterns are becoming very sensitive to process variations. Meanwhile, the high pressure of yield ramp is always there due to time-to-market competition. The company that achieves patterning maturity earlier than others will have a great advantage and a better chance to realize maximum profit margins. For debugging silicon failures, DFT diagnostics can identify which nets or cells caused the yield loss. But normally, a long time period is needed with many resources to identify which failures are due to one common layout pattern or structure. This paper will present a new yield diagnostic flow, based on preliminary EFA results, to show how pattern analysis can more efficiently detect pattern related systematic defects. Increased visibility on design pattern related failures also allows more precise yield loss estimation.

  16. Automated pattern recognition system for noise analysis

    Sides, W.H. Jr.; Piety, K.R.

    1980-01-01

    A pattern recognition system was developed at ORNL for on-line monitoring of noise signals from sensors in a nuclear power plant. The system continuousy measures the power spectral density (PSD) values of the signals and the statistical characteristics of the PSDs in unattended operation. Through statistical comparison of current with past PSDs (pattern recognition), the system detects changes in the noise signals. Because the noise signals contain information about the current operational condition of the plant, a change in these signals could indicate a change, either normal or abnormal, in the operational condition

  17. Automated analysis of long-term grooming behavior in Drosophila using a k-nearest neighbors classifier

    Allen, Victoria W; Shirasu-Hiza, Mimi

    2018-01-01

    Despite being pervasive, the control of programmed grooming is poorly understood. We addressed this gap by developing a high-throughput platform that allows long-term detection of grooming in Drosophila melanogaster. In our method, a k-nearest neighbors algorithm automatically classifies fly behavior and finds grooming events with over 90% accuracy in diverse genotypes. Our data show that flies spend ~13% of their waking time grooming, driven largely by two major internal programs. One of these programs regulates the timing of grooming and involves the core circadian clock components cycle, clock, and period. The second program regulates the duration of grooming and, while dependent on cycle and clock, appears to be independent of period. This emerging dual control model in which one program controls timing and another controls duration, resembles the two-process regulatory model of sleep. Together, our quantitative approach presents the opportunity for further dissection of mechanisms controlling long-term grooming in Drosophila. PMID:29485401

  18. IAEA safeguards and classified materials

    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

  19. Hidden pattern discovery on epileptic EEG with 1-D local binary patterns and epileptic seizures detection by grey relational analysis.

    Kaya, Yılmaz

    2015-09-01

    This paper proposes a novel approach to detect epilepsy seizures by using Electroencephalography (EEG), which is one of the most common methods for the diagnosis of epilepsy, based on 1-Dimension Local Binary Pattern (1D-LBP) and grey relational analysis (GRA) methods. The main aim of this paper is to evaluate and validate a novel approach, which is a computer-based quantitative EEG analyzing method and based on grey systems, aimed to help decision-maker. In this study, 1D-LBP, which utilizes all data points, was employed for extracting features in raw EEG signals, Fisher score (FS) was employed to select the representative features, which can also be determined as hidden patterns. Additionally, GRA is performed to classify EEG signals through these Fisher scored features. The experimental results of the proposed approach, which was employed in a public dataset for validation, showed that it has a high accuracy in identifying epileptic EEG signals. For various combinations of epileptic EEG, such as A-E, B-E, C-E, D-E, and A-D clusters, 100, 96, 100, 99.00 and 100% were achieved, respectively. Also, this work presents an attempt to develop a new general-purpose hidden pattern determination scheme, which can be utilized for different categories of time-varying signals.

  20. Analysis of Facebook content demand patterns

    Kihl, Maria; Larsson, Robin; Unnervik, Niclas; Haberkamm, Jolina; Arvidsson, Åke; Aurelius, Andreas

    2014-01-01

    Data volumes in communication networks increase rapidly. Further, usage of social network applications is very wide spread among users, and among these applications, Facebook is the most popular. In this paper, we analyse user demands patterns and content popularity of Facebook generated traffic. The data comes from residential users in two metropolitan access networks in Sweden, and we analyse more than 17 million images downloaded by almost 16,000 Facebook users. We show that the distributi...

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

    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).

  2. Dynamical Networks for Smog Pattern Analysis

    Zong, Linqi; Gong, Xinyi; Zhu, Jia

    2015-01-01

    Smog, as a form of air pollution, poses as a serious problem to the environment, health, and economy of the world[1-4] . Previous studies on smog mostly focused on the components and the effects of smog [5-10]. However, as the smog happens with increased frequency and duration, the smog pattern which is critical for smog forecast and control, is rarely investigated, mainly due to the complexity of the components, the causes, and the spreading processes of smog. Here we report the first analys...

  3. Phenotype analysis of early risk factors from electronic medical records improves image-derived diagnostic classifiers for optic nerve pathology

    Chaganti, Shikha; Nabar, Kunal P.; Nelson, Katrina M.; Mawn, Louise A.; Landman, Bennett A.

    2017-03-01

    We examine imaging and electronic medical records (EMR) of 588 subjects over five major disease groups that affect optic nerve function. An objective evaluation of the role of imaging and EMR data in diagnosis of these conditions would improve understanding of these diseases and help in early intervention. We developed an automated image processing pipeline that identifies the orbital structures within the human eyes from computed tomography (CT) scans, calculates structural size, and performs volume measurements. We customized the EMR-based phenome-wide association study (PheWAS) to derive diagnostic EMR phenotypes that occur at least two years prior to the onset of the conditions of interest from a separate cohort of 28,411 ophthalmology patients. We used random forest classifiers to evaluate the predictive power of image-derived markers, EMR phenotypes, and clinical visual assessments in identifying disease cohorts from a control group of 763 patients without optic nerve disease. Image-derived markers showed more predictive power than clinical visual assessments or EMR phenotypes. However, the addition of EMR phenotypes to the imaging markers improves the classification accuracy against controls: the AUC improved from 0.67 to 0.88 for glaucoma, 0.73 to 0.78 for intrinsic optic nerve disease, 0.72 to 0.76 for optic nerve edema, 0.72 to 0.77 for orbital inflammation, and 0.81 to 0.85 for thyroid eye disease. This study illustrates the importance of diagnostic context for interpretation of image-derived markers and the proposed PheWAS technique provides a flexible approach for learning salient features of patient history and incorporating these data into traditional machine learning analyses.

  4. Spectroscopic vector analysis for fast pattern quality monitoring

    Sohn, Younghoon; Ryu, Sungyoon; Lee, Chihoon; Yang, Yusin

    2018-03-01

    In semiconductor industry, fast and effective measurement of pattern variation has been key challenge for assuring massproduct quality. Pattern measurement techniques such as conventional CD-SEMs or Optical CDs have been extensively used, but these techniques are increasingly limited in terms of measurement throughput and time spent in modeling. In this paper we propose time effective pattern monitoring method through the direct spectrum-based approach. In this technique, a wavelength band sensitive to a specific pattern change is selected from spectroscopic ellipsometry signal scattered by pattern to be measured, and the amplitude and phase variation in the wavelength band are analyzed as a measurement index of the pattern change. This pattern change measurement technique is applied to several process steps and verified its applicability. Due to its fast and simple analysis, the methods can be adapted to the massive process variation monitoring maximizing measurement throughput.

  5. An Analysis of Interaction Patterns in the Focus Group Interview

    Gavora Peter

    2015-12-01

    Full Text Available This paper is based on the analysis of a focus group interview of a moderator and a group of undergraduate students on the topic of self-regulation of learning. The purpose of the investigation was to identify interaction patterns that appeared in the talk of participants and the moderator. In the stream of communication two rudimentary interaction patterns were recognized. The first pattern was named the Catalogue. It consists of a sequence of turns of participants who respond to a request of the moderator and who provide their answers, one by one, without reacting on the content of the previous partner(s talk. The other interaction pattern was called the Domino. In this pattern participants respond to each other. The Catalogue pattern prevailed in the interview. Alongside with identification of patterns of interaction the study demonstrated the functions of the common ground and its accomplishment in the talk of the moderator and participants.

  6. PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data.

    Hanke, Michael; Halchenko, Yaroslav O; Sederberg, Per B; Hanson, Stephen José; Haxby, James V; Pollmann, Stefan

    2009-01-01

    Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.

  7. HPLC fingerprint analysis combined with chemometrics for pattern recognition of ginger.

    Feng, Xu; Kong, Weijun; Wei, Jianhe; Ou-Yang, Zhen; Yang, Meihua

    2014-03-01

    Ginger, the fresh rhizome of Zingiber officinale Rosc. (Zingiberaceae), has been used worldwide; however, for a long time, there has been no standard approbated internationally for its quality control. To establish an efficacious and combinational method and pattern recognition technique for quality control of ginger. A simple, accurate and reliable method based on high-performance liquid chromatography with photodiode array (HPLC-PDA) detection was developed for establishing the chemical fingerprints of 10 batches of ginger from different markets in China. The method was validated in terms of precision, reproducibility and stability; and the relative standard deviations were all less than 1.57%. On the basis of this method, the fingerprints of 10 batches of ginger samples were obtained, which showed 16 common peaks. Coupled with similarity evaluation software, the similarities between each fingerprint of the sample and the simulative mean chromatogram were in the range of 0.998-1.000. Then, the chemometric techniques, including similarity analysis, hierarchical clustering analysis and principal component analysis were applied to classify the ginger samples. Consistent results were obtained to show that ginger samples could be successfully classified into two groups. This study revealed that HPLC-PDA method was simple, sensitive and reliable for fingerprint analysis, and moreover, for pattern recognition and quality control of ginger.

  8. Exploring the potential of data mining techniques for the analysis of accident patterns

    Prato, Carlo Giacomo; Bekhor, Shlomo; Galtzur, Ayelet

    2010-01-01

    Research in road safety faces major challenges: individuation of the most significant determinants of traffic accidents, recognition of the most recurrent accident patterns, and allocation of resources necessary to address the most relevant issues. This paper intends to comprehend which data mining...... and association rules) data mining techniques are implemented for the analysis of traffic accidents occurred in Israel between 2001 and 2004. Results show that descriptive techniques are useful to classify the large amount of analyzed accidents, even though introduce problems with respect to the clear...... importance of input and intermediate neurons, and the relative importance of hundreds of association rules. Further research should investigate whether limiting the analysis to fatal accidents would simplify the task of data mining techniques in recognizing accident patterns without the “noise” probably...

  9. Mass spectrometry-based serum proteome pattern analysis in molecular diagnostics of early stage breast cancer

    Stobiecki Maciej

    2009-07-01

    Full Text Available Abstract Background Mass spectrometric analysis of the blood proteome is an emerging method of clinical proteomics. The approach exploiting multi-protein/peptide sets (fingerprints detected by mass spectrometry that reflect overall features of a specimen's proteome, termed proteome pattern analysis, have been already shown in several studies to have applicability in cancer diagnostics. We aimed to identify serum proteome patterns specific for early stage breast cancer patients using MALDI-ToF mass spectrometry. Methods Blood samples were collected before the start of therapy in a group of 92 patients diagnosed at stages I and II of the disease, and in a group of age-matched healthy controls (104 women. Serum specimens were purified and the low-molecular-weight proteome fraction was examined using MALDI-ToF mass spectrometry after removal of albumin and other high-molecular-weight serum proteins. Protein ions registered in a mass range between 2,000 and 10,000 Da were analyzed using a new bioinformatic tool created in our group, which included modeling spectra as a sum of Gaussian bell-shaped curves. Results We have identified features of serum proteome patterns that were significantly different between blood samples of healthy individuals and early stage breast cancer patients. The classifier built of three spectral components that differentiated controls and cancer patients had 83% sensitivity and 85% specificity. Spectral components (i.e., protein ions that were the most frequent in such classifiers had approximate m/z values of 2303, 2866 and 3579 Da (a biomarker built from these three components showed 88% sensitivity and 78% specificity. Of note, we did not find a significant correlation between features of serum proteome patterns and established prognostic or predictive factors like tumor size, nodal involvement, histopathological grade, estrogen and progesterone receptor expression. In addition, we observed a significantly (p = 0

  10. Iceberg Semantics For Count Nouns And Mass Nouns: Classifiers, measures and portions

    Fred Landman

    2016-12-01

    It is the analysis of complex NPs and their mass-count properties that is the focus of the second part of this paper. There I develop an analysis of English and Dutch pseudo- partitives, in particular, measure phrases like three liters of wine and classifier phrases like three glasses of wine. We will study measure interpretations and classifier interpretations of measures and classifiers, and different types of classifier interpretations: container interpretations, contents interpretations, and - indeed - portion interpretations. Rothstein 2011 argues that classifier interpretations (including portion interpretations of pseudo partitives pattern with count nouns, but that measure interpretations pattern with mass nouns. I will show that this distinction follows from the very basic architecture of Iceberg semantics.

  11. Describing Old Czech Declension Patterns for Automatic Text Analysis

    Jínová, P.; Lehečka, Boris; Oliva jr., Karel

    -, č. 13 (2014), s. 7-17 ISSN 1579-8372 Institutional support: RVO:68378092 Keywords : Old Czech morphology * declension patterns * automatic text analysis * i-stems * ja-stems Subject RIV: AI - Linguistics

  12. Two dimensional Fourier transform methods for fringe pattern analysis

    Sciammarella, C. A.; Bhat, G.

    An overview of the use of FFTs for fringe pattern analysis is presented, with emphasis on fringe patterns containing displacement information. The techniques are illustrated via analysis of the displacement and strain distributions in the direction perpendicular to the loading, in a disk under diametral compression. The experimental strain distribution is compared to the theoretical, and the agreement is found to be excellent in regions where the elasticity solution models well the actual problem.

  13. Data analysis and pattern recognition in multiple databases

    Adhikari, Animesh; Pedrycz, Witold

    2014-01-01

    Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyse them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery, and mining patterns of select items provide different...

  14. Pattern theory the stochastic analysis of real-world signals

    Mumford, David

    2010-01-01

    Pattern theory is a distinctive approach to the analysis of all forms of real-world signals. At its core is the design of a large variety of probabilistic models whose samples reproduce the look and feel of the real signals, their patterns, and their variability. Bayesian statistical inference then allows you to apply these models in the analysis of new signals. This book treats the mathematical tools, the models themselves, and the computational algorithms for applying statistics to analyze six representative classes of signals of increasing complexity. The book covers patterns in text, sound

  15. Dietary patterns and colorectal cancer risk: a meta-analysis.

    Feng, Yu-Liang; Shu, Long; Zheng, Pei-Fen; Zhang, Xiao-Yan; Si, Cai-Juan; Yu, Xiao-Long; Gao, Wei; Zhang, Lun

    2017-05-01

    The analysis of dietary patterns has recently drawn considerable attention as a method of investigating the association between the overall whole diet and the risk of colorectal cancer. However, the results have yielded conflicting findings. Here, we carried out a meta-analysis to identify the association between dietary patterns and the risk of colorectal cancer. A total of 40 studies fulfilled the inclusion criteria and were included in this meta-analysis. The highest category of 'healthy' dietary pattern compared with the lowest category was apparently associated with a decreased risk for colorectal cancer [odds ratio (OR)=0.75; confidence interval (CI): 0.68-0.83; Pcolorectal cancer was shown for the highest compared with the lowest category of a 'western-style' dietary pattern (OR=1.40; CI: 1.26-1.56; Pcolorectal cancer in the highest compared with the lowest category of 'alcohol-consumption' pattern (OR=1.44; CI: 1.13-1.82; P=0.003). The results of this meta-analysis indicate that a 'healthy' dietary pattern may decrease the risk of colorectal cancer, whereas 'western-style' and 'alcohol-consumption' patterns may increase the risk of colorectal cancer.

  16. Multivariate analysis of 2-DE protein patterns - Practical approaches

    Jacobsen, Charlotte; Jacobsen, Susanne; Grove, H.

    2007-01-01

    Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two...... of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times...

  17. Development and analysis of a low-cost screening tool to identify and classify hearing loss in children: a proposal for developing countries

    Alessandra Giannella Samelli

    2011-01-01

    Full Text Available OBJECTIVE: A lack of attention has been given to hearing health in primary care in developing countries. A strategy involving low-cost screening tools may fill the current gap in hearing health care provided to children. Therefore, it is necessary to establish and adopt lower-cost procedures that are accessible to underserved areas that lack other physical or human resources that would enable the identification of groups at risk for hearing loss. The aim of this study was to develop and analyze the efficacy of a low-cost screening tool to identify and classify hearing loss in children. METHODS: A total of 214 2-to-10 year-old children participated in this study. The study was conducted by providing a questionnaire to the parents and comparing the answers with the results of a complete audiological assessment. Receiver operating characteristic (ROC curves were constructed, and discriminant analysis techniques were used to classify each child based on the total score. RESULTS: We found conductive hearing loss in 39.3% of children, sensorineural hearing loss in 7.4% and normal hearing in 53.3%. The discriminant analysis technique provided the following classification rule for the total score on the questionnaire: 0 to 4 points - normal hearing; 5 to 7 points - conductive hearing loss; over 7 points - sensorineural hearing loss. CONCLUSION: Our results suggest that the questionnaire could be used as a screening tool to classify children with normal hearing or hearing loss and according to the type of hearing loss based on the total questionnaire score

  18. 3D Facial Pattern Analysis for Autism

    2010-07-01

    et al. (2001) proposed a two-level Garbor wavelet network (GWN) to detect eight facial features. In Bhuiyan et al. (2003) six facial features are...Toyama, K., Krüger, V., 2001. Hierarchical Wavelet Networks for Facial Feature Localization. ICCV’01 Workshop on Recognition, Analysis and... pathological  (red) and normal structure (blue) (b)  signed distance map (negative distance indicates the  pathological  shape is inside) (c) raw

  19. Truncation in diffraction pattern analysis. Pt. 1

    Delhez, R.; Keijser, T.H. de; Mittemeijer, E.J.; Langford, J.I.

    1986-01-01

    An evaluation of the concept of a line profile is provoked by truncation of the range of intensity measurement in practice. The measured truncated line profile can be considered either as part of the total intensity distribution which peaks at or near the reciprocal-lattice points (approach 1), or as part of a component line profile which is confined to a single reciprocal-lattice point (approach 2). Some false conceptions in line-profile analysis can then be avoided and recipes can be developed for the extrapolation of the tails of the truncated line profile. Fourier analysis of line profiles, according to the first approach, implies a Fourier series development of the total intensity distribution defined within [l - 1/2, l + 1/2] (l indicates the node considered in reciprocal space); the second approach implies a Fourier transformation of the component line profile defined within [ - ∞, + ∞]. Exact descriptions of size broadening are provided by both approaches, whereas combined size and strain broadening can only be evaluated adequately within the first approach. Straightforward methods are given for obtaining truncation-corrected values for the average crystallite size. (orig.)

  20. Analysis of transaction records of live freshwater finfish in China: A case study of customers’ claims of fish mortality using cross-classified modeling

    Beibei Jia

    2016-11-01

    Full Text Available Customers of finfish in China place a high priority on healthy fish at the point of sale but factors that increase the risk of morbidity and mortality during transportation have had limited study. We designed a case study to investigate variation of mortalities claimed by customers receiving fish at markets with above-normal mortalities. We used daily transaction records of the 3 species transported from a company located in Guangdong province to its destination markets in Beijing between April and July 2013: largemouth bass (Micropterus salmoides, Chinese perch (Siniperca chuatsi, and longsnout catfish (Leiocassis longirostris. We quantified magnitudes and patterns of weekly mortalities of transported fish, and used cross-classified random-effect modeling to explore variation and clustering of fish mortality claims at wholesale destinations. Random effects for customer and market-week were interpreted by variance partition coefficients (VPC and intraclass correlation coefficients (ICC. A significant fixed effect of market was found in the model of mortality claims for longsnout catfish (p < 0.05, and changing patterns of VPC and ICC suggested that customers ordering longsnout catfish had more variation in claims than those ordering the other 2 species. Our findings indicate a need for better customer communication for live fish transportation and a need for detailed measurements during the process including physiological factors and transportation conditions, to better understand their role in reported mortalities.

  1. Authentication and distinction of Shenmai injection with HPLC fingerprint analysis assisted by pattern recognition techniques

    Xue-Feng Lu

    2012-10-01

    Full Text Available In this paper, the feasibility and advantages of employing high performance liquid chromatographic (HPLC fingerprints combined with pattern recognition techniques for quality control of Shenmai injection were investigated and demonstrated. The Similarity Evaluation System was employed to evaluate the similarities of samples of Shenmai injection, and the HPLC generated chromatographic data were analyzed using hierarchical clustering analysis (HCA and soft independent modeling of class analogy (SIMCA. Consistent results were obtained to show that the authentic samples and the blended samples were successfully classified by SIMCA, which could be applied to accurate discrimination and quality control of Shenmai injection. Furthermore, samples could also be grouped in accordance with manufacturers. Our results revealed that the developed method has potential perspective for the original discrimination and quality control of Shenmai injection. Keywords: Shenmai injection, High performance liquid chromatography, Fingerprint, Pattern recognition

  2. Single molecule analysis of c-myb alternative splicing reveals novel classifiers for precursor B-ALL.

    Ye E Zhou

    Full Text Available The c-Myb transcription factor, a key regulator of proliferation and differentiation in hematopoietic and other cell types, has an N-terminal DNA binding domain and a large C-terminal domain responsible for transcriptional activation, negative regulation and determining target gene specificity. Overexpression and rearrangement of the c-myb gene (MYB has been reported in some patients with leukemias and other types of cancers, implicating activated alleles of c-myb in the development of human tumors. Alternative RNA splicing can produce variants of c-myb with qualitatively distinct transcriptional activities that may be involved in transformation and leukemogenesis. Here, by performing a detailed, single molecule assay we found that c-myb alternative RNA splicing was elevated and much more complex in leukemia samples than in cell lines or CD34+ hematopoietic progenitor cells from normal donors. The results revealed that leukemia samples express more than 60 different c-myb splice variants, most of which have multiple alternative splicing events and were not detectable by conventional microarray or PCR approaches. For example, the single molecule assay detected 21 and 22 splice variants containing the 9B and 9S exons, respectively, most of which encoded unexpected variant forms of c-Myb protein. Furthermore, the detailed analysis identified some splice variants whose expression correlated with poor survival in a small cohort of precursor B-ALL samples. Our findings indicate that single molecule assays can reveal complexities in c-myb alternative splicing that have potential as novel biomarkers and could help explain the role of c-Myb variants in the development of human leukemia.

  3. Description and Analysis Pattern for Theses and Dissertations

    Sirous Alidousti

    2009-07-01

    Full Text Available Dissertations and theses that are generated in course of research at PhD and Masters levels are considered to be important scientific documents in every country. Data description and analysis of such documents collected together, could automatically - especially when compared with data from other resources - provide new information that is very valuable. Nevertheless, no comprehensive, integrated pattern exists for such description and analysis. The present paper offers the findings of a research conducted for devising an information analysis pattern for dissertations and theses. It also puts forward information categories derived from such documents that could be described and analyzed.

  4. Upgrade of the Automatic Analysis System in the TJ-II Thomson Scattering Diagnostic: New Image Recognition Classifier and Fault Condition Detection

    Makili, L.; Dormido-Canto, S. [UNED, Madrid (Spain); Vega, J.; Pastor, I.; Pereira, A.; Portas, A.; Perez-Risco, D.; Rodriguez-Fernandez, M. [Association EuratomCIEMAT para Fusion, Madrid (Spain); Busch, P. [FOM Instituut voor PlasmaFysica Rijnhuizen, Nieuwegein (Netherlands)

    2009-07-01

    Full text of publication follows: An automatic image classification system has been in operation for years in the TJ-II Thomson diagnostic. It recognizes five different types of images: CCD camera background, measurement of stray light without plasma or in a collapsed discharge, image during ECH phase, image during NBI phase and image after reaching the cut o density during ECH heating. Each kind of image implies the execution of different application software. Therefore, the classification system was developed to launch the corresponding software in an automatic way. The method to recognize the several classes was based on a learning system, in particular Support Vector Machines (SVM). Since the first implementation of the classifier, a relevant improvement has been accomplished in the diagnostic: a new notch filter is in operation, having a larger stray-light rejection at the ruby wavelength than the previous filter. On the other hand, its location in the optical system has been modified. As a consequence, the stray light pattern in the CCD image is located in a different position. In addition to these transformations, the power of neutral beams injected in the TJ-II plasma has been increased about a factor of 2. Due to the fact that the recognition system is based on a learning system and major modifications have been carried out in both the diagnostic (optics) and TJ-II plasmas (injected power), the classifier model is no longer valid. The creation of a new model (also based on SVM) under the present conditions has been necessary. Finally, specific error conditions in the data acquisition process can automatically be detected now. The recovering process can be automated, thereby avoiding the loss of data in ensuing discharges. (authors)

  5. Cognitive approaches for patterns analysis and security applications

    Ogiela, Marek R.; Ogiela, Lidia

    2017-08-01

    In this paper will be presented new opportunities for developing innovative solutions for semantic pattern classification and visual cryptography, which will base on cognitive and bio-inspired approaches. Such techniques can be used for evaluation of the meaning of analyzed patterns or encrypted information, and allow to involve such meaning into the classification task or encryption process. It also allows using some crypto-biometric solutions to extend personalized cryptography methodologies based on visual pattern analysis. In particular application of cognitive information systems for semantic analysis of different patterns will be presented, and also a novel application of such systems for visual secret sharing will be described. Visual shares for divided information can be created based on threshold procedure, which may be dependent on personal abilities to recognize some image details visible on divided images.

  6. Classifying Returns as Extreme

    Christiansen, Charlotte

    2014-01-01

    I consider extreme returns for the stock and bond markets of 14 EU countries using two classification schemes: One, the univariate classification scheme from the previous literature that classifies extreme returns for each market separately, and two, a novel multivariate classification scheme tha...

  7. Multi-spatial analysis of aeolian dune-field patterns

    Ewing, Ryan C.; McDonald, George D.; Hayes, Alex G.

    2015-07-01

    Aeolian dune-fields are composed of different spatial scales of bedform patterns that respond to changes in environmental boundary conditions over a wide range of time scales. This study examines how variations in spatial scales of dune and ripple patterns found within dune fields are used in environmental reconstructions on Earth, Mars and Titan. Within a single bedform type, different spatial scales of bedforms emerge as a pattern evolves from an initial state into a well-organized pattern, such as with the transition from protodunes to dunes. Additionally, different types of bedforms, such as ripples, coarse-grained ripples and dunes, coexist at different spatial scales within a dune-field. Analysis of dune-field patterns at the intersection of different scales and types of bedforms at different stages of development provides a more comprehensive record of sediment supply and wind regime than analysis of a single scale and type of bedform. Interpretations of environmental conditions from any scale of bedform, however, are limited to environmental signals associated with the response time of that bedform. Large-scale dune-field patterns integrate signals over long-term climate cycles and reveal little about short-term variations in wind or sediment supply. Wind ripples respond instantly to changing conditions, but reveal little about longer-term variations in wind or sediment supply. Recognizing the response time scales across different spatial scales of bedforms maximizes environmental interpretations from dune-field patterns.

  8. Numerical analysis of a neural network with hierarchically organized patterns

    Bacci, Silvia; Wiecko, Cristina; Parga, Nestor

    1988-01-01

    A numerical analysis of the retrieval behaviour of an associative memory model where the memorized patterns are stored hierarchically is performed. It is found that the model is able to categorize errors. For a finite number of categories, these are retrieved correctly even when the stored patterns are not. Instead, when they are allowed to increase with the number of neurons, their retrieval quality deteriorates above a critical category capacity. (Author)

  9. LCC: Light Curves Classifier

    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.

  10. Learning to classify organic and conventional wheat - a machine-learning driven approach using the MeltDB 2.0 metabolomics analysis platform

    Nikolas eKessler

    2015-03-01

    Full Text Available We present results of our machine learning approach to the problem of classifying GC-MS data originating from wheat grains of different farming systems. The aim is to investigate the potential of learning algorithms to classify GC-MS data to be either from conventionally grown or from organically grown samples and considering different cultivars. The motivation of our work is rather obvious on the background of nowadays increased demand for organic food in post-industrialized societies and the necessity to prove organic food authenticity. The background of our data set is given by up to eleven wheat cultivars that have been cultivated in both farming systems, organic and conventional, throughout three years. More than 300 GC-MS measurements were recorded and subsequently processed and analyzed in the MeltDB 2.0 metabolomics analysis platform, being briefly outlined in this paper. We further describe how unsupervised (t-SNE, PCA and supervised (RF, SVM methods can be applied for sample visualization and classification. Our results clearly show that years have most and wheat cultivars have second-most influence on the metabolic composition of a sample. We can also show, that for a given year and cultivar, organic and conventional cultivation can be distinguished by machine-learning algorithms.

  11. A Combined Approach to Classifying Land Surface Cover of Urban Domestic Gardens Using Citizen Science Data and High Resolution Image Analysis

    Fraser Baker

    2018-03-01

    Full Text Available Domestic gardens are an important component of cities, contributing significantly to urban green infrastructure (GI and its associated ecosystem services. However, domestic gardens are incredibly heterogeneous which presents challenges for quantifying their GI contribution and associated benefits for sustainable urban development. This study applies an innovative methodology that combines citizen science data with high resolution image analysis to create a garden dataset in the case study city of Manchester, UK. An online Citizen Science Survey (CSS collected estimates of proportional coverage for 10 garden land surface types from 1031 city residents. High resolution image analysis was conducted to validate the CSS estimates, and to classify 7 land surface cover categories for all garden parcels in the city. Validation of the CSS land surface estimations revealed a mean accuracy of 76.63% (s = 15.24%, demonstrating that citizens are able to provide valid estimates of garden surface coverage proportions. An Object Based Image Analysis (OBIA classification achieved an estimated overall accuracy of 82%, with further processing required to classify shadow objects. CSS land surface estimations were then extrapolated across the entire classification through calculation of within image class proportions, to provide the proportional coverage of 10 garden land surface types (buildings, hard impervious surfaces, hard pervious surfaces, bare soil, trees, shrubs, mown grass, rough grass, cultivated land, water within every garden parcel in the city. The final dataset provides a better understanding of the composition of GI in domestic gardens and how this varies across the city. An average garden in Manchester has 50.23% GI, including trees (16.54%, mown grass (14.46%, shrubs (9.19%, cultivated land (7.62%, rough grass (1.97% and water (0.45%. At the city scale, Manchester has 49.0% GI, and around one fifth (20.94% of this GI is contained within domestic

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

    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.

  13. Data and statistical methods for analysis of trends and patterns

    Atwood, C.L.; Gentillon, C.D.; Wilson, G.E.

    1992-11-01

    This report summarizes topics considered at a working meeting on data and statistical methods for analysis of trends and patterns in US commercial nuclear power plants. This meeting was sponsored by the Office of Analysis and Evaluation of Operational Data (AEOD) of the Nuclear Regulatory Commission (NRC). Three data sets are briefly described: Nuclear Plant Reliability Data System (NPRDS), Licensee Event Report (LER) data, and Performance Indicator data. Two types of study are emphasized: screening studies, to see if any trends or patterns appear to be present; and detailed studies, which are more concerned with checking the analysis assumptions, modeling any patterns that are present, and searching for causes. A prescription is given for a screening study, and ideas are suggested for a detailed study, when the data take of any of three forms: counts of events per time, counts of events per demand, and non-event data

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

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

    2018-04-01

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

  15. Attractor structure discriminates sleep states: recurrence plot analysis applied to infant breathing patterns.

    Terrill, Philip Ian; Wilson, Stephen James; Suresh, Sadasivam; Cooper, David M; Dakin, Carolyn

    2010-05-01

    Breathing patterns are characteristically different between infant active sleep (AS) and quiet sleep (QS), and statistical quantifications of interbreath interval (IBI) data have previously been used to discriminate between infant sleep states. It has also been identified that breathing patterns are governed by a nonlinear controller. This study aims to investigate whether nonlinear quantifications of infant IBI data are characteristically different between AS and QS, and whether they may be used to discriminate between these infant sleep states. Polysomnograms were obtained from 24 healthy infants at six months of age. Periods of AS and QS were identified, and IBI data extracted. Recurrence quantification analysis (RQA) was applied to each period, and recurrence calculated for a fixed radius in the range of 0-8 in steps of 0.02, and embedding dimensions of 4, 6, 8, and 16. When a threshold classifier was trained, the RQA variable recurrence was able to correctly classify 94.3% of periods in a test dataset. It was concluded that RQA of IBI data is able to accurately discriminate between infant sleep states. This is a promising step toward development of a minimal-channel automatic sleep state classification system.

  16. Hierarchical cluster analysis of progression patterns in open-angle glaucoma patients with medical treatment.

    Bae, Hyoung Won; Rho, Seungsoo; Lee, Hye Sun; Lee, Naeun; Hong, Samin; Seong, Gong Je; Sung, Kyung Rim; Kim, Chan Yun

    2014-04-29

    To classify medically treated open-angle glaucoma (OAG) by the pattern of progression using hierarchical cluster analysis, and to determine OAG progression characteristics by comparing clusters. Ninety-five eyes of 95 OAG patients who received medical treatment, and who had undergone visual field (VF) testing at least once per year for 5 or more years. OAG was classified into subgroups using hierarchical cluster analysis based on the following five variables: baseline mean deviation (MD), baseline visual field index (VFI), MD slope, VFI slope, and Glaucoma Progression Analysis (GPA) printout. After that, other parameters were compared between clusters. Two clusters were made after a hierarchical cluster analysis. Cluster 1 showed -4.06 ± 2.43 dB baseline MD, 92.58% ± 6.27% baseline VFI, -0.28 ± 0.38 dB per year MD slope, -0.52% ± 0.81% per year VFI slope, and all "no progression" cases in GPA printout, whereas cluster 2 showed -8.68 ± 3.81 baseline MD, 77.54 ± 12.98 baseline VFI, -0.72 ± 0.55 MD slope, -2.22 ± 1.89 VFI slope, and seven "possible" and four "likely" progression cases in GPA printout. There were no significant differences in age, sex, mean IOP, central corneal thickness, and axial length between clusters. However, cluster 2 included more high-tension glaucoma patients and used a greater number of antiglaucoma eye drops significantly compared with cluster 1. Hierarchical cluster analysis of progression patterns divided OAG into slow and fast progression groups, evidenced by assessing the parameters of glaucomatous progression in VF testing. In the fast progression group, the prevalence of high-tension glaucoma was greater and the number of antiglaucoma medications administered was increased versus the slow progression group. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

  17. Analysis of synonymous codon usage patterns in the genus Rhizobium.

    Wang, Xinxin; Wu, Liang; Zhou, Ping; Zhu, Shengfeng; An, Wei; Chen, Yu; Zhao, Lin

    2013-11-01

    The codon usage patterns of rhizobia have received increasing attention. However, little information is available regarding the conserved features of the codon usage patterns in a typical rhizobial genus. The codon usage patterns of six completely sequenced strains belonging to the genus Rhizobium were analysed as model rhizobia in the present study. The relative neutrality plot showed that selection pressure played a role in codon usage in the genus Rhizobium. Spearman's rank correlation analysis combined with correspondence analysis (COA) showed that the codon adaptation index and the effective number of codons (ENC) had strong correlation with the first axis of the COA, which indicated the important role of gene expression level and the ENC in the codon usage patterns in this genus. The relative synonymous codon usage of Cys codons had the strongest correlation with the second axis of the COA. Accordingly, the usage of Cys codons was another important factor that shaped the codon usage patterns in Rhizobium genomes and was a conserved feature of the genus. Moreover, the comparison of codon usage between highly and lowly expressed genes showed that 20 unique preferred codons were shared among Rhizobium genomes, revealing another conserved feature of the genus. This is the first report of the codon usage patterns in the genus Rhizobium.

  18. Intelligent Garbage Classifier

    Ignacio Rodríguez Novelle

    2008-12-01

    Full Text Available IGC (Intelligent Garbage Classifier is a system for visual classification and separation of solid waste products. Currently, an important part of the separation effort is based on manual work, from household separation to industrial waste management. Taking advantage of the technologies currently available, a system has been built that can analyze images from a camera and control a robot arm and conveyor belt to automatically separate different kinds of waste.

  19. Classifying Linear Canonical Relations

    Lorand, Jonathan

    2015-01-01

    In this Master's thesis, we consider the problem of classifying, up to conjugation by linear symplectomorphisms, linear canonical relations (lagrangian correspondences) from a finite-dimensional symplectic vector space to itself. We give an elementary introduction to the theory of linear canonical relations and present partial results toward the classification problem. This exposition should be accessible to undergraduate students with a basic familiarity with linear algebra.

  20. Classifying and assembling two-dimensional X-ray laser diffraction patterns of a single particle to reconstruct the three-dimensional diffraction intensity function: resolution limit due to the quantum noise.

    Tokuhisa, Atsushi; Taka, Junichiro; Kono, Hidetoshi; Go, Nobuhiro

    2012-05-01

    A new two-step algorithm is developed for reconstructing the three-dimensional diffraction intensity of a globular biological macromolecule from many experimentally measured quantum-noise-limited two-dimensional X-ray laser diffraction patterns, each for an unknown orientation. The first step is classification of the two-dimensional patterns into groups according to the similarity of direction of the incident X-rays with respect to the molecule and an averaging within each group to reduce the noise. The second step is detection of common intersecting circles between the signal-enhanced two-dimensional patterns to identify their mutual location in the three-dimensional wavenumber space. The newly developed algorithm enables one to detect a signal for classification in noisy experimental photon-count data with as low as ~0.1 photons per effective pixel. The wavenumber of such a limiting pixel determines the attainable structural resolution. From this fact, the resolution limit due to the quantum noise attainable by this new method of analysis as well as two important experimental parameters, the number of two-dimensional patterns to be measured (the load for the detector) and the number of pairs of two-dimensional patterns to be analysed (the load for the computer), are derived as a function of the incident X-ray intensity and quantities characterizing the target molecule. © 2012 International Union of Crystallography

  1. Reactor noise analysis by statistical pattern recognition methods

    Howington, L.C.; Gonzalez, R.C.

    1976-01-01

    A multivariate statistical pattern recognition system for reactor noise analysis is presented. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, updating, and data compacting capabilities. System design emphasizes control of the false-alarm rate. Its abilities to learn normal patterns, to recognize deviations from these patterns, and to reduce the dimensionality of data with minimum error were evaluated by experiments at the Oak Ridge National Laboratory (ORNL) High-Flux Isotope Reactor. Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the pattern recognition system

  2. Multivariate pattern analysis of obsessive-compulsive disorder using structural neuroanatomy.

    Hu, Xinyu; Liu, Qi; Li, Bin; Tang, Wanjie; Sun, Huaiqiang; Li, Fei; Yang, Yanchun; Gong, Qiyong; Huang, Xiaoqi

    2016-02-01

    Magnetic resonance imaging (MRI) studies have revealed brain structural abnormalities in obsessive-compulsive disorder (OCD) patients, involving both gray matter (GM) and white matter (WM). However, the results of previous publications were based on average differences between groups, which limited their usages in clinical practice. Therefore, the aim of this study was to examine whether the application of multivariate pattern analysis (MVPA) to high-dimensional structural images would allow accurate discrimination between OCD patients and healthy control subjects (HCS). High-resolution T1-weighted images were acquired from 33 OCD patients and 33 demographically matched HCS in a 3.0 T scanner. Differences in GM and WM volume between OCD and HCS were examined using two types of well-established MVPA techniques: support vector machine (SVM) and Gaussian process classifier (GPC). We also drew a receiver operating characteristic (ROC) curve to evaluate the performance of each classifier. The classification accuracies for both classifiers using GM and WM anatomy were all above 75%. The highest classification accuracy (81.82%, P<0.001) was achieved with the SVM classifier using WM information. Regional brain anomalies with high discriminative power were based on three distributed networks including the fronto-striatal circuit, the temporo-parieto-occipital junction and the cerebellum. Our study illustrated that both GM and WM anatomical features may be useful in differentiating OCD patients from HCS. WM volume using the SVM approach showed the highest accuracy in our population for revealing group differences, which suggested its potential diagnostic role in detecting highly enriched OCD patients at the level of the individual. Copyright © 2015 Elsevier B.V. and ECNP. All rights reserved.

  3. Modeling activity patterns of wildlife using time-series analysis.

    Zhang, Jindong; Hull, Vanessa; Ouyang, Zhiyun; He, Liang; Connor, Thomas; Yang, Hongbo; Huang, Jinyan; Zhou, Shiqiang; Zhang, Zejun; Zhou, Caiquan; Zhang, Hemin; Liu, Jianguo

    2017-04-01

    The study of wildlife activity patterns is an effective approach to understanding fundamental ecological and evolutionary processes. However, traditional statistical approaches used to conduct quantitative analysis have thus far had limited success in revealing underlying mechanisms driving activity patterns. Here, we combine wavelet analysis, a type of frequency-based time-series analysis, with high-resolution activity data from accelerometers embedded in GPS collars to explore the effects of internal states (e.g., pregnancy) and external factors (e.g., seasonal dynamics of resources and weather) on activity patterns of the endangered giant panda ( Ailuropoda melanoleuca ). Giant pandas exhibited higher frequency cycles during the winter when resources (e.g., water and forage) were relatively poor, as well as during spring, which includes the giant panda's mating season. During the summer and autumn when resources were abundant, pandas exhibited a regular activity pattern with activity peaks every 24 hr. A pregnant individual showed distinct differences in her activity pattern from other giant pandas for several months following parturition. These results indicate that animals adjust activity cycles to adapt to seasonal variation of the resources and unique physiological periods. Wavelet coherency analysis also verified the synchronization of giant panda activity level with air temperature and solar radiation at the 24-hr band. Our study also shows that wavelet analysis is an effective tool for analyzing high-resolution activity pattern data and its relationship to internal and external states, an approach that has the potential to inform wildlife conservation and management across species.

  4. Electrocardiogram Pattern Recognition and Analysis Based on Artificial Neural Networks and Support Vector Machines: A Review

    Mario Sansone

    2013-01-01

    Full Text Available Computer systems for Electrocardiogram (ECG analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units or in prompt detection of dangerous events (e.g., ventricular fibrillation. Together with clinical applications (arrhythmia detection and heart rate variability analysis, ECG is currently being investigated in biometrics (human identification, an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned.

  5. Dietary patterns and depression risk: A meta-analysis.

    Li, Ye; Lv, Mei-Rong; Wei, Yan-Jin; Sun, Ling; Zhang, Ji-Xiang; Zhang, Huai-Guo; Li, Bin

    2017-07-01

    Although some studies have reported potential associations of dietary patterns with depression risk, a consistent perspective hasn't been estimated to date. Therefore, we conducted this meta-analysis to evaluate the relation between dietary patterns and the risk of depression. A literature research was conducted searching MEDLINE and EMBASE databases up to September 2016. In total, 21 studies from ten countries met the inclusion criteria and were included in the present meta-analysis. A dietary pattern characterized by a high intakes of fruit, vegetables, whole grain, fish, olive oil, low-fat dairy and antioxidants and low intakes of animal foods was apparently associated with a decreased risk of depression. A dietary pattern characterized by a high consumption of red and/or processed meat, refined grains, sweets, high-fat dairy products, butter, potatoes and high-fat gravy, and low intakes of fruits and vegetables is associated with an increased risk of depression. The results of this meta-analysis suggest that healthy pattern may decrease the risk of depression, whereas western-style may increase the risk of depression. However, more randomized controlled trails and cohort studies are urgently required to confirm this findings. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

  6. Role of Dietary Pattern Analysis in Determining Cognitive Status in Elderly Australian Adults

    Kimberly Ashby-Mitchell

    2015-02-01

    Full Text Available Principal Component Analysis (PCA was used to determine the association between dietary patterns and cognitive function and to examine how classification systems based on food groups and food items affect levels of association between diet and cognitive function. The present study focuses on the older segment of the Australian Diabetes, Obesity and Lifestyle Study (AusDiab sample (age 60+ that completed the food frequency questionnaire at Wave 1 (1999/2000 and the mini-mental state examination and tests of memory, verbal ability and processing speed at Wave 3 (2012. Three methods were used in order to classify these foods before applying PCA. In the first instance, the 101 individual food items asked about in the questionnaire were used (no categorisation. In the second and third instances, foods were combined and reduced to 32 and 20 food groups, respectively, based on nutrient content and culinary usage—a method employed in several other published studies for PCA. Logistic regression analysis and generalized linear modelling was used to analyse the relationship between PCA-derived dietary patterns and cognitive outcome. Broader food group classifications resulted in a greater proportion of food use variance in the sample being explained (use of 101 individual foods explained 23.22% of total food use, while use of 32 and 20 food groups explained 29.74% and 30.74% of total variance in food use in the sample, respectively. Three dietary patterns were found to be associated with decreased odds of cognitive impairment (CI. Dietary patterns derived from 101 individual food items showed that for every one unit increase in ((Fruit and Vegetable Pattern: p = 0.030, OR 1.061, confidence interval: 1.006–1.118; (Fish, Legumes and Vegetable Pattern: p = 0.040, OR 1.032, confidence interval: 1.001–1.064; (Dairy, Cereal and Eggs Pattern: p = 0.003, OR 1.020, confidence interval: 1.007–1.033, the odds of cognitive impairment decreased. Different

  7. Investigating Convergence Patterns for Numerical Methods Using Data Analysis

    Gordon, Sheldon P.

