Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths.

PubMed

Liu, Zhicheng; Wang, Yang; Dontcheva, Mira; Hoffman, Matthew; Walker, Seth; Wilson, Alan

2017-01-01

Modern web clickstream data consists of long, high-dimensional sequences of multivariate events, making it difficult to analyze. Following the overarching principle that the visual interface should provide information about the dataset at multiple levels of granularity and allow users to easily navigate across these levels, we identify four levels of granularity in clickstream analysis: patterns, segments, sequences and events. We present an analytic pipeline consisting of three stages: pattern mining, pattern pruning and coordinated exploration between patterns and sequences. Based on this approach, we discuss properties of maximal sequential patterns, propose methods to reduce the number of patterns and describe design considerations for visualizing the extracted sequential patterns and the corresponding raw sequences. We demonstrate the viability of our approach through an analysis scenario and discuss the strengths and limitations of the methods based on user feedback.

Isoform-level gene expression patterns in single-cell RNA-sequencing data.

PubMed

Vu, Trung Nghia; Wills, Quin F; Kalari, Krishna R; Niu, Nifang; Wang, Liewei; Pawitan, Yudi; Rantalainen, Mattias

2018-02-27

RNA sequencing of single cells enables characterization of transcriptional heterogeneity in seemingly homogeneous cell populations. Single-cell sequencing has been applied in a wide range of researches fields. However, few studies have focus on characterization of isoform-level expression patterns at the single-cell level. In this study we propose and apply a novel method, ISOform-Patterns (ISOP), based on mixture modeling, to characterize the expression patterns of isoform pairs from the same gene in single-cell isoform-level expression data. We define six principal patterns of isoform expression relationships and describe a method for differential-pattern analysis. We demonstrate ISOP through analysis of single-cell RNA-sequencing data from a breast cancer cell line, with replication in three independent datasets. We assigned the pattern types to each of 16,562 isoform-pairs from 4,929 genes. Among those, 26% of the discovered patterns were significant (p<0.05), while remaining patterns are possibly effects of transcriptional bursting, drop-out and stochastic biological heterogeneity. Furthermore, 32% of genes discovered through differential-pattern analysis were not detected by differential-expression analysis. The effect of drop-out events, mean expression level, and properties of the expression distribution on the performances of ISOP were also investigated through simulated datasets. To conclude, ISOP provides a novel approach for characterization of isoformlevel preference, commitment and heterogeneity in single-cell RNA-sequencing data. The ISOP method has been implemented as a R package and is available at https://github.com/nghiavtr/ISOP under a GPL-3 license. mattias.rantalainen@ki.se. Supplementary data are available at Bioinformatics online.

Segmentation of touching handwritten Japanese characters using the graph theory method

NASA Astrophysics Data System (ADS)

Suwa, Misako

2000-12-01

Projection analysis methods have been widely used to segment Japanese character strings. However, if adjacent characters have overhanging strokes or a touching point doesn't correspond to the histogram minimum, the methods are prone to result in errors. In contrast, non-projection analysis methods being proposed for use on numerals or alphabet characters cannot be simply applied for Japanese characters because of the differences in the structure of the characters. Based on the oversegmenting strategy, a new pre-segmentation method is presented in this paper: touching patterns are represented as graphs and touching strokes are regarded as the elements of proper edge cutsets. By using the graph theoretical technique, the cutset martrix is calculated. Then, by applying pruning rules, potential touching strokes are determined and the patterns are over segmented. Moreover, this algorithm was confirmed to be valid for touching patterns with overhanging strokes and doubly connected patterns in simulations.

[Methods of a posteriori identification of food patterns in Brazilian children: a systematic review].

PubMed

Carvalho, Carolina Abreu de; Fonsêca, Poliana Cristina de Almeida; Nobre, Luciana Neri; Priore, Silvia Eloiza; Franceschini, Sylvia do Carmo Castro

2016-01-01

The objective of this study is to provide guidance for identifying dietary patterns using the a posteriori approach, and analyze the methodological aspects of the studies conducted in Brazil that identified the dietary patterns of children. Articles were selected from the Latin American and Caribbean Literature on Health Sciences, Scientific Electronic Library Online and Pubmed databases. The key words were: Dietary pattern; Food pattern; Principal Components Analysis; Factor analysis; Cluster analysis; Reduced rank regression. We included studies that identified dietary patterns of children using the a posteriori approach. Seven studies published between 2007 and 2014 were selected, six of which were cross-sectional and one cohort, Five studies used the food frequency questionnaire for dietary assessment; one used a 24-hour dietary recall and the other a food list. The method of exploratory approach used in most publications was principal components factor analysis, followed by cluster analysis. The sample size of the studies ranged from 232 to 4231, the values of the Kaiser-Meyer-Olkin test from 0.524 to 0.873, and Cronbach's alpha from 0.51 to 0.69. Few Brazilian studies identified dietary patterns of children using the a posteriori approach and principal components factor analysis was the technique most used.

A Note on Procrustean Rotation in Exploratory Factor Analysis: A Computer Intensive Approach to Goodness-of-Fit Evaluation.

ERIC Educational Resources Information Center

Raykov, Tenko; Little, Todd D.

1999-01-01

Describes a method for evaluating results of Procrustean rotation to a target factor pattern matrix in exploratory factor analysis. The approach, based on the bootstrap method, yields empirical approximations of the sampling distributions of: (1) differences between target elements and rotated factor pattern matrices; and (2) the overall…

Sub-pattern based multi-manifold discriminant analysis for face recognition

NASA Astrophysics Data System (ADS)

Dai, Jiangyan; Guo, Changlu; Zhou, Wei; Shi, Yanjiao; Cong, Lin; Yi, Yugen

2018-04-01

In this paper, we present a Sub-pattern based Multi-manifold Discriminant Analysis (SpMMDA) algorithm for face recognition. Unlike existing Multi-manifold Discriminant Analysis (MMDA) approach which is based on holistic information of face image for recognition, SpMMDA operates on sub-images partitioned from the original face image and then extracts the discriminative local feature from the sub-images separately. Moreover, the structure information of different sub-images from the same face image is considered in the proposed method with the aim of further improve the recognition performance. Extensive experiments on three standard face databases (Extended YaleB, CMU PIE and AR) demonstrate that the proposed method is effective and outperforms some other sub-pattern based face recognition methods.

Quantitative assessment of soft tissue deformation using digital speckle pattern interferometry: studies on phantom breast models.

PubMed

Karuppanan, Udayakumar; Unni, Sujatha Narayanan; Angarai, Ganesan R

2017-01-01

Assessment of mechanical properties of soft matter is a challenging task in a purely noninvasive and noncontact environment. As tissue mechanical properties play a vital role in determining tissue health status, such noninvasive methods offer great potential in framing large-scale medical screening strategies. The digital speckle pattern interferometry (DSPI)-based image capture and analysis system described here is capable of extracting the deformation information from a single acquired fringe pattern. Such a method of analysis would be required in the case of the highly dynamic nature of speckle patterns derived from soft tissues while applying mechanical compression. Soft phantoms mimicking breast tissue optical and mechanical properties were fabricated and tested in the DSPI out of plane configuration set up. Hilbert transform (HT)-based image analysis algorithm was developed to extract the phase and corresponding deformation of the sample from a single acquired fringe pattern. The experimental fringe contours were found to correlate with numerically simulated deformation patterns of the sample using Abaqus finite element analysis software. The extracted deformation from the experimental fringe pattern using the HT-based algorithm is compared with the deformation value obtained using numerical simulation under similar conditions of loading and the results are found to correlate with an average %error of 10. The proposed method is applied on breast phantoms fabricated with included subsurface anomaly mimicking cancerous tissue and the results are analyzed.

A Comparison of Component and Factor Patterns: A Monte Carlo Approach.

ERIC Educational Resources Information Center

Velicer, Wayne F.; And Others

1982-01-01

Factor analysis, image analysis, and principal component analysis are compared with respect to the factor patterns they would produce under various conditions. The general conclusion that is reached is that the three methods produce results that are equivalent. (Author/JKS)

Spectroscopic vector analysis for fast pattern quality monitoring

NASA Astrophysics Data System (ADS)

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.

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.

PubMed

Formisano, Elia; De Martino, Federico; Valente, Giancarlo

2008-09-01

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

Geostatistics and spatial analysis in biological anthropology.

PubMed

Relethford, John H

2008-05-01

A variety of methods have been used to make evolutionary inferences based on the spatial distribution of biological data, including reconstructing population history and detection of the geographic pattern of natural selection. This article provides an examination of geostatistical analysis, a method used widely in geology but which has not often been applied in biological anthropology. Geostatistical analysis begins with the examination of a variogram, a plot showing the relationship between a biological distance measure and the geographic distance between data points and which provides information on the extent and pattern of spatial correlation. The results of variogram analysis are used for interpolating values of unknown data points in order to construct a contour map, a process known as kriging. The methods of geostatistical analysis and discussion of potential problems are applied to a large data set of anthropometric measures for 197 populations in Ireland. The geostatistical analysis reveals two major sources of spatial variation. One pattern, seen for overall body and craniofacial size, shows an east-west cline most likely reflecting the combined effects of past population dispersal and settlement. The second pattern is seen for craniofacial height and shows an isolation by distance pattern reflecting rapid spatial changes in the midlands region of Ireland, perhaps attributable to the genetic impact of the Vikings. The correspondence of these results with other analyses of these data and the additional insights generated from variogram analysis and kriging illustrate the potential utility of geostatistical analysis in biological anthropology. (c) 2008 Wiley-Liss, Inc.

Statistical analysis on the signals monitoring multiphase flow patterns in pipeline-riser system

NASA Astrophysics Data System (ADS)

Ye, Jing; Guo, Liejin

2013-07-01

The signals monitoring petroleum transmission pipeline in offshore oil industry usually contain abundant information about the multiphase flow on flow assurance which includes the avoidance of most undesirable flow pattern. Therefore, extracting reliable features form these signals to analyze is an alternative way to examine the potential risks to oil platform. This paper is focused on characterizing multiphase flow patterns in pipeline-riser system that is often appeared in offshore oil industry and finding an objective criterion to describe the transition of flow patterns. Statistical analysis on pressure signal at the riser top is proposed, instead of normal prediction method based on inlet and outlet flow conditions which could not be easily determined during most situations. Besides, machine learning method (least square supported vector machine) is also performed to classify automatically the different flow patterns. The experiment results from a small-scale loop show that the proposed method is effective for analyzing the multiphase flow pattern.

The phase interrogation method for optical fiber sensor by analyzing the fork interference pattern

NASA Astrophysics Data System (ADS)

Lv, Riqing; Qiu, Liqiang; Hu, Haifeng; Meng, Lu; Zhang, Yong

2018-02-01

The phase interrogation method for optical fiber sensor is proposed based on the fork interference pattern between the orbital angular momentum beam and plane wave. The variation of interference pattern with phase difference between the two light beams is investigated to realize the phase interrogation. By employing principal component analysis method, the features of the interference pattern can be extracted. Moreover, the experimental system is designed to verify the theoretical analysis, as well as feasibility of phase interrogation. In this work, the Mach-Zehnder interferometer was employed to convert the strain applied on sensing fiber to the phase difference between the reference and measuring paths. This interrogation method is also applicable for the measurements of other physical parameters, which can produce the phase delay in optical fiber. The performance of the system can be further improved by employing highlysensitive materials and fiber structures.

Edge detection and localization with edge pattern analysis and inflection characterization

NASA Astrophysics Data System (ADS)

Jiang, Bo

2012-05-01

In general edges are considered to be abrupt changes or discontinuities in two dimensional image signal intensity distributions. The accuracy of front-end edge detection methods in image processing impacts the eventual success of higher level pattern analysis downstream. To generalize edge detectors designed from a simple ideal step function model to real distortions in natural images, research on one dimensional edge pattern analysis to improve the accuracy of edge detection and localization proposes an edge detection algorithm, which is composed by three basic edge patterns, such as ramp, impulse, and step. After mathematical analysis, general rules for edge representation based upon the classification of edge types into three categories-ramp, impulse, and step (RIS) are developed to reduce detection and localization errors, especially reducing "double edge" effect that is one important drawback to the derivative method. But, when applying one dimensional edge pattern in two dimensional image processing, a new issue is naturally raised that the edge detector should correct marking inflections or junctions of edges. Research on human visual perception of objects and information theory pointed out that a pattern lexicon of "inflection micro-patterns" has larger information than a straight line. Also, research on scene perception gave an idea that contours have larger information are more important factor to determine the success of scene categorization. Therefore, inflections or junctions are extremely useful features, whose accurate description and reconstruction are significant in solving correspondence problems in computer vision. Therefore, aside from adoption of edge pattern analysis, inflection or junction characterization is also utilized to extend traditional derivative edge detection algorithm. Experiments were conducted to test my propositions about edge detection and localization accuracy improvements. The results support the idea that these edge detection method improvements are effective in enhancing the accuracy of edge detection and localization.

Beyond mind-reading: multi-voxel pattern analysis of fMRI data.

PubMed

Norman, Kenneth A; Polyn, Sean M; Detre, Greg J; Haxby, James V

2006-09-01

A key challenge for cognitive neuroscience is determining how mental representations map onto patterns of neural activity. Recently, researchers have started to address this question by applying sophisticated pattern-classification algorithms to distributed (multi-voxel) patterns of functional MRI data, with the goal of decoding the information that is represented in the subject's brain at a particular point in time. This multi-voxel pattern analysis (MVPA) approach has led to several impressive feats of mind reading. More importantly, MVPA methods constitute a useful new tool for advancing our understanding of neural information processing. We review how researchers are using MVPA methods to characterize neural coding and information processing in domains ranging from visual perception to memory search.

Development of test methods for scale model simulation of aerial applications in the NASA Langley Vortex Research Facility. [agricultural aircraft

NASA Technical Reports Server (NTRS)

Jordan, F. L., Jr.

1980-01-01

As part of basic research to improve aerial applications technology, methods were developed at the Langley Vortex Research Facility to simulate and measure deposition patterns of aerially-applied sprays and granular materials by means of tests with small-scale models of agricultural aircraft and dynamically-scaled test particles. Interactions between the aircraft wake and the dispersed particles are being studied with the objective of modifying wake characteristics and dispersal techniques to increase swath width, improve deposition pattern uniformity, and minimize drift. The particle scaling analysis, test methods for particle dispersal from the model aircraft, visualization of particle trajectories, and measurement and computer analysis of test deposition patterns are described. An experimental validation of the scaling analysis and test results that indicate improved control of chemical drift by use of winglets are presented to demonstrate test methods.

Investigation of Portevin-Le Chatelier band with temporal phase analysis of speckle interferometry

NASA Astrophysics Data System (ADS)

Jiang, Zhenyu; Zhang, Qingchuan; Wu, Xiaoping

2003-04-01

A new method combining temporal phase analysis with dynamic digital speckle pattern interferometry is proposed to study Portevin-Le Chatelier effect quantitatively. The principle bases on that the phase difference of interference speckle patterns is a time-dependent function related to the object deformation. The interference speckle patterns of specimen are recorded with high sampling rate while PLC effect occurs, and the 2D displacement map of PLC band and its width are obtained by analyzing the displacement of specimen with proposed method.

Fan fault diagnosis based on symmetrized dot pattern analysis and image matching

NASA Astrophysics Data System (ADS)

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.

Automating pattern quantification: new tools for analysing anisotropy and inhomogeneity of 2d patterns

NASA Astrophysics Data System (ADS)

Gerik, A.; Kruhl, J. H.

2006-12-01

The quantitative analysis of patterns as a geometric arrangement of material domains with specific geometric or crystallographic properties such as shape, size or crystallographic orientation has been shown to be a valuable tool with a wide field of applications in geo- and material sciences. Pattern quantification allows an unbiased comparison of experimentally generated or theoretical patterns with patterns of natural origin. In addition to this, the application of different methods can also provide information about different pattern forming processes. This information includes the distribution of crystals in a matrix - to analyze i.e. the nature and orientation of flow within a melt - or the governing shear strain regime at the point of time the pattern was formed as well as nature of fracture patterns of different scales, all of which are of great interest not only in structural and engineering geology, but also in material sciences. Different approaches to this problem have been discussed over the past fifteen years, yet only few of the methods were applied successfully at least to single examples (i.e. Velde et al., 1990; Harris et al., 1991; Peternell et al., 2003; Volland &Kruhl, 2004). One of the reasons for this has been the high expenditure of time that was necessary to prepare and analyse the samples. To overcome this problem, a first selection of promising methods have been implemented into a growing collection of software tools: (1) The modifications that Harris et al. (1991) have suggested for the Cantor's dust method (Velde et al., 1990) and which have been applied by Volland &Kruhl (2004) to show the anisotropy in a breccia sample. (2) A map-counting method that uses local box-counting dimensions to map the inhomogeneity of a crystal distribution pattern. Peternell et al. (2003) have used this method to analyze the distribution of phenocrysts in a porphyric granite. (3) A modified perimeter method that relates the directional dependence of the perimeter of grain boundaries to the anisotropy of the pattern (Peternell et al., 2003). We have used the resulting new possibilities to analyze numerous patterns of natural, experimental and mathematical origin in order to determine the scope of applicability of the different methods and present these results along with an evaluation of their individual sensitivities and limitations. References: Harris, C., Franssen, R. &Loosveld, R. (1991): Fractal analysis of fractures in rocks: the Cantor's Dust method comment. Tectonophysics 198: 107-111. Peternell, M., Andries, F. &Kruhl, J.H. (2003): Magmatic flow-pattern anisotropies - analyzed on the basis of a new 'map-mounting' fractal geometry method. DRT Tectonics conference, St. Malo, Book of Abstracts. Velde, B., Dubois, J., Touchard, G. &Badri, A. (1990): Fractal analysis of fractures in rocks: the Cantor's Dust method. Tectonophysics (179): 345-352. Volland, S. &Kruhl, J.H. (2004): Anisotropy quantification: the application of fractal geometry methods on tectonic fracture patterns of a Hercynian fault zone in NW-Sardinia. Journal of Structural Geology 26: 1499- 1510.

Dynamic mapping of the structures of content in clinical settings (DMSC). A new coding system for analysing the patient's narratives.

PubMed

Salvatore, Sergio; Gennaro, Alessandro; Auletta, Andrea; Grassi, Rossano; Rocco, Diego

2012-12-01

The paper presents a method of content analysis framed within a semiotic and contextual model of the psychotherapy process as a situated dynamics of sensemaking: the Dynamic Mapping of the Structures of Content in Clinical Settings (DMSC). DMSC is a system of content analysis focused on a generalized level of meaning, concerning basic aspects of the patient's narrative (e.g., if the narrative concerns herself or other than herself). The paper presents the result of the application of DMSC to an intensive single-case analysis (Katja). The method has been applied by judges to the transcripts of sessions and is aimed at identifying patterns of combinations (defined: Patterns of content) of the categories characterizing the patient's narratives (pattern analysis approach) as well as at mapping the transition among these patterns (sequential analysis approach). These results provide evidence of its construct validity. In accordance with the theoretical model grounding the method, we have found that: (a) DMSC provides a meaningful representation of the patient's narratives in terms of Patterns of content; (b) the probability of transition among the Patterns of content have proved to be significantly associated with the clinical quality of the sessions. The DMSC has to be considered an attempt paving the way for further investigations aimed at developing a deeper understanding of the role played by the dynamics of sensemaking in the psychotherapy process. ©2011 The British Psychological Society.

REQUIREMENTS PATTERNS FOR FORMAL CONTRACTS IN ARCHITECTURAL ANALYSIS AND DESIGN LANGUAGE (AADL) MODELS

DTIC Science & Technology

2017-04-17

Cyberphysical Systems, Formal Methods , Requirements Patterns, AADL, Assume Guarantee Reasoning Environment 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF...5 3. Methods , Assumptions, and Procedures...Rockwell Collins has been addressing these challenges by developing compositional reasoning methods that permit the verification of systems that exceed

Quantitative assessment of soft tissue deformation using digital speckle pattern interferometry: studies on phantom breast models

PubMed Central

Karuppanan, Udayakumar; Unni, Sujatha Narayanan; Angarai, Ganesan R.

2017-01-01

Abstract. Assessment of mechanical properties of soft matter is a challenging task in a purely noninvasive and noncontact environment. As tissue mechanical properties play a vital role in determining tissue health status, such noninvasive methods offer great potential in framing large-scale medical screening strategies. The digital speckle pattern interferometry (DSPI)–based image capture and analysis system described here is capable of extracting the deformation information from a single acquired fringe pattern. Such a method of analysis would be required in the case of the highly dynamic nature of speckle patterns derived from soft tissues while applying mechanical compression. Soft phantoms mimicking breast tissue optical and mechanical properties were fabricated and tested in the DSPI out of plane configuration set up. Hilbert transform (HT)-based image analysis algorithm was developed to extract the phase and corresponding deformation of the sample from a single acquired fringe pattern. The experimental fringe contours were found to correlate with numerically simulated deformation patterns of the sample using Abaqus finite element analysis software. The extracted deformation from the experimental fringe pattern using the HT-based algorithm is compared with the deformation value obtained using numerical simulation under similar conditions of loading and the results are found to correlate with an average %error of 10. The proposed method is applied on breast phantoms fabricated with included subsurface anomaly mimicking cancerous tissue and the results are analyzed. PMID:28180134

Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data.

PubMed

Grootswagers, Tijl; Wardle, Susan G; Carlson, Thomas A

2017-04-01

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain-computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to "decode" different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.

A Quantitative Method for the Analysis of Nomothetic Relationships between Idiographic Structures: Dynamic Patterns Create Attractor States for Sustained Posttreatment Change

ERIC Educational Resources Information Center

Fisher, Aaron J.; Newman, Michelle G.; Molenaar, Peter C. M.

2011-01-01

Objective: The present article aimed to demonstrate that the establishment of dynamic patterns during the course of psychotherapy can create attractor states for continued adaptive change following the conclusion of treatment. Method: This study is a secondary analysis of T. D. Borkovec and E. Costello (1993). Of the 55 participants in the…

Application of syntactic methods of pattern recognition for data mining and knowledge discovery in medicine

NASA Astrophysics Data System (ADS)

Ogiela, Marek R.; Tadeusiewicz, Ryszard

2000-04-01

This paper presents and discusses possibilities of application of selected algorithms belonging to the group of syntactic methods of patten recognition used to analyze and extract features of shapes and to diagnose morphological lesions seen on selected medical images. This method is particularly useful for specialist morphological analysis of shapes of selected organs of abdominal cavity conducted to diagnose disease symptoms occurring in the main pancreatic ducts, upper segments of ureters and renal pelvis. Analysis of the correct morphology of these organs is possible with the application of the sequential and tree method belonging to the group of syntactic methods of pattern recognition. The objective of this analysis is to support early diagnosis of disease lesions, mainly characteristic for carcinoma and pancreatitis, based on examinations of ERCP images and a diagnosis of morphological lesions in ureters as well as renal pelvis based on an analysis of urograms. In the analysis of ERCP images the main objective is to recognize morphological lesions in pancreas ducts characteristic for carcinoma and chronic pancreatitis, while in the case of kidney radiogram analysis the aim is to diagnose local irregularities of ureter lumen and to examine the morphology of renal pelvis and renal calyxes. Diagnosing the above mentioned lesion has been conducted with the use of syntactic methods of pattern recognition, in particular the languages of description of features of shapes and context-free sequential attributed grammars. These methods allow to recognize and describe in a very efficient way the aforementioned lesions on images obtained as a result of initial image processing of width diagrams of the examined structures. Additionally, in order to support the analysis of the correct structure of renal pelvis a method using the tree grammar for syntactic pattern recognition to define its correct morphological shapes has been presented.

Two dimensional Fourier transform methods for fringe pattern analysis

NASA Astrophysics Data System (ADS)

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.

An open-access CMIP5 pattern library for temperature and precipitation: Description and methodology

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

Lynch, Cary D.; Hartin, Corinne A.; Bond-Lamberty, Benjamin

Pattern scaling is used to efficiently emulate general circulation models and explore uncertainty in climate projections under multiple forcing scenarios. Pattern scaling methods assume that local climate changes scale with a global mean temperature increase, allowing for spatial patterns to be generated for multiple models for any future emission scenario. For uncertainty quantification and probabilistic statistical analysis, a library of patterns with descriptive statistics for each file would be beneficial, but such a library does not presently exist. Of the possible techniques used to generate patterns, the two most prominent are the delta and least squared regression methods. We exploremore » the differences and statistical significance between patterns generated by each method and assess performance of the generated patterns across methods and scenarios. Differences in patterns across seasons between methods and epochs were largest in high latitudes (60-90°N/S). Bias and mean errors between modeled and pattern predicted output from the linear regression method were smaller than patterns generated by the delta method. Across scenarios, differences in the linear regression method patterns were more statistically significant, especially at high latitudes. We found that pattern generation methodologies were able to approximate the forced signal of change to within ≤ 0.5°C, but choice of pattern generation methodology for pattern scaling purposes should be informed by user goals and criteria. As a result, this paper describes our library of least squared regression patterns from all CMIP5 models for temperature and precipitation on an annual and sub-annual basis, along with the code used to generate these patterns.« less

An open-access CMIP5 pattern library for temperature and precipitation: Description and methodology

DOE PAGES

Lynch, Cary D.; Hartin, Corinne A.; Bond-Lamberty, Benjamin; ...

2017-05-15

Pattern scaling is used to efficiently emulate general circulation models and explore uncertainty in climate projections under multiple forcing scenarios. Pattern scaling methods assume that local climate changes scale with a global mean temperature increase, allowing for spatial patterns to be generated for multiple models for any future emission scenario. For uncertainty quantification and probabilistic statistical analysis, a library of patterns with descriptive statistics for each file would be beneficial, but such a library does not presently exist. Of the possible techniques used to generate patterns, the two most prominent are the delta and least squared regression methods. We exploremore » the differences and statistical significance between patterns generated by each method and assess performance of the generated patterns across methods and scenarios. Differences in patterns across seasons between methods and epochs were largest in high latitudes (60-90°N/S). Bias and mean errors between modeled and pattern predicted output from the linear regression method were smaller than patterns generated by the delta method. Across scenarios, differences in the linear regression method patterns were more statistically significant, especially at high latitudes. We found that pattern generation methodologies were able to approximate the forced signal of change to within ≤ 0.5°C, but choice of pattern generation methodology for pattern scaling purposes should be informed by user goals and criteria. As a result, this paper describes our library of least squared regression patterns from all CMIP5 models for temperature and precipitation on an annual and sub-annual basis, along with the code used to generate these patterns.« less

Pattern Activity Clustering and Evaluation (PACE)

NASA Astrophysics Data System (ADS)

Blasch, Erik; Banas, Christopher; Paul, Michael; Bussjager, Becky; Seetharaman, Guna

2012-06-01

With the vast amount of network information available on activities of people (i.e. motions, transportation routes, and site visits) there is a need to explore the salient properties of data that detect and discriminate the behavior of individuals. Recent machine learning approaches include methods of data mining, statistical analysis, clustering, and estimation that support activity-based intelligence. We seek to explore contemporary methods in activity analysis using machine learning techniques that discover and characterize behaviors that enable grouping, anomaly detection, and adversarial intent prediction. To evaluate these methods, we describe the mathematics and potential information theory metrics to characterize behavior. A scenario is presented to demonstrate the concept and metrics that could be useful for layered sensing behavior pattern learning and analysis. We leverage work on group tracking, learning and clustering approaches; as well as utilize information theoretical metrics for classification, behavioral and event pattern recognition, and activity and entity analysis. The performance evaluation of activity analysis supports high-level information fusion of user alerts, data queries and sensor management for data extraction, relations discovery, and situation analysis of existing data.

Atherosclerotic plaque characterization by spatial and temporal speckle pattern analysis

NASA Astrophysics Data System (ADS)

Tearney, Guillermo J.; Bouma, Brett E.

2002-04-01

Improved methods are needed to identify the vulnerable coronary plaques responsible for acute myocardial infraction or sudden cardiac death. We describe a method for characterizing the structure and biomechanical properties of atherosclerotic plaques based on speckle pattern fluctuations. Near-field speckle images were acquired from five human aortic specimens ex vivo. The speckle decorrelation time constant varied significantly for vulnerable aortic plaques (τ = 40 ms) versus stable plaques (τ = 400 ms) and normal aorta (τ = 500 ms). These initial results indicate that different atherosclerotic plaque types may be distinguished by analysis of temporal and spatial speckle pattern fluctuations.

Classification and Sequential Pattern Analysis for Improving Managerial Efficiency and Providing Better Medical Service in Public Healthcare Centers

PubMed Central

Chung, Sukhoon; Rhee, Hyunsill; Suh, Yongmoo

2010-01-01

Objectives This study sought to find answers to the following questions: 1) Can we predict whether a patient will revisit a healthcare center? 2) Can we anticipate diseases of patients who revisit the center? Methods For the first question, we applied 5 classification algorithms (decision tree, artificial neural network, logistic regression, Bayesian networks, and Naïve Bayes) and the stacking-bagging method for building classification models. To solve the second question, we performed sequential pattern analysis. Results We determined: 1) In general, the most influential variables which impact whether a patient of a public healthcare center will revisit it or not are personal burden, insurance bill, period of prescription, age, systolic pressure, name of disease, and postal code. 2) The best plain classification model is dependent on the dataset. 3) Based on average of classification accuracy, the proposed stacking-bagging method outperformed all traditional classification models and our sequential pattern analysis revealed 16 sequential patterns. Conclusions Classification models and sequential patterns can help public healthcare centers plan and implement healthcare service programs and businesses that are more appropriate to local residents, encouraging them to revisit public health centers. PMID:21818426

Using Two Different Approaches to Assess Dietary Patterns: Hypothesis-Driven and Data-Driven Analysis.

PubMed

Previdelli, Ágatha Nogueira; de Andrade, Samantha Caesar; Fisberg, Regina Mara; Marchioni, Dirce Maria

2016-09-23

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.

Measuring patterns in team interaction sequences using a discrete recurrence approach.

PubMed

Gorman, Jamie C; Cooke, Nancy J; Amazeen, Polemnia G; Fouse, Shannon

2012-08-01

Recurrence-based measures of communication determinism and pattern information are described and validated using previously collected team interaction data. Team coordination dynamics has revealed that"mixing" team membership can lead to flexible interaction processes, but keeping a team "intact" can lead to rigid interaction processes. We hypothesized that communication of intact teams would have greater determinism and higher pattern information compared to that of mixed teams. Determinism and pattern information were measured from three-person Uninhabited Air Vehicle team communication sequences over a series of 40-minute missions. Because team members communicated using push-to-talk buttons, communication sequences were automatically generated during each mission. The Composition x Mission determinism effect was significant. Intact teams' determinism increased over missions, whereas mixed teams' determinism did not change. Intact teams had significantly higher maximum pattern information than mixed teams. Results from these new communication analysis methods converge with content-based methods and support our hypotheses. Because they are not content based, and because they are automatic and fast, these new methods may be amenable to real-time communication pattern analysis.

[Analysis of the swimming pattern and the velocity of bacteria using video tracking method].

PubMed

Shigematsu, M

1997-04-01

The swimming patterns and the velocities of several flagellated bacteria were measured by a computer assisted video tracking method. The moving path of the individual bacterium revealed that the bacterium frequently changed its swimming direction and velocity. The velocity among bacterial strains varies widely. In low viscous environment. Campylobacter jejuni has characteristic swimming pattern with frequent changes in their swimming direction. As the viscosity increase, C. jejuni increases its velocity at a little higher viscosity of 3 centipoise (cP) and secondly increases at about 40 cP. Different from other flagellated bacteria, the swimming pattern of C. jejuni in these two velocity peaks were changed. C. jejuni exhibited continuously forward moving path in the first peak, but in the second it repeated back and forth swimming pattern. We thus assumed that C. jejuni may use a different swimming mode in high viscous media from the original mode mediated by the propelling force of the flagella. This method is useful for a detail analysis of bacterial movement and moving patterns in different environmental conditions.

Analysis of ELA-DQB exon 2 polymorphism in Argentine Creole horses by PCR-RFLP and PCR-SSCP.

PubMed

Villegas-Castagnasso, E E; Díaz, S; Giovambattista, G; Dulout, F N; Peral-García, P

2003-08-01

The second exon of equine leucocyte antigen (ELA)-DQB genes was amplified from genomic DNA of 32 Argentine Creole horses by PCR. Amplified DNA was analysed by PCR-restriction fragment length polymorphism (RFLP) and PCR-single-strand conformation polymorphism (SSCP). The PCR-RFLP analysis revealed two HaeIII patterns, four RsaI patterns, five MspI patterns and two HinfI patterns. EcoRI showed no variation in the analysed sample. Additional patterns that did not account for known exon 2 DNA sequences were observed, suggesting the existence of novel ELA-DQB alleles. PCR-SSCP analysis exhibited seven different band patterns, and the number of bands per animal ranged from four to nine. Both methods indicated that at least two DQB genes are present. The presence of more than two alleles in each animal showed that the primers employed in this work are not specific for a unique DQB locus. The improvement of this PCR-RFLP method should provide a simple and rapid technique for an accurate definition of ELA-DQB typing in horses.

An image-processing methodology for extracting bloodstain pattern features.

PubMed

Arthur, Ravishka M; Humburg, Philomena J; Hoogenboom, Jerry; Baiker, Martin; Taylor, Michael C; de Bruin, Karla G

2017-08-01

There is a growing trend in forensic science to develop methods to make forensic pattern comparison tasks more objective. This has generally involved the application of suitable image-processing methods to provide numerical data for identification or comparison. This paper outlines a unique image-processing methodology that can be utilised by analysts to generate reliable pattern data that will assist them in forming objective conclusions about a pattern. A range of features were defined and extracted from a laboratory-generated impact spatter pattern. These features were based in part on bloodstain properties commonly used in the analysis of spatter bloodstain patterns. The values of these features were consistent with properties reported qualitatively for such patterns. The image-processing method developed shows considerable promise as a way to establish measurable discriminating pattern criteria that are lacking in current bloodstain pattern taxonomies. Copyright © 2017 Elsevier B.V. All rights reserved.

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

PubMed

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.

An open-access CMIP5 pattern library for temperature and precipitation: description and methodology

NASA Astrophysics Data System (ADS)

Lynch, Cary; Hartin, Corinne; Bond-Lamberty, Ben; Kravitz, Ben

2017-05-01

Pattern scaling is used to efficiently emulate general circulation models and explore uncertainty in climate projections under multiple forcing scenarios. Pattern scaling methods assume that local climate changes scale with a global mean temperature increase, allowing for spatial patterns to be generated for multiple models for any future emission scenario. For uncertainty quantification and probabilistic statistical analysis, a library of patterns with descriptive statistics for each file would be beneficial, but such a library does not presently exist. Of the possible techniques used to generate patterns, the two most prominent are the delta and least squares regression methods. We explore the differences and statistical significance between patterns generated by each method and assess performance of the generated patterns across methods and scenarios. Differences in patterns across seasons between methods and epochs were largest in high latitudes (60-90° N/S). Bias and mean errors between modeled and pattern-predicted output from the linear regression method were smaller than patterns generated by the delta method. Across scenarios, differences in the linear regression method patterns were more statistically significant, especially at high latitudes. We found that pattern generation methodologies were able to approximate the forced signal of change to within ≤ 0.5 °C, but the choice of pattern generation methodology for pattern scaling purposes should be informed by user goals and criteria. This paper describes our library of least squares regression patterns from all CMIP5 models for temperature and precipitation on an annual and sub-annual basis, along with the code used to generate these patterns. The dataset and netCDF data generation code are available at doi:10.5281/zenodo.495632.

Method to mosaic gratings that relies on analysis of far-field intensity patterns in two wavelengths

NASA Astrophysics Data System (ADS)

Hu, Yao; Zeng, Lijiang; Li, Lifeng

2007-01-01

We propose an experimental method to coherently mosaic two planar diffraction gratings. The method uses a Twyman-Green interferometer to guarantee the planar parallelism of the two sub-aperture gratings, and obtains the in-plane rotational error and the two translational errors from analysis of the far-field diffraction intensity patterns in two alignment wavelengths. We adjust the relative attitude and position of the two sub-aperture gratings to produce Airy disk diffraction patterns in both wavelengths. In our experiment, the repeatability of in-plane rotation adjustment was 2.35 μrad and that of longitudinal adjustment was 0.11 μm. The accuracy of lateral adjustment was about 2.9% of the grating period.

How bootstrap can help in forecasting time series with more than one seasonal pattern

NASA Astrophysics Data System (ADS)

Cordeiro, Clara; Neves, M. Manuela

2012-09-01

The search for the future is an appealing challenge in time series analysis. The diversity of forecasting methodologies is inevitable and is still in expansion. Exponential smoothing methods are the launch platform for modelling and forecasting in time series analysis. Recently this methodology has been combined with bootstrapping revealing a good performance. The algorithm (Boot. EXPOS) using exponential smoothing and bootstrap methodologies, has showed promising results for forecasting time series with one seasonal pattern. In case of more than one seasonal pattern, the double seasonal Holt-Winters methods and the exponential smoothing methods were developed. A new challenge was now to combine these seasonal methods with bootstrap and carry over a similar resampling scheme used in Boot. EXPOS procedure. The performance of such partnership will be illustrated for some well-know data sets existing in software.

Dietary patterns and socioeconomic position.

PubMed

Mullie, P; Clarys, P; Hulens, M; Vansant, G

2010-03-01

To test a socioeconomic hypothesis on three dietary patterns and to describe the relation between three commonly used methods to determine dietary patterns, namely Healthy Eating Index, Mediterranean Diet Score and principal component analysis. Cross-sectional design in 1852 military men. Using mailed questionnaires, the food consumption frequency was recorded. The correlation coefficients between the three dietary patterns varied between 0.43 and 0.62. The highest correlation was found between Healthy Eating Index and Healthy Dietary Pattern (principal components analysis). Cohen's kappa coefficient of agreement varied between 0.10 and 0.20. After age-adjustment, education and income remained associated with the most healthy dietary pattern. Even when both socioeconomic indicators were used together in one model, higher income and education were associated with higher scores for Healthy Eating Index, Mediterranean Diet Score and Healthy Dietary Pattern. The least healthy quintiles of dietary pattern as measured by the three methods were associated with a clustering of unhealthy behaviors, that is, smoking, low physical activity, highest intake of total fat and saturated fatty acids, and low intakes of fruits and vegetables. The three dietary patterns used indicated that the most healthy patterns were associated with a higher socioeconomic position, while lower patterns were associated with several unhealthy behaviors.

An enhanced cluster analysis program with bootstrap significance testing for ecological community analysis

USGS Publications Warehouse

McKenna, J.E.

2003-01-01

The biosphere is filled with complex living patterns and important questions about biodiversity and community and ecosystem ecology are concerned with structure and function of multispecies systems that are responsible for those patterns. Cluster analysis identifies discrete groups within multivariate data and is an effective method of coping with these complexities, but often suffers from subjective identification of groups. The bootstrap testing method greatly improves objective significance determination for cluster analysis. The BOOTCLUS program makes cluster analysis that reliably identifies real patterns within a data set more accessible and easier to use than previously available programs. A variety of analysis options and rapid re-analysis provide a means to quickly evaluate several aspects of a data set. Interpretation is influenced by sampling design and a priori designation of samples into replicate groups, and ultimately relies on the researcher's knowledge of the organisms and their environment. However, the BOOTCLUS program provides reliable, objectively determined groupings of multivariate data.

Observer-Pattern Modeling and Slow-Scale Bifurcation Analysis of Two-Stage Boost Inverters

NASA Astrophysics Data System (ADS)

Zhang, Hao; Wan, Xiaojin; Li, Weijie; Ding, Honghui; Yi, Chuanzhi

2017-06-01

This paper deals with modeling and bifurcation analysis of two-stage Boost inverters. Since the effect of the nonlinear interactions between source-stage converter and load-stage inverter causes the “hidden” second-harmonic current at the input of the downstream H-bridge inverter, an observer-pattern modeling method is proposed by removing time variance originating from both fundamental frequency and hidden second harmonics in the derived averaged equations. Based on the proposed observer-pattern model, the underlying mechanism of slow-scale instability behavior is uncovered with the help of eigenvalue analysis method. Then eigenvalue sensitivity analysis is used to select some key system parameters of two-stage Boost inverter, and some behavior boundaries are given to provide some design-oriented information for optimizing the circuit. Finally, these theoretical results are verified by numerical simulations and circuit experiment.

Relating interesting quantitative time series patterns with text events and text features

NASA Astrophysics Data System (ADS)

Wanner, Franz; Schreck, Tobias; Jentner, Wolfgang; Sharalieva, Lyubka; Keim, Daniel A.

2013-12-01

In many application areas, the key to successful data analysis is the integrated analysis of heterogeneous data. One example is the financial domain, where time-dependent and highly frequent quantitative data (e.g., trading volume and price information) and textual data (e.g., economic and political news reports) need to be considered jointly. Data analysis tools need to support an integrated analysis, which allows studying the relationships between textual news documents and quantitative properties of the stock market price series. In this paper, we describe a workflow and tool that allows a flexible formation of hypotheses about text features and their combinations, which reflect quantitative phenomena observed in stock data. To support such an analysis, we combine the analysis steps of frequent quantitative and text-oriented data using an existing a-priori method. First, based on heuristics we extract interesting intervals and patterns in large time series data. The visual analysis supports the analyst in exploring parameter combinations and their results. The identified time series patterns are then input for the second analysis step, in which all identified intervals of interest are analyzed for frequent patterns co-occurring with financial news. An a-priori method supports the discovery of such sequential temporal patterns. Then, various text features like the degree of sentence nesting, noun phrase complexity, the vocabulary richness, etc. are extracted from the news to obtain meta patterns. Meta patterns are defined by a specific combination of text features which significantly differ from the text features of the remaining news data. Our approach combines a portfolio of visualization and analysis techniques, including time-, cluster- and sequence visualization and analysis functionality. We provide two case studies, showing the effectiveness of our combined quantitative and textual analysis work flow. The workflow can also be generalized to other application domains such as data analysis of smart grids, cyber physical systems or the security of critical infrastructure, where the data consists of a combination of quantitative and textual time series data.

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

PubMed

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.

Lindemann histograms as a new method to analyse nano-patterns and phases

NASA Astrophysics Data System (ADS)

Makey, Ghaith; Ilday, Serim; Tokel, Onur; Ibrahim, Muhamet; Yavuz, Ozgun; Pavlov, Ihor; Gulseren, Oguz; Ilday, Omer

The detection, observation, and analysis of material phases and atomistic patterns are of great importance for understanding systems exhibiting both equilibrium and far-from-equilibrium dynamics. As such, there is intense research on phase transitions and pattern dynamics in soft matter, statistical and nonlinear physics, and polymer physics. In order to identify phases and nano-patterns, the pair correlation function is commonly used. However, this approach is limited in terms of recognizing competing patterns in dynamic systems, and lacks visualisation capabilities. In order to solve these limitations, we introduce Lindemann histogram quantification as an alternative method to analyse solid, liquid, and gas phases, along with hexagonal, square, and amorphous nano-pattern symmetries. We show that the proposed approach based on Lindemann parameter calculated per particle maps local number densities to material phase or particles pattern. We apply the Lindemann histogram method on dynamical colloidal self-assembly experimental data and identify competing patterns.

An Analysis of Selected Factors Influencing Enrollment Patterns.

ERIC Educational Resources Information Center

Heck, James

This report presents an analysis of factors influencing enrollment patterns at Lake City Community College (LCCC; Florida) and recommends ways to increase enrollments at the college. Section I reviews the methods of collecting data for the report, which included interviews with key college personnel, an examination of social indicators such as…

The analysis method of the DRAM cell pattern hotspot

NASA Astrophysics Data System (ADS)

Lee, Kyusun; Lee, Kweonjae; Chang, Jinman; Kim, Taeheon; Han, Daehan; Hong, Aeran; Kim, Yonghyeon; Kang, Jinyoung; Choi, Bumjin; Lee, Joosung; Lee, Jooyoung; Hong, Hyeongsun; Lee, Kyupil; Jin, Gyoyoung

2015-03-01

It is increasingly difficult to determine degree of completion of the patterning and the distribution at the DRAM Cell Patterns. When we research DRAM Device Cell Pattern, there are three big problems currently, it is as follows. First, due to etch loading, it is difficult to predict the potential defect. Second, due to under layer topology, it is impossible to demonstrate the influence of the hotspot. Finally, it is extremely difficult to predict final ACI pattern by the photo simulation, because current patterning process is double patterning technology which means photo pattern is completely different from final etch pattern. Therefore, if the hotspot occurs in wafer, it is very difficult to find it. CD-SEM is the most common pattern measurement tool in semiconductor fabrication site. CD-SEM is used to accurately measure small region of wafer pattern primarily. Therefore, there is no possibility of finding places where unpredictable defect occurs. Even though, "Current Defect detector" can measure a wide area, every chip has same pattern issue, the detector cannot detect critical hotspots. Because defect detecting algorithm of bright field machine is based on image processing, if same problems occur on compared and comparing chip, the machine cannot identify it. Moreover this instrument is not distinguished the difference of distribution about 1nm~3nm. So, "Defect detector" is difficult to handle the data for potential weak point far lower than target CD. In order to solve those problems, another method is needed. In this paper, we introduce the analysis method of the DRAM Cell Pattern Hotspot.

EventThread: Visual Summarization and Stage Analysis of Event Sequence Data.

PubMed

Guo, Shunan; Xu, Ke; Zhao, Rongwen; Gotz, David; Zha, Hongyuan; Cao, Nan

2018-01-01

Event sequence data such as electronic health records, a person's academic records, or car service records, are ordered series of events which have occurred over a period of time. Analyzing collections of event sequences can reveal common or semantically important sequential patterns. For example, event sequence analysis might reveal frequently used care plans for treating a disease, typical publishing patterns of professors, and the patterns of service that result in a well-maintained car. It is challenging, however, to visually explore large numbers of event sequences, or sequences with large numbers of event types. Existing methods focus on extracting explicitly matching patterns of events using statistical analysis to create stages of event progression over time. However, these methods fail to capture latent clusters of similar but not identical evolutions of event sequences. In this paper, we introduce a novel visualization system named EventThread which clusters event sequences into threads based on tensor analysis and visualizes the latent stage categories and evolution patterns by interactively grouping the threads by similarity into time-specific clusters. We demonstrate the effectiveness of EventThread through usage scenarios in three different application domains and via interviews with an expert user.

Identification and handling of artifactual gene expression profiles emerging in microarray hybridization experiments

PubMed Central

Brodsky, Leonid; Leontovich, Andrei; Shtutman, Michael; Feinstein, Elena

2004-01-01

Mathematical methods of analysis of microarray hybridizations deal with gene expression profiles as elementary units. However, some of these profiles do not reflect a biologically relevant transcriptional response, but rather stem from technical artifacts. Here, we describe two technically independent but rationally interconnected methods for identification of such artifactual profiles. Our diagnostics are based on detection of deviations from uniformity, which is assumed as the main underlying principle of microarray design. Method 1 is based on detection of non-uniformity of microarray distribution of printed genes that are clustered based on the similarity of their expression profiles. Method 2 is based on evaluation of the presence of gene-specific microarray spots within the slides’ areas characterized by an abnormal concentration of low/high differential expression values, which we define as ‘patterns of differentials’. Applying two novel algorithms, for nested clustering (method 1) and for pattern detection (method 2), we can make a dual estimation of the profile’s quality for almost every printed gene. Genes with artifactual profiles detected by method 1 may then be removed from further analysis. Suspicious differential expression values detected by method 2 may be either removed or weighted according to the probabilities of patterns that cover them, thus diminishing their input in any further data analysis. PMID:14999086

Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.

PubMed

Tan, Qihua; Thomassen, Mads; Burton, Mark; Mose, Kristian Fredløv; Andersen, Klaus Ejner; Hjelmborg, Jacob; Kruse, Torben

2017-06-06

Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

RFLP and sequence analysis of the cytochrome b gene of selected animals and man: methodology and forensic application.

PubMed

Zehner, R; Zimmermann, S; Mebs, D

1998-01-01

To identify common animal species by analysis of the cytochrome b gene a method has been developed to obtain PCR products of a large domain of the cytochrome b gene (981 bp out of 1140 bp) in humans, selected mammals and birds using the same specifically designed primers. Species-specific RFLP patterns are generated by co-restriction with the restriction endonucleases ALU I and NCO I. The RFLP patterns obtained are conclusive even in mixtures of two or more species. The results were confirmed by sequence analysis which in addition explained intraspecies variations in the RFLP patterns. The method has been applied to forensic casework studies where the origin of roasted meat, stomach contents and a bone sample has been successfully identified.

Prioritising lexical patterns to increase axiomatisation in biomedical ontologies. The role of localisation and modularity.

PubMed

Quesada-Martínez, M; Fernández-Breis, J T; Stevens, R; Mikroyannidi, E

2015-01-01

This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". In previous work, we have defined methods for the extraction of lexical patterns from labels as an initial step towards semi-automatic ontology enrichment methods. Our previous findings revealed that many biomedical ontologies could benefit from enrichment methods using lexical patterns as a starting point.Here, we aim to identify which lexical patterns are appropriate for ontology enrichment, driving its analysis by metrics to prioritised the patterns. We propose metrics for suggesting which lexical regularities should be the starting point to enrich complex ontologies. Our method determines the relevance of a lexical pattern by measuring its locality in the ontology, that is, the distance between the classes associated with the pattern, and the distribution of the pattern in a certain module of the ontology. The methods have been applied to four significant biomedical ontologies including the Gene Ontology and SNOMED CT. The metrics provide information about the engineering of the ontologies and the relevance of the patterns. Our method enables the suggestion of links between classes that are not made explicit in the ontology. We propose a prioritisation of the lexical patterns found in the analysed ontologies. The locality and distribution of lexical patterns offer insights into the further engineering of the ontology. Developers can use this information to improve the axiomatisation of their ontologies.

Exploring patterns enriched in a dataset with contrastive principal component analysis.

PubMed

Abid, Abubakar; Zhang, Martin J; Bagaria, Vivek K; Zou, James

2018-05-30

Visualization and exploration of high-dimensional data is a ubiquitous challenge across disciplines. Widely used techniques such as principal component analysis (PCA) aim to identify dominant trends in one dataset. However, in many settings we have datasets collected under different conditions, e.g., a treatment and a control experiment, and we are interested in visualizing and exploring patterns that are specific to one dataset. This paper proposes a method, contrastive principal component analysis (cPCA), which identifies low-dimensional structures that are enriched in a dataset relative to comparison data. In a wide variety of experiments, we demonstrate that cPCA with a background dataset enables us to visualize dataset-specific patterns missed by PCA and other standard methods. We further provide a geometric interpretation of cPCA and strong mathematical guarantees. An implementation of cPCA is publicly available, and can be used for exploratory data analysis in many applications where PCA is currently used.

Time-series analysis of foreign exchange rates using time-dependent pattern entropy

NASA Astrophysics Data System (ADS)

Ishizaki, Ryuji; Inoue, Masayoshi

2013-08-01

Time-dependent pattern entropy is a method that reduces variations to binary symbolic dynamics and considers the pattern of symbols in a sliding temporal window. We use this method to analyze the instability of daily variations in foreign exchange rates, in particular, the dollar-yen rate. The time-dependent pattern entropy of the dollar-yen rate was found to be high in the following periods: before and after the turning points of the yen from strong to weak or from weak to strong, and the period after the Lehman shock.

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

PubMed

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.

Modest validity and fair reproducibility of dietary patterns derived by cluster analysis.

PubMed

Funtikova, Anna N; Benítez-Arciniega, Alejandra A; Fitó, Montserrat; Schröder, Helmut

2015-03-01

Cluster analysis is widely used to analyze dietary patterns. We aimed to analyze the validity and reproducibility of the dietary patterns defined by cluster analysis derived from a food frequency questionnaire (FFQ). We hypothesized that the dietary patterns derived by cluster analysis have fair to modest reproducibility and validity. Dietary data were collected from 107 individuals from population-based survey, by an FFQ at baseline (FFQ1) and after 1 year (FFQ2), and by twelve 24-hour dietary recalls (24-HDR). Repeatability and validity were measured by comparing clusters obtained by the FFQ1 and FFQ2 and by the FFQ2 and 24-HDR (reference method), respectively. Cluster analysis identified a "fruits & vegetables" and a "meat" pattern in each dietary data source. Cluster membership was concordant for 66.7% of participants in FFQ1 and FFQ2 (reproducibility), and for 67.0% in FFQ2 and 24-HDR (validity). Spearman correlation analysis showed reasonable reproducibility, especially in the "fruits & vegetables" pattern, and lower validity also especially in the "fruits & vegetables" pattern. κ statistic revealed a fair validity and reproducibility of clusters. Our findings indicate a reasonable reproducibility and fair to modest validity of dietary patterns derived by cluster analysis. Copyright © 2015 Elsevier Inc. All rights reserved.

Feature-space-based FMRI analysis using the optimal linear transformation.

PubMed

Sun, Fengrong; Morris, Drew; Lee, Wayne; Taylor, Margot J; Mills, Travis; Babyn, Paul S

2010-09-01

The optimal linear transformation (OLT), an image analysis technique of feature space, was first presented in the field of MRI. This paper proposes a method of extending OLT from MRI to functional MRI (fMRI) to improve the activation-detection performance over conventional approaches of fMRI analysis. In this method, first, ideal hemodynamic response time series for different stimuli were generated by convolving the theoretical hemodynamic response model with the stimulus timing. Second, constructing hypothetical signature vectors for different activity patterns of interest by virtue of the ideal hemodynamic responses, OLT was used to extract features of fMRI data. The resultant feature space had particular geometric clustering properties. It was then classified into different groups, each pertaining to an activity pattern of interest; the applied signature vector for each group was obtained by averaging. Third, using the applied signature vectors, OLT was applied again to generate fMRI composite images with high SNRs for the desired activity patterns. Simulations and a blocked fMRI experiment were employed for the method to be verified and compared with the general linear model (GLM)-based analysis. The simulation studies and the experimental results indicated the superiority of the proposed method over the GLM-based analysis in detecting brain activities.

Computer-based classification of bacteria species by analysis of their colonies Fresnel diffraction patterns

NASA Astrophysics Data System (ADS)

Suchwalko, Agnieszka; Buzalewicz, Igor; Podbielska, Halina

2012-01-01

In the presented paper the optical system with converging spherical wave illumination for classification of bacteria species, is proposed. It allows for compression of the observation space, observation of Fresnel patterns, diffraction pattern scaling and low level of optical aberrations, which are not possessed by other optical configurations. Obtained experimental results have shown that colonies of specific bacteria species generate unique diffraction signatures. Analysis of Fresnel diffraction patterns of bacteria colonies can be fast and reliable method for classification and recognition of bacteria species. To determine the unique features of bacteria colonies diffraction patterns the image processing analysis was proposed. Classification can be performed by analyzing the spatial structure of diffraction patterns, which can be characterized by set of concentric rings. The characteristics of such rings depends on the bacteria species. In the paper, the influence of basic features and ring partitioning number on the bacteria classification, is analyzed. It is demonstrated that Fresnel patterns can be used for classification of following species: Salmonella enteritidis, Staplyococcus aureus, Proteus mirabilis and Citrobacter freundii. Image processing is performed by free ImageJ software, for which a special macro with human interaction, was written. LDA classification, CV method, ANOVA and PCA visualizations preceded by image data extraction were conducted using the free software R.

Identifying scales of pattern in ecological data: a comparison of lacunarity, spectral and wavelet analyses

Treesearch

Sari C. Saunders; Jiquan Chen; Thomas D. Drummer; Eric J. Gustafson; Kimberley D. Brosofske

2005-01-01

Identifying scales of pattern in ecological systems and coupling patterns to processes that create them are ongoing challenges. We examined the utility of three techniques (lacunarity, spectral, and wavelet analysis) for detecting scales of pattern of ecological data. We compared the information obtained using these methods for four datasets, including: surface...

Analoguing Creativity & Culture: A Method for Metaphors.

ERIC Educational Resources Information Center

Thompson, Timothy N.

Adding to the benefits of using metaphors as tools, "analoguing" (a method of analysis that focuses on metaphors for meanings in use and meanings of metaphors in use) helps avoid excessive categorization and separation by looking for unities and patterns in phenomena rather than for divisions. Six months of observation of patterns of…

Building and degradation of secondary cell walls: are there common patterns of lamellar assembly of cellulose microfibrils and cell wall delamination?

PubMed

De Micco, Veronica; Ruel, Katia; Joseleau, Jean-Paul; Aronne, Giovanna

2010-08-01

During cell wall formation and degradation, it is possible to detect cellulose microfibrils assembled into thicker and thinner lamellar structures, respectively, following inverse parallel patterns. The aim of this study was to analyse such patterns of microfibril aggregation and cell wall delamination. The thickness of microfibrils and lamellae was measured on digital images of both growing and degrading cell walls viewed by means of transmission electron microscopy. To objectively detect, measure and classify microfibrils and lamellae into thickness classes, a method based on the application of computerized image analysis combined with graphical and statistical methods was developed. The method allowed common classes of microfibrils and lamellae in cell walls to be identified from different origins. During both the formation and degradation of cell walls, a preferential formation of structures with specific thickness was evidenced. The results obtained with the developed method allowed objective analysis of patterns of microfibril aggregation and evidenced a trend of doubling/halving lamellar structures, during cell wall formation/degradation in materials from different origin and which have undergone different treatments.

Coping with confounds in multivoxel pattern analysis: what should we do about reaction time differences? A comment on Todd, Nystrom & Cohen 2013.

PubMed

Woolgar, Alexandra; Golland, Polina; Bode, Stefan

2014-09-01

Multivoxel pattern analysis (MVPA) is a sensitive and increasingly popular method for examining differences between neural activation patterns that cannot be detected using classical mass-univariate analysis. Recently, Todd et al. ("Confounds in multivariate pattern analysis: Theory and rule representation case study", 2013, NeuroImage 77: 157-165) highlighted a potential problem for these methods: high sensitivity to confounds at the level of individual participants due to the use of directionless summary statistics. Unlike traditional mass-univariate analyses where confounding activation differences in opposite directions tend to approximately average out at group level, group level MVPA results may be driven by any activation differences that can be discriminated in individual participants. In Todd et al.'s empirical data, factoring out differences in reaction time (RT) reduced a classifier's ability to distinguish patterns of activation pertaining to two task rules. This raises two significant questions for the field: to what extent have previous multivoxel discriminations in the literature been driven by RT differences, and by what methods should future studies take RT and other confounds into account? We build on the work of Todd et al. and compare two different approaches to remove the effect of RT in MVPA. We show that in our empirical data, in contrast to that of Todd et al., the effect of RT on rule decoding is negligible, and results were not affected by the specific details of RT modelling. We discuss the meaning of and sensitivity for confounds in traditional and multivoxel approaches to fMRI analysis. We observe that the increased sensitivity of MVPA comes at a price of reduced specificity, meaning that these methods in particular call for careful consideration of what differs between our conditions of interest. We conclude that the additional complexity of the experimental design, analysis and interpretation needed for MVPA is still not a reason to favour a less sensitive approach. Copyright © 2014 Elsevier Inc. All rights reserved.

Discriminant analysis of resting-state functional connectivity patterns on the Grassmann manifold

NASA Astrophysics Data System (ADS)

Fan, Yong; Liu, Yong; Jiang, Tianzi; Liu, Zhening; Hao, Yihui; Liu, Haihong

2010-03-01

The functional networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive functions and neurological diseases. In this paper, we propose a novel algorithm for discriminant analysis of functional networks encoded by spatial independent components. The functional networks of each individual are used as bases for a linear subspace, referred to as a functional connectivity pattern, which facilitates a comprehensive characterization of temporal signals of fMRI data. The functional connectivity patterns of different individuals are analyzed on the Grassmann manifold by adopting a principal angle based subspace distance. In conjunction with a support vector machine classifier, a forward component selection technique is proposed to select independent components for constructing the most discriminative functional connectivity pattern. The discriminant analysis method has been applied to an fMRI based schizophrenia study with 31 schizophrenia patients and 31 healthy individuals. The experimental results demonstrate that the proposed method not only achieves a promising classification performance for distinguishing schizophrenia patients from healthy controls, but also identifies discriminative functional networks that are informative for schizophrenia diagnosis.

Application of physico-chemical typing methods for the epidemiological analysis of Salmonella enteritidis strains of phage type 25/17.

PubMed Central

Seltmann, G.; Voigt, W.; Beer, W.

1994-01-01

Eighty-nine Salmonella enteritidis phage type 25/17 strains isolated from a localized outbreak in the German state Nordrhein-Westfalen (outbreak NWI) could not be further differentiated by biochemotyping and plasmid pattern analysis. They were submitted to a complex typing system consisting of modern physico-chemical analytical procedures. In lipopolysaccharide pattern analysis the strains proved to be homogeneous. In multilocus enzyme electrophoresis, outer membrane and whole cell protein pattern (WCPP) analysis, and Fourier-transform infrared (FT-IR) spectroscopy (increasing extent of differentiation in the given order) strains deviating from each basal pattern were found. The extent of correspondence in these deviations was satisfactory. Forty-six strains of the same sero- and phage type, however, obtained from different outbreaks, were additionally typed. The results obtained with them indicate that the data of the first group were not restricted to strains from outbreak NWI, but of general validity. It was found that both WCPP and FT-IR represent valuable methods for the sub-grouping of bacteria. Images Fig. 1 Fig. 2 Fig. 3 PMID:7995351

Performance analysis of a brushless dc motor due to magnetization distribution in a continuous ring magnet

NASA Astrophysics Data System (ADS)

Hur, Jin; Jung, In-Soung; Sung, Ha-Gyeong; Park, Soon-Sup

2003-05-01

This paper represents the force performance of a brushless dc motor with a continuous ring-type permanent magnet (PM), considering its magnetization patterns: trapezoidal, trapezoidal with dead zone, and unbalanced trapezoidal magnetization with dead zone. The radial force density in PM motor causes vibration, because vibration is induced the traveling force from the rotating PM acting on the stator. Magnetization distribution of the PM as well as the shape of the teeth determines the distribution of force density. In particular, the distribution has a three-dimensional (3-D) pattern because of overhang, that is, it is not uniform in axial direction. Thus, the analysis of radial force density required dynamic analysis considering the 3-D shape of the teeth and overhang. The results show that the force density as a source of vibration varies considerably depending on the overhang and magnetization distribution patterns. In addition, the validity of the developed method, coupled 3-D equivalent magnetic circuit network method, with driving circuit and motion equation, is confirmed by comparison of conventional method using 3D finite element method.

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

PubMed Central

2010-01-01

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

Modeling Eye Gaze Patterns in Clinician-Patient Interaction with Lag Sequential Analysis

PubMed Central

Montague, E; Xu, J; Asan, O; Chen, P; Chewning, B; Barrett, B

2011-01-01

Objective The aim of this study was to examine whether lag-sequential analysis could be used to describe eye gaze orientation between clinicians and patients in the medical encounter. This topic is particularly important as new technologies are implemented into multi-user health care settings where trust is critical and nonverbal cues are integral to achieving trust. This analysis method could lead to design guidelines for technologies and more effective assessments of interventions. Background Nonverbal communication patterns are important aspects of clinician-patient interactions and may impact patient outcomes. Method Eye gaze behaviors of clinicians and patients in 110-videotaped medical encounters were analyzed using the lag-sequential method to identify significant behavior sequences. Lag-sequential analysis included both event-based lag and time-based lag. Results Results from event-based lag analysis showed that the patients’ gaze followed that of clinicians, while clinicians did not follow patients. Time-based sequential analysis showed that responses from the patient usually occurred within two seconds after the initial behavior of the clinician. Conclusion Our data suggest that the clinician’s gaze significantly affects the medical encounter but not the converse. Application Findings from this research have implications for the design of clinical work systems and modeling interactions. Similar research methods could be used to identify different behavior patterns in clinical settings (physical layout, technology, etc.) to facilitate and evaluate clinical work system designs. PMID:22046723

Patterns of Contraceptive Adoption, Continuation, and Switching after Delivery among Malawian Women.

PubMed

Kopp, Dawn M; Rosenberg, Nora E; Stuart, Gretchen S; Miller, William C; Hosseinipour, Mina C; Bonongwe, Phylos; Mwale, Mwawi; Tang, Jennifer H

2017-01-01

Women who report use of postpartum family planning may not continue their initial method or use it consistently. Understanding the patterns of method uptake, discontinuation, and switching among women after delivery is important to promote uptake and continuation of effective methods of contraception. This is a secondary analysis of 634 Malawian women enrolled into a prospective cohort study after delivery. They completed baseline surveys upon enrollment and follow-up telephone surveys 3, 6, and 12 months post-delivery. Women were included in this analysis if they had completed at least the 3- and 6-month post-delivery surveys. Descriptive statistics were used to assess contraceptive method mix and patterns of switching, whereas Pearson's χ2 tests were used for bivariable analyses to compare characteristics of women who continued and discontinued their initial post-delivery contraceptive method. Among the 479 women included in this analysis, the use of abstinence/traditional methods decreased and the use of long-acting and permanent methods (LAPM) increased over time. Almost half (47%) discontinued the contraceptive method reported at 3-months post-delivery; women using injectables or LAPM at 3-months post-delivery were significantly more likely to continue their method than those using non-modern methods (p<0.001). Of the 216 women who switched methods, 82% switched to a more or equally effective method. The change in contraceptive method mix and high rate of contraceptive switching in the first 12 months postpartum highlights a need to assist women in accessing effective contraceptives soon after delivery.

Scoring Tools for the Analysis of Infant Respiratory Inductive Plethysmography Signals.

PubMed

Robles-Rubio, Carlos Alejandro; Bertolizio, Gianluca; Brown, Karen A; Kearney, Robert E

2015-01-01

Infants recovering from anesthesia are at risk of life threatening Postoperative Apnea (POA). POA events are rare, and so the study of POA requires the analysis of long cardiorespiratory records. Manual scoring is the preferred method of analysis for these data, but it is limited by low intra- and inter-scorer repeatability. Furthermore, recommended scoring rules do not provide a comprehensive description of the respiratory patterns. This work describes a set of manual scoring tools that address these limitations. These tools include: (i) a set of definitions and scoring rules for 6 mutually exclusive, unique patterns that fully characterize infant respiratory inductive plethysmography (RIP) signals; (ii) RIPScore, a graphical, manual scoring software to apply these rules to infant data; (iii) a library of data segments representing each of the 6 patterns; (iv) a fully automated, interactive formal training protocol to standardize the analysis and establish intra- and inter-scorer repeatability; and (v) a quality control method to monitor scorer ongoing performance over time. To evaluate these tools, three scorers from varied backgrounds were recruited and trained to reach a performance level similar to that of an expert. These scorers used RIPScore to analyze data from infants at risk of POA in two separate, independent instances. Scorers performed with high accuracy and consistency, analyzed data efficiently, had very good intra- and inter-scorer repeatability, and exhibited only minor confusion between patterns. These results indicate that our tools represent an excellent method for the analysis of respiratory patterns in long data records. Although the tools were developed for the study of POA, their use extends to any study of respiratory patterns using RIP (e.g., sleep apnea, extubation readiness). Moreover, by establishing and monitoring scorer repeatability, our tools enable the analysis of large data sets by multiple scorers, which is essential for longitudinal and multicenter studies.

Chemometrics Methods for Specificity, Authenticity and Traceability Analysis of Olive Oils: Principles, Classifications and Applications.

PubMed

Messai, Habib; Farman, Muhammad; Sarraj-Laabidi, Abir; Hammami-Semmar, Asma; Semmar, Nabil

2016-11-17

Olive oils (OOs) show high chemical variability due to several factors of genetic, environmental and anthropic types. Genetic and environmental factors are responsible for natural compositions and polymorphic diversification resulting in different varietal patterns and phenotypes. Anthropic factors, however, are at the origin of different blends' preparation leading to normative, labelled or adulterated commercial products. Control of complex OO samples requires their (i) characterization by specific markers; (ii) authentication by fingerprint patterns; and (iii) monitoring by traceability analysis. These quality control and management aims require the use of several multivariate statistical tools: specificity highlighting requires ordination methods; authentication checking calls for classification and pattern recognition methods; traceability analysis implies the use of network-based approaches able to separate or extract mixed information and memorized signals from complex matrices. This chapter presents a review of different chemometrics methods applied for the control of OO variability from metabolic and physical-chemical measured characteristics. The different chemometrics methods are illustrated by different study cases on monovarietal and blended OO originated from different countries. Chemometrics tools offer multiple ways for quantitative evaluations and qualitative control of complex chemical variability of OO in relation to several intrinsic and extrinsic factors.