    2013-01-01

    The article investigates the patterns that arise in the convergence of numerical methods, particularly those in the errors involved in successive iterations, using data analysis and curve fitting methods. In particular, the results obtained are used to convey a deeper level of understanding of the concepts of linear, quadratic, and cubic…

  8. Patterns of Intergenerational Occupational Movements: A Smallest-Space Analysis

    Mortimer, Jeylan T.

    1974-01-01

    Data collected by the smallest-space analysis technique indicates a pattern of occupational inheritance from father to son and the tendency of sons to choose work offering their fathers' vocational experiences, which supports the hypothesis that attributes of fathers' occupations are related to values transmitted to sons and reflected in their…

  9. Classifying and assembling two-dimensional X-ray laser diffraction patterns of a single particle to reconstruct the three-dimensional diffraction intensity function: resolution limit due to the quantum noise

    Tokuhisa, Atsushi; Taka, Junichiro; Kono, Hidetoshi; Go, Nobuhiro

    2012-01-01

    A new algorithm is developed for reconstructing the high-resolution three-dimensional diffraction intensity function of a globular biological macromolecule from many quantum-noise-limited two-dimensional X-ray laser diffraction patterns, each for an unknown orientation. The structural resolution is expressed as a function of the incident X-ray intensity and quantities characterizing the target molecule. A new two-step algorithm is developed for reconstructing the three-dimensional diffraction intensity of a globular biological macromolecule from many experimentally measured quantum-noise-limited two-dimensional X-ray laser diffraction patterns, each for an unknown orientation. The first step is classification of the two-dimensional patterns into groups according to the similarity of direction of the incident X-rays with respect to the molecule and an averaging within each group to reduce the noise. The second step is detection of common intersecting circles between the signal-enhanced two-dimensional patterns to identify their mutual location in the three-dimensional wavenumber space. The newly developed algorithm enables one to detect a signal for classification in noisy experimental photon-count data with as low as ∼0.1 photons per effective pixel. The wavenumber of such a limiting pixel determines the attainable structural resolution. From this fact, the resolution limit due to the quantum noise attainable by this new method of analysis as well as two important experimental parameters, the number of two-dimensional patterns to be measured (the load for the detector) and the number of pairs of two-dimensional patterns to be analysed (the load for the computer), are derived as a function of the incident X-ray intensity and quantities characterizing the target molecule

  10. Pattern recognition in menstrual bleeding diaries by statistical cluster analysis

    Wessel Jens

    2009-07-01

    Full Text Available Abstract Background The aim of this paper is to empirically identify a treatment-independent statistical method to describe clinically relevant bleeding patterns by using bleeding diaries of clinical studies on various sex hormone containing drugs. Methods We used the four cluster analysis methods single, average and complete linkage as well as the method of Ward for the pattern recognition in menstrual bleeding diaries. The optimal number of clusters was determined using the semi-partial R2, the cubic cluster criterion, the pseudo-F- and the pseudo-t2-statistic. Finally, the interpretability of the results from a gynecological point of view was assessed. Results The method of Ward yielded distinct clusters of the bleeding diaries. The other methods successively chained the observations into one cluster. The optimal number of distinctive bleeding patterns was six. We found two desirable and four undesirable bleeding patterns. Cyclic and non cyclic bleeding patterns were well separated. Conclusion Using this cluster analysis with the method of Ward medications and devices having an impact on bleeding can be easily compared and categorized.

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

    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.

  12. Urban Image Classification: Per-Pixel Classifiers, Sub-Pixel Analysis, Object-Based Image Analysis, and Geospatial Methods. 10; Chapter

    Myint, Soe W.; Mesev, Victor; Quattrochi, Dale; Wentz, Elizabeth A.

    2013-01-01

    Remote sensing methods used to generate base maps to analyze the urban environment rely predominantly on digital sensor data from space-borne platforms. This is due in part from new sources of high spatial resolution data covering the globe, a variety of multispectral and multitemporal sources, sophisticated statistical and geospatial methods, and compatibility with GIS data sources and methods. The goal of this chapter is to review the four groups of classification methods for digital sensor data from space-borne platforms; per-pixel, sub-pixel, object-based (spatial-based), and geospatial methods. Per-pixel methods are widely used methods that classify pixels into distinct categories based solely on the spectral and ancillary information within that pixel. They are used for simple calculations of environmental indices (e.g., NDVI) to sophisticated expert systems to assign urban land covers. Researchers recognize however, that even with the smallest pixel size the spectral information within a pixel is really a combination of multiple urban surfaces. Sub-pixel classification methods therefore aim to statistically quantify the mixture of surfaces to improve overall classification accuracy. While within pixel variations exist, there is also significant evidence that groups of nearby pixels have similar spectral information and therefore belong to the same classification category. Object-oriented methods have emerged that group pixels prior to classification based on spectral similarity and spatial proximity. Classification accuracy using object-based methods show significant success and promise for numerous urban 3 applications. Like the object-oriented methods that recognize the importance of spatial proximity, geospatial methods for urban mapping also utilize neighboring pixels in the classification process. The primary difference though is that geostatistical methods (e.g., spatial autocorrelation methods) are utilized during both the pre- and post

  13. Identifying clinical course patterns in SMS data using cluster analysis

    Kent, Peter; Kongsted, Alice

    2012-01-01

    ABSTRACT: BACKGROUND: Recently, there has been interest in using the short message service (SMS or text messaging), to gather frequent information on the clinical course of individual patients. One possible role for identifying clinical course patterns is to assist in exploring clinically important...... showed that clinical course patterns can be identified by cluster analysis using all SMS time points as cluster variables. This method is simple, intuitive and does not require a high level of statistical skill. However, there are alternative ways of managing SMS data and many different methods...

  14. Application of pattern recognition techniques to crime analysis

    Bender, C.F.; Cox, L.A. Jr.; Chappell, G.A.

    1976-08-15

    The initial goal was to evaluate the capabilities of current pattern recognition techniques when applied to existing computerized crime data. Performance was to be evaluated both in terms of the system's capability to predict crimes and to optimize police manpower allocation. A relation was sought to predict the crime's susceptibility to solution, based on knowledge of the crime type, location, time, etc. The preliminary results of this work are discussed. They indicate that automatic crime analysis involving pattern recognition techniques is feasible, and that efforts to determine optimum variables and techniques are warranted. 47 figures (RWR)

  15. Multitemporal spatial pattern analysis of Tulum's tropical coastal landscape

    Ramírez-Forero, Sandra Carolina; López-Caloca, Alejandra; Silván-Cárdenas, José Luis

    2011-11-01

    The tropical coastal landscape of Tulum in Quintana Roo, Mexico has a high ecological, economical, social and cultural value, it provides environmental and tourism services at global, national, regional and local levels. The landscape of the area is heterogeneous and presents random fragmentation patterns. In recent years, tourist services of the region has been increased promoting an accelerate expansion of hotels, transportation and recreation infrastructure altering the complex landscape. It is important to understand the environmental dynamics through temporal changes on the spatial patterns and to propose a better management of this ecological area to the authorities. This paper addresses a multi-temporal analysis of land cover changes from 1993 to 2000 in Tulum using Thematic Mapper data acquired by Landsat-5. Two independent methodologies were applied for the analysis of changes in the landscape and for the definition of fragmentation patterns. First, an Iteratively Multivariate Alteration Detection (IR-MAD) algorithm was used to detect and localize land cover change/no-change areas. Second, the post-classification change detection evaluated using the Support Vector Machine (SVM) algorithm. Landscape metrics were calculated from the results of IR-MAD and SVM. The analysis of the metrics indicated, among other things, a higher fragmentation pattern along roadways.

  16. Analysis of metabolomic patterns in thoroughbreds before and after exercise

    Hyun-Jun Jang

    2017-11-01

    Full Text Available Objective Evaluation of exercise effects in racehorses is important in horseracing industry and animal health care. In this study, we compared metabolic patterns between before and after exercise to screen metabolic biomarkers for exercise effects in thoroughbreds. Methods The concentration of metabolites in muscle, plasma, and urine was measured by 1H nuclear magnetic resonance (NMR spectroscopy analysis and the relative metabolite levels in the three samples were compared between before and after exercise. Subsequently, multivariate data analysis based on the metabolic profiles was performed using orthogonal partial least square discriminant analysis (OPLS-DA and variable important plots and t-test was used for basic statistical analysis. Results From 1H NMR spectroscopy analysis, 35, 25, and 34 metabolites were detected in the muscle, plasma, and urine. Aspartate, betaine, choline, cysteine, ethanol, and threonine were increased over 2-fold in the muscle; propionate and trimethylamine were increased over 2-fold in the plasma; and alanine, glycerol, inosine, lactate, and pyruvate were increased over 2-fold whereas acetoacetate, arginine, citrulline, creatine, glutamine, glutarate, hippurate, lysine, methionine, phenylacetylglycine, taurine, trigonelline, trimethylamine, and trimethylamine N-oxide were decreased below 0.5-fold in the urine. The OPLS-DA showed clear separation of the metabolic patterns before and after exercise in the muscle, plasma, and urine. Statistical analysis showed that after exercise, acetoacetate, arginine, glutamine, hippurate, phenylacetylglycine trimethylamine, trimethylamine N-oxide, and trigonelline were significantly decreased and alanine, glycerol, inosine, lactate, and pyruvate were significantly increased in the urine (p<0.05. Conclusion In conclusion, we analyzed integrated metabolic patterns in the muscle, plasma, and urine before and after exercise in racehorses. We found changed patterns of metabolites in

  17. Properties Analysis on Travel Intensity of Land Use Patterns

    Lishan Sun

    2014-01-01

    Full Text Available Quantization of the relationship between travel intensity and land use patterns is still a critical problem in urban transportation planning. Achieved researches on land use patterns are restricted to macrodata such as population and area, which failed to provide detail travel information for transportation planners. There is still problem on how to reflect the relationship between transport and land use accurately. This paper presents a study that is reflective of such an effort. A data extraction method is developed to get the travel origin and destination (OD between traffic zones based on the mobile data of 100,000 residents in Beijing. Then Point of Interests (POIs data in typical traffic zones was analyzed combined with construction area investigation. Based on the analysis of travel OD and POI data, the average travel intensity of each land use pattern is quantified. Research results could provide a quantitative basis for the optimization of urban transportation planning.

  18. Multivariate statistical pattern recognition system for reactor noise analysis

    Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

    1976-01-01

    A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system

  19. Multivariate statistical pattern recognition system for reactor noise analysis

    Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

    1975-01-01

    A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system. 19 references

  20. Rainfall Patterns Analysis over Ampangan Muda, Kedah from 2007 - 2016

    Chooi Tan, Kok

    2018-04-01

    The scientific knowledge about climate change and climate variability over Malaysia pertaining to the extreme water-related disaster such as drought and flood. A deficit or increment in precipitation occurred over the past century becomes a useful tool to understand the climate change in Malaysia. The purpose of this work is to examine the rainfall patterns over Ampangan Muda, Kedah. Daily rainfall data is acquired from Malaysian Meteorological Department to analyse the temporal and trends of the monthly and annual rainfall over the study area from 2007 to 2016. The obtained results show that the temporal and patterns of the rainfall over Ampangan Muda, Kedah is largely affected by the regional phenomena such as monsoon, El Niño Southern Oscillation (ENSO), and the Madden-Julian Oscillation. In addition, backward trajectories analysis is also used to identify the patterns for long-range of synoptic circulation over the region.

  1. Advanced morphological analysis of patterns of thin anodic porous alumina

    Toccafondi, C. [Istituto Italiano di Tecnologia, Department of Nanophysics, Via Morego 30, Genova I 16163 (Italy); Istituto Italiano di Tecnologia, Department of Nanostructures, Via Morego 30, Genova I 16163 (Italy); Stępniowski, W.J. [Department of Advanced Materials and Technologies, Faculty of Advanced Technologies and Chemistry, Military University of Technology, 2 Kaliskiego Str., 00-908 Warszawa (Poland); Leoncini, M. [Istituto Italiano di Tecnologia, Department of Nanostructures, Via Morego 30, Genova I 16163 (Italy); Salerno, M., E-mail: marco.salerno@iit.it [Istituto Italiano di Tecnologia, Department of Nanophysics, Via Morego 30, Genova I 16163 (Italy)

    2014-08-15

    Different conditions of fabrication of thin anodic porous alumina on glass substrates have been explored, obtaining two sets of samples with varying pore density and porosity, respectively. The patterns of pores have been imaged by high resolution scanning electron microscopy and analyzed by innovative methods. The regularity ratio has been extracted from radial profiles of the fast Fourier transforms of the images. Additionally, the Minkowski measures have been calculated. It was first observed that the regularity ratio averaged across all directions is properly corrected by the coefficient previously determined in the literature. Furthermore, the angularly averaged regularity ratio for the thin porous alumina made during short single-step anodizations is lower than that of hexagonal patterns of pores as for thick porous alumina from aluminum electropolishing and two-step anodization. Therefore, the regularity ratio represents a reliable measure of pattern order. At the same time, the lower angular spread of the regularity ratio shows that disordered porous alumina is more isotropic. Within each set, when changing either pore density or porosity, both regularity and isotropy remain rather constant, showing consistent fabrication quality of the experimental patterns. Minor deviations are tentatively discussed with the aid of the Minkowski measures, and the slight decrease in both regularity and isotropy for the final data-points of the porosity set is ascribed to excess pore opening and consequent pore merging. - Highlights: • Thin porous alumina is partly self-ordered and pattern analysis is required. • Regularity ratio is often misused: we fix the averaging and consider its spread. • We also apply the mathematical tool of Minkowski measures, new in this field. • Regularity ratio shows pattern isotropy and Minkowski helps in assessment. • General agreement with perfect artificial patterns confirms the good manufacturing.

  2. Adaptive pattern recognition in real-time video-based soccer analysis

    Schlipsing, Marc; Salmen, Jan; Tschentscher, Marc

    2017-01-01

    are taken into account. Our contribution is twofold: (1) the deliberate use of machine learning and pattern recognition techniques allows us to achieve high classification accuracy in varying environments. We systematically evaluate combinations of image features and learning machines in the given online......Computer-aided sports analysis is demanded by coaches and the media. Image processing and machine learning techniques that allow for "live" recognition and tracking of players exist. But these methods are far from collecting and analyzing event data fully autonomously. To generate accurate results......, human interaction is required at different stages including system setup, calibration, supervision of classifier training, and resolution of tracking conflicts. Furthermore, the real-time constraints are challenging: in contrast to other object recognition and tracking applications, we cannot treat data...

  3. Analysis of Usage Patterns in Large Multimedia Websites

    Singh, Rahul; Bhattarai, Bibek

    User behavior in a website is a critical indicator of the web site's usability and success. Therefore an understanding of usage patterns is essential to website design optimization. In this context, large multimedia websites pose a significant challenge for comprehension of the complex and diverse user behaviors they sustain. This is due to the complexity of analyzing and understanding user-data interactions in media-rich contexts. In this chapter we present a novel multi-perspective approach for usability analysis of large media rich websites. Our research combines multimedia web content analysis with elements of web-log analysis and visualization/visual mining of web usage metadata. Multimedia content analysis allows direct estimation of the information-cues presented to a user by the web content. Analysis of web logs and usage-metadata, such as location, type, and frequency of interactions provides a complimentary perspective on the site's usage. The entire set of information is leveraged through powerful visualization and interactive querying techniques to provide analysis of usage patterns, measure of design quality, as well as the ability to rapidly identify problems in the web-site design. Experiments on media rich sites including the SkyServer - a large multimedia web-based astronomy information repository demonstrate the efficacy and promise of the proposed approach.

  4. Fast Most Similar Neighbor (MSN) classifiers for Mixed Data

    Hernández Rodríguez, Selene

    2010-01-01

    The k nearest neighbor (k-NN) classifier has been extensively used in Pattern Recognition because of its simplicity and its good performance. However, in large datasets applications, the exhaustive k-NN classifier becomes impractical. Therefore, many fast k-NN classifiers have been developed; most of them rely on metric properties (usually the triangle inequality) to reduce the number of prototype comparisons. Hence, the existing fast k-NN classifiers are applicable only when the comparison f...

  5. ICAP: An Interactive Cluster Analysis Procedure for analyzing remotely sensed data. [to classify the radiance data to produce a thematic map

    Wharton, S. W.

    1980-01-01

    An Interactive Cluster Analysis Procedure (ICAP) was developed to derive classifier training statistics from remotely sensed data. The algorithm interfaces the rapid numerical processing capacity of a computer with the human ability to integrate qualitative information. Control of the clustering process alternates between the algorithm, which creates new centroids and forms clusters and the analyst, who evaluate and elect to modify the cluster structure. Clusters can be deleted or lumped pairwise, or new centroids can be added. A summary of the cluster statistics can be requested to facilitate cluster manipulation. The ICAP was implemented in APL (A Programming Language), an interactive computer language. The flexibility of the algorithm was evaluated using data from different LANDSAT scenes to simulate two situations: one in which the analyst is assumed to have no prior knowledge about the data and wishes to have the clusters formed more or less automatically; and the other in which the analyst is assumed to have some knowledge about the data structure and wishes to use that information to closely supervise the clustering process. For comparison, an existing clustering method was also applied to the two data sets.

  6. SOA Modeling Patterns for Service Oriented Discovery and Analysis

    Bell, Michael

    2010-01-01

    Learn the essential tools for developing a sound service-oriented architecture. SOA Modeling Patterns for Service-Oriented Discovery and Analysis introduces a universal, easy-to-use, and nimble SOA modeling language to facilitate the service identification and examination life cycle stage. This business and technological vocabulary will benefit your service development endeavors and foster organizational software asset reuse and consolidation, and reduction of expenditure. Whether you are a developer, business architect, technical architect, modeler, business analyst, team leader, or manager,

  7. Fringe pattern analysis for optical metrology theory, algorithms, and applications

    Servin, Manuel; Padilla, Moises

    2014-01-01

    The main objective of this book is to present the basic theoretical principles and practical applications for the classical interferometric techniques and the most advanced methods in the field of modern fringe pattern analysis applied to optical metrology. A major novelty of this work is the presentation of a unified theoretical framework based on the Fourier description of phase shifting interferometry using the Frequency Transfer Function (FTF) along with the theory of Stochastic Process for the straightforward analysis and synthesis of phase shifting algorithms with desired properties such

  8. Fractal Analysis of Radiologists Visual Scanning Pattern in Screening Mammography

    Alamudun, Folami T [ORNL; Yoon, Hong-Jun [ORNL; Hudson, Kathy [University of Tennessee, Knoxville (UTK); Morin-Ducote, Garnetta [University of Tennessee, Knoxville (UTK); Tourassi, Georgia [ORNL

    2015-01-01

    Several investigators have investigated radiologists visual scanning patterns with respect to features such as total time examining a case, time to initially hit true lesions, number of hits, etc. The purpose of this study was to examine the complexity of the radiologists visual scanning pattern when viewing 4-view mammographic cases, as they typically do in clinical practice. Gaze data were collected from 10 readers (3 breast imaging experts and 7 radiology residents) while reviewing 100 screening mammograms (24 normal, 26 benign, 50 malignant). The radiologists scanpaths across the 4 mammographic views were mapped to a single 2-D image plane. Then, fractal analysis was applied on the derived scanpaths using the box counting method. For each case, the complexity of each radiologist s scanpath was estimated using fractal dimension. The association between gaze complexity, case pathology, case density, and radiologist experience was evaluated using 3 factor fixed effects ANOVA. ANOVA showed that case pathology, breast density, and experience level are all independent predictors of the visual scanning pattern complexity. Visual scanning patterns are significantly different for benign and malignant cases than for normal cases as well as when breast parenchyma density changes.

  9. Patterns of regional development in Serbia: A multivariate statistical analysis

    Čoček Ladislav

    2010-01-01

    Full Text Available The primary objective of this paper is to examine patterns of regional development in Serbia and to identify underlying geographical factors of these patterns. Principal component analysis is used to reveal the basic dimensions of regional differentiation. Its results are described in the context of findings from thematically similar research on Central European countries. An area's position in the national settlement system hierarchy has been identified as the strongest determinant of regional differentiation in Serbia. Other strong patterns seem to be connected with macro-geographical position. Success in economic development is most apparent in regions near the metropolitan area of Belgrade, and the general development level, along with a predisposition for agriculture, exhibits a strong north-south polarization. Specific attention is directed at demographic development, which is characterized by a west-east gradient. Central patterns of regional differentiation are similar to those uncovered in previous Central European research. Regional policy in Serbia should, therefore, try to learn from experience within this region to cope with processes and problems that are often quite similar. .

  10. Profile agreement indices in Rietveld and pattern-fitting analysis

    Hill, R.J.; Fischer, R.X.

    1990-01-01

    Two definitions of profile agreement indices are now in common use for estimating the degree of fit in Rietveld refinement and in structure-independent pattern-fitting methods of powder diffraction analysis. In the original program written by Rietveld, the background was subtracted and the 'non-peak' regions of the pattern were removed from further consideration in a preliminary data-reduction stage prior to structure refinement. However, the agreement indices used in many of the more recent programs retain the background counts in the observed step intensities and include all portions of the pattern in the sums. These latter definitions are strongly dependent on the signal-to-noise ratio and on the relative amount of 'background-only' regions and do not, therefore, provide a sound basis for comparing the degree of fit of peak profile and crystal structure model refinements in the general case. The extent of this dependence is illustrated quantitatively using conventional and synchrotron X-ray and constant-wavelength and time-of-flight neutron data sets with different inherent background levels and peak densities. The unweighted background-corrected peak-only profile agreement index R' p =Σ i vertical strokeY io -Y ic vertical stroke/Σ i vertical strokeY io -Y ib vertical stroke (and, to a lesser extent, its weighted equivalent) is recommended as the most appropriate criterion of fit for comparative work between diffraction patterns of all kinds. (orig.)

  11. Visual and Quantitative Analysis Methods of Respiratory Patterns for Respiratory Gated PET/CT.

    Son, Hye Joo; Jeong, Young Jin; Yoon, Hyun Jin; Park, Jong-Hwan; Kang, Do-Young

    2016-01-01

    We integrated visual and quantitative methods for analyzing the stability of respiration using four methods: phase space diagrams, Fourier spectra, Poincaré maps, and Lyapunov exponents. Respiratory patterns of 139 patients were grouped based on the combination of the regularity of amplitude, period, and baseline positions. Visual grading was done by inspecting the shape of diagram and classified into two states: regular and irregular. Quantitation was done by measuring standard deviation of x and v coordinates of Poincaré map (SD x , SD v ) or the height of the fundamental peak ( A 1 ) in Fourier spectrum or calculating the difference between maximal upward and downward drift. Each group showed characteristic pattern on visual analysis. There was difference of quantitative parameters (SD x , SD v , A 1 , and MUD-MDD) among four groups (one way ANOVA, p = 0.0001 for MUD-MDD, SD x , and SD v , p = 0.0002 for A 1 ). In ROC analysis, the cutoff values were 0.11 for SD x (AUC: 0.982, p quantitative indices of respiratory stability and determining quantitative cutoff value for differentiating regular and irregular respiration.

  12. Geostatistical analysis of allele presence patterns among American black bears in eastern North Carolina

    Thompson, L.M.; Van Manen, F.T.; King, T.L.

    2005-01-01

    primary orientation of the best habitat areas. Furthermore, the patterns we observed suggest directions of potential source populations beyond the 2 study areas. Indeed, nearby areas classified as core black bear habitat exist in the directions indicated by our analysis. Geostatistical analysis of allele occurrence patterns may provide a useful technique to identify potential barriers to gene flow among bear populations.

  13. Accident patterns for construction-related workers: a cluster analysis

    Liao, Chia-Wen; Tyan, Yaw-Yauan

    2012-01-01

    The construction industry has been identified as one of the most hazardous industries. The risk of constructionrelated workers is far greater than that in a manufacturing based industry. However, some steps can be taken to reduce worker risk through effective injury prevention strategies. In this article, k-means clustering methodology is employed in specifying the factors related to different worker types and in identifying the patterns of industrial occupational accidents. Accident reports during the period 1998 to 2008 are extracted from case reports of the Northern Region Inspection Office of the Council of Labor Affairs of Taiwan. The results show that the cluster analysis can indicate some patterns of occupational injuries in the construction industry. Inspection plans should be proposed according to the type of construction-related workers. The findings provide a direction for more effective inspection strategies and injury prevention programs.

  14. Distinct patterns of Internet and smartphone-related problems among adolescents by gender: Latent class analysis.

    Lee, Seung-Yup; Lee, Donghwan; Nam, Cho Rong; Kim, Da Yea; Park, Sera; Kwon, Jun-Gun; Kweon, Yong-Sil; Lee, Youngjo; Kim, Dai Jin; Choi, Jung-Seok

    2018-05-23

    Background and objectives The ubiquitous Internet connections by smartphones weakened the traditional boundaries between computers and mobile phones. We sought to explore whether smartphone-related problems differ from those of computer use according to gender using latent class analysis (LCA). Methods After informed consents, 555 Korean middle-school students completed surveys on gaming, Internet use, and smartphone usage patterns. They also completed various psychosocial instruments. LCA was performed for the whole group and by gender. In addition to ANOVA and χ 2 tests, post-hoc tests were conducted to examine differences among the LCA subgroups. Results In the whole group (n = 555), four subtypes were identified: dual-problem users (49.5%), problematic Internet users (7.7%), problematic smartphone users (32.1%), and "healthy" users (10.6%). Dual-problem users scored highest for addictive behaviors and other psychopathologies. The gender-stratified LCA revealed three subtypes for each gender. With dual-problem and healthy subgroup as common, problematic Internet subgroup was classified in the males, whereas problematic smartphone subgroup was classified in the females in the gender-stratified LCA. Thus, distinct patterns were observed according to gender with higher proportion of dual-problem present in males. While gaming was associated with problematic Internet use in males, aggression and impulsivity demonstrated associations with problematic smartphone use in females. Conclusions An increase in the number of digital media-related problems was associated with worse outcomes in various psychosocial scales. Gaming may play a crucial role in males solely displaying Internet-related problems. The heightened impulsivity and aggression seen in our female problematic smartphone users requires further research.

  15. High dimensional classifiers in the imbalanced case

    Bak, Britta Anker; Jensen, Jens Ledet

    We consider the binary classification problem in the imbalanced case where the number of samples from the two groups differ. The classification problem is considered in the high dimensional case where the number of variables is much larger than the number of samples, and where the imbalance leads...... to a bias in the classification. A theoretical analysis of the independence classifier reveals the origin of the bias and based on this we suggest two new classifiers that can handle any imbalance ratio. The analytical results are supplemented by a simulation study, where the suggested classifiers in some...

  16. Allergen Sensitization Pattern by Sex: A Cluster Analysis in Korea.

    Ohn, Jungyoon; Paik, Seung Hwan; Doh, Eun Jin; Park, Hyun-Sun; Yoon, Hyun-Sun; Cho, Soyun

    2017-12-01

    Allergens tend to sensitize simultaneously. Etiology of this phenomenon has been suggested to be allergen cross-reactivity or concurrent exposure. However, little is known about specific allergen sensitization patterns. To investigate the allergen sensitization characteristics according to gender. Multiple allergen simultaneous test (MAST) is widely used as a screening tool for detecting allergen sensitization in dermatologic clinics. We retrospectively reviewed the medical records of patients with MAST results between 2008 and 2014 in our Department of Dermatology. A cluster analysis was performed to elucidate the allergen-specific immunoglobulin (Ig)E cluster pattern. The results of MAST (39 allergen-specific IgEs) from 4,360 cases were analyzed. By cluster analysis, 39items were grouped into 8 clusters. Each cluster had characteristic features. When compared with female, the male group tended to be sensitized more frequently to all tested allergens, except for fungus allergens cluster. The cluster and comparative analysis results demonstrate that the allergen sensitization is clustered, manifesting allergen similarity or co-exposure. Only the fungus cluster allergens tend to sensitize female group more frequently than male group.

  17. EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures.

    Wang, Lei; Long, Xi; Arends, Johan B A M; Aarts, Ronald M

    2017-10-01

    The traditional EEG features in the time and frequency domain show limited seizure detection performance in the epileptic population with intellectual disability (ID). In addition, the influence of EEG seizure patterns on detection performance was less studied. A single-channel EEG signal can be mapped into visibility graphs (VGS), including basic visibility graph (VG), horizontal VG (HVG), and difference VG (DVG). These graphs were used to characterize different EEG seizure patterns. To demonstrate its effectiveness in identifying EEG seizure patterns and detecting generalized seizures, EEG recordings of 615h on one EEG channel from 29 epileptic patients with ID were analyzed. A novel feature set with discriminative power for seizure detection was obtained by using the VGS method. The degree distributions (DDs) of DVG can clearly distinguish EEG of each seizure pattern. The degree entropy and power-law degree power in DVG were proposed here for the first time, and they show significant difference between seizure and non-seizure EEG. The connecting structure measured by HVG can better distinguish seizure EEG from background than those by VG and DVG. A traditional EEG feature set based on frequency analysis was used here as a benchmark feature set. With a support vector machine (SVM) classifier, the seizure detection performance of the benchmark feature set (sensitivity of 24%, FD t /h of 1.8s) can be improved by combining our proposed VGS features extracted from one EEG channel (sensitivity of 38%, FD t /h of 1.4s). The proposed VGS-based features can help improve seizure detection for ID patients. Copyright © 2017 Elsevier B.V. All rights reserved.

  18. Adaptive Human aware Navigation based on Motion Pattern Analysis

    Tranberg, Søren; Svenstrup, Mikael; Andersen, Hans Jørgen

    2009-01-01

    Respecting people’s social spaces is an important prerequisite for acceptable and natural robot navigation in human environments. In this paper, we describe an adaptive system for mobile robot navigation based on estimates of whether a person seeks to interact with the robot or not. The estimates...... are based on run-time motion pattern analysis compared to stored experience in a database. Using a potential field centered around the person, the robot positions itself at the most appropriate place relative to the person and the interaction status. The system is validated through qualitative tests...

  19. Analysis of Stomatal Patterning in Selected Mutants of MAPK Pathways

    Felemban, Abrar

    2016-05-01

    Stomata are cellular valves in plants that play an essential role in the regulation of gas exchange and are distributed in the epidermis of aerial organs. In Arabidopsis thaliana, stomatal production and development are coordinated by the mitogen-activated protein kinase (MAPK) signalling pathway, which modulates a variety of other processes, including cell proliferation, regulation of cytokinesis, programed cell death, and response to abiotic and biotic stress. The environment also plays a role in stomatal development, by influencing the frequency at which stomata develop in leaves. This thesis presents an analysis of stomatal development in Arabidopsis mutants in two MAPK pathways: MEKK1-MKK1/MKK2-MPK4, and MAP3K17/18-MKK3. Obtained results demonstrate the effect of stress conditions on stomatal development and specify the involvement of analysed MAPK in stomatal patterning. First, both analysed pathways modulate stomatal patterning in Arabidopsis cotyledons. Second, plant growth-promoting bacteria tested enhance stomatal density and affect guard cell morphology. Third, the sucrose or mannitol treatment increases defects in stomatal patterning. Finally, salt stress or high temperature can suppress stomatal defects in mutants of the MEKK1-MKK1/MKK2-MPK4 pathway.

  20. Pattern analysis of planning and management at the radiographic dept. in hospital

    Yanagisawa, Makoto; Taniguchi, Gen; Imai, Shoji.

    1979-01-01

    This papers attempt to make the planning method, and the relationships between planning and management by the circulation studies. We investigated the circulation of radiographic departments in 3 hospitals, the managements of 20 hospitals, and the planning layouts of 63 hospitals. Now we made 9 typical diagrammatic layouts to classify many plans and some patterns to classify many management types. In this process we used some items to classify as follows. (1) Staffs' works; there are diagnosis, photographing, nursing, developing, assistant or management works, and so on. (2) Management manners; there are three types, such as only photographing facility type, photographing and nursing facility type, and diagnosis facility type. (3) Classify how to developing and how to do assistant or management works. (4) Planning types; table-6 1) Patients' spaces are separate or not. 2) Photographing staffs' corners are independent or not. 3) Developing spaces are centralized or not. 4) Are there or not, the connections between patients' zones and staffs'. (author)

  1. Recognize and classify pneumoconiosis

    Hering, K.G.; Hofmann-Preiss, K.

    2014-01-01

    In the year 2012, out of the 10 most frequently recognized occupational diseases 6 were forms of pneumoconiosis. With respect to healthcare and economic aspects, silicosis and asbestos-associated diseases are of foremost importance. The latter are to be found everywhere and are not restricted to large industrial areas. Radiology has a central role in the diagnosis and evaluation of occupational lung disorders. In cases of known exposure mainly to asbestos and quartz, the diagnosis of pneumoconiosis, with few exceptions will be established primarily by the radiological findings. As these disorders are asymptomatic for a long time they are quite often detected as incidental findings in examinations for other reasons. Therefore, radiologists have to be familiar with the pattern of findings of the most frequent forms of pneumoconiosis and the differential diagnoses. For reasons of equal treatment of the insured a quality-based, standardized performance, documentation and evaluation of radiological examinations is required in preventive procedures and evaluations. Above all, a standardized low-dose protocol has to be used in computed tomography (CT) examinations, although individualized concerning the dose, in order to keep radiation exposure as low as possible for the patient. The International Labour Office (ILO) classification for the coding of chest X-rays and the international classification of occupational and environmental respiratory diseases (ICOERD) classification used since 2004 for CT examinations meet the requirements of the insured and the occupational insurance associations as a means of reproducible and comparable data for decision-making. (orig.) [de

  2. [Applications of 2D and 3D landscape pattern indices in landscape pattern analysis of mountainous area at county level].

    Lu, Chao; Qi, Wei; Li, Le; Sun, Yao; Qin, Tian-Tian; Wang, Na-Na

    2012-05-01

    Landscape pattern indices are the commonly used tools for the quantitative analysis of landscape pattern. However, the traditional 2D landscape pattern indices neglect the effects of terrain on landscape, existing definite limitations in quantitatively describing the landscape patterns in mountains areas. Taking the Qixia City, a typical mountainous and hilly region in Shandong Province of East China, as a case, this paper compared the differences between 2D and 3D landscape pattern indices in quantitatively describing the landscape patterns and their dynamic changes in mountainous areas. On the basis of terrain structure analysis, a set of landscape pattern indices were selected, including area and density (class area and mean patch size), edge and shape (edge density, landscape shape index, and fractal dimension of mean patch), diversity (Shannon's diversity index and evenness index) , and gathering and spread (contagion index). There existed obvious differences between the 3D class area, mean patch area, and edge density and the corresponding 2D indices, but no significant differences between the 3D landscape shape index, fractal dimension of mean patch, and Shannon' s diversity index and evenness index and the corresponding 2D indices. The 3D contagion index and 2D contagion index had no difference. Because the 3D landscape pattern indices were calculated by using patch surface area and surface perimeter whereas the 2D landscape pattern indices were calculated by adopting patch projective area and projective perimeter, the 3D landscape pattern indices could be relative accurate and efficient in describing the landscape area, density and borderline, in mountainous areas. However, there were no distinct differences in describing landscape shape, diversity, and gathering and spread between the 3D and 2D landscape pattern indices. Generally, by introducing 3D landscape pattern indices to topographic pattern, the description of landscape pattern and its dynamic

  3. Different Patterns of the Urban Heat Island Intensity from Cluster Analysis

    Silva, F. B.; Longo, K.

    2014-12-01

    This study analyzes the different variability patterns of the Urban Heat Island intensity (UHII) in the Metropolitan Area of Rio de Janeiro (MARJ), one of the largest urban agglomerations in Brazil. The UHII is defined as the difference in the surface air temperature between the urban/suburban and rural/vegetated areas. To choose one or more stations that represent those areas we used the technique of cluster analysis on the air temperature observations from 14 surface weather stations in the MARJ. The cluster analysis aims to classify objects based on their characteristics, gathering similar groups. The results show homogeneity patterns between air temperature observations, with 6 homogeneous groups being defined. Among those groups, one might be a natural choice for the representative urban area (Central station); one corresponds to suburban area (Afonsos station); and another group referred as rural area is compound of three stations (Ecologia, Santa Cruz and Xerém) that are located in vegetated regions. The arithmetic mean of temperature from the three rural stations is taken to represent the rural station temperature. The UHII is determined from these homogeneous groups. The first UHII is estimated from urban and rural temperature areas (Case 1), whilst the second UHII is obtained from suburban and rural temperature areas (Case 2). In Case 1, the maximum UHII occurs in two periods, one in the early morning and the other at night, while the minimum UHII occurs in the afternoon. In Case 2, the maximum UHII is observed during afternoon/night and the minimum during dawn/early morning. This study demonstrates that the stations choice reflects different UHII patterns, evidencing that distinct behaviors of this phenomenon can be identified.

  4. Finger crease pattern recognition using Legendre moments and principal component analysis

    Luo, Rongfang; Lin, Tusheng

    2007-03-01

    The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis (PCA). After obtaining the region of interest (ROI) for each finger image in the pre-processing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve (K-L) transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.

  5. Quantitative analysis of breast echotexture patterns in automated breast ultrasound images

    Chang, Ruey-Feng; Hou, Yu-Ling; Lo, Chung-Ming; Huang, Chiun-Sheng; Chen, Jeon-Hor; Kim, Won Hwa; Chang, Jung Min; Bae, Min Sun; Moon, Woo Kyung

    2015-01-01

    Purpose: Breast tissue composition is considered to be associated with breast cancer risk. This study aimed to develop a computer-aided classification (CAC) system to automatically classify echotexture patterns as heterogeneous or homogeneous using automated breast ultrasound (ABUS) images. Methods: A CAC system was proposed that can recognize breast echotexture patterns in ABUS images. For each case, the echotexture pattern was assessed by two expert radiologists and classified as heterogeneous or homogeneous. After neutrosophic image transformation and fuzzy c-mean clusterings, the lower and upper boundaries of the fibroglandular tissues were defined. Then, the number of hypoechoic regions and histogram features were extracted from the fibroglandular tissues, and the support vector machine model with the leave-one-out cross-validation method was utilized as the classifier. The authors’ database included a total of 208 ABUS images of the breasts of 104 females. Results: The accuracies of the proposed system for the classification of heterogeneous and homogeneous echotexture patterns were 93.48% (43/46) and 92.59% (150/162), respectively, with an overall Az (area under the receiver operating characteristic curve) of 0.9786. The agreement between the radiologists and the proposed system was almost perfect, with a kappa value of 0.814. Conclusions: The use of ABUS and the proposed method can provide quantitative information on the echotexture patterns of the breast and can be used to evaluate whether breast echotexture patterns are associated with breast cancer risk in the future

  6. Quantitative analysis of breast echotexture patterns in automated breast ultrasound images

    Chang, Ruey-Feng [Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan and Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan (China); Hou, Yu-Ling [Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan (China); Lo, Chung-Ming [Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan (China); Huang, Chiun-Sheng [Department of Surgery, National Taiwan University Hospital, Taipei 10617, Taiwan (China); Chen, Jeon-Hor [Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung 82445, Taiwan and Tu and Yuen Center for Functional Onco-Imaging and Department of Radiological Science, University of California, Irvine, California 92697 (United States); Kim, Won Hwa; Chang, Jung Min; Bae, Min Sun; Moon, Woo Kyung, E-mail: moonwk@snu.ac.kr [Department of Radiology, Seoul National University Hospital, Seoul 110-744 (Korea, Republic of)

    2015-08-15

    Purpose: Breast tissue composition is considered to be associated with breast cancer risk. This study aimed to develop a computer-aided classification (CAC) system to automatically classify echotexture patterns as heterogeneous or homogeneous using automated breast ultrasound (ABUS) images. Methods: A CAC system was proposed that can recognize breast echotexture patterns in ABUS images. For each case, the echotexture pattern was assessed by two expert radiologists and classified as heterogeneous or homogeneous. After neutrosophic image transformation and fuzzy c-mean clusterings, the lower and upper boundaries of the fibroglandular tissues were defined. Then, the number of hypoechoic regions and histogram features were extracted from the fibroglandular tissues, and the support vector machine model with the leave-one-out cross-validation method was utilized as the classifier. The authors’ database included a total of 208 ABUS images of the breasts of 104 females. Results: The accuracies of the proposed system for the classification of heterogeneous and homogeneous echotexture patterns were 93.48% (43/46) and 92.59% (150/162), respectively, with an overall Az (area under the receiver operating characteristic curve) of 0.9786. The agreement between the radiologists and the proposed system was almost perfect, with a kappa value of 0.814. Conclusions: The use of ABUS and the proposed method can provide quantitative information on the echotexture patterns of the breast and can be used to evaluate whether breast echotexture patterns are associated with breast cancer risk in the future.

  7. TESTING TREE-CLASSIFIER VARIANTS AND ALTERNATE MODELING METHODOLOGIES IN THE EAST GREAT BASIN MAPPING UNIT OF THE SOUTHWEST REGIONAL GAP ANALYSIS PROJECT (SW REGAP)

    We tested two methods for dataset generation and model construction, and three tree-classifier variants to identify the most parsimonious and thematically accurate mapping methodology for the SW ReGAP project. Competing methodologies were tested in the East Great Basin mapping un...

  8. Analysis of abnormally thickened endometrial patterns on transvaginal sonography

    Lee, Myung Sook; Cho, Hyeun Cha

    1999-01-01

    To determine whether the transvaginal sonographic appearance of the thickened endometrium can help to predict the underlying endometrial pathologic process. The sonogram reports of fall 41 pre- and 21 postmenopausal women who underwent transvaginal sonogram were retrospectively analyzed. The women undergoing estrogen replacement therapy, tamoxifen therapy or having abnormal cervical cytology were excluded from this study. The analysis of sonographic and histologic results was performed in all patients. Three distinct sonographic patterns were encountered. Type I consisted of heterogeneous endometrial thickening with internal hypoechoic areas (normal [n=4], polyp [n=1] and cancer [n=4] in premenopausal women and cancer [n=4] in postmenopausal women). Type II consisted of echogenic endometrial thickening with or without tiny cysts (normal[n=5], and hyperplasia [n=7] in premenopausal women and normal [n=4], polyp [n=2], and hyperplasia [n=1] in postmenopausal women). Type III consisted of localized well defined endoluminal lesion (normal [n=1], polyp [n=14], hyperplasia [n=1], cancer [n=1], and submucosal mass [n=3] in premenopausal women and normal [n=4], polyp [n=2],submucosal mass [n=3], and hematoma [n=1] in postmenopausal women). The measurement of the endometrial thickness combined with analysis of sonographic echo patterns may be helpful in prediction and differentiation of endometrial disease in pre- and postmenopausal women. Also it can contribute to avoiding unnecessary D and C.

  9. Optimizing human activity patterns using global sensitivity analysis.

    Fairchild, Geoffrey; Hickmann, Kyle S; Mniszewski, Susan M; Del Valle, Sara Y; Hyman, James M

    2014-12-01

    Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule's regularity for a population. We show how to tune an activity's regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimization problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. We use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations.

  10. Neural Network Classifiers for Local Wind Prediction.

    Kretzschmar, Ralf; Eckert, Pierre; Cattani, Daniel; Eggimann, Fritz

    2004-05-01

    This paper evaluates the quality of neural network classifiers for wind speed and wind gust prediction with prediction lead times between +1 and +24 h. The predictions were realized based on local time series and model data. The selection of appropriate input features was initiated by time series analysis and completed by empirical comparison of neural network classifiers trained on several choices of input features. The selected input features involved day time, yearday, features from a single wind observation device at the site of interest, and features derived from model data. The quality of the resulting classifiers was benchmarked against persistence for two different sites in Switzerland. The neural network classifiers exhibited superior quality when compared with persistence judged on a specific performance measure, hit and false-alarm rates.

  11. Evaluation of chemical transport model predictions of primary organic aerosol for air masses classified by particle component-based factor analysis

    C. A. Stroud

    2012-09-01

    Full Text Available Observations from the 2007 Border Air Quality and Meteorology Study (BAQS-Met 2007 in Southern Ontario, Canada, were used to evaluate predictions of primary organic aerosol (POA and two other carbonaceous species, black carbon (BC and carbon monoxide (CO, made for this summertime period by Environment Canada's AURAMS regional chemical transport model. Particle component-based factor analysis was applied to aerosol mass spectrometer measurements made at one urban site (Windsor, ON and two rural sites (Harrow and Bear Creek, ON to derive hydrocarbon-like organic aerosol (HOA factors. A novel diagnostic model evaluation was performed by investigating model POA bias as a function of HOA mass concentration and indicator ratios (e.g. BC/HOA. Eight case studies were selected based on factor analysis and back trajectories to help classify model bias for certain POA source types. By considering model POA bias in relation to co-located BC and CO biases, a plausible story is developed that explains the model biases for all three species.

    At the rural sites, daytime mean PM1 POA mass concentrations were under-predicted compared to observed HOA concentrations. POA under-predictions were accentuated when the transport arriving at the rural sites was from the Detroit/Windsor urban complex and for short-term periods of biomass burning influence. Interestingly, the daytime CO concentrations were only slightly under-predicted at both rural sites, whereas CO was over-predicted at the urban Windsor site with a normalized mean bias of 134%, while good agreement was observed at Windsor for the comparison of daytime PM1 POA and HOA mean values, 1.1 μg m−3 and 1.2 μg m−3, respectively. Biases in model POA predictions also trended from positive to negative with increasing HOA values. Periods of POA over-prediction were most evident at the urban site on calm nights due to an overly-stable model surface layer

  12. Integrative Analysis of DCE-MRI and Gene Expression Profiles in Construction of a Gene Classifier for Assessment of Hypoxia-Related Risk of Chemoradiotherapy Failure in Cervical Cancer

    Fjeldbo, Christina S; Julin, Cathinka H; Lando, Malin

    2016-01-01

    platforms. The prognostic value was independent of existing clinical markers, regardless of clinical endpoints. CONCLUSIONS: A robust DCE-MRI-associated gene classifier has been constructed that may be used to achieve an early indication of patients' risk of hypoxia-related chemoradiotherapy failure.......PURPOSE: A 31-gene expression signature reflected in dynamic contrast enhanced (DCE)-MR images and correlated with hypoxia-related aggressiveness in cervical cancer was identified in previous work. We here aimed to construct a dichotomous classifier with key signature genes and a predefined...... as an indicator of hypoxia. RESULTS: Classifier candidates were constructed by integrative analysis of ABrix and gene expression profiles in the training cohort and evaluated by a leave-one-out cross-validation approach. On the basis of their ability to separate patients correctly according to hypoxia status, a 6...

  13. Development of Methodology to Classify Historical Panchromatic Aerial Photography. Analysis of Landscape Features on Point Au Fer Island, Louisiana - from 1956 to 2009: A Case Study

    2011-12-01

    7,386 acres). Hurricanes and other extreme extratropical storms have been shown to contribute to extensive shoreline erosion and breaching, and the...that provide protection from storms ; serve as species habitat; act as a control for nutrient and pollution transfer; support fish, agriculture...quickly and accurately classify historical panchromatic photography in order to identify storm -induced land loss and impacts (Morton et al. 2005; Barras

  14. Electromyographic Pattern Analysis and Classification for a Robotic Prosthetic Arm

    M. José H. Erazo Macias

    2006-01-01

    Full Text Available This paper deals with the statistical analysis and pattern classification of electromyographic signals from the biceps of a person with amputation below the humerus. Such signals collected from an amputation simulator are synergistically generated to produce discrete elbow movements. The purpose of this study is to utilise these signals to control an electrically driven prosthetic or orthotic elbow with minimum extra mental effort on the part of the subject. The results show very good separability of classes of movements when a learning pattern classification scheme is used, and a superposition of any composite motion to the three basic primitive motions—humeral rotation in and out, flexion and extension, and pronation and supination. Since no synergy was detected for the wrist movement, different inputs have to be provided for a grip. In addition, the method described is not limited by the location of the electrodes. For amputees with shorter stumps, synergistic signals could be obtained from the shoulder muscles. However, the presentation in this paper is limited to biceps signal classification only.

  15. Pattern Recognition of Gene Expression with Singular Spectrum Analysis

    Hossein Hassani

    2014-07-01

    Full Text Available Drosophila segmentation as a model organism is one of the most highly studied. Among many maternal segmentation coordinate genes, bicoid protein pattern plays a significant role during Drosophila embryogenesis, since this gradient determines most aspects of head and thorax development. Despite the fact that several models have been proposed to describe the bicoid gradient, due to its association with considerable error, each can only partially explain bicoid characteristics. In this paper, a modified version of singular spectrum analysis is examined for filtering and extracting the bicoid gene expression signal. The results with strong evidence indicate that the proposed technique is able to remove noise more effectively and can be considered as a promising method for filtering gene expression measurements for other applications.

  16. Transcriptome analysis reveals novel patterning and pigmentation genes underlying Heliconius butterfly wing pattern variation

    Hines Heather M

    2012-06-01

    Full Text Available Abstract Background Heliconius butterfly wing pattern diversity offers a unique opportunity to investigate how natural genetic variation can drive the evolution of complex adaptive phenotypes. Positional cloning and candidate gene studies have identified a handful of regulatory and pigmentation genes implicated in Heliconius wing pattern variation, but little is known about the greater developmental networks within which these genes interact to pattern a wing. Here we took a large-scale transcriptomic approach to identify the network of genes involved in Heliconius wing pattern development and variation. This included applying over 140 transcriptome microarrays to assay gene expression in dissected wing pattern elements across a range of developmental stages and wing pattern morphs of Heliconius erato. Results We identified a number of putative early prepattern genes with color-pattern related expression domains. We also identified 51 genes differentially expressed in association with natural color pattern variation. Of these, the previously identified color pattern “switch gene” optix was recovered as the first transcript to show color-specific differential expression. Most differentially expressed genes were transcribed late in pupal development and have roles in cuticle formation or pigment synthesis. These include previously undescribed transporter genes associated with ommochrome pigmentation. Furthermore, we observed upregulation of melanin-repressing genes such as ebony and Dat1 in non-melanic patterns. Conclusions This study identifies many new genes implicated in butterfly wing pattern development and provides a glimpse into the number and types of genes affected by variation in genes that drive color pattern evolution.

  17. Generation and Analysis of Constrained Random Sampling Patterns

    Pierzchlewski, Jacek; Arildsen, Thomas

    2016-01-01

    Random sampling is a technique for signal acquisition which is gaining popularity in practical signal processing systems. Nowadays, event-driven analog-to-digital converters make random sampling feasible in practical applications. A process of random sampling is defined by a sampling pattern, which...... indicates signal sampling points in time. Practical random sampling patterns are constrained by ADC characteristics and application requirements. In this paper, we introduce statistical methods which evaluate random sampling pattern generators with emphasis on practical applications. Furthermore, we propose...... algorithm generates random sampling patterns dedicated for event-driven-ADCs better than existed sampling pattern generators. Finally, implementation issues of random sampling patterns are discussed....

  18. Fingerprint prediction using classifier ensembles

    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...

  19. Fisher classifier and its probability of error estimation

    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.

  20. Qualitative analysis fingertip patterns in ABO blood group

    S. V. KShirsagar

    2013-01-01

    The inheritance of the dermatoglyphic patterns is polygenic. The genetic basis of the blood group is well established. The correlation between the dermatoglyphic patterns and the ABO blood group is studied by some workers in different populations. In the present study, the correlation between dermatoglyphics and ABO blood group is studied in the Marathwada Region of Maharashtra. The qualitative data included fingertip patterns and three indices. It was observed that, the Arch pattern is more ...

  1. Analysis of Architecture Pattern Usage in Legacy System Architecture Documentation

    Harrison, Neil B.; Avgeriou, Paris

    2008-01-01

    Architecture patterns are an important tool in architectural design. However, while many architecture patterns have been identified, there is little in-depth understanding of their actual use in software architectures. For instance, there is no overview of how many patterns are used per system or

  2. A Gaussian mixture model based adaptive classifier for fNIRS brain-computer interfaces and its testing via simulation

    Li, Zheng; Jiang, Yi-han; Duan, Lian; Zhu, Chao-zhe

    2017-08-01

    Objective. Functional near infra-red spectroscopy (fNIRS) is a promising brain imaging technology for brain-computer interfaces (BCI). Future clinical uses of fNIRS will likely require operation over long time spans, during which neural activation patterns may change. However, current decoders for fNIRS signals are not designed to handle changing activation patterns. The objective of this study is to test via simulations a new adaptive decoder for fNIRS signals, the Gaussian mixture model adaptive classifier (GMMAC). Approach. GMMAC can simultaneously classify and track activation pattern changes without the need for ground-truth labels. This adaptive classifier uses computationally efficient variational Bayesian inference to label new data points and update mixture model parameters, using the previous model parameters as priors. We test GMMAC in simulations in which neural activation patterns change over time and compare to static decoders and unsupervised adaptive linear discriminant analysis classifiers. Main results. Our simulation experiments show GMMAC can accurately decode under time-varying activation patterns: shifts of activation region, expansions of activation region, and combined contractions and shifts of activation region. Furthermore, the experiments show the proposed method can track the changing shape of the activation region. Compared to prior work, GMMAC performed significantly better than the other unsupervised adaptive classifiers on a difficult activation pattern change simulation: 99% versus  brain-computer interfaces, including neurofeedback training systems, where operation over long time spans is required.

  3. Classifier Fusion With Contextual Reliability Evaluation.

    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.

  4. Classified

    Computer Security Team

    2011-01-01

    In the last issue of the Bulletin, we have discussed recent implications for privacy on the Internet. But privacy of personal data is just one facet of data protection. Confidentiality is another one. However, confidentiality and data protection are often perceived as not relevant in the academic environment of CERN.   But think twice! At CERN, your personal data, e-mails, medical records, financial and contractual documents, MARS forms, group meeting minutes (and of course your password!) are all considered to be sensitive, restricted or even confidential. And this is not all. Physics results, in particular when being preliminary and pending scrutiny, are sensitive, too. Just recently, an ATLAS collaborator copy/pasted the abstract of an ATLAS note onto an external public blog, despite the fact that this document was clearly marked as an "Internal Note". Such an act was not only embarrassing to the ATLAS collaboration, and had negative impact on CERN’s reputation --- i...

  5. Pre-emergency-department care-seeking patterns are associated with the severity of presenting condition for emergency department visit and subsequent adverse events: a timeframe episode analysis.

    Chien-Lung Chan

    Full Text Available Many patients treated in Emergency Department (ED visits can be treated at primary or urgent care sectors, despite the fact that a number of ED visitors seek other forms of care prior to an ED visit. However, little is known regarding how the pre-ED activity episodes affect ED visits.We investigated whether care-seeking patterns involve the use of health care services of various types prior to ED visits and examined the associations of these patterns with the severity of the presenting condition for the ED visit (EDVS and subsequent events.This retrospective observational study used administrative data on beneficiaries of the universal health care insurance program in Taiwan. The service type, treatment capacity, and relative diagnosis were used to classify pre-ED visits into 8 care types. Frequent pattern analysis was used to identify sequential care-seeking patterns and to classify 667,183 eligible pre-ED episodes into patterns. Generalized linear models were developed using generalized estimating equations to examine the associations of these patterns with EDVS and subsequent events.The results revealed 17 care-seeking patterns. The EDVS and likelihood of subsequent events significantly differed among patterns. The ED severity index of patterns differ from patterns seeking directly ED care (coefficients ranged from -0.05 to 0.13, and the odds-ratios for the likelihood of subsequent ED visits and hospitalization ranged from 1.18 to 1.86 and 1.16 to 2.84, respectively.The pre-ED care-seeking patterns differ in severity of presenting condition and subsequent events that may represent different causes of ED visit. Future health policy maker may adopt different intervention strategies for targeted population to reduce unnecessary ED visit effectively.

  6. Pre-emergency-department care-seeking patterns are associated with the severity of presenting condition for emergency department visit and subsequent adverse events: a timeframe episode analysis.

    Chan, Chien-Lung; Lin, Wender; Yang, Nan-Ping; Lai, K Robert; Huang, Hsin-Tsung

    2015-01-01

    Many patients treated in Emergency Department (ED) visits can be treated at primary or urgent care sectors, despite the fact that a number of ED visitors seek other forms of care prior to an ED visit. However, little is known regarding how the pre-ED activity episodes affect ED visits. We investigated whether care-seeking patterns involve the use of health care services of various types prior to ED visits and examined the associations of these patterns with the severity of the presenting condition for the ED visit (EDVS) and subsequent events. This retrospective observational study used administrative data on beneficiaries of the universal health care insurance program in Taiwan. The service type, treatment capacity, and relative diagnosis were used to classify pre-ED visits into 8 care types. Frequent pattern analysis was used to identify sequential care-seeking patterns and to classify 667,183 eligible pre-ED episodes into patterns. Generalized linear models were developed using generalized estimating equations to examine the associations of these patterns with EDVS and subsequent events. The results revealed 17 care-seeking patterns. The EDVS and likelihood of subsequent events significantly differed among patterns. The ED severity index of patterns differ from patterns seeking directly ED care (coefficients ranged from -0.05 to 0.13), and the odds-ratios for the likelihood of subsequent ED visits and hospitalization ranged from 1.18 to 1.86 and 1.16 to 2.84, respectively. The pre-ED care-seeking patterns differ in severity of presenting condition and subsequent events that may represent different causes of ED visit. Future health policy maker may adopt different intervention strategies for targeted population to reduce unnecessary ED visit effectively.

  7. Identification, classification and expression pattern analysis of sugarcane cysteine proteinases

    Gustavo Coelho Correa

    2001-12-01

    Full Text Available Cysteine proteases are peptidyl hydrolyses dependent on a cysteine residue at the active center. The physical and chemical properties of cysteine proteases have been extensively characterized, but their precise biological functions have not yet been completely understood, although it is known that they are involved in a number of events such as protein turnover, cancer, germination, programmed cell death and senescence. Protein sequences from different cysteine proteinases, classified as members of the E.C.3.4.22 sub-sub-class, were used to perform a T-BLAST-n search on the Brazilian Sugarcane Expressed Sequence Tags project (SUCEST data bank. Sequence homology was found with 76 cluster sequences that corresponded to possible cysteine proteinases. The alignments of these SUCEST clusters with the sequence of cysteine proteinases of known origins provided important information about the classification and possible function of these sugarcane enzymes. Inferences about the expression pattern of each gene were made by direct correlation with the SUCEST cDNA libraries from which each cluster was derived. Since no previous reports of sugarcane cysteine proteinases genes exists, this study represents a first step in the study of new biochemical, physiological and biotechnological aspects of sugarcane cysteine proteases.Proteinases cisteínicas são peptidil-hidrolases dependentes de um resíduo de cisteína em seu sítio ativo. As propriedades físico-químicas destas proteinases têm sido amplamente caracterizadas, entretanto suas funções biológicas ainda não foram completamente elucidadas. Elas estão envolvidas em um grande número de eventos, tais como: processamento e degradação protéica, câncer, germinação, morte celular programada e processos de senescência. Diferentes proteinases cisteínicas, classificadas pelo Comitê de Nomenclatura da União Internacional de Bioquímica e Biologia Molecular (IUBMB como pertencentes à sub

  8. Classifying Sluice Occurrences in Dialogue

    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. Classification of natural circulation two-phase flow patterns using fuzzy inference on image analysis

    Mesquita, R.N. de; Masotti, P.H.F.; Penha, R.M.L.; Andrade, D.A.; Sabundjian, G.; Torres, W.M.

    2012-01-01

    Highlights: ► A fuzzy classification system for two-phase flow instability patterns is developed. ► Flow patterns are classified based on images of natural circulation experiments. ► Fuzzy inference is optimized to use single grayscale profiles as input. - Abstract: Two-phase flow on natural circulation phenomenon has been an important theme on recent studies related to nuclear reactor designs. The accuracy of heat transfer estimation has been improved with new models that require precise prediction of pattern transitions of flow. In this work, visualization of natural circulation cycles is used to study two-phase flow patterns associated with phase transients and static instabilities of flow. A Fuzzy Flow-type Classification System (FFCS) was developed to classify these patterns based only on image extracted features. Image acquisition and temperature measurements were simultaneously done. Experiments in natural circulation facility were adjusted to generate a series of characteristic two-phase flow instability periodic cycles. The facility is composed of a loop of glass tubes, a heat source using electrical heaters, a cold source using a helicoidal heat exchanger, a visualization section and thermocouples positioned over different loop sections. The instability cyclic period is estimated based on temperature measurements associated with the detection of a flow transition image pattern. FFCS shows good results provided that adequate image acquisition parameters and pre-processing adjustments are used.

  10. Distributional patterns of cecropia (Cecropiaceae: a panbiogeographic analysis

    Franco Rosselli Pilar

    1997-06-01

    Full Text Available A panbiogeographic analysis of the distributional patterns of 60 species of Cecropia was carried out. Based on the distributional ranges of 36 species, we found eight generalized tracks for Cecropia species. whereas distributional patterns of 24 species were uninformative for the analysis. The major concentration of species of Cecropia is in the Neotropical Andean region. where there are three generalized tracks and two nodes. The northern Andes in Colombia and Ecuador are richer than the Central Andes in Perú. they contain two generalized tracks; one to the west and another to the east, formed by individual tracks of eight species each. There are four generalized tracks outside the Andean region: two in the Amazonian region in Guayana-Pará and in Manaus. one in Roraima. one in Serra do Mar in the Atlantic forest of Brazil and one in Central America. Speciation in Cecropia may be related to the Andean first uplift.Con base en la distribución de 60 especies del género Cecropia, se hizo un análisis panbiogeográfico. Se construyeron 8 trazos generalizados con base en el patrón de distribución de 36 especies; la distribución de las demás especies no aportaba información para la definición de los trazos. La región andina tiene la mayor concentración de especies de Cecropia representada por la presencia de tres trazos generalizados y dos nodos; los dos trazos con mayor número de especies se localizan en su parte norte, en Colombia y Ecuador y el otro en los Andes centrales en Perú. Se encontraron además, cuatro trazos extrandinos: dos en la región amazónica, en Pará-Guayana y en Manaus, uno en Roraima, uno en Serra do Mar en la Selva Atlánfíca del Brasil y uno en Centro América. La especiación en Cecropia parece estar relacionada con el primer levantamiento de los Andes.

  11. An analysis of error patterns in children′s backward digit recall in noise

    Homira Osman

    2015-01-01

    Full Text Available The purpose of the study was to determine whether perceptual masking or cognitive processing accounts for a decline in working memory performance in the presence of competing speech. The types and patterns of errors made on the backward digit span in quiet and multitalker babble at -5 dB signal-to-noise ratio (SNR were analyzed. The errors were classified into two categories: item (if digits that were not presented in a list were repeated and order (if correct digits were repeated but in an incorrect order. Fifty five children with normal hearing were included. All the children were aged between 7 years and 10 years. Repeated measures of analysis of variance (RM-ANOVA revealed the main effects for error type and digit span length. In terms of listening condition interaction, it was found that the order errors occurred more frequently than item errors in the degraded listening condition compared to quiet. In addition, children had more difficulty recalling the correct order of intermediate items, supporting strong primacy and recency effects. Decline in children′s working memory performance was not primarily related to perceptual difficulties alone. The majority of errors was related to the maintenance of sequential order information, which suggests that reduced performance in competing speech may result from increased cognitive processing demands in noise.

  12. An analysis of error patterns in children's backward digit recall in noise

    Osman, Homira; Sullivan, Jessica R.

    2015-01-01

    The purpose of the study was to determine whether perceptual masking or cognitive processing accounts for a decline in working memory performance in the presence of competing speech. The types and patterns of errors made on the backward digit span in quiet and multitalker babble at -5 dB signal-to-noise ratio (SNR) were analyzed. The errors were classified into two categories: item (if digits that were not presented in a list were repeated) and order (if correct digits were repeated but in an incorrect order). Fifty five children with normal hearing were included. All the children were aged between 7 years and 10 years. Repeated measures of analysis of variance (RM-ANOVA) revealed the main effects for error type and digit span length. In terms of listening condition interaction it was found that the order errors occurred more frequently than item errors in the degraded listening condition compared to quiet. In addition, children had more difficulty recalling the correct order of intermediate items, supporting strong primacy and recency effects. Decline in children's working memory performance was not primarily related to perceptual difficulties alone. The majority of errors was related to the maintenance of sequential order information, which suggests that reduced performance in competing speech may result from increased cognitive processing demands in noise. PMID:26168949

  13. Quantitative analysis of crystalline pharmaceuticals in tablets by pattern-fitting procedure using X-ray diffraction pattern.

    Takehira, Rieko; Momose, Yasunori; Yamamura, Shigeo

    2010-10-15

    A pattern-fitting procedure using an X-ray diffraction pattern was applied to the quantitative analysis of binary system of crystalline pharmaceuticals in tablets. Orthorhombic crystals of isoniazid (INH) and mannitol (MAN) were used for the analysis. Tablets were prepared under various compression pressures using a direct compression method with various compositions of INH and MAN. Assuming that X-ray diffraction pattern of INH-MAN system consists of diffraction intensities from respective crystals, observed diffraction intensities were fitted to analytic expression based on X-ray diffraction theory and separated into two intensities from INH and MAN crystals by a nonlinear least-squares procedure. After separation, the contents of INH were determined by using the optimized normalization constants for INH and MAN. The correction parameter including all the factors that are beyond experimental control was required for quantitative analysis without calibration curve. The pattern-fitting procedure made it possible to determine crystalline phases in the range of 10-90% (w/w) of the INH contents. Further, certain characteristics of the crystals in the tablets, such as the preferred orientation, size of crystallite, and lattice disorder were determined simultaneously. This method can be adopted to analyze compounds whose crystal structures are known. It is a potentially powerful tool for the quantitative phase analysis and characterization of crystals in tablets and powders using X-ray diffraction patterns. Copyright 2010 Elsevier B.V. All rights reserved.

  14. Quantum ensembles of quantum classifiers.

    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.

  15. Reverse inference of memory retrieval processes underlying metacognitive monitoring of learning using multivariate pattern analysis.

    Stiers, Peter; Falbo, Luciana; Goulas, Alexandros; van Gog, Tamara; de Bruin, Anique

    2016-05-15

    Monitoring of learning is only accurate at some time after learning. It is thought that immediate monitoring is based on working memory, whereas later monitoring requires re-activation of stored items, yielding accurate judgements. Such interpretations are difficult to test because they require reverse inference, which presupposes specificity of brain activity for the hidden cognitive processes. We investigated whether multivariate pattern classification can provide this specificity. We used a word recall task to create single trial examples of immediate and long term retrieval and trained a learning algorithm to discriminate them. Next, participants performed a similar task involving monitoring instead of recall. The recall-trained classifier recognized the retrieval patterns underlying immediate and long term monitoring and classified delayed monitoring examples as long-term retrieval. This result demonstrates the feasibility of decoding cognitive processes, instead of their content. Copyright © 2016 Elsevier Inc. All rights reserved.

  16. Analysis of different vibration patterns to guide blind people.

    Durá-Gil, Juan V; Bazuelo-Ruiz, Bruno; Moro-Pérez, David; Mollà-Domenech, Fernando

    2017-01-01

    The literature indicates the best vibration positions and frequencies on the human body where tactile information is transmitted. However, there is a lack of knowledge about how to combine tactile stimuli for navigation. The aim of this study is to compare different vibration patterns outputted to blind people and to determine the most intuitive vibration patterns to indicate direction for navigation purposes through a tactile belt. The vibration patterns that stimulate the front side of the waist are preferred for indicating direction. Vibration patterns applied on the back side of the waist could be suitable for sending messages such as stop.

  17. Analysis of different vibration patterns to guide blind people

    Juan V. Durá-Gil

    2017-03-01

    Full Text Available The literature indicates the best vibration positions and frequencies on the human body where tactile information is transmitted. However, there is a lack of knowledge about how to combine tactile stimuli for navigation. The aim of this study is to compare different vibration patterns outputted to blind people and to determine the most intuitive vibration patterns to indicate direction for navigation purposes through a tactile belt. The vibration patterns that stimulate the front side of the waist are preferred for indicating direction. Vibration patterns applied on the back side of the waist could be suitable for sending messages such as stop.

  18. Upgrade of the automatic analysis system in the TJ-II Thomson Scattering diagnostic: New image recognition classifier and fault condition detection

    Makili, L.; Vega, J.; Dormido-Canto, S.; Pastor, I.; Pereira, A.; Farias, G.; Portas, A.; Perez-Risco, D.; Rodriguez-Fernandez, M.C.; Busch, P.

    2010-01-01

    An automatic image classification system based on support vector machines (SVM) has been in operation for years in the TJ-II Thomson Scattering diagnostic. It recognizes five different types of images: CCD camera background, measurement of stray light without plasma or in a collapsed discharge, image during ECH phase, image during NBI phase and image after reaching the cut off density during ECH heating. Each kind of image implies the execution of different application software. Due to the fact that the recognition system is based on a learning system and major modifications have been carried out in both the diagnostic (optics) and TJ-II plasmas (injected power), the classifier model is no longer valid. A new SVM model has been developed with the current conditions. Also, specific error conditions in the data acquisition process can automatically be detected and managed now. The recovering process has been automated, thereby avoiding the loss of data in ensuing discharges.

  19. Upgrade of the automatic analysis system in the TJ-II Thomson Scattering diagnostic: New image recognition classifier and fault condition detection

    Makili, L. [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Dormido-Canto, S., E-mail: sebas@dia.uned.e [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Pastor, I.; Pereira, A. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Farias, G. [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Portas, A.; Perez-Risco, D.; Rodriguez-Fernandez, M.C. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Busch, P. [FOM Institut voor PlasmaFysica Rijnhuizen, Nieuwegein (Netherlands)

    2010-07-15

    An automatic image classification system based on support vector machines (SVM) has been in operation for years in the TJ-II Thomson Scattering diagnostic. It recognizes five different types of images: CCD camera background, measurement of stray light without plasma or in a collapsed discharge, image during ECH phase, image during NBI phase and image after reaching the cut off density during ECH heating. Each kind of image implies the execution of different application software. Due to the fact that the recognition system is based on a learning system and major modifications have been carried out in both the diagnostic (optics) and TJ-II plasmas (injected power), the classifier model is no longer valid. A new SVM model has been developed with the current conditions. Also, specific error conditions in the data acquisition process can automatically be detected and managed now. The recovering process has been automated, thereby avoiding the loss of data in ensuing discharges.

  20. Methodologies for analysis of patterning in the mouse RPE sheet

    Boatright, Jeffrey H.; Dalal, Nupur; Chrenek, Micah A.; Gardner, Christopher; Ziesel, Alison; Jiang, Yi; Grossniklaus, Hans E.

    2015-01-01

    -analyzed results were compared. Whether tallied manually or automatically with software, the resulting cell measurements were in close agreement. We compared normal with diseased RPE cells during aging with quantitative cell size and shape metrics. Subtle differences between the RPE sheet characteristics of young and old mice were identified. The IRBP−/− mouse RPE sheet did not differ from C57BL/6J (wild type, WT), suggesting that IRBP does not play a direct role in maintaining the health of the RPE cell, while the slow loss of photoreceptor (PhR) cells previously established in this knockout does support a role in the maintenance of PhR cells. Rd8 mice exhibited several measurable changes in patterns of RPE cells compared to WT, suggesting a slow degeneration of the RPE sheet that had not been previously noticed in rd8. Conclusions An optimized dissection method and a series of programs were used to establish a rapid and hands-off analysis. The software-aided, high-sampling-size approach performed as well as trained human scorers, but was considerably faster and easier. This method allows tens to hundreds of thousands of cells to be analyzed, each with 23 metrics. With this combination of dissection and image analysis of the RPE sheet, we can now analyze cell-to-cell interactions of immediate neighbors. In the future, we may be able to observe interactions of second, third, or higher ring neighbors and analyze tension in sheets, which might be expected to deviate from normal near large bumps in the RPE sheet caused by druse or when large frank holes in the RPE sheet are observed in geographic atrophy. This method and software can be readily applied to other aspects of vision science, neuroscience, and epithelial biology where patterns may exist in a sheet or surface of cells. PMID:25593512

  1. Vents Pattern Analysis at Etna volcano (Sicily, Italy).

    Brancato, Alfonso; Tusa, Giuseppina; Coltelli, Mauro; Proietti, Cristina; Branca, Stefano

    2014-05-01

    Mount Etna is a composite stratovolcano located along the Ionian coast of eastern Sicily. It is characterized by basaltic eruptions, both effusive and explosive, occurred during a complex eruptive history over the last 500 ka. Flank eruptions occur at an interval of decades, mostly concentrated along the NE, S and W rift zones. A vent clustering at various scales is a common feature in many volcanic settings. In order to identify the clusters within the studied area, a spatial point pattern analysis is undertaken using vent positions, both known and reconstructed. It reveals both clustering and spatial regularity in the Etna region at different distances. The visual inspection of the vent spatial distribution suggests a clustering on the rift zones of Etna volcano. To confirm this evidence, a coarse analysis is performed by the application of Ξ2- and t-test simple statistics. Then, a refined analysis is performed by using the Ripley K-function (Ripley, 1976), whose estimator K(d), knowing the area of the study region and the number of vents, allow us to calculate the distance among two different location of events. The above estimator can be easier transformed by using the Besag L-function (Besag, 1977); the peaks of positive L(d)=[K(d)/π]1/2 -d values indicate clustering while troughs of negative values stand for regularity for their corresponding distances d (L(d)=0 indicates complete spatial randomness). Spatial pattern of flank vents is investigated in order to model the spatial distribution of likely eruptive vents for the next event, basically in terms of relative probabilities. For this, a Gaussian kernel technique is used, and the L(d) function is adopted to generate an optimal smoothing bandwidth based on the clustering behaviour of the Etna volcano. A total of 154 vents (among which 36 are reconstructed), related to Etna flank activity of the last 4.0 ka, is used to model future vent opening. The investigated region covers an area of 850 km2, divided

  2. An Analysis on change of household electricity demand pattern

    Na, In Gang [Korea Energy Economics Institute, Euiwang (Korea)

    1999-01-01

    The object of this study is to analyze the behavioral pattern change of household electricity demand. Through the cross section analysis using materials from the energy total research report, the change in income elasticity of household electricity demand was studied. In this study, two methodologies were used. Firstly, it was shown that the effect of an income variable was very significant with a positive value in simultaneous equations model using exponential equations of electrical appliances holding. Cross section income effect showed a various distribution according to the season or income level. Overall, it was calculated at 0.111 when the appliances are fixed and 0.432 when even appliances are changed. Secondly, using a choice convenient correction model, it is resulted that lambda, the choice convenient correction factor, has a positive value and is statistically significant. In 1996, income elasticity of electricity demand for households with air-conditioning was 0.305 and for households without air-conditioning was 0.172. Income elasticity of households with air-conditioning is increasing as time goes by while income elasticity of households without air-conditioning is decreasing. (author). 32 refs., 35 tabs.

  3. Topological analysis of long-chain branching patterns in polyolefins.

    Bonchev, D; Markel, E; Dekmezian, A

    2001-01-01

    Patterns in molecular topology and complexity for long-chain branching are quantitatively described. The Wiener number, the topological complexity index, and a new index of 3-starness are used to quantify polymer structure. General formulas for these indices were derived for the cases of 3-arm star, H-shaped, and B-arm comb polymers. The factors affecting complexity in monodisperse polymer systems are ranked as follows: number of arms > arm length > arm central position approximately equal to arm clustering > total molecular weight approximately equal to backbone molecular weight. Topological indices change rapidly and then plateau as the molecular weight of branches on a polyolefin backbone increases from 0 to 5 kD. Complexity calculations relate 2-arm or 3-arm comb structures to the corresponding 3-arm stars of equivalent complexity but much higher molecular weight. In a subsequent paper, we report the application of topological analysis for developing structure/property relationships for monodisperse polymers. While the focus of the present work is on the description of monodisperse, well-defined architectures, the methods may be extended to the description of polydisperse systems.

  4. Developments In Electronic Speckle Pattern Interferometry For Automotive Vibration Analysis.

    Davies, Jeremy C.; Buckberry, Clive H.; Jones, Julian D. C.; Pannell, Chris N.

    1989-01-01

    The incorporation of monomode fibre optics into an argon ion powered Electronic Speckle Pattern Interferometer (ESPI) is reported. The system, consisting of an optics assembly linked to the laser and a CCD camera transceiver, flexibly connected by 40m of monomode fibre optic cable to the optics, has been used to analyse the modal behaviour of structures up to 5m X 3m X 2m in size. Phase modulation of the reference beam in order to operate in a heterodyne mode has been implemented using a piezo-electric crystal operating on the monomode fibre. A new mode of operation - sequential time-average subtraction - and the results of a new processing algorithm are also reported. Their implementation enables speckle free, time-average vibration maps to be generated in real-time on large, unstable structures. Example results for a four cylinder power unit, a vehicle body shell component and an engine oil pan are included. In all cases the analysis was conducted in a general workshop environment without the need for vibration isolation facilities.

  5. Multivariate pattern analysis reveals anatomical connectivity differences between the left and right mesial temporal lobe epilepsy

    Peng Fang

    2015-01-01

    Full Text Available Previous studies have demonstrated differences of clinical signs and functional brain network organizations between the left and right mesial temporal lobe epilepsy (mTLE, but the anatomical connectivity differences underlying functional variance between the left and right mTLE remain uncharacterized. We examined 43 (22 left, 21 right mTLE patients with hippocampal sclerosis and 39 healthy controls using diffusion tensor imaging. After the whole-brain anatomical networks were constructed for each subject, multivariate pattern analysis was applied to classify the left mTLE from the right mTLE and extract the anatomical connectivity differences between the left and right mTLE patients. The classification results reveal 93.0% accuracy for the left mTLE versus the right mTLE, 93.4% accuracy for the left mTLE versus controls and 90.0% accuracy for the right mTLE versus controls. Compared with the right mTLE, the left mTLE exhibited a different connectivity pattern in the cortical-limbic network and cerebellum. The majority of the most discriminating anatomical connections were located within or across the cortical-limbic network and cerebellum, thereby indicating that these disease-related anatomical network alterations may give rise to a portion of the complex of emotional and memory deficit between the left and right mTLE. Moreover, the orbitofrontal gyrus, cingulate cortex, hippocampus and parahippocampal gyrus, which exhibit high discriminative power in classification, may play critical roles in the pathophysiology of mTLE. The current study demonstrated that anatomical connectivity differences between the left mTLE and the right mTLE may have the potential to serve as a neuroimaging biomarker to guide personalized diagnosis of the left and right mTLE.

  6. Multivariate pattern analysis reveals anatomical connectivity differences between the left and right mesial temporal lobe epilepsy.

    Fang, Peng; An, Jie; Zeng, Ling-Li; Shen, Hui; Chen, Fanglin; Wang, Wensheng; Qiu, Shijun; Hu, Dewen

    2015-01-01

    Previous studies have demonstrated differences of clinical signs and functional brain network organizations between the left and right mesial temporal lobe epilepsy (mTLE), but the anatomical connectivity differences underlying functional variance between the left and right mTLE remain uncharacterized. We examined 43 (22 left, 21 right) mTLE patients with hippocampal sclerosis and 39 healthy controls using diffusion tensor imaging. After the whole-brain anatomical networks were constructed for each subject, multivariate pattern analysis was applied to classify the left mTLE from the right mTLE and extract the anatomical connectivity differences between the left and right mTLE patients. The classification results reveal 93.0% accuracy for the left mTLE versus the right mTLE, 93.4% accuracy for the left mTLE versus controls and 90.0% accuracy for the right mTLE versus controls. Compared with the right mTLE, the left mTLE exhibited a different connectivity pattern in the cortical-limbic network and cerebellum. The majority of the most discriminating anatomical connections were located within or across the cortical-limbic network and cerebellum, thereby indicating that these disease-related anatomical network alterations may give rise to a portion of the complex of emotional and memory deficit between the left and right mTLE. Moreover, the orbitofrontal gyrus, cingulate cortex, hippocampus and parahippocampal gyrus, which exhibit high discriminative power in classification, may play critical roles in the pathophysiology of mTLE. The current study demonstrated that anatomical connectivity differences between the left mTLE and the right mTLE may have the potential to serve as a neuroimaging biomarker to guide personalized diagnosis of the left and right mTLE.

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

    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.

  8. Preliminary analysis of the nestedness patterns of Montane forest ...

    Results show that the species ordering is significantly non-random. The discussion and conclusions focus on the nested subset patterns exhibited by 14 species and, to a lesser extent, 'idiosyncratic' species and islands. Factors that may have contributed to this pattern include selective extinction and colonisation; however, ...

  9. Analysis of time-varying psoriasis lesion image patterns

    Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær; Nielsen, Allan Aasbjerg

    2004-01-01

    The multivariate alteration detection transform is applied to pairs of within and between time varying registered psoriasis image patterns. Color band contribution to the variates explaining maximal change is analyzed.......The multivariate alteration detection transform is applied to pairs of within and between time varying registered psoriasis image patterns. Color band contribution to the variates explaining maximal change is analyzed....

  10. Pattern of abdominal wall herniae in females: a retrospective analysis

    Background: Gender differences are expected to influence the pattern and outcome of management of abdominal wall hernias. Some of these are left to speculations with few published articles on hernias in females. Objectives: To describe the clinical pattern of abdominal wall hernias in females. Method: A 5 year ...

  11. Binary pattern analysis for 3D facial action unit detection

    Sandbach, Georgia; Zafeiriou, Stefanos; Pantic, Maja

    2012-01-01

    In this paper we propose new binary pattern features for use in the problem of 3D facial action unit (AU) detection. Two representations of 3D facial geometries are employed, the depth map and the Azimuthal Projection Distance Image (APDI). To these the traditional Local Binary Pattern is applied,

  12. Individual Differences in Consumer Buying Patterns: A Behavioral Economic Analysis

    Cavalcanti, Paulo R.; Oliveira-Castro, Jorge M.; Foxall, Gordon R.

    2013-01-01

    Although previous studies have identified several regularities in buying behavior, no integrated view of individual differences related to such patterns has been yet proposed. The present research examined individual differences in patterns of buying behavior of fast-moving consumer goods, using panel data with information concerning purchases of…

  13. Characterization, expression patterns and functional analysis of the MAPK and MAPKK genes in watermelon (Citrullus lanatus).

    Song, Qiuming; Li, Dayong; Dai, Yi; Liu, Shixia; Huang, Lei; Hong, Yongbo; Zhang, Huijuan; Song, Fengming

    2015-12-23

    Mitogen-activated protein kinase (MAPK) cascades, which consist of three functionally associated protein kinases, namely MEKKs, MKKs and MPKs, are universal signaling modules in all eukaryotes and have been shown to play critical roles in many physiological and biochemical processes in plants. However, little or nothing is known about the MPK and MKK families in watermelon. In the present study, we performed a systematic characterization of the ClMPK and ClMKK families including the identification and nomenclature, chromosomal localization, phylogenetic relationships, ClMPK-ClMKK interactions, expression patterns in different tissues and in response to abiotic and biotic stress and transient expression-based functional analysis for their roles in disease resistance. Genome-wide survey identified fifteen ClMPK and six ClMKK genes in watermelon genome and phylogenetic analysis revealed that both of the ClMPK and ClMKK families can be classified into four distinct groups. Yeast two-hybrid assays demonstrated significant interactions between members of the ClMPK and ClMKK families, defining putative ClMKK2-1/ClMKK6-ClMPK4-1/ClMPK4-2/ClMPK13 and ClMKK5-ClMPK6 cascades. Most of the members in the ClMPK and ClMKK families showed differential expression patterns in different tissues and in response to abiotic (e.g. drought, salt, cold and heat treatments) and biotic (e.g. infection of Fusarium oxysporum f. sp. niveum) stresses. Transient expression of ClMPK1, ClMPK4-2 and ClMPK7 in Nicotiana benthamiana resulted in enhanced resistance to Botrytis cinerea and upregulated expression of defense genes while transient expression of ClMPK6 and ClMKK2-2 led to increased susceptibility to B. cinerea. Furthermore, transient expression of ClMPK7 also led to hypersensitive response (HR)-like cell death and significant accumulation of H2O2 in N. benthamiana. We identified fifteen ClMPK and six ClMKK genes from watermelon and analyzed their phylogenetic relationships, expression

  14. New method of classifying human errors at nuclear power plants and the analysis results of applying this method to maintenance errors at domestic plants

    Takagawa, Kenichi; Miyazaki, Takamasa; Gofuku, Akio; Iida, Hiroyasu

    2007-01-01

    Since many of the adverse events that have occurred in nuclear power plants in Japan and abroad have been related to maintenance or operation, it is necessary to plan preventive measures based on detailed analyses of human errors made by maintenance workers or operators. Therefore, before planning preventive measures, we developed a new method of analyzing human errors. Since each human error is an unsafe action caused by some misjudgement made by a person, we decided to classify them into six categories according to the stage in the judgment process in which the error was made. By further classifying each error into either an omission-type or commission-type, we produced 12 categories of errors. Then, we divided them into the two categories of basic error tendencies and individual error tendencies, and categorized background factors into four categories: imperfect planning; imperfect facilities or tools; imperfect environment; and imperfect instructions or communication. We thus defined the factors in each category to make it easy to identify factors that caused the error. Then using this method, we studied the characteristics of human errors that involved maintenance workers and planners since many maintenance errors have occurred. Among the human errors made by workers (worker errors) during the implementation stage, the following three types were prevalent with approximately 80%: commission-type 'projection errors', omission-type comprehension errors' and commission type 'action errors'. The most common among the individual factors of worker errors was 'repetition or habit' (schema), based on the assumption of a typical situation, and the half number of the 'repetition or habit' cases (schema) were not influenced by any background factors. The most common background factor that contributed to the individual factor was 'imperfect work environment', followed by 'insufficient knowledge'. Approximately 80% of the individual factors were 'repetition or habit' or

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

    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

  16. Manual versus Automated Narrative Analysis of Agrammatic Production Patterns: The Northwestern Narrative Language Analysis and Computerized Language Analysis

    Hsu, Chien-Ju; Thompson, Cynthia K.

    2018-01-01

    Purpose: The purpose of this study is to compare the outcomes of the manually coded Northwestern Narrative Language Analysis (NNLA) system, which was developed for characterizing agrammatic production patterns, and the automated Computerized Language Analysis (CLAN) system, which has recently been adopted to analyze speech samples of individuals…

  17. Facial soft tissue analysis among various vertical facial patterns

    Jeelani, W.; Fida, M.; Shaikh, A.