Scalable Kernel Methods and Algorithms for General Sequence Analysis

ERIC Educational Resources Information Center

Kuksa, Pavel

2011-01-01

Analysis of large-scale sequential data has become an important task in machine learning and pattern recognition, inspired in part by numerous scientific and technological applications such as the document and text classification or the analysis of biological sequences. However, current computational methods for sequence comparison still lack…

Comparison of 3 Methods for Identifying Dietary Patterns Associated With Risk of Disease

PubMed Central

DiBello, Julia R.; Kraft, Peter; McGarvey, Stephen T.; Goldberg, Robert; Campos, Hannia

2008-01-01

Reduced rank regression and partial least-squares regression (PLS) are proposed alternatives to principal component analysis (PCA). Using all 3 methods, the authors derived dietary patterns in Costa Rican data collected on 3,574 cases and controls in 1994–2004 and related the resulting patterns to risk of first incident myocardial infarction. Four dietary patterns associated with myocardial infarction were identified. Factor 1, characterized by high intakes of lean chicken, vegetables, fruit, and polyunsaturated oil, was generated by all 3 dietary pattern methods and was associated with a significantly decreased adjusted risk of myocardial infarction (28%–46%, depending on the method used). PCA and PLS also each yielded a pattern associated with a significantly decreased risk of myocardial infarction (31% and 23%, respectively); this pattern was characterized by moderate intake of alcohol and polyunsaturated oil and low intake of high-fat dairy products. The fourth factor derived from PCA was significantly associated with a 38% increased risk of myocardial infarction and was characterized by high intakes of coffee and palm oil. Contrary to previous studies, the authors found PCA and PLS to produce more patterns associated with cardiovascular disease than reduced rank regression. The most effective method for deriving dietary patterns related to disease may vary depending on the study goals. PMID:18945692

a Three-Step Spatial-Temporal Clustering Method for Human Activity Pattern Analysis

NASA Astrophysics Data System (ADS)

Huang, W.; Li, S.; Xu, S.

2016-06-01

How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time) to four dimensions (space, time and semantics). More specifically, not only a location and time that people stay and spend are collected, but also what people "say" for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The results show that the approximate 55% spatiotemporal clusters distributed in different locations can be eventually grouped as the same type of clusters with consideration of semantic aspect.

Infrared face recognition based on LBP histogram and KW feature selection

NASA Astrophysics Data System (ADS)

Xie, Zhihua

2014-07-01

The conventional LBP-based feature as represented by the local binary pattern (LBP) histogram still has room for performance improvements. This paper focuses on the dimension reduction of LBP micro-patterns and proposes an improved infrared face recognition method based on LBP histogram representation. To extract the local robust features in infrared face images, LBP is chosen to get the composition of micro-patterns of sub-blocks. Based on statistical test theory, Kruskal-Wallis (KW) feature selection method is proposed to get the LBP patterns which are suitable for infrared face recognition. The experimental results show combination of LBP and KW features selection improves the performance of infrared face recognition, the proposed method outperforms the traditional methods based on LBP histogram, discrete cosine transform(DCT) or principal component analysis(PCA).

Development of neural network techniques for finger-vein pattern classification

NASA Astrophysics Data System (ADS)

Wu, Jian-Da; Liu, Chiung-Tsiung; Tsai, Yi-Jang; Liu, Jun-Ching; Chang, Ya-Wen

2010-02-01

A personal identification system using finger-vein patterns and neural network techniques is proposed in the present study. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis and classification. The biometric system for verification consists of a combination of feature extraction using principal component analysis and pattern classification using both back-propagation network and adaptive neuro-fuzzy inference systems. Finger-vein features are first extracted by principal component analysis method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed adaptive neuro-fuzzy inference system in the pattern classification, the back-propagation network is compared with the proposed system. The experimental results indicated the proposed system using adaptive neuro-fuzzy inference system demonstrated a better performance than the back-propagation network for personal identification using the finger-vein patterns.

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

PubMed

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.

Chemometrics Methods for Specificity, Authenticity and Traceability Analysis of Olive Oils: Principles, Classifications and Applications

PubMed Central

Messai, Habib; Farman, Muhammad; Sarraj-Laabidi, Abir; Hammami-Semmar, Asma; Semmar, Nabil

2016-01-01

Background. Olive oils (OOs) show high chemical variability due to several factors of genetic, environmental and anthropic types. Genetic and environmental factors are responsible for natural compositions and polymorphic diversification resulting in different varietal patterns and phenotypes. Anthropic factors, however, are at the origin of different blends’ preparation leading to normative, labelled or adulterated commercial products. Control of complex OO samples requires their (i) characterization by specific markers; (ii) authentication by fingerprint patterns; and (iii) monitoring by traceability analysis. Methods. These quality control and management aims require the use of several multivariate statistical tools: specificity highlighting requires ordination methods; authentication checking calls for classification and pattern recognition methods; traceability analysis implies the use of network-based approaches able to separate or extract mixed information and memorized signals from complex matrices. Results. This chapter presents a review of different chemometrics methods applied for the control of OO variability from metabolic and physical-chemical measured characteristics. The different chemometrics methods are illustrated by different study cases on monovarietal and blended OO originated from different countries. Conclusion. Chemometrics tools offer multiple ways for quantitative evaluations and qualitative control of complex chemical variability of OO in relation to several intrinsic and extrinsic factors. PMID:28231172

Using Exploratory Spatial Data Analysis to Leverage Social Indicator Databases: The Discovery of Interesting Patterns

ERIC Educational Resources Information Center

Anselin, Luc; Sridharan, Sanjeev; Gholston, Susan

2007-01-01

With the proliferation of social indicator databases, the need for powerful techniques to study patterns of change has grown. In this paper, the utility of spatial data analytical methods such as exploratory spatial data analysis (ESDA) is suggested as a means to leverage the information contained in social indicator databases. The principles…

Race and Older Mothers’ Differentiation: A Sequential Quantitative and Qualitative Analysis

PubMed Central

Sechrist, Jori; Suitor, J. Jill; Riffin, Catherine; Taylor-Watson, Kadari; Pillemer, Karl

2011-01-01

The goal of this paper is to demonstrate a process by which qualitative and quantitative approaches are combined to reveal patterns in the data that are unlikely to be detected and confirmed by either method alone. Specifically, we take a sequential approach to combining qualitative and quantitative data to explore race differences in how mothers differentiate among their adult children. We began with a standard multivariate analysis examining race differences in mothers’ differentiation among their adult children regarding emotional closeness and confiding. Finding no race differences in this analysis, we conducted an in-depth comparison of the Black and White mothers’ narratives to determine whether there were underlying patterns that we had been unable to detect in our first analysis. Using this method, we found that Black mothers were substantially more likely than White mothers to emphasize interpersonal relationships within the family when describing differences among their children. In our final step, we developed a measure of familism based on the qualitative data and conducted a multivariate analysis to confirm the patterns revealed by the in-depth comparison of the mother’s narratives. We conclude that using such a sequential mixed methods approach to data analysis has the potential to shed new light on complex family relations. PMID:21967639

Auxiliary drying to prevent pattern collapse in high aspect ratio nanostructures

NASA Astrophysics Data System (ADS)

Liu, Gang; Zhou, Jie; Xiong, Ying; Zhang, Xiaobo; Tian, Yangchao

2011-07-01

Many defects are generated in densely packed high aspect ratio structures during nanofabrication. Pattern collapse is one of the serious problems that may arise, mainly due to the capillary force during drying after the rinsing process. In this paper, a method of auxiliary drying is presented to prevent pattern collapse in high aspect ratio nanostructures by adding an auxiliary substrate as a reinforcing rib to restrict deformation and to balance the capillary force. The principle of the method is presented based on the analysis of pattern collapse. A finite element method is then applied to analyze the deformation of the resist beams caused by the surface tension using the ANSYS software, and the effect of the nanostructure's length to width ratio simulated and analyzed. Finally, the possible range of applications based on the proposed method is discussed. Our results show that the aspect ratio may be increased 2.6 times without pattern collapse; furthermore, this method can be widely used in the removal of solvents in micro- and nanofabrication.

Auxiliary drying to prevent pattern collapse in high aspect ratio nanostructures.

PubMed

Liu, Gang; Zhou, Jie; Xiong, Ying; Zhang, Xiaobo; Tian, Yangchao

2011-07-29

Many defects are generated in densely packed high aspect ratio structures during nanofabrication. Pattern collapse is one of the serious problems that may arise, mainly due to the capillary force during drying after the rinsing process. In this paper, a method of auxiliary drying is presented to prevent pattern collapse in high aspect ratio nanostructures by adding an auxiliary substrate as a reinforcing rib to restrict deformation and to balance the capillary force. The principle of the method is presented based on the analysis of pattern collapse. A finite element method is then applied to analyze the deformation of the resist beams caused by the surface tension using the ANSYS software, and the effect of the nanostructure's length to width ratio simulated and analyzed. Finally, the possible range of applications based on the proposed method is discussed. Our results show that the aspect ratio may be increased 2.6 times without pattern collapse; furthermore, this method can be widely used in the removal of solvents in micro- and nanofabrication.

Empirical OPC rule inference for rapid RET application

NASA Astrophysics Data System (ADS)

Kulkarni, Anand P.

2006-10-01

A given technological node (45 nm, 65 nm) can be expected to process thousands of individual designs. Iterative methods applied at the node consume valuable days in determining proper placement of OPC features, and manufacturing and testing mask correspondence to wafer patterns in a trial-and-error fashion for each design. Repeating this fabrication process for each individual design is a time-consuming and expensive process. We present a novel technique which sidesteps the requirement to iterate through the model-based OPC analysis and pattern verification cycle on subsequent designs at the same node. Our approach relies on the inference of rules from a correct pattern at the wafer surface it relates to the OPC and pre-OPC pattern layout files. We begin with an offline phase where we obtain a "gold standard" design file that has been fab-tested at the node with a prepared, post-OPC layout file that corresponds to the intended on-wafer pattern. We then run an offline analysis to infer rules to be used in this method. During the analysis, our method implicitly identifies contextual OPC strategies for optimal placement of RET features on any design at that node. Using these strategies, we can apply OPC to subsequent designs at the same node with accuracy comparable to the original design file but significantly smaller expected runtimes. The technique promises to offer a rapid and accurate complement to existing RET application strategies.

Splitting a droplet for femtoliter liquid patterns and single cell isolation.

PubMed

Li, Huizeng; Yang, Qiang; Li, Guannan; Li, Mingzhu; Wang, Shutao; Song, Yanlin

2015-05-06

Well-defined microdroplet generation has attracted great interest, which is important for the high-resolution patterning and matrix distribution for chemical reactions and biological assays. By sliding a droplet on a patterned superhydrophilic/superhydrophobic substrate, tiny microdroplet arrays low to femtoliter were achieved with uniform volume and composition. Using this method, cells were successfully isolated, resulting in a single cell array. The droplet-splitting method is facile, sample-effective, and low-cost, which will be of great potential for the development of microdroplet arrays for biological analysis as well as patterning system and devices.

Method of locating underground mines fires

DOEpatents

Laage, Linneas; Pomroy, William

1992-01-01

An improved method of locating an underground mine fire by comparing the pattern of measured combustion product arrival times at detector locations with a real time computer-generated array of simulated patterns. A number of electronic fire detection devices are linked thru telemetry to a control station on the surface. The mine's ventilation is modeled on a digital computer using network analysis software. The time reguired to locate a fire consists of the time required to model the mines' ventilation, generate the arrival time array, scan the array, and to match measured arrival time patterns to the simulated patterns.

Consistency and Generalizability of Dietary Patterns in a Multiethnic Working Population.

PubMed

Eng, Jui-Yee; Moy, Foong-Ming; Bulgiba, Awang; Rampal, Sanjay

2018-03-31

Dietary pattern analysis is a complementary method to nutrient analysis in evaluating overall diet-disease hypotheses. Although studies have been conducted to derive dietary patterns among Malaysians, their consistency across subgroups has not been examined. The study aimed to derive dietary patterns empirically and to examine the consistency and generalizability of patterns across sex, ethnicity, and urban status in a working population. This was a cross-sectional study using data from the Clustering of Lifestyle Risk Factors and Understanding its Association with Stress on Health and Well-Being among School Teachers in Malaysia study collected between August 2014 and November 2015. Dietary intake was assessed using a food frequency questionnaire, and dietary patterns were derived using factor analysis. Participants were teachers from selected public schools from three states in Peninsular Malaysia (n=4,618). Dietary patterns derived using factor analysis. Separate factor analysis was conducted by sex, ethnicity, and urban status to identify dietary patterns. Eigenvalue >2, scree plot, Velicer's minimum average partial analysis, and Horn's parallel analysis were used to determine the number of factors to retain. The interpretability of each dietary pattern was evaluated. The consistency and generalizability of dietary patterns across subgroups were assessed using the Tucker congruence coefficient. There was no subgroup-specific dietary pattern found. Thus, dietary patterns were derived using the pooled sample in the final model. Two dietary patterns (Western and Prudent) were derived. The Western dietary pattern explained 15.4% of total variance, characterized by high intakes of refined grains, animal-based foods, added fat, and sugar-sweetened beverages as well as fast food. The Prudent dietary pattern explained 11.1% of total variance and was loaded with pulses, legumes, vegetables, and fruits. The derived Western and Prudent dietary patterns were consistent and generalizable across subgroups of sex, ethnicity, and urban status. Further research is needed to explore associations between these dietary patterns and chronic diseases. Copyright © 2018 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Nonlinear gamma correction via normed bicoherence minimization in optical fringe projection metrology

NASA Astrophysics Data System (ADS)

Kamagara, Abel; Wang, Xiangzhao; Li, Sikun

2018-03-01

We propose a method to compensate for the projector intensity nonlinearity induced by gamma effect in three-dimensional (3-D) fringe projection metrology by extending high-order spectra analysis and bispectral norm minimization to digital sinusoidal fringe pattern analysis. The bispectrum estimate allows extraction of vital signal information features such as spectral component correlation relationships in fringe pattern images. Our approach exploits the fact that gamma introduces high-order harmonic correlations in the affected fringe pattern image. Estimation and compensation of projector nonlinearity is realized by detecting and minimizing the normed bispectral coherence of these correlations. The proposed technique does not require calibration information and technical knowledge or specification of fringe projection unit. This is promising for developing a modular and calibration-invariant model for intensity nonlinear gamma compensation in digital fringe pattern projection profilometry. Experimental and numerical simulation results demonstrate this method to be efficient and effective in improving the phase measuring accuracies with phase-shifting fringe pattern projection profilometry.

Rapid Discrimination for Traditional Complex Herbal Medicines from Different Parts, Collection Time, and Origins Using High-Performance Liquid Chromatography and Near-Infrared Spectral Fingerprints with Aid of Pattern Recognition Methods

PubMed Central

Fu, Haiyan; Fan, Yao; Zhang, Xu; Lan, Hanyue; Yang, Tianming; Shao, Mei; Li, Sihan

2015-01-01

As an effective method, the fingerprint technique, which emphasized the whole compositions of samples, has already been used in various fields, especially in identifying and assessing the quality of herbal medicines. High-performance liquid chromatography (HPLC) and near-infrared (NIR), with their unique characteristics of reliability, versatility, precision, and simple measurement, played an important role among all the fingerprint techniques. In this paper, a supervised pattern recognition method based on PLSDA algorithm by HPLC and NIR has been established to identify the information of Hibiscus mutabilis L. and Berberidis radix, two common kinds of herbal medicines. By comparing component analysis (PCA), linear discriminant analysis (LDA), and particularly partial least squares discriminant analysis (PLSDA) with different fingerprint preprocessing of NIR spectra variables, PLSDA model showed perfect functions on the analysis of samples as well as chromatograms. Most important, this pattern recognition method by HPLC and NIR can be used to identify different collection parts, collection time, and different origins or various species belonging to the same genera of herbal medicines which proved to be a promising approach for the identification of complex information of herbal medicines. PMID:26345990

Time-series analysis of multiple foreign exchange rates using time-dependent pattern entropy

NASA Astrophysics Data System (ADS)

Ishizaki, Ryuji; Inoue, Masayoshi

2018-01-01

Time-dependent pattern entropy is a method that reduces variations to binary symbolic dynamics and considers the pattern of symbols in a sliding temporal window. We use this method to analyze the instability of daily variations in multiple foreign exchange rates. The time-dependent pattern entropy of 7 foreign exchange rates (AUD/USD, CAD/USD, CHF/USD, EUR/USD, GBP/USD, JPY/USD, and NZD/USD) was found to be high in the long period after the Lehman shock, and be low in the long period after Mar 2012. We compared the correlation matrix between exchange rates in periods of high and low of the time-dependent pattern entropy.

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

NASA Astrophysics Data System (ADS)

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.

Syntactic methods of shape feature description and its application in analysis of medical images

NASA Astrophysics Data System (ADS)

Ogiela, Marek R.; Tadeusiewicz, Ryszard

2000-02-01

The paper presents specialist algorithms of morphologic analysis of shapes of selected organs of abdominal cavity proposed in order to diagnose disease symptoms occurring in the main pancreatic ducts and upper segments of ureters. Analysis of the correct morphology of these structures has been conducted with the use of syntactic methods of pattern recognition. Its main objective is computer-aided support to early diagnosis of neoplastic lesions and pancreatitis based on images taken in the course of examination with the endoscopic retrograde cholangiopancreatography (ERCP) method and a diagnosis of morphological lesions in ureter based on kidney radiogram analysis. In the analysis of ERCP images, the main objective is to recognize morphological lesions in pancreas ducts characteristic for carcinoma and chronic pancreatitis. In the case of kidney radiogram analysis the aim is to diagnose local irregularity of ureter lumen. Diagnosing the above mentioned lesion has been conducted with the use of syntactic methods of pattern recognition, in particular the languages of shape features description and context-free attributed grammars. These methods allow to recognize and describe in a very efficient way the aforementioned lesions on images obtained as a result of initial image processing into diagrams of widths of the examined structures.

Stability-based validation of dietary patterns obtained by cluster analysis.

PubMed

Sauvageot, Nicolas; Schritz, Anna; Leite, Sonia; Alkerwi, Ala'a; Stranges, Saverio; Zannad, Faiez; Streel, Sylvie; Hoge, Axelle; Donneau, Anne-Françoise; Albert, Adelin; Guillaume, Michèle

2017-01-14

Cluster analysis is a data-driven method used to create clusters of individuals sharing similar dietary habits. However, this method requires specific choices from the user which have an influence on the results. Therefore, there is a need of an objective methodology helping researchers in their decisions during cluster analysis. The objective of this study was to use such a methodology based on stability of clustering solutions to select the most appropriate clustering method and number of clusters for describing dietary patterns in the NESCAV study (Nutrition, Environment and Cardiovascular Health), a large population-based cross-sectional study in the Greater Region (N = 2298). Clustering solutions were obtained with K-means, K-medians and Ward's method and a number of clusters varying from 2 to 6. Their stability was assessed with three indices: adjusted Rand index, Cramer's V and misclassification rate. The most stable solution was obtained with K-means method and a number of clusters equal to 3. The "Convenient" cluster characterized by the consumption of convenient foods was the most prevalent with 46% of the population having this dietary behaviour. In addition, a "Prudent" and a "Non-Prudent" patterns associated respectively with healthy and non-healthy dietary habits were adopted by 25% and 29% of the population. The "Convenient" and "Non-Prudent" clusters were associated with higher cardiovascular risk whereas the "Prudent" pattern was associated with a decreased cardiovascular risk. Associations with others factors showed that the choice of a specific dietary pattern is part of a wider lifestyle profile. This study is of interest for both researchers and public health professionals. From a methodological standpoint, we showed that using stability of clustering solutions could help researchers in their choices. From a public health perspective, this study showed the need of targeted health promotion campaigns describing the benefits of healthy dietary patterns.

Patterns of Contraceptive Adoption, Continuation, and Switching after Delivery among Malawian Women

PubMed Central

Rosenberg, Nora E.; Stuart, Gretchen S.; Miller, William C.; Hosseinipour, Mina C.; Bonongwe, Phylos; Mwale, Mwawi; Tang, Jennifer H.

2017-01-01

Women who report use of postpartum family planning may not continue their initial method or use it consistently. Understanding the patterns of method uptake, discontinuation, and switching among women after delivery is important to promote uptake and continuation of effective methods of contraception. This is a secondary analysis of 634 Malawian women enrolled into a prospective cohort study after delivery. They completed baseline surveys upon enrollment and follow-up telephone surveys 3, 6, and 12 months post-delivery. Women were included in this analysis if they had completed at least the 3- and 6-month post-delivery surveys. Descriptive statistics were used to assess contraceptive method mix and patterns of switching, whereas Pearson’s χ2 tests were used for bivariable analyses to compare characteristics of women who continued and discontinued their initial post-delivery contraceptive method. Among the 479 women included in this analysis, the use of abstinence/traditional methods decreased and the use of long-acting and permanent methods (LAPM) increased over time. Almost half (47%) discontinued the contraceptive method reported at 3-months post-delivery; women using injectables or LAPM at 3-months post-delivery were significantly more likely to continue their method than those using non-modern methods (p<0.001). Of the 216 women who switched methods, 82% switched to a more or equally effective method. The change in contraceptive method mix and high rate of contraceptive switching in the first 12 months postpartum highlights a need to assist women in accessing effective contraceptives soon after delivery. PMID:28107404

Quantifying the pattern of beta/A4 amyloid protein distribution in Alzheimer's disease by image analysis.

PubMed

Bruce, C V; Clinton, J; Gentleman, S M; Roberts, G W; Royston, M C

1992-04-01

We have undertaken a study of the distribution of the beta/A4 amyloid deposited in the cerebral cortex in Alzheimer's disease. Previous studies which have examined the differential distribution of amyloid in the cortex in order to determine the laminar pattern of cortical pathology have not proved to be conclusive. We have developed an alternative method for the solution of this problem. It involves the immunostaining of sections followed by computer-enhanced image analysis. A mathematical model is then used to describe both the amount and the pattern of amyloid across the cortex. This method is both accurate and reliable and also removes many of the problems concerning inter and intra-rater variability in measurement. This method will provide the basis for further quantitative studies on the differential distribution of amyloid in Alzheimer's disease and other cases of dementia where cerebral amyloidosis occurs.

Panel data analysis of cardiotocograph (CTG) data.

PubMed

Horio, Hiroyuki; Kikuchi, Hitomi; Ikeda, Tomoaki

2013-01-01

Panel data analysis is a statistical method, widely used in econometrics, which deals with two-dimensional panel data collected over time and over individuals. Cardiotocograph (CTG) which monitors fetal heart rate (FHR) using Doppler ultrasound and uterine contraction by strain gage is commonly used in intrapartum treatment of pregnant women. Although the relationship between FHR waveform pattern and the outcome such as umbilical blood gas data at delivery has long been analyzed, there exists no accumulated FHR patterns from large number of cases. As time-series economic fluctuations in econometrics such as consumption trend has been studied using panel data which consists of time-series and cross-sectional data, we tried to apply this method to CTG data. The panel data composed of a symbolized segment of FHR pattern can be easily handled, and a perinatologist can get the whole FHR pattern view from the microscopic level of time-series FHR data.

Mining sequential patterns for protein fold recognition.

PubMed

Exarchos, Themis P; Papaloukas, Costas; Lampros, Christos; Fotiadis, Dimitrios I

2008-02-01

Protein data contain discriminative patterns that can be used in many beneficial applications if they are defined correctly. In this work sequential pattern mining (SPM) is utilized for sequence-based fold recognition. Protein classification in terms of fold recognition plays an important role in computational protein analysis, since it can contribute to the determination of the function of a protein whose structure is unknown. Specifically, one of the most efficient SPM algorithms, cSPADE, is employed for the analysis of protein sequence. A classifier uses the extracted sequential patterns to classify proteins in the appropriate fold category. For training and evaluating the proposed method we used the protein sequences from the Protein Data Bank and the annotation of the SCOP database. The method exhibited an overall accuracy of 25% in a classification problem with 36 candidate categories. The classification performance reaches up to 56% when the five most probable protein folds are considered.

Micro Dot Patterning on the Light Guide Panel Using Powder Blasting

PubMed Central

Jang, Ho Su; Cho, Myeong Woo; Park, Dong Sam

2008-01-01

This study is to develop a micromachining technology for a light guide panel(LGP) mold, whereby micro dot patterns are formed on a LGP surface by a single injection process instead of existing screen printing processes. The micro powder blasting technique is applied to form micro dot patterns on the LGP mold surface. The optimal conditions for masking, laminating, exposure, and developing processes to form the micro dot patterns are first experimentally investigated. A LGP mold with masked micro patterns is then machined using the micro powder blasting method and the machinability of the micro dot patterns is verified. A prototype LGP is test- injected using the developed LGP mold and a shape analysis of the patterns and performance testing of the injected LGP are carried out. As an additional approach, matte finishing, a special surface treatment method, is applied to the mold surface to improve the light diffusion characteristics, uniformity and brightness of the LGP. The results of this study show that the applied powder blasting method can be successfully used to manufacture LGPs with micro patterns by just single injection using the developed mold and thereby replace existing screen printing methods. PMID:27879740

Quantitative analysis of crystalline pharmaceuticals in powders and tablets by a pattern-fitting procedure using X-ray powder diffraction data.

PubMed

Yamamura, S; Momose, Y

2001-01-16

A pattern-fitting procedure for quantitative analysis of crystalline pharmaceuticals in solid dosage forms using X-ray powder diffraction data is described. This method is based on a procedure for pattern-fitting in crystal structure refinement, and observed X-ray scattering intensities were fitted to analytical expressions including some fitting parameters, i.e. scale factor, peak positions, peak widths and degree of preferred orientation of the crystallites. All fitting parameters were optimized by the non-linear least-squares procedure. Then the weight fraction of each component was determined from the optimized scale factors. In the present study, well-crystallized binary systems, zinc oxide-zinc sulfide (ZnO-ZnS) and salicylic acid-benzoic acid (SA-BA), were used as the samples. In analysis of the ZnO-ZnS system, the weight fraction of ZnO or ZnS could be determined quantitatively in the range of 5-95% in the case of both powders and tablets. In analysis of the SA-BA systems, the weight fraction of SA or BA could be determined quantitatively in the range of 20-80% in the case of both powders and tablets. Quantitative analysis applying this pattern-fitting procedure showed better reproducibility than other X-ray methods based on the linear or integral intensities of particular diffraction peaks. Analysis using this pattern-fitting procedure also has the advantage that the preferred orientation of the crystallites in solid dosage forms can be also determined in the course of quantitative analysis.

Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE.

PubMed

Quaglio, Pietro; Yegenoglu, Alper; Torre, Emiliano; Endres, Dominik M; Grün, Sonja

2017-01-01

Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis.

Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE

PubMed Central

Quaglio, Pietro; Yegenoglu, Alper; Torre, Emiliano; Endres, Dominik M.; Grün, Sonja

2017-01-01

Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis. PMID:28596729

Proceedings of the Third Annual Symposium on Mathematical Pattern Recognition and Image Analysis

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr.

1985-01-01

Topics addressed include: multivariate spline method; normal mixture analysis applied to remote sensing; image data analysis; classifications in spatially correlated environments; probability density functions; graphical nonparametric methods; subpixel registration analysis; hypothesis integration in image understanding systems; rectification of satellite scanner imagery; spatial variation in remotely sensed images; smooth multidimensional interpolation; and optimal frequency domain textural edge detection filters.

Watershed identification of polygonal patterns in noisy SAR images.

PubMed

Moreels, Pierre; Smrekar, Suzanne E

2003-01-01

This paper describes a new approach to pattern recognition in synthetic aperture radar (SAR) images. A visual analysis of the images provided by NASA's Magellan mission to Venus has revealed a number of zones showing polygonal-shaped faults on the surface of the planet. The goal of the paper is to provide a method to automate the identification of such zones. The high level of noise in SAR images and its multiplicative nature make automated image analysis difficult and conventional edge detectors, like those based on gradient images, inefficient. We present a scheme based on an improved watershed algorithm and a two-scale analysis. The method extracts potential edges in the SAR image, analyzes the patterns obtained, and decides whether or not the image contains a "polygon area". This scheme can also be applied to other SAR or visual images, for instance in observation of Mars and Jupiter's satellite Europa.

Analysis of noise-induced temporal correlations in neuronal spike sequences

NASA Astrophysics Data System (ADS)

Reinoso, José A.; Torrent, M. C.; Masoller, Cristina

2016-11-01

We investigate temporal correlations in sequences of noise-induced neuronal spikes, using a symbolic method of time-series analysis. We focus on the sequence of time-intervals between consecutive spikes (inter-spike-intervals, ISIs). The analysis method, known as ordinal analysis, transforms the ISI sequence into a sequence of ordinal patterns (OPs), which are defined in terms of the relative ordering of consecutive ISIs. The ISI sequences are obtained from extensive simulations of two neuron models (FitzHugh-Nagumo, FHN, and integrate-and-fire, IF), with correlated noise. We find that, as the noise strength increases, temporal order gradually emerges, revealed by the existence of more frequent ordinal patterns in the ISI sequence. While in the FHN model the most frequent OP depends on the noise strength, in the IF model it is independent of the noise strength. In both models, the correlation time of the noise affects the OP probabilities but does not modify the most probable pattern.

Automated Gait Analysis Through Hues and Areas (AGATHA): a method to characterize the spatiotemporal pattern of rat gait

PubMed Central

Kloefkorn, Heidi E.; Pettengill, Travis R.; Turner, Sara M. F.; Streeter, Kristi A.; Gonzalez-Rothi, Elisa J.; Fuller, David D.; Allen, Kyle D.

2016-01-01

While rodent gait analysis can quantify the behavioral consequences of disease, significant methodological differences exist between analysis platforms and little validation has been performed to understand or mitigate these sources of variance. By providing the algorithms used to quantify gait, open-source gait analysis software can be validated and used to explore methodological differences. Our group is introducing, for the first time, a fully-automated, open-source method for the characterization of rodent spatiotemporal gait patterns, termed Automated Gait Analysis Through Hues and Areas (AGATHA). This study describes how AGATHA identifies gait events, validates AGATHA relative to manual digitization methods, and utilizes AGATHA to detect gait compensations in orthopaedic and spinal cord injury models. To validate AGATHA against manual digitization, results from videos of rodent gait, recorded at 1000 frames per second (fps), were compared. To assess one common source of variance (the effects of video frame rate), these 1000 fps videos were re-sampled to mimic several lower fps and compared again. While spatial variables were indistinguishable between AGATHA and manual digitization, low video frame rates resulted in temporal errors for both methods. At frame rates over 125 fps, AGATHA achieved a comparable accuracy and precision to manual digitization for all gait variables. Moreover, AGATHA detected unique gait changes in each injury model. These data demonstrate AGATHA is an accurate and precise platform for the analysis of rodent spatiotemporal gait patterns. PMID:27554674

Automated Gait Analysis Through Hues and Areas (AGATHA): A Method to Characterize the Spatiotemporal Pattern of Rat Gait.

PubMed

Kloefkorn, Heidi E; Pettengill, Travis R; Turner, Sara M F; Streeter, Kristi A; Gonzalez-Rothi, Elisa J; Fuller, David D; Allen, Kyle D

2017-03-01

While rodent gait analysis can quantify the behavioral consequences of disease, significant methodological differences exist between analysis platforms and little validation has been performed to understand or mitigate these sources of variance. By providing the algorithms used to quantify gait, open-source gait analysis software can be validated and used to explore methodological differences. Our group is introducing, for the first time, a fully-automated, open-source method for the characterization of rodent spatiotemporal gait patterns, termed Automated Gait Analysis Through Hues and Areas (AGATHA). This study describes how AGATHA identifies gait events, validates AGATHA relative to manual digitization methods, and utilizes AGATHA to detect gait compensations in orthopaedic and spinal cord injury models. To validate AGATHA against manual digitization, results from videos of rodent gait, recorded at 1000 frames per second (fps), were compared. To assess one common source of variance (the effects of video frame rate), these 1000 fps videos were re-sampled to mimic several lower fps and compared again. While spatial variables were indistinguishable between AGATHA and manual digitization, low video frame rates resulted in temporal errors for both methods. At frame rates over 125 fps, AGATHA achieved a comparable accuracy and precision to manual digitization for all gait variables. Moreover, AGATHA detected unique gait changes in each injury model. These data demonstrate AGATHA is an accurate and precise platform for the analysis of rodent spatiotemporal gait patterns.

Analysis for collapse behavior of resist pattern in short develop time process using atomic force microscope

NASA Astrophysics Data System (ADS)

Sanada, Masakazu; Tamada, Osamu; Ishikawa, Atsushi; Kawai, Akira

2005-05-01

Adhesion property of resist is characterized with DPAT (direct peeling with atomic force microscope (AFM) tip) method using 193 nm resist patterns of 180 nm dot shape which were developed for various developing time between 12 and 120 seconds in order to analyze the phenomenon which the short develop time process had led to suppress the pattern collapse. Surface free energy and refractive index of resist film treated with the developing time were also investigated from a thermodynamic point of view. The balance model among surface energy was adopted for analyzing intrusion phenomenon of developer solution into the resist-substrate interface. It can be explained quantitatively that the intrusion energy of developer solution acts to weaken the adhesion strength of resist pattern to the substrate. Furthermore, the intrusion energy became larger with increasing developing time. Analysis with the DPAT method indicates that the pattern collapse occurs accompanied with interface and cohesion destruction. Interface-scientifically speaking, the short develop time process proved to be effective to suppress the pattern collapse because of higher adhesion energy of the resist pattern to the substrate in shorter developing time.

Analytic Methods for Evaluating Patterns of Multiple Congenital Anomalies in Birth Defect Registries.

PubMed

Agopian, A J; Evans, Jane A; Lupo, Philip J

2018-01-15

It is estimated that 20 to 30% of infants with birth defects have two or more birth defects. Among these infants with multiple congenital anomalies (MCA), co-occurring anomalies may represent either chance (i.e., unrelated etiologies) or pathogenically associated patterns of anomalies. While some MCA patterns have been recognized and described (e.g., known syndromes), others have not been identified or characterized. Elucidating these patterns may result in a better understanding of the etiologies of these MCAs. This article reviews the literature with regard to analytic methods that have been used to evaluate patterns of MCAs, in particular those using birth defect registry data. A popular method for MCA assessment involves a comparison of the observed to expected ratio for a given combination of MCAs, or one of several modified versions of this comparison. Other methods include use of numerical taxonomy or other clustering techniques, multiple regression analysis, and log-linear analysis. Advantages and disadvantages of these approaches, as well as specific applications, were outlined. Despite the availability of multiple analytic approaches, relatively few MCA combinations have been assessed. The availability of large birth defects registries and computing resources that allow for automated, big data strategies for prioritizing MCA patterns may provide for new avenues for better understanding co-occurrence of birth defects. Thus, the selection of an analytic approach may depend on several considerations. Birth Defects Research 110:5-11, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Differentiation of tea varieties using UV-Vis spectra and pattern recognition techniques

NASA Astrophysics Data System (ADS)

Palacios-Morillo, Ana; Alcázar, Ángela.; de Pablos, Fernando; Jurado, José Marcos

2013-02-01

Tea, one of the most consumed beverages all over the world, is of great importance in the economies of a number of countries. Several methods have been developed to classify tea varieties or origins based in pattern recognition techniques applied to chemical data, such as metal profile, amino acids, catechins and volatile compounds. Some of these analytical methods become tedious and expensive to be applied in routine works. The use of UV-Vis spectral data as discriminant variables, highly influenced by the chemical composition, can be an alternative to these methods. UV-Vis spectra of methanol-water extracts of tea have been obtained in the interval 250-800 nm. Absorbances have been used as input variables. Principal component analysis was used to reduce the number of variables and several pattern recognition methods, such as linear discriminant analysis, support vector machines and artificial neural networks, have been applied in order to differentiate the most common tea varieties. A successful classification model was built by combining principal component analysis and multilayer perceptron artificial neural networks, allowing the differentiation between tea varieties. This rapid and simple methodology can be applied to solve classification problems in food industry saving economic resources.

Fingerprint pattern restoration by digital image processing techniques.

PubMed

Wen, Che-Yen; Yu, Chiu-Chung

2003-09-01

Fingerprint evidence plays an important role in solving criminal problems. However, defective (lacking information needed for completeness) or contaminated (undesirable information included) fingerprint patterns make identifying and recognizing processes difficult. Unfortunately. this is the usual case. In the recognizing process (enhancement of patterns, or elimination of "false alarms" so that a fingerprint pattern can be searched in the Automated Fingerprint Identification System (AFIS)), chemical and physical techniques have been proposed to improve pattern legibility. In the identifying process, a fingerprint examiner can enhance contaminated (but not defective) fingerprint patterns under guidelines provided by the Scientific Working Group on Friction Ridge Analysis, Study and Technology (SWGFAST), the Scientific Working Group on Imaging Technology (SWGIT), and an AFIS working group within the National Institute of Justice. Recently, the image processing techniques have been successfully applied in forensic science. For example, we have applied image enhancement methods to improve the legibility of digital images such as fingerprints and vehicle plate numbers. In this paper, we propose a novel digital image restoration technique based on the AM (amplitude modulation)-FM (frequency modulation) reaction-diffusion method to restore defective or contaminated fingerprint patterns. This method shows its potential application to fingerprint pattern enhancement in the recognizing process (but not for the identifying process). Synthetic and real images are used to show the capability of the proposed method. The results of enhancing fingerprint patterns by the manual process and our method are evaluated and compared.

How memory of direct animal interactions can lead to territorial pattern formation.

PubMed

Potts, Jonathan R; Lewis, Mark A

2016-05-01

Mechanistic home range analysis (MHRA) is a highly effective tool for understanding spacing patterns of animal populations. It has hitherto focused on populations where animals defend their territories by communicating indirectly, e.g. via scent marks. However, many animal populations defend their territories using direct interactions, such as ritualized aggression. To enable application of MHRA to such populations, we construct a model of direct territorial interactions, using linear stability analysis and energy methods to understand when territorial patterns may form. We show that spatial memory of past interactions is vital for pattern formation, as is memory of 'safe' places, where the animal has visited but not suffered recent territorial encounters. Additionally, the spatial range over which animals make decisions to move is key to understanding the size and shape of their resulting territories. Analysis using energy methods, on a simplified version of our system, shows that stability in the nonlinear system corresponds well to predictions of linear analysis. We also uncover a hysteresis in the process of territory formation, so that formation may depend crucially on initial space-use. Our analysis, in one dimension and two dimensions, provides mathematical groundwork required for extending MHRA to situations where territories are defended by direct encounters. © 2016 The Author(s).

The Analysis Performance Method Naive Bayes Andssvm Determine Pattern Groups of Disease

NASA Astrophysics Data System (ADS)

Sitanggang, Rianto; Tulus; Situmorang, Zakarias

2017-12-01

Information is a very important element and into the daily needs of the moment, to get a precise and accurate information is not easy, this research can help decision makers and make a comparison. Researchers perform data mining techniques to analyze the performance of methods and algorithms naïve Bayes methods Smooth Support Vector Machine (ssvm) in the grouping of the disease.The pattern of disease that is often suffered by people in the group can be in the detection area of the collection of information contained in the medical record. Medical records have infromasi disease by patients in coded according to standard WHO. Processing of medical record data to find patterns of this group of diseases that often occur in this community take the attribute address, sex, type of disease, and age. Determining the next analysis is grouping of four ersebut attribute. From the results of research conducted on the dataset fever diabete mellitus, naïve Bayes method produces an average value of 99% and an accuracy and SSVM method produces an average value of 93% accuracy

Comparison of transferrin isoform analysis by capillary electrophoresis and HPLC for screening congenital disorders of glycosylation.

PubMed

Dave, Mihika B; Dherai, Alpa J; Udani, Vrajesh P; Hegde, Anaita U; Desai, Neelu A; Ashavaid, Tester F

2018-01-01

Transferrin, a major glycoprotein has different isoforms depending on the number of sialic acid residues present on its oligosaccharide chain. Genetic variants of transferrin as well as the primary (CDG) & secondary glycosylation defects lead to an altered transferrin pattern. Isoform analysis methods are based on charge/mass variations. We aimed to compare the performance of commercially available capillary electrophoresis CDT kit for diagnosing congenital disorders of glycosylation with our in-house optimized HPLC method for transferrin isoform analysis. The isoform pattern of 30 healthy controls & 50 CDG-suspected patients was determined by CE using a Carbohydrate-Deficient Transferrin kit. The results were compared with in-house HPLC-based assay for transferrin isoforms. Transferrin isoform pattern for healthy individuals showed a predominant tetrasialo transferrin fraction followed by pentasialo, trisialo, and disialotransferrin. Two of 50 CDG-suspected patients showed the presence of asialylated isoforms. The results were comparable with isoform pattern obtained by HPLC. The commercial controls showed a <20% CV for each isoform. Bland Altman plot showed the difference plot to be within +1.96 with no systemic bias in the test results by HPLC & CE. The CE method is rapid, reproducible and comparable with HPLC and can be used for screening Glycosylation defects. © 2017 Wiley Periodicals, Inc.

Using Spider-Web Patterns To Determine Toxicity

NASA Technical Reports Server (NTRS)

Noever, David A.; Cronise, Raymond J.; Relwani, Rachna A.

1995-01-01

Method of determining toxicities of chemicals involves recording and analysis of spider-web patterns. Based on observation spiders exposed to various chemicals spin webs that differ, in various ways, from normal webs. Potential alternative to toxicity testing on higher animals.

The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data

PubMed Central

Hebart, Martin N.; Görgen, Kai; Haynes, John-Dylan

2015-01-01

The multivariate analysis of brain signals has recently sparked a great amount of interest, yet accessible and versatile tools to carry out decoding analyses are scarce. Here we introduce The Decoding Toolbox (TDT) which represents a user-friendly, powerful and flexible package for multivariate analysis of functional brain imaging data. TDT is written in Matlab and equipped with an interface to the widely used brain data analysis package SPM. The toolbox allows running fast whole-brain analyses, region-of-interest analyses and searchlight analyses, using machine learning classifiers, pattern correlation analysis, or representational similarity analysis. It offers automatic creation and visualization of diverse cross-validation schemes, feature scaling, nested parameter selection, a variety of feature selection methods, multiclass capabilities, and pattern reconstruction from classifier weights. While basic users can implement a generic analysis in one line of code, advanced users can extend the toolbox to their needs or exploit the structure to combine it with external high-performance classification toolboxes. The toolbox comes with an example data set which can be used to try out the various analysis methods. Taken together, TDT offers a promising option for researchers who want to employ multivariate analyses of brain activity patterns. PMID:25610393

The new analysis method of PWQ in the DRAM pattern

NASA Astrophysics Data System (ADS)

Han, Daehan; Chang, Jinman; Kim, Taeheon; Lee, Kyusun; Kim, Yonghyeon; Kang, Jinyoung; Hong, Aeran; Choi, Bumjin; Lee, Joosung; Kim, Hyoung Jun; Lee, Kweonjae; Hong, Hyoungsun; Jin, Gyoyoung

2016-03-01

In a sub 2Xnm node process, the feedback of pattern weak points is more and more significant. Therefore, it is very important to extract the systemic defect in Double Patterning Technology(DPT), however, it is impossible to predict exact systemic defect at the recent photo simulation tool.[1] Therefore, the method of Process Window Qualification (PWQ) is very serious and essential these days. Conventional PWQ methods are die to die image comparison by using an e-beam or bright field machine. Results are evaluated by the person, who reviews the images, in some cases. However, conventional die to die comparison method has critical problem. If reference die and comparison die have same problem, such as both of dies have pattern problems, the issue patterns are not detected by current defect detecting approach. Aside from the inspection accuracy, reviewing the wafer requires much effort and time to justify the genuine issue patterns. Therefore, our company adopts die to data based matching PWQ method that is using NGR machine. The main features of the NGR are as follows. First, die to data based matching, second High speed, finally massive data were used for evaluation of pattern inspection.[2] Even though our die to data based matching PWQ method measures the mass data, our margin decision process is based on image shape. Therefore, it has some significant problems. First, because of the long analysis time, the developing period of new device is increased. Moreover, because of the limitation of resources, it may not examine the full chip area. Consequently, the result of PWQ weak points cannot represent the all the possible defects. Finally, since the PWQ margin is not decided by the mathematical value, to make the solid definition of killing defect is impossible. To overcome these problems, we introduce a statistical values base process window qualification method that increases the accuracy of process margin and reduces the review time. Therefore, it is possible to see the genuine margin of the critical pattern issue which we cannot see on our conventional PWQ inspection; hence we can enhance the accuracy of PWQ margin.

The "Promise" of Three Methods of Word Association Analysis to L2 Lexical Research

ERIC Educational Resources Information Center

Zareva, Alla; Wolter, Brent

2012-01-01

The present study is an attempt to empirically test and compare the results of three methods of word association (WA) analysis. Two of the methods--namely, associative commonality and nativelikeness, and lexico-syntactic patterns of associative organization--have been traditionally used in both first language (L1) and second language (L2)…

Moire technique utilization for detection and measurement of scoliosis

NASA Astrophysics Data System (ADS)

Zawieska, Dorota; Podlasiak, Piotr

1993-02-01

Moire projection method enables non-contact measurement of the shape or deformation of different surfaces and constructions by fringe pattern analysis. The fringe map acquisition of the whole surface of the object under test is one of the main advantages compared with 'point by point' methods. The computer analyzes the shape of the whole surface and next user can selected different points or cross section of the object map. In this paper a few typical examples of an application of the moire technique in solving different medical problems will be presented. We will also present to you the equipment the moire pattern analysis is done in real time using the phase stepping method with CCD camera.

Generation 1.5 Written Error Patterns: A Comparative Study

ERIC Educational Resources Information Center

Doolan, Stephen M.; Miller, Donald

2012-01-01

In an attempt to contribute to existing research on Generation 1.5 students, the current study uses quantitative and qualitative methods to compare error patterns in a corpus of Generation 1.5, L1, and L2 community college student writing. This error analysis provides one important way to determine if error patterns in Generation 1.5 student…

[Analysis of dietary pattern and diabetes mellitus influencing factors identified by classification tree model in adults of Fujian].

PubMed

Yu, F L; Ye, Y; Yan, Y S

2017-05-10

Objective: To find out the dietary patterns and explore the relationship between environmental factors (especially dietary patterns) and diabetes mellitus in the adults of Fujian. Methods: Multi-stage sampling method were used to survey residents aged ≥18 years by questionnaire, physical examination and laboratory detection in 10 disease surveillance points in Fujian. Factor analysis was used to identify the dietary patterns, while logistic regression model was applied to analyze relationship between dietary patterns and diabetes mellitus, and classification tree model was adopted to identify the influencing factors for diabetes mellitus. Results: There were four dietary patterns in the population, including meat, plant, high-quality protein, and fried food and beverages patterns. The result of logistic analysis showed that plant pattern, which has higher factor loading of fresh fruit-vegetables and cereal-tubers, was a protective factor for non-diabetes mellitus. The risk of diabetes mellitus in the population at T2 and T3 levels of factor score were 0.727 (95 %CI: 0.561-0.943) times and 0.736 (95 %CI : 0.573-0.944) times higher, respectively, than those whose factor score was in lowest quartile. Thirteen influencing factors and eleven group at high-risk for diabetes mellitus were identified by classification tree model. The influencing factors were dyslipidemia, age, family history of diabetes, hypertension, physical activity, career, sex, sedentary time, abdominal adiposity, BMI, marital status, sleep time and high-quality protein pattern. Conclusion: There is a close association between dietary patterns and diabetes mellitus. It is necessary to promote healthy and reasonable diet, strengthen the monitoring and control of blood lipids, blood pressure and body weight, and have good lifestyle for the prevention and control of diabetes mellitus.

Analysis of biomedical time signals for characterization of cutaneous diabetic micro-angiopathy

NASA Astrophysics Data System (ADS)

Kraitl, Jens; Ewald, Hartmut

2007-02-01

Photo-plethysmography (PPG) is frequently used in research on microcirculation of blood. It is a non-invasive procedure and takes minimal time to be carried out. Usually PPG time series are analyzed by conventional linear methods, mainly Fourier analysis. These methods may not be optimal for the investigation of nonlinear effects of the hearth circulation system like vasomotion, autoregulation, thermoregulation, breathing, heartbeat and vessels. The wavelet analysis of the PPG time series is a specific, sensitive nonlinear method for the in vivo identification of hearth circulation patterns and human health status. This nonlinear analysis of PPG signals provides additional information which cannot be detected using conventional approaches. The wavelet analysis has been used to study healthy subjects and to characterize the health status of patients with a functional cutaneous microangiopathy which was associated with diabetic neuropathy. The non-invasive in vivo method is based on the radiation of monochromatic light through an area of skin on the finger. A Photometrical Measurement Device (PMD) has been developed. The PMD is suitable for non-invasive continuous online monitoring of one or more biologic constituent values and blood circulation patterns.

A generalized method for high throughput in-situ experiment data analysis: An example of battery materials exploration

NASA Astrophysics Data System (ADS)

Aoun, Bachir; Yu, Cun; Fan, Longlong; Chen, Zonghai; Amine, Khalil; Ren, Yang

2015-04-01

A generalized method is introduced to extract critical information from series of ranked correlated data. The method is generally applicable to all types of spectra evolving as a function of any arbitrary parameter. This approach is based on correlation functions and statistical scedasticity formalism. Numerous challenges in analyzing high throughput experimental data can be tackled using the herein proposed method. We applied this method to understand the reactivity pathway and formation mechanism of a Li-ion battery cathode material during high temperature synthesis using in-situ high-energy X-ray diffraction. We demonstrate that Pearson's correlation function can easily unravel all major phase transition and, more importantly, the minor structural changes which cannot be revealed by conventionally inspecting the series of diffraction patterns. Furthermore, a two-dimensional (2D) reactivity pattern calculated as the scedasticity along all measured reciprocal space of all successive diffraction pattern pairs unveils clearly the structural evolution path and the active areas of interest during the synthesis. The methods described here can be readily used for on-the-fly data analysis during various in-situ operando experiments in order to quickly evaluate and optimize experimental conditions, as well as for post data analysis and large data mining where considerable amount of data hinders the feasibility of the investigation through point-by-point inspection.

A generalized method for high throughput in-situ experiment data analysis: An example of battery materials exploration

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

Aoun, Bachir; Yu, Cun; Fan, Longlong

A generalized method is introduced to extract critical information from series of ranked correlated data. The method is generally applicable to all types of spectra evolving as a function of any arbitrary parameter. This approach is based on correlation functions and statistical scedasticity formalism. Numerous challenges in analyzing high throughput experimental data can be tackled using the herein proposed method. We applied this method to understand the reactivity pathway and formation mechanism of a Li-ion battery cathode material during high temperature synthesis using in-situ highenergy X-ray diffraction. We demonstrate that Pearson's correlation function can easily unravel all major phase transitionmore » and, more importantly, the minor structural changes which cannot be revealed by conventionally inspecting the series of diffraction patterns. Furthermore, a two-dimensional (2D) reactivity pattern calculated as the scedasticity along all measured reciprocal space of all successive diffraction pattern pairs unveils clearly the structural evolution path and the active areas of interest during the synthesis. The methods described here can be readily used for on-the-fly data analysis during various in-situ operando experiments in order to quickly evaluate and optimize experimental conditions, as well as for post data analysis and large data mining where considerable amount of data hinders the feasibility of the investigation through point-by-point inspection.« less

Particle detection for patterned wafers of 100nm design rule by evanescent light illumination: analysis of evanescent light scattering using Finite-Difference Time-Domain (FDTD) method

NASA Astrophysics Data System (ADS)

Yoshioka, Toshie; Miyoshi, Takashi; Takaya, Yasuhiro

2005-12-01

To realize high productivity and reliability of the semiconductor, patterned wafers inspection technology to maintain high yield becomes essential in modern semiconductor manufacturing processes. As circuit feature is scaled below 100nm, the conventional imaging and light scattering methods are impossible to apply to the patterned wafers inspection technique, because of diffraction limit and lower S/N ratio. So, we propose a new particle detection method using annular evanescent light illumination. In this method, a converging annular light used as a light source is incident on a micro-hemispherical lens. When the converging angle is larger than critical angle, annular evanescent light is generated under the bottom surface of the hemispherical lens. Evanescent light is localized near by the bottom surface and decays exponentially away from the bottom surface. So, the evanescent light selectively illuminates the particles on the patterned wafer surface, because it can't illuminate the patterned wafer surface. The proposed method evaluates particles on a patterned wafer surface by detecting scattered evanescent light distribution from particles. To analyze the fundamental characteristics of the proposed method, the computer simulation was performed using FDTD method. The simulation results show that the proposed method is effective for detecting 100nm size particle on patterned wafer of 100nm lines and spaces, particularly under the condition that the evanescent light illumination with p-polarization and parallel incident to the line orientation. Finally, the experiment results suggest that 220nm size particle on patterned wafer of about 200nm lines and spaces can be detected.

Modeling eye gaze patterns in clinician-patient interaction with lag sequential analysis.

PubMed

Montague, Enid; Xu, Jie; Chen, Ping-Yu; Asan, Onur; Barrett, Bruce P; Chewning, Betty

2011-10-01

The aim of this study was to examine whether lag sequential analysis could be used to describe eye gaze orientation between clinicians and patients in the medical encounter. This topic is particularly important as new technologies are implemented into multiuser health care settings in which trust is critical and nonverbal cues are integral to achieving trust. This analysis method could lead to design guidelines for technologies and more effective assessments of interventions. Nonverbal communication patterns are important aspects of clinician-patient interactions and may affect patient outcomes. The eye gaze behaviors of clinicians and patients in 110 videotaped medical encounters were analyzed using the lag sequential method to identify significant behavior sequences. Lag sequential analysis included both event-based lag and time-based lag. Results from event-based lag analysis showed that the patient's gaze followed that of the clinician, whereas the clinician's gaze did not follow the patient's. Time-based sequential analysis showed that responses from the patient usually occurred within 2 s after the initial behavior of the clinician. Our data suggest that the clinician's gaze significantly affects the medical encounter but that the converse is not true. Findings from this research have implications for the design of clinical work systems and modeling interactions. Similar research methods could be used to identify different behavior patterns in clinical settings (physical layout, technology, etc.) to facilitate and evaluate clinical work system designs.

Software phantom with realistic speckle modeling for validation of image analysis methods in echocardiography

NASA Astrophysics Data System (ADS)

Law, Yuen C.; Tenbrinck, Daniel; Jiang, Xiaoyi; Kuhlen, Torsten

2014-03-01

Computer-assisted processing and interpretation of medical ultrasound images is one of the most challenging tasks within image analysis. Physical phenomena in ultrasonographic images, e.g., the characteristic speckle noise and shadowing effects, make the majority of standard methods from image analysis non optimal. Furthermore, validation of adapted computer vision methods proves to be difficult due to missing ground truth information. There is no widely accepted software phantom in the community and existing software phantoms are not exible enough to support the use of specific speckle models for different tissue types, e.g., muscle and fat tissue. In this work we propose an anatomical software phantom with a realistic speckle pattern simulation to _ll this gap and provide a exible tool for validation purposes in medical ultrasound image analysis. We discuss the generation of speckle patterns and perform statistical analysis of the simulated textures to obtain quantitative measures of the realism and accuracy regarding the resulting textures.

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

PubMed Central

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

2016-01-01

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. PMID:27754328

Development of Pattern Recognition Techniques for the Evaluation of Toxicant Impacts to Multispecies Systems

DTIC Science & Technology

1993-06-18

the exception. In the Standardized Aquatic Microcosm and the Mixed Flask Culture (MFC) microcosms, multivariate analysis and clustering methods...rule rather than the exception. In the Standardized Aquatic Microcosm and the Mixed Flask Culture (MFC) microcosms, multivariate analysis and...experiments using two microcosm protocols. We use nonmetric clustering, a multivariate pattern recognition technique developed by Matthews and Heame (1991

3D capillary stop valves for versatile patterning inside microfluidic chips.

PubMed

Papadimitriou, V A; Segerink, L I; van den Berg, A; Eijkel, J C T

2018-02-13

The patterning of antibodies in microfluidics chips is always a delicate process that is usually done in an open chip before bonding. Typical bonding techniques such as plasma treatment can harm the antibodies with as result that they are removed from our fabrication toolbox. Here we propose a method, based on capillary phenomena using 3D capillary valves, that autonomously and conveniently allows us to pattern liquids inside closed chips. We theoretically analyse the system and demonstrate how our analysis can be used as a design tool for various applications. Chips patterned with the method were used for simple immunodetection of a cardiac biomarker which demonstrates its suitability for antibody patterning. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Disease clusters, exact distributions of maxima, and P-values.

PubMed

Grimson, R C

1993-10-01

This paper presents combinatorial (exact) methods that are useful in the analysis of disease cluster data obtained from small environments, such as buildings and neighbourhoods. Maxwell-Boltzmann and Fermi-Dirac occupancy models are compared in terms of appropriateness of representation of disease incidence patterns (space and/or time) in these environments. The methods are illustrated by a statistical analysis of the incidence pattern of bone fractures in a setting wherein fracture clustering was alleged to be occurring. One of the methodological results derived in this paper is the exact distribution of the maximum cell frequency in occupancy models.

Morphology filter bank for extracting nodular and linear patterns in medical images.

PubMed

Hashimoto, Ryutaro; Uchiyama, Yoshikazu; Uchimura, Keiichi; Koutaki, Gou; Inoue, Tomoki

2017-04-01

Using image processing to extract nodular or linear shadows is a key technique of computer-aided diagnosis schemes. This study proposes a new method for extracting nodular and linear patterns of various sizes in medical images. We have developed a morphology filter bank that creates multiresolution representations of an image. Analysis bank of this filter bank produces nodular and linear patterns at each resolution level. Synthesis bank can then be used to perfectly reconstruct the original image from these decomposed patterns. Our proposed method shows better performance based on a quantitative evaluation using a synthesized image compared with a conventional method based on a Hessian matrix, often used to enhance nodular and linear patterns. In addition, experiments show that our method can be applied to the followings: (1) microcalcifications of various sizes in mammograms can be extracted, (2) blood vessels of various sizes in retinal fundus images can be extracted, and (3) thoracic CT images can be reconstructed while removing normal vessels. Our proposed method is useful for extracting nodular and linear shadows or removing normal structures in medical images.

GazeAppraise v. 0.1

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

Wilson, Andrew; Haass, Michael; Rintoul, Mark Daniel

GazeAppraise advances the state of the art of gaze pattern analysis using methods that simultaneously analyze spatial and temporal characteristics of gaze patterns. GazeAppraise enables novel research in visual perception and cognition; for example, using shape features as distinguishing elements to assess individual differences in visual search strategy. Given a set of point-to-point gaze sequences, hereafter referred to as scanpaths, the method constructs multiple descriptive features for each scanpath. Once the scanpath features have been calculated, they are used to form a multidimensional vector representing each scanpath and cluster analysis is performed on the set of vectors from all scanpaths.more » An additional benefit of this method is the identification of causal or correlated characteristics of the stimuli, subjects, and visual task through statistical analysis of descriptive metadata distributions within and across clusters.« less

Multiplex titration RT-PCR: rapid determination of gene expression patterns for a large number of genes

NASA Technical Reports Server (NTRS)

Nebenfuhr, A.; Lomax, T. L.

1998-01-01

We have developed an improved method for determination of gene expression levels with RT-PCR. The procedure is rapid and does not require extensive optimization or densitometric analysis. Since the detection of individual transcripts is PCR-based, small amounts of tissue samples are sufficient for the analysis of expression patterns in large gene families. Using this method, we were able to rapidly screen nine members of the Aux/IAA family of auxin-responsive genes and identify those genes which vary in message abundance in a tissue- and light-specific manner. While not offering the accuracy of conventional semi-quantitative or competitive RT-PCR, our method allows quick screening of large numbers of genes in a wide range of RNA samples with just a thermal cycler and standard gel analysis equipment.

Investigation of computer-aided colonic crypt pattern analysis

NASA Astrophysics Data System (ADS)

Qi, Xin; Pan, Yinsheng; Sivak, Michael V., Jr.; Olowe, Kayode; Rollins, Andrew M.

2007-02-01

Colorectal cancer is the second leading cause of cancer-related death in the United States. Approximately 50% of these deaths could be prevented by earlier detection through screening. Magnification chromoendoscopy is a technique which utilizes tissue stains applied to the gastrointestinal mucosa and high-magnification endoscopy to better visualize and characterize lesions. Prior studies have shown that shapes of colonic crypts change with disease and show characteristic patterns. Current methods for assessing colonic crypt patterns are somewhat subjective and not standardized. Computerized algorithms could be used to standardize colonic crypt pattern assessment. We have imaged resected colonic mucosa in vitro (N = 70) using methylene blue dye and a surgical microscope to approximately simulate in vivo imaging with magnification chromoendoscopy. We have developed a method of computerized processing to analyze the crypt patterns in the images. The quantitative image analysis consists of three steps. First, the crypts within the region of interest of colonic tissue are semi-automatically segmented using watershed morphological processing. Second, crypt size and shape parameters are extracted from the segmented crypts. Third, each sample is assigned to a category according to the Kudo criteria. The computerized classification is validated by comparison with human classification using the Kudo classification criteria. The computerized colonic crypt pattern analysis algorithm will enable a study of in vivo magnification chromoendoscopy of colonic crypt pattern correlated with risk of colorectal cancer. This study will assess the feasibility of screening and surveillance of the colon using magnification chromoendoscopy.

Bone marrow cavity segmentation using graph-cuts with wavelet-based texture feature.

PubMed

Shigeta, Hironori; Mashita, Tomohiro; Kikuta, Junichi; Seno, Shigeto; Takemura, Haruo; Ishii, Masaru; Matsuda, Hideo

2017-10-01

Emerging bioimaging technologies enable us to capture various dynamic cellular activities [Formula: see text]. As large amounts of data are obtained these days and it is becoming unrealistic to manually process massive number of images, automatic analysis methods are required. One of the issues for automatic image segmentation is that image-taking conditions are variable. Thus, commonly, many manual inputs are required according to each image. In this paper, we propose a bone marrow cavity (BMC) segmentation method for bone images as BMC is considered to be related to the mechanism of bone remodeling, osteoporosis, and so on. To reduce manual inputs to segment BMC, we classified the texture pattern using wavelet transformation and support vector machine. We also integrated the result of texture pattern classification into the graph-cuts-based image segmentation method because texture analysis does not consider spatial continuity. Our method is applicable to a particular frame in an image sequence in which the condition of fluorescent material is variable. In the experiment, we evaluated our method with nine types of mother wavelets and several sets of scale parameters. The proposed method with graph-cuts and texture pattern classification performs well without manual inputs by a user.

2D pair distribution function analysis of anisotropic small-angle scattering patterns from elongated nano-composite hydrogels.

PubMed

Nishi, Kengo; Shibayama, Mitsuhiro

2017-05-03

Small angle scattering (SAS) on polymer nanocomposites under elongation or shear flow is an important experimental method to investigate the reinforcement effects of the mechanical properties by fillers. However, the anisotropic scattering patterns that appear in SAS are very complicated and difficult to interpret. A representative example is a four-spot scattering pattern observed in the case of polymer materials containing silica nanoparticles, the origin of which is still in debate because of the lack of quantitative analysis. The difficulties in the interpretation of anisotropic scattering patterns mainly arise from the abstract nature of the reciprocal space. Here, we focus on the 2D pair distribution function (PDF) directly evaluated from anisotropic scattering patterns. We applied this method to elongated poly(N,N-dimethylacrylamide) gels containing silica nanoparticles (PDAM-NP gel), which show a four-spot scattering pattern under elongation. From 2D PDFs, we obtained detailed and concrete structural information about the elongated PDAM-NP gel, such as affine and non-affine displacements of directly attached and homogeneously dispersed silica nanoparticles, respectively. We proposed that nanoparticles homogeneously dispersed in the perpendicular direction are not displaced due to the collision of the adsorbed polymer layer during elongation, while those in the parallel direction are displaced in an affine way. We assumed that this suppression of the lateral compression is the origin of the four-spot pattern in this study. These results strongly indicate that our 2D PDF analysis will provide deep insight into the internal structure of polymer nanocomposites hidden in the anisotropic scattering patterns.

Application of Concepts from Cross-Recurrence Analysis in Speech Production: An Overview and Comparison with Other Nonlinear Methods

ERIC Educational Resources Information Center

Lancia, Leonardo; Fuchs, Susanne; Tiede, Mark

2014-01-01

Purpose: The aim of this article was to introduce an important tool, cross-recurrence analysis, to speech production applications by showing how it can be adapted to evaluate the similarity of multivariate patterns of articulatory motion. The method differs from classical applications of cross-recurrence analysis because no phase space…

Bloodstain Pattern Analysis: implementation of a fluid dynamic model for position determination of victims

PubMed Central

Laan, Nick; de Bruin, Karla G.; Slenter, Denise; Wilhelm, Julie; Jermy, Mark; Bonn, Daniel

2015-01-01

Bloodstain Pattern Analysis is a forensic discipline in which, among others, the position of victims can be determined at crime scenes on which blood has been shed. To determine where the blood source was investigators use a straight-line approximation for the trajectory, ignoring effects of gravity and drag and thus overestimating the height of the source. We determined how accurately the location of the origin can be estimated when including gravity and drag into the trajectory reconstruction. We created eight bloodstain patterns at one meter distance from the wall. The origin’s location was determined for each pattern with: the straight-line approximation, our method including gravity, and our method including both gravity and drag. The latter two methods require the volume and impact velocity of each bloodstain, which we are able to determine with a 3D scanner and advanced fluid dynamics, respectively. We conclude that by including gravity and drag in the trajectory calculation, the origin’s location can be determined roughly four times more accurately than with the straight-line approximation. Our study enables investigators to determine if the victim was sitting or standing, or it might be possible to connect wounds on the body to specific patterns, which is important for crime scene reconstruction. PMID:26099070

Bloodstain Pattern Analysis: implementation of a fluid dynamic model for position determination of victims.

PubMed

Laan, Nick; de Bruin, Karla G; Slenter, Denise; Wilhelm, Julie; Jermy, Mark; Bonn, Daniel

2015-06-22

Bloodstain Pattern Analysis is a forensic discipline in which, among others, the position of victims can be determined at crime scenes on which blood has been shed. To determine where the blood source was investigators use a straight-line approximation for the trajectory, ignoring effects of gravity and drag and thus overestimating the height of the source. We determined how accurately the location of the origin can be estimated when including gravity and drag into the trajectory reconstruction. We created eight bloodstain patterns at one meter distance from the wall. The origin's location was determined for each pattern with: the straight-line approximation, our method including gravity, and our method including both gravity and drag. The latter two methods require the volume and impact velocity of each bloodstain, which we are able to determine with a 3D scanner and advanced fluid dynamics, respectively. We conclude that by including gravity and drag in the trajectory calculation, the origin's location can be determined roughly four times more accurately than with the straight-line approximation. Our study enables investigators to determine if the victim was sitting or standing, or it might be possible to connect wounds on the body to specific patterns, which is important for crime scene reconstruction.

Pattern recall skills of talented soccer players: Two new methods applied.

PubMed

van Maarseveen, Mariëtte J J; Oudejans, Raôul R D; Savelsbergh, Geert J P

2015-06-01

In this study we analyzed the pattern recall skills of talented soccer players by means of two innovative methods of analysis and gaze behavior data. Twenty-two young female soccer players watched video clips of 3 vs. 3 small-sided games and, after occlusion, had to reproduce the positions of the players. Recall performance was measured by calculating the spatial error of the recalled player positions at the moment of occlusion and at consecutive 33ms increments. We analyzed player positions relative to each other, by assessing geometric pattern features in terms of angles between players, and we transformed the data into real-world coordinates to exclude the effects of the 2D perspective in the video clips. The results showed that the participants anticipated the movements of the patterns. In real-world coordinates, the more experienced players anticipated the pattern further in advance than the less experienced players and demonstrated a higher search rate, a shorter fixation duration and a higher fixation order. The differences in recall accuracy between the defensive and offensive elements were not consistent across the methods of analysis and, therefore, we propose that perspective effects of the video clip should be taken into account in further research. Copyright © 2015 Elsevier B.V. All rights reserved.

Bloodstain Pattern Analysis: implementation of a fluid dynamic model for position determination of victims

NASA Astrophysics Data System (ADS)

Laan, Nick; de Bruin, Karla G.; Slenter, Denise; Wilhelm, Julie; Jermy, Mark; Bonn, Daniel

2015-06-01

Bloodstain Pattern Analysis is a forensic discipline in which, among others, the position of victims can be determined at crime scenes on which blood has been shed. To determine where the blood source was investigators use a straight-line approximation for the trajectory, ignoring effects of gravity and drag and thus overestimating the height of the source. We determined how accurately the location of the origin can be estimated when including gravity and drag into the trajectory reconstruction. We created eight bloodstain patterns at one meter distance from the wall. The origin’s location was determined for each pattern with: the straight-line approximation, our method including gravity, and our method including both gravity and drag. The latter two methods require the volume and impact velocity of each bloodstain, which we are able to determine with a 3D scanner and advanced fluid dynamics, respectively. We conclude that by including gravity and drag in the trajectory calculation, the origin’s location can be determined roughly four times more accurately than with the straight-line approximation. Our study enables investigators to determine if the victim was sitting or standing, or it might be possible to connect wounds on the body to specific patterns, which is important for crime scene reconstruction.

Time-Course Gene Set Analysis for Longitudinal Gene Expression Data

PubMed Central

Hejblum, Boris P.; Skinner, Jason; Thiébaut, Rodolphe

2015-01-01

Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA) introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR) measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indicates if the time patterns of gene sets significantly differ according to these conditions. The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial), and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA) for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses. In our simulation study TcGSA exhibits good statistical properties, and an increased power compared to other approaches for analyzing time-course expression patterns of gene sets. The method is made available for the community through an R package. PMID:26111374

Assessment of Gait Characteristics in Total Knee Arthroplasty Patients Using a Hierarchical Partial Least Squares Method.