    2016-01-01

    Background: The emergence of soft tissue paradigm in orthodontics has made various soft tissue parameters an integral part of the orthodontic problem list. The purpose of this study was to determine and compare various facial soft tissue parameters on lateral cephalograms among patients with short, average and long facial patterns. Methods: A cross-sectional study was conducted on the lateral cephalograms of 180 adult subjects divided into three equal groups, i.e., short, average and long face according to the vertical facial pattern. Incisal display at rest, nose height, upper and lower lip lengths, degree of lip procumbency and the nasolabial angle were measured for each individual. The gender differences for these soft tissue parameters were determined using Mann-Whitney U test while the comparison among different facial patterns was performed using Kruskal-Wallis test. Results: Significant differences in the incisal display at rest, total nasal height, lip procumbency, the nasolabial angle and the upper and lower lip lengths were found among the three vertical facial patterns. A significant positive correlation of nose and lip dimensions was found with the underlying skeletal pattern. Similarly, the incisal display at rest, upper and lower lip procumbency and the nasolabial angle were significantly correlated with the lower anterior facial height. Conclusion: Short facial pattern is associated with minimal incisal display, recumbent upper and lower lips and acute nasolabial angle while the long facial pattern is associated with excessive incisal display, procumbent upper and lower lips and obtuse nasolabial angle. (author)

  18. New York Household Travel Patterns: A Comparison Analysis

    Hu, Patricia S [ORNL; Reuscher, Tim [ORNL

    2007-05-01

    In 1969, the U. S. Department of Transportation began collecting detailed data on personal travel to address various transportation planning issues. These issues range from assessing transportation investment programs to developing new technologies to alleviate congestion. This 1969 survey was the birth of the Nationwide Personal Transportation Survey (NPTS). The survey was conducted again in 1977, 1983, 1990 and 1995. Longer-distance travel was collected in 1977 and 1995. In 2001, the survey was renamed to the National Household Travel Survey (NHTS) and collected both daily and longer-distance trips in one survey. In addition to the number of sample households that the national NPTS/NHTS survey allotted to New York State (NYS), the state procured an additional sample of households in both the 1995 and 2001 surveys. In the 1995 survey, NYS procured an addition sample of more than 9,000 households, increasing the final NY NPTS sample size to a total of 11,004 households. Again in 2001, NYS procured 12,000 additional sample households, increasing the final New York NHTS sample size to a total of 13,423 households with usable data. These additional sample households allowed NYS to address transportation planning issues pertinent to geographic areas significantly smaller than for what the national NPTS and NHTS data are intended. Specifically, these larger sample sizes enable detailed analysis of twelve individual Metropolitan Planning Organizations (MPOs). Furthermore, they allowed NYS to address trends in travel behavior over time. In this report, travel data for the entire NYS were compared to those of the rest of the country with respect to personal travel behavior and key travel determinants. The influence of New York City (NYC) data on the comparisons of the state of New York to the rest of the country was also examined. Moreover, the analysis examined the relationship between population density and travel patterns, and the similarities and differences among New

  19. Comparing cosmic web classifiers using information theory

    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.

  20. Comparing cosmic web classifiers using information theory

    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.

  1. Distorted Pattern Recognition and Analysis with the Help of IEf Graph Representation

    Adam Sedziwy

    2002-01-01

    Full Text Available An algorithm for distorted pattern recognition is presented. lt's generalization of M Flasinski results (Pattern Recognition, 27, 1-16, 1992. A new formalism allows to make both qualitative and quantitive distortion analysis. It also enlarges parser flexibility by extending the set of patterns which may be recognized.

  2. A Western Dietary Pattern Increases Prostate Cancer Risk: A Systematic Review and Meta-Analysis.

    Fabiani, Roberto; Minelli, Liliana; Bertarelli, Gaia; Bacci, Silvia

    2016-10-12

    Dietary patterns were recently applied to examine the relationship between eating habits and prostate cancer (PC) risk. While the associations between PC risk with the glycemic index and Mediterranean score have been reviewed, no meta-analysis is currently available on dietary patterns defined by "a posteriori" methods. A literature search was carried out (PubMed, Web of Science) to identify studies reporting the relationship between dietary patterns and PC risk. Relevant dietary patterns were selected and the risks estimated were calculated by a random-effect model. Multivariable-adjusted odds ratios (ORs), for a first-percentile increase in dietary pattern score, were combined by a dose-response meta-analysis. Twelve observational studies were included in the meta-analysis which identified a "Healthy pattern" and a "Western pattern". The Healthy pattern was not related to PC risk (OR = 0.96; 95% confidence interval (CI): 0.88-1.04) while the Western pattern significantly increased it (OR = 1.34; 95% CI: 1.08-1.65). In addition, the "Carbohydrate pattern", which was analyzed in four articles, was positively associated with a higher PC risk (OR = 1.64; 95% CI: 1.35-2.00). A significant linear trend between the Western ( p = 0.011) pattern, the Carbohydrate ( p = 0.005) pattern, and the increment of PC risk was observed. The small number of studies included in the meta-analysis suggests that further investigation is necessary to support these findings.

  3. Electricity demand forecasting using regression, scenarios and pattern analysis

    Khuluse, S

    2009-02-01

    Full Text Available The objective of the study is to forecast national electricity demand patterns for a period of twenty years: total annual consumption and understanding seasonal effects. No constraint on the supply of electricity was assumed...

  4. Bibliometric Analysis of Publication Output Patterns of Faculty ...

    The study is set out to analyse publication research output patterns of the faculty members of Agriculture and Veterinary Complex of Ahmadu Bello University, Zaria ... Faculty of Agriculture (FOA), National Agricultural Extension and Research ...

  5. 3D Bayesian contextual classifiers

    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....

  6. Analysis of Roanoke Region Weather Patterns Under Global Teleconnections

    LaRocque, Eric John

    2006-01-01

    This work attempts to relate global teleconnections, through physical phenomena such as the El Nino-Southern Oscillation (ENSO), Artic Oscillation (AO), North Atlantic Oscillation (NAO), and the Pacific North American (PNA) pattern to synoptic-scale weather patterns and precipitation in the Roanoke, Virginia region. The first chapter describes the behavior of the El Nino-Southern Oscillation (ENSO) by implementing non-homogeneous and homogeneous Markov Chain models on a monthly time series o...

  7. Spatial Distribution Characteristics of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and Correlation Analysis

    Jianhua Ni

    2016-08-01

    Full Text Available The spatial distribution of urban service facilities is largely constrained by the road network. In this study, network point pattern analysis and correlation analysis were used to analyze the relationship between road network and healthcare facility distribution. The weighted network kernel density estimation method proposed in this study identifies significant differences between the outside and inside areas of the Ming city wall. The results of network K-function analysis show that private hospitals are more evenly distributed than public hospitals, and pharmacy stores tend to cluster around hospitals along the road network. After computing the correlation analysis between different categorized hospitals and street centrality, we find that the distribution of these hospitals correlates highly with the street centralities, and that the correlations are higher with private and small hospitals than with public and large hospitals. The comprehensive analysis results could help examine the reasonability of existing urban healthcare facility distribution and optimize the location of new healthcare facilities.

  8. Early-life school, neighborhood, and family influences on adult health: a multilevel cross-classified analysis of the Aberdeen children of the 1950s study.

    Dundas, Ruth; Leyland, Alastair H; Macintyre, Sally

    2014-07-15

    Lifetime exposures to adverse social environments influence adult health, as do exposures in early life. It is usual to examine the influences of school on teenage health and of adult area of residence on adult health. We examined the combined long-term association of the school attended, as well as the area of residence in childhood, with adult health. A total of 6,285 children from Aberdeen, Scotland, who were aged 5-12 years in 1962, were followed up at a mean age of 47 years in 2001. Cross-classified multilevel logistic regression was used to estimate the associations of family, school, and area of residence with self-reported adult health and mental health, adjusting for childhood family-, school-, and neighborhood-level factors, as well as current adult occupational position. Low early-life social position (as determined by the father's occupational level) was associated with poor adult self-rated health but not poor mental health. There were small contextual associations between childhood school environment (median odds ratio = 1.08) and neighborhood environment (median odds ratio = 1.05) and adult self-rated health. The share of the total variance in health at the family level was 10.1% compared with 89.6% at the individual level. Both socioeconomic context and composition in early life appear to have an influence on adult health, even after adjustment for current occupational position. © The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

  9. Stochastic Turing Patterns: Analysis of Compartment-Based Approaches

    Cao, Yang; Erban, Radek

    2014-01-01

    © 2014, Society for Mathematical Biology. Turing patterns can be observed in reaction-diffusion systems where chemical species have different diffusion constants. In recent years, several studies investigated the effects of noise on Turing patterns and showed that the parameter regimes, for which stochastic Turing patterns are observed, can be larger than the parameter regimes predicted by deterministic models, which are written in terms of partial differential equations (PDEs) for species concentrations. A common stochastic reaction-diffusion approach is written in terms of compartment-based (lattice-based) models, where the domain of interest is divided into artificial compartments and the number of molecules in each compartment is simulated. In this paper, the dependence of stochastic Turing patterns on the compartment size is investigated. It has previously been shown (for relatively simpler systems) that a modeler should not choose compartment sizes which are too small or too large, and that the optimal compartment size depends on the diffusion constant. Taking these results into account, we propose and study a compartment-based model of Turing patterns where each chemical species is described using a different set of compartments. It is shown that the parameter regions where spatial patterns form are different from the regions obtained by classical deterministic PDE-based models, but they are also different from the results obtained for the stochastic reaction-diffusion models which use a single set of compartments for all chemical species. In particular, it is argued that some previously reported results on the effect of noise on Turing patterns in biological systems need to be reinterpreted.

  10. Stochastic Turing Patterns: Analysis of Compartment-Based Approaches

    Cao, Yang

    2014-11-25

    © 2014, Society for Mathematical Biology. Turing patterns can be observed in reaction-diffusion systems where chemical species have different diffusion constants. In recent years, several studies investigated the effects of noise on Turing patterns and showed that the parameter regimes, for which stochastic Turing patterns are observed, can be larger than the parameter regimes predicted by deterministic models, which are written in terms of partial differential equations (PDEs) for species concentrations. A common stochastic reaction-diffusion approach is written in terms of compartment-based (lattice-based) models, where the domain of interest is divided into artificial compartments and the number of molecules in each compartment is simulated. In this paper, the dependence of stochastic Turing patterns on the compartment size is investigated. It has previously been shown (for relatively simpler systems) that a modeler should not choose compartment sizes which are too small or too large, and that the optimal compartment size depends on the diffusion constant. Taking these results into account, we propose and study a compartment-based model of Turing patterns where each chemical species is described using a different set of compartments. It is shown that the parameter regions where spatial patterns form are different from the regions obtained by classical deterministic PDE-based models, but they are also different from the results obtained for the stochastic reaction-diffusion models which use a single set of compartments for all chemical species. In particular, it is argued that some previously reported results on the effect of noise on Turing patterns in biological systems need to be reinterpreted.

  11. Analysis of Insulating Material of XLPE Cables considering Innovative Patterns of Partial Discharges

    Fernando Figueroa Godoy

    2017-01-01

    Full Text Available This paper aims to analyze the quality of insulation in high voltage underground cables XLPE using a prototype which classifies the following usual types of patterns of partial discharge (PD: (1 internal PD, (2 superficial PD, (3 corona discharge in air, and (4 corona discharge in oil, in addition to considering two new PD patterns: (1 false contact and (2 floating ground. The tests and measurements to obtain the patterns and study cases of partial discharges were performed at the Testing Laboratory Equipment and Materials (LEPEM of the Federal Electricity Commission of Mexico (CFE using a measuring equipment LDIC and norm IEC60270. To classify the six patterns of partial discharges mentioned above a Probabilistic Neural Network Bayesian Modified (PNNBM method having the feature of using a large amount of data will be used and it is not saturated. In addition, PNN converges, always finding a solution in a short period of time with low computational cost. The insulation of two high voltage cables with different characteristics was analyzed. The test results allow us to conclude which wire has better insulation.

  12. Knowledge Uncertainty and Composed Classifier

    Klimešová, Dana; Ocelíková, E.

    2007-01-01

    Roč. 1, č. 2 (2007), s. 101-105 ISSN 1998-0140 Institutional research plan: CEZ:AV0Z10750506 Keywords : Boosting architecture * contextual modelling * composed classifier * knowledge management, * knowledge * uncertainty Subject RIV: IN - Informatics, Computer Science

  13. Correlation Dimension-Based Classifier

    Jiřina, Marcel; Jiřina jr., M.

    2014-01-01

    Roč. 44, č. 12 (2014), s. 2253-2263 ISSN 2168-2267 R&D Projects: GA MŠk(CZ) LG12020 Institutional support: RVO:67985807 Keywords : classifier * multidimensional data * correlation dimension * scaling exponent * polynomial expansion Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 3.469, year: 2014

  14. Improving the analysis of near-spectroscopy data with multivariate classification of hemodynamic patterns: a theoretical formulation and validation.

    Gemignani, Jessica; Middell, Eike; Barbour, Randall L; Graber, Harry L; Blankertz, Benjamin

    2018-04-04

    The statistical analysis of functional near infrared spectroscopy (fNIRS) data based on the general linear model (GLM) is often made difficult by serial correlations, high inter-subject variability of the hemodynamic response, and the presence of motion artifacts. In this work we propose to extract information on the pattern of hemodynamic activations without using any a priori model for the data, by classifying the channels as 'active' or 'not active' with a multivariate classifier based on linear discriminant analysis (LDA). This work is developed in two steps. First we compared the performance of the two analyses, using a synthetic approach in which simulated hemodynamic activations were combined with either simulated or real resting-state fNIRS data. This procedure allowed for exact quantification of the classification accuracies of GLM and LDA. In the case of real resting-state data, the correlations between classification accuracy and demographic characteristics were investigated by means of a Linear Mixed Model. In the second step, to further characterize the reliability of the newly proposed analysis method, we conducted an experiment in which participants had to perform a simple motor task and data were analyzed with the LDA-based classifier as well as with the standard GLM analysis. The results of the simulation study show that the LDA-based method achieves higher classification accuracies than the GLM analysis, and that the LDA results are more uniform across different subjects and, in contrast to the accuracies achieved by the GLM analysis, have no significant correlations with any of the demographic characteristics. Findings from the real-data experiment are consistent with the results of the real-plus-simulation study, in that the GLM-analysis results show greater inter-subject variability than do the corresponding LDA results. The results obtained suggest that the outcome of GLM analysis is highly vulnerable to violations of theoretical assumptions

  15. National youth sedentary behavior and physical activity daily patterns using latent class analysis applied to accelerometry.

    Evenson, Kelly R; Wen, Fang; Hales, Derek; Herring, Amy H

    2016-05-03

    Applying latent class analysis (LCA) to accelerometry can help elucidated underlying patterns. This study described the patterns of accelerometer-determined sedentary behavior and physical activity among youth by applying LCA to a nationally representative United States (US) sample. Using 2003-2006 National Health and Nutrition Examination Survey data, 3998 youths 6-17 years wore an ActiGraph 7164 accelerometer for one week, providing > =3 days of wear for > =8 h/day from 6:00 am-midnight. Cutpoints defined sedentary behavior ( = 2296 counts/minute), and vigorous activity (> = 4012 counts/minute). To account for wear time differences, outcomes were expressed as percent of day in a given intensity. LCA was used to classify daily (Monday through Sunday) patterns of average counts/minute, sedentary behavior, light activity, MVPA, and vigorous activity separately. The latent classes were explored overall and by age (6-11, 12-14, 15-17 years), gender, and whether or not youth attended school during measurement. Estimates were weighted to account for the sampling frame. For average counts/minute/day, four classes emerged from least to most active: 40.9% of population (mean 323.5 counts/minute/day), 40.3% (559.6 counts/minute/day), 16.5% (810.0 counts/minute/day), and 2.3% (1132.9 counts/minute/day). For percent of sedentary behavior, four classes emerged: 13.5% of population (mean 544.6 min/day), 30.1% (455.1 min/day), 38.5% (357.7 min/day), and 18.0% (259.2 min/day). For percent of light activity, four classes emerged: 12.3% of population (mean 222.6 min/day), 29.3% (301.7 min/day), 41.8% (384.0 min/day), and 16.6% (455.5 min/day). For percent of MVPA, four classes emerged: 59.9% of population (mean 25.0 min/day), 33.3% (60.9 min/day), 3.1% (89.0 min/day), and 3.6% (109.3 min/day). For percent of vigorous activity, three classes emerged: 76.8% of population (mean 7.1 min/day), 18.5% (23.9 min/day), and 4.7% (47.4 min/day). Classes were developed by age

  16. Automated measurement and classification of pulmonary blood-flow velocity patterns using phase-contrast MRI and correlation analysis.

    van Amerom, Joshua F P; Kellenberger, Christian J; Yoo, Shi-Joon; Macgowan, Christopher K

    2009-01-01

    An automated method was evaluated to detect blood flow in small pulmonary arteries and classify each as artery or vein, based on a temporal correlation analysis of their blood-flow velocity patterns. The method was evaluated using velocity-sensitive phase-contrast magnetic resonance data collected in vitro with a pulsatile flow phantom and in vivo in 11 human volunteers. The accuracy of the method was validated in vitro, which showed relative velocity errors of 12% at low spatial resolution (four voxels per diameter), but was reduced to 5% at increased spatial resolution (16 voxels per diameter). The performance of the method was evaluated in vivo according to its reproducibility and agreement with manual velocity measurements by an experienced radiologist. In all volunteers, the correlation analysis was able to detect and segment peripheral pulmonary vessels and distinguish arterial from venous velocity patterns. The intrasubject variability of repeated measurements was approximately 10% of peak velocity, or 2.8 cm/s root-mean-variance, demonstrating the high reproducibility of the method. Excellent agreement was obtained between the correlation analysis and radiologist measurements of pulmonary velocities, with a correlation of R2=0.98 (P<.001) and a slope of 0.99+/-0.01.

  17. Associations between Food Outlets around Schools and BMI among Primary Students in England: A Cross-Classified Multi-Level Analysis.

    Julianne Williams

    Full Text Available Researchers and policy-makers are interested in the influence that food retailing around schools may have on child obesity risk. Most previous research comes from North America, uses data aggregated at the school-level and focuses on associations between fast food outlets and school obesity rates. This study examines associations between food retailing and BMI among a large sample of primary school students in Berkshire, England. By controlling for individual, school and home characteristics and stratifying results across the primary school years, we aimed to identify if the food environment around schools had an effect on BMI, independent of socio-economic variables.We measured the densities of fast food outlets and food stores found within schoolchildren's home and school environments using Geographic Information Systems (GIS and data from local councils. We linked these data to measures from the 2010/11 National Child Measurement Programme and used a cross-classified multi-level approach to examine associations between food retailing and BMI z-scores. Analyses were stratified among Reception (aged 4-5 and Year 6 (aged 10-11 students to measure associations across the primary school years.Our multilevel model had three levels to account for individual (n = 16,956, home neighbourhood (n = 664 and school (n = 268 factors. After controlling for confounders, there were no significant associations between retailing near schools and student BMI, but significant positive associations between fast food outlets in home neighbourhood and BMI z-scores. Year 6 students living in areas with the highest density of fast food outlets had an average BMI z-score that was 0.12 (95% CI: 0.04, 0.20 higher than those living in areas with none.We found little evidence to suggest that food retailing around schools influences student BMI. There is some evidence to suggest that fast food outlet densities in a child's home neighbourhood may have an effect on BMI

  18. Associations between Food Outlets around Schools and BMI among Primary Students in England: A Cross-Classified Multi-Level Analysis.

    Williams, Julianne; Scarborough, Peter; Townsend, Nick; Matthews, Anne; Burgoine, Thomas; Mumtaz, Lorraine; Rayner, Mike

    2015-01-01

    Researchers and policy-makers are interested in the influence that food retailing around schools may have on child obesity risk. Most previous research comes from North America, uses data aggregated at the school-level and focuses on associations between fast food outlets and school obesity rates. This study examines associations between food retailing and BMI among a large sample of primary school students in Berkshire, England. By controlling for individual, school and home characteristics and stratifying results across the primary school years, we aimed to identify if the food environment around schools had an effect on BMI, independent of socio-economic variables. We measured the densities of fast food outlets and food stores found within schoolchildren's home and school environments using Geographic Information Systems (GIS) and data from local councils. We linked these data to measures from the 2010/11 National Child Measurement Programme and used a cross-classified multi-level approach to examine associations between food retailing and BMI z-scores. Analyses were stratified among Reception (aged 4-5) and Year 6 (aged 10-11) students to measure associations across the primary school years. Our multilevel model had three levels to account for individual (n = 16,956), home neighbourhood (n = 664) and school (n = 268) factors. After controlling for confounders, there were no significant associations between retailing near schools and student BMI, but significant positive associations between fast food outlets in home neighbourhood and BMI z-scores. Year 6 students living in areas with the highest density of fast food outlets had an average BMI z-score that was 0.12 (95% CI: 0.04, 0.20) higher than those living in areas with none. We found little evidence to suggest that food retailing around schools influences student BMI. There is some evidence to suggest that fast food outlet densities in a child's home neighbourhood may have an effect on BMI, particularly

  19. Classification using diffraction patterns for single-particle analysis

    Hu, Hongli; Zhang, Kaiming [Department of Biophysics, the Health Science Centre, Peking University, Beijing 100191 (China); Meng, Xing, E-mail: xmeng101@gmail.com [Wadsworth Centre, New York State Department of Health, Albany, New York 12201 (United States)

    2016-05-15

    An alternative method has been assessed; diffraction patterns derived from the single particle data set were used to perform the first round of classification in creating the initial averages for proteins data with symmetrical morphology. The test protein set was a collection of Caenorhabditis elegans small heat shock protein 17 obtained by Cryo EM, which has a tetrahedral (12-fold) symmetry. It is demonstrated that the initial classification on diffraction patterns is workable as well as the real-space classification that is based on the phase contrast. The test results show that the information from diffraction patterns has the enough details to make the initial model faithful. The potential advantage using the alternative method is twofold, the ability to handle the sets with poor signal/noise or/and that break the symmetry properties. - Highlights: • New classification method. • Create the accurate initial model. • Better in handling noisy data.

  20. Classification using diffraction patterns for single-particle analysis

    Hu, Hongli; Zhang, Kaiming; Meng, Xing

    2016-01-01

    An alternative method has been assessed; diffraction patterns derived from the single particle data set were used to perform the first round of classification in creating the initial averages for proteins data with symmetrical morphology. The test protein set was a collection of Caenorhabditis elegans small heat shock protein 17 obtained by Cryo EM, which has a tetrahedral (12-fold) symmetry. It is demonstrated that the initial classification on diffraction patterns is workable as well as the real-space classification that is based on the phase contrast. The test results show that the information from diffraction patterns has the enough details to make the initial model faithful. The potential advantage using the alternative method is twofold, the ability to handle the sets with poor signal/noise or/and that break the symmetry properties. - Highlights: • New classification method. • Create the accurate initial model. • Better in handling noisy data.

  1. Analysis of pattern formation in systems with competing range interactions

    Zhao, H J; Misko, V R; Peeters, F M

    2012-01-01

    We analyzed pattern formation and identified various morphologies in a system of particles interacting through a non-monotonic potential with a competing range interaction characterized by a repulsive core (r c ) and an attractive tail (r > r c ), using molecular-dynamics simulations. Depending on parameters, the interaction potential models the inter-particle interaction in various physical systems ranging from atoms, molecules and colloids to vortices in low κ type-II superconductors and in recently discovered ‘type-1.5’ superconductors. We constructed a ‘morphology diagram’ in the plane ‘critical radius r c -density n’ and proposed a new approach to characterizing the different types of patterns. Namely, we elaborated a set of quantitative criteria in order to identify the different pattern types, using the radial distribution function (RDF), the local density function and the occupation factor. (paper)

  2. Attack Pattern Analysis Framework for a Multiagent Intrusion Detection System

    Krzysztof Juszczyszyn

    2008-08-01

    Full Text Available The paper proposes the use of attack pattern ontology and formal framework for network traffic anomalies detection within a distributed multi-agent Intrusion Detection System architecture. Our framework assumes ontology-based attack definition and distributed processing scheme with exchange of communicates between agents. The role of traffic anomalies detection was presented then it has been discussed how some specific values characterizing network communication can be used to detect network anomalies caused by security incidents (worm attack, virus spreading. Finally, it has been defined how to use the proposed techniques in distributed IDS using attack pattern ontology.

  3. Bayes classifiers for imbalanced traffic accidents datasets.

    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.

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

    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.

  5. Refractory nonconvulsive status epilepticus in coma: analysis of the evolution of ictal patterns

    Paulo Breno Noronha Liberalesso

    2012-07-01

    Full Text Available OBJECTIVE: Nonconvulsive status epilepticus (NCSE is currently considered as one of the most frequent types of status epilepticus (SE. The objective of the present study was to identify the natural history of the electrographical evolution of refractory NCSE and to establish the relationship between ictal patterns and prognosis. METHODS: We analyzed, retrospectively, 14 patients with loss of consciousness and NCSE. The ictal patterns were classified as discrete seizures (DS, merging seizures (MS, continuous ictal discharges (CID, continuous ictal discharges with flat periods (CID-F, and periodic lateralized epileptiform discharges (PLEDs. RESULTS: The ictal patterns were DS (n=7; 50.0%, PLEDs (n=3; 1.4%, CID (n=2; 14.3%, MS (n=1; 7.1%, and CID-F (n=1; 7.1%. CONCLUSIONS: NCSE electrographic findings are heterogeneous and do not follow a stereotyped sequence. PLEDs were related to a higher probability of neurological morbidity and mortality.

  6. A systematic comparison of supervised classifiers.

    Diego Raphael Amancio

    Full Text Available Pattern recognition has been employed in a myriad of industrial, commercial and academic applications. Many techniques have been devised to tackle such a diversity of applications. Despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, as many techniques as possible should be considered in high accuracy applications. Typical related works either focus on the performance of a given algorithm or compare various classification methods. In many occasions, however, researchers who are not experts in the field of machine learning have to deal with practical classification tasks without an in-depth knowledge about the underlying parameters. Actually, the adequate choice of classifiers and parameters in such practical circumstances constitutes a long-standing problem and is one of the subjects of the current paper. We carried out a performance study of nine well-known classifiers implemented in the Weka framework and compared the influence of the parameter configurations on the accuracy. The default configuration of parameters in Weka was found to provide near optimal performance for most cases, not including methods such as the support vector machine (SVM. In addition, the k-nearest neighbor method frequently allowed the best accuracy. In certain conditions, it was possible to improve the quality of SVM by more than 20% with respect to their default parameter configuration.

  7. Citation Analysis and Authorship Patterns of Two Linguistics Journals

    Ezema, Ifeanyi J.; Asogwa, Brendan E.

    2014-01-01

    This article analyzes the sources cited in articles published in two linguistics journals, "Applied Linguistics and Journal of Linguistics," from 2001 to 2010. A retrospective descriptive study was conducted using bibliometric indicators, such as types of cited sources, timeliness of cited sources, authorship patterns, rank lists of the…

  8. Patterns of Reading Performance in Acute Stroke: A Descriptive Analysis

    Lauren L. Cloutman

    2010-01-01

    Full Text Available One of the main sources of information regarding the underlying processes involved in both normal and impaired reading has been the study of reading deficits that occur as a result of brain damage. However, patterns of reading deficits found acutely after brain injury have been little explored. The observed patterns of performance in chronic stroke patients might reflect reorganization of the cognitive processes underlying reading or development of compensatory strategies that are not normally used to read. Method: 112 acute left hemisphere stroke patients were administered a task of oral reading of words and pseudowords within 1–2 days of hospital admission; performance was examined for error rate and type, and compared to that on tasks involving visual lexical decision, visual/auditory comprehension, and naming. Results: Several distinct patterns of performance were identified. Although similarities were found between the patterns of reading performance observed acutely and the classical acquired dyslexias generally identified more chronically, some notable differences were observed. Of interest was the finding that no patient produced any pure semantic errors in reading, despite finding such errors in comprehension and naming.

  9. Analysis of Spatial Voting Patterns: An Approach in Political Socialization

    Klimasewski, Ted

    1973-01-01

    Passage of the 26th Amendment gave young adults the right to vote. This study attempts to further student understanding of the electoral process by presenting a method for analyzing spatial voting patterns. The spatial emphasis adds another dimension to the temporal and behavioral-structural approaches in studying the American electoral system.…

  10. Analysis of morphophonemic patterns of Gujii dialect: an insight from ...

    The Gujii dialect which is one of southern dialects of Afaan Oromoo is highly characterized by assimilation patterns. This assimilation is dictated by some linguistic and non-linguistic factors and it has impact on the communication held between Gujii dialect speakers and school text version speakers. Therefore, this paper ...

  11. Do pattern recognition skills transfer across sports? A preliminary analysis.

    Smeeton, Nicholas J; Ward, Paul; Williams, A Mark

    2004-02-01

    The ability to recognize patterns of play is fundamental to performance in team sports. While typically assumed to be domain-specific, pattern recognition skills may transfer from one sport to another if similarities exist in the perceptual features and their relations and/or the strategies used to encode and retrieve relevant information. A transfer paradigm was employed to compare skilled and less skilled soccer, field hockey and volleyball players' pattern recognition skills. Participants viewed structured and unstructured action sequences from each sport, half of which were randomly represented with clips not previously seen. The task was to identify previously viewed action sequences quickly and accurately. Transfer of pattern recognition skill was dependent on the participant's skill, sport practised, nature of the task and degree of structure. The skilled soccer and hockey players were quicker than the skilled volleyball players at recognizing structured soccer and hockey action sequences. Performance differences were not observed on the structured volleyball trials between the skilled soccer, field hockey and volleyball players. The skilled field hockey and soccer players were able to transfer perceptual information or strategies between their respective sports. The less skilled participants' results were less clear. Implications for domain-specific expertise, transfer and diversity across domains are discussed.

  12. Demand response driven load pattern elasticity analysis for smart households

    Paterakis, N.G.; Catalao, J.P.S.; Tascikaraoglu, A.; Bakirtzis, A.G.; Erdinc, O.

    2015-01-01

    The recent interest in smart grid vision enables several smart applications in different parts of the power grid structure, where specific importance should be given to the demand side. As a result, changes in load patterns due to demand response (DR) activities at end-user premises, such as smart

  13. Corpus-Based Rhythmic Pattern Analysis of Ragtime Syncopation

    Koops, Hendrik Vincent; Volk, A.; de Haas, W.B.

    2015-01-01

    This paper presents a corpus-based study on rhythmic patterns in the RAG-collection of approximately 11.000 symbolically encoded ragtime pieces. While characteristic musical features that define ragtime as a genre have been debated since its inception, musicologists argue that specific syncopation

  14. A cross-cultural analysis of communication patterns between two ...

    Employing the mixed-method research design, the study revealed the cultural affinity in both ethnic groups' communication patterns in the use of honorific greeting, silence, expressiveness (direct or indirectness and touch) and eye contact. This shows that culture has a significant influence on some of the communication ...

  15. Analysis of Conceptualization Patterns across Groups of People

    Glückstad, Fumiko Kano; Herlau, Tue; Schmidt, Mikkel Nørgaard

    2013-01-01

    This paper analyzes patterns of conceptualizations possessed by different groups of subjects. The eventual goal of this work is to dynamically learn and structure semantic representations for groups of people sharing domain knowledge. In this paper, we conduct a survey for collecting data...

  16. Polysubstance Use Patterns in Underground Rave Attenders: A Cluster Analysis

    Fernandez-Calderon, Fermin; Lozano, Oscar M.; Vidal, Claudio; Ortega, Josefa Gutierrez; Vergara, Esperanza; Gonzalez-Saiz, Francisco; Bilbao, Izaskun; Caluente, Marta; Cano, Tomas; Cid, Francisco; Dominguez, Celia; Izquierdo, Emcarni; Perez, Maria I.

    2011-01-01

    Drug use in mainstream rave parties has been widely documented in a large number of studies. However, not much is known about drug use in underground raves. The purpose of this study is to find out the polysubstance use patterns at underground raves. Two hundred and fifty-two young people between the ages of 18 and 30 who went to underground raves…

  17. An Analysis of Primary School Dropout Patterns in Honduras

    Sekiya, Takeshi; Ashida, Akemi

    2017-01-01

    This study hypothesized that repeating a grade is one reason why Honduran primary students drop out of school but not the main reason. Using longitudinal data, we analyzed student enrollment patterns up until students left school. The results revealed that many students dropped out suddenly without having previously repeated a grade, although many…

  18. Prescription pattern and cost analysis of antipsychotics in a tertiary ...

    Our study revealed that the prescription patterns at the hospital studied were not in conformity with the WHO guidelines. Atypicals, are very expensive and unaffordable to the majority of patients in the study setting. This indicates the need for measures to reduce cost of newer psychotropic drugs, to increase their availability ...

  19. Systematic analysis of stability patterns in plant primary metabolism.

    Dorothee Girbig

    Full Text Available Metabolic networks are characterized by complex interactions and regulatory mechanisms between many individual components. These interactions determine whether a steady state is stable to perturbations. Structural kinetic modeling (SKM is a framework to analyze the stability of metabolic steady states that allows the study of the system Jacobian without requiring detailed knowledge about individual rate equations. Stability criteria can be derived by generating a large number of structural kinetic models (SK-models with randomly sampled parameter sets and evaluating the resulting Jacobian matrices. Until now, SKM experiments applied univariate tests to detect the network components with the largest influence on stability. In this work, we present an extended SKM approach relying on supervised machine learning to detect patterns of enzyme-metabolite interactions that act together in an orchestrated manner to ensure stability. We demonstrate its application on a detailed SK-model of the Calvin-Benson cycle and connected pathways. The identified stability patterns are highly complex reflecting that changes in dynamic properties depend on concerted interactions between several network components. In total, we find more patterns that reliably ensure stability than patterns ensuring instability. This shows that the design of this system is strongly targeted towards maintaining stability. We also investigate the effect of allosteric regulators revealing that the tendency to stability is significantly increased by including experimentally determined regulatory mechanisms that have not yet been integrated into existing kinetic models.

  20. Pattern of abdominal wall herniae in females: a retrospective analysis.

    Result: There were 181 female patients with 184 hernias representing 27.9% of the total ... It is not unexpected to find variations in the pattern of hernia presentation and outcome of man- .... with majority typically found in elderly females with a.

  1. Application of multivariate statistical methods to classify archaeological pottery from Tel-Alramad site, Syria, based on x-ray fluorescence analysis

    Bakraji, E. H.

    2007-01-01

    Radioisotopic x-ray fluorescence (XRF) analysis has been utilized to determine the elemental composition of 55 archaeological pottery samples by the determination of 17 chemical elements. Fifty-four of them came from the Tel-Alramad Site in Katana town, near Damascus city, Syria, and one sample came from Brazil. The XRF results have been processed using two multivariate statistical methods, cluster and factor analysis, in order to determine similarities and correlation between the selected samples based on their elemental composition. The methodology successfully separates the samples where four distinct chemical groups were identified. (author)

  2. An electromagnetic signals monitoring and analysis wireless platform employing personal digital assistants and pattern analysis techniques

    Ninos, K.; Georgiadis, P.; Cavouras, D.; Nomicos, C.

    2010-05-01

    This study presents the design and development of a mobile wireless platform to be used for monitoring and analysis of seismic events and related electromagnetic (EM) signals, employing Personal Digital Assistants (PDAs). A prototype custom-developed application was deployed on a 3G enabled PDA that could connect to the FTP server of the Institute of Geodynamics of the National Observatory of Athens and receive and display EM signals at 4 receiver frequencies (3 KHz (E-W, N-S), 10 KHz (E-W, N-S), 41 MHz and 46 MHz). Signals may originate from any one of the 16 field-stations located around the Greek territory. Employing continuous recordings of EM signals gathered from January 2003 till December 2007, a Support Vector Machines (SVM)-based classification system was designed to distinguish EM precursor signals within noisy background. EM-signals corresponding to recordings preceding major seismic events (Ms≥5R) were segmented, by an experienced scientist, and five features (mean, variance, skewness, kurtosis, and a wavelet based feature), derived from the EM-signals were calculated. These features were used to train the SVM-based classification scheme. The performance of the system was evaluated by the exhaustive search and leave-one-out methods giving 87.2% overall classification accuracy, in correctly identifying EM precursor signals within noisy background employing all calculated features. Due to the insufficient processing power of the PDAs, this task was performed on a typical desktop computer. This optimal trained context of the SVM classifier was then integrated in the PDA based application rendering the platform capable to discriminate between EM precursor signals and noise. System's efficiency was evaluated by an expert who reviewed 1/ multiple EM-signals, up to 18 days prior to corresponding past seismic events, and 2/ the possible EM-activity of a specific region employing the trained SVM classifier. Additionally, the proposed architecture can form a

  3. Classified facilities for environmental protection

    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.)

  4. A Study on Diffusion Pattern of Technology Convergence: Patent Analysis for Korea

    Jae Young Choi

    2015-08-01

    Full Text Available Technology convergence indicates that technologies of different application areas are converted into a new and common unity of technology. Its range spans from inter-field, whereby technologies are converged between heterogeneous fields in homogeneous sector, to a wider inter-sector, whereby technologies belong to heterogeneous technology sector are converged. This paper determined the definition of technology convergence from previous literature and classified patents into technology category depending on patent information. Furthermore, we empirically measure technology convergence degree based on co-classification analysis and estimate its diffusion trend at the entire technology domain level by using overall 1,476,967 of patents filed to the KIPO (Korean Intellectual Property Office from 1998 to 2010. As a result, potential size and growth rate of technology convergence are varied by both technology and type of technology convergence, i.e., inter-field and inter-sector technology convergence. Diffusion pattern of inter-sector technology convergence appears as the more various form than that of inter-field technology convergence. In a relationship between potential size and growth rate of technology convergence, growth rate of technology convergence is in inverse proportion to potential size of technology convergence in general. That is, the faster the growth rate of technology convergence, the smaller the potential size of technology convergence. In addition, this paper found that technology convergence of the instrument and chemistry sector is actively progressing in both inter-field and inter-sector convergence, while the technologies related to Information and Communication Technology (ICT in electrical engineering sector have relatively mature progress of technology convergence, especially in inter-sector technology convergence.

  5. Energy-Efficient Neuromorphic Classifiers.

    Martí, Daniel; Rigotti, Mattia; Seok, Mingoo; Fusi, Stefano

    2016-10-01

    Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of the brain. The energy consumptions promised by neuromorphic engineering are extremely low, comparable to those of the nervous system. Until now, however, the neuromorphic approach has been restricted to relatively simple circuits and specialized functions, thereby obfuscating a direct comparison of their energy consumption to that used by conventional von Neumann digital machines solving real-world tasks. Here we show that a recent technology developed by IBM can be leveraged to realize neuromorphic circuits that operate as classifiers of complex real-world stimuli. Specifically, we provide a set of general prescriptions to enable the practical implementation of neural architectures that compete with state-of-the-art classifiers. We also show that the energy consumption of these architectures, realized on the IBM chip, is typically two or more orders of magnitude lower than that of conventional digital machines implementing classifiers with comparable performance. Moreover, the spike-based dynamics display a trade-off between integration time and accuracy, which naturally translates into algorithms that can be flexibly deployed for either fast and approximate classifications, or more accurate classifications at the mere expense of longer running times and higher energy costs. This work finally proves that the neuromorphic approach can be efficiently used in real-world applications and has significant advantages over conventional digital devices when energy consumption is considered.

  6. Patterns of functional vision loss in glaucoma determined with archetypal analysis

    Elze, Tobias; Pasquale, Louis R.; Shen, Lucy Q.; Chen, Teresa C.; Wiggs, Janey L.; Bex, Peter J.

    2015-01-01

    Glaucoma is an optic neuropathy accompanied by vision loss which can be mapped by visual field (VF) testing revealing characteristic patterns related to the retinal nerve fibre layer anatomy. While detailed knowledge about these patterns is important to understand the anatomic and genetic aspects of glaucoma, current classification schemes are typically predominantly derived qualitatively. Here, we classify glaucomatous vision loss quantitatively by statistically learning prototypical patterns on the convex hull of the data space. In contrast to component-based approaches, this method emphasizes distinct aspects of the data and provides patterns that are easier to interpret for clinicians. Based on 13 231 reliable Humphrey VFs from a large clinical glaucoma practice, we identify an optimal solution with 17 glaucomatous vision loss prototypes which fit well with previously described qualitative patterns from a large clinical study. We illustrate relations of our patterns to retinal structure by a previously developed mathematical model. In contrast to the qualitative clinical approaches, our results can serve as a framework to quantify the various subtypes of glaucomatous visual field loss. PMID:25505132

  7. Analysis of blood flow patterns in aortic aneurysm by cine magnetic resonance imaging

    Matsuoka, Hiroshi

    1993-01-01

    Cine MRI (0.5 T) using rephased gradient echo technique was performed to study the patterns of blood flow in the aortic aneurysm of 16 patients with aortic aneurysm, and the data were compared with those of 5 healthy volunteers. In the transaxial section, the blood flow in normal aorta appeared as homogeneous high intensity during systole. On the other hand, the blood flow in the aneurysm appeared as inhomogeneous flow enhancement with flow void. In the sagittal scan, the homogeneous flow enhancement in a normal aorta was also observed during systole and its apex of flow enhancement was 'taper'. The blood flow patterns in the aneurysm were classified as 'irregular', 'zonal', 'eddy', and 'obscure' depending on the contrast of flow enhancement and flow void. Their apexes were 'taper' or 'round'. The blood flow patterns in the aneurysm were related to the size of aneurysm. In patients with a large size 'aneurysm, their flow patterns were 'eddy' or 'obscure' and the flow enhancement was 'round'. On the other hand, in patients with a small size aneurysm, their flow patterns were 'irregular' or 'zonal', and their flow enhancement was 'taper'. Though the exact mechanism of abnormal flow patterns in an aortic aneurysm remains to be determined, cine MRI gives helpful informations in assessing blood flow dynamics in the aneurysm. (author)

  8. Evaluation of chemical transport model predictions of primary organic aerosol for air masses classified by particle-component-based factor analysis

    C. A. Stroud; M. D. Moran; P. A. Makar; S. Gong; W. Gong; J. Zhang; J. G. Slowik; J. P. D. Abbatt; G. Lu; J. R. Brook; C. Mihele; Q. Li; D. Sills; K. B. Strawbridge; M. L. McGuire

    2012-01-01

    Observations from the 2007 Border Air Quality and Meteorology Study (BAQS-Met 2007) in Southern Ontario, Canada, were used to evaluate predictions of primary organic aerosol (POA) and two other carbonaceous species, black carbon (BC) and carbon monoxide (CO), made for this summertime period by Environment Canada's AURAMS regional chemical transport model. Particle component-based factor analysis was applied to aerosol mass spectrometer measurements made at one urban site (Windsor, ON) and two...

  9. [Quantitative analysis of landscape patterns at the juncture of Shaanxi, Shanxi and Inner Mongolia, based on remote sensing data--taking Yulin sheet TM image as an example].

    Li, Tuansheng

    2004-03-01

    Based on the TM image of Yulin sheet and with the help of ERDAS, ARC/INFO and ARC/VIEW software, the landscape of Yulin sheet was classified. Using the spatial pattern analysis software FRAGSTATS of the vector version, a set of landscape indices were calculated at three scale levels of patches, classes and landscape. The results showed that landscape pattern indices could be successfully used in characterizing the spatial pattern of the studied area. However, this study should be further extended to the landscape of the same area in other period to analyze its dynamic change. FRAGSTATS was a good software, but should be improved by adding some indices such as PD2 developed by us.

  10. Sleep wake pattern analysis: Study of 131 medical students

    Nita Ninama; Jaydeep Kangathara

    2012-01-01

    Objective:Sleep is part of the rhythm of life. Without a good sleep the mind is less adapts, mood is altered and the body loses the ability to refresh. The sleep wake cycle of the students is quite different and characterized by delayed onset, partial sleep deprivation, poor sleep quality, insufficient sleep duration and occurrence of napping episodes during the day The aim of the present study is to know sleep wake pattern in medical student, role of residence and individual characterization...

  11. Pattern recognition as a method of data analysis

    Caputo, M.

    1978-11-15

    The method of pattern recognition has been used in biological and social sciences and has been recently introduced for the solution of geological and geophysical problems such as oil and ore prospecting and seismological prediction. The method is briefly illustrated by an application to earthquake prediction in Italy in which topographic and geologic maps are used in conjunction with earthquake catalogs. 3 figures, 1 table.

  12. Energy consumption patterns. A theoretical analysis; Energieverbrauchsverhalten. Eine theoretische Analyse

    Flandrich, D.

    2006-07-01

    The author questions the methodological and methodical foundations of energy consumption research and attempts a theory of energy consumption patterns on the basis of psychology, opening up a quite new perspective that has been neglected so far. Energy policy and energy marketing are two fields of applications which are getting more important in these times of increasing prices of energy resources, high public awareness of environmental issues, and deregulated energy markets. (orig.)

  13. Analysis of co-authorship patterns at the individual level

    Wolfgang Glänzel

    Full Text Available Publication activity, citation impact and communication patterns, in general, change in the course of a scientist's career. Mobility and radical changes in a scientist's research environment or profile are among the most spectacular factors that have effect on individual collaboration patterns. Although bibliometrics at this level should be applied with the utmost care, characteristic patterns of an individual scientist's research collaboration and changes in these in the course of a career can be well depicted using bibliometric methods. A wide variety of indicators and network tools are chosen to follow up the evolution and to visualise and to quantify collaboration and performance profiles of individual researchers. These methods are, however, designed to supplement expert-opinion based assessment and other qualitative assessments, and should not be used as stand-alone evaluation tools. This study presents part of the results published in an earlier study by Zhang and Glänzel (20124 as well as new applications of these methods.