PubMed

Wang, Wei; Ackland, David C; McClelland, Jodie A; Webster, Kate E; Halgamuge, Saman

2018-01-01

Quantitative gait analysis is an important tool in objective assessment and management of total knee arthroplasty (TKA) patients. Studies evaluating gait patterns in TKA patients have tended to focus on discrete data such as spatiotemporal information, joint range of motion and peak values of kinematics and kinetics, or consider selected principal components of gait waveforms for analysis. These strategies may not have the capacity to capture small variations in gait patterns associated with each joint across an entire gait cycle, and may ultimately limit the accuracy of gait classification. The aim of this study was to develop an automatic feature extraction method to analyse patterns from high-dimensional autocorrelated gait waveforms. A general linear feature extraction framework was proposed and a hierarchical partial least squares method derived for discriminant analysis of multiple gait waveforms. The effectiveness of this strategy was verified using a dataset of joint angle and ground reaction force waveforms from 43 patients after TKA surgery and 31 healthy control subjects. Compared with principal component analysis and partial least squares methods, the hierarchical partial least squares method achieved generally better classification performance on all possible combinations of waveforms, with the highest classification accuracy . The novel hierarchical partial least squares method proposed is capable of capturing virtually all significant differences between TKA patients and the controls, and provides new insights into data visualization. The proposed framework presents a foundation for more rigorous classification of gait, and may ultimately be used to evaluate the effects of interventions such as surgery and rehabilitation.

Unifying cost and information in information-theoretic competitive learning.

PubMed

Kamimura, Ryotaro

2005-01-01

In this paper, we introduce costs into the framework of information maximization and try to maximize the ratio of information to its associated cost. We have shown that competitive learning is realized by maximizing mutual information between input patterns and competitive units. One shortcoming of the method is that maximizing information does not necessarily produce representations faithful to input patterns. Information maximizing primarily focuses on some parts of input patterns that are used to distinguish between patterns. Therefore, we introduce the cost, which represents average distance between input patterns and connection weights. By minimizing the cost, final connection weights reflect input patterns well. We applied the method to a political data analysis, a voting attitude problem and a Wisconsin cancer problem. Experimental results confirmed that, when the cost was introduced, representations faithful to input patterns were obtained. In addition, improved generalization performance was obtained within a relatively short learning time.

Two-dimensional single-cell patterning with one cell per well driven by surface acoustic waves

PubMed Central

Collins, David J.; Morahan, Belinda; Garcia-Bustos, Jose; Doerig, Christian; Plebanski, Magdalena; Neild, Adrian

2015-01-01

In single-cell analysis, cellular activity and parameters are assayed on an individual, rather than population-average basis. Essential to observing the activity of these cells over time is the ability to trap, pattern and retain them, for which previous single-cell-patterning work has principally made use of mechanical methods. While successful as a long-term cell-patterning strategy, these devices remain essentially single use. Here we introduce a new method for the patterning of multiple spatially separated single particles and cells using high-frequency acoustic fields with one cell per acoustic well. We characterize and demonstrate patterning for both a range of particle sizes and the capture and patterning of cells, including human lymphocytes and red blood cells infected by the malarial parasite Plasmodium falciparum. This ability is made possible by a hitherto unexplored regime where the acoustic wavelength is on the same order as the cell dimensions. PMID:26522429

Addressing the issue of insufficient information in data-based bridge health monitoring : final report.

DOT National Transportation Integrated Search

2015-11-01

One of the most efficient ways to solve the damage detection problem using the statistical pattern recognition : approach is that of exploiting the methods of outlier analysis. Cast within the pattern recognition framework, : damage detection assesse...

Automatic classification of canine PRG neuronal discharge patterns using K-means clustering.

PubMed

Zuperku, Edward J; Prkic, Ivana; Stucke, Astrid G; Miller, Justin R; Hopp, Francis A; Stuth, Eckehard A

2015-02-01

Respiratory-related neurons in the parabrachial-Kölliker-Fuse (PB-KF) region of the pons play a key role in the control of breathing. The neuronal activities of these pontine respiratory group (PRG) neurons exhibit a variety of inspiratory (I), expiratory (E), phase spanning and non-respiratory related (NRM) discharge patterns. Due to the variety of patterns, it can be difficult to classify them into distinct subgroups according to their discharge contours. This report presents a method that automatically classifies neurons according to their discharge patterns and derives an average subgroup contour of each class. It is based on the K-means clustering technique and it is implemented via SigmaPlot User-Defined transform scripts. The discharge patterns of 135 canine PRG neurons were classified into seven distinct subgroups. Additional methods for choosing the optimal number of clusters are described. Analysis of the results suggests that the K-means clustering method offers a robust objective means of both automatically categorizing neuron patterns and establishing the underlying archetypical contours of subtypes based on the discharge patterns of group of neurons. Published by Elsevier B.V.

Micro Dot Patterning on the Light Guide Panel Using Powder Blasting.

PubMed

Jang, Ho Su; Cho, Myeong Woo; Park, Dong Sam

2008-02-08

This study is to develop a micromachining technology for a light guidepanel(LGP) mold, whereby micro dot patterns are formed on a LGP surface by a singleinjection process instead of existing screen printing processes. The micro powder blastingtechnique is applied to form micro dot patterns on the LGP mold surface. The optimalconditions for masking, laminating, exposure, and developing processes to form the microdot patterns are first experimentally investigated. A LGP mold with masked micro patternsis then machined using the micro powder blasting method and the machinability of themicro dot patterns is verified. A prototype LGP is test- injected using the developed LGPmold and a shape analysis of the patterns and performance testing of the injected LGP arecarried out. As an additional approach, matte finishing, a special surface treatment method,is applied to the mold surface to improve the light diffusion characteristics, uniformity andbrightness of the LGP. The results of this study show that the applied powder blastingmethod can be successfully used to manufacture LGPs with micro patterns by just singleinjection using the developed mold and thereby replace existing screen printing methods.

A Holarctic Biogeographical Analysis of the Collembola (Arthropoda, Hexapoda) Unravels Recent Post-Glacial Colonization Patterns

PubMed Central

Ávila-Jiménez, María Luisa; Coulson, Stephen James

2011-01-01

We aimed to describe the main Arctic biogeographical patterns of the Collembola, and analyze historical factors and current climatic regimes determining Arctic collembolan species distribution. Furthermore, we aimed to identify possible dispersal routes, colonization sources and glacial refugia for Arctic collembola. We implemented a Gaussian Mixture Clustering method on species distribution ranges and applied a distance- based parametric bootstrap test on presence-absence collembolan species distribution data. Additionally, multivariate analysis was performed considering species distributions, biodiversity, cluster distribution and environmental factors (temperature and precipitation). No clear relation was found between current climatic regimes and species distribution in the Arctic. Gaussian Mixture Clustering found common elements within Siberian areas, Atlantic areas, the Canadian Arctic, a mid-Siberian cluster and specific Beringian elements, following the same pattern previously described, using a variety of molecular methods, for Arctic plants. Species distribution hence indicate the influence of recent glacial history, as LGM glacial refugia (mid-Siberia, and Beringia) and major dispersal routes to high Arctic island groups can be identified. Endemic species are found in the high Arctic, but no specific biogeographical pattern can be clearly identified as a sign of high Arctic glacial refugia. Ocean currents patterns are suggested as being an important factor shaping the distribution of Arctic Collembola, which is consistent with Antarctic studies in collembolan biogeography. The clear relations between cluster distribution and geographical areas considering their recent glacial history, lack of relationship of species distribution with current climatic regimes, and consistency with previously described Arctic patterns in a series of organisms inferred using a variety of methods, suggest that historical phenomena shaping contemporary collembolan distribution can be inferred through biogeographical analysis. PMID:26467728

Digital versus conventional techniques for pattern fabrication of implant-supported frameworks

PubMed Central

Alikhasi, Marzieh; Rohanian, Ahmad; Ghodsi, Safoura; Kolde, Amin Mohammadpour

2018-01-01

Objective: The aim of this experimental study was to compare retention of frameworks cast from wax patterns fabricated by three different methods. Materials and Methods: Thirty-six implant analogs connected to one-piece abutments were divided randomly into three groups according to the wax pattern fabrication method (n = 12). Computer-aided design/computer-aided manufacturing (CAD/CAM) milling machine, three-dimensional printer, and conventional technique were used for fabrication of waxing patterns. All laboratory procedures were performed by an expert-reliable technician to eliminate intra-operator bias. The wax patterns were cast, finished, and seated on related abutment analogs. The number of adjustment times was recorded and analyzed by Kruskal–Wallis test. Frameworks were cemented on the corresponding analogs with zinc phosphate cement and tensile resistance test was used to measure retention value. Statistical Analysis Used: One-way analysis of variance (ANOVA) and post hoc Tukey tests were used for statistical analysis. Level of significance was set at P < 0.05. Results: The mean retentive values of 680.36 ± 21.93 N, 440.48 ± 85.98 N, and 407.23 ± 67.48 N were recorded for CAD/CAM, rapid prototyping, and conventional group, respectively. One-way ANOVA test revealed significant differences among the three groups (P < 0.001). The post hoc Tukey test showed significantly higher retention for CAD/CAM group (P < 0.001), while there was no significant difference between the two other groups (P = 0.54). CAD/CAM group required significantly more adjustments (P < 0.001). Conclusions: CAD/CAM-fabricated wax patterns showed significantly higher retention for implant-supported cement-retained frameworks; this could be a valuable help when there are limitations in the retention of single-unit implant restorations. PMID:29657528

Automated Defect and Correlation Length Analysis of Block Copolymer Thin Film Nanopatterns

PubMed Central

Murphy, Jeffrey N.; Harris, Kenneth D.; Buriak, Jillian M.

2015-01-01

Line patterns produced by lamellae- and cylinder-forming block copolymer (BCP) thin films are of widespread interest for their potential to enable nanoscale patterning over large areas. In order for such patterning methods to effectively integrate with current technologies, the resulting patterns need to have low defect densities, and be produced in a short timescale. To understand whether a given polymer or annealing method might potentially meet such challenges, it is necessary to examine the evolution of defects. Unfortunately, few tools are readily available to researchers, particularly those engaged in the synthesis and design of new polymeric systems with the potential for patterning, to measure defects in such line patterns. To this end, we present an image analysis tool, which we have developed and made available, to measure the characteristics of such patterns in an automated fashion. Additionally we apply the tool to six cylinder-forming polystyrene-block-poly(2-vinylpyridine) polymers thermally annealed to explore the relationship between the size of each polymer and measured characteristics including line period, line-width, defect density, line-edge roughness (LER), line-width roughness (LWR), and correlation length. Finally, we explore the line-edge roughness, line-width roughness, defect density, and correlation length as a function of the image area sampled to determine each in a more rigorous fashion. PMID:26207990

A hidden two-locus disease association pattern in genome-wide association studies

PubMed Central

2011-01-01

Background Recent association analyses in genome-wide association studies (GWAS) mainly focus on single-locus association tests (marginal tests) and two-locus interaction detections. These analysis methods have provided strong evidence of associations between genetics variances and complex diseases. However, there exists a type of association pattern, which often occurs within local regions in the genome and is unlikely to be detected by either marginal tests or interaction tests. This association pattern involves a group of correlated single-nucleotide polymorphisms (SNPs). The correlation among SNPs can lead to weak marginal effects and the interaction does not play a role in this association pattern. This phenomenon is due to the existence of unfaithfulness: the marginal effects of correlated SNPs do not express their significant joint effects faithfully due to the correlation cancelation. Results In this paper, we develop a computational method to detect this association pattern masked by unfaithfulness. We have applied our method to analyze seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). The analysis for each data set takes about one week to finish the examination of all pairs of SNPs. Based on the empirical result of these real data, we show that this type of association masked by unfaithfulness widely exists in GWAS. Conclusions These newly identified associations enrich the discoveries of GWAS, which may provide new insights both in the analysis of tagSNPs and in the experiment design of GWAS. Since these associations may be easily missed by existing analysis tools, we can only connect some of them to publicly available findings from other association studies. As independent data set is limited at this moment, we also have difficulties to replicate these findings. More biological implications need further investigation. Availability The software is freely available at http://bioinformatics.ust.hk/hidden_pattern_finder.zip. PMID:21569557

Effects of gyrokinesis exercise on the gait pattern of female patients with chronic low back pain

PubMed Central

Seo, Kook-Eun; Park, Tae-Jin

2016-01-01

[Purpose] The purpose of the present study was to use kinematic variables to identify the effects of 8/weeks’ performance of a gyrokinesis exercise on the gait pattern of females with chronic low back pain. [Subjects] The subjects of the present study were females in their late 20s to mid 30s who were chronic back pain patients. [Methods] A 3-D motion analysis system was used to measure the changes in their gait patterns between pre and post-gyrokintic exercise. The SPSS 21.0 statistics program was used to perform the paired t-test, to compare the gait patterns of pre-post-gyrokinesis exercise. [Results] In the gait analysis, pre-post-gyrokinesis exercise gait patterns showed statistically significant differences in right and left step length, stride length, right-left step widths, and stride speed. [Conclusion] Gait pattern analysis revealed increases in step length, stride length, and stride speed along with a decrease in step width after 8 weeks of gyrokinesis exercise, demonstrating it improved gait pattern. PMID:27065537

On mining complex sequential data by means of FCA and pattern structures

NASA Astrophysics Data System (ADS)

Buzmakov, Aleksey; Egho, Elias; Jay, Nicolas; Kuznetsov, Sergei O.; Napoli, Amedeo; Raïssi, Chedy

2016-02-01

Nowadays data-sets are available in very complex and heterogeneous ways. Mining of such data collections is essential to support many real-world applications ranging from healthcare to marketing. In this work, we focus on the analysis of "complex" sequential data by means of interesting sequential patterns. We approach the problem using the elegant mathematical framework of formal concept analysis and its extension based on "pattern structures". Pattern structures are used for mining complex data (such as sequences or graphs) and are based on a subsumption operation, which in our case is defined with respect to the partial order on sequences. We show how pattern structures along with projections (i.e. a data reduction of sequential structures) are able to enumerate more meaningful patterns and increase the computing efficiency of the approach. Finally, we show the applicability of the presented method for discovering and analysing interesting patient patterns from a French healthcare data-set on cancer. The quantitative and qualitative results (with annotations and analysis from a physician) are reported in this use-case which is the main motivation for this work.

Principal Component Analysis in the Spectral Analysis of the Dynamic Laser Speckle Patterns

NASA Astrophysics Data System (ADS)

Ribeiro, K. M.; Braga, R. A., Jr.; Horgan, G. W.; Ferreira, D. D.; Safadi, T.

2014-02-01

Dynamic laser speckle is a phenomenon that interprets an optical patterns formed by illuminating a surface under changes with coherent light. Therefore, the dynamic change of the speckle patterns caused by biological material is known as biospeckle. Usually, these patterns of optical interference evolving in time are analyzed by graphical or numerical methods, and the analysis in frequency domain has also been an option, however involving large computational requirements which demands new approaches to filter the images in time. Principal component analysis (PCA) works with the statistical decorrelation of data and it can be used as a data filtering. In this context, the present work evaluated the PCA technique to filter in time the data from the biospeckle images aiming the reduction of time computer consuming and improving the robustness of the filtering. It was used 64 images of biospeckle in time observed in a maize seed. The images were arranged in a data matrix and statistically uncorrelated by PCA technique, and the reconstructed signals were analyzed using the routine graphical and numerical methods to analyze the biospeckle. Results showed the potential of the PCA tool in filtering the dynamic laser speckle data, with the definition of markers of principal components related to the biological phenomena and with the advantage of fast computational processing.

Nonprincipal plane scattering of flat plates and pattern control of horn antennas

NASA Technical Reports Server (NTRS)

Balanis, Constantine A.; Polka, Lesley A.; Liu, Kefeng

1989-01-01

Using the geometrical theory of diffraction, the traditional method of high frequency scattering analysis, the prediction of the radar cross section of a perfectly conducting, flat, rectangular plate is limited to principal planes. Part A of this report predicts the radar cross section in nonprincipal planes using the method of equivalent currents. This technique is based on an asymptotic end-point reduction of the surface radiation integrals for an infinite wedge and enables nonprincipal plane prediction. The predicted radar cross sections for both horizontal and vertical polarizations are compared to moment method results and experimental data from Arizona State University's anechoic chamber. In part B, a variational calculus approach to the pattern control of the horn antenna is outlined. The approach starts with the optimization of the aperture field distribution so that the control of the radiation pattern in a range of directions can be realized. A control functional is thus formulated. Next, a spectral analysis method is introduced to solve for the eigenfunctions from the extremal condition of the formulated functional. Solutions to the optimized aperture field distribution are then obtained.

Rapid Prototyping Technology for Manufacturing GTE Turbine Blades

NASA Astrophysics Data System (ADS)

Balyakin, A. V.; Dobryshkina, E. M.; Vdovin, R. A.; Alekseev, V. P.

2018-03-01

The conventional approach to manufacturing turbine blades by investment casting is expensive and time-consuming, as it takes a lot of time to make geometrically precise and complex wax patterns. Turbine blade manufacturing in pilot production can be sped up by accelerating the casting process while keeping the geometric precision of the final product. This paper compares the rapid prototyping method (casting the wax pattern composition into elastic silicone molds) to the conventional technology. Analysis of the size precision of blade casts shows that silicon-mold casting features sufficient geometric precision. Thus, this method for making wax patterns can be a cost-efficient solution for small-batch or pilot production of turbine blades for gas-turbine units (GTU) and gas-turbine engines (GTE). The paper demonstrates how additive technology and thermographic analysis can speed up the cooling of wax patterns in silicone molds. This is possible at an optimal temperature and solidification time, which make the process more cost-efficient while keeping the geometric quality of the final product.

A Parallel Genetic Algorithm to Discover Patterns in Genetic Markers that Indicate Predisposition to Multifactorial Disease

PubMed Central

Rausch, Tobias; Thomas, Alun; Camp, Nicola J.; Cannon-Albright, Lisa A.; Facelli, Julio C.

2008-01-01

This paper describes a novel algorithm to analyze genetic linkage data using pattern recognition techniques and genetic algorithms (GA). The method allows a search for regions of the chromosome that may contain genetic variations that jointly predispose individuals for a particular disease. The method uses correlation analysis, filtering theory and genetic algorithms (GA) to achieve this goal. Because current genome scans use from hundreds to hundreds of thousands of markers, two versions of the method have been implemented. The first is an exhaustive analysis version that can be used to visualize, explore, and analyze small genetic data sets for two marker correlations; the second is a GA version, which uses a parallel implementation allowing searches of higher-order correlations in large data sets. Results on simulated data sets indicate that the method can be informative in the identification of major disease loci and gene-gene interactions in genome-wide linkage data and that further exploration of these techniques is justified. The results presented for both variants of the method show that it can help genetic epidemiologists to identify promising combinations of genetic factors that might predispose to complex disorders. In particular, the correlation analysis of IBD expression patterns might hint to possible gene-gene interactions and the filtering might be a fruitful approach to distinguish true correlation signals from noise. PMID:18547558

Pattern recognition of satellite cloud imagery for improved weather prediction

NASA Technical Reports Server (NTRS)

Gautier, Catherine; Somerville, Richard C. J.; Volfson, Leonid B.

1986-01-01

The major accomplishment was the successful development of a method for extracting time derivative information from geostationary meteorological satellite imagery. This research is a proof-of-concept study which demonstrates the feasibility of using pattern recognition techniques and a statistical cloud classification method to estimate time rate of change of large-scale meteorological fields from remote sensing data. The cloud classification methodology is based on typical shape function analysis of parameter sets characterizing the cloud fields. The three specific technical objectives, all of which were successfully achieved, are as follows: develop and test a cloud classification technique based on pattern recognition methods, suitable for the analysis of visible and infrared geostationary satellite VISSR imagery; develop and test a methodology for intercomparing successive images using the cloud classification technique, so as to obtain estimates of the time rate of change of meteorological fields; and implement this technique in a testbed system incorporating an interactive graphics terminal to determine the feasibility of extracting time derivative information suitable for comparison with numerical weather prediction products.

Dietary Patterns Derived Using Exploratory and Confirmatory Factor Analysis are Stable and Generalizable Across Race, Region, and Gender Subgroups in the REGARDS Study.

PubMed

Judd, Suzanne E; Letter, Abraham J; Shikany, James M; Roth, David L; Newby, P K

2014-01-01

Examining diet as a whole using dietary patterns as exposures is a complementary method to using single food or nutrients in studies of diet and disease, but the generalizability of intake patterns across race, region, and gender in the United States has not been established. To employ rigorous statistical analysis to empirically derive dietary patterns in a large bi-racial, geographically diverse population and examine whether results are stable across population subgroups. The present analysis utilized data from 21,636 participants in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study who completed the Block 98 food frequency questionnaire. We employed exploratory factor analysis and confirmatory factor analyses on 56 different food groups iteratively and examined differences by race, region, and sex to determine the optimal factor solution in our sample. Five dietary patterns emerged: the "Convenience" pattern was characterized by mixed dishes; the "Plant-based" pattern by fruits, vegetables, and fish; the "Sweets/Fats" pattern by sweet snacks, desserts, and fats and oils; the "Southern" pattern by fried foods, organ meat, and sweetened beverages; and the "Alcohol/Salads" pattern by beer, wine, liquor, and salads. Differences were most pronounced in the Southern pattern with black participants, those residing in the Southeast, and participants not completing high school having the highest scores. Five meaningful dietary patterns emerged in the REGARDS study and showed strong congruence across race, sex, and region. Future research will examine associations between these patterns and health outcomes to better understand racial disparities in disease and inform prevention efforts.

Electropalatography in home training of retracted articulation in a Swedish child with cleft palate: effect on articulation pattern and speech.

PubMed

Lohmander, Anette; Henriksson, Cecilia; Havstam, Christina

2010-12-01

The aim was to evaluate the effectiveness of electropalatography (EPG) in home training of persistent articulation errors in an 11-year-old Swedish girl born with isolated cleft palate. The /t/ and /s/ sounds were trained in a single subject design across behaviours during an eight month period using a portable training unit (PTU). Both EPG analysis and perceptual analysis showed an improvement in the production of /t/ and /s/ in words and sentences after therapy. Analysis of tongue-contact patterns showed that the participant had more normal articulatory patterns of /t/ and /s/ after just 2 months (after approximately 8 hours of training) respectively. No statistically significant transfer by means of intelligibility in connected speech was found. The present results show that EPG home training can be a sufficient method for treating persistent speech disorders associated with cleft palate. Methods for transfer from function (articulation) to activity (intelligibility) need to be explored.

Statistical approaches to account for missing values in accelerometer data: Applications to modeling physical activity.

PubMed

Yue Xu, Selene; Nelson, Sandahl; Kerr, Jacqueline; Godbole, Suneeta; Patterson, Ruth; Merchant, Gina; Abramson, Ian; Staudenmayer, John; Natarajan, Loki

2018-04-01

Physical inactivity is a recognized risk factor for many chronic diseases. Accelerometers are increasingly used as an objective means to measure daily physical activity. One challenge in using these devices is missing data due to device nonwear. We used a well-characterized cohort of 333 overweight postmenopausal breast cancer survivors to examine missing data patterns of accelerometer outputs over the day. Based on these observed missingness patterns, we created psuedo-simulated datasets with realistic missing data patterns. We developed statistical methods to design imputation and variance weighting algorithms to account for missing data effects when fitting regression models. Bias and precision of each method were evaluated and compared. Our results indicated that not accounting for missing data in the analysis yielded unstable estimates in the regression analysis. Incorporating variance weights and/or subject-level imputation improved precision by >50%, compared to ignoring missing data. We recommend that these simple easy-to-implement statistical tools be used to improve analysis of accelerometer data.

Quantitative analysis of the patellofemoral motion pattern using semi-automatic processing of 4D CT data.

PubMed

Forsberg, Daniel; Lindblom, Maria; Quick, Petter; Gauffin, Håkan

2016-09-01

To present a semi-automatic method with minimal user interaction for quantitative analysis of the patellofemoral motion pattern. 4D CT data capturing the patellofemoral motion pattern of a continuous flexion and extension were collected for five patients prone to patellar luxation both pre- and post-surgically. For the proposed method, an observer would place landmarks in a single 3D volume, which then are automatically propagated to the other volumes in a time sequence. From the landmarks in each volume, the measures patellar displacement, patellar tilt and angle between femur and tibia were computed. Evaluation of the observer variability showed the proposed semi-automatic method to be favorable over a fully manual counterpart, with an observer variability of approximately 1.5[Formula: see text] for the angle between femur and tibia, 1.5 mm for the patellar displacement, and 4.0[Formula: see text]-5.0[Formula: see text] for the patellar tilt. The proposed method showed that surgery reduced the patellar displacement and tilt at maximum extension with approximately 10-15 mm and 15[Formula: see text]-20[Formula: see text] for three patients but with less evident differences for two of the patients. A semi-automatic method suitable for quantification of the patellofemoral motion pattern as captured by 4D CT data has been presented. Its observer variability is on par with that of other methods but with the distinct advantage to support continuous motions during the image acquisition.

Convergence and divergence across construction methods for human brain white matter networks: an assessment based on individual differences.

PubMed

Zhong, Suyu; He, Yong; Gong, Gaolang

2015-05-01

Using diffusion MRI, a number of studies have investigated the properties of whole-brain white matter (WM) networks with differing network construction methods (node/edge definition). However, how the construction methods affect individual differences of WM networks and, particularly, if distinct methods can provide convergent or divergent patterns of individual differences remain largely unknown. Here, we applied 10 frequently used methods to construct whole-brain WM networks in a healthy young adult population (57 subjects), which involves two node definitions (low-resolution and high-resolution) and five edge definitions (binary, FA weighted, fiber-density weighted, length-corrected fiber-density weighted, and connectivity-probability weighted). For these WM networks, individual differences were systematically analyzed in three network aspects: (1) a spatial pattern of WM connections, (2) a spatial pattern of nodal efficiency, and (3) network global and local efficiencies. Intriguingly, we found that some of the network construction methods converged in terms of individual difference patterns, but diverged with other methods. Furthermore, the convergence/divergence between methods differed among network properties that were adopted to assess individual differences. Particularly, high-resolution WM networks with differing edge definitions showed convergent individual differences in the spatial pattern of both WM connections and nodal efficiency. For the network global and local efficiencies, low-resolution and high-resolution WM networks for most edge definitions consistently exhibited a highly convergent pattern in individual differences. Finally, the test-retest analysis revealed a decent temporal reproducibility for the patterns of between-method convergence/divergence. Together, the results of the present study demonstrated a measure-dependent effect of network construction methods on the individual difference of WM network properties. © 2015 Wiley Periodicals, Inc.

An algol program for dissimilarity analysis: a divisive-omnithetic clustering technique

USGS Publications Warehouse

Tipper, J.C.

1979-01-01

Clustering techniques are used properly to generate hypotheses about patterns in data. Of the hierarchical techniques, those which are divisive and omnithetic possess many theoretically optimal properties. One such method, dissimilarity analysis, is implemented here in ALGOL 60, and determined to be competitive computationally with most other methods. ?? 1979.

An improved principal component analysis based region matching method for fringe direction estimation

NASA Astrophysics Data System (ADS)

He, A.; Quan, C.

2018-04-01

The principal component analysis (PCA) and region matching combined method is effective for fringe direction estimation. However, its mask construction algorithm for region matching fails in some circumstances, and the algorithm for conversion of orientation to direction in mask areas is computationally-heavy and non-optimized. We propose an improved PCA based region matching method for the fringe direction estimation, which includes an improved and robust mask construction scheme, and a fast and optimized orientation-direction conversion algorithm for the mask areas. Along with the estimated fringe direction map, filtered fringe pattern by automatic selective reconstruction modification and enhanced fast empirical mode decomposition (ASRm-EFEMD) is used for Hilbert spiral transform (HST) to demodulate the phase. Subsequently, windowed Fourier ridge (WFR) method is used for the refinement of the phase. The robustness and effectiveness of proposed method are demonstrated by both simulated and experimental fringe patterns.

Characterizing Bonding Patterns in Diradicals and Triradicals by Density-Based Wave Function Analysis: A Uniform Approach.

PubMed

Orms, Natalie; Rehn, Dirk R; Dreuw, Andreas; Krylov, Anna I

2018-02-13

Density-based wave function analysis enables unambiguous comparisons of the electronic structure computed by different methods and removes ambiguity of orbital choices. We use this tool to investigate the performance of different spin-flip methods for several prototypical diradicals and triradicals. In contrast to previous calibration studies that focused on energy gaps between high- and low spin-states, we focus on the properties of the underlying wave functions, such as the number of effectively unpaired electrons. Comparison of different density functional and wave function theory results provides insight into the performance of the different methods when applied to strongly correlated systems such as polyradicals. We show that canonical molecular orbitals for species like large copper-containing diradicals fail to correctly represent the underlying electronic structure due to highly non-Koopmans character, while density-based analysis of the same wave function delivers a clear picture of the bonding pattern.

A Method for Analyzing A+2 Isotope Patterns for Use in Undergraduate Organic Courses

ERIC Educational Resources Information Center

Gross, Ray A.

2007-01-01

A novel ratio method is developed and automated for finding the bromine-chlorine-sulfur stoichiometry in the molecular formula of an unknown. This method is also useful in spectrometric analysis or beginning organic chemistry.

Dietary Patterns and Overweight/Obesity: A Review Article

PubMed Central

MU, Min; XU, Li-Fa; HU, Dong; WU, Jing; BAI, Ming-Jie

2017-01-01

Background: Dietary patterns analysis may provide insights into the influence of overall diet on overweight/obesity. In the past two decades, the relation between dietary patterns and overweight/obesity has been a research focus and a number of results were reported in the research field. Methods: An electronic literature search was conducted in PubMed and Web of Science, to identify human studies published by Mar 2015 and written in English. The following keywords or phrases were involved: dietary patterns, dietary pattern, factor analysis, principal component analysis, diet, obesity, adiposity, overweight and BMI. All the studies were retrieved and prudent/healthy (n=17) and western/unhealthy (n=18) dietary patterns were identified. Results: When compared with the lowest categories of a prudent/healthy dietary pattern, a reduced overweight/obesity risk was shown in the highest (OR=0.64; 95% CI: 0.52, 0.78; P<0.0001). While there was an increased overweight/obesity risk in the highest when compared with the lowest categories of a western/unhealthy dietary pattern (OR=1.65; 95% CI: 1.45, 1.87; P<0.0001). Conclusion: A prudent/healthy dietary pattern and limit intake of western/unhealthy dietary pattern should be followed, which helps to keep a healthy body mass. PMID:28845396

Dietary pattern analysis: a comparison between matched vegetarian and omnivorous subjects

PubMed Central

2013-01-01

Background 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. Methods 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. Results 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). Conclusions 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. PMID:23758767

Redundancy analysis allows improved detection of methylation changes in large genomic regions.

PubMed

Ruiz-Arenas, Carlos; González, Juan R

2017-12-14

DNA methylation is an epigenetic process that regulates gene expression. Methylation can be modified by environmental exposures and changes in the methylation patterns have been associated with diseases. Methylation microarrays measure methylation levels at more than 450,000 CpGs in a single experiment, and the most common analysis strategy is to perform a single probe analysis to find methylation probes associated with the outcome of interest. However, methylation changes usually occur at the regional level: for example, genomic structural variants can affect methylation patterns in regions up to several megabases in length. Existing DMR methods provide lists of Differentially Methylated Regions (DMRs) of up to only few kilobases in length, and cannot check if a target region is differentially methylated. Therefore, these methods are not suitable to evaluate methylation changes in large regions. To address these limitations, we developed a new DMR approach based on redundancy analysis (RDA) that assesses whether a target region is differentially methylated. Using simulated and real datasets, we compared our approach to three common DMR detection methods (Bumphunter, blockFinder, and DMRcate). We found that Bumphunter underestimated methylation changes and blockFinder showed poor performance. DMRcate showed poor power in the simulated datasets and low specificity in the real data analysis. Our method showed very high performance in all simulation settings, even with small sample sizes and subtle methylation changes, while controlling type I error. Other advantages of our method are: 1) it estimates the degree of association between the DMR and the outcome; 2) it can analyze a targeted or region of interest; and 3) it can evaluate the simultaneous effects of different variables. The proposed methodology is implemented in MEAL, a Bioconductor package designed to facilitate the analysis of methylation data. We propose a multivariate approach to decipher whether an outcome of interest alters the methylation pattern of a region of interest. The method is designed to analyze large target genomic regions and outperforms the three most popular methods for detecting DMRs. Our method can evaluate factors with more than two levels or the simultaneous effect of more than one continuous variable, which is not possible with the state-of-the-art methods.

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

Shumway, R.H.; McQuarrie, A.D.

Robust statistical approaches to the problem of discriminating between regional earthquakes and explosions are developed. We compare linear discriminant analysis using descriptive features like amplitude and spectral ratios with signal discrimination techniques using the original signal waveforms and spectral approximations to the log likelihood function. Robust information theoretic techniques are proposed and all methods are applied to 8 earthquakes and 8 mining explosions in Scandinavia and to an event from Novaya Zemlya of unknown origin. It is noted that signal discrimination approaches based on discrimination information and Renyi entropy perform better in the test sample than conventional methods based onmore » spectral ratios involving the P and S phases. Two techniques for identifying the ripple-firing pattern for typical mining explosions are proposed and shown to work well on simulated data and on several Scandinavian earthquakes and explosions. We use both cepstral analysis in the frequency domain and a time domain method based on the autocorrelation and partial autocorrelation functions. The proposed approach strips off underlying smooth spectral and seasonal spectral components corresponding to the echo pattern induced by two simple ripple-fired models. For two mining explosions, a pattern is identified whereas for two earthquakes, no pattern is evident.« less

Synchronized diffusive-wave spectroscopy: Principle and application to sound propagation in aqueous foams.

PubMed

Crassous, Jérôme; Chasle, Patrick; Pierre, Juliette; Saint-Jalmes, Arnaud; Dollet, Benjamin

2016-03-01

We present an experimental method to measure oscillatory strains in turbid material. The material is illuminated with a laser, and the speckle patterns are recorded. The analysis of the deformations of the optical path length shows that the speckle patterns are modulated at the strain frequency. By recording those patterns synchronously with the strain source, we are able to measure the amplitude and the phase of the strain. This method is tested in the specific case of an aqueous foam where an acoustic wave propagates. The effects of material internal dynamics and heterogeneous deformations are also discussed.

Synchronized diffusive-wave spectroscopy: Principle and application to sound propagation in aqueous foams

NASA Astrophysics Data System (ADS)

Crassous, Jérôme; Chasle, Patrick; Pierre, Juliette; Saint-Jalmes, Arnaud; Dollet, Benjamin

2016-03-01

We present an experimental method to measure oscillatory strains in turbid material. The material is illuminated with a laser, and the speckle patterns are recorded. The analysis of the deformations of the optical path length shows that the speckle patterns are modulated at the strain frequency. By recording those patterns synchronously with the strain source, we are able to measure the amplitude and the phase of the strain. This method is tested in the specific case of an aqueous foam where an acoustic wave propagates. The effects of material internal dynamics and heterogeneous deformations are also discussed.

Parallel log structured file system collective buffering to achieve a compact representation of scientific and/or dimensional data

DOEpatents

Grider, Gary A.; Poole, Stephen W.

2015-09-01

Collective buffering and data pattern solutions are provided for storage, retrieval, and/or analysis of data in a collective parallel processing environment. For example, a method can be provided for data storage in a collective parallel processing environment. The method comprises receiving data to be written for a plurality of collective processes within a collective parallel processing environment, extracting a data pattern for the data to be written for the plurality of collective processes, generating a representation describing the data pattern, and saving the data and the representation.

Array magnetics modal analysis for the DIII-D tokamak based on localized time-series modelling

DOE PAGES

Olofsson, K. Erik J.; Hanson, Jeremy M.; Shiraki, Daisuke; ...

2014-07-14

Here, time-series analysis of magnetics data in tokamaks is typically done using block-based fast Fourier transform methods. This work presents the development and deployment of a new set of algorithms for magnetic probe array analysis. The method is based on an estimation technique known as stochastic subspace identification (SSI). Compared with the standard coherence approach or the direct singular value decomposition approach, the new technique exhibits several beneficial properties. For example, the SSI method does not require that frequencies are orthogonal with respect to the timeframe used in the analysis. Frequencies are obtained directly as parameters of localized time-series models.more » The parameters are extracted by solving small-scale eigenvalue problems. Applications include maximum-likelihood regularized eigenmode pattern estimation, detection of neoclassical tearing modes, including locked mode precursors, and automatic clustering of modes, and magnetics-pattern characterization of sawtooth pre- and postcursors, edge harmonic oscillations and fishbones.« less

Clustering change patterns using Fourier transformation with time-course gene expression data.

PubMed

Kim, Jaehee

2011-01-01

To understand the behavior of genes, it is important to explore how the patterns of gene expression change over a period of time because biologically related gene groups can share the same change patterns. In this study, the problem of finding similar change patterns is induced to clustering with the derivative Fourier coefficients. This work is aimed at discovering gene groups with similar change patterns which share similar biological properties. We developed a statistical model using derivative Fourier coefficients to identify similar change patterns of gene expression. We used a model-based method to cluster the Fourier series estimation of derivatives. We applied our model to cluster change patterns of yeast cell cycle microarray expression data with alpha-factor synchronization. It showed that, as the method clusters with the probability-neighboring data, the model-based clustering with our proposed model yielded biologically interpretable results. We expect that our proposed Fourier analysis with suitably chosen smoothing parameters could serve as a useful tool in classifying genes and interpreting possible biological change patterns.

Analysis of lead twist in modern high-performance grinding methods

NASA Astrophysics Data System (ADS)

Kundrák, J.; Gyáni, K.; Felhő, C.; Markopoulos, AP; Deszpoth, I.

2016-11-01

According to quality requirements of road vehicles shafts, which bear dynamic seals, twisted-pattern micro-geometrical topography is not allowed. It is a question whether newer modern grinding methods - such as quick-point grinding and peel grinding - could provide twist- free topography. According to industrial experience, twist-free surfaces can be made, however with certain settings, same twist occurs. In this paper it is proved by detailed chip-geometrical analysis that the topography generated by the new procedures is theoretically twist-patterned because of the feeding motion of the CBN tool. The presented investigation was carried out by a single-grain wheel model and computer simulation.

Cellular automata rule characterization and classification using texture descriptors

NASA Astrophysics Data System (ADS)

Machicao, Jeaneth; Ribas, Lucas C.; Scabini, Leonardo F. S.; Bruno, Odermir M.

2018-05-01

The cellular automata (CA) spatio-temporal patterns have attracted the attention from many researchers since it can provide emergent behavior resulting from the dynamics of each individual cell. In this manuscript, we propose an approach of texture image analysis to characterize and classify CA rules. The proposed method converts the CA spatio-temporal patterns into a gray-scale image. The gray-scale is obtained by creating a binary number based on the 8-connected neighborhood of each dot of the CA spatio-temporal pattern. We demonstrate that this technique enhances the CA rule characterization and allow to use different texture image analysis algorithms. Thus, various texture descriptors were evaluated in a supervised training approach aiming to characterize the CA's global evolution. Our results show the efficiency of the proposed method for the classification of the elementary CA (ECAs), reaching a maximum of 99.57% of accuracy rate according to the Li-Packard scheme (6 classes) and 94.36% for the classification of the 88 rules scheme. Moreover, within the image analysis context, we found a better performance of the method by means of a transformation of the binary states to a gray-scale.

Three-Dimensional Analysis of Spiny Dendrites Using Straightening and Unrolling Transforms

PubMed Central

Morales, Juan; Benavides-Piccione, Ruth; Pastor, Luis; Yuste, Rafael; DeFelipe, Javier

2014-01-01

Current understanding of the synaptic organization of the brain depends to a large extent on knowledge about the synaptic inputs to the neurons. Indeed, the dendritic surfaces of pyramidal cells (the most common neuron in the cerebral cortex) are covered by thin protrusions named dendritic spines. These represent the targets of most excitatory synapses in the cerebral cortex and therefore, dendritic spines prove critical in learning, memory and cognition. This paper presents a new method that facilitates the analysis of the 3D structure of spine insertions in dendrites, providing insight on spine distribution patterns. This method is based both on the implementation of straightening and unrolling transformations to move the analysis process to a planar, unfolded arrangement, and on the design of DISPINE, an interactive environment that supports the visual analysis of 3D patterns. PMID:22644869

Pattern Analysis and Decision Support for Cancer through Clinico-Genomic Profiles

NASA Astrophysics Data System (ADS)

Exarchos, Themis P.; Giannakeas, Nikolaos; Goletsis, Yorgos; Papaloukas, Costas; Fotiadis, Dimitrios I.

Advances in genome technology are playing a growing role in medicine and healthcare. With the development of new technologies and opportunities for large-scale analysis of the genome, genomic data have a clear impact on medicine. Cancer prognostics and therapeutics are among the first major test cases for genomic medicine, given that all types of cancer are related with genomic instability. In this paper we present a novel system for pattern analysis and decision support in cancer. The system integrates clinical data from electronic health records and genomic data. Pattern analysis and data mining methods are applied to these integrated data and the discovered knowledge is used for cancer decision support. Through this integration, conclusions can be drawn for early diagnosis, staging and cancer treatment.

Forecasting of hourly load by pattern recognition in a small area power system

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

Dehdashti-Shahrokh, A.

1982-01-01

An intuitive, logical, simple and efficient method of forecasting hourly load in a small area power system is presented. A pattern recognition approach is used in developing the forecasting model. Pattern recognition techniques are powerful tools in the field of artificial intelligence (cybernetics) and simulate the way the human brain operates to make decisions. Pattern recognition is generally used in analysis of processes where the total physical nature behind the process variation is unkown but specific kinds of measurements explain their behavior. In this research basic multivariate analyses, in conjunction with pattern recognition techniques, are used to develop a linearmore » deterministic model to forecast hourly load. This method assumes that load patterns in the same geographical area are direct results of climatological changes (weather sensitive load), and have occurred in the past as a result of similar climatic conditions. The algorithm described in here searches for the best possible pattern from a seasonal library of load and weather data in forecasting hourly load. To accommodate the unpredictability of weather and the resulting load, the basic twenty-four load pattern was divided into eight three-hour intervals. This division was made to make the model adaptive to sudden climatic changes. The proposed method offers flexible lead times of one to twenty-four hours. The results of actual data testing had indicated that this proposed method is computationally efficient, highly adaptive, with acceptable data storage size and accuracy that is comparable to many other existing methods.« less

Inactivity periods and postural change speed can explain atypical postural change patterns of Caenorhabditis elegans mutants.

PubMed

Fukunaga, Tsukasa; Iwasaki, Wataru

2017-01-19

With rapid advances in genome sequencing and editing technologies, systematic and quantitative analysis of animal behavior is expected to be another key to facilitating data-driven behavioral genetics. The nematode Caenorhabditis elegans is a model organism in this field. Several video-tracking systems are available for automatically recording behavioral data for the nematode, but computational methods for analyzing these data are still under development. In this study, we applied the Gaussian mixture model-based binning method to time-series postural data for 322 C. elegans strains. We revealed that the occurrence patterns of the postural states and the transition patterns among these states have a relationship as expected, and such a relationship must be taken into account to identify strains with atypical behaviors that are different from those of wild type. Based on this observation, we identified several strains that exhibit atypical transition patterns that cannot be fully explained by their occurrence patterns of postural states. Surprisingly, we found that two simple factors-overall acceleration of postural movement and elimination of inactivity periods-explained the behavioral characteristics of strains with very atypical transition patterns; therefore, computational analysis of animal behavior must be accompanied by evaluation of the effects of these simple factors. Finally, we found that the npr-1 and npr-3 mutants have similar behavioral patterns that were not predictable by sequence homology, proving that our data-driven approach can reveal the functions of genes that have not yet been characterized. We propose that elimination of inactivity periods and overall acceleration of postural change speed can explain behavioral phenotypes of strains with very atypical postural transition patterns. Our methods and results constitute guidelines for effectively finding strains that show "truly" interesting behaviors and systematically uncovering novel gene functions by bioimage-informatic approaches.

Geometrical optics analysis of the structural imperfection of retroreflection corner cubes with a nonlinear conjugate gradient method.

PubMed

Kim, Hwi; Min, Sung-Wook; Lee, Byoungho

2008-12-01

Geometrical optics analysis of the structural imperfection of retroreflection corner cubes is described. In the analysis, a geometrical optics model of six-beam reflection patterns generated by an imperfect retroreflection corner cube is developed, and its structural error extraction is formulated as a nonlinear optimization problem. The nonlinear conjugate gradient method is employed for solving the nonlinear optimization problem, and its detailed implementation is described. The proposed method of analysis is a mathematical basis for the nondestructive optical inspection of imperfectly fabricated retroreflection corner cubes.

The Use of Satellite Observed Cloud Patterns in Northern Hemisphere 300 mb and 1000/300 mb Numerical Analysis.

DTIC Science & Technology

1984-02-01

prediction Extratropical cyclones Objective analysis Bogus techniques 20. ABSTRACT (Continue on reverse aide If necooearn mid Identify by block number) Jh A...quasi-objective statistical method for deriving 300 mb geopotential heights and 1000/300 mb thicknesses in the vicinity of extratropical cyclones 0I...with the aid of satellite imagery is presented. The technique utilizes satellite observed extratropical spiral cloud pattern parameters in conjunction

Local variance for multi-scale analysis in geomorphometry.

PubMed

Drăguţ, Lucian; Eisank, Clemens; Strasser, Thomas

2011-07-15

Increasing availability of high resolution Digital Elevation Models (DEMs) is leading to a paradigm shift regarding scale issues in geomorphometry, prompting new solutions to cope with multi-scale analysis and detection of characteristic scales. We tested the suitability of the local variance (LV) method, originally developed for image analysis, for multi-scale analysis in geomorphometry. The method consists of: 1) up-scaling land-surface parameters derived from a DEM; 2) calculating LV as the average standard deviation (SD) within a 3 × 3 moving window for each scale level; 3) calculating the rate of change of LV (ROC-LV) from one level to another, and 4) plotting values so obtained against scale levels. We interpreted peaks in the ROC-LV graphs as markers of scale levels where cells or segments match types of pattern elements characterized by (relatively) equal degrees of homogeneity. The proposed method has been applied to LiDAR DEMs in two test areas different in terms of roughness: low relief and mountainous, respectively. For each test area, scale levels for slope gradient, plan, and profile curvatures were produced at constant increments with either resampling (cell-based) or image segmentation (object-based). Visual assessment revealed homogeneous areas that convincingly associate into patterns of land-surface parameters well differentiated across scales. We found that the LV method performed better on scale levels generated through segmentation as compared to up-scaling through resampling. The results indicate that coupling multi-scale pattern analysis with delineation of morphometric primitives is possible. This approach could be further used for developing hierarchical classifications of landform elements.

Local variance for multi-scale analysis in geomorphometry

PubMed Central

Drăguţ, Lucian; Eisank, Clemens; Strasser, Thomas

2011-01-01

Increasing availability of high resolution Digital Elevation Models (DEMs) is leading to a paradigm shift regarding scale issues in geomorphometry, prompting new solutions to cope with multi-scale analysis and detection of characteristic scales. We tested the suitability of the local variance (LV) method, originally developed for image analysis, for multi-scale analysis in geomorphometry. The method consists of: 1) up-scaling land-surface parameters derived from a DEM; 2) calculating LV as the average standard deviation (SD) within a 3 × 3 moving window for each scale level; 3) calculating the rate of change of LV (ROC-LV) from one level to another, and 4) plotting values so obtained against scale levels. We interpreted peaks in the ROC-LV graphs as markers of scale levels where cells or segments match types of pattern elements characterized by (relatively) equal degrees of homogeneity. The proposed method has been applied to LiDAR DEMs in two test areas different in terms of roughness: low relief and mountainous, respectively. For each test area, scale levels for slope gradient, plan, and profile curvatures were produced at constant increments with either resampling (cell-based) or image segmentation (object-based). Visual assessment revealed homogeneous areas that convincingly associate into patterns of land-surface parameters well differentiated across scales. We found that the LV method performed better on scale levels generated through segmentation as compared to up-scaling through resampling. The results indicate that coupling multi-scale pattern analysis with delineation of morphometric primitives is possible. This approach could be further used for developing hierarchical classifications of landform elements. PMID:21779138

Dietary patterns in the Avon Longitudinal Study of Parents and Children

PubMed Central

Jones, Louise R.; Northstone, Kate

2015-01-01

Publications from the Avon Longitudinal Study of Parents and Children that used empirically derived dietary patterns were reviewed. The relationships of dietary patterns with socioeconomic background and childhood development were examined. Diet was assessed using food frequency questionnaires and food records. Three statistical methods were used: principal components analysis, cluster analysis, and reduced rank regression. Throughout childhood, children and parents have similar dietary patterns. The “health-conscious” and “traditional” patterns were associated with high intakes of fruits and/or vegetables and better nutrient profiles than the “processed” patterns. There was evidence of tracking in childhood diet, with the “health-conscious” patterns tracking most strongly, followed by the “processed” pattern. An “energy-dense, low-fiber, high-fat” dietary pattern was extracted using reduced rank regression; high scores on this pattern were associated with increasing adiposity. Maternal education was a strong determinant of pattern score or cluster membership; low educational attainment was associated with higher scores on processed, energy-dense patterns in both parents and children. The Avon Longitudinal Study of Parents and Children has provided unique insights into the value of empirically derived dietary patterns and has demonstrated that they are a useful tool in nutritional epidemiology. PMID:26395343

PROSPECT improves cis-acting regulatory element prediction by integrating expression profile data with consensus pattern searches

PubMed Central

Fujibuchi, Wataru; Anderson, John S. J.; Landsman, David

2001-01-01

Consensus pattern and matrix-based searches designed to predict cis-acting transcriptional regulatory sequences have historically been subject to large numbers of false positives. We sought to decrease false positives by incorporating expression profile data into a consensus pattern-based search method. We have systematically analyzed the expression phenotypes of over 6000 yeast genes, across 121 expression profile experiments, and correlated them with the distribution of 14 known regulatory elements over sequences upstream of the genes. Our method is based on a metric we term probabilistic element assessment (PEA), which is a ranking of potential sites based on sequence similarity in the upstream regions of genes with similar expression phenotypes. For eight of the 14 known elements that we examined, our method had a much higher selectivity than a naïve consensus pattern search. Based on our analysis, we have developed a web-based tool called PROSPECT, which allows consensus pattern-based searching of gene clusters obtained from microarray data. PMID:11574681

Dynamic analysis of patterns of renal sympathetic nerve activity: implications for renal function.

PubMed

DiBona, Gerald F

2005-03-01

Methods of dynamic analysis are used to provide additional understanding of the renal sympathetic neural control of renal function. The concept of functionally specific subgroups of renal sympathetic nerve fibres conveying information encoded in the frequency domain is presented. Analog pulse modulation and pseudorandom binary sequence stimulation patterns are used for the determination of renal vascular frequency response. Transfer function analysis is used to determine the effects of non-renal vasoconstrictor and vasoconstrictor intensities of renal sympathetic nerve activity on dynamic autoregulation of renal blood flow.

The reliability and validity of subjective notational analysis in comparison to global positioning system tracking to assess athlete movement patterns.

PubMed

Doğramac, Sera N; Watsford, Mark L; Murphy, Aron J

2011-03-01

Subjective notational analysis can be used to track players and analyse movement patterns during match-play of team sports such as futsal. The purpose of this study was to establish the validity and reliability of the Event Recorder for subjective notational analysis. A course was designed, replicating ten minutes of futsal match-play movement patterns, where ten participants undertook the course. The course allowed a comparison of data derived from subjective notational analysis, to the known distances of the course, and to GPS data. The study analysed six locomotor activity categories, focusing on total distance covered, total duration of activities and total frequency of activities. The values between the known measurements and the Event Recorder were similar, whereas the majority of significant differences were found between the Event Recorder and GPS values. The reliability of subjective notational analysis was established with all ten participants being analysed on two occasions, as well as analysing five random futsal players twice during match-play. Subjective notational analysis is a valid and reliable method of tracking player movements, and may be a preferred and more effective method than GPS, particularly for indoor sports such as futsal, and field sports where short distances and changes in direction are observed.

Characterization of Multidrug Resistant E. faecalis Strains from Pigs of Local Origin by ADSRRS-Fingerprinting and MALDI -TOF MS; Evaluation of the Compatibility of Methods Employed for Multidrug Resistance Analysis

PubMed Central

Nowakiewicz, Aneta; Ziółkowska, Grażyna; Zięba, Przemysław; Gnat, Sebastian; Trościańczyk, Aleksandra; Adaszek, Łukasz

2017-01-01

The aim of this study was to characterize multidrug resistant E. faecalis strains from pigs of local origin and to analyse the relationship between resistance and genotypic and proteomic profiles by amplification of DNA fragments surrounding rare restriction sites (ADSRRS-fingerprinting) and matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI -TOF MS). From the total pool of Enterococcus spp. isolated from 90 pigs, we selected 36 multidrug resistant E. faecalis strains, which represented three different phenotypic resistance profiles. Phenotypic resistance to tetracycline, macrolides, phenicols, and lincomycin and high-level resistance to aminoglycosides were confirmed by the occurrence of at least one corresponding resistance gene in each strain. Based on the analysis of the genotypic and phenotypic resistance of the strains tested, five distinct resistance profiles were generated. As a complement of this analysis, profiles of virulence genes were determined and these profiles corresponded to the phenotypic resistance profiles. The demonstration of resistance to a wide panel of antimicrobials by the strains tested in this study indicates the need of typing to determine the spread of resistance also at the local level. It seems that in the case of E. faecalis, type and scope of resistance strongly determines the genotypic pattern obtained with the ADSRRS-fingerprinting method. The ADSRRS-fingerprinting analysis showed consistency of the genetic profiles with the resistance profiles, while analysis of data with the use of the MALDI- TOF MS method did not demonstrate direct reproduction of the clustering pattern obtained with this method. Our observations were confirmed by statistical analysis (Simpson’s index of diversity, Rand and Wallace coefficients). Even though the MALDI -TOF MS method showed slightly higher discrimination power than ADSRRS-fingerprinting, only the latter method allowed reproduction of the clustering pattern of isolates based on phenotypic resistance and analysis of resistance and virulence genes (Wallace coefficient 1.0). This feature seems to be the most useful for epidemiological purposes and short-term analysis. PMID:28135327

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

PubMed

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.

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

PubMed

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

2011-05-01

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

A Guide to Analyzing Message-Response Sequences and Group Interaction Patterns in Computer-Mediated Communication

ERIC Educational Resources Information Center

Jeong, Allan

2005-01-01

This paper proposes a set of methods and a framework for evaluating, modeling, and predicting group interactions in computer-mediated communication. The method of sequential analysis is described along with specific software tools and techniques to facilitate the analysis of message-response sequences. In addition, the Dialogic Theory and its…

An E-plane analysis of aperture-matched horn antennas using the moment method and the uniform geometrical theory of diffraction

NASA Technical Reports Server (NTRS)

Heedy, D. J.; Burnside, W. D.

1984-01-01

The moment method and the uniform geometrical theory of diffraction are utilized to obtain two separate solutions for the E-plane field pattern of an aperture-matched horn antenna. This particular horn antenna consists of a standard pyramidal horn with the following modifications: a rolled edge section attached to the aperture edges and a curved throat section. The resulting geometry provides significantly better performance in terms of the pattern, impedance, and frequency characteristics than normally obtainable. The moment method is used to calculate the E-plane pattern and BSWR of the antenna. However, at higher frequencies, large amounts of computation time are required. The uniform geometrical theory of diffraction provides a quick and efficient high frequency solution for the E-plane field pattern. In fact, the uniform geometrical theory of diffraction may be used to initially design the antenna; then, the moment method may be applied to fine tune the design. This procedure has been successfully applied to a compact range feed design.

Dietary patterns and household food insecurity in rural populations of Kilosa district, Tanzania.

PubMed

Ntwenya, Julius Edward; Kinabo, Joyce; Msuya, John; Mamiro, Peter; Majili, Zahara Saidi

2015-01-01

Few studies have investigated the relationship between dietary pattern and household food insecurity. The objective of the present analysis was to describe the food consumption patterns and to relate these with the prevalence of food insecurity in the context of a rural community. Three hundred and seven (307) randomly selected households in Kilosa district participated in the study. Data were collected during the rainy season (February-May) and post harvest season (September-October) in the year 2011. Food consumption pattern was determined using a 24-h dietary recall method. Food insecurity data were based on the 30 day recall experience to food insecurity in the household. Factor analysis method using Principal Components extraction function was used to derive the dietary patterns and correlation analysis was used to establish the existing relationship between household food insecurity and dietary patterns factor score. Four food consumption patterns namely (I) Meat and milk; (II) Pulses, legumes, nuts and cooking oils; (III) fish (and other sea foods), roots and tubers; (IV) Cereals, vegetables and fruits consumption patterns were identified during harvest season. Dietary patterns identified during the rainy season were as follows: (I) Fruits, cooking oils, fats, roots and tubers (II) Eggs, meat, milk and milk products (III) Fish, other sea foods, vegetables, roots and tubers and (IV) Pulses, legumes, nuts, cereals and vegetables. Household food insecurity was 80% and 69% during rainy and harvest-seasons, respectively (P = 0.01). Household food insecurity access scale score was negatively correlated with the factor scores on household dietary diversity. Food consumption patterns and food insecurity varied by seasons with worst scenarios most prevalent during the rainy season. The risk for inadequate dietary diversity was higher among food insecure households compared to food secure households. Effort geared at alleviating household food insecurity could contribute to consumption of a wide range of food items at the household level.

Model-based document categorization employing semantic pattern analysis and local structure clustering

NASA Astrophysics Data System (ADS)

Fume, Kosei; Ishitani, Yasuto

2008-01-01

We propose a document categorization method based on a document model that can be defined externally for each task and that categorizes Web content or business documents into a target category in accordance with the similarity of the model. The main feature of the proposed method consists of two aspects of semantics extraction from an input document. The semantics of terms are extracted by the semantic pattern analysis and implicit meanings of document substructure are specified by a bottom-up text clustering technique focusing on the similarity of text line attributes. We have constructed a system based on the proposed method for trial purposes. The experimental results show that the system achieves more than 80% classification accuracy in categorizing Web content and business documents into 15 or 70 categories.

Mycobacterium avium restriction fragment length polymorphism-IS IS1245 and the simple double repetitive element polymerase chain reaction typing method to screen genetic diversity in Brazilian strains.

PubMed

Sequeira, Patrícia Carvalho de; Fonseca, Leila de Souza; Silva, Marlei Gomes da; Saad, Maria Helena Féres

2005-11-01

Simple double repetitive element polymerase chain reaction (MaDRE-PCR) and Pvu II-IS1245 restriction fragment length polymorphism (RFLP) typing methods were used to type 41 Mycobacterium avium isolates obtained from 14 AIDS inpatients and 10 environment and animals specimens identified among 53 mycobacteria isolated from 237 food, chicken, and pig. All environmental and animals strains showed orphan patterns by both methods. By MaDRE-PCR four patients, with multiple isolates, showed different patterns, suggesting polyclonal infection that was confirmed by RFLP in two of them. This first evaluation of MaDRE-PCR on Brazilian M. avium strains demonstrated that the method seems to be useful as simple and less expensive typing method for screening genetic diversity in M. avium strains on selected epidemiological studies, although with limitation on analysis identical patterns except for one band.

Dietary Patterns Derived Using Exploratory and Confirmatory Factor Analysis are Stable and Generalizable Across Race, Region, and Gender Subgroups in the REGARDS Study

PubMed Central

Judd, Suzanne E.; Letter, Abraham J.; Shikany, James M.; Roth, David L.; Newby, P. K.

2015-01-01

Background: Examining diet as a whole using dietary patterns as exposures is a complementary method to using single food or nutrients in studies of diet and disease, but the generalizability of intake patterns across race, region, and gender in the United States has not been established. Objective: To employ rigorous statistical analysis to empirically derive dietary patterns in a large bi-racial, geographically diverse population and examine whether results are stable across population subgroups. Design: The present analysis utilized data from 21,636 participants in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study who completed the Block 98 food frequency questionnaire. We employed exploratory factor analysis and confirmatory factor analyses on 56 different food groups iteratively and examined differences by race, region, and sex to determine the optimal factor solution in our sample. Results: Five dietary patterns emerged: the “Convenience” pattern was characterized by mixed dishes; the “Plant-based” pattern by fruits, vegetables, and fish; the “Sweets/Fats” pattern by sweet snacks, desserts, and fats and oils; the “Southern” pattern by fried foods, organ meat, and sweetened beverages; and the “Alcohol/Salads” pattern by beer, wine, liquor, and salads. Differences were most pronounced in the Southern pattern with black participants, those residing in the Southeast, and participants not completing high school having the highest scores. Conclusion: Five meaningful dietary patterns emerged in the REGARDS study and showed strong congruence across race, sex, and region. Future research will examine associations between these patterns and health outcomes to better understand racial disparities in disease and inform prevention efforts. PMID:25988129

Data handling and analysis for the 1971 corn blight watch experiment.

NASA Technical Reports Server (NTRS)

Anuta, P. E.; Phillips, T. L.; Landgrebe, D. A.

1972-01-01

Review of the data handling and analysis methods used in the near-operational test of remote sensing systems provided by the 1971 corn blight watch experiment. The general data analysis techniques and, particularly, the statistical multispectral pattern recognition methods for automatic computer analysis of aircraft scanner data are described. Some of the results obtained are examined, and the implications of the experiment for future data communication requirements of earth resource survey systems are discussed.

Surface patterning of soft polymer film-coated cylinders via an electric field.

PubMed

Li, Bo; Li, Yue; Xu, Guang-Kui; Feng, Xi-Qiao

2009-11-04

Using the linear stability analysis method, we investigate the surface wrinkling of a thin polymer coating on a cylinder in an externally applied electric field. It is demonstrated that energy competition between surface energy, van der Waals interactive potential energy and electrostatic interaction energy may lead to ordered patterns on the film surface. The analytical solutions are derived for the critical conditions of both longitudinal and circumferential instabilities. The wavelengths of the generated surface patterns can be mediated by changing the magnitude of the electric field. Our analysis shows that the surface morphology is sensitive to the curvature radius of the fiber, especially in the micrometer and nanometer length scales. Furthermore, we suggest a potential approach for fabricating hierarchical patterns on curved surfaces.

Drop-on-Demand Single Cell Isolation and Total RNA Analysis

PubMed Central

Moon, Sangjun; Kim, Yun-Gon; Dong, Lingsheng; Lombardi, Michael; Haeggstrom, Edward; Jensen, Roderick V.; Hsiao, Li-Li; Demirci, Utkan

2011-01-01

Technologies that rapidly isolate viable single cells from heterogeneous solutions have significantly contributed to the field of medical genomics. Challenges remain both to enable efficient extraction, isolation and patterning of single cells from heterogeneous solutions as well as to keep them alive during the process due to a limited degree of control over single cell manipulation. Here, we present a microdroplet based method to isolate and pattern single cells from heterogeneous cell suspensions (10% target cell mixture), preserve viability of the extracted cells (97.0±0.8%), and obtain genomic information from isolated cells compared to the non-patterned controls. The cell encapsulation process is both experimentally and theoretically analyzed. Using the isolated cells, we identified 11 stem cell markers among 1000 genes and compare to the controls. This automated platform enabling high-throughput cell manipulation for subsequent genomic analysis employs fewer handling steps compared to existing methods. PMID:21412416

Detecting dominant motion patterns in crowds of pedestrians

NASA Astrophysics Data System (ADS)

Saqib, Muhammad; Khan, Sultan Daud; Blumenstein, Michael

2017-02-01

As the population of the world increases, urbanization generates crowding situations which poses challenges to public safety and security. Manual analysis of crowded situations is a tedious job and usually prone to errors. In this paper, we propose a novel technique of crowd analysis, the aim of which is to detect different dominant motion patterns in real-time videos. A motion field is generated by computing the dense optical flow. The motion field is then divided into blocks. For each block, we adopt an Intra-clustering algorithm for detecting different flows within the block. Later on, we employ Inter-clustering for clustering the flow vectors among different blocks. We evaluate the performance of our approach on different real-time videos. The experimental results show that our proposed method is capable of detecting distinct motion patterns in crowded videos. Moreover, our algorithm outperforms state-of-the-art methods.

Nonlinear analysis of human physical activity patterns in health and disease.

PubMed

Paraschiv-Ionescu, A; Buchser, E; Rutschmann, B; Aminian, K

2008-02-01

The reliable and objective assessment of chronic disease state has been and still is a very significant challenge in clinical medicine. An essential feature of human behavior related to the health status, the functional capacity, and the quality of life is the physical activity during daily life. A common way to assess physical activity is to measure the quantity of body movement. Since human activity is controlled by various factors both extrinsic and intrinsic to the body, quantitative parameters only provide a partial assessment and do not allow for a clear distinction between normal and abnormal activity. In this paper, we propose a methodology for the analysis of human activity pattern based on the definition of different physical activity time series with the appropriate analysis methods. The temporal pattern of postures, movements, and transitions between postures was quantified using fractal analysis and symbolic dynamics statistics. The derived nonlinear metrics were able to discriminate patterns of daily activity generated from healthy and chronic pain states.

The art and learning patterns of knowing in nursing.

PubMed

Baixinho, Cristina Lavareda; Ferraz, Isabel Carvalho Beato; Ferreira, Óscar Manuel Ramos; Rafael, Helga Marilia da Silva

2014-12-01

Objective To identify the perception of the students about the use of art as a pedagogical strategy in learning the patterns of knowing in nursing; to identify the dimensions of each pattern valued in the analysis of pieces of art. Method Descriptive mixed study. Data collection used a questionnaire applied to 31 nursing students. Results In the analysis of the students' discourse, it was explicit that empirical knowledge includes scientific knowledge, tradition and nature of care. The aesthetic knowledge implies expressiveness, subjectivity and sensitivity. Self-knowledge, experience, reflective attitude and relationships with others are the subcategories of personal knowledge and the moral and ethics support ethical knowledge. Conclusion It is possible to learn patterns of knowledge through art, especially the aesthetic, ethical and personal. It is necessary to investigate further pedagogical strategies that contribute to the learning patterns of nursing knowledge.

Application of the empirical orthogonal function to study the rainfall pattern in Daerah Istimewa Yogyakarta province

NASA Astrophysics Data System (ADS)

Adi-Kusumo, Fajar; Gunardi, Utami, Herni; Nurjani, Emilya; Sopaheluwakan, Ardhasena; Aluicius, Irwan Endrayanto; Christiawan, Titus

2016-02-01

We consider the Empirical Orthogonal Function (EOF) to study the rainfall pattern in Daerah Istimewa Yogyakarta (DIY) Province, Indonesia. The EOF is one of the important methods to study the dominant pattern of the data using dimension reduction technique. EOF makes possible to reduce the huge dimension of observed data into a smaller one without losing its significant information in order to figures the whole data. The methods is also known as Principal Components Analysis (PCA) which is conducted to find the pattern of the data. DIY Province is one of the province in Indonesia which has special characteristics related to the rainfall pattern. This province has an active volcano, karst, highlands, and also some lower area including beach. This province is bounded by the Indonesian ocean which is one of the important factor to provide the rainfall. We use at least ten years rainfall monthly data of all stations in this area and study the rainfall characteristics based on the four regencies of the province. EOF analysis is conducted to analyze data in order to decide the station groups which have similar characters.

Exploratory factor analysis of the 12-item Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale in people newly diagnosed with advanced cancer.

PubMed

Bai, Mei; Dixon, Jane K

2014-01-01

The purpose of this study was to reexamine the factor pattern of the 12-item Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale (FACIT-Sp-12) using exploratory factor analysis in people newly diagnosed with advanced cancer. Principal components analysis (PCA) and 3 common factor analysis methods were used to explore the factor pattern of the FACIT-Sp-12. Factorial validity was assessed in association with quality of life (QOL). Principal factor analysis (PFA), iterative PFA, and maximum likelihood suggested retrieving 3 factors: Peace, Meaning, and Faith. Both Peace and Meaning positively related to QOL, whereas only Peace uniquely contributed to QOL. This study supported the 3-factor model of the FACIT-Sp-12. Suggestions for revision of items and further validation of the identified factor pattern were provided.

Metacarpophalangeal pattern profile analysis in Leri-Weill dyschondrosteosis.

PubMed

Laurencikas, E; Soderman, E; Grigelioniene, G; Hagenäs, L; Jorulf, H

2005-04-01

To analyze the metacarpophalangeal profile (MCPP) in individuals with Leri-Weill dyschondrosteosis (LWD) and to assess its value as a possible contributor to early diagnosis. Hand profiles of 39 individuals with a diagnosis of LWD were calculated and analyzed. Discriminant analysis was applied to differentiate between LWD and normal individuals. There was a distinct pattern profile in LWD. Mean pattern profile showed two bone-shortening gradients, with increasing shortening from distal to proximal and from medial to lateral. Distal phalanx 2 was disproportionately long and second metacarpal was disproportionately short. Discriminant analysis yielded correct classification in 72% of analyzed cases. MCPP is not age-related and the analysis can be applied at any age, facilitating early diagnosis of LWD. In view of its availability, low costs, and diagnostic value, MCPP analysis should be considered as a routine method in the patients of short stature where LWD is suspected.

Medium-throughput processing of whole mount in situ hybridisation experiments into gene expression domains.