  14. 76 FR 34761 - Classified National Security Information

    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. Comparative analysis of Edwardsiella isolates from fish in the eastern United States identifies two distinct genetic taxa amongst organisms phenotypically classified as E. tarda

    Griffin, Matt J.; Quiniou, Sylvie M.; Cody, Theresa; Tabuchi, Maki; Ware, Cynthia; Cipriano, Rocco C.; Mauel, Michael J.; Soto, Esteban

    2013-01-01

    Edwardsiella tarda, a Gram-negative member of the family Enterobacteriaceae, has been implicated in significant losses in aquaculture facilities worldwide. Here, we assessed the intra-specific variability of E. tarda isolates from 4 different fish species in the eastern United States. Repetitive sequence mediated PCR (rep-PCR) using 4 different primer sets (ERIC I & II, ERIC II, BOX, and GTG5) and multi-locus sequence analysis of 16S SSU rDNA, groEl, gyrA, gyrB, pho, pgi, pgm, and rpoA gene fragments identified two distinct genotypes of E. tarda (DNA group I; DNA group II). Isolates that fell into DNA group II demonstrated more similarity to E. ictaluri than DNA group I, which contained the reference E. tarda strain (ATCC #15947). Conventional PCR analysis using published E. tarda-specific primer sets yielded variable results, with several primer sets producing no observable amplification of target DNA from some isolates. Fluorometric determination of G + C content demonstrated 56.4% G + C content for DNA group I, 60.2% for DNA group II, and 58.4% for E. ictaluri. Surprisingly, these isolates were indistinguishable using conventional biochemical techniques, with all isolates demonstrating phenotypic characteristics consistent with E. tarda. Analysis using two commercial test kits identified multiple phenotypes, although no single metabolic characteristic could reliably discriminate between genetic groups. Additionally, anti-microbial susceptibility and fatty acid profiles did not demonstrate remarkable differences between groups. The significant genetic variation (<90% similarity at gyrA, gyrB, pho, phi and pgm; <40% similarity by rep-PCR) between these groups suggests organisms from DNA group II may represent an unrecognized, genetically distinct taxa of Edwardsiella that is phenotypically indistinguishable from E. tarda.

  16. Principal Component Analysis Coupled with Artificial Neural Networks—A Combined Technique Classifying Small Molecular Structures Using a Concatenated Spectral Database

    Mihail Lucian Birsa

    2011-10-01

    Full Text Available In this paper we present several expert systems that predict the class identity of the modeled compounds, based on a preprocessed spectral database. The expert systems were built using Artificial Neural Networks (ANN and are designed to predict if an unknown compound has the toxicological activity of amphetamines (stimulant and hallucinogen, or whether it is a nonamphetamine. In attempts to circumvent the laws controlling drugs of abuse, new chemical structures are very frequently introduced on the black market. They are obtained by slightly modifying the controlled molecular structures by adding or changing substituents at various positions on the banned molecules. As a result, no substance similar to those forming a prohibited class may be used nowadays, even if it has not been specifically listed. Therefore, reliable, fast and accessible systems capable of modeling and then identifying similarities at molecular level, are highly needed for epidemiological, clinical, and forensic purposes. In order to obtain the expert systems, we have preprocessed a concatenated spectral database, representing the GC-FTIR (gas chromatography-Fourier transform infrared spectrometry and GC-MS (gas chromatography-mass spectrometry spectra of 103 forensic compounds. The database was used as input for a Principal Component Analysis (PCA. The scores of the forensic compounds on the main principal components (PCs were then used as inputs for the ANN systems. We have built eight PC-ANN systems (principal component analysis coupled with artificial neural network with a different number of input variables: 15 PCs, 16 PCs, 17 PCs, 18 PCs, 19 PCs, 20 PCs, 21 PCs and 22 PCs. The best expert system was found to be the ANN network built with 18 PCs, which accounts for an explained variance of 77%. This expert system has the best sensitivity (a rate of classification C = 100% and a rate of true positives TP = 100%, as well as a good selectivity (a rate of true negatives TN

  17. Patterns of urban violent injury: a spatio-temporal analysis.

    Michael Cusimano

    2010-01-01

    Full Text Available Injury related to violent acts is a problem in every society. Although some authors have examined the geography of violent crime, few have focused on the spatio-temporal patterns of violent injury and none have used an ambulance dataset to explore the spatial characteristics of injury. The purpose of this study was to describe the combined spatial and temporal characteristics of violent injury in a large urban centre.Using a geomatics framework and geographic information systems software, we studied 4,587 ambulance dispatches and 10,693 emergency room admissions for violent injury occurrences among adults (aged 18-64 in Toronto, Canada, during 2002 and 2004, using population-based datasets. We created kernel density and choropleth maps for 24-hour periods and four-hour daily time periods and compared location of ambulance dispatches and patient residences with local land use and socioeconomic characteristics. We used multivariate regressions to control for confounding factors. We found the locations of violent injury and the residence locations of those injured were both closely related to each other and clearly clustered in certain parts of the city characterised by high numbers of bars, social housing units, and homeless shelters, as well as lower household incomes. The night and early morning showed a distinctive peak in injuries and a shift in the location of injuries to a "nightlife" district. The locational pattern of patient residences remained unchanged during those times.Our results demonstrate that there is a distinctive spatio-temporal pattern in violent injury reflected in the ambulance data. People injured in this urban centre more commonly live in areas of social deprivation. During the day, locations of injury and locations of residences are similar. However, later at night, the injury location of highest density shifts to a "nightlife" district, whereas the residence locations of those most at risk of injury do not change.

  18. An analysis on vegetation pattern of ecotone in North China

    Jia, J.C.; Zhang, H.Y. [North China Electric Power Univ., Beijing (China). Energy and Environmental Research Center

    2008-07-01

    Vegetation pattern is influenced by several natural factors, including climatic elements, elevation factors and soil conditions. Since soil formation and soil types are influenced by water-temperature conditions, much can be learned about vegetation distribution patterns by studying the relationship between water-temperature conditions and vegetation distribution. This paper presented the results of a study whose purpose was to provide scientific evidence for exploiting natural resources, planting trees, and restoring grassland from cropland. A warmth index (WI ) and humidity index (HI) were used to examine the relation between the distribution of vegetation and the water-temperature condition in North China's ecotone, the transition area between two adjacent but different plant communities, including steppe, bush and forest ecosystems. A vegetation map of the study site was digitized and then converted into a vegetation grid map from which 17 different vegetation types were chosen as the study object. A monthly mean temperature grid map and precipitation grid map of the study site were made based on the method of spatial interpolation, by using 119 meteorological data for 50 years during the period from 1951 to 2000. The thermal distribution curves and humidity distribution curves of 17 vegetation types in North China, determined the whole range and optimum range of WI and HI of 17 vegetation types. The relative proportion of each vegetation type distributed in the optimum range of WI and HI were calculated. The vegetation pattern was analyzed according to the WI and HI standard, and was described by species and their relative amount. 10 refs., 5 tabs., 3 figs.

  19. Pattern analysis in MR imaging of muscle diseases

    Kaiser, W.A.; Schalke, B.C.G.

    1987-01-01

    Between March 1984 and March 1987, 161 patients with muscle diseases underwent MR imaging performed with a 1.0-T superconductive magnet. Forty-four had progressive muscular dystrophies, 25 had different types of myositis, 19 had spinal or neural muscular atrophies, 16 had myotonic dystrophy, 22 had metabolic disorders, and 35 had other muscle disease, including muscle tumors, posttraumatic muscular atrophies, and postradiation effects. The advantages of MR imaging are the high sensitivity and soft-tissue contrast, as well as the depiction of typical distribution patterns of affected muscle groups, which can be used in diagnosis, biopsy planning, and design of therapy

  20. Identification of Design Work Patterns by Retrospective Analysis of Work Sheets

    Hansen, Claus Thorp

    1999-01-01

    project is carried out where we seek to identify design work patterns by retrospective analysis of documentation created during design projects.An elements to satisfy the wish for an efficient design process could be to identify work patterns applied by engineering designers, evaluate these patterns...... with respect to their efficiency, and reuse the most efficient in future projects. Thus, the objective of this research is to analyse design projects in order to identify the work patterns applied. Based on an evaluation of identified work patterns we expect a recommendation of work patterns supporting...... an efficient design process can be established.In this paper we describe the analysis method, and present observations from analyses of three projects....

  1. An analysis of whorl patterns for determination of hand.

    Kapoor, Neeti; Badiye, Ashish

    2015-05-01

    On crime scenes, whole set of the ten digit fingerprints are rarely found and usually chance prints in the form of single digit fingerprint are encountered. Determination of hand (Right or left) can be of vital importance to reduce the burden on the investigator and may thereby aid in fixation of absolute identity of the donor. In the present investigation, 500 randomly selected and bilateral rolled fingerprints of 250 healthy, consenting adult subjects of a central Indian (Marathi) population with whorl patterns were examined to determine the hand. It was found that by studying various parameters like; slope of apex ridges (towards right, left or absent), rotation of innermost ridges (either clockwise, anti-clockwise or absent), angle formed at both sides of core, position of the perpendicular bisector on the delta line (with respect to core), ridge tracing (outer, inner or meeting), higher ridge count, angle between deltas and core (at deltas), direction of the pattern (tilting/inclination) and distance between the deltas & the core; it is possible to successfully determine the hand of the print. Applying chi-square test, the results were found to be statistically significant at p < 0.01 levels. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

  2. An electromyographic analysis of two handwriting grasp patterns.

    de Almeida, Pedro Henrique Tavares Queiroz; da Cruz, Daniel Marinho Cezar; Magna, Luis Alberto; Ferrigno, Iracema Serrat Vergotti

    2013-08-01

    Handwriting is a fundamental skill needed for the development of daily-life activities during lifetime and can be performed using different forms to hold the writing object. In this study, we monitored the sEMG activity of trapezius, biceps brachii, extensor carpi radialis brevis and flexor digitorum superficialis during a handwriting task with two groups of subjects using different grasp patterns. Twenty-four university students (thirteen males and eleven females; mean age of 22.04±2.8years) were included in this study. We randomly invited 12 subjects that used the Dynamic Tripod grasp and 12 subjects that used the Static Tripod grasp. The static tripod group showed statistically significant changes in the sEMG activity of trapezium and biceps brachii muscles during handwriting when compared to dynamic tripod group's subjects. No significant differences were found in extensor carpi radialis brevis and flexor digitorum superficialis activities among the two groups. The findings in this study suggest an increased activity of proximal muscles among subjects using a transitional grasp, indicating potential higher energy expenditure and muscular harm with the maintenance of this motor pattern in handwriting tasks, especially during the progression in academic life. Copyright © 2013 Elsevier Ltd. All rights reserved.

  3. Dynamic analysis and pattern visualization of forest fires.

    Lopes, António M; Tenreiro Machado, J A

    2014-01-01

    This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.

  4. Patterns of Puffery: An Analysis of Non-Fiction Blurbs

    Cronin, Blaise; La Barre, Kathryn

    2005-01-01

    The blurb is a paratextual element which has not previously been subjected to systematic analysis. We describe the nature and purpose of this publishing epiphenomenon, highlight some of the related marketing issues and ethical concerns and provide a statistical analysis of almost 2000 blurbs identified in a sample of 450 non-fiction books.…

  5. Pharmacokinetic Tumor Heterogeneity as a Prognostic Biomarker for Classifying Breast Cancer Recurrence Risk.

    Mahrooghy, Majid; Ashraf, Ahmed B; Daye, Dania; McDonald, Elizabeth S; Rosen, Mark; Mies, Carolyn; Feldman, Michael; Kontos, Despina

    2015-06-01

    Heterogeneity in cancer can affect response to therapy and patient prognosis. Histologic measures have classically been used to measure heterogeneity, although a reliable noninvasive measurement is needed both to establish baseline risk of recurrence and monitor response to treatment. Here, we propose using spatiotemporal wavelet kinetic features from dynamic contrast-enhanced magnetic resonance imaging to quantify intratumor heterogeneity in breast cancer. Tumor pixels are first partitioned into homogeneous subregions using pharmacokinetic measures. Heterogeneity wavelet kinetic (HetWave) features are then extracted from these partitions to obtain spatiotemporal patterns of the wavelet coefficients and the contrast agent uptake. The HetWave features are evaluated in terms of their prognostic value using a logistic regression classifier with genetic algorithm wrapper-based feature selection to classify breast cancer recurrence risk as determined by a validated gene expression assay. Receiver operating characteristic analysis and area under the curve (AUC) are computed to assess classifier performance using leave-one-out cross validation. The HetWave features outperform other commonly used features (AUC = 0.88 HetWave versus 0.70 standard features). The combination of HetWave and standard features further increases classifier performance (AUCs 0.94). The rate of the spatial frequency pattern over the pharmacokinetic partitions can provide valuable prognostic information. HetWave could be a powerful feature extraction approach for characterizing tumor heterogeneity, providing valuable prognostic information.

  6. Prediction of Mortality in Patients with Isolated Traumatic Subarachnoid Hemorrhage Using a Decision Tree Classifier: A Retrospective Analysis Based on a Trauma Registry System.

    Rau, Cheng-Shyuan; Wu, Shao-Chun; Chien, Peng-Chen; Kuo, Pao-Jen; Chen, Yi-Chun; Hsieh, Hsiao-Yun; Hsieh, Ching-Hua

    2017-11-22

    Background: In contrast to patients with traumatic subarachnoid hemorrhage (tSAH) in the presence of other types of intracranial hemorrhage, the prognosis of patients with isolated tSAH is good. The incidence of mortality in these patients ranges from 0-2.5%. However, few data or predictive models are available for the identification of patients with a high mortality risk. In this study, we aimed to construct a model for mortality prediction using a decision tree (DT) algorithm, along with data obtained from a population-based trauma registry, in a Level 1 trauma center. Methods: Five hundred and forty-five patients with isolated tSAH, including 533 patients who survived and 12 who died, between January 2009 and December 2016, were allocated to training ( n = 377) or test ( n = 168) sets. Using the data on demographics and injury characteristics, as well as laboratory data of the patients, classification and regression tree (CART) analysis was performed based on the Gini impurity index, using the rpart function in the rpart package in R. Results: In this established DT model, three nodes (head Abbreviated Injury Scale (AIS) score ≤4, creatinine (Cr) 4 died, as did the 57% of those with an AIS score ≤4, but Cr ≥1.4 and age ≥76 years. All patients who did not meet the above-mentioned criteria survived. With all the variables in the model, the DT achieved an accuracy of 97.9% (sensitivity of 90.9% and specificity of 98.1%) and 97.7% (sensitivity of 100% and specificity of 97.7%), for the training set and test set, respectively. Conclusions: The study established a DT model with three nodes (head AIS score ≤4, Cr decision-making algorithm may help identify patients with a high risk of mortality.

  7. Dietary Patterns Derived by Cluster Analysis are Associated with Cognitive Function among Korean Older Adults.

    Kim, Jihye; Yu, Areum; Choi, Bo Youl; Nam, Jung Hyun; Kim, Mi Kyung; Oh, Dong Hoon; Yang, Yoon Jung

    2015-05-29

    The objective of this study was to investigate major dietary patterns among older Korean adults through cluster analysis and to determine an association between dietary patterns and cognitive function. This is a cross-sectional study. The data from the Korean Multi-Rural Communities Cohort Study was used. Participants included 765 participants aged 60 years and over. A quantitative food frequency questionnaire with 106 items was used to investigate dietary intake. The Korean version of the MMSE-KC (Mini-Mental Status Examination-Korean version) was used to assess cognitive function. Two major dietary patterns were identified using K-means cluster analysis. The "MFDF" dietary pattern indicated high consumption of Multigrain rice, Fish, Dairy products, Fruits and fruit juices, while the "WNC" dietary pattern referred to higher intakes of White rice, Noodles, and Coffee. Means of the total MMSE-KC and orientation score of the participants in the MFDF dietary pattern were higher than those of the WNC dietary pattern. Compared with the WNC dietary pattern, the MFDF dietary pattern showed a lower risk of cognitive impairment after adjusting for covariates (OR 0.64, 95% CI 0.44-0.94). The MFDF dietary pattern, with high consumption of multigrain rice, fish, dairy products, and fruits may be related to better cognition among Korean older adults.

  8. Dietary Patterns Derived by Cluster Analysis are Associated with Cognitive Function among Korean Older Adults

    Jihye Kim

    2015-05-01

    Full Text Available The objective of this study was to investigate major dietary patterns among older Korean adults through cluster analysis and to determine an association between dietary patterns and cognitive function. This is a cross-sectional study. The data from the Korean Multi-Rural Communities Cohort Study was used. Participants included 765 participants aged 60 years and over. A quantitative food frequency questionnaire with 106 items was used to investigate dietary intake. The Korean version of the MMSE-KC (Mini-Mental Status Examination–Korean version was used to assess cognitive function. Two major dietary patterns were identified using K-means cluster analysis. The “MFDF” dietary pattern indicated high consumption of Multigrain rice, Fish, Dairy products, Fruits and fruit juices, while the “WNC” dietary pattern referred to higher intakes of White rice, Noodles, and Coffee. Means of the total MMSE-KC and orientation score of the participants in the MFDF dietary pattern were higher than those of the WNC dietary pattern. Compared with the WNC dietary pattern, the MFDF dietary pattern showed a lower risk of cognitive impairment after adjusting for covariates (OR 0.64, 95% CI 0.44–0.94. The MFDF dietary pattern, with high consumption of multigrain rice, fish, dairy products, and fruits may be related to better cognition among Korean older adults.

  9. Using Conditional Analysis to Investigate Spatial and Temporal patterns in Upland Rainfall

    Sakamoto Ferranti, Emma Jayne; Whyatt, James Duncan; Timmis, Roger James

    2010-05-01

    The seasonality and characteristics of rainfall in the UK are altering under a changing climate. Summer rainfall is generally decreasing whereas winter rainfall is increasing, particularly in northern and western areas (Maraun et al., 2008) and recent research suggests these rainfall increases are amplified in upland areas (Burt and Ferranti, 2010). Conditional analysis has been used to investigate these rainfall patterns in Cumbria, an upland area in northwest England. Cumbria was selected as an example of a topographically diverse mid-latitude region that has a predominately maritime and westerly-defined climate. Moreover it has a dense network of more than 400 rain gauges that have operated for periods between 1900 and present day. Cumbria has experienced unprecedented flooding in the past decade and understanding the spatial and temporal changes in this and other upland regions is important for water resource and ecosystem management. The conditional analysis method examines the spatial and temporal variations in rainfall under different synoptic conditions and in different geographic sub-regions (Ferranti et al., 2009). A daily synoptic typing scheme, the Lamb Weather Catalogue, was applied to classify rainfall into different weather types, for example: south-westerly, westerly, easterly or cyclonic. Topographic descriptors developed using GIS were used to classify rain gauges into 6 directionally-dependant geographic sub-regions: coastal, windward-lowland, windward-upland, leeward-upland, leeward-lowland, secondary upland. Combining these classification methods enabled seasonal rainfall climatologies to be produced for specific weather types and sub-regions. Winter rainfall climatologies were constructed for all 6 sub-regions for 3 weather types - south-westerly (SW), westerly (W), and cyclonic (C); these weather types contribute more than 50% of total winter rainfall. The frequency of wet-days (>0.3mm), the total winter rainfall and the average wet day

  10. Pattern analysis of defecography in patients with chronic functional constipation: is it predictable for the responsiveness of biofeedback therapy?

    Yang, Hye Rin; Kim, Ah Young; Hong, Seong Sook; Byun, Jae Ho; Myung Seung Jae; Ha, Hyun Kwon [University of Ulsan of Medicine, Seoul (Korea, Republic of)

    2005-08-15

    To determine of pattern analysis of defecography can predict the responsiveness of biofeedback therapy in patients with chronic functional constipation. Over a two-year period, 104 patients with chronic functional constipation underwent defecography and biofeedback therapy. Two blinded readers analyzed the defecographic findings and classified them into six types; I = normal defecation, II = hypertonic lower anal sphincter (poor anal opening due to a persistent contraction of the lower anal sphincter), III dyskinetic puborectal sling (inadequate laxity of the puborectal sling), IV spastic pelvic floor syndrome (persistent contraction of both the puborectal sling and the lower and sphincter), V unclassified (including paradoxical contraction of the anal sphincter), VI anatomical obstruction. In addition, the degree of rectal contraction during defecation was scored (grade 0 to 3). After biofeedback therapy, the differences in the defecography patterns or rectal contraction between the two groups, the responsive or non-responsive group, were analyzed. The defecograms revealed that the type IV of the spastic pelvic floor syndrome was most common (50 of 104 patients, 48%), followed by II (21/104, 20%), III (12/104, 11.5%), V (9/104, 9%) and VI (12/104, 11.5%). Biofeedback therapy showed a therapeutic response in 71 out of 104 patients (68%) but failed in 33 patients (32%). However, there were no significant differences in the defecographic pattern between the responsive and non-responsive groups ({rho} = 0.630). The defecograms revealed contractions in 78 patients (75%) and moderate to vigorous contractions (more than grade 2) in 66 patients. Most of the biofeedback-responsive group showed rectal contractions (66 of 71 patients, 93%, {rho} < 0.001). In patients with chronic functional constipation, there was no significant difference in the morphological patterns of the defecogram between the responsive and non-responsive biofeedback groups. However, the presence of

  11. Pattern analysis of defecography in patients with chronic functional constipation: is it predictable for the responsiveness of biofeedback therapy?

    Yang, Hye Rin; Kim, Ah Young; Hong, Seong Sook; Byun, Jae Ho; Myung Seung Jae; Ha, Hyun Kwon

    2005-01-01

    To determine of pattern analysis of defecography can predict the responsiveness of biofeedback therapy in patients with chronic functional constipation. Over a two-year period, 104 patients with chronic functional constipation underwent defecography and biofeedback therapy. Two blinded readers analyzed the defecographic findings and classified them into six types; I = normal defecation, II = hypertonic lower anal sphincter (poor anal opening due to a persistent contraction of the lower anal sphincter), III dyskinetic puborectal sling (inadequate laxity of the puborectal sling), IV spastic pelvic floor syndrome (persistent contraction of both the puborectal sling and the lower and sphincter), V unclassified (including paradoxical contraction of the anal sphincter), VI anatomical obstruction. In addition, the degree of rectal contraction during defecation was scored (grade 0 to 3). After biofeedback therapy, the differences in the defecography patterns or rectal contraction between the two groups, the responsive or non-responsive group, were analyzed. The defecograms revealed that the type IV of the spastic pelvic floor syndrome was most common (50 of 104 patients, 48%), followed by II (21/104, 20%), III (12/104, 11.5%), V (9/104, 9%) and VI (12/104, 11.5%). Biofeedback therapy showed a therapeutic response in 71 out of 104 patients (68%) but failed in 33 patients (32%). However, there were no significant differences in the defecographic pattern between the responsive and non-responsive groups (ρ = 0.630). The defecograms revealed contractions in 78 patients (75%) and moderate to vigorous contractions (more than grade 2) in 66 patients. Most of the biofeedback-responsive group showed rectal contractions (66 of 71 patients, 93%, ρ < 0.001). In patients with chronic functional constipation, there was no significant difference in the morphological patterns of the defecogram between the responsive and non-responsive biofeedback groups. However, the presence of rectal

  12. Pattern recognition in spaces of probability distributions for the analysis of edge-localized modes in tokamak plasmas

    Shabbir, Aqsa

    2016-07-07

    scaling (MDS) and landmark multidimensional scaling (LMDS) for data visualization (dimensionality reduction). Furthermore, two new classification schemes are developed: a distance-to-centroid classifier (D2C) and a principal geodesic classifier (PGC). D2C classifies on the basis of the minimum GD to the class centroids and PGC considers the shape of the class on the manifold by determining the minimum distance to the principal geodesic of each class. The methods are validated by their application to the classification and retrieval of colored texture images represented in the wavelet domain. Both methods prove to be computationally efficient, yield high accuracy and also clearly exhibit the adequacy of the GD and its superiority over the Euclidean distance, for comparing PDFs. The second main goal of the work targets ELM analysis at three fronts, using pattern recognition and probabilistic modeling: (i) We first concentrate on visualization of ELM characteristics by creating maps containing projections of multidimensional ELM data, as well as the corresponding probabilistic models. In particular, GD-based MDS is used for representing the complete distributions of the multidimensional data characterizing the operational space of ELMs onto two-dimensional maps. Clusters corresponding to type I and type III ELMs are identified and the maps enable tracking of trends in plasma parameters across the operational space. It is shown that the maps can also be used with reasonable accuracy for predicting the values of the plasma parameters at a certain point in the operational space. (ii) Our second application concerns fast, standardized and automated classification of ELM types. The presented classification schemes are aimed at complementing the phenomenological characterization using standardized methods that are less susceptible to subjective interpretation, while considerably reducing the effort of ELM experts in identifying ELM types. To this end, different classification

  13. Pattern recognition in spaces of probability distributions for the analysis of edge-localized modes in tokamak plasmas

    Shabbir, Aqsa

    2016-01-01

    scaling (MDS) and landmark multidimensional scaling (LMDS) for data visualization (dimensionality reduction). Furthermore, two new classification schemes are developed: a distance-to-centroid classifier (D2C) and a principal geodesic classifier (PGC). D2C classifies on the basis of the minimum GD to the class centroids and PGC considers the shape of the class on the manifold by determining the minimum distance to the principal geodesic of each class. The methods are validated by their application to the classification and retrieval of colored texture images represented in the wavelet domain. Both methods prove to be computationally efficient, yield high accuracy and also clearly exhibit the adequacy of the GD and its superiority over the Euclidean distance, for comparing PDFs. The second main goal of the work targets ELM analysis at three fronts, using pattern recognition and probabilistic modeling: (i) We first concentrate on visualization of ELM characteristics by creating maps containing projections of multidimensional ELM data, as well as the corresponding probabilistic models. In particular, GD-based MDS is used for representing the complete distributions of the multidimensional data characterizing the operational space of ELMs onto two-dimensional maps. Clusters corresponding to type I and type III ELMs are identified and the maps enable tracking of trends in plasma parameters across the operational space. It is shown that the maps can also be used with reasonable accuracy for predicting the values of the plasma parameters at a certain point in the operational space. (ii) Our second application concerns fast, standardized and automated classification of ELM types. The presented classification schemes are aimed at complementing the phenomenological characterization using standardized methods that are less susceptible to subjective interpretation, while considerably reducing the effort of ELM experts in identifying ELM types. To this end, different classification

  14. An analysis of factors related to the tail-like pattern of myxofibrosarcoma seen on MRI

    Kikuta, Kazutaka; Kubota, Daisuke; Chuuman, Hirokazu; Kawai, Akira; Yoshida, Akihiko; Morioka, Hideo; Toyama, Yoshiaki

    2015-01-01

    Myxofibrosarcoma (MFS) is characterized by a high frequency of local recurrence after surgery because of infiltrative growth of the tumor cells. This infiltrative growth creates a characteristic 'tail-like' pattern on magnetic resonance imaging (MRI), and it has been reported that this pattern is especially obvious on gadolinium-enhanced MRI (Gd MRI). However, the relationship between the tail-like pattern seen on Gd MRI and clinicopathological features of MFS is still not clear. In this study, we performed a retrospective analysis to identify clinicopathological factors related to the tail-like pattern of the MRI findings in patients with MFS. We retrospectively analyzed 50 patients with MFS to identify factors related to the tail-like pattern. On Gd MRI, 32 of the 50 patients presented the tail-like pattern, whereas 18 presented a solid pattern. The clincopathological factors related to the tail-like pattern were evaluated by chi-squared test. A superficial origin (p = 0.0009) was most significantly related to the tail-like pattern. The 5-year recurrence-free survival (RFS) rate was 75.6 % for patients showing the tail-like pattern and 90.9 % for those showing the solid pattern. The corresponding 5-year disease-free survival (DFS) rates were 64.7 and 79.3 %, respectively. Thus in terms of both 5-year RFS and DFS, patients with the tail-like pattern tended to have a poorer outcome. A superficial origin of MFS is significantly related to a tail-like pattern on Gd MRI. The tail-like pattern is associated with poorer prognosis. Further studies of tumor depth and the tail-like pattern on Gd MRI are needed. (orig.)

  15. Implementing SCRUM using Business Process Management and Pattern Analysis Methodologies

    Ron S. Kenett

    2013-11-01

    Full Text Available The National Institute of Standards and Technology in the US has estimated that software defects and problems annually cost 59.5 billions the U.S. economy (http://www.abeacha.com/NIST_press_release_bugs_cost.htm. The study is only one of many that demonstrate the need for significant improvements in software development processes and practices. US Federal agencies, that depend on IT to support their missions and spent at least $76 billion on IT in fiscal year 2011, experienced numerous examples of lengthy IT projects that incurred cost overruns and schedule delays while contributing little to mission-related outcomes (www.gao.gov/products/GAO-12-681. To reduce the risk of such problems, the US Office of Management and Budget recommended deploying an agile software delivery, which calls for producing software in small, short increments (GAO, 2012. Consistent with this recommendation, this paper is about the application of Business Process Management to the improvement of software and system development through SCRUM or agile techniques. It focuses on how organizational behavior and process management techniques can be integrated with knowledge management approaches to deploy agile development. The context of this work is a global company developing software solutions for service operators such as cellular phone operators. For a related paper with a comprehensive overview of agile methods in project management see Stare (2013. Through this comprehensive case study we demonstrate how such an integration can be achieved. SCRUM is a paradigm shift in many organizations in that it results in a new balance between focus on results and focus on processes. In order to describe this new paradigm of business processes this work refers to Enterprise Knowledge Development (EKD, a comprehensive approach to map and document organizational patterns. In that context, the paper emphasizes the concept of patterns, reviews the main elements of SCRUM and shows how

  16. The Determination of Children's Knowledge of Global Lunar Patterns from Online Essays Using Text Mining Analysis

    Cheon, Jongpil; Lee, Sangno; Smith, Walter; Song, Jaeki; Kim, Yongjin

    2013-01-01

    The purpose of this study was to use text mining analysis of early adolescents' online essays to determine their knowledge of global lunar patterns. Australian and American students in grades five to seven wrote about global lunar patterns they had discovered by sharing observations with each other via the Internet. These essays were analyzed for…

  17. MELDOQ - astrophysical image and pattern analysis in medicine: early recognition of malignant melanomas of the skin by digital image analysis. Final report

    Bunk, W.; Pompl, R.; Morfill, G.; Stolz, W.; Abmayr, W.

    1999-01-01

    Dermatoscopy is at present the most powerful clinical method for early detection of malignant melanomas. However, the application requires a lot of expertise and experience. Therefore, a quantitative image analysis system has been developed in order to assist dermatologists in 'on site diagnosis' and to improve the detection efficiency. Based on a very extensive dataset of dermatoscopic images, recorded in a standardized manner, a number of features for quantitative characterization of complex patterns in melanocytic skin lesions has been developed. The derived classifier improved the detection rate of malignant and benign melanocytic lesions to over 90% (sensitivity =91.5% and specificity =93.4% in the test set), using only six measures. A distinguishing feature of the system is the visualization of the quantified characteristics that are based on the dermatoscopic ABCD-rule. The developed prototype of a dermatoscopic workplace consists of defined procedures for standardized image acquisition and documentation, components of a necessary data pre-processing (e.g. shading- and colour-correction, removal of artefacts), quantification algorithms (evaluating asymmetry properties, border characteristics, the content of colours and structural components) and classification routines. In 2000 an industrial partner will begin marketing the digital imaging system including the specialized software for the early detection of skin cancer, which is suitable for clinicians and practitioners. The primary used nonlinear analysis techniques (e.g. scaling index method and others) can identify and characterize complex patterns in images and have a diagnostic potential in many other applications. (orig.) [de

  18. Dysphonic Voice Pattern Analysis of Patients in Parkinson’s Disease Using Minimum Interclass Probability Risk Feature Selection and Bagging Ensemble Learning Methods

    Yunfeng Wu

    2017-01-01

    Full Text Available Analysis of quantified voice patterns is useful in the detection and assessment of dysphonia and related phonation disorders. In this paper, we first study the linear correlations between 22 voice parameters of fundamental frequency variability, amplitude variations, and nonlinear measures. The highly correlated vocal parameters are combined by using the linear discriminant analysis method. Based on the probability density functions estimated by the Parzen-window technique, we propose an interclass probability risk (ICPR method to select the vocal parameters with small ICPR values as dominant features and compare with the modified Kullback-Leibler divergence (MKLD feature selection approach. The experimental results show that the generalized logistic regression analysis (GLRA, support vector machine (SVM, and Bagging ensemble algorithm input with the ICPR features can provide better classification results than the same classifiers with the MKLD selected features. The SVM is much better at distinguishing normal vocal patterns with a specificity of 0.8542. Among the three classification methods, the Bagging ensemble algorithm with ICPR features can identify 90.77% vocal patterns, with the highest sensitivity of 0.9796 and largest area value of 0.9558 under the receiver operating characteristic curve. The classification results demonstrate the effectiveness of our feature selection and pattern analysis methods for dysphonic voice detection and measurement.

  19. Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier.

    Li, Qiang; Gu, Yu; Jia, Jing

    2017-01-30

    Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS) and support vector machine (SVM) algorithms in a quartz crystal microbalance (QCM)-based electronic nose (e-nose) we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3%) showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN) classifier (93.3%) and moving average-linear discriminant analysis (MA-LDA) classifier (87.6%). The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization) performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

  20. Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier

    Qiang Li

    2017-01-01

    Full Text Available Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS and support vector machine (SVM algorithms in a quartz crystal microbalance (QCM-based electronic nose (e-nose we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3% showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN classifier (93.3% and moving average-linear discriminant analysis (MA-LDA classifier (87.6%. The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

  1. Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

    Linn, Kristin A; Gaonkar, Bilwaj; Satterthwaite, Theodore D; Doshi, Jimit; Davatzikos, Christos; Shinohara, Russell T

    2016-05-15

    Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases. Copyright © 2016 Elsevier Inc. All rights reserved.

  2. Irradiation Pattern Analysis for Designing Light Sources-Based on Light Emitting Diodes

    Rojas, E.; Stolik, S.; La Rosa, J. de; Valor, A.

    2016-01-01

    Nowadays it is possible to design light sources with a specific irradiation pattern for many applications. Light Emitting Diodes present features like high luminous efficiency, durability, reliability, flexibility, among others as the result of its rapid development. In this paper the analysis of the irradiation pattern of the light emitting diodes is presented. The approximation of these irradiation patterns to both, a Lambertian, as well as a Gaussian functions for the design of light sources is proposed. Finally, the obtained results and the functionality of bringing the irradiation pattern of the light emitting diodes to these functions are discussed. (Author)

  3. Using visual information analysis to explore complex patterns in the activity of designers

    Cash, Philip; Stanković, Tino; Štorga, Mario

    2014-01-01

    The analysis of complex interlinked datasets poses a significant problem for design researchers. This is addressed by proposing an information visualisation method for analysing patterns of design activity, qualitatively and quantitatively, with respect to time. This method visualises the tempora...

  4. Classifying smoking urges via machine learning.

    Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

    2016-12-01

    Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights

  5. Forest fragmentation in Massachusetts, USA: a town-level assessment using Morphological Spatial Pattern Analysis and affinity propagation

    J. Rogan; T.M. Wright; J. Cardille; H. Pearsall; Y. Ogneva-Himmelberger; Rachel Riemann; Kurt Riitters; K. Partington

    2016-01-01

    Forest fragmentation has been studied extensively with respect to biodiversity loss, disruption of ecosystem services, and edge effects although the relationship between forest fragmentation and human activities is still not well understood. We classified the pattern of forests in Massachusetts using fragmentation indicators to address...

  6. Waste classifying and separation device

    Kakiuchi, Hiroki.

    1997-01-01

    A flexible plastic bags containing solid wastes of indefinite shape is broken and the wastes are classified. The bag cutting-portion of the device has an ultrasonic-type or a heater-type cutting means, and the cutting means moves in parallel with the transferring direction of the plastic bags. A classification portion separates and discriminates the plastic bag from the contents and conducts classification while rotating a classification table. Accordingly, the plastic bag containing solids of indefinite shape can be broken and classification can be conducted efficiently and reliably. The device of the present invention has a simple structure which requires small installation space and enables easy maintenance. (T.M.)

  7. Defining and Classifying Interest Groups

    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....

  8. GEOSPATIAL ANALYSIS OF URBAN LAND USE PATTERN ANALYSIS FOR HEMORRHAGIC FEVER RISK – A REVIEW

    L. N. Izzah

    2016-09-01

    Full Text Available Human modification of the natural environment continues to create habitats in which vectors of a wide variety of human and animal pathogens (such as Plasmodium, Aedes aegypti, Arenavirus etc. thrive if unabated with an enormous potential to negatively affect public health. Typical examples of these modifications include impoundments, dams, irrigation systems, landfills and so on that provide enabled environment for the transmission of Hemorrhagic fever such as malaria, dengue, avian flu, Lassa fever etc. Furthermore, contemporary urban dwelling pattern appears to be associated with the prevalence of Hemorrhagic diseases in recent years. These observations are not peculiar to the developing world, as urban expansion also contributes significantly to mosquito and other vectors habitats. This habitats offer breeding ground to some vector virus populations. The key to disease control is developing an understanding of the contribution of human landscape modification to vector-borne pathogen transmission and how a balance may be achieved between human development, public health, and responsible urban land use. A comprehensive review of urban land use Pattern Analysis for Hemorrhagic fever risk has been conducted in this paper. The study found that most of the available literatures dwell more on the impact of urban land use on malaria and dengue fevers; however, studies are yet to be found discussing the implications of urban land use on the risk of Ebola, Lassa and other non-mosquito borne VHFs. A relational model for investigating the influence of urban land use change pattern on the risk of Hemorrhagic fever has been proposed in this study.

  9. Substance Use Patterns Among Adolescents in Europe: A Latent Class Analysis.

    Göbel, Kristin; Scheithauer, Herbert; Bräker, Astrid-Britta; Jonkman, Harrie; Soellner, Renate

    2016-07-28

    Several researchers have investigated substance use patterns using a latent class analysis; however, hardly no studies exist on substance use patterns across countries. Adolescent substance use patterns, demographic factors, and international differences in the prevalence of substance use patterns were explored. Data from 25 European countries were used to identify patterns of adolescent (12-16 years, 50.6% female) substance use (N = 33,566). Latent class analysis revealed four substance use classes: nonusers (68%), low-alcohol users (recent use of beer, wine, and alcopops; 16.1%), alcohol users (recent use of alcohol and lifetime use of marijuana; 11.2%), and polysubstance users (recent use of alcohol, marijuana, and other illicit drugs; 4.7%). Results support a general pattern of adolescent substance use across all countries; however, the prevalence rates of use patterns vary for each country. The present research provides insight into substance use patterns across Europe by using a large international adolescent sample, multidimensional indicators and a variety of substances. Substance use patterns are helpful when targeting policy and prevention strategies.

  10. Analysis of the diffraction pattern obtained by the Laue method

    Riquet, J. par; Bonnet, R.

    1978-01-01

    A computation method is presented which allows a rapid indexing of any unknown spot pattern obtained by back-reflection or transmission Laue methods. The Cartesian coordinates of n spots are measured in an orthonormal frame referred to the photographic film. Two spots 1 and 2 separated by a wide angular distance αsup(m) are carefully chosen. Their indices are assumed to be less than 5. The set (E) of all the pairs of planes (h 1 k 1 l 1 ) and (h 2 k 2 l 2 ) making an angle α close to αsup(m) is then computed. Since the pair of reflecting planes related to spots 1 and 2 belongs to (E), each computed pair of planes is tried, in order to determine the orientation of the crystal and to check whether the coordinates of the (n-2) other spots can be matched to dense planes of indices less than 8. If the uncertainty of the measurements is high or if n is too low, this method gives the possible orientations for the crystal. Plane indices less than 8 have been identified in cubic, tetragonal and orthorhombic crystals. (Auth.)

  11. Analysis of periodically patterned metallic nanostructures for infrared absorber

    Peng, Sha; Yuan, Ying; Long, Huabao; Liu, Runhan; Wei, Dong; Zhang, Xinyu; Wang, Haiwei; Xie, Changsheng

    2018-02-01

    With rapid advancement of infrared detecting technology in both military and civil domains, the photo-electronic performances of near-infrared detectors have been widely concerned. Currently, near-infrared detectors demonstrate some problems such as low sensitivity, low detectivity, and relatively small array scale. The current studies show that surface plasmons (SPs) stimulated over the surface of metallic nanostructures by incident light can be used to break the diffraction limit and thus concentrate light into sub-wavelength scale, so as to indicate a method to develop a new type of infrared absorber or detector with very large array. In this paper, we present the design and characterization of periodically patterned metallic nanostructures that combine nanometer thickness aluminum film with silicon wafer. Numerical computations show that there are some valleys caused by surface plasmons in the reflection spectrum in the infrared region, and both red shift and blue shift of the reflection spectrum were observed through changing the nanostructural parameters such as angle α and diameters D. Moreover, the strong E-field intensity is located at the sharp corner of the nano-structures.

  12. Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches

    Wang, Wenshuo; Xi, Junqiang; Zhao, Ding

    2017-01-01

    Analysis and recognition of driving styles are profoundly important to intelligent transportation and vehicle calibration. This paper presents a novel driving style analysis framework using the primitive driving patterns learned from naturalistic driving data. In order to achieve this, first, a Bayesian nonparametric learning method based on a hidden semi-Markov model (HSMM) is introduced to extract primitive driving patterns from time series driving data without prior knowledge of the number...

  13. A study on association and correlation of lip and finger print pattern analysis for gender identification

    Surapaneni Ratheesh Kumar Nandan

    2015-01-01

    Conclusion: Lip print analysis is a challenging area in the personal identification during forensic dentistry examination. The study revealed the weaker correlation and approachable significance of lip and finger print pattern in gender identification. Future studies should be encouraged in the direction of software based identification for lip and finger print analysis in gender identification. Such studies may benefit this study pattern in more accurate way.

  14. Pattern analysis of aligned nanowires in a microchannel

    Jeon, Young Jin; Kang, Hyun Wook; Ko, Seung Hwan; Sung, Hyung Jin

    2013-01-01

    An image processing method for evaluating the quality of nanowire alignment in a microchannel is described. A solution containing nanowires flowing into a microchannel will tend to deposit the nanowires on the bottom surface of the channel via near-wall shear flows. The deposited nanowires generally form complex directional structures along the direction of flow, and the physical properties of these structures depend on the structural morphology, including the alignment quality. A quantitative analysis approach to characterizing the nanowire alignment is needed to estimate the useful features of the nanowire structures. This analysis consists of several image processing methods, including ridge detection, texton analysis and autocorrelation function (ACF) calculation. The ridge detection method improved the ACF by extracting nanowire frames 1–2 pixels in width. Dilation filters were introduced to permit a comparison of the ACF results calculated from different images, regardless of the nanowire orientation. An ACF based on the FFT was then calculated over a square interrogation window. The alignment angle probability distribution was obtained using texton analysis. Monte Carlo simulations of artificially generated images were carried out, and the new algorithm was applied to images collected using two types of microscopy. (paper)

  15. Analysis of spatial pattern of settlements in the federal capital ...

    Human settlements are important, seemingly static but dynamic, features of the cultural landscape that have attracted several studies due to the important role they play in human life. This paper examined the spatial distribution of settlements in the Federal Capital Territory (FCT) of Nigeria. The analysis uses vector based ...

  16. Comparative analysis on some spatial-domain filters for fringe pattern denoising.

    Wang, Haixia; Kemao, Qian

    2011-04-20

    Fringe patterns produced by various optical interferometric techniques encode information such as shape, deformation, and refractive index. Noise affects further processing of the fringe patterns. Denoising is often needed before fringe pattern demodulation. Filtering along the fringe orientation is an effective option. Such filters include coherence enhancing diffusion, spin filtering with curve windows, second-order oriented partial-differential equations, and the regularized quadratic cost function for oriented fringe pattern filtering. These filters are analyzed to establish the relationships among them. Theoretical analysis shows that the four filters are largely equivalent to each other. Quantitative results are given on simulated fringe patterns to validate the theoretical analysis and to compare the performance of these filters. © 2011 Optical Society of America

  17. Long-term patterns of dental attendance and caries experience among British adults: a retrospective analysis.

    Aldossary, Arwa; Harrison, Victoria E; Bernabé, Eduardo

    2015-02-01

    There is inconclusive evidence on the value of regular dental attendance. This study explored the relationship between long-term patterns of dental attendance and caries experience. We used retrospective data from 3,235 adults, ≥ 16 yrs of age, who participated in the Adult Dental Health Survey in the UK. Participants were classified into four groups (always, current, former, and never regular-attenders) based on their responses to three questions on lifetime dental-attendance patterns. The association between dental-attendance patterns and caries experience, as measured using the decayed, missing, or filled teeth (DMFT) index, was tested in negative binomial regression models, adjusting for demographic (sex, age, and country of residence) and socio-economic (educational attainment, household income, and social class) factors. A consistent pattern of association between long-term dental attendance and caries experience was found in adjusted models. Former and never regular-attenders had a significantly higher DMFT score and numbers of decayed and missing teeth, but fewer filled teeth, than always regular-attenders. No differences in DMFT or its components were found between current and always regular-attenders. The findings of this study show that adults with different lifetime trajectories of dental attendance had different dental statuses. © 2014 Eur J Oral Sci.