PubMed

Crombach, Anton; Cicin-Sain, Damjan; Wotton, Karl R; Jaeger, Johannes

2012-01-01

Understanding the function and evolution of developmental regulatory networks requires the characterisation and quantification of spatio-temporal gene expression patterns across a range of systems and species. However, most high-throughput methods to measure the dynamics of gene expression do not preserve the detailed spatial information needed in this context. For this reason, quantification methods based on image bioinformatics have become increasingly important over the past few years. Most available approaches in this field either focus on the detailed and accurate quantification of a small set of gene expression patterns, or attempt high-throughput analysis of spatial expression through binary pattern extraction and large-scale analysis of the resulting datasets. Here we present a robust, "medium-throughput" pipeline to process in situ hybridisation patterns from embryos of different species of flies. It bridges the gap between high-resolution, and high-throughput image processing methods, enabling us to quantify graded expression patterns along the antero-posterior axis of the embryo in an efficient and straightforward manner. Our method is based on a robust enzymatic (colorimetric) in situ hybridisation protocol and rapid data acquisition through wide-field microscopy. Data processing consists of image segmentation, profile extraction, and determination of expression domain boundary positions using a spline approximation. It results in sets of measured boundaries sorted by gene and developmental time point, which are analysed in terms of expression variability or spatio-temporal dynamics. Our method yields integrated time series of spatial gene expression, which can be used to reverse-engineer developmental gene regulatory networks across species. It is easily adaptable to other processes and species, enabling the in silico reconstitution of gene regulatory networks in a wide range of developmental contexts.

Optimal Ranking Regime Analysis of TreeFlow Dendrohydrological Reconstructions

NASA Astrophysics Data System (ADS)

Mauget, S. A.

2017-12-01

The Optimal Ranking Regime (ORR) method was used to identify 6-100 year time windows containing significant ranking sequences in 55 western U.S. streamflow reconstructions, and reconstructions of the level of the Great Salt Lake and San Francisco Bay salinity during 1500-2007. The method's ability to identify optimally significant and non-overlapping runs of low and high rankings allows it to re-express a reconstruction time series as a simplified sequence of regime segments marking intra- to multi-decadal (IMD) periods of low or high streamflow, lake level, or salinity. Those ORR sequences, referred to here as Z-lines, can be plotted to identify consistent regime patterns in the analysis of numerous reconstructions. The Z-lines for the 57 reconstructions evaluated here show a common pattern of IMD cycles of drought and pluvial periods during the late 16th and 17th centuries, a relatively dormant period during the 18th century, and the reappearance of alternating dry and wet IMD periods during the 19th and early 20th centuries. Although this pattern suggests the possibility of similarly active and inactive oceanic modes in the North Pacific and North Atlantic, such centennial-scale patterns are not evident in the ORR analyses of reconstructed Pacific Decadal Oscillation (PDO), El Niño-Southern Oscillation, and North Atlantic seas-surface temperature variation. But given the inconsistency in the analyses of four PDO reconstructions the possible role of centennial-scale oceanic mechanisms is uncertain. In future research the ORR method might be applied to climate reconstructions around the Pacific Basin to try to resolve this uncertainty. Given its ability to compare regime patterns in climate reconstructions derived using different methods and proxies, the method may also be used in future research to evaluate long-term regional temperature reconstructions.

ERTS: A multispectral image analysis contribution for the geomorphological evaluation of southern Maracaibo Lake Basin. [geological survey and drainage patterns

NASA Technical Reports Server (NTRS)

Salas, F.; Cabello, O.; Alarcon, F.; Ferrer, C.

1974-01-01

Multispectral analysis of ERTS-A images at scales of 1:1,000,000 and 1:500,000 has been conducted with conventional photointerpretation methods. Specific methods have been developed for the geomorphological analysis of southern Maracaibo Lake Basin which comprises part of the Venezuelan Andean Range, Perija Range, the Tachira gap and the Southern part of the Maracaibo Lake depression. A steplike analysis was conducted to separate macroforms, landscapes and relief units as well as drainage patterns and tectonic features, which permitted the delineation of tectonic provinces, stratigraphic units, geomorphologic units and geomorphologic positions. The geomorphologic synthesis obtained compares favorably with conventional analysis made on this area for accuracy of 1:100,000 scale, and in some features with details obtained through conventional analysis for accuracy of 1:15,000 and field work. Geomorphological units in the mountains were identified according to changes in tone, texture, forms orientation of interfluves and tectonic characteristics which control interfluvial disimetrics.

[Identification of Dens Draconis and Os Draconis by XRD method].

PubMed

Chen, Guang-Yun; Wu, Qi-Nan; Shen, Bei; Chen, Rong

2012-04-01

To establish an XRD method for evaluating the quality of Os Draconis and Dens Draconis and applying in judgement of the counterfeit. Dens Draconis, Os Draconis and the counterfeit of Os Draconis were analyzed by XRD. Their diffraction patterns were clustered analysis and evaluated their similarity degree. Established the analytical method of Dens Draconis and Os Draconis basing the features fingerprint information of the 10 common peaks by XRD pattern. Obtained the XRD pattern of the counterfeit of Os Draconis. The similarity degree of separate sources of Dens Draconis was high,while the similarity degree of separate sources of Os Draconis was significant different from each other. This method can be used for identification and evaluation of Os Draconis and Dens Draconis. It also can be used for identification the counterfeit of Os Draconis effectively.

Relationship Between Landcover Pattern and Surface Net Radiation in AN Coastal City

NASA Astrophysics Data System (ADS)

Zhao, X.; Liu, L.; Liu, X.; Zhao, Y.

2016-06-01

Taking Xiamen city as the study area this research first retrieved surface net radiation using meteorological data and Landsat 5 TM images of the four seasons in the year 2009. Meanwhile the 65 different landscape metrics of each analysis unit were acquired using landscape analysis method. Then the most effective landscape metrics affecting surface net radiation were determined by correlation analysis, partial correlation analysis, stepwise regression method, etc. At both class and landscape levels, this paper comprehensively analyzed the temporal and spatial variations of the surface net radiation as well as the effects of land cover pattern on it in Xiamen from a multi-seasonal perspective. The results showed that the spatial composition of land cover pattern shows significant influence on surface net radiation while the spatial allocation of land cover pattern does not. The proportions of bare land and forest land are effective and important factors which affect the changes of surface net radiation all the year round. Moreover, the proportion of forest land is more capable for explaining surface net radiation than the proportion of bare land. So the proportion of forest land is the most important and continuously effective factor which affects and explains the cross-seasonal differences of surface net radiation. This study is helpful in exploring the formation and evolution mechanism of urban heat island. It also gave theoretical hints and realistic guidance for urban planning and sustainable development.

Development of indirect EFBEM for radiating noise analysis including underwater problems

NASA Astrophysics Data System (ADS)

Kwon, Hyun-Wung; Hong, Suk-Yoon; Song, Jee-Hun

2013-09-01

For the analysis of radiating noise problems in medium-to-high frequency ranges, the Energy Flow Boundary Element Method (EFBEM) was developed. EFBEM is the analysis technique that applies the Boundary Element Method (BEM) to Energy Flow Analysis (EFA). The fundamental solutions representing spherical wave property for radiating noise problems in open field and considering the free surface effect in underwater are developed. Also the directivity factor is developed to express wave's directivity patterns in medium-to-high frequency ranges. Indirect EFBEM by using fundamental solutions and fictitious source was applied to open field and underwater noise problems successfully. Through numerical applications, the acoustic energy density distributions due to vibration of a simple plate model and a sphere model were compared with those of commercial code, and the comparison showed good agreement in the level and pattern of the energy density distributions.

Fine needle aspiration (FNA) of synovial sarcoma--a comparative histological-cytological study of 15 cases, including immunohistochemical, electron microscopic and cytogenetic examination and DNA-ploidy analysis.

PubMed

Akerman, M; Willén, H; Carlén, B; Mandahl, N; Mertens, F

1996-06-01

A retrospective study of 25 FNAs (11 aspirates from primary tumours and 14 from recurrencies and metastases) from 15 synovial sarcomas was performed. The cytological findings were correlated with the histopathology and the value of immunohistochemical and electron microscopic examination as well as DNA-ploidy and cytogenetic analysis for diagnosis were assessed. A reproducible cellular pattern with a reliable diagnosis of spindle cell sarcoma was possible provided that the aspirates were cell rich. However, a true biphasic pattern indicative of synovial sarcoma was only seen in one of the 25 specimens. Electron microscopic examination of the aspirates was a valuable adjunctive diagnostic method, whereas immunocytochemistry and DNA-ploidy analysis were not. Immunohistochemical, electron microscopic and cytogenetic analysis were all valuable ancillary methods when performed on surgical specimens. Malignant haemangiopericytoma and fibrosarcoma were the most important differential diagnoses in the FNA specimens.

Application of concepts from cross-recurrence analysis in speech production: an overview and comparison with other nonlinear methods.

PubMed

Lancia, Leonardo; Fuchs, Susanne; Tiede, Mark

2014-06-01

The aim of this article was to introduce an important tool, cross-recurrence analysis, to speech production applications by showing how it can be adapted to evaluate the similarity of multivariate patterns of articulatory motion. The method differs from classical applications of cross-recurrence analysis because no phase space reconstruction is conducted, and a cleaning algorithm removes the artifacts from the recurrence plot. The main features of the proposed approach are robustness to nonstationarity and efficient separation of amplitude variability from temporal variability. The authors tested these claims by applying their method to synthetic stimuli whose variability had been carefully controlled. The proposed method was also demonstrated in a practical application: It was used to investigate the role of biomechanical constraints in articulatory reorganization as a consequence of speeded repetition of CVCV utterances containing a labial and a coronal consonant. Overall, the proposed approach provided more reliable results than other methods, particularly in the presence of high variability. The proposed method is a useful and appropriate tool for quantifying similarity and dissimilarity in patterns of speech articulator movement, especially in such research areas as speech errors and pathologies, where unpredictable divergent behavior is expected.

Quantitative estimation of time-variable earthquake hazard by using fuzzy set theory

NASA Astrophysics Data System (ADS)

Deyi, Feng; Ichikawa, M.

1989-11-01

In this paper, the various methods of fuzzy set theory, called fuzzy mathematics, have been applied to the quantitative estimation of the time-variable earthquake hazard. The results obtained consist of the following. (1) Quantitative estimation of the earthquake hazard on the basis of seismicity data. By using some methods of fuzzy mathematics, seismicity patterns before large earthquakes can be studied more clearly and more quantitatively, highly active periods in a given region and quiet periods of seismic activity before large earthquakes can be recognized, similarities in temporal variation of seismic activity and seismic gaps can be examined and, on the other hand, the time-variable earthquake hazard can be assessed directly on the basis of a series of statistical indices of seismicity. Two methods of fuzzy clustering analysis, the method of fuzzy similarity, and the direct method of fuzzy pattern recognition, have been studied is particular. One method of fuzzy clustering analysis is based on fuzzy netting, and another is based on the fuzzy equivalent relation. (2) Quantitative estimation of the earthquake hazard on the basis of observational data for different precursors. The direct method of fuzzy pattern recognition has been applied to research on earthquake precursors of different kinds. On the basis of the temporal and spatial characteristics of recognized precursors, earthquake hazards in different terms can be estimated. This paper mainly deals with medium-short-term precursors observed in Japan and China.

Visual Aggregate Analysis of Eligibility Features of Clinical Trials

PubMed Central

He, Zhe; Carini, Simona; Sim, Ida; Weng, Chunhua

2015-01-01

Objective To develop a method for profiling the collective populations targeted for recruitment by multiple clinical studies addressing the same medical condition using one eligibility feature each time. Methods Using a previously published database COMPACT as the backend, we designed a scalable method for visual aggregate analysis of clinical trial eligibility features. This method consists of four modules for eligibility feature frequency analysis, query builder, distribution analysis, and visualization, respectively. This method is capable of analyzing (1) frequently used qualitative and quantitative features for recruiting subjects for a selected medical condition, (2) distribution of study enrollment on consecutive value points or value intervals of each quantitative feature, and (3) distribution of studies on the boundary values, permissible value ranges, and value range widths of each feature. All analysis results were visualized using Google Charts API. Five recruited potential users assessed the usefulness of this method for identifying common patterns in any selected eligibility feature for clinical trial participant selection. Results We implemented this method as a Web-based analytical system called VITTA (Visual Analysis Tool of Clinical Study Target Populations). We illustrated the functionality of VITTA using two sample queries involving quantitative features BMI and HbA1c for conditions “hypertension” and “Type 2 diabetes”, respectively. The recruited potential users rated the user-perceived usefulness of VITTA with an average score of 86.4/100. Conclusions We contributed a novel aggregate analysis method to enable the interrogation of common patterns in quantitative eligibility criteria and the collective target populations of multiple related clinical studies. A larger-scale study is warranted to formally assess the usefulness of VITTA among clinical investigators and sponsors in various therapeutic areas. PMID:25615940

An approach to the language discrimination in different scripts using adjacent local binary pattern

NASA Astrophysics Data System (ADS)

Brodić, D.; Amelio, A.; Milivojević, Z. N.

2017-09-01

The paper proposes a language discrimination method of documents. First, each letter is encoded with the certain script type according to its status in baseline area. Such a cipher text is subjected to a feature extraction process. Accordingly, the local binary pattern as well as its expanded version called adjacent local binary pattern are extracted. Because of the difference in the language characteristics, the above analysis shows significant diversity. This type of diversity is a key aspect in the decision-making differentiation of the languages. Proposed method is tested on an example of documents. The experiments give encouraging results.

Double-pulse digital speckle pattern interferometry for vibration analysis

NASA Astrophysics Data System (ADS)

Zhang, Dazhi; Xue, Jingfeng; Chen, Lu; Wen, Juying; Wang, Jingjing

2014-12-01

The double-pulse Digital Speckle Pattern Interferometry (DSPI) in the laboratory is established. Two good performances have been achieved at the same time, which is uniform distribution of laser beam energy by space filter and recording two successive pictures by a CCD camera successfully. Then two-dimensional discrete orthogonal wavelet transform method is used for the process of filtering method. By using the DSPI, speckle pattern of a vibrated object is obtained with interval of (2~800)μs, and 3D plot of the transient vibration is achieved. Moreover, good agreements of the mode shapes and displacement are obtained by comparing with Laser Doppler Vibrometer (LDV) .

Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran

NASA Astrophysics Data System (ADS)

Sarparandeh, Mohammadali; Hezarkhani, Ardeshir

2017-12-01

The use of efficient methods for data processing has always been of interest to researchers in the field of earth sciences. Pattern recognition techniques are appropriate methods for high-dimensional data such as geochemical data. Evaluation of the geochemical distribution of rare earth elements (REEs) requires the use of such methods. In particular, the multivariate nature of REE data makes them a good target for numerical analysis. The main subject of this paper is application of unsupervised pattern recognition approaches in evaluating geochemical distribution of REEs in the Kiruna type magnetite-apatite deposit of Se-Chahun. For this purpose, 42 bulk lithology samples were collected from the Se-Chahun iron ore deposit. In this study, 14 rare earth elements were measured with inductively coupled plasma mass spectrometry (ICP-MS). Pattern recognition makes it possible to evaluate the relations between the samples based on all these 14 features, simultaneously. In addition to providing easy solutions, discovery of the hidden information and relations of data samples is the advantage of these methods. Therefore, four clustering methods (unsupervised pattern recognition) - including a modified basic sequential algorithmic scheme (MBSAS), hierarchical (agglomerative) clustering, k-means clustering and self-organizing map (SOM) - were applied and results were evaluated using the silhouette criterion. Samples were clustered in four types. Finally, the results of this study were validated with geological facts and analysis results from, for example, scanning electron microscopy (SEM), X-ray diffraction (XRD), ICP-MS and optical mineralogy. The results of the k-means clustering and SOM methods have the best matches with reality, with experimental studies of samples and with field surveys. Since only the rare earth elements are used in this division, a good agreement of the results with lithology is considerable. It is concluded that the combination of the proposed methods and geological studies leads to finding some hidden information, and this approach has the best results compared to using only one of them.

DASS-GUI: a user interface for identification and analysis of significant patterns in non-sequential data.

PubMed

Hollunder, Jens; Friedel, Maik; Kuiper, Martin; Wilhelm, Thomas

2010-04-01

Many large 'omics' datasets have been published and many more are expected in the near future. New analysis methods are needed for best exploitation. We have developed a graphical user interface (GUI) for easy data analysis. Our discovery of all significant substructures (DASS) approach elucidates the underlying modularity, a typical feature of complex biological data. It is related to biclustering and other data mining approaches. Importantly, DASS-GUI also allows handling of multi-sets and calculation of statistical significances. DASS-GUI contains tools for further analysis of the identified patterns: analysis of the pattern hierarchy, enrichment analysis, module validation, analysis of additional numerical data, easy handling of synonymous names, clustering, filtering and merging. Different export options allow easy usage of additional tools such as Cytoscape. Source code, pre-compiled binaries for different systems, a comprehensive tutorial, case studies and many additional datasets are freely available at http://www.ifr.ac.uk/dass/gui/. DASS-GUI is implemented in Qt.

Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications

USDA-ARS?s Scientific Manuscript database

Analysis of DNA methylation patterns relies increasingly on sequencing-based profiling methods. The four most frequently used sequencing-based technologies are the bisulfite-based methods MethylC-seq and reduced representation bisulfite sequencing (RRBS), and the enrichment-based techniques methylat...

[Applications of stable isotope analysis in the trophic ecology studies of cephalopods].

PubMed

Li, Yun-Kai; Gong, Yi; Chen, Xin-Jun

2014-05-01

Cephalopods play an important role in marine food webs, however, knowledge about their complex life history, especially their feeding ecology, remains limited. With the rapidly increasing use of stable isotope analysis (SIA) in ecology, it becomes a powerful tool and complement of traditional methods for investigating the trophic ecology and migration patterns of invertebrates. Here, after summarizing the current methods for trophic ecology investigation of cephalopods, applications of SIA in studying the trophic ecology of cephalopods were reviewed, including the key issues such as standardization of available tissues for SIA analyzing, diet shift and migration patterns of cephalopods, with the aim of advancing its application in the biology of cephalopods in the future.

Geographic patterns of networks derived from extreme precipitation over the Indian subcontinent

NASA Astrophysics Data System (ADS)

Stolbova, Veronika; Bookhagen, Bodo; Marwan, Norbert; Kurths, Juergen

2014-05-01

Complex networks (CN) and event synchronization (ES) methods have been applied to study a number of climate phenomena such as Indian Summer Monsoon (ISM), South-American Monsoon, and African Monsoon. These methods proved to be powerful tools to infer interdependencies in climate dynamics between geographical sites, spatial structures, and key regions of the considered climate phenomenon. Here, we use these methods to study the spatial temporal variability of the extreme rainfall over the Indian subcontinent, in order to filter the data by coarse-graining the network, and to identify geographic patterns that are signature features (spatial signatures) of the ISM. We find four main geographic patterns of networks derived from extreme precipitation over the Indian subcontinent using up-to-date satellite-derived, and high temporal and spatial resolution rain-gauge interpolated daily rainfall datasets. In order to prove that our results are also relevant for other climatic variables like pressure and temperature, we use re-analysis data provided by the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR). We find that two of the patterns revealed from the CN extreme rainfall analysis coincide with those obtained for the pressure and temperature fields, and all four above mentioned patterns can be explained by topography, winds, and monsoon circulation. CN and ES enable to select the most informative regions for the ISM, providing realistic description of the ISM dynamics with fewer data, and also help to infer geographic pattern that are spatial signatures of the ISM. These patterns deserve a special attention for the meteorologists and can be used as markers of the ISM variability.

Cross-Modal Multivariate Pattern Analysis

PubMed Central

Meyer, Kaspar; Kaplan, Jonas T.

2011-01-01

Multivariate pattern analysis (MVPA) is an increasingly popular method of analyzing functional magnetic resonance imaging (fMRI) data1-4. Typically, the method is used to identify a subject's perceptual experience from neural activity in certain regions of the brain. For instance, it has been employed to predict the orientation of visual gratings a subject perceives from activity in early visual cortices5 or, analogously, the content of speech from activity in early auditory cortices6. Here, we present an extension of the classical MVPA paradigm, according to which perceptual stimuli are not predicted within, but across sensory systems. Specifically, the method we describe addresses the question of whether stimuli that evoke memory associations in modalities other than the one through which they are presented induce content-specific activity patterns in the sensory cortices of those other modalities. For instance, seeing a muted video clip of a glass vase shattering on the ground automatically triggers in most observers an auditory image of the associated sound; is the experience of this image in the "mind's ear" correlated with a specific neural activity pattern in early auditory cortices? Furthermore, is this activity pattern distinct from the pattern that could be observed if the subject were, instead, watching a video clip of a howling dog? In two previous studies7,8, we were able to predict sound- and touch-implying video clips based on neural activity in early auditory and somatosensory cortices, respectively. Our results are in line with a neuroarchitectural framework proposed by Damasio9,10, according to which the experience of mental images that are based on memories - such as hearing the shattering sound of a vase in the "mind's ear" upon seeing the corresponding video clip - is supported by the re-construction of content-specific neural activity patterns in early sensory cortices. PMID:22105246

FE-DEM Analysis of the Effect of Tread Pattern on the Tractive Performance of Tires Operating on Sand

NASA Astrophysics Data System (ADS)

Nakashima, Hiroshi; Takatsu, Yuzuru; Shinone, Hisanori; Matsukawa, Hisao; Kasetani, Takahiro

Soil-tire system interaction is a fundamental and important research topic in terramechanics. We applied a 2D finite element, discrete element method (FE-DEM), using FEM for the tire and the bottom soil layer and DEM for the surface soil layer. Satisfactory performance analysis was achieved. In this study, to clarify the capabilities and limitations of the method for soil-tire interaction analysis, the tractive performance of real automobile tires with two different tread patterns—smooth and grooved—was analyzed by FE-DEM, and the numerical results compared with the experimental results obtained using an indoor traction measurement system. The analysis of tractive performance could be performed with sufficient accuracy by the proposed 2D dynamic FE-DEM. FE-DEM obtained larger drawbar pull for a tire with a grooved tread pattern, which was verified by the experimental results. Moreover, the result for the grooved tire showed almost the same gross tractive effort and similar running resistance as in experiments. However, for a tire with smooth tread pattern, the analyzed gross tractive effort and running resistance behaved differently than the experimental results, largely due to the difference in tire sinkage in FE-DEM.

How does modifying a DEM to reflect known hydrology affect subsequent terrain analysis?

NASA Astrophysics Data System (ADS)

Callow, John Nikolaus; Van Niel, Kimberly P.; Boggs, Guy S.

2007-01-01

SummaryMany digital elevation models (DEMs) have difficulty replicating hydrological patterns in flat landscapes. Efforts to improve DEM performance in replicating known hydrology have included a variety of soft (i.e. algorithm-based approaches) and hard techniques, such as " Stream burning" or "surface reconditioning" (e.g. Agree or ANUDEM). Using a representation of the known stream network, these methods trench or mathematically warp the original DEM to improve how accurately stream position, stream length and catchment boundaries replicate known hydrological conditions. However, these techniques permanently alter the DEM and may affect further analyses (e.g. slope). This paper explores the impact that commonly used hydrological correction methods ( Stream burning, Agree.aml and ANUDEM v4.6.3 and ANUDEM v5.1) have on the overall nature of a DEM, finding that different methods produce non-convergent outcomes for catchment parameters (such as catchment boundaries, stream position and length), and differentially compromise secondary terrain analysis. All hydrological correction methods successfully improved calculation of catchment area, stream position and length as compared to using the DEM without any modification, but they all increased catchment slope. No single method performing best across all categories. Different hydrological correction methods changed elevation and slope in different spatial patterns and magnitudes, compromising the ability to derive catchment parameters and conduct secondary terrain analysis from a single DEM. Modification of a DEM to better reflect known hydrology can be useful, however knowledge of the magnitude and spatial pattern of the changes are required before using a DEM for subsequent analyses.

A Hybrid One-Way ANOVA Approach for the Robust and Efficient Estimation of Differential Gene Expression with Multiple Patterns

PubMed Central

Mollah, Mohammad Manir Hossain; Jamal, Rahman; Mokhtar, Norfilza Mohd; Harun, Roslan; Mollah, Md. Nurul Haque

2015-01-01

Background Identifying genes that are differentially expressed (DE) between two or more conditions with multiple patterns of expression is one of the primary objectives of gene expression data analysis. Several statistical approaches, including one-way analysis of variance (ANOVA), are used to identify DE genes. However, most of these methods provide misleading results for two or more conditions with multiple patterns of expression in the presence of outlying genes. In this paper, an attempt is made to develop a hybrid one-way ANOVA approach that unifies the robustness and efficiency of estimation using the minimum β-divergence method to overcome some problems that arise in the existing robust methods for both small- and large-sample cases with multiple patterns of expression. Results The proposed method relies on a β-weight function, which produces values between 0 and 1. The β-weight function with β = 0.2 is used as a measure of outlier detection. It assigns smaller weights (≥ 0) to outlying expressions and larger weights (≤ 1) to typical expressions. The distribution of the β-weights is used to calculate the cut-off point, which is compared to the observed β-weight of an expression to determine whether that gene expression is an outlier. This weight function plays a key role in unifying the robustness and efficiency of estimation in one-way ANOVA. Conclusion Analyses of simulated gene expression profiles revealed that all eight methods (ANOVA, SAM, LIMMA, EBarrays, eLNN, KW, robust BetaEB and proposed) perform almost identically for m = 2 conditions in the absence of outliers. However, the robust BetaEB method and the proposed method exhibited considerably better performance than the other six methods in the presence of outliers. In this case, the BetaEB method exhibited slightly better performance than the proposed method for the small-sample cases, but the the proposed method exhibited much better performance than the BetaEB method for both the small- and large-sample cases in the presence of more than 50% outlying genes. The proposed method also exhibited better performance than the other methods for m > 2 conditions with multiple patterns of expression, where the BetaEB was not extended for this condition. Therefore, the proposed approach would be more suitable and reliable on average for the identification of DE genes between two or more conditions with multiple patterns of expression. PMID:26413858

Automated Analysis of Renewable Energy Datasets ('EE/RE Data Mining')

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

Bush, Brian; Elmore, Ryan; Getman, Dan

This poster illustrates methods to substantially improve the understanding of renewable energy data sets and the depth and efficiency of their analysis through the application of statistical learning methods ('data mining') in the intelligent processing of these often large and messy information sources. The six examples apply methods for anomaly detection, data cleansing, and pattern mining to time-series data (measurements from metering points in buildings) and spatiotemporal data (renewable energy resource datasets).

Predicting variations of perceptual performance across individuals from neural activity using pattern classifiers.

PubMed

Das, Koel; Giesbrecht, Barry; Eckstein, Miguel P

2010-07-15

Within the past decade computational approaches adopted from the field of machine learning have provided neuroscientists with powerful new tools for analyzing neural data. For instance, previous studies have applied pattern classification algorithms to electroencephalography data to predict the category of presented visual stimuli, human observer decision choices and task difficulty. Here, we quantitatively compare the ability of pattern classifiers and three ERP metrics (peak amplitude, mean amplitude, and onset latency of the face-selective N170) to predict variations across individuals' behavioral performance in a difficult perceptual task identifying images of faces and cars embedded in noise. We investigate three different pattern classifiers (Classwise Principal Component Analysis, CPCA; Linear Discriminant Analysis, LDA; and Support Vector Machine, SVM), five training methods differing in the selection of training data sets and three analyses procedures for the ERP measures. We show that all three pattern classifier algorithms surpass traditional ERP measurements in their ability to predict individual differences in performance. Although the differences across pattern classifiers were not large, the CPCA method with training data sets restricted to EEG activity for trials in which observers expressed high confidence about their decisions performed the highest at predicting perceptual performance of observers. We also show that the neural activity predicting the performance across individuals was distributed through time starting at 120ms, and unlike the face-selective ERP response, sustained for more than 400ms after stimulus presentation, indicating that both early and late components contain information correlated with observers' behavioral performance. Together, our results further demonstrate the potential of pattern classifiers compared to more traditional ERP techniques as an analysis tool for modeling spatiotemporal dynamics of the human brain and relating neural activity to behavior. Copyright 2010 Elsevier Inc. All rights reserved.

Differences in Looking at Own- and Other-Race Faces Are Subtle and Analysis-Dependent: An Account of Discrepant Reports.

PubMed

Arizpe, Joseph; Kravitz, Dwight J; Walsh, Vincent; Yovel, Galit; Baker, Chris I

2016-01-01

The Other-Race Effect (ORE) is the robust and well-established finding that people are generally poorer at facial recognition of individuals of another race than of their own race. Over the past four decades, much research has focused on the ORE because understanding this phenomenon is expected to elucidate fundamental face processing mechanisms and the influence of experience on such mechanisms. Several recent studies of the ORE in which the eye-movements of participants viewing own- and other-race faces were tracked have, however, reported highly conflicting results regarding the presence or absence of differential patterns of eye-movements to own- versus other-race faces. This discrepancy, of course, leads to conflicting theoretical interpretations of the perceptual basis for the ORE. Here we investigate fixation patterns to own- versus other-race (African and Chinese) faces for Caucasian participants using different analysis methods. While we detect statistically significant, though subtle, differences in fixation pattern using an Area of Interest (AOI) approach, we fail to detect significant differences when applying a spatial density map approach. Though there were no significant differences in the spatial density maps, the qualitative patterns matched the results from the AOI analyses reflecting how, in certain contexts, Area of Interest (AOI) analyses can be more sensitive in detecting the differential fixation patterns than spatial density analyses, due to spatial pooling of data with AOIs. AOI analyses, however, also come with the limitation of requiring a priori specification. These findings provide evidence that the conflicting reports in the prior literature may be at least partially accounted for by the differences in the statistical sensitivity associated with the different analysis methods employed across studies. Overall, our results suggest that detection of differences in eye-movement patterns can be analysis-dependent and rests on the assumptions inherent in the given analysis.

Differences in Looking at Own- and Other-Race Faces Are Subtle and Analysis-Dependent: An Account of Discrepant Reports

PubMed Central

Arizpe, Joseph; Kravitz, Dwight J.; Walsh, Vincent; Yovel, Galit; Baker, Chris I.

2016-01-01

The Other-Race Effect (ORE) is the robust and well-established finding that people are generally poorer at facial recognition of individuals of another race than of their own race. Over the past four decades, much research has focused on the ORE because understanding this phenomenon is expected to elucidate fundamental face processing mechanisms and the influence of experience on such mechanisms. Several recent studies of the ORE in which the eye-movements of participants viewing own- and other-race faces were tracked have, however, reported highly conflicting results regarding the presence or absence of differential patterns of eye-movements to own- versus other-race faces. This discrepancy, of course, leads to conflicting theoretical interpretations of the perceptual basis for the ORE. Here we investigate fixation patterns to own- versus other-race (African and Chinese) faces for Caucasian participants using different analysis methods. While we detect statistically significant, though subtle, differences in fixation pattern using an Area of Interest (AOI) approach, we fail to detect significant differences when applying a spatial density map approach. Though there were no significant differences in the spatial density maps, the qualitative patterns matched the results from the AOI analyses reflecting how, in certain contexts, Area of Interest (AOI) analyses can be more sensitive in detecting the differential fixation patterns than spatial density analyses, due to spatial pooling of data with AOIs. AOI analyses, however, also come with the limitation of requiring a priori specification. These findings provide evidence that the conflicting reports in the prior literature may be at least partially accounted for by the differences in the statistical sensitivity associated with the different analysis methods employed across studies. Overall, our results suggest that detection of differences in eye-movement patterns can be analysis-dependent and rests on the assumptions inherent in the given analysis. PMID:26849447

Analysis of forest naturalness and tree mortality patterns in Estonia

Treesearch

J.A. Stanturf

2009-01-01

New methods for evaluating structural properties of stands and individual tree mortality within forests are needed to enhance biodiversity assessment in forest inventories. One approach is to assess the degree of naturalness in a forest. We assessed forest naturalness by examining patterns and causes of mortality and deadwood amount and...

Analysis of Spatial Voting Patterns: An Approach in Political Socialization

ERIC Educational Resources Information Center

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.…

[Investigation on pattern and methods of quality control for Chinese materia medica based on dao-di herbs and bioassay - bioassay for Coptis chinensis].

PubMed

Yan, Dan; Xiao, Xiao-he

2011-05-01

Establishment of bioassay methods is the technical issues to be faced with in the bioassay of Chinese materia medica. Taking the bioassay of Coptis chinensis Franch. as an example, the establishment process and application of the bioassay methods (including bio-potency and bio-activity fingerprint) were explained from the aspects of methodology, principle of selection, experimental design, method confirmation and data analysis. The common technologies were extracted and formed with the above aspects, so as to provide technical support for constructing pattern and method of the quality control for Chinese materia medica based on the dao-di herbs and bioassay.

Evaluation of pedestrian safety at intersections: A theoretical framework based on pedestrian-vehicle interaction patterns.

PubMed

Ni, Ying; Wang, Menglong; Sun, Jian; Li, Keping

2016-11-01

Pedestrians are the most vulnerable road users, and pedestrian safety has become a major research focus in recent years. Regarding the quality and quantity issues with collision data, conflict analysis using surrogate safety measures has become a useful method to study pedestrian safety. However, given the inequality between pedestrians and vehicles in encounters and the multiple interactions between pedestrians and vehicles, it is insufficient to simply use the same indicator(s) or the same way to aggregate indicators for all conditions. In addition, behavioral factors cannot be neglected. To better use information extracted from trajectories for safety evaluation and pay more attention on effects of behavioral factors, this paper develops a more sophisticated framework for pedestrian conflict analysis that takes pedestrian-vehicle interactions into consideration. A concept of three interaction patterns has been proposed for the first time, namely "hard interaction," "no interaction," and "soft-interaction." Interactions have been categorized under one of these patterns by analyzing profiles of speed and conflict indicators during the whole interactive processes. In this paper, a support vector machine (SVM) approach has been adopted to classify severity levels for a dataset including 1144 events extracted from three intersections in Shanghai, China, followed by an analysis of variable importance. The results revealed that different conflict indicators have different contributions to indicating the severity level under various interaction patterns. Therefore, it is recommended either to use specific conflict indicators or to use weighted indicator aggregation for each interaction pattern when evaluating pedestrian safety. The implementation has been carried out at the fourth crosswalk, and the results indicate that the proposed method can achieve a higher accuracy and better robustness than conventional methods. Furthermore, the method is helpful for better understanding underlying levels of safety from the behavioral perspective, which can also provide evidence for targeted traffic education on proper behaviors. Copyright © 2016 Elsevier Ltd. All rights reserved.

A method for studying the development pattern of urban commercial service facilities based on customer reviews from social media

NASA Astrophysics Data System (ADS)

Wang, Y. D.; Jiang, B. T.; Ye, X. Y.

2016-06-01

Urbanization is one of the most important human social activities in the 21st century (Chaolin et al., 2012). With an increasing number of people visiting cities, the provision of adequate urban service facilities, including public and commercial service facilities, in locations where people live has become an important guarantee of the success of urbanization. Exploring the commercial service facilities in a specific area of a city can help us understand the progress and trends of urban renewal in the area, provide a quantitative basis for evaluating the rationality of planning implementation, and facilitate an analysis of the effects of different factors on the regional development of a city (Schor et al. 2003). In this paper, we proposed a data processing and analysis method for studying the distribution and development pattern of urban commercial facilities based on customer reviews. In addition, based on road network constraints, we explored the patterns contained in customer reviews data, including patterns for the spatial distribution and spatial-temporal evolution of facilities as well as the number of facilities and degree of satisfaction.