  18. Robust Combining of Disparate Classifiers Through Order Statistics

    Tumer, Kagan; Ghosh, Joydeep

    2001-01-01

    Integrating the outputs of multiple classifiers via combiners or meta-learners has led to substantial improvements in several difficult pattern recognition problems. In this article we investigate a family of combiners based on order statistics, for robust handling of situations where there are large discrepancies in performance of individual classifiers. Based on a mathematical modeling of how the decision boundaries are affected by order statistic combiners, we derive expressions for the reductions in error expected when simple output combination methods based on the the median, the maximum and in general, the ith order statistic, are used. Furthermore, we analyze the trim and spread combiners, both based on linear combinations of the ordered classifier outputs, and show that in the presence of uneven classifier performance, they often provide substantial gains over both linear and simple order statistics combiners. Experimental results on both real world data and standard public domain data sets corroborate these findings.

  19. Hydrodynamic-driven stability analysis of morphological patterns on stalactites and implications for cave paleoflow reconstructions.

    Camporeale, Carlo; Ridolfi, Luca

    2012-06-08

    A novel hydrodynamic-driven stability analysis is presented for surface patterns on speleothems, i.e., secondary sedimentary cave deposits, by coupling fluid dynamics to the geochemistry of calcite precipitation or dissolution. Falling film theory provides the solution for the flow-field and depth perturbations, the latter being crucial to triggering patterns known as crenulations. In a wide range of Reynolds numbers, the model provides the dominant wavelengths and pattern celerities, in fair agreement with field data. The analysis of the phase velocity of ridges on speleothems has a potential as a proxy of past film flow rates, thus suggesting a new support for paleoclimate analyses.

  20. Analysis of the cephalometric pattern of Brazilian achondroplastic adult subjects

    Renato Cardoso

    2012-12-01

    Full Text Available OBJECTIVE: The aim of this study was to assess the position of the cranial base, maxilla, and mandible of Brazilian achondroplastic adult subjects through cephalometric measurements of the cranio-dento-facial complex, and to compare the results to normal patterns established in literature. METHODS: Fourteen achondroplastic adult subjects were evaluated based on their radiographic cephalometric measurements, which were obtained using the tracings proposed by Downs, Steinner, Bjork, Ricketts and McNamara. Statistical comparison of the means was performed with Student's t test. RESULTS: When compared to normal patterns, the cranial base presented a smaller size in both its anterior and posterior portions, the cranial base angle was acute and there was an anterior projection of the porion; the maxilla was found to be smaller in size in both the anteroposterior and transversal directions, it was inclined anteriorly with anterior vertical excess, and retropositioned in relation to the cranial base and to the mandible; the mandible presented a normal-sized ramus, a decreased body and transverse dimension, a tendency towards vertical growth and clockwise rotation, and it was slightly protruded in relation to the cranial base and maxilla. CONCLUSION: Although we observed wide individual variation in some parameters, it was possible to identify significant differences responsible for the phenotypical characteristics of achondroplastic patients.OBJETIVO: avaliar o tamanho e o posicionamento da base do crânio, da maxila e da mandíbula de indivíduos acondroplásicos brasileiros adultos, a partir de medidas cefalométricas do complexo dentoesqueletofacial. Confrontar os dados obtidos aos padrões de normalidade estabelecidos na literatura. MÉTODOS: foram avaliados 14 indivíduos acondroplásicos adultos, utilizando algumas grandezas cefalométricas radiográficas obtidas a partir dos traçados preconizados por Downs, Steinner, Björk, Ricketts e Mc

  1. Advanced analysis of free visual exploration patterns in schizophrenia

    Andreas eSprenger

    2013-10-01

    Full Text Available Background: Visual scanpath analyses provide important information about attention allocation and attention shifting during visual exploration of social situations. This study investigated whether patients with schizophrenia simply show restricted free visual exploration behaviour reflected by reduced saccade frequency and increased fixation duration or whether patients use qualitatively different exploration strategies than healthy controls. Methods: Scanpaths of 32 patients with schizophrenia and age-matched 33 healthy controls were assessed while participants freely explored six photos of daily life situations (20 seconds/photo evaluated for cognitive complexity and emotional strain. Using fixation and saccade parameters, we compared temporal changes in exploration behaviour, cluster analyses, attentional landscapes and analyses of scanpath similarities between both groups. Results: We found fewer fixation clusters, longer fixation durations within a cluster, fewer changes between clusters, and a greater increase of fixation duration over time in patients compared to controls. Scanpath patterns and attentional landscapes in patients also differed significantly from those of controls. Generally, cognitive complexity and emotional strain had significant effects on visual exploration behaviour. This effect was similar in both groups as were physical properties of fixation locations.Conclusions: Longer attention allocation to a given feature in a scene and less attention shifts in patients suggest a more focal processing mode compared to a more ambient exploration strategy in controls. These visual exploration alterations were present in patients independently of cognitive complexity, emotional strain or physical properties of visual cues implying that they represent a rather general deficit. Despite this impairment, patients were able to adapt their scanning behaviour to changes in cognitive complexity and emotional strain similar to controls.

  2. Relevance analysis of mammographic parenchymal patterns and breast cancer

    Feng Rendong; Lv Xiangyang; Li Shaolin; Gao Ming; Miao Liqiong

    2009-01-01

    Objective: Discussing the relativity of Mammographic parenchymal patterns and breast cancer, implementing the intervention treatment and regularly traces to the breast high dangerous crowd, in order to reduce the occurrence rate of beast cancer and the mortality rate. Methods: Mammary gland type was marked according to X ray on 500 breast cancer subjects and 1000 control subjects. Peri-cancer histological sections of the subtypes of the breast cancer group and histological section of the subtypes of the control group were studied contrastively to analyze the breast cancer risk index in every subtype and the occurrence rate in every age group. The types and the occurrence rates were counted. Results: (1)The lowest risk group: the subtypes with OR 0.3 and the cancer incidence rate ranging from 5% to 10% were IV b, II b, III b. (4)High-risk group: the subtypes with OR> 1 and the cancer incidence rate above 10% were III c, IV c. High dangerous age sections of breast cancer: 35 to 55 years old in IVc and IIIc (the age section of IIIc may lengthen to 60 years old), 31 to 50 years old in IVb, 50 to 60 years old in IIIb and IIb. Conclusion: IIIc and IVc belong to the high dangerous subtypes. People of these subtypes reach 67.4% of all breast cancer examples, so these people are the main subjects of the mammary gland general survey and tracing. Patient aged from 35 to 55 should be reexamined once a year. When necessary, the intervention treatment may be carried out to prevent breast cancer and to reduce the occurrence rate of beast cancer. Discovery and treatment in early phase can improve the breast cancer's survival quality, and reduce the mortality rate. (authors)

  3. Value of self-monitoring blood glucose pattern analysis in improving diabetes outcomes.

    Parkin, Christopher G; Davidson, Jaime A

    2009-05-01

    Self-monitoring of blood glucose (SMBG) is an important adjunct to hemoglobin A1c (HbA1c) testing. This action can distinguish between fasting, preprandial, and postprandial hyperglycemia; detect glycemic excursions; identify and monitor resolution of hypoglycemia; and provide immediate feedback to patients about the effect of food choices, activity, and medication on glycemic control. Pattern analysis is a systematic approach to identifying glycemic patterns within SMBG data and then taking appropriate action based upon those results. The use of pattern analysis involves: (1) establishing pre- and postprandial glucose targets; (2) obtaining data on glucose levels, carbohydrate intake, medication administration (type, dosages, timing), activity levels and physical/emotional stress; (3) analyzing data to identify patterns of glycemic excursions, assessing any influential factors, and implementing appropriate action(s); and (4) performing ongoing SMBG to assess the impact of any therapeutic changes made. Computer-based and paper-based data collection and management tools can be developed to perform pattern analysis for identifying patterns in SMBG data. This approach to interpreting SMBG data facilitates rational therapeutic adjustments in response to this information. Pattern analysis of SMBG data can be of equal or greater value than measurement of HbA1c levels. 2009 Diabetes Technology Society.

  4. Analysis of Activity Patterns and Performance in Polio Survivors

    2006-10-01

    correlations ( clustering ) from multiple observations on the same subject. Multivariable mixed models with random intercepts or both random...0.001 (0.001) (0.59) (ɘ.001) * The top p values compared the means between the three groups in using unpaired t tests, taking clustering of...May 2004. Talaty M. Models for Gait Analysis. 5th SIAMOC (Societa Italiana Di Analisi Del Movimento in Clinica) Congress, Loano, Italy November

  5. Levels and Patterns in the Analysis of the Organizational Culture

    Mariana Aida Cimpeanu

    2011-01-01

    Knowledge and analysis of the component elements of the organizational culture helps us greatly understand the respective culture, establish the main guidelines of the company values and understand the behaviours and attitudes of the employees. M. Thevenet indentifies two levels at which the culture manifests itself: the external level – the outside culture (which refers to local, regional or national culture), and the inner level –the internal culture (including organizational culture, profe...

  6. Classifying cognitive profiles using machine learning with privileged information in Mild Cognitive Impairment

    Hanin Hamdan Alahmadi

    2016-11-01

    Full Text Available Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalised Matrix Learning Vector Quantization (GMLVQ classifiers to discriminate patients with Mild Cognitive Impairment (MCI from healthy controls based on their cognitive skills. Further, we adopted a ``Learning with privileged information'' approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants.MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls based on the learning performance and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on the learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1 when overall fMRI signal for structured stimuli is

  7. Classification of viral zoonosis through receptor pattern analysis.

    Bae, Se-Eun; Son, Hyeon Seok

    2011-04-13

    Viral zoonosis, the transmission of a virus from its primary vertebrate reservoir species to humans, requires ubiquitous cellular proteins known as receptor proteins. Zoonosis can occur not only through direct transmission from vertebrates to humans, but also through intermediate reservoirs or other environmental factors. Viruses can be categorized according to genotype (ssDNA, dsDNA, ssRNA and dsRNA viruses). Among them, the RNA viruses exhibit particularly high mutation rates and are especially problematic for this reason. Most zoonotic viruses are RNA viruses that change their envelope proteins to facilitate binding to various receptors of host species. In this study, we sought to predict zoonotic propensity through the analysis of receptor characteristics. We hypothesized that the major barrier to interspecies virus transmission is that receptor sequences vary among species--in other words, that the specific amino acid sequence of the receptor determines the ability of the viral envelope protein to attach to the cell. We analysed host-cell receptor sequences for their hydrophobicity/hydrophilicity characteristics. We then analysed these properties for similarities among receptors of different species and used a statistical discriminant analysis to predict the likelihood of transmission among species. This study is an attempt to predict zoonosis through simple computational analysis of receptor sequence differences. Our method may be useful in predicting the zoonotic potential of newly discovered viral strains.

  8. Classifying regularized sensor covariance matrices: An alternative to CSP

    Roijendijk, L.M.M.; Gielen, C.C.A.M.; Farquhar, J.D.R.

    2016-01-01

    Common spatial patterns ( CSP) is a commonly used technique for classifying imagined movement type brain-computer interface ( BCI) datasets. It has been very successful with many extensions and improvements on the basic technique. However, a drawback of CSP is that the signal processing pipeline

  9. Classifying regularised sensor covariance matrices: An alternative to CSP

    Roijendijk, L.M.M.; Gielen, C.C.A.M.; Farquhar, J.D.R.

    2016-01-01

    Common spatial patterns (CSP) is a commonly used technique for classifying imagined movement type brain computer interface (BCI) datasets. It has been very successful with many extensions and improvements on the basic technique. However, a drawback of CSP is that the signal processing pipeline

  10. Levels and Patterns in the Analysis of the Organizational Culture

    Mariana Aida Cimpeanu

    2011-05-01

    Full Text Available Knowledge and analysis of the component elements of the organizational culture helps us greatly understand the respective culture, establish the main guidelines of the company values and understand the behaviours and attitudes of the employees. M. Thevenet indentifies two levels at which the culture manifests itself: the external level – the outside culture (which refers to local, regional or national culture, and the inner level –the internal culture (including organizational culture, professional culture, the culture of a group. Starting from this assumption, one can identify the main components of the organizational culture: founders, the organization’s history, values, beliefs and symbols, the way of thinking, the standards of behaviour etc. Some of these are visible, forming a cultural foundation surface, while others create a less visible foundation of culture – the hidden level. Kotter and Heskett agree that these two levels of analysis are very connected and influence each other. Considering their importance, other authors identify three, four or more levels of culture (Denison, Hofstede, Shein, bringing forth first the values then the rituals, heroes and symbols. Different models of culture analysis help us explain the elements of culture and understand its importance by providing for the researchers a starting point in explaining specific aspects related to the organizational culture and the organizational behaviour. By understanding the organizational culture, the members of an organization are able to shape their behaviour, can recognize their rights and obligations inside the company and the style of internal communication. They can determine the style of clothing and the dominant attitude inside the company, the way in which the management defines and implements its decisions and the staff policy.

  11. Analysis of Trends in Cooperative Network Patterns for KAERI Researchers

    Chun, Young Choon; Lee, Hyun Soo

    2016-01-01

    There has been a trend toward faster results of research and accelerating inter-disciplinary convergence, under constraints in available resources. Under such reality, national and international cooperation with inter-sectoral research on science-technology-industry is becoming inevitable as a strategic approach for enhancing competitive edge on global dimension. This study gives an analysis on the cooperative network in nuclear research which bears multi-disciplinary technical feature. The study aims to visualize the cooperative network of KAERI(Korea Atomic Energy Research Institute) researchers, as the hub of the network, including academics and industry, with a view to provide insight on strengthening the cooperative network in nuclear research. This study accounted for the paper entries in SCI(E) in 2013 (538 papers) and 2015 (551 papers) with a view to identify cooperative research activities centered for KAERI. On international cooperation, the analysis showed a trend toward, first of all, diversification of partner countries. There were 118 entries of co-authorship with 22 countries in 2013 (41 with USA, 12 with Japan, 9 with India), which evolved to 121 entries in 2015 (34 for USA, 11 with China, 10 each with Japan and India). Secondly, there was a trend toward more number of countries evenly spread in 2015 compared to 2013, except a few major countries like USA, Japan, and India

  12. Analysis of Trends in Cooperative Network Patterns for KAERI Researchers

    Chun, Young Choon; Lee, Hyun Soo [KAERI, Daejeon (Korea, Republic of)

    2016-05-15

    There has been a trend toward faster results of research and accelerating inter-disciplinary convergence, under constraints in available resources. Under such reality, national and international cooperation with inter-sectoral research on science-technology-industry is becoming inevitable as a strategic approach for enhancing competitive edge on global dimension. This study gives an analysis on the cooperative network in nuclear research which bears multi-disciplinary technical feature. The study aims to visualize the cooperative network of KAERI(Korea Atomic Energy Research Institute) researchers, as the hub of the network, including academics and industry, with a view to provide insight on strengthening the cooperative network in nuclear research. This study accounted for the paper entries in SCI(E) in 2013 (538 papers) and 2015 (551 papers) with a view to identify cooperative research activities centered for KAERI. On international cooperation, the analysis showed a trend toward, first of all, diversification of partner countries. There were 118 entries of co-authorship with 22 countries in 2013 (41 with USA, 12 with Japan, 9 with India), which evolved to 121 entries in 2015 (34 for USA, 11 with China, 10 each with Japan and India). Secondly, there was a trend toward more number of countries evenly spread in 2015 compared to 2013, except a few major countries like USA, Japan, and India.

  13. Global patterns of materials use. A socioeconomic and geophysical analysis

    Steinberger, Julia K.; Krausmann, Fridolin; Eisenmenger, Nina [Institute of Social Ecology Vienna, IFF, University of Klagenfurt, Schottenfeldgasse 29, A-1070 Wien (Austria)

    2010-03-15

    Human use of materials is a major driver of global environmental change. The links between materials use and economic development are central to the challenge of decoupling of materials use and economic growth (dematerialization). This article presents a new global material flow dataset compiled for the year 2000, covering 175 countries, including both extraction and trade flows, and comprising four major material categories: biomass, construction minerals, fossil energy carriers and ores/industrial minerals. First, we quantify the variability and distributional inequality (Gini coefficients) in international material consumption. We then measure the influence of the drivers population, GDP, land area and climate. This analysis yields international income elasticities of material use. Finally, we examine the coupling between material flows, and between income and material productivity, measured in economic production per tonne material consumed. Material productivity is strongly coupled to income, and may thus not be suitable as an international indicator of environmental progress - a finding which we relate to the economic inelasticity of material consumption. The results demonstrate striking differences between the material groups. Biomass is the most equitably distributed resource, economically the most inelastic, and is not correlated to any of the mineral materials. The three mineral material groups are closely coupled to each other and economic activity, indicating that the challenge of dematerializing industrial economies may require fundamental structural transformation. Our analysis provides a first systematic investigation of international differences in material use and their drivers, and thus serves as the basis for more detailed future work. (author)

  14. [Accidents in equestrian sports : Analysis of injury mechanisms and patterns].

    Schröter, C; Schulte-Sutum, A; Zeckey, C; Winkelmann, M; Krettek, C; Mommsen, P

    2017-02-01

    Equestrian sports are one of the most popular forms of sport in Germany, while also being one of the most accident-prone sports. Furthermore, riding accidents are frequently associated with a high degree of severity of injuries and mortality. Nevertheless, there are insufficient data regarding incidences, demographics, mechanisms of accidents, injury severity and patterns and outcome of injured persons in amateur equestrian sports. Accordingly, it was the aim of the present study to retrospectively analyze these aspects. A total of 503 patients were treated in the emergency room of the Hannover Medical School because of an accident during recreational horse riding between 2006 and 2011. The female gender was predominantly affected with 89.5 %. The mean age of the patients was 26.2 ± 14.9 years and women (24.5 ± 12.5 years) were on average younger than men (40.2 ± 23.9 years). A special risk group was girls and young women aged between 10 and 39 years. The overall injury severity was measured using the injury severity score (ISS). Based on the total population, head injuries were the most common location of injuries with 17.3 % followed by injuries to the upper extremities with 15.2 % and the thoracic and lumbar spine with 10.9 %. The three most common injury locations after falling from a horse were the head (17.5 %), the upper extremities (17.4 %), the thoracic and lumbar spine (12.9 %). The most frequent injuries while handling horses were foot injuries (17.2 %), followed by head (16.6 %) and mid-facial injuries (15.0 %). With respect to the mechanism of injury accidents while riding were predominant (74 %), while accidents when handling horses accounted for only 26 %. The median ISS was 9.8 points. The proportion of multiple trauma patients (ISS > 16) was 18.1 %. Based on the total sample, the average in-hospital patient stay was 5.3 ± 5.4 days with a significantly higher proportion of hospitalized patients in the

  15. Patterns of Communication through Interpreters: A Detailed Sociolinguistic Analysis

    Aranguri, Cesar; Davidson, Brad; Ramirez, Robert

    2006-01-01

    BACKGROUND Numerous articles have detailed how the presence of an interpreter leads to less satisfactory communication with physicians; few have studied how actual communication takes place through an interpreter in a clinical setting. OBJECTIVE Record and analyze physician-interpreter-patient interactions. DESIGN Primary care physicians with high-volume Hispanic practices were recruited for a communication study. Dyslipidemic Hispanic patients, either monolingual Spanish or bilingual Spanish-English, were recruited on the day of a normally scheduled appointment and, once consented, recorded without a researcher present in the room. Separate postvisit interviews were conducted with the patient and the physician. All interactions were fully transcribed and analyzed. PARTICIPANTS Sixteen patients were recorded interacting with 9 physicians. Thirteen patients used an interpreter with 8 physicians, and 3 patients spoke Spanish with the 1 bilingual physician. APPROACH Transcript analysis based on sociolinguistic and discourse analytic techniques, including but not limited to time speaking, analysis of questions asked and answered, and the loss of semantic information. RESULTS Speech was significantly reduced and revised by the interpreter, resulting in an alteration of linguistic features such as content, meaning, reinforcement/validation, repetition, and affect. In addition, visits that included an interpreter had virtually no rapport-building “small talk,” which typically enables the physician to gain comprehensive patient history, learn clinically relevant information, and increase emotional engagement in treatment. CONCLUSIONS The presence of an interpreter increases the difficulty of achieving good physician-patient communication. Physicians and interpreters should be trained in the process of communication and interpretation, to minimize conversational loss and maximize the information and relational exchange with interpreted patients. PMID:16808747

  16. Developing a complex independent component analysis technique to extract non-stationary patterns from geophysical time-series

    Forootan, Ehsan; Kusche, Jürgen

    2016-04-01

    Geodetic/geophysical observations, such as the time series of global terrestrial water storage change or sea level and temperature change, represent samples of physical processes and therefore contain information about complex physical interactionswith many inherent time scales. Extracting relevant information from these samples, for example quantifying the seasonality of a physical process or its variability due to large-scale ocean-atmosphere interactions, is not possible by rendering simple time series approaches. In the last decades, decomposition techniques have found increasing interest for extracting patterns from geophysical observations. Traditionally, principal component analysis (PCA) and more recently independent component analysis (ICA) are common techniques to extract statistical orthogonal (uncorrelated) and independent modes that represent the maximum variance of observations, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the auto-covariance matrix or diagonalizing higher (than two)-order statistical tensors from centered time series. However, the stationary assumption is obviously not justifiable for many geophysical and climate variables even after removing cyclic components e.g., the seasonal cycles. In this paper, we present a new decomposition method, the complex independent component analysis (CICA, Forootan, PhD-2014), which can be applied to extract to non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of real-valued ICA (Forootan and Kusche, JoG-2012), where we (i) define a new complex data set using a Hilbert transformation. The complex time series contain the observed values in their real part, and the temporal rate of variability in their imaginary part. (ii) An ICA algorithm based on diagonalization of fourth-order cumulants is then applied to decompose the new complex data set in (i

  17. The Importance of Bloodstain Pattern Analysis in the Investigation of Road Traffic Accidents: A Case Report

    Younis M. Albalooshi

    2015-12-01

    Full Text Available Bloodstain pattern analysis has become a field of specialization in Forensic sciences and plays an important role in the reconstruction of events at a crime scene. Research, books, and articles have been published on the analysis and interpretation of bloodstain patterns We present a case study of a road traffic accident in which bloodstain pattern analysis helped us to solve the discrepancy between reports produced by forensic examiners and by the forensic biology department. The case was of a 22-year-old man who died immediately and a 31- year-old woman who survived a road traffic accident. They were both found outside their overturned car and it was impossible to ascertain from initial observations which of the victims was driving the car at the time of the accident. An external examination of the man revealed multiple injuries, and the cause of his death was severe brain injury. The woman survived with a fracture of the forearm, dislocated clavicle bone, and other minor injuries. After initial examination of the car and based on the pattern of injuries the deceased received, forensic examiner concluded that the man was the driving the car at the time of accident. On the other hand, the forensic DNA analysis of bloodstains obtained from the driver's seat matched that of the woman, suggesting that she was the driver. This apparent discrepancy directed the forensic examiner to carry out a bloodstain pattern analysis on the driver's seat. The bloodstain pattern analysis helped resolve the discrepancy and enabled the investigators to identify the driver correctly. This case report emphasizes the importance of bloodstain pattern analysis in the reconstruction of cases involving road traffic accidents.

  18. Analysis on leisure patterns of the pre-elderly adults.

    Cho, Gun-Sang; Yi, Eun-Surk

    2013-01-01

    The purpose of study is to analyze how leisure activities affect the near elders' preparation for successful and productive aging. To achieve the purpose of the study, this study was conducted in 2012 and the data was collected by using multi-stage stratified cluster random sampling method in the great city area (6 places), metropolitan area (7 places), medium-sized urban area (6 places), and rural area (6 places). Out of the total number of 1,000 copies of questionnaire distributed to pre-elders (Baby-boomers from 55 yr to 64 yr), 978 were collected and used for data analysis. According to the result, the more time, frequency and intensity in leisure and recreational participation, the higher the satisfaction level and the happiness level in their life. It means that leisure and recreational activities play an important role for their life. In other words, for pre-elders, leisure activities can be regarded as the important element for preparation of their old age. Therefore, the leisure and recreation for pre-elderly adults should not be recognized as a tool for improving the economic productivity but for reinforcing the recovery resilience.

  19. Eating patterns in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil: an exploratory analysis

    Letícia de Oliveira Cardoso

    2016-01-01

    Full Text Available Abstract: The food consumption of 15,071 public employees was analyzed in six Brazilian cities participating in the baseline for Brazilian Longitudinal Study of Adult Health (ELSA-Brasil, 2008-2010 with the aim of identifying eating patterns and their relationship to socio-demographic variables. Multiple correspondence and cluster analysis were applied. Four patterns were identified, with their respective frequencies: "traditional" (48%; "fruits and vegetables" (25%; "pastry shop" (24%; and "diet/light" (5% The "traditional" and "pastry shop" patterns were more frequent among men, younger individuals, and those with less schooling. "Fruits and vegetables" and "diet/light" were more frequent in women, older individuals, and those with more schooling. Our findings show the inclusion of new items in the "traditional" pattern and the appearance of the "low sugar/low fat" pattern among the eating habits of Brazilian workers, and signal socio-demographic and regional differences.

  20. Eating patterns in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil): an exploratory analysis.

    Cardoso, Letícia de Oliveira; Carvalho, Marilia Sá; Cruz, Oswaldo Gonçalves; Melere, Cristiane; Luft, Vivian Cristine; Molina, Maria Del Carmen Bisi; Faria, Carolina Perim de; Benseñor, Isabela M; Matos, Sheila Maria Alvim; Fonseca, Maria de Jesus Mendes da; Griep, Rosane Harter; Chor, Dóra

    2016-01-01

    The food consumption of 15,071 public employees was analyzed in six Brazilian cities participating in the baseline for Brazilian Longitudinal Study of Adult Health (ELSA-Brasil, 2008-2010) with the aim of identifying eating patterns and their relationship to socio-demographic variables. Multiple correspondence and cluster analysis were applied. Four patterns were identified, with their respective frequencies: "traditional" (48%); "fruits and vegetables" (25%); "pastry shop" (24%); and "diet/light" (5%) The "traditional" and "pastry shop" patterns were more frequent among men, younger individuals, and those with less schooling. "Fruits and vegetables" and "diet/light" were more frequent in women, older individuals, and those with more schooling. Our findings show the inclusion of new items in the "traditional" pattern and the appearance of the "low sugar/low fat" pattern among the eating habits of Brazilian workers, and signal socio-demographic and regional differences.

  1. In-depth motivic analysis based on multiparametric closed pattern and cyclic sequence mining

    Lartillot, Olivier

    2014-01-01

    presents a much simpler description and justification of this general strategy, as well as significant simplifications of the model, in particular concerning the management of pattern cyclicity. A new method for automated bundling of patterns belonging to same motivic or thematic classes is also presented....... The good performance of the method is shown through the analysis of a piece from the JKUPDD database. Ground-truth motives are detected, while additional relevant information completes the ground-truth musicological analysis. The system, implemented in Matlab, is made publicly available as part of Mining......Suite, a new open-source framework for audio and music analysis....

  2. Composite Classifiers for Automatic Target Recognition

    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...

  3. Classifying Transition Behaviour in Postural Activity Monitoring

    James BRUSEY

    2009-10-01

    Full Text Available A few accelerometers positioned on different parts of the body can be used to accurately classify steady state behaviour, such as walking, running, or sitting. Such systems are usually built using supervised learning approaches. Transitions between postures are, however, difficult to deal with using posture classification systems proposed to date, since there is no label set for intermediary postures and also the exact point at which the transition occurs can sometimes be hard to pinpoint. The usual bypass when using supervised learning to train such systems is to discard a section of the dataset around each transition. This leads to poorer classification performance when the systems are deployed out of the laboratory and used on-line, particularly if the regimes monitored involve fast paced activity changes. Time-based filtering that takes advantage of sequential patterns is a potential mechanism to improve posture classification accuracy in such real-life applications. Also, such filtering should reduce the number of event messages needed to be sent across a wireless network to track posture remotely, hence extending the system’s life. To support time-based filtering, understanding transitions, which are the major event generators in a classification system, is a key. This work examines three approaches to post-process the output of a posture classifier using time-based filtering: a naïve voting scheme, an exponentially weighted voting scheme, and a Bayes filter. Best performance is obtained from the exponentially weighted voting scheme although it is suspected that a more sophisticated treatment of the Bayes filter might yield better results.

  4. WISC-R Subtest Pattern Stability and Learning Disabilities: A Profile Analysis.

    Mealor, David J.; Abrams, Pamela F.

    Profile analysis was performed on Wechsler Intelligence Scale for Children-Revised (WISC-R) scores of 29 learning disabled students (6-10 years old) in a Specific Learning Disabilities (SLD) program, to determine whether subtest patterns for initial and re-evaluation WISC-R administrations would differ significantly. Profile analysis was applied…

  5. Patterns of Enquiry: Textual Analysis of a Classroom Discussion Unit on Bee Feeding Behaviour.

    Binns, Richard W.

    This paper constitutes an analysis of "Honey Bee Communication: An Enquiry into Two Concepts of Animal Behavior," a unit of classroom discussion modules developed by the Patterns of Enquiry Project at the Ontario Institute for Studies in Education. The conceptual framework of the analysis consists of four major items: (1) descriptive…

  6. Mycoplasma pneumonia in children: radiographic pattern analysis and difference in resolution

    Jeong, Myeong Ja; Jeong, Sung Eun; Kim, Joung Sook; Hur, Gham; Park, Jeung Uk [Inje Univ. College of Medicine, Seoul (Korea, Republic of)

    1997-11-01

    By analysing frequency and disease progression, this study aimed to investigate and predict the prognosis of mycoplasma pneumonia according to radiographic pattern. We retrospectively reviewed plain chest radiographs of 230 patients in whom mycoplasm pneumonia had been serologically confirmed. Their age ranged from two months to 14 years and two months, and 203(88.3%) were younger than eight years. Radiographic patterns were classified as air space consolidation, bronchopneumonic, interstitial pneumonic or diffuse mixed infiltrating type. The radiologic resolution period for each type was analysed by the resolution of symptoms and normalization of radiologic findings. The bronchopneumonic type, which was the most common, was seen in 82 patients(35.6%), airspace consolidation in 58(25.2%), interstitial in 55(23.9%), and diffuse mixed in 22(9.57%). In thirteen patients(5.7%), chest radiographs were normal, though the clinical and radiologic resolution period for each type was variable. The mean resolution period of the air space consolidation type was 14.5 days, bronchopneumonic, 7.6 days ; interstitial, 10.5 days, and diffuse mixed, 15.6 days. The airspace consolidation type needed the longest recovery period, exceeded only by the diffuse mixed type. The bronchopneumonic type was the most common radiographic pattern of mycoplasma pneumonia. The prognosis of the airspace consolidation type seems to be poorest, since this required the longest recovery period.

  7. An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition Control.

    Adewuyi, Adenike A; Hargrove, Levi J; Kuiken, Todd A

    2016-04-01

    Pattern recognition control combined with surface electromyography (EMG) from the extrinsic hand muscles has shown great promise for control of multiple prosthetic functions for transradial amputees. There is, however, a need to adapt this control method when implemented for partial-hand amputees, who possess both a functional wrist and information-rich residual intrinsic hand muscles. We demonstrate that combining EMG data from both intrinsic and extrinsic hand muscles to classify hand grasps and finger motions allows up to 19 classes of hand grasps and individual finger motions to be decoded, with an accuracy of 96% for non-amputees and 85% for partial-hand amputees. We evaluated real-time pattern recognition control of three hand motions in seven different wrist positions. We found that a system trained with both intrinsic and extrinsic muscle EMG data, collected while statically and dynamically varying wrist position increased completion rates from 73% to 96% for partial-hand amputees and from 88% to 100% for non-amputees when compared to a system trained with only extrinsic muscle EMG data collected in a neutral wrist position. Our study shows that incorporating intrinsic muscle EMG data and wrist motion can significantly improve the robustness of pattern recognition control for application to partial-hand prosthetic control.

  8. Pattern Decomposition Method and a New Vegetation Index for Hyper-Multispectral Satellite Data Analysis

    Muramatsu, K.; Furumi, S.; Hayashi, A.; Shiono, Y.; Ono, A.; Fujiwara, N.; Daigo, M.; Ochiai, F.

    We have developed the ``pattern decomposition method'' based on linear spectral mixing of ground objects for n-dimensional satellite data. In this method, spectral response patterns for each pixel in an image are decomposed into three components using three standard spectral shape patterns determined from the image data. Applying this method to AMSS (Airborne Multi-Spectral Scanner) data, eighteen-dimensional data are successfully transformed into three-dimensional data. Using the three components, we have developed a new vegetation index in which all the multispectral data are reflected. We consider that the index should be linear to the amount of vegetation and vegetation vigor. To validate the index, its relations to vegetation types, vegetation cover ratio, and chlorophyll contents of a leaf were studied using spectral reflectance data measured in the field with a spectrometer. The index was sensitive to vegetation types and vegetation vigor. This method and index are very useful for assessment of vegetation vigor, classifying land cover types and monitoring vegetation changes

  9. Qualitative Analysis of Primary Fingerprint Pattern in Different Blood Group and Gender in Nepalese

    Sudikshya KC

    2018-01-01

    Full Text Available Dermatoglyphics, the study of epidermal ridges on palm, sole, and digits, is considered as most effective and reliable evidence of identification. The fingerprints were studied in 300 Nepalese of known blood groups of different ages and classified into primary patterns and then analyzed statistically. In both sexes, incidence of loops was highest in ABO blood group and Rh +ve blood types, followed by whorls and arches, while the incidence of whorls was highest followed by loops and arches in Rh −ve blood types. Loops were higher in all blood groups except “A –ve” and “B –ve” where whorls were predominant. The fingerprint pattern in Rh blood types of blood group “A” was statistically significant while in others it was insignificant. In middle and little finger, loops were higher whereas in ring finger whorls were higher in all blood groups. Whorls were higher in thumb and index finger except in blood group “O” where loops were predominant. This study concludes that distribution of primary pattern of fingerprint is not related to gender and blood group but is related to individual digits.

  10. [Emergy analysis on different planting patterns of typical watersheds in Loess Plateau.

    Deng, Jian; Zhao, Fa Zhu; Han, Xin Hui; Feng, Yong Zhong; Yang, Gai He

    2016-05-01

    To objectively evaluate and compare the stability and sustainability of different planting patterns of typical watersheds in Loess Plateau of China after the Grain for Green Project, this paper used the emergy analysis method to quantify the emergy inputs and outputs of three watersheds with different planting patterns, i.e., both grains and fruit trees (Gaoxigou watershed), mainly grains (Wuliwan watershed) and mainly fruit trees (Miaozuigou watershed). In addition, an emergy analysis system was established to evaluate the suitability of the three patterns from the perspectives of natural resources pressure as well as social and economic development levels. More than 75% of the total emergy inputs of all the three watersheds were purchased, and nonrenewable emergy inputs had a much larger contribution than renewable emergy inputs, indicating the characteristic of low emergy self-sufficient ratio and considerable high environmental loading ratio. The pattern of planting grains had high emergy inputs but low emergy outputs, while the patterns of planting fruit trees and planting both had high emergy inputs and outputs. The energy densities of all three patterns reached two times of the average of agricultural systems in China. Especially, the net emergy of planting grains pattern was the lowest while that of planting both grains and fruit trees was the highest. The environmental sustainability index (ESI) of planting grains pattern was less than 1 and both emergy and ESI were much lower than national averages. The ESI of planting both grains and fruit trees pattern was the highest. In summary, comparison of the three patterns showed that planting both grains and fruit trees had better sustainability and high stability and the emergy production efficiency was high. Thus, it was suggested to change the agricultural development from watershed based units to multi-industry integrated mode.

  11. Automated CBED processing: Sample thickness estimation based on analysis of zone-axis CBED pattern

    Klinger, M., E-mail: klinger@post.cz; Němec, M.; Polívka, L.; Gärtnerová, V.; Jäger, A.

    2015-03-15

    An automated processing of convergent beam electron diffraction (CBED) patterns is presented. The proposed methods are used in an automated tool for estimating the thickness of transmission electron microscopy (TEM) samples by matching an experimental zone-axis CBED pattern with a series of patterns simulated for known thicknesses. The proposed tool detects CBED disks, localizes a pattern in detected disks and unifies the coordinate system of the experimental pattern with the simulated one. The experimental pattern is then compared disk-by-disk with a series of simulated patterns each corresponding to different known thicknesses. The thickness of the most similar simulated pattern is then taken as the thickness estimate. The tool was tested on [0 1 1] Si, [0 1 0] α-Ti and [0 1 1] α-Ti samples prepared using different techniques. Results of the presented approach were compared with thickness estimates based on analysis of CBED patterns in two beam conditions. The mean difference between these two methods was 4.1% for the FIB-prepared silicon samples, 5.2% for the electro-chemically polished titanium and 7.9% for Ar{sup +} ion-polished titanium. The proposed techniques can also be employed in other established CBED analyses. Apart from the thickness estimation, it can potentially be used to quantify lattice deformation, structure factors, symmetry, defects or extinction distance. - Highlights: • Automated TEM sample thickness estimation using zone-axis CBED is presented. • Computer vision and artificial intelligence are employed in CBED processing. • This approach reduces operator effort, analysis time and increases repeatability. • Individual parts can be employed in other analyses of CBED/diffraction pattern.

  12. Analysis of plant LTR-retrotransposons at the fine-scale family level reveals individual molecular patterns

    Domingues Douglas S

    2012-04-01

    Full Text Available Abstract Background Sugarcane is an important crop worldwide for sugar production and increasingly, as a renewable energy source. Modern cultivars have polyploid, large complex genomes, with highly unequal contributions from ancestral genomes. Long Terminal Repeat retrotransposons (LTR-RTs are the single largest components of most plant genomes and can substantially impact the genome in many ways. It is therefore crucial to understand their contribution to the genome and transcriptome, however a detailed study of LTR-RTs in sugarcane has not been previously carried out. Results Sixty complete LTR-RT elements were classified into 35 families within four Copia and three Gypsy lineages. Structurally, within lineages elements were similar, between lineages there were large size differences. FISH analysis resulted in the expected pattern of Gypsy/heterochromatin, Copia/euchromatin, but in two lineages there was localized clustering on some chromosomes. Analysis of related ESTs and RT-PCR showed transcriptional variation between tissues and families. Four distinct patterns were observed in sRNA mapping, the most unusual of which was that of Ale1, with very large numbers of 24nt sRNAs in the coding region. The results presented support the conclusion that distinct small RNA-regulated pathways in sugarcane target the lineages of LTR-RT elements. Conclusions Individual LTR-RT sugarcane families have distinct structures, and transcriptional and regulatory signatures. Our results indicate that in sugarcane individual LTR-RT families have distinct behaviors and can potentially impact the genome in diverse ways. For instance, these transposable elements may affect nearby genes by generating a diverse set of small RNA's that trigger gene silencing mechanisms. There is also some evidence that ancestral genomes contribute significantly different element numbers from particular LTR-RT lineages to the modern sugarcane cultivar genome.

  13. 15 CFR 4.8 - Classified Information.

    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...

  14. Statistical-Synoptic Analysis of the Atmosphere Thickness Pattern of Iran’s Pervasive Frosts

    Iman Rousta

    2016-08-01

    Full Text Available The present study aimed at analyzing the synoptic pattern of atmospheric thickness of winter pervasive frosts in Iran. To this end, the data related to the daily minimum temperature of a 50-year period (1961–2010 were gathered from 451 synoptic and climatology stations. Then, the instances in which the temperature was below 0 °C for at least two consecutive days and this phenomenon covered at least 50% of the entirety of Iran were selected. Subsequently, the atmosphere thickness pattern was extracted for these days, with the representative day being identified and analyzed through cluster analysis. The results showed that the Siberian high pressure plays a significant role in the occurrence of pervasive frosts in Iran. In some other cases, the northeast–southwest direction of this pattern leads to its combination with the East Europe high pressure, causing widespread frosts in Iran. Furthermore, the interaction between counter clockwise currents in this system and the clockwise currents in the Azores high pressure tongue directs cold weather from northern parts of Europe toward Iran. The formation of blocking systems leads to the stagnation of cold weather over Iran, a phenomenon that results in significant reduction of temperature and severe frosts in these areas. In addition, the omega pattern (the fifth pattern and Deep Eastern European trough and polar low pressure pattern (the fourth pattern were the most dominant and severe frost patterns in Iran respectively.

  15. Localizing genes to cerebellar layers by classifying ISH images.

    Lior Kirsch

    Full Text Available Gene expression controls how the brain develops and functions. Understanding control processes in the brain is particularly hard since they involve numerous types of neurons and glia, and very little is known about which genes are expressed in which cells and brain layers. Here we describe an approach to detect genes whose expression is primarily localized to a specific brain layer and apply it to the mouse cerebellum. We learn typical spatial patterns of expression from a few markers that are known to be localized to specific layers, and use these patterns to predict localization for new genes. We analyze images of in-situ hybridization (ISH experiments, which we represent using histograms of local binary patterns (LBP and train image classifiers and gene classifiers for four layers of the cerebellum: the Purkinje, granular, molecular and white matter layer. On held-out data, the layer classifiers achieve accuracy above 94% (AUC by representing each image at multiple scales and by combining multiple image scores into a single gene-level decision. When applied to the full mouse genome, the classifiers predict specific layer localization for hundreds of new genes in the Purkinje and granular layers. Many genes localized to the Purkinje layer are likely to be expressed in astrocytes, and many others are involved in lipid metabolism, possibly due to the unusual size of Purkinje cells.

  16. Application of pattern mixture models to address missing data in longitudinal data analysis using SPSS.

    Son, Heesook; Friedmann, Erika; Thomas, Sue A

    2012-01-01

    Longitudinal studies are used in nursing research to examine changes over time in health indicators. Traditional approaches to longitudinal analysis of means, such as analysis of variance with repeated measures, are limited to analyzing complete cases. This limitation can lead to biased results due to withdrawal or data omission bias or to imputation of missing data, which can lead to bias toward the null if data are not missing completely at random. Pattern mixture models are useful to evaluate the informativeness of missing data and to adjust linear mixed model (LMM) analyses if missing data are informative. The aim of this study was to provide an example of statistical procedures for applying a pattern mixture model to evaluate the informativeness of missing data and conduct analyses of data with informative missingness in longitudinal studies using SPSS. The data set from the Patients' and Families' Psychological Response to Home Automated External Defibrillator Trial was used as an example to examine informativeness of missing data with pattern mixture models and to use a missing data pattern in analysis of longitudinal data. Prevention of withdrawal bias, omitted data bias, and bias toward the null in longitudinal LMMs requires the assessment of the informativeness of the occurrence of missing data. Missing data patterns can be incorporated as fixed effects into LMMs to evaluate the contribution of the presence of informative missingness to and control for the effects of missingness on outcomes. Pattern mixture models are a useful method to address the presence and effect of informative missingness in longitudinal studies.

  17. Mercury⊕: An evidential reasoning image classifier

    Peddle, Derek R.

    1995-12-01

    MERCURY⊕ is a multisource evidential reasoning classification software system based on the Dempster-Shafer theory of evidence. The design and implementation of this software package is described for improving the classification and analysis of multisource digital image data necessary for addressing advanced environmental and geoscience applications. In the remote-sensing context, the approach provides a more appropriate framework for classifying modern, multisource, and ancillary data sets which may contain a large number of disparate variables with different statistical properties, scales of measurement, and levels of error which cannot be handled using conventional Bayesian approaches. The software uses a nonparametric, supervised approach to classification, and provides a more objective and flexible interface to the evidential reasoning framework using a frequency-based method for computing support values from training data. The MERCURY⊕ software package has been implemented efficiently in the C programming language, with extensive use made of dynamic memory allocation procedures and compound linked list and hash-table data structures to optimize the storage and retrieval of evidence in a Knowledge Look-up Table. The software is complete with a full user interface and runs under Unix, Ultrix, VAX/VMS, MS-DOS, and Apple Macintosh operating system. An example of classifying alpine land cover and permafrost active layer depth in northern Canada is presented to illustrate the use and application of these ideas.

  18. Executed Movement Using EEG Signals through a Naive Bayes Classifier

    Juliano Machado

    2014-11-01

    Full Text Available Recent years have witnessed a rapid development of brain-computer interface (BCI technology. An independent BCI is a communication system for controlling a device by human intension, e.g., a computer, a wheelchair or a neuroprosthes is, not depending on the brain’s normal output pathways of peripheral nerves and muscles, but on detectable signals that represent responsive or intentional brain activities. This paper presents a comparative study of the usage of the linear discriminant analysis (LDA and the naive Bayes (NB classifiers on describing both right- and left-hand movement through electroencephalographic signal (EEG acquisition. For the analysis, we considered the following input features: the energy of the segments of a band pass-filtered signal with the frequency band in sensorimotor rhythms and the components of the spectral energy obtained through the Welch method. We also used the common spatial pattern (CSP filter, so as to increase the discriminatory activity among movement classes. By using the database generated by this experiment, we obtained hit rates up to 70%. The results are compatible with previous studies.

  19. EFFECTS OF HETEROGENIETY ON SPATIAL PATTERN ANALYSIS OF WILD PISTACHIO TREES IN ZAGROS WOODLANDS, IRAN

    Y. Erfanifard

    2014-10-01

    Full Text Available Vegetation heterogeneity biases second-order summary statistics, e.g., Ripley's K-function, applied for spatial pattern analysis in ecology. Second-order investigation based on Ripley's K-function and related statistics (i.e., L- and pair correlation function g is widely used in ecology to develop hypothesis on underlying processes by characterizing spatial patterns of vegetation. The aim of this study was to demonstrate effects of underlying heterogeneity of wild pistachio (Pistacia atlantica Desf. trees on the second-order summary statistics of point pattern analysis in a part of Zagros woodlands, Iran. The spatial distribution of 431 wild pistachio trees was accurately mapped in a 40 ha stand in the Wild Pistachio & Almond Research Site, Fars province, Iran. Three commonly used second-order summary statistics (i.e., K-, L-, and g-functions were applied to analyse their spatial pattern. The two-sample Kolmogorov-Smirnov goodness-of-fit test showed that the observed pattern significantly followed an inhomogeneous Poisson process null model in the study region. The results also showed that heterogeneous pattern of wild pistachio trees biased the homogeneous form of K-, L-, and g-functions, demonstrating a stronger aggregation of the trees at the scales of 0–50 m than actually existed and an aggregation at scales of 150–200 m, while regularly distributed. Consequently, we showed that heterogeneity of point patterns may bias the results of homogeneous second-order summary statistics and we also suggested applying inhomogeneous summary statistics with related null models for spatial pattern analysis of heterogeneous vegetations.

  20. Effects of Heterogeniety on Spatial Pattern Analysis of Wild Pistachio Trees in Zagros Woodlands, Iran

    Erfanifard, Y.; Rezayan, F.

    2014-10-01

    Vegetation heterogeneity biases second-order summary statistics, e.g., Ripley's K-function, applied for spatial pattern analysis in ecology. Second-order investigation based on Ripley's K-function and related statistics (i.e., L- and pair correlation function g) is widely used in ecology to develop hypothesis on underlying processes by characterizing spatial patterns of vegetation. The aim of this study was to demonstrate effects of underlying heterogeneity of wild pistachio (Pistacia atlantica Desf.) trees on the second-order summary statistics of point pattern analysis in a part of Zagros woodlands, Iran. The spatial distribution of 431 wild pistachio trees was accurately mapped in a 40 ha stand in the Wild Pistachio & Almond Research Site, Fars province, Iran. Three commonly used second-order summary statistics (i.e., K-, L-, and g-functions) were applied to analyse their spatial pattern. The two-sample Kolmogorov-Smirnov goodness-of-fit test showed that the observed pattern significantly followed an inhomogeneous Poisson process null model in the study region. The results also showed that heterogeneous pattern of wild pistachio trees biased the homogeneous form of K-, L-, and g-functions, demonstrating a stronger aggregation of the trees at the scales of 0-50 m than actually existed and an aggregation at scales of 150-200 m, while regularly distributed. Consequently, we showed that heterogeneity of point patterns may bias the results of homogeneous second-order summary statistics and we also suggested applying inhomogeneous summary statistics with related null models for spatial pattern analysis of heterogeneous vegetations.

  1. Generating Geospatially Realistic Driving Patterns Derived From Clustering Analysis Of Real EV Driving Data

    Pedersen, Anders Bro; Aabrandt, Andreas; Østergaard, Jacob

    2014-01-01

    In order to provide a vehicle fleet that realistically represents the predicted Electric Vehicle (EV) penetration for the future, a model is required that mimics people driving behaviour rather than simply playing back collected data. When the focus is broadened from on a traditional user...... scales, which calls for a statistically correct, yet flexible model. This paper describes a method for modelling EV, based on non-categorized data, which takes into account the plug in locations of the vehicles. By using clustering analysis to extrapolate and classify the primary locations where...

  2. Predict or classify: The deceptive role of time-locking in brain signal classification

    Rusconi, Marco; Valleriani, Angelo

    2016-06-01

    Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

  3. Longitudinal analysis of dietary patterns in Chinese adults from 1991 to 2009.

    Batis, Carolina; Sotres-Alvarez, Daniela; Gordon-Larsen, Penny; Mendez, Michelle A; Adair, Linda; Popkin, Barry

    2014-04-28

    In the present study, we aimed to identify the changes or stability in the structure of dietary patterns and tracking, trends and factors related to the adherence to these dietary patterns in China from 1991 to 2009. We analysed dietary data collected during seven waves of the China Health and Nutrition Survey and included 9253 adults with complete dietary data for three or more waves. Dietary intake assessment was carried out over a 3 d period with 24 h recalls and a household food inventory. Using factor analysis in each wave, we found that the structure of the two dietary patterns identified remained stable over the studied period. The traditional southern dietary pattern was characterised by high intakes of rice, fresh leafy vegetables, low-fat red meat, pork, organ meats, poultry and fish/seafood and low intakes of wheat flour and maize/coarse grains and the modern high-wheat dietary pattern was characterised by high intakes of wheat buns/breads, cakes/cookies/pastries, deep-fried wheat, nuts/seeds, starchy root/tuber products, fruits, eggs/egg products, soya milk, animal-based milk and instant noodles/frozen dumplings. Temporal tracking (maintenance of a relative position over time) was higher for the traditional southern dietary pattern, whereas adherence to the modern high-wheat dietary pattern had an upward trend over time. Higher income, education and urbanicity levels were positively associated with both the dietary patterns, but the association became weaker in the later years. These results suggest that even in the context of rapid economic changes in China, the way people chose to combine their foods remained relatively stable. However, the increasing popularity of the modern high-wheat dietary pattern, a pattern associated with several energy-dense foods, is a cause of concern.

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

    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.

  5. X-ray diffraction from thin films : Size/strain analysis and whole pattern fitting

    Scardi, P [Trento Univ. (Italy). Dept. of Materials Engineering

    1996-09-01

    Line Profile Analysis (LPA) and whole pattern fitting may be used with success for the characterization of thin films from XRD data collected with the traditional Bragg-Brentano geometry. The size/strain analysis was conducted by an integrated procedure of profile modelling-assisted Fourier analysis, in order to measure the content of lattice imperfections and crystalline domain size along the growth direction in heteroepitaxial thin films. The microstructure of these films is typical of several PVD processes for the production of highly textured and low-defect thin crystalline layers. The same analysis could be conducted on random thin films as well, and in this case it is possible to determine an average crystallite size and shape. As will be shown in the paper, structural and microstructural parameters obtained by these methods may be correlated with thin film properties of technological interest. The whole pattern analysis may be used to obtain the information contained in a wide region of the diffraction pattern. This approach, currently used for the quantitative analysis of phase mixtures in traditional powder samples, was modified to account both for the size/strain effects, according to a simplified LPA, and for the structure of thin films and multi-layer systems. In this way, a detailed analysis based on a structural model for the present phases can be performed considering the real geometry of these samples. In particular, the quantitative phase analysis could be conducted in terms of layer thickness instead of volume or weight fractions.

  6. X-ray diffraction from thin films : Size/strain analysis and whole pattern fitting

    Scardi, P.

    1996-01-01

    Line Profile Analysis (LPA) and whole pattern fitting may be used with success for the characterization of thin films from XRD data collected with the traditional Bragg-Brentano geometry. The size/strain analysis was conducted by an integrated procedure of profile modelling-assisted Fourier analysis, in order to measure the content of lattice imperfections and crystalline domain size along the growth direction in heteroepitaxial thin films. The microstructure of these films is typical of several PVD processes for the production of highly textured and low-defect thin crystalline layers. The same analysis could be conducted on random thin films as well, and in this case it is possible to determine an average crystallite size and shape. As will be shown in the paper, structural and microstructural parameters obtained by these methods may be correlated with thin film properties of technological interest. The whole pattern analysis may be used to obtain the information contained in a wide region of the diffraction pattern. This approach, currently used for the quantitative analysis of phase mixtures in traditional powder samples, was modified to account both for the size/strain effects, according to a simplified LPA, and for the structure of thin films and multi-layer systems. In this way, a detailed analysis based on a structural model for the present phases can be performed considering the real geometry of these samples. In particular, the quantitative phase analysis could be conducted in terms of layer thickness instead of volume or weight fractions

  7. Identifying typical patterns of vulnerability: A 5-step approach based on cluster analysis

    Sietz, Diana; Lüdeke, Matthias; Kok, Marcel; Lucas, Paul; Carsten, Walther; Janssen, Peter

    2013-04-01

    Specific processes that shape the vulnerability of socio-ecological systems to climate, market and other stresses derive from diverse background conditions. Within the multitude of vulnerability-creating mechanisms, distinct processes recur in various regions inspiring research on typical patterns of vulnerability. The vulnerability patterns display typical combinations of the natural and socio-economic properties that shape a systems' vulnerability to particular stresses. Based on the identification of a limited number of vulnerability patterns, pattern analysis provides an efficient approach to improving our understanding of vulnerability and decision-making for vulnerability reduction. However, current pattern analyses often miss explicit descriptions of their methods and pay insufficient attention to the validity of their groupings. Therefore, the question arises as to how do we identify typical vulnerability patterns in order to enhance our understanding of a systems' vulnerability to stresses? A cluster-based pattern recognition applied at global and local levels is scrutinised with a focus on an applicable methodology and practicable insights. Taking the example of drylands, this presentation demonstrates the conditions necessary to identify typical vulnerability patterns. They are summarised in five methodological steps comprising the elicitation of relevant cause-effect hypotheses and the quantitative indication of mechanisms as well as an evaluation of robustness, a validation and a ranking of the identified patterns. Reflecting scale-dependent opportunities, a global study is able to support decision-making with insights into the up-scaling of interventions when available funds are limited. In contrast, local investigations encourage an outcome-based validation. This constitutes a crucial step in establishing the credibility of the patterns and hence their suitability for informing extension services and individual decisions. In this respect, working at

  8. Analysis of antenal sensilla patterns of Rhodnius prolixus from Colombia and Venezuela

    Lyda Esteban

    2005-12-01

    Full Text Available Antennal sensilla patterns were used to analyze population variation of domestic Rhodnius prolixus from six departments and states representing three biogeographical regions of Colombia and Venezuela. Discriminant analysis of the patterns of mechanoreceptors and of three types of chemoreceptors on the pedicel and flagellar segments showed clear differentiation between R. prolixus populations east and west of the Andean Cordillera. The distribution of thick and thin-walled trichoids on the second flagellar segment also showed correlation with latitude, but this was not seen in the patterns of other sensilla. The results of the sensilla patterns appear to be reflecting biogeographic features or population isolation rather than characters associated with different habitats and lend support to the idea that domestic R. prolixus originated in the eastern region of the Andes.

  9. Peptide Pattern Recognition for high-throughput protein sequence analysis and clustering

    Busk, Peter Kamp

    2017-01-01

    Large collections of protein sequences with divergent sequences are tedious to analyze for understanding their phylogenetic or structure-function relation. Peptide Pattern Recognition is an algorithm that was developed to facilitate this task but the previous version does only allow a limited...... number of sequences as input. I implemented Peptide Pattern Recognition as a multithread software designed to handle large numbers of sequences and perform analysis in a reasonable time frame. Benchmarking showed that the new implementation of Peptide Pattern Recognition is twenty times faster than...... the previous implementation on a small protein collection with 673 MAP kinase sequences. In addition, the new implementation could analyze a large protein collection with 48,570 Glycosyl Transferase family 20 sequences without reaching its upper limit on a desktop computer. Peptide Pattern Recognition...

  10. Identification of dietary patterns using factor analysis in an epidemiological study in São Paulo

    Dirce Maria Lobo Marchioni

    Full Text Available CONTEXT AND OBJECTIVE: Diet and nutrition are environmental factors in health/disease relationships. From the epidemiological viewpoint, diet represents a complex set of highly correlated exposures. Our objective was to identify patterns of food intake in a group of individuals living in São Paulo, and to develop objective dietary measurements for epidemiological purposes. DESIGN AND LOCAL: Exploratory factor analysis of data in a case-control study in seven teaching hospitals in São Paulo. METHODS: The participants were 517 patients (260 oral cancer cases and 257 controls admitted to the study hospitals between November 1998 and March 2001. The weekly intake frequencies for dairy products, cereals, meat, processed meat, vegetables, pulses, fruits and sweets were assessed by means of a semi-quantitative food frequency questionnaire. Dietary patterns were identified by factor analysis, based on the intake of the eight food groups, using principal component analysis as an extraction method followed by varimax rotation. RESULTS: Factor analysis identified three patterns that accounted for 55% of the total variability within the sample. The first pattern ("prudent" was characterized by vegetable, fruit and meat intake; the second ("traditional" by cereals (mainly rice and pulses (mainly beans; and the third ("snacks" by dairy products and processed meat. CONCLUSION: This study identified food intake patterns through an a posteriori approach. Such analysis may be useful for nutritional intervention programs and, after computing scores for each individual according to the patterns identified, for establishing a relationship between diet and other epidemiological measurements of interest.

  11. [Scale effect of Nanjing urban green infrastructure network pattern and connectivity analysis.

    Yu, Ya Ping; Yin, Hai Wei; Kong, Fan Hua; Wang, Jing Jing; Xu, Wen Bin

    2016-07-01

    Based on ArcGIS, Erdas, GuidosToolbox, Conefor and other software platforms, using morphological spatial pattern analysis (MSPA) and landscape connectivity analysis methods, this paper quantitatively analysed the scale effect, edge effect and distance effect of the Nanjing urban green infrastructure network pattern in 2013 by setting different pixel sizes (P) and edge widths in MSPA analysis, and setting different dispersal distance thresholds in landscape connectivity analysis. The results showed that the type of landscape acquired based on the MSPA had a clear scale effect and edge effect, and scale effects only slightly affected landscape types, whereas edge effects were more obvious. Different dispersal distances had a great impact on the landscape connectivity, 2 km or 2.5 km dispersal distance was a critical threshold for Nanjing. When selecting the pixel size 30 m of the input data and the edge wide 30 m used in the morphological model, we could get more detailed landscape information of Nanjing UGI network. Based on MSPA and landscape connectivity, analysis of the scale effect, edge effect, and distance effect on the landscape types of the urban green infrastructure (UGI) network was helpful for selecting the appropriate size, edge width, and dispersal distance when developing these networks, and for better understanding the spatial pattern of UGI networks and the effects of scale and distance on the ecology of a UGI network. This would facilitate a more scientifically valid set of design parameters for UGI network spatiotemporal pattern analysis. The results of this study provided an important reference for Nanjing UGI networks and a basis for the analysis of the spatial and temporal patterns of medium-scale UGI landscape networks in other regions.

  12. Dietary patterns and cardiometabolic risk factors among adolescents: systematic review and meta-analysis.

    Cunha, Carla de Magalhães; Costa, Priscila R F; de Oliveira, Lucivalda P M; Queiroz, Valterlinda A de O; Pitangueira, Jacqueline C D; Oliveira, Ana Marlúcia

    2018-04-01

    This study systematised and synthesised the results of observational studies that were aimed at supporting the association between dietary patterns and cardiometabolic risk (CMR) factors among adolescents. Relevant scientific articles were searched in PUBMED, EMBASE, SCIENCE DIRECT, LILACS, WEB OF SCIENCE and SCOPUS. Observational studies that included the measurement of any CMR factor in healthy adolescents and dietary patterns were included. The search strategy retained nineteen articles for qualitative analysis. Among retained articles, the effects of dietary pattern on the means of BMI (n 18), waist circumference (WC) (n 9), systolic blood pressure (n 7), diastolic blood pressure (n 6), blood glucose (n 5) and lipid profile (n 5) were examined. Systematised evidence showed that an unhealthy dietary pattern appears to be associated with poor mean values of CMR factors among adolescents. However, evidence of a protective effect of healthier dietary patterns in this group remains unclear. Considering the number of studies with available information, a meta-analysis of anthropometric measures showed that dietary patterns characterised by the highest intake of unhealthy foods resulted in a higher mean BMI (0·57 kg/m²; 95 % CI 0·51, 0·63) and WC (0·57 cm; 95 % CI 0·47, 0·67) compared with low intake of unhealthy foods. Controversially, patterns characterised by a low intake of healthy foods were associated with a lower mean BMI (-0·41 kg/m²; 95 % CI -0·46,-0·36) and WC (-0·43 cm; 95 % CI -0·52,-0·33). An unhealthy dietary pattern may influence markers of CMR among adolescents, but considering the small number and limitations of the studies included, further studies are warranted to strengthen the evidence of this relation.

  13. Genome-wide analysis of the sox family in the calcareous sponge Sycon ciliatum: multiple genes with unique expression patterns

    Fortunato Sofia

    2012-07-01

    Full Text Available Abstract Background Sox genes are HMG-domain containing transcription factors with important roles in developmental processes in animals; many of them appear to have conserved functions among eumetazoans. Demosponges have fewer Sox genes than eumetazoans, but their roles remain unclear. The aim of this study is to gain insight into the early evolutionary history of the Sox gene family by identification and expression analysis of Sox genes in the calcareous sponge Sycon ciliatum. Methods Calcaronean Sox related sequences were retrieved by searching recently generated genomic and transcriptome sequence resources and analyzed using variety of phylogenetic methods and identification of conserved motifs. Expression was studied by whole mount in situ hybridization. Results We have identified seven Sox genes and four Sox-related genes in the complete genome of Sycon ciliatum. Phylogenetic and conserved motif analyses showed that five of Sycon Sox genes represent groups B, C, E, and F present in cnidarians and bilaterians. Two additional genes are classified as Sox genes but cannot be assigned to specific subfamilies, and four genes are more similar to Sox genes than to other HMG-containing genes. Thus, the repertoire of Sox genes is larger in this representative of calcareous sponges than in the demosponge Amphimedon queenslandica. It remains unclear whether this is due to the expansion of the gene family in Sycon or a secondary reduction in the Amphimedon genome. In situ hybridization of Sycon Sox genes revealed a variety of expression patterns during embryogenesis and in specific cell types of adult sponges. Conclusions In this study, we describe a large family of Sox genes in Sycon ciliatum with dynamic expression patterns, indicating that Sox genes are regulators in development and cell type determination in sponges, as observed in higher animals. The revealed differences between demosponge and calcisponge Sox genes repertoire highlight the need to

  14. Latent class analysis of multimorbidity patterns and associated outcomes in Spanish older adults: a prospective cohort study.

    Olaya, Beatriz; Moneta, Maria Victoria; Caballero, Francisco Félix; Tyrovolas, Stefanos; Bayes, Ivet; Ayuso-Mateos, José Luis; Haro, Josep Maria

    2017-08-18

    This study sought to identify multimorbidity patterns and determine the association between these latent classes with several outcomes, including health, functioning, disability, quality of life and use of services, at baseline and after 3 years of follow-up. We analyzed data from a representative Spanish cohort of 3541 non-institutionalized people aged 50 years old and over. Measures were taken at baseline and after 3 years of follow-up. Latent Class Analysis (LCA) was conducted using eleven common chronic conditions. Generalized linear models were conducted to determine the adjusted association of multimorbidity latent classes with several outcomes. 63.8% of participants were assigned to the "healthy" class, with minimum disease, 30% were classified under the "metabolic/stroke" class and 6% were assigned to the "cardiorespiratory/mental/arthritis" class. Significant cross-sectional associations were found between membership of both multimorbidity classes and poorer memory, quality of life, greater burden and more use of services. After 3 years of follow-up, the "metabolic/stroke" class was a significant predictor of lower levels of verbal fluency while the two multimorbidity classes predicted poor quality of life, problems in independent living, higher risk of hospitalization and greater use of health services. Common chronic conditions in older people cluster together in broad categories. These broad clusters are qualitatively distinct and are important predictors of several health and functioning outcomes. Future studies are needed to understand underlying mechanisms and common risk factors for patterns of multimorbidity and to propose more effective treatments.

  15. The use of protein patterns in genetic diversity analysis in some Brassica napus cultivars

    Roya Razavizadeh

    2013-11-01

    Full Text Available In this study, protein variations of seeds and five-day old cotyledonal leaves of four selected Brassica napus cultivars including Elite, Ocapy, Tasilo and Zarfam were analyzed by SDS-PAGE to identify protein markers. The amount of total soluble protein of seed storage proteins did not show significant differences in all cultivars whereas it was different in cotyledonal leaves. Protein patterns of seeds and cotyledonal leaves showed significant differences using SDS-PAGE and consequence analysis of bands by ImageJ program. Relative expression of six protein bands in seeds and five-day old cotyledonal leaves were significantly different. Three protein markers were identified by protein patterns of seed and cotyledonal leaves. The results of relationship analysis based on presence and absence of the specific protein bands in protein pattern of seed storage proteins showed that Tasilo and Elite cultivars had the highest similarities.

  16. Transient pattern analysis for fault detection and diagnosis of HVAC systems

    Cho, Sung-Hwan; Yang, Hoon-Cheol; Zaheer-uddin, M.; Ahn, Byung-Cheon

    2005-01-01

    Modern building HVAC systems are complex and consist of a large number of interconnected sub-systems and components. In the event of a fault, it becomes very difficult for the operator to locate and isolate the faulty component in such large systems using conventional fault detection methods. In this study, transient pattern analysis is explored as a tool for fault detection and diagnosis of an HVAC system. Several tests involving different fault replications were conducted in an environmental chamber test facility. The results show that the evolution of fault residuals forms clear and distinct patterns that can be used to isolate faults. It was found that the time needed to reach steady state for a typical building HVAC system is at least 50-60 min. This means incorrect diagnosis of faults can happen during online monitoring if the transient pattern responses are not considered in the fault detection and diagnosis analysis

  17. Dietary pattern and asthma: a systematic review and meta-analysis

    Lv N

    2014-08-01

    Full Text Available Nan Lv,1 Lan Xiao,1 Jun Ma1,2 1Palo Alto Medical Foundation Research Institute, 2Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA Background: The literature on the relationship between diet and asthma has largely focused on individual nutrients, with conflicting results. People consume a combination of foods from various groups that form a dietary pattern. Studying the role of dietary patterns in asthma is an emerging area of research. The purpose of this study was to systematically review dietary patterns and asthma outcomes in adults and children, to review maternal diet and child asthma, and to conduct a meta-analysis on the association between asthma prevalence and dietary patterns in adults. Methods: We searched Medline, Scopus, and ISI Web of Knowledge up to January 2014. Two researchers independently reviewed studies meeting the inclusion criteria using the American Dietetic Association quality criteria. A linear mixed model was used to derive the pooled effect size (95% confidence interval for each of three dietary pattern categories (healthy, unhealthy, and neutral. Results: Thirty-one studies were identified (16 cross-sectional, one case-control, 13 cohort, and one randomized controlled trial, including 12 in adults, 13 in children, five in pregnant woman–child pairs, and one in both children and pregnant woman–child pairs. Six of the 12 adult studies reported significant associations between dietary patterns and asthma outcomes (eg, ever asthma and forced expiratory volume in one second. Seven of ten studies examining the Mediterranean diet showed protective effects on child asthma and/or wheeze. Four of the six studies in mother-child pairs showed that maternal dietary patterns during pregnancy were not associated with child asthma or wheeze. The meta-analysis including six adult studies, the primary outcome of which was the prevalence of current or ever asthma, showed no association with healthy

  18. Local-global classifier fusion for screening chest radiographs

    Ding, Meng; Antani, Sameer; Jaeger, Stefan; Xue, Zhiyun; Candemir, Sema; Kohli, Marc; Thoma, George

    2017-03-01

    Tuberculosis (TB) is a severe comorbidity of HIV and chest x-ray (CXR) analysis is a necessary step in screening for the infective disease. Automatic analysis of digital CXR images for detecting pulmonary abnormalities is critical for population screening, especially in medical resource constrained developing regions. In this article, we describe steps that improve previously reported performance of NLM's CXR screening algorithms and help advance the state of the art in the field. We propose a local-global classifier fusion method where two complementary classification systems are combined. The local classifier focuses on subtle and partial presentation of the disease leveraging information in radiology reports that roughly indicates locations of the abnormalities. In addition, the global classifier models the dominant spatial structure in the gestalt image using GIST descriptor for the semantic differentiation. Finally, the two complementary classifiers are combined using linear fusion, where the weight of each decision is calculated by the confidence probabilities from the two classifiers. We evaluated our method on three datasets in terms of the area under the Receiver Operating Characteristic (ROC) curve, sensitivity, specificity and accuracy. The evaluation demonstrates the superiority of our proposed local-global fusion method over any single classifier.

  19. A Healthy Dietary Pattern Reduces Lung Cancer Risk: A Systematic Review and Meta-Analysis

    Yanlai Sun

    2016-03-01

    Full Text Available Background: Diet and nutrients play an important role in cancer development and progress; a healthy dietary pattern has been found to be associated with several types of cancer. However, the association between a healthy eating pattern and lung cancer risk is still unclear. Objective: Therefore, we conducted a systematic review with meta-analysis to evaluate whether a healthy eating pattern might reduce lung cancer risk. Methods: We identified relevant studies from the PubMed and Embase databases up to October 2015, and the relative risks were extracted and combined by the fixed-effects model when no substantial heterogeneity was observed; otherwise, the random-effects model was employed. Subgroup and publication bias analyses were also performed. Results: Finally, eight observational studies were included in the meta-analysis. The pooled relative risk of lung cancer for the highest vs. lowest category of healthy dietary pattern was 0.81 (95% confidence interval, CI: 0.75–0.86, and no significant heterogeneity was detected. The relative risks (RRs for non-smokers, former smokers and current smokers were 0.89 (95% CI: 0.63–1.27, 0.74 (95% CI: 0.62–0.89 and 0.86 (95% CI: 0.79–0.93, respectively. The results remained stable in subgroup analyses by other confounders and sensitivity analysis. Conclusions: The results of our meta-analysis suggest that a healthy dietary pattern is associated with a lower lung cancer risk, and they provide more beneficial evidence for changing the diet pattern in the general population.

  20. A Healthy Dietary Pattern Reduces Lung Cancer Risk: A Systematic Review and Meta-Analysis.

    Sun, Yanlai; Li, Zhenxiang; Li, Jianning; Li, Zengjun; Han, Jianjun

    2016-03-04

    Diet and nutrients play an important role in cancer development and progress; a healthy dietary pattern has been found to be associated with several types of cancer. However, the association between a healthy eating pattern and lung cancer risk is still unclear. Therefore, we conducted a systematic review with meta-analysis to evaluate whether a healthy eating pattern might reduce lung cancer risk. We identified relevant studies from the PubMed and Embase databases up to October 2015, and the relative risks were extracted and combined by the fixed-effects model when no substantial heterogeneity was observed; otherwise, the random-effects model was employed. Subgroup and publication bias analyses were also performed. Finally, eight observational studies were included in the meta-analysis. The pooled relative risk of lung cancer for the highest vs. lowest category of healthy dietary pattern was 0.81 (95% confidence interval, CI: 0.75-0.86), and no significant heterogeneity was detected. The relative risks (RRs) for non-smokers, former smokers and current smokers were 0.89 (95% CI: 0.63-1.27), 0.74 (95% CI: 0.62-0.89) and 0.86 (95% CI: 0.79-0.93), respectively. The results remained stable in subgroup analyses by other confounders and sensitivity analysis. The results of our meta-analysis suggest that a healthy dietary pattern is associated with a lower lung cancer risk, and they provide more beneficial evidence for changing the diet pattern in the general population.

  1. Error minimizing algorithms for nearest eighbor classifiers

    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.

  2. Inferring Drosophila gap gene regulatory network: Pattern analysis of simulated gene expression profiles and stability analysis

    Fomekong-Nanfack, Y.; Postma, M.; Kaandorp, J.A.

    2009-01-01

    Abstract Background Inference of gene regulatory networks (GRNs) requires accurate data, a method to simulate the expression patterns and an efficient optimization algorithm to estimate the unknown parameters. Using this approach it is possible to obtain alternative circuits without making any a priori assumptions about the interactions, which all simulate the observed patterns. It is important to analyze the properties of the circuits. Findings We have analyzed the simulated gene expression ...

  3. Data Stream Classification Based on the Gamma Classifier

    Abril Valeria Uriarte-Arcia

    2015-01-01

    Full Text Available The ever increasing data generation confronts us with the problem of handling online massive amounts of information. One of the biggest challenges is how to extract valuable information from these massive continuous data streams during single scanning. In a data stream context, data arrive continuously at high speed; therefore the algorithms developed to address this context must be efficient regarding memory and time management and capable of detecting changes over time in the underlying distribution that generated the data. This work describes a novel method for the task of pattern classification over a continuous data stream based on an associative model. The proposed method is based on the Gamma classifier, which is inspired by the Alpha-Beta associative memories, which are both supervised pattern recognition models. The proposed method is capable of handling the space and time constrain inherent to data stream scenarios. The Data Streaming Gamma classifier (DS-Gamma classifier implements a sliding window approach to provide concept drift detection and a forgetting mechanism. In order to test the classifier, several experiments were performed using different data stream scenarios with real and synthetic data streams. The experimental results show that the method exhibits competitive performance when compared to other state-of-the-art algorithms.

  4. Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers

    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.

  5. Determining patterns of variability in ecological communities: time lag analysis revisited

    Kampichler, C.; Van der Jeugd, H.P.

    2013-01-01

    All ecological communities experience change over time. One method to quantify temporal variation in the patterns of relative abundance of communities is time lag analysis (TLA). It uses a distance-based approach to study temporal community dynamics by regressing community dissimilarity over

  6. Cross-Cultural Patterns of Attachment: A Meta-Analysis of t?Y Strange Situation.

    van IJzendoorn, Marinus H.; Kroonenberg, Pieter M.

    1988-01-01

    Examines 2,000 Strange Situation classifications obtained in eight different countries. Differences and similarities between distributions in classifications of samples are investigated using correspondence analysis. Substantial intracultural differences are established; data also suggest a pattern of cross-cultural differences. (Author/RWB)

  7. Experience in performing trends and patterns analysis of nuclear power plant operational data

    Novak, T.M.; Williams, M.H.; Dennig, R.L.

    1990-01-01

    The Office for Analysis and Evaluation of Operational Data (AEOD) of the U.S. Nuclear Regulatory Commission (USNRC) has conducted a formal trends and patterns program since 1982. Since that time, the methods and end products of the program have evolved through experience and changes in the environment for trends and patterns analysis, i.e., increasing regulatory emphasis on operations and balance of plant performance, emergence of performance indicators, the availability of personal computer hardware and software to perform analysis, and changes in the information reported to the USNRC. This paper discusses the technical milestones of the AEOD trends and patterns program in terms of: 1) Sources of operational data, e.g., pre- and post- 1984 Licensee Event Reports, NPRDS, 2) Data storage and retrieval, e.g., Sequence Coding and Search System (SCSS), 3) Statistical methods, e.g., contingency table analysis, 4) Types of results. The paper summarizes the major lessons learned in the process of implementing a trends and patterns program and outlines future direction

  8. An Application of Discriminant Analysis to Pattern Recognition of Selected Contaminated Soil Features in Thin Sections

    Ribeiro, Alexandra B.; Nielsen, Allan Aasbjerg

    1997-01-01

    qualitative microprobe results: present elements Al, Si, Cr, Fe, As (associated with others). Selected groups of calibrated images (same light conditions and magnification) submitted to discriminant analysis, in order to find a pattern of recognition in the soil features corresponding to contamination already...

  9. Report on the first workshop on Movement Pattern Analysis MPA10

    Patrick Olivier Laube

    2011-01-01

    Full Text Available This paper reports on the 1st Workshop on Movement Pattern Analysis, held as a pre-GIScience 2010 workshop in September 2010 in Zurich, Switzerland. The report outlines the scientific motivation for the event, summarizes its main contributions and outcomes, discusses the implications of the gathering, and indicates directions for the road ahead.

  10. French registry of acute leukemia and myelodysplastic syndromes. Age distribution and hemogram analysis of the 4496 cases recorded during 1982-1983 and classified according to FAB criteria. Groupe Francais de Morphologie Hematologique

    Anon.

    1987-01-01

    During 1982 and 1983, 4496 new cases were recorded in the French Registry of acute leukemia and myelodysplastic syndromes by the French Group of Hematologic Morphology. This cooperative group associated members of 37 university centers spread throughout France; these centers handle the overwhelming majority of acute leukemias diagnoses. The cases were all classified according to FAB guidelines. Two thousand four hundred ninety-nine cases of acute myeloid leukemia were recorded, with similar total recruitment and distribution by cytologic subclass for both years. Hemogram data analysis revealed significant differences between different classes for certain parameters, particularly leukocytosis. A greater proportion of the acute myelogenous leukemias (AMLs) secondary to chemotherapy and/or radiotherapy (n = 145) were unclassifiable according to the French-American-British (FAB) system than the de novo AMLs (n = 1954). Eight hundred twenty cases of myelodysplastic syndromes were analyzed. Their frequency was underestimated due to optional reporting during the first year and the less favorable position of the university centers for recruiting these syndromes. The characteristics of the hemograms were established for acquired idiopathic sideroblastic anemia (n = 107), refractory anemia with excess blasts (RAEB) (n = 329), chronic myelomonocytic leukemia (n = 129) and RAEB in transformation (n = 65). Analysis of the 1177 acute lymphoblastic leukemias (ALLs) recorded showed good stability from one year to the next in terms of numbers of cases and distribution in the subclasses L1, L2, and L3. The distribution among these three subclasses by age also was determined. For L1 and L2 the hemogram data were examined separately for adults and children. The study of 74 cases of type L3 ALL enabled us to detail the hematologic presentation of this rare form of leukemia

  11. Social sensing of urban land use based on analysis of Twitter users' mobility patterns.

    Soliman, Aiman; Soltani, Kiumars; Yin, Junjun; Padmanabhan, Anand; Wang, Shaowen

    2017-01-01

    A number of recent studies showed that digital footprints around built environments, such as geo-located tweets, are promising data sources for characterizing urban land use. However, challenges for achieving this purpose exist due to the volume and unstructured nature of geo-located social media. Previous studies focused on analyzing Twitter data collectively resulting in coarse resolution maps of urban land use. We argue that the complex spatial structure of a large collection of tweets, when viewed through the lens of individual-level human mobility patterns, can be simplified to a series of key locations for each user, which could be used to characterize urban land use at a higher spatial resolution. Contingent issues that could affect our approach, such as Twitter users' biases and tendencies at locations where they tweet the most, were systematically investigated using 39 million geo-located Tweets and two independent datasets of the City of Chicago: 1) travel survey and 2) parcel-level land use map. Our results support that the majority of Twitter users show a preferential return, where their digital traces are clustered around a few key locations. However, we did not find a general relation among users between the ranks of locations for an individual-based on the density of tweets-and their land use types. On the contrary, temporal patterns of tweeting at key locations were found to be coherent among the majority of users and significantly associated with land use types of these locations. Furthermore, we used these temporal patterns to classify key locations into generic land use types with an overall classification accuracy of 0.78. The contribution of our research is twofold: a novel approach to resolving land use types at a higher resolution, and in-depth understanding of Twitter users' location-related and temporal biases, promising to benefit human mobility and urban studies in general.

  12. Social sensing of urban land use based on analysis of Twitter users’ mobility patterns

    Soltani, Kiumars; Yin, Junjun; Padmanabhan, Anand; Wang, Shaowen

    2017-01-01

    A number of recent studies showed that digital footprints around built environments, such as geo-located tweets, are promising data sources for characterizing urban land use. However, challenges for achieving this purpose exist due to the volume and unstructured nature of geo-located social media. Previous studies focused on analyzing Twitter data collectively resulting in coarse resolution maps of urban land use. We argue that the complex spatial structure of a large collection of tweets, when viewed through the lens of individual-level human mobility patterns, can be simplified to a series of key locations for each user, which could be used to characterize urban land use at a higher spatial resolution. Contingent issues that could affect our approach, such as Twitter users’ biases and tendencies at locations where they tweet the most, were systematically investigated using 39 million geo-located Tweets and two independent datasets of the City of Chicago: 1) travel survey and 2) parcel-level land use map. Our results support that the majority of Twitter users show a preferential return, where their digital traces are clustered around a few key locations. However, we did not find a general relation among users between the ranks of locations for an individual—based on the density of tweets—and their land use types. On the contrary, temporal patterns of tweeting at key locations were found to be coherent among the majority of users and significantly associated with land use types of these locations. Furthermore, we used these temporal patterns to classify key locations into generic land use types with an overall classification accuracy of 0.78. The contribution of our research is twofold: a novel approach to resolving land use types at a higher resolution, and in-depth understanding of Twitter users’ location-related and temporal biases, promising to benefit human mobility and urban studies in general. PMID:28723936

  13. Nutrient-derived dietary patterns and risk of colorectal cancer: a factor analysis in Uruguay.

    De Stefani, Eduardo; Ronco, Alvaro L; Boffetta, Paolo; Deneo-Pellegrini, Hugo; Correa, Pelayo; Acosta, Gisele; Mendilaharsu, Maria

    2012-01-01

    In order to explore the role of nutrients and bioactive related substances in colorectal cancer, we conducted a case-control in Uruguay, which is the country with the highest production of beef in the world. Six hundred and eleven (611) cases afflicted with colorectal cancer and 1,362 controls drawn from the same hospitals in the same time period were analyzed through unconditional multiple logistic regression. This base population was submitted to a principal components factor analysis and three factors were retained. They were labeled as the meat-based, plant-based, and carbohydrates patterns. They were rotated using orthogonal varimax method. The highest risk was positively associated with the meat-based pattern (OR for the highest quartile versus the lowest one 1.63, 95 % CI 1.22-2.18, P value for trend = 0.001), whereas the plant-based pattern was strongly protective (OR 0.60, 95 % CI 0.45-0.81, P value for trend pattern was only positively associated with colon cancer risk (OR 1.46, 95 % CI 1.02-2.09). The meat-based pattern was rich in saturated fat, animal protein, cholesterol, and phosphorus, nutrients originated in red meat. Since herocyclic amines are formed in the well-done red meat through the action of amino acids and creatine, it is suggestive that this pattern could be an important etiologic agent for colorectal cancer.

  14. Dietary patterns and risk of colorectal cancer: a factor analysis in uruguay.

    Stefani, Eduardo De; Deneo-Pellegrini, Hugo; Ronco, Alvaro L; Correa, Pelayo; Boffetta, Paolo; Aune, Dagfinn; Acosta, Gisele; Mendilaharsu, Maria; Luaces, Maria E; Lando, Gabriel; Silva, Cecilia

    2011-01-01

    In the time period 1996-2004, a case-control study of colorectal cancer was conducted in Montevideo, Uruguay. The study included 610 cases and 1,220 controls, frequency matched for age, sex, and residence. All cases were newly diagnosed and microscopically confirmed and controls were drawn from the same hospitals. Controls were submitted to factor analysis (principal components method) and 4 dietary patterns for men (prudent, traditional, Western, drinker) and 3 for women (prudent, Western, drinker) were retained. These were rotated and normalized by the Kaiser method. Scores were applied to all participants (cases and controls) and odds ratios were estimated by logistic regression and polynomial regression. The Western pattern showed an OR of 2.62 (95 % CI 1.36-5.08) for colon cancer among men, and women displayed a similar increase in risk. However, rectal cancer was not associated with this diet, rather being inversely associated with the prudent and traditional patterns among men (OR 0.49, 95 % CI 0.28-0.57 for the traditional pattern). In conclusion, whereas the Western pattern was directly associated with colon cancer, the prudent pattern was strongly protective for rectal cancer.

  15. Partial Least Square Discriminant Analysis Discovered a Dietary Pattern Inversely Associated with Nasopharyngeal Carcinoma Risk.

    Lo, Yen-Li; Pan, Wen-Harn; Hsu, Wan-Lun; Chien, Yin-Chu; Chen, Jen-Yang; Hsu, Mow-Ming; Lou, Pei-Jen; Chen, I-How; Hildesheim, Allan; Chen, Chien-Jen

    2016-01-01

    Evidence on the association between dietary component, dietary pattern and nasopharyngeal carcinoma (NPC) is scarce. A major challenge is the high degree of correlation among dietary constituents. We aimed to identify dietary pattern associated with NPC and to illustrate the dose-response relationship between the identified dietary pattern scores and the risk of NPC. Taking advantage of a matched NPC case-control study, data from a total of 319 incident cases and 319 matched controls were analyzed. Dietary pattern was derived employing partial least square discriminant analysis (PLS-DA) performed on energy-adjusted food frequencies derived from a 66-item food-frequency questionnaire. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated with multiple conditional logistic regression models, linking pattern scores and NPC risk. A high score of the PLS-DA derived pattern was characterized by high intakes of fruits, milk, fresh fish, vegetables, tea, and eggs ordered by loading values. We observed that one unit increase in the scores was associated with a significantly lower risk of NPC (ORadj = 0.73, 95% CI = 0.60-0.88) after controlling for potential confounders. Similar results were observed among Epstein-Barr virus seropositive subjects. An NPC protective diet is indicated with more phytonutrient-rich plant foods (fruits, vegetables), milk, other protein-rich foods (in particular fresh fish and eggs), and tea. This information may be used to design potential dietary regimen for NPC prevention.

  16. Cluster analysis in soft X-ray spectromicroscopy: Finding the patterns in complex specimens

    Lerotic, M. [Department of Physics and Astronomy, State University of New York at Stony Brook, Stony Brook, NY 11794-3800 (United States)]. E-mail: lerotic@xray1.physics.sunysb.edu; Jacobsen, C. [Department of Physics and Astronomy, State University of New York at Stony Brook, Stony Brook, NY 11794-3800 (United States); Gillow, J.B. [Environmental Sciences Department, Brookhaven National Laboratory, Upton, NY 11973 (United States); Francis, A.J. [Environmental Sciences Department, Brookhaven National Laboratory, Upton, NY 11973 (United States); Wirick, S. [Department of Physics and Astronomy, State University of New York at Stony Brook, Stony Brook, NY 11794-3800 (United States); Vogt, S. [Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439 (United States); Maser, J. [Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439 (United States)

    2005-06-15

    Soft X-ray spectromicroscopy provides spectral data on the chemical speciation of light elements at sub-100 nanometer spatial resolution. If all chemical species in a specimen are known and separately characterized, existing approaches can be used to measure the concentration of each component at each pixel. In other situations such as in biology or environmental science, this approach may not be possible. We have previously described [M. Lerotic, C. Jacobsen, T. Schaefer, S. Vogt, Ultramicroscopy 100 (1-2) (2004) 35] the use of principle component analysis (PCA) to orthogonalize and noise-filter spectromicroscopy data, and cluster analysis (Canada) to classify the analyzed data and obtain thickness maps of representative spectra. We describe here an extension of that work employing an angle distance measure; this measure provides better classification based on spectral signatures alone in specimens with significant thickness variations. The method is illustrated using simulated data, and also to examine sporulation in the bacterium Clostridium sp.