Singularity analysis based on wavelet transform of fractal measures for identifying geochemical anomaly in mineral exploration

NASA Astrophysics Data System (ADS)

Chen, Guoxiong; Cheng, Qiuming

2016-02-01

Multi-resolution and scale-invariance have been increasingly recognized as two closely related intrinsic properties endowed in geofields such as geochemical and geophysical anomalies, and they are commonly investigated by using multiscale- and scaling-analysis methods. In this paper, the wavelet-based multiscale decomposition (WMD) method was proposed to investigate the multiscale natures of geochemical pattern from large scale to small scale. In the light of the wavelet transformation of fractal measures, we demonstrated that the wavelet approximation operator provides a generalization of box-counting method for scaling analysis of geochemical patterns. Specifically, the approximation coefficient acts as the generalized density-value in density-area fractal modeling of singular geochemical distributions. Accordingly, we presented a novel local singularity analysis (LSA) using the WMD algorithm which extends the conventional moving averaging to a kernel-based operator for implementing LSA. Finally, the novel LSA was validated using a case study dealing with geochemical data (Fe2O3) in stream sediments for mineral exploration in Inner Mongolia, China. In comparison with the LSA implemented using the moving averaging method the novel LSA using WMD identified improved weak geochemical anomalies associated with mineralization in covered area.

A new similarity index for nonlinear signal analysis based on local extrema patterns

NASA Astrophysics Data System (ADS)

Niknazar, Hamid; Motie Nasrabadi, Ali; Shamsollahi, Mohammad Bagher

2018-02-01

Common similarity measures of time domain signals such as cross-correlation and Symbolic Aggregate approximation (SAX) are not appropriate for nonlinear signal analysis. This is because of the high sensitivity of nonlinear systems to initial points. Therefore, a similarity measure for nonlinear signal analysis must be invariant to initial points and quantify the similarity by considering the main dynamics of signals. The statistical behavior of local extrema (SBLE) method was previously proposed to address this problem. The SBLE similarity index uses quantized amplitudes of local extrema to quantify the dynamical similarity of signals by considering patterns of sequential local extrema. By adding time information of local extrema as well as fuzzifying quantized values, this work proposes a new similarity index for nonlinear and long-term signal analysis, which extends the SBLE method. These new features provide more information about signals and reduce noise sensitivity by fuzzifying them. A number of practical tests were performed to demonstrate the ability of the method in nonlinear signal clustering and classification on synthetic data. In addition, epileptic seizure detection based on electroencephalography (EEG) signal processing was done by the proposed similarity to feature the potentials of the method as a real-world application tool.

Interference pattern period measurement at picometer level

NASA Astrophysics Data System (ADS)

Xiang, Xiansong; Wei, Chunlong; Jia, Wei; Zhou, Changhe; Li, Minkang; Lu, Yancong

2016-10-01

To produce large scale gratings by Scanning Beam Interference Lithography (SBIL), a light spot containing grating pattern is generated by two beams interfering, and a scanning stage is used to drive the substrate moving under the light spot. In order to locate the stage at the proper exposure positions, the period of the Interference pattern must be measured accurately. We developed a set of process to obtain the period value of two interfering beams at picometer level. The process includes data acquisition and data analysis. The data is received from a photodiode and a laser interferometer with sub-nanometer resolution. Data analysis differs from conventional analyzing methods like counting wave peaks or using Fourier transform to get the signal period, after a preprocess of filtering and envelope removing, the mean square error is calculated between the received signal and ideal sinusoid waves to find the best-fit frequency, thus an accuracy period value is acquired, this method has a low sensitivity to amplitude noise and a high resolution of frequency. With 405nm laser beams interfering, a pattern period value around 562nm is acquired by employing this process, fitting diagram of the result shows the accuracy of the period value reaches picometer level, which is much higher than the results of conventional methods.

From atomistic interfaces to dendritic patterns

NASA Astrophysics Data System (ADS)

Galenko, P. K.; Alexandrov, D. V.

2018-01-01

Transport processes around phase interfaces, together with thermodynamic properties and kinetic phenomena, control the formation of dendritic patterns. Using the thermodynamic and kinetic data of phase interfaces obtained on the atomic scale, one can analyse the formation of a single dendrite and the growth of a dendritic ensemble. This is the result of recent progress in theoretical methods and computational algorithms calculated using powerful computer clusters. Great benefits can be attained from the development of micro-, meso- and macro-levels of analysis when investigating the dynamics of interfaces, interpreting experimental data and designing the macrostructure of samples. The review and research articles in this theme issue cover the spectrum of scales (from nano- to macro-length scales) in order to exhibit recently developing trends in the theoretical analysis and computational modelling of dendrite pattern formation. Atomistic modelling, the flow effect on interface dynamics, the transition from diffusion-limited to thermally controlled growth existing at a considerable driving force, two-phase (mushy) layer formation, the growth of eutectic dendrites, the formation of a secondary dendritic network due to coalescence, computational methods, including boundary integral and phase-field methods, and experimental tests for theoretical models-all these themes are highlighted in the present issue. This article is part of the theme issue `From atomistic interfaces to dendritic patterns'.

Impact of the difference in the plantar flexor strength of the ankle joint in the affected side among hemiplegic patients on the plantar pressure and walking asymmetry.

PubMed

You, Young Youl; Chung, Sin Ho; Lee, Hyung Jin

2016-11-01

[Purpose] This study was to examine the changes in the gait lines and plantar pressures in static and dynamic circumstances, according to the differences in the strengths of the plantar flexors in the ankle joints on the affected sides of hemiplegic patients, and to determine their impacts on walking symmetry. [Subjects and Methods] A total of thirty hospitalized stroke patients suffering from hemiplegia were selected in this study. The subjects had ankylosing patterns in the ankle joints of the affected sides. Fifteen of the patients had plantar flexor manual muscle testing scores between poor and fair, while fifteen of the patients had zero and trace. [Results] The contact pattern of the plantar surface with the ground is a reliable method for walking analysis, which is an important index for understanding the ankle mechanism and the relationship between the plantar surface and the ground. [Conclusion] The functional improvement of patients with stroke could be supported through a verification of the analysis methods of the therapy strategy and walking pattern.

Finger crease pattern recognition using Legendre moments and principal component analysis

NASA Astrophysics Data System (ADS)

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.

Knowledge Discovery from Vibration Measurements

PubMed Central

Li, Jian; Wang, Daoyao

2014-01-01

The framework as well as the particular algorithms of pattern recognition process is widely adopted in structural health monitoring (SHM). However, as a part of the overall process of knowledge discovery from data bases (KDD), the results of pattern recognition are only changes and patterns of changes of data features. In this paper, based on the similarity between KDD and SHM and considering the particularity of SHM problems, a four-step framework of SHM is proposed which extends the final goal of SHM from detecting damages to extracting knowledge to facilitate decision making. The purposes and proper methods of each step of this framework are discussed. To demonstrate the proposed SHM framework, a specific SHM method which is composed by the second order structural parameter identification, statistical control chart analysis, and system reliability analysis is then presented. To examine the performance of this SHM method, real sensor data measured from a lab size steel bridge model structure are used. The developed four-step framework of SHM has the potential to clarify the process of SHM to facilitate the further development of SHM techniques. PMID:24574933

Using spatial analysis to demonstrate the heterogeneity of the cardiovascular drug-prescribing pattern in Taiwan

PubMed Central

2011-01-01

Background Geographic Information Systems (GIS) combined with spatial analytical methods could be helpful in examining patterns of drug use. Little attention has been paid to geographic variation of cardiovascular prescription use in Taiwan. The main objective was to use local spatial association statistics to test whether or not the cardiovascular medication-prescribing pattern is homogenous across 352 townships in Taiwan. Methods The statistical methods used were the global measures of Moran's I and Local Indicators of Spatial Association (LISA). While Moran's I provides information on the overall spatial distribution of the data, LISA provides information on types of spatial association at the local level. LISA statistics can also be used to identify influential locations in spatial association analysis. The major classes of prescription cardiovascular drugs were taken from Taiwan's National Health Insurance Research Database (NHIRD), which has a coverage rate of over 97%. The dosage of each prescription was converted into defined daily doses to measure the consumption of each class of drugs. Data were analyzed with ArcGIS and GeoDa at the township level. Results The LISA statistics showed an unusual use of cardiovascular medications in the southern townships with high local variation. Patterns of drug use also showed more low-low spatial clusters (cold spots) than high-high spatial clusters (hot spots), and those low-low associations were clustered in the rural areas. Conclusions The cardiovascular drug prescribing patterns were heterogeneous across Taiwan. In particular, a clear pattern of north-south disparity exists. Such spatial clustering helps prioritize the target areas that require better education concerning drug use. PMID:21609462

The Synthetic Experiment: E. B. Titchener's Cornell Psychological Laboratory and the Test of Introspective Analysis.

PubMed

Evans, Rand B

2017-01-01

Beginning in 1 9a0, a major thread of research was added to E. B. Titchener's Cornell laboratory: the synthetic experiment. Titchener and his graduate students used introspective analysis to reduce a perception, a complex experience, into its simple sensory constituents. To test the validity of that analysis, stimulus patterns were selected to reprodiuce the patterns of sensations found in the introspective analyses. If the original perception can be reconstructed in this way, then the analysis was considered validated. This article reviews development of the synthetic method in E. B. Titchener's laboratory at Cornell University and examines its impact on psychological research.

Discrimination between glycosylation patterns of therapeutic antibodies using a microfluidic platform, MALDI-MS and multivariate statistics.

PubMed

Thuy, Tran Thi; Tengstrand, Erik; Aberg, Magnus; Thorsén, Gunnar

2012-11-01

Optimal glycosylation with respect to the efficacy, serum half-life time, and immunogenic properties is essential in the generation of therapeutic antibodies. The glycosylation pattern can be affected by several different parameters during the manufacture of antibodies and may change significantly over cultivation time. Fast and robust methods for determination of the glycosylation patterns of therapeutic antibodies are therefore needed. We have recently presented an efficient method for the determination of glycans on therapeutic antibodies using a microfluidic CD platform for sample preparation prior to matrix-assisted laser-desorption mass spectrometry analysis. In the present work, this method is applied to analyse the glycosylation patterns of three commercially available therapeutic antibodies and one intended for therapeutic use. Two of the antibodies produced in mouse myeloma cell line (SP2/0) and one produced in Chinese hamster ovary (CHO) cells exhibited similar glycosylation patterns but could still be readily differentiated from each other using multivariate statistical methods. The two antibodies with most similar glycosylation patterns were also studied in an assessment of the method's applicability for quality control of therapeutic antibodies. The method presented in this paper is highly automated and rapid. It can therefore efficiently generate data that helps to keep a production process within the desired design space or assess that an identical product is being produced after changes to the process. Copyright © 2012 Elsevier B.V. All rights reserved.

Cluster analysis of multiple planetary flow regimes

NASA Technical Reports Server (NTRS)

Mo, Kingtse; Ghil, Michael

1987-01-01

A modified cluster analysis method was developed to identify spatial patterns of planetary flow regimes, and to study transitions between them. This method was applied first to a simple deterministic model and second to Northern Hemisphere (NH) 500 mb data. The dynamical model is governed by the fully-nonlinear, equivalent-barotropic vorticity equation on the sphere. Clusters of point in the model's phase space are associated with either a few persistent or with many transient events. Two stationary clusters have patterns similar to unstable stationary model solutions, zonal, or blocked. Transient clusters of wave trains serve as way stations between the stationary ones. For the NH data, cluster analysis was performed in the subspace of the first seven empirical orthogonal functions (EOFs). Stationary clusters are found in the low-frequency band of more than 10 days, and transient clusters in the bandpass frequency window between 2.5 and 6 days. In the low-frequency band three pairs of clusters determine, respectively, EOFs 1, 2, and 3. They exhibit well-known regional features, such as blocking, the Pacific/North American (PNA) pattern and wave trains. Both model and low-pass data show strong bimodality. Clusters in the bandpass window show wave-train patterns in the two jet exit regions. They are related, as in the model, to transitions between stationary clusters.

High-resolution melting analysis for prenatal diagnosis of beta-thalassemia in northern Thailand.

PubMed

Charoenkwan, Pimlak; Sirichotiyakul, Supatra; Phusua, Arunee; Suanta, Sudjai; Fanhchaksai, Kanda; Sae-Tung, Rattika; Sanguansermsri, Torpong

2017-12-01

High-resolution melting (HRM) analysis is a rapid mutation analysis which assesses the pattern of reduction of fluorescence signal after subjecting the amplified PCR product with saturated fluorescence dye to an increasing temperature. We used HRM analysis for prenatal diagnosis of beta-thalassemia disease in northern Thailand. Five PCR-HRM protocols were used to detect point mutations in five different segments of the beta-globin gene, and one protocol to detect the 3.4 kb beta-globin deletion. We sought to characterize the mutations in carriers and to enable prenatal diagnosis in 126 couples at risk of having a fetus with beta-thalassemia disease. The protocols identified 18 common mutations causing beta-thalassemia, including the rare codon 132 (A-T) mutation. Each mutation showed a specific HRM pattern and all results were in concordance with those from direct DNA sequencing or gap-PCR methods. In cases of beta-thalassemia disease resulting from homozygosity for a mutation or compound heterozygosity for two mutations on the same amplified segment, the HRM patterns were different to those of a single mutation and were specific for each combination. HRM analysis is a simple and useful method for mutation identification in beta-thalassemia carriers and prenatal diagnosis of beta-thalassemia in northern Thailand.

Learning To Recognize Visual Concepts: Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning

DTIC Science & Technology

1993-01-01

Maria and My Parents, Helena and Andrzej IV ACKNOWLEDGMENTS I would like to first of all thank my advisor. Dr. Ryszard Michalski. who introduced...represent the current state of the art in machine learning methodology. The most popular method. the minimization of Bayes risk [ Duda and Hart. 1973]. is a...34 Pattern Recognition, Vol. 23, no. 3-4, pp. 291-309, 1990. Duda , O. and P. Hart, Pattern Classification and Scene Analysis, John Wiley & Sons. 1973

A morphometric analysis of vegetation patterns in dryland ecosystems

PubMed Central

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

2017-01-01

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. PMID:28386414

A morphometric analysis of vegetation patterns in dryland ecosystems.

PubMed

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.

A morphometric analysis of vegetation patterns in dryland ecosystems

NASA Astrophysics Data System (ADS)

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.

Two-dimensional wavelet analysis based classification of gas chromatogram differential mobility spectrometry signals.

PubMed

Zhao, Weixiang; Sankaran, Shankar; Ibáñez, Ana M; Dandekar, Abhaya M; Davis, Cristina E

2009-08-04

This study introduces two-dimensional (2-D) wavelet analysis to the classification of gas chromatogram differential mobility spectrometry (GC/DMS) data which are composed of retention time, compensation voltage, and corresponding intensities. One reported method to process such large data sets is to convert 2-D signals to 1-D signals by summing intensities either across retention time or compensation voltage, but it can lose important signal information in one data dimension. A 2-D wavelet analysis approach keeps the 2-D structure of original signals, while significantly reducing data size. We applied this feature extraction method to 2-D GC/DMS signals measured from control and disordered fruit and then employed two typical classification algorithms to testify the effects of the resultant features on chemical pattern recognition. Yielding a 93.3% accuracy of separating data from control and disordered fruit samples, 2-D wavelet analysis not only proves its feasibility to extract feature from original 2-D signals but also shows its superiority over the conventional feature extraction methods including converting 2-D to 1-D and selecting distinguishable pixels from training set. Furthermore, this process does not require coupling with specific pattern recognition methods, which may help ensure wide applications of this method to 2-D spectrometry data.

Multi-resolution analysis using integrated microscopic configuration with local patterns for benign-malignant mass classification

NASA Astrophysics Data System (ADS)

Rabidas, Rinku; Midya, Abhishek; Chakraborty, Jayasree; Sadhu, Anup; Arif, Wasim

2018-02-01

In this paper, Curvelet based local attributes, Curvelet-Local configuration pattern (C-LCP), is introduced for the characterization of mammographic masses as benign or malignant. Amid different anomalies such as micro- calcification, bilateral asymmetry, architectural distortion, and masses, the reason for targeting the mass lesions is due to their variation in shape, size, and margin which makes the diagnosis a challenging task. Being efficient in classification, multi-resolution property of the Curvelet transform is exploited and local information is extracted from the coefficients of each subband using Local configuration pattern (LCP). The microscopic measures in concatenation with the local textural information provide more discriminating capability than individual. The measures embody the magnitude information along with the pixel-wise relationships among the neighboring pixels. The performance analysis is conducted with 200 mammograms of the DDSM database containing 100 mass cases of each benign and malignant. The optimal set of features is acquired via stepwise logistic regression method and the classification is carried out with Fisher linear discriminant analysis. The best area under the receiver operating characteristic curve and accuracy of 0.95 and 87.55% are achieved with the proposed method, which is further compared with some of the state-of-the-art competing methods.

CRD's Daniela Ushizima Receives DOE Early Career Award

Science.gov Websites

Science. The award will fund research into developing new methods to help scientists extract more -the-art data analysis methods with emphasis on pattern recognition and machine learning emerging sources, multidisciplinary teams to interpret the data and the computational methods to automate some of

PatterNet: a system to learn compact physical design pattern representations for pattern-based analytics

NASA Astrophysics Data System (ADS)

Lutich, Andrey

2017-07-01

This research considers the problem of generating compact vector representations of physical design patterns for analytics purposes in semiconductor patterning domain. PatterNet uses a deep artificial neural network to learn mapping of physical design patterns to a compact Euclidean hyperspace. Distances among mapped patterns in this space correspond to dissimilarities among patterns defined at the time of the network training. Once the mapping network has been trained, PatterNet embeddings can be used as feature vectors with standard machine learning algorithms, and pattern search, comparison, and clustering become trivial problems. PatterNet is inspired by the concepts developed within the framework of generative adversarial networks as well as the FaceNet. Our method facilitates a deep neural network (DNN) to learn directly the compact representation by supplying it with pairs of design patterns and dissimilarity among these patterns defined by a user. In the simplest case, the dissimilarity is represented by an area of the XOR of two patterns. Important to realize that our PatterNet approach is very different to the methods developed for deep learning on image data. In contrast to "conventional" pictures, the patterns in the CAD world are the lists of polygon vertex coordinates. The method solely relies on the promise of deep learning to discover internal structure of the incoming data and learn its hierarchical representations. Artificial intelligence arising from the combination of PatterNet and clustering analysis very precisely follows intuition of patterning/optical proximity correction experts paving the way toward human-like and human-friendly engineering tools.

Microbead-regulated surface wrinkling patterns in a film-substrate system

NASA Astrophysics Data System (ADS)

Zhang, Cheng; Wang, Jiawen; Cao, Yan-Ping; Lu, Conghua; Li, Bo; Feng, Xi-Qiao

2017-10-01

The control of surface wrinkling patterns at the microscale is a concern in many applications. In this letter, we regulate surface wrinkling patterns on a film-substrate system by introducing microbeads atop the film. Both experiments and theoretical analysis reveal the changes in surface wrinkles induced by microbeads. Under equibiaxial compression, the film-substrate system without microbeads bonded on its upper surface often buckles into global, uniform labyrinths, whereas the labyrinthine pattern locally gives way to radial stripes emanating from the microbeads. This regulation of surface wrinkles depends on the sizes and spacing of microbeads. We combine the finite element method and the Fourier spectral method to explore the physical mechanisms underlying the phenomena. This study offers a viable technique for engineering surfaces with tunable functions.

Sinusoidal Analysis-Synthesis of Audio Using Perceptual Criteria

NASA Astrophysics Data System (ADS)

Painter, Ted; Spanias, Andreas

2003-12-01

This paper presents a new method for the selection of sinusoidal components for use in compact representations of narrowband audio. The method consists of ranking and selecting the most perceptually relevant sinusoids. The idea behind the method is to maximize the matching between the auditory excitation pattern associated with the original signal and the corresponding auditory excitation pattern associated with the modeled signal that is being represented by a small set of sinusoidal parameters. The proposed component-selection methodology is shown to outperform the maximum signal-to-mask ratio selection strategy in terms of subjective quality.

Linguistic Analysis of the Human Heartbeat Using Frequency and Rank Order Statistics

NASA Astrophysics Data System (ADS)

Yang, Albert C.-C.; Hseu, Shu-Shya; Yien, Huey-Wen; Goldberger, Ary L.; Peng, C.-K.

2003-03-01

Complex physiologic signals may carry unique dynamical signatures that are related to their underlying mechanisms. We present a method based on rank order statistics of symbolic sequences to investigate the profile of different types of physiologic dynamics. We apply this method to heart rate fluctuations, the output of a central physiologic control system. The method robustly discriminates patterns generated from healthy and pathologic states, as well as aging. Furthermore, we observe increased randomness in the heartbeat time series with physiologic aging and pathologic states and also uncover nonrandom patterns in the ventricular response to atrial fibrillation.

Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition

PubMed Central

Cui, Zhiming; Zhao, Pengpeng

2014-01-01

A motion trajectory is an intuitive representation form in time-space domain for a micromotion behavior of moving target. Trajectory analysis is an important approach to recognize abnormal behaviors of moving targets. Against the complexity of vehicle trajectories, this paper first proposed a trajectory pattern learning method based on dynamic time warping (DTW) and spectral clustering. It introduced the DTW distance to measure the distances between vehicle trajectories and determined the number of clusters automatically by a spectral clustering algorithm based on the distance matrix. Then, it clusters sample data points into different clusters. After the spatial patterns and direction patterns learned from the clusters, a recognition method for detecting vehicle abnormal behaviors based on mixed pattern matching was proposed. The experimental results show that the proposed technical scheme can recognize main types of traffic abnormal behaviors effectively and has good robustness. The real-world application verified its feasibility and the validity. PMID:24605045

Automatic Assessment and Reduction of Noise using Edge Pattern Analysis in Non-Linear Image Enhancement

NASA Technical Reports Server (NTRS)

Jobson, Daniel J.; Rahman, Zia-Ur; Woodell, Glenn A.; Hines, Glenn D.

2004-01-01

Noise is the primary visibility limit in the process of non-linear image enhancement, and is no longer a statistically stable additive noise in the post-enhancement image. Therefore novel approaches are needed to both assess and reduce spatially variable noise at this stage in overall image processing. Here we will examine the use of edge pattern analysis both for automatic assessment of spatially variable noise and as a foundation for new noise reduction methods.

[Relations between biomedical variables: mathematical analysis or linear algebra?].

PubMed

Hucher, M; Berlie, J; Brunet, M

1977-01-01

The authors, after a short reminder of one pattern's structure, stress on the possible double approach of relations uniting the variables of this pattern: use of fonctions, what is within the mathematical analysis sphere, use of linear algebra profiting by matricial calculation's development and automatiosation. They precise the respective interests on these methods, their bounds and the imperatives for utilization, according to the kind of variables, of data, and the objective for work, understanding phenomenons or helping towards decision.

Analysis of Short and Long Range Atomic Order in Nanocrystalline Diamonds with Application of Powder Diffractometry

NASA Technical Reports Server (NTRS)

Palosz, B.; Grzanka, E.; Stelmakh, S.; Pielaszek, R.; Bismayer, U.; Neuefiend, J.; Weber, H.-P.; Proffen, T.; VonDreele, R.; Palosz, W.;

Winter Precipitation Forecast in the European and Mediterranean Regions Using Cluster Analysis

NASA Astrophysics Data System (ADS)

Totz, Sonja; Tziperman, Eli; Coumou, Dim; Pfeiffer, Karl; Cohen, Judah

2017-12-01

The European climate is changing under global warming, and especially the Mediterranean region has been identified as a hot spot for climate change with climate models projecting a reduction in winter rainfall and a very pronounced increase in summertime heat waves. These trends are already detectable over the historic period. Hence, it is beneficial to forecast seasonal droughts well in advance so that water managers and stakeholders can prepare to mitigate deleterious impacts. We developed a new cluster-based empirical forecast method to predict precipitation anomalies in winter. This algorithm considers not only the strength but also the pattern of the precursors. We compare our algorithm with dynamic forecast models and a canonical correlation analysis-based prediction method demonstrating that our prediction method performs better in terms of time and pattern correlation in the Mediterranean and European regions.

Genomic analysis of codon usage shows influence of mutation pressure, natural selection, and host features on Marburg virus evolution.

PubMed

Nasrullah, Izza; Butt, Azeem M; Tahir, Shifa; Idrees, Muhammad; Tong, Yigang

2015-08-26

The Marburg virus (MARV) has a negative-sense single-stranded RNA genome, belongs to the family Filoviridae, and is responsible for several outbreaks of highly fatal hemorrhagic fever. Codon usage patterns of viruses reflect a series of evolutionary changes that enable viruses to shape their survival rates and fitness toward the external environment and, most importantly, their hosts. To understand the evolution of MARV at the codon level, we report a comprehensive analysis of synonymous codon usage patterns in MARV genomes. Multiple codon analysis approaches and statistical methods were performed to determine overall codon usage patterns, biases in codon usage, and influence of various factors, including mutation pressure, natural selection, and its two hosts, Homo sapiens and Rousettus aegyptiacus. Nucleotide composition and relative synonymous codon usage (RSCU) analysis revealed that MARV shows mutation bias and prefers U- and A-ended codons to code amino acids. Effective number of codons analysis indicated that overall codon usage among MARV genomes is slightly biased. The Parity Rule 2 plot analysis showed that GC and AU nucleotides were not used proportionally which accounts for the presence of natural selection. Codon usage patterns of MARV were also found to be influenced by its hosts. This indicates that MARV have evolved codon usage patterns that are specific to both of its hosts. Moreover, selection pressure from R. aegyptiacus on the MARV RSCU patterns was found to be dominant compared with that from H. sapiens. Overall, mutation pressure was found to be the most important and dominant force that shapes codon usage patterns in MARV. To our knowledge, this is the first detailed codon usage analysis of MARV and extends our understanding of the mechanisms that contribute to codon usage and evolution of MARV.

Characterizing bonding patterns in diradicals and triradicals by density-based wave function analysis: A uniform approach

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

Orms, Natalie; Rehn, Dirk; Dreuw, Andreas

Density-based wave function analysis enables unambiguous comparisons of electronic structure computed by different methods and removes ambiguity of orbital choices. Here, we use this tool to investigate the performance of different spin-flip methods for several prototypical diradicals and triradicals. In contrast to previous calibration studies that focused on energy gaps between high and low spin-states, we focus on the properties of the underlying wave functions, such as the number of effectively unpaired electrons. Comparison of different density functional and wave function theory results provides insight into the performance of the different methods when applied to strongly correlated systems such asmore » polyradicals. We also show that canonical molecular orbitals for species like large copper-containing diradicals fail to correctly represent the underlying electronic structure due to highly non-Koopmans character, while density-based analysis of the same wave function delivers a clear picture of bonding pattern.« less

Characterizing bonding patterns in diradicals and triradicals by density-based wave function analysis: A uniform approach

DOE PAGES

Orms, Natalie; Rehn, Dirk; Dreuw, Andreas; ...

2017-12-21

Density-based wave function analysis enables unambiguous comparisons of electronic structure computed by different methods and removes ambiguity of orbital choices. Here, we use this tool to investigate the performance of different spin-flip methods for several prototypical diradicals and triradicals. In contrast to previous calibration studies that focused on energy gaps between high and low spin-states, we focus on the properties of the underlying wave functions, such as the number of effectively unpaired electrons. Comparison of different density functional and wave function theory results provides insight into the performance of the different methods when applied to strongly correlated systems such asmore » polyradicals. We also show that canonical molecular orbitals for species like large copper-containing diradicals fail to correctly represent the underlying electronic structure due to highly non-Koopmans character, while density-based analysis of the same wave function delivers a clear picture of bonding pattern.« less

Visual aggregate analysis of eligibility features of clinical trials.

PubMed

He, Zhe; Carini, Simona; Sim, Ida; Weng, Chunhua

2015-04-01

To develop a method for profiling the collective populations targeted for recruitment by multiple clinical studies addressing the same medical condition using one eligibility feature each time. Using a previously published database COMPACT as the backend, we designed a scalable method for visual aggregate analysis of clinical trial eligibility features. This method consists of four modules for eligibility feature frequency analysis, query builder, distribution analysis, and visualization, respectively. This method is capable of analyzing (1) frequently used qualitative and quantitative features for recruiting subjects for a selected medical condition, (2) distribution of study enrollment on consecutive value points or value intervals of each quantitative feature, and (3) distribution of studies on the boundary values, permissible value ranges, and value range widths of each feature. All analysis results were visualized using Google Charts API. Five recruited potential users assessed the usefulness of this method for identifying common patterns in any selected eligibility feature for clinical trial participant selection. We implemented this method as a Web-based analytical system called VITTA (Visual Analysis Tool of Clinical Study Target Populations). We illustrated the functionality of VITTA using two sample queries involving quantitative features BMI and HbA1c for conditions "hypertension" and "Type 2 diabetes", respectively. The recruited potential users rated the user-perceived usefulness of VITTA with an average score of 86.4/100. We contributed a novel aggregate analysis method to enable the interrogation of common patterns in quantitative eligibility criteria and the collective target populations of multiple related clinical studies. A larger-scale study is warranted to formally assess the usefulness of VITTA among clinical investigators and sponsors in various therapeutic areas. Copyright © 2015 Elsevier Inc. All rights reserved.

Ontogenetic patterns in the dreams of women across the lifespan.

PubMed

Dale, Allyson; Lortie-Lussier, Monique; De Koninck, Joseph

2015-12-01

The present study supports and extends previous research on the developmental differences in women's dreams across the lifespan. The participants included 75 Canadian women in each of 5 age groups from adolescence to old age including 12-17, 18-24, 25-39, 40-64, and 65-85, totaling 375 women. One dream per participant was scored by two independent judges using the method of content analysis. Trend analysis was used to determine the ontogenetic pattern of the dream content categories. Results demonstrated significant ontogenetic decreases (linear trends) for female and familiar characters, activities, aggression, and friendliness. These patterns of dream imagery reflect the waking developmental patterns as proposed by social theories and recognized features of aging as postulated by the continuity hypothesis. Limitations and suggestions for future research including the examining of developmental patterns in the dreams of males are discussed. Copyright © 2015 Elsevier Inc. All rights reserved.

[Spatial distribution pattern and fractal analysis of Larix chinensis populations in Qinling Mountain].

PubMed

Guo, Hua; Wang, Xiaoan; Xiao, Yaping

2005-02-01

In this paper, the fractal characters of Larix chinensis populations in Qinling Mountain were studied by contiguous grid quadrate sampling method and by boxing-counting dimension and information dimension. The results showed that the high boxing-counting dimension (1.8087) and information dimension (1.7931) reflected a higher spatial occupational degree of L. chinensis populations. Judged by the dispersal index and Morisita's pattern index, L. chinensis populations clumped at three different age stages (0-25, 25-50 and over 50 years). From Greig-Smiths' mean variance analysis, the figure of pattern scale showed that L. chinensis populations clumped in 128 m2 and 512 m2, and the different age groups clumped in different scales. The pattern intensities decreased with increasing age, and tended to reduce with increasing area when detected by Kershaw's PI index. The spatial pattern characters of L. chinensis populations may be their responses to environmental factors.

Navier-Stokes flow field analysis of compressible flow in a high pressure safety relief valve

NASA Technical Reports Server (NTRS)

Vu, Bruce; Wang, Ten-See; Shih, Ming-Hsin; Soni, Bharat

1993-01-01

The objective of this study is to investigate the complex three-dimensional flowfield of an oxygen safety pressure relieve valve during an incident, with a computational fluid dynamic (CFD) analysis. Specifically, the analysis will provide a flow pattern that would lead to the expansion of the eventual erosion pattern of the hardware, so as to combine it with other findings to piece together a most likely scenario for the investigation. The CFD model is a pressure based solver. An adaptive upwind difference scheme is employed for the spatial discretization, and a predictor, multiple corrector method is used for the velocity-pressure coupling. The computational result indicated vortices formation near the opening of the valve which matched the erosion pattern of the damaged hardware.

[Limitations of landscape pattern analysis based on landscape indices: a case study of Lize-jian wetland in Yilan of Taiwan Province, China].

PubMed

Lin, Meng-lung; Cao, Yu; Wang, Shin

2008-01-01

In this paper, the Lizejian wetland landscape patterns in northeastern Taiwan of China were established by landscape indices and aerial photo interpretation, and a parallel analysis was made on them. The results showed that landscape indices could only indicate the landscape geometric characteristics of the wetland at patch and landscape levels, but could not present its spatial and functional characteristics observed from aerial photos. Combining aerial photo interpretation with landscape indices could be helpful to the holistic understanding of Lizejian wetland' s landscape structure and function, and improve the landscape pattern analysis. The new method for assessing landscape structure from a holistic point of view would play an important role in future landscape ecology research.

A comparison of heuristic and model-based clustering methods for dietary pattern analysis.

PubMed

Greve, Benjamin; Pigeot, Iris; Huybrechts, Inge; Pala, Valeria; Börnhorst, Claudia

2016-02-01

Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward's method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data. The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice. The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward's methods, the performance of k-means was better in 64-100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a 'non-processed' cluster with a high consumption of fruits, vegetables and wholemeal bread, a 'balanced' cluster with only slight preferences of single foods and a 'junk food' cluster. The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.

Complex amplitude reconstruction for dynamic beam quality M² factor measurement with self-referencing interferometer wavefront sensor.

PubMed

Du, Yongzhao; Fu, Yuqing; Zheng, Lixin

2016-12-20

A real-time complex amplitude reconstruction method for determining the dynamic beam quality M² factor based on a Mach-Zehnder self-referencing interferometer wavefront sensor is developed. By using the proposed complex amplitude reconstruction method, full characterization of the laser beam, including amplitude (intensity profile) and phase information, can be reconstructed from a single interference pattern with the Fourier fringe pattern analysis method in a one-shot measurement. With the reconstructed complex amplitude, the beam fields at any position z along its propagation direction can be obtained by first utilizing the diffraction integral theory. Then the beam quality M² factor of the dynamic beam is calculated according to the specified method of the Standard ISO11146. The feasibility of the proposed method is demonstrated with the theoretical analysis and experiment, including the static and dynamic beam process. The experimental method is simple, fast, and operates without movable parts and is allowed in order to investigate the laser beam in inaccessible conditions using existing methods.

Heritable patterns of tooth decay in the permanent dentition: principal components and factor analyses.

PubMed

Shaffer, John R; Feingold, Eleanor; Wang, Xiaojing