  17. Quantitative Folding Pattern Analysis of Early Primary Sulci in Human Fetuses with Brain Abnormalities.

    Im, K; Guimaraes, A; Kim, Y; Cottrill, E; Gagoski, B; Rollins, C; Ortinau, C; Yang, E; Grant, P E

    2017-07-01

    Aberrant gyral folding is a key feature in the diagnosis of many cerebral malformations. However, in fetal life, it is particularly challenging to confidently diagnose aberrant folding because of the rapid spatiotemporal changes of gyral development. Currently, there is no resource to measure how an individual fetal brain compares with normal spatiotemporal variations. In this study, we assessed the potential for automatic analysis of early sulcal patterns to detect individual fetal brains with cerebral abnormalities. Triplane MR images were aligned to create a motion-corrected volume for each individual fetal brain, and cortical plate surfaces were extracted. Sulcal basins were automatically identified on the cortical plate surface and compared with a combined set generated from 9 normal fetal brain templates. Sulcal pattern similarities to the templates were quantified by using multivariate geometric features and intersulcal relationships for 14 normal fetal brains and 5 fetal brains that were proved to be abnormal on postnatal MR imaging. Results were compared with the gyrification index. Significantly reduced sulcal pattern similarities to normal templates were found in all abnormal individual fetuses compared with normal fetuses (mean similarity [normal, abnormal], left: 0.818, 0.752; P the primary distinguishing features. The gyrification index was not significantly different between the normal and abnormal groups. Automated analysis of interrelated patterning of early primary sulci could outperform the traditional gyrification index and has the potential to quantitatively detect individual fetuses with emerging abnormal sulcal patterns. © 2017 by American Journal of Neuroradiology.

  18. High-resolution computed tomography to differentiate chronic diffuse interstitial lung diseases with predominant ground-glass pattern using logical analysis of data

    Martin, Sophie Grivaud; Brauner, Michel W.; Rety, Frederique; Kronek, Louis-Philippe; Brauner, Nadia; Valeyre, Dominique; Nunes, Hilario; Brillet, Pierre-Yves

    2010-01-01

    We evaluated the performance of high-resolution computed tomography (HRCT) to differentiate chronic diffuse interstitial lung diseases (CDILD) with predominant ground-glass pattern by using logical analysis of data (LAD). A total of 162 patients were classified into seven categories: sarcoidosis (n = 38), connective tissue disease (n = 32), hypersensitivity pneumonitis (n = 18), drug-induced lung disease (n = 15), alveolar proteinosis (n = 12), idiopathic non-specific interstitial pneumonia (n = 10) and miscellaneous (n = 37). First, 40 CT attributes were investigated by the LAD to build up patterns characterising a category. From the association of patterns, LAD determined models specific to each CDILD. Second, data were recomputed by adding eight clinical attributes to the analysis. The 20 x 5 cross-folding method was used for validation. Models could be individualised for sarcoidosis, hypersensitivity pneumonitis, connective tissue disease and alveolar proteinosis. An additional model was individualised for drug-induced lung disease by adding clinical data. No model was demonstrated for idiopathic non-specific interstitial pneumonia and the miscellaneous category. The results showed that HRCT had a good sensitivity (≥64%) and specificity (≥78%) and a high negative predictive value (≥93%) for diseases with a model. Higher sensitivity (≥78%) and specificity (≥89%) were achieved by adding clinical data. The diagnostic performance of HRCT is high and can be increased by adding clinical data. (orig.)

  19. Pictorial binding: endeavor to classify

    Zinchenko S.

    2015-01-01

    Full Text Available The article is devoted to the classification of bindings of the 1-19th centuries with a unique and untypical book binding decoration technique (encaustic, tempera and oil paintings. Analysis of design features, materials and techniques of art decoration made it possible to identify them as a separate type - pictorial bindings and divide them into four groups. The first group consists of Coptic bindings, decorated with icon-painting images in encaustic technique. The second group is made up of leather Western bindings of the 13-14th centuries, which have the decoration and technique of ornamentation close to iconography. The third group involves parchment bindings, ornamentation technique of which is closer to the miniature. The last group comprises bindings of East Slavic origin of the 15-19th centuries, decorated with icon-painting pictures made in the technique of tempera or oil painting. The proposed classification requires further basic research as several specific kinds of bindings have not yet been investigated

  20. Using Two Different Approaches to Assess Dietary Patterns: Hypothesis-Driven and Data-Driven Analysis

    Ágatha Nogueira Previdelli

    2016-09-01

    Full Text Available The use of dietary patterns to assess dietary intake has become increasingly common in nutritional epidemiology studies due to the complexity and multidimensionality of the diet. Currently, two main approaches have been widely used to assess dietary patterns: data-driven and hypothesis-driven analysis. Since the methods explore different angles of dietary intake, using both approaches simultaneously might yield complementary and useful information; thus, we aimed to use both approaches to gain knowledge of adolescents’ dietary patterns. Food intake from a cross-sectional survey with 295 adolescents was assessed by 24 h dietary recall (24HR. In hypothesis-driven analysis, based on the American National Cancer Institute method, the usual intake of Brazilian Healthy Eating Index Revised components were estimated. In the data-driven approach, the usual intake of foods/food groups was estimated by the Multiple Source Method. In the results, hypothesis-driven analysis showed low scores for Whole grains, Total vegetables, Total fruit and Whole fruits, while, in data-driven analysis, fruits and whole grains were not presented in any pattern. High intakes of sodium, fats and sugars were observed in hypothesis-driven analysis with low total scores for Sodium, Saturated fat and SoFAA (calories from solid fat, alcohol and added sugar components in agreement, while the data-driven approach showed the intake of several foods/food groups rich in these nutrients, such as butter/margarine, cookies, chocolate powder, whole milk, cheese, processed meat/cold cuts and candies. In this study, using both approaches at the same time provided consistent and complementary information with regard to assessing the overall dietary habits that will be important in order to drive public health programs, and improve their efficiency to monitor and evaluate the dietary patterns of populations.

  1. Image processing and pattern recognition algorithms for evaluation of crossed immunoelectrophoretic patterns (crossed radioimmunoelectrophoresis analysis manager; CREAM)

    Søndergaard, I; Poulsen, L K; Hagerup, M

    1987-01-01

    points along the precipitation curve in the curve-fitting process. The system has been tested on crossed immunoelectrophoretic patterns as well as crossed radioimmunoelectrophoretic patterns and it has been shown that the system can recognize the same precipitation curves on different immunoplates...

  2. Simultaneous-Fault Diagnosis of Gas Turbine Generator Systems Using a Pairwise-Coupled Probabilistic Classifier

    Zhixin Yang

    2013-01-01

    Full Text Available A reliable fault diagnostic system for gas turbine generator system (GTGS, which is complicated and inherent with many types of component faults, is essential to avoid the interruption of electricity supply. However, the GTGS diagnosis faces challenges in terms of the existence of simultaneous-fault diagnosis and high cost in acquiring the exponentially increased simultaneous-fault vibration signals for constructing the diagnostic system. This research proposes a new diagnostic framework combining feature extraction, pairwise-coupled probabilistic classifier, and decision threshold optimization. The feature extraction module adopts wavelet packet transform and time-domain statistical features to extract vibration signal features. Kernel principal component analysis is then applied to further reduce the redundant features. The features of single faults in a simultaneous-fault pattern are extracted and then detected using a probabilistic classifier, namely, pairwise-coupled relevance vector machine, which is trained with single-fault patterns only. Therefore, the training dataset of simultaneous-fault patterns is unnecessary. To optimize the decision threshold, this research proposes to use grid search method which can ensure a global solution as compared with traditional computational intelligence techniques. Experimental results show that the proposed framework performs well for both single-fault and simultaneous-fault diagnosis and is superior to the frameworks without feature extraction and pairwise coupling.

  3. Fan fault diagnosis based on symmetrized dot pattern analysis and image matching

    Xu, Xiaogang; Liu, Haixiao; Zhu, Hao; Wang, Songling

    2016-07-01

    To detect the mechanical failure of fans, a new diagnostic method based on the symmetrized dot pattern (SDP) analysis and image matching is proposed. Vibration signals of 13 kinds of running states are acquired on a centrifugal fan test bed and reconstructed by the SDP technique. The SDP pattern templates of each running state are established. An image matching method is performed to diagnose the fault. In order to improve the diagnostic accuracy, the single template, multiple templates and clustering fault templates are used to perform the image matching.

  4. Analysis of stresses in filament-wound spherical pressure vessels produced by the delta-axisymmetric pattern

    Knight, C.E. Jr.

    1975-01-01

    Spherical pressure vessels may be produced by filament winding the composite material with a delta-axisymmetric pattern. This particular pattern yields a composite with high fiber density and efficient and reproducible structures. The pattern is readily defined mathematically and, thus, eases the analysis problem. (U.S.)

  5. Biomechanical Differences of Foot-Strike Patterns During Running: A Systematic Review With Meta-analysis.

    Almeida, Matheus O; Davis, Irene S; Lopes, Alexandre D

    2015-10-01

    Systematic review with meta-analysis. To determine the biomechanical differences between foot-strike patterns used when running. Strike patterns during running have received attention in the recent literature due to their potential mechanical differences and associated injury risks. Electronic databases (MEDLINE, Embase, LILACS, SciELO, and SPORTDiscus) were searched through July 2014. Studies (cross-sectional, case-control, prospective, and retrospective) comparing the biomechanical characteristics of foot-strike patterns during running in distance runners at least 18 years of age were included in this review. Two independent reviewers evaluated the risk of bias. A meta-analysis with a random-effects model was used to combine the data from the included studies. Sixteen studies were included in the final analysis. In the meta-analyses of kinematic variables, significant differences between forefoot and rearfoot strikers were found for foot and knee angle at initial contact and knee flexion range of motion. A forefoot-strike pattern resulted in a plantar-flexed ankle position and a more flexed knee position, compared to a dorsiflexed ankle position and a more extended knee position for the rearfoot strikers, at initial contact with the ground. In the comparison of rearfoot and midfoot strikers, midfoot strikers demonstrated greater ankle dorsiflexion range of motion and decreased knee flexion range of motion compared to rearfoot strikers. For kinetic variables, the meta-analysis revealed that rearfoot strikers had higher vertical loading rates compared to forefoot strikers. There are differences in kinematic and kinetic characteristics between foot-strike patterns when running. Clinicians should be aware of these characteristics to help in the management of running injuries and advice on training.

  6. The Analysis of Tree Species Distribution Information Extraction and Landscape Pattern Based on Remote Sensing Images

    Yi Zeng

    2017-08-01

    Full Text Available The forest ecosystem is the largest land vegetation type, which plays the role of unreplacement with its unique value. And in the landscape scale, the research on forest landscape pattern has become the current hot spot, wherein the study of forest canopy structure is very important. They determines the process and the strength of forests energy flow, which influences the adjustments of ecosystem for climate and species diversity to some extent. The extraction of influencing factors of canopy structure and the analysis of the vegetation distribution pattern are especially important. To solve the problems, remote sensing technology, which is superior to other technical means because of its fine timeliness and large-scale monitoring, is applied to the study. Taking Lingkong Mountain as the study area, the paper uses the remote sensing image to analyze the forest distribution pattern and obtains the spatial characteristics of canopy structure distribution, and DEM data are as the basic data to extract the influencing factors of canopy structure. In this paper, pattern of trees distribution is further analyzed by using terrain parameters, spatial analysis tools and surface processes quantitative simulation. The Hydrological Analysis tool is used to build distributed hydrological model, and corresponding algorithm is applied to determine surface water flow path, rivers network and basin boundary. Results show that forest vegetation distribution of dominant tree species present plaque on the landscape scale and their distribution have spatial heterogeneity which is related to terrain factors closely. After the overlay analysis of aspect, slope and forest distribution pattern respectively, the most suitable area for stand growth and the better living condition are obtained.

  7. Hierarchical mixtures of naive Bayes classifiers

    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

  8. Comparing classifiers for pronunciation error detection

    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

  9. Feature extraction for dynamic integration of classifiers

    Pechenizkiy, M.; Tsymbal, A.; Puuronen, S.; Patterson, D.W.

    2007-01-01

    Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. In this paper, we present an algorithm for the dynamic integration of classifiers in the space of extracted features (FEDIC). It is based on the technique

  10. Genome-wide analysis of WRKY transcription factors in white pear (Pyrus bretschneideri) reveals evolution and patterns under drought stress.

    Huang, Xiaosan; Li, Kongqing; Xu, Xiaoyong; Yao, Zhenghong; Jin, Cong; Zhang, Shaoling

    2015-12-24

    WRKY transcription factors (TFs) constitute one of the largest protein families in higher plants, and its members contain one or two conserved WRKY domains, about 60 amino acid residues with the WRKYGQK sequence followed by a C2H2 or C2HC zinc finger motif. WRKY proteins play significant roles in plant development, and in responses to biotic and abiotic stresses. Pear (Pyrus bretschneideri) is one of the most important fruit crops in the world and is frequently threatened by abiotic stress, such as drought, affecting growth, development and productivity. Although the pear genome sequence has been released, little is known about the WRKY TFs in pear, especially in respond to drought stress at the genome-wide level. We identified a total of 103 WRKY TFs in the pear genome. Based on the structural features of WRKY proteins and topology of the phylogenetic tree, the pear WRKY (PbWRKY) family was classified into seven groups (Groups 1, 2a-e, and 3). The microsyteny analysis indicated that 33 (32%) PbWRKY genes were tandemly duplicated and 57 genes (55.3%) were segmentally duplicated. RNA-seq experiment data and quantitative real-time reverse transcription PCR revealed that PbWRKY genes in different groups were induced by drought stress, and Group 2a and 3 were mainly involved in the biological pathways in response to drought stress. Furthermore, adaptive evolution analysis detected a significant positive selection for Pbr001425 in Group 3, and its expression pattern differed from that of other members in this group. The present study provides a solid foundation for further functional dissection and molecular evolution of WRKY TFs in pear, especially for improving the water-deficient resistance of pear through manipulation of the PbWRKYs.

  11. Changing Authorship Patterns and Publishing Habits in the European Journal of Pediatric Surgery: A 10-Year Analysis.

    Pintér, András

    2015-08-01

    The aim of this study is an analysis of the changing authorship patterns and publishing habits encountered in papers published in the European Journal of Pediatric Surgery (EJPS) over the past 10 years. Furthermore, it seeks to anticipate the trends in the years ahead. We conducted a retrospective review of articles published in the EJPS during a 10-year period (January 1, 2003-December 31, 2012). Each article was classified as an Original Report/Original Article (OR/OA) or as a Case Report/Case Gallery (CR/CG), and they were analyzed separately. For investigation of the percentage distribution of publications according to the number of authors per articles, papers were combined and into three groups (1-2, 3-5, and 6 ≤ authors). The analysis focused on whether the work was done by members of one institution, or in collaboration with other units of the same institution, or in collaboration with other national or multinational institutes. In the past 10 years, the EJPS published 996 articles (616 ORs/OAs, 380 CRs/CGs). The one and two authored publications (125) have not decreased, the three to five authored articles (552) changed minimally, whereas the number of 6 ≤ authored publications (319) has increased. Of 996 publications, 348 were from single institutes and 648 were written in collaboration with two or more other institutes. In addition, in this 10-year period, the number of multinational articles has increased significantly from 24 to 59. Increase in cooperation within and between institutions is a positive trend, aiming with the goal of improving quality of publications. Georg Thieme Verlag KG Stuttgart · New York.

  12. Deconvolution When Classifying Noisy Data Involving Transformations

    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.

  13. Deconvolution When Classifying Noisy Data Involving Transformations.

    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.

  14. Deconvolution When Classifying Noisy Data Involving Transformations

    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.

  15. Two-dimensional wavelet transform for reliability-guided phase unwrapping in optical fringe pattern analysis.

    Li, Sikun; Wang, Xiangzhao; Su, Xianyu; Tang, Feng

    2012-04-20

    This paper theoretically discusses modulus of two-dimensional (2D) wavelet transform (WT) coefficients, calculated by using two frequently used 2D daughter wavelet definitions, in an optical fringe pattern analysis. The discussion shows that neither is good enough to represent the reliability of the phase data. The differences between the two frequently used 2D daughter wavelet definitions in the performance of 2D WT also are discussed. We propose a new 2D daughter wavelet definition for reliability-guided phase unwrapping of optical fringe pattern. The modulus of the advanced 2D WT coefficients, obtained by using a daughter wavelet under this new daughter wavelet definition, includes not only modulation information but also local frequency information of the deformed fringe pattern. Therefore, it can be treated as a good parameter that represents the reliability of the retrieved phase data. Computer simulation and experimentation show the validity of the proposed method.

  16. Internet Connection Control based on Idle Time Using User Behavior Pattern Analysis

    Fadilah Fahrul Hardiansyah

    2014-12-01

    Full Text Available The increase of smartphone ability is rapidly increasing the power consumption. Many methods have been proposed to reduce smartphone power consumption. Most of these methods use the internet connection control based on the availability of the battery power level regardless of when and where a waste of energy occurs. This paper proposes a new approach to control the internet connection based on idle time using user behavior pattern analysis. User behavior patterns are used to predict idle time duration. Internet connection control performed during idle time. During idle time internet connection periodically switched on and off by a certain time interval. This method effectively reduces a waste of energy. Control of the internet connection does not interfere the user because it is implemented on idle time. Keywords: Smartphone, User Behavior, Pattern Recognition, Idle Time, Internet Connection Control

  17. Distribution patterns of firearm discharge residues as revealed by neutron activation analysis

    Pillay, K.K.S.; Driscoll, D.C.; Jester, W.A.

    1975-01-01

    A systematic investigation using a variety of handguns has revealed the existence of distinguisable distribution patterns of firearm discharge residues on surfaces below the flight path of a bullet. The residues are identificable even at distances of 12 meters from the gun using nondestructive neutron activation analysis. The results of these investigations show that the distribution pattern for a gun is reproducible using similar ammunition and that there exist two distinct regions to the patterns developed between the firearm and the target-one with respect to the position of the gun and the other in the vicinity of the target. The judicious applications of these findings could be of significant value in criminal investigations. (T.G.)

  18. A Social Network Analysis of Tourist Movement Patterns in Blogs: Korean Backpackers in Europe

    Hee Chung Chung

    2017-12-01

    Full Text Available Given recent developments in information and communication technology, the number of individual tourists enjoying free travel without the advice of travel agencies is increasing. Therefore, such tourists can visit more tourist destinations and create more complex movement patterns than mass tourists. These tourist movement patterns are a key factor in understanding tourist behavior and they contain various information that is important for tourism marketers. In this vein, this study aims to investigate tourist movement patterns in Europe. We acquired 122 data points from posts on the NAVER blog, which is the most famous social media platform in Korea. These data were transformed into matrix data for social network analysis and analyzed for centrality. The results suggest that Korean backpackers in Europe tend to enter Europe through London and Paris. Venezia and Firenze are also key cities.

  19. Inferring Drosophila gap gene regulatory network: Pattern analysis of simulated gene expression profiles and stability analysis

    Fomekong-Nanfack, Y.; Postma, M.; Kaandorp, J.A.

    2009-01-01

    Background: Inference of gene regulatory networks (GRNs) requires accurate data, a method to simulate the expression patterns and an efficient optimization algorithm to estimate the unknown parameters. Using this approach it is possible to obtain alternative circuits without making any a priori

  20. Recurrent daily rainfall patterns over South Africa and associated dynamics during the core of the austral summer

    Cretat, J

    2010-12-01

    Full Text Available field instead of atmospheric processes and dynamics. An original agglomerative hierarchical clustering approach is used to classify daily rainfall patterns recorded at 5352 stations from DJF 1971 to DJF 1999. Five clusters are retained for analysis...

  1. Metabolic patterns in prion diseases: an FDG PET voxel-based analysis

    Prieto, Elena; Dominguez-Prado, Ines; Jesus Ribelles, Maria; Arbizu, Javier [Clinica Universidad de Navarra, Nuclear Medicine Department, Pamplona (Spain); Riverol, Mario; Ortega-Cubero, Sara; Rosario Luquin, Maria; Castro, Purificacion de [Clinica Universidad de Navarra, Neurology Department, Pamplona (Spain)

    2015-09-15

    Clinical diagnosis of human prion diseases can be challenging since symptoms are common to other disorders associated with rapidly progressive dementia. In this context, {sup 18}F-fluorodeoxyglucose (FDG) positron emission tomography (PET) might be a useful complementary tool. The aim of this study was to determine the metabolic pattern in human prion diseases, particularly sporadic Creutzfeldt-Jakob disease (sCJD), the new variant of Creutzfeldt-Jakob disease (vCJD) and fatal familial insomnia (FFI). We retrospectively studied 17 patients with a definitive, probable or possible prion disease who underwent FDG PET in our institution. Of these patients, 12 were diagnosed as sCJD (9 definitive, 2 probable and 1 possible), 1 was diagnosed as definitive vCJD and 4 were diagnosed as definitive FFI. The hypometabolic pattern of each individual and comparisons across the groups of subjects (control subjects, sCJD and FFI) were evaluated using a voxel-based analysis. The sCJD group exhibited a pattern of hypometabolism that affected both subcortical (bilateral caudate, thalamus) and cortical (frontal cortex) structures, while the FFI group only presented a slight hypometabolism in the thalamus. Individual analysis demonstrated a considerable variability of metabolic patterns among patients, with the thalamus and basal ganglia the most frequently affected areas, combined in some cases with frontal and temporal hypometabolism. Patients with a prion disease exhibit a characteristic pattern of brain metabolism presentation in FDG PET imaging. Consequently, in patients with rapidly progressive cognitive impairment, the detection of these patterns in the FDG PET study could orient the diagnosis to a prion disease. (orig.)

  2. Metabolic patterns in prion diseases: an FDG PET voxel-based analysis

    Prieto, Elena; Dominguez-Prado, Ines; Jesus Ribelles, Maria; Arbizu, Javier; Riverol, Mario; Ortega-Cubero, Sara; Rosario Luquin, Maria; Castro, Purificacion de

    2015-01-01

    Clinical diagnosis of human prion diseases can be challenging since symptoms are common to other disorders associated with rapidly progressive dementia. In this context, 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) might be a useful complementary tool. The aim of this study was to determine the metabolic pattern in human prion diseases, particularly sporadic Creutzfeldt-Jakob disease (sCJD), the new variant of Creutzfeldt-Jakob disease (vCJD) and fatal familial insomnia (FFI). We retrospectively studied 17 patients with a definitive, probable or possible prion disease who underwent FDG PET in our institution. Of these patients, 12 were diagnosed as sCJD (9 definitive, 2 probable and 1 possible), 1 was diagnosed as definitive vCJD and 4 were diagnosed as definitive FFI. The hypometabolic pattern of each individual and comparisons across the groups of subjects (control subjects, sCJD and FFI) were evaluated using a voxel-based analysis. The sCJD group exhibited a pattern of hypometabolism that affected both subcortical (bilateral caudate, thalamus) and cortical (frontal cortex) structures, while the FFI group only presented a slight hypometabolism in the thalamus. Individual analysis demonstrated a considerable variability of metabolic patterns among patients, with the thalamus and basal ganglia the most frequently affected areas, combined in some cases with frontal and temporal hypometabolism. Patients with a prion disease exhibit a characteristic pattern of brain metabolism presentation in FDG PET imaging. Consequently, in patients with rapidly progressive cognitive impairment, the detection of these patterns in the FDG PET study could orient the diagnosis to a prion disease. (orig.)

  3. Soil Moisture Retrieval and Spatiotemporal Pattern Analysis Using Sentinel-1 Data of Dahra, Senegal

    Zhiqu Liu

    2017-11-01

    Full Text Available The spatiotemporal pattern of soil moisture is of great significance for the understanding of the water exchange between the land surface and the atmosphere. The two-satellite constellation of the Sentinel-1 mission provides C-band synthetic aperture radar (SAR observations with high spatial and temporal resolutions, which are suitable for soil moisture monitoring. In this paper, we aim to assess the capability of pattern analysis based on the soil moisture retrieved from Sentinel-1 time-series data of Dahra in Senegal. The look-up table (LUT method is used in the retrieval with the backscattering coefficients that are simulated by the advanced integrated equation Model (AIEM for the soil layer and the Michigan microwave canopy scattering (MIMICS model for the vegetation layer. The temporal trend of Sentinel-1A soil moisture is evaluated by the ground measurements from the site at Dahra, with an unbiased root-mean-squared deviation (ubRMSD of 0.053 m3/m3, a mean average deviation (MAD of 0.034 m3/m3, and an R value of 0.62. The spatial variation is also compared with the existing microwave products at a coarse scale, which confirms the reliability of the Sentinel-1A soil moisture. The spatiotemporal patterns are analyzed by empirical orthogonal functions (EOF, and the geophysical factors that are affecting soil moisture are discussed. The first four EOFs of soil moisture explain 77.2% of the variance in total and the primary EOF explains 66.2%, which shows the dominant pattern at the study site. Soil texture and the normalized difference vegetation index are more closely correlated with the primary pattern than the topography and temperature in the study area. The investigation confirms the potential for soil moisture retrieval and spatiotemporal pattern analysis using Sentinel-1 images.

  4. ANALYSIS OF RAILWAY USER TRAVEL BEHAVIOUR PATTERNS OF DIFFERENT AGE GROUPS

    Takamasa AKIYAMA

    2009-01-01

    Full Text Available In recent years, there have been requirments for a transport environment that will foster the development of safe, comfortable townships. The study of urban activities amid an aging society and effective use of public transport modes in addressing environmental problems have become particularly important issues. This study analyzes travel behaviour patterns of varying age groups using urban railways in order to examine the relationship between urban public transport use and urban activities. specifically, it analyzes the composition of urban activity and travel behaviour patterns among urban railway users in the Keihanshin (Kyoto-Osaka-Kobe metropolitan area. This paper looks at urban activities within aging societies and identifies the differences in travel behaviour of railway users by separating them into young, middle aged and senior citizen age groups. Analysis makes particular use of the Railway station Database, which is a compilation of existing studies into attributes of railway stations and their surroundings, and results of person trip surveys. Rail use behaviour characteristics have been sorted by age group because mobility via urban railway systems is varied by age group. As a result, differences in railway usage patterns (travel objectives, distance and time, and number of transfers, etc. have been identified and so too have differences in urban activity patterns related to free activities (shopping, recreation. Furthermore, the study developed a travel behaviour pattern estimation model which is capable of categorizing specific transport behaviour patterns and estimating rail users and transport behaviour patterns from the relationship with areas surrounding railway stations to ensure future mobility by public transport for older age groups. The results make it possible to put forward proposals for urban rail services that will facilitate urban activities for the different age groups. Eventually, it will be possible to understand

  5. Oblique decision trees using embedded support vector machines in classifier ensembles

    Menkovski, V.; Christou, I.; Efremidis, S.

    2008-01-01

    Classifier ensembles have emerged in recent years as a promising research area for boosting pattern recognition systems' performance. We present a new base classifier that utilizes oblique decision tree technology based on support vector machines for the construction of oblique (non-axis parallel)

  6. Variants of the Borda count method for combining ranked classifier hypotheses

    van Erp, Merijn; Schomaker, Lambert; Schomaker, Lambert; Vuurpijl, Louis

    2000-01-01

    The Borda count is a simple yet effective method of combining rankings. In pattern recognition, classifiers are often able to return a ranked set of results. Several experiments have been conducted to test the ability of the Borda count and two variant methods to combine these ranked classifier

  7. A cluster analysis of patterns of objectively measured physical activity in Hong Kong.

    Lee, Paul H; Yu, Ying-Ying; McDowell, Ian; Leung, Gabriel M; Lam, T H

    2013-08-01

    The health benefits of exercise are clear. In targeting interventions it would be valuable to know whether characteristic patterns of physical activity (PA) are associated with particular population subgroups. The present study used cluster analysis to identify characteristic hourly PA patterns measured by accelerometer. Cross-sectional design. Objectively measured PA in Hong Kong adults. Four-day accelerometer data were collected during 2009 to 2011 for 1714 participants in Hong Kong (mean age 44?2 years, 45?9% male). Two clusters were identified, one more active than the other. The ‘active cluster’ (n 480) was characterized by a routine PA pattern on weekdays and a more active and varied pattern on weekends; the other, the ‘less active cluster’ (n 1234), by a consistently low PA pattern on both weekdays and weekends with little variation from day to day. Demographic, lifestyle, PA level and health characteristics of the two clusters were compared. They differed in age, sex, smoking, income and level of PA required at work. The odds of having any chronic health conditions was lower for the active group (adjusted OR50?62, 95% CI 0?46, 0?84) but the two groups did not differ in terms of specific chronic health conditions or obesity. Implications are drawn for targeting exercise promotion programmes at the population level.

  8. Longitudinal Physical Activity Patterns Among Older Adults: A Latent Transition Analysis.

    Mooney, Stephen J; Joshi, Spruha; Cerdá, Magdalena; Kennedy, Gary J; Beard, John R; Rundle, Andrew G

    2018-05-14

    Most epidemiologic studies of physical activity measure either total energy expenditure or engagement in a single activity type, such as walking. These approaches may gloss over important nuances in activity patterns. We performed a latent transition analysis to identify patterns of activity types as well as neighborhood and individual determinants of changes in those activity patterns over two years in a cohort of 2,023 older adult residents of New York City, NY, surveyed between 2011 and 2013. We identified seven latent classes: 1) Mostly Inactive, 2) Walking, 3) Exercise, 4) Household Activities and Walking, 5) Household Activities and Exercise, 6) Gardening and Household Activities, and 7) Gardening, Household Activities, and Exercise. The majority of subjects retained the same activity patterns between waves (54% unchanged between waves 1 and 2, 66% unchanged between waves 2 and 3).Most latent class transitions were between classes distinguished only by one form of activity, and only neighborhood unemployment was consistently associated with changing between activity latent classes. Future latent transition analyses of physical activity would benefit from larger cohorts and longer follow-up periods to assess predictors of and long-term impacts of changes in activity patterns.

  9. Dietary pattern analysis: a comparison between matched vegetarian and omnivorous subjects.

    Clarys, Peter; Deriemaeker, Peter; Huybrechts, Inge; Hebbelinck, Marcel; Mullie, Patrick

    2013-06-13

    Dietary pattern analysis, based on the concept that foods eaten together are as important as a reductive methodology characterized by a single food or nutrient analysis, has emerged as an alternative approach to study the relation between nutrition and disease. The aim of the present study was to compare nutritional intake and the results of dietary pattern analysis in properly matched vegetarian and omnivorous subjects. Vegetarians (n = 69) were recruited via purposeful sampling and matched non-vegetarians (n = 69) with same age, gender, health and lifestyle characteristics were searched for via convenience sampling. Two dietary pattern analysis methods, the Healthy Eating Index-2010 (HEI-2010) and the Mediterranean Diet Score (MDS) were calculated and analysed in function of the nutrient intake. Mean total energy intake was comparable between vegetarians and omnivorous subjects (p > 0.05). Macronutrient analysis revealed significant differences between the mean values for vegetarians and omnivorous subjects (absolute and relative protein and total fat intake were significantly lower in vegetarians, while carbohydrate and fibre intakes were significantly higher in vegetarians than in omnivorous subjects). The HEI and MDS were significantly higher for the vegetarians (HEI = 53.8.1 ± 11.2; MDS = 4.3 ± 1.3) compared to the omnivorous subjects (HEI = 46.4 ± 15.3; MDS = 3.8 ± 1.4). Our results indicate a more nutrient dense pattern, closer to the current dietary recommendations for the vegetarians compared to the omnivorous subjects. Both indexing systems were able to discriminate between the vegetarians and the non-vegetarians with higher scores for the vegetarian subjects.

  10. Hidden Markov model analysis of maternal behavior patterns in inbred and reciprocal hybrid mice.

    Valeria Carola

    Full Text Available Individual variation in maternal care in mammals shows a significant heritable component, with the maternal behavior of daughters resembling that of their mothers. In laboratory mice, genetically distinct inbred strains show stable differences in maternal care during the first postnatal week. Moreover, cross fostering and reciprocal breeding studies demonstrate that differences in maternal care between inbred strains persist in the absence of genetic differences, demonstrating a non-genetic or epigenetic contribution to maternal behavior. In this study we applied a mathematical tool, called hidden Markov model (HMM, to analyze the behavior of female mice in the presence of their young. The frequency of several maternal behaviors in mice has been previously described, including nursing/grooming pups and tending to the nest. However, the ordering, clustering, and transitions between these behaviors have not been systematically described and thus a global description of maternal behavior is lacking. Here we used HMM to describe maternal behavior patterns in two genetically distinct mouse strains, C57BL/6 and BALB/c, and their genetically identical reciprocal hybrid female offspring. HMM analysis is a powerful tool to identify patterns of events that cluster in time and to determine transitions between these clusters, or hidden states. For the HMM analysis we defined seven states: arched-backed nursing, blanket nursing, licking/grooming pups, grooming, activity, eating, and sleeping. By quantifying the frequency, duration, composition, and transition probabilities of these states we were able to describe the pattern of maternal behavior in mouse and identify aspects of these patterns that are under genetic and nongenetic inheritance. Differences in these patterns observed in the experimental groups (inbred and hybrid females were detected only after the application of HMM analysis whereas classical statistical methods and analyses were not able to

  11. Comparison of cluster and principal component analysis techniques to derive dietary patterns in Irish adults.

    Hearty, Aine P; Gibney, Michael J

    2009-02-01

    The aims of the present study were to examine and compare dietary patterns in adults using cluster and factor analyses and to examine the format of the dietary variables on the pattern solutions (i.e. expressed as grams/day (g/d) of each food group or as the percentage contribution to total energy intake). Food intake data were derived from the North/South Ireland Food Consumption Survey 1997-9, which was a randomised cross-sectional study of 7 d recorded food and nutrient intakes of a representative sample of 1379 Irish adults aged 18-64 years. Cluster analysis was performed using the k-means algorithm and principal component analysis (PCA) was used to extract dietary factors. Food data were reduced to thirty-three food groups. For cluster analysis, the most suitable format of the food-group variable was found to be the percentage contribution to energy intake, which produced six clusters: 'Traditional Irish'; 'Continental'; 'Unhealthy foods'; 'Light-meal foods & low-fat milk'; 'Healthy foods'; 'Wholemeal bread & desserts'. For PCA, food groups in the format of g/d were found to be the most suitable format, and this revealed four dietary patterns: 'Unhealthy foods & high alcohol'; 'Traditional Irish'; 'Healthy foods'; 'Sweet convenience foods & low alcohol'. In summary, cluster and PCA identified similar dietary patterns when presented with the same dataset. However, the two dietary pattern methods required a different format of the food-group variable, and the most appropriate format of the input variable should be considered in future studies.

  12. Growth patterns of an intertidal gastropod as revealed by oxygen isotope analysis

    Bean, J. R.; Hill, T. M.; Guerra, C.

    2007-12-01

    The size and morphology of mollusk shells are affected by environmental conditions. As a result, it is difficult to assess growth rate, population age structure, shell morphologies associated with ontogenetic stages, and to compare life history patterns across various environments. Oxygen isotope analysis is a useful tool for estimating minimum ages and growth rates of calcium carbonate secreting organisms. Calcite shell material from members of two northern California populations of the intertidal muricid gastropod Acanthinucella spirata was sampled for isotopic analysis. Individual shells were sampled from apex to margin, thus providing a sequential record of juvenile and adult growth. A. spirata were collected from a sheltered habitat in Tomales Bay and from an exposed reef in Bolinas. Abiotic factors, such as temperature, wave exposure, and substrate consistency, and biotic composition differ significantly between these sites, possibly resulting in local adaptations and variation in life history and growth patterns. Shell morphology of A. spirata changes with age as internal shell margin thickenings of denticle rows associated with external growth bands are irregularly accreted. It is not known when, either seasonally and/or ontogentically, these thickenings and bands form or whether inter or intra-populational variation exists. Preliminary results demonstrate the seasonal oxygen isotopic variability present at the two coastal sites, indicating 5-6 degC changes from winter to summertime temperatures; these data are consistent with local intertidal temperature records. Analysis of the seasonal patterns indicate that: 1) differences in growth rate and seasonal growth patterns at different ontogenetic stages within populations, and 2) differences in growth patterns and possibly age structure between the two A. spirata populations. These findings indicate that isotopic analyses, in addition to field observations and morphological measurements, are necessary to

  13. Comparing rainfall patterns between regions in Peninsular Malaysia via a functional data analysis technique

    Suhaila, Jamaludin; Jemain, Abdul Aziz; Hamdan, Muhammad Fauzee; Wan Zin, Wan Zawiah

    2011-12-01

    SummaryNormally, rainfall data is collected on a daily, monthly or annual basis in the form of discrete observations. The aim of this study is to convert these rainfall values into a smooth curve or function which could be used to represent the continuous rainfall process at each region via a technique known as functional data analysis. Since rainfall data shows a periodic pattern in each region, the Fourier basis is introduced to capture these variations. Eleven basis functions with five harmonics are used to describe the unimodal rainfall pattern for stations in the East while five basis functions which represent two harmonics are needed to describe the rainfall pattern in the West. Based on the fitted smooth curve, the wet and dry periods as well as the maximum and minimum rainfall values could be determined. Different rainfall patterns are observed among the studied regions based on the smooth curve. Using the functional analysis of variance, the test results indicated that there exist significant differences in the functional means between each region. The largest differences in the functional means are found between the East and Northwest regions and these differences may probably be due to the effect of topography and, geographical location and are mostly influenced by the monsoons. Therefore, the same inputs or approaches might not be useful in modeling the hydrological process for different regions.

  14. Comparative analysis of taxonomic, functional, and metabolic patterns of microbiomes from 14 full-scale biogas reactors by metagenomic sequencing and radioisotopic analysis.

    Luo, Gang; Fotidis, Ioannis A; Angelidaki, Irini

    2016-01-01

    Biogas production is a very complex process due to the high complexity in diversity and interactions of the microorganisms mediating it, and only limited and diffuse knowledge exists about the variation of taxonomic and functional patterns of microbiomes across different biogas reactors, and their relationships with the metabolic patterns. The present study used metagenomic sequencing and radioisotopic analysis to assess the taxonomic, functional, and metabolic patterns of microbiomes from 14 full-scale biogas reactors operated under various conditions treating either sludge or manure. The results from metagenomic analysis showed that the dominant methanogenic pathway revealed by radioisotopic analysis was not always correlated with the taxonomic and functional compositions. It was found by radioisotopic experiments that the aceticlastic methanogenic pathway was dominant, while metagenomics analysis showed higher relative abundance of hydrogenotrophic methanogens. Principal coordinates analysis showed the sludge-based samples were clearly distinct from the manure-based samples for both taxonomic and functional patterns, and canonical correspondence analysis showed that the both temperature and free ammonia were crucial environmental variables shaping the taxonomic and functional patterns. The study further the overall patterns of functional genes were strongly correlated with overall patterns of taxonomic composition across different biogas reactors. The discrepancy between the metabolic patterns determined by metagenomic analysis and metabolic pathways determined by radioisotopic analysis was found. Besides, a clear correlation between taxonomic and functional patterns was demonstrated for biogas reactors, and also the environmental factors that shaping both taxonomic and functional genes patterns were identified.

  15. Logarithmic learning for generalized classifier neural network.

    Ozyildirim, Buse Melis; Avci, Mutlu

    2014-12-01

    Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. Neural network classifier of attacks in IP telephony

    Safarik, Jakub; Voznak, Miroslav; Mehic, Miralem; Partila, Pavol; Mikulec, Martin

    2014-05-01

    Various types of monitoring mechanism allow us to detect and monitor behavior of attackers in VoIP networks. Analysis of detected malicious traffic is crucial for further investigation and hardening the network. This analysis is typically based on statistical methods and the article brings a solution based on neural network. The proposed algorithm is used as a classifier of attacks in a distributed monitoring network of independent honeypot probes. Information about attacks on these honeypots is collected on a centralized server and then classified. This classification is based on different mechanisms. One of them is based on the multilayer perceptron neural network. The article describes inner structure of used neural network and also information about implementation of this network. The learning set for this neural network is based on real attack data collected from IP telephony honeypot called Dionaea. We prepare the learning set from real attack data after collecting, cleaning and aggregation of this information. After proper learning is the neural network capable to classify 6 types of most commonly used VoIP attacks. Using neural network classifier brings more accurate attack classification in a distributed system of honeypots. With this approach is possible to detect malicious behavior in a different part of networks, which are logically or geographically divided and use the information from one network to harden security in other networks. Centralized server for distributed set of nodes serves not only as a collector and classifier of attack data, but also as a mechanism for generating a precaution steps against attacks.

  17. An analysis of the physiological FDG uptake pattern in the stomach

    Koga, Hirofumi; Kuwabara, Yasuo; Hiraka, Kiyohisa; Nakagawa, Makoto; Abe, Koichiro; Kaneko, Koichiro; Hayashi, Kazutaka; Honda, Hiroshi; Sasaki, Masayuki

    2003-01-01

    The purpose of this study was to clarify the normal gastric FDG uptake pattern to provide basic information to make an accurate diagnosis of gastric lesions by FDG PET. We examined 22 cases, including 9 of malignant lymphoma, 8 of lung cancer, 2 of esophageal cancer, and 3 of other malignancies. No gastric lesions were observed in any of the 22 cases on upper gastrointestinal examinations using either barium meal or endoscopic techniques. The intervals between FDG PET and the gastrointestinal examination were within one week in all cases. The stomach regions were classified into the following three areas: U (upper)-area, M (middle)-area, and L (lower)-area. The degree of FDG uptake in these three gastric regions was qualitatively evaluated by visual grading into 4 degrees, and then a semiquantitative evaluation was carried out using the standardized uptake value (SUV). Based on a visual grading evaluation, the mean FDG uptake score in the U-, M-, and L-areas was 1.14±0.96, 0.82±0.96, and 0.36±0.49 (mean±S.D.), respectively. The FDG uptake scores obtained in the three areas were significantly different (Friedman test, p M>L. In conclusion, the physiological gastric FDG uptake was significantly higher at the oral end. A stronger gastric FDG uptake at the anal end may therefore be suggestive of a pathological uptake. (author)

  18. Human vigilance investigation analysis of the pattern array test (further data analysis). Final report

    Have, A.C.

    1979-04-01

    This report analyzes a test which was designed to help solve problems of human vigilance encountered in a material safeguard system. The test was designed to determine the efficiency of an operator when processing large amounts of information from a video screen over extended periods of time. In the test eight objects, either circles, squares, or triangles, were set in a 5 x 5 matrix which appeared on a video screen. The eight objects were shown for a specified length of time, the screen blanked out for another specified period, then eight objects in the same 5 x 5 matrix were again shown. The observer was tested on his ability to discern changes in patterns and/or symbols from frame to frame. The testees were able to identify changes in pattern easier than changes in symbols

  19. A Stylistic Analysis of Linguistic Patterns in Chichamanda Ngozi Adichie’s Purple Hibiscus

    Muchamad Sholakhuddin Al Fajri

    2017-06-01

    Full Text Available This study aims to carry out a detailed and systematic stylistic analysis of linguistic patterns in Purple Hibiscus Novel by Chichamanda Ngozi Adichie. It particularly analyses a specific extract of the novel in terms of narration and point of view, conversational analysis, speech and thought presentations and mind style, and how these linguistic devices and patterns are employed by the author to shape characters’ personalities and relationships between them in the reader’s mind. The result appears to suggest that the author successfully represents the protagonist, Kambili as an obedient and a salient daughter who respects deeply his father, while her father, Eugene, is constructed as a strict father and religious who imposes an absolute control on his daughter.

  20. A DATA-MINING BASED METHOD FOR THE GAIT PATTERN ANALYSIS

    Marcelo Rudek

    2015-12-01

    Full Text Available The paper presents a method developed for the gait classification based on the analysis of the trajectory of the pressure centres (CoP extracted from the contact points of the feet with the ground during walking. The data acquirement is performed ba means of a walkway with embedded tactile sensors. The proposed method includes capturing procedures, standardization of data, creation of an organized repository (data warehouse, and development of a process mining. A graphical analysis is applied to looking at the footprint signature patterns. The aim is to obtain a visual interpretation of the grouping by situating it into the normal walking patterns or deviations associated with an individual way of walking. The method consists of data classification automation which divides them into healthy and non-healthy subjects in order to assist in rehabilitation treatments for the people with related mobility problems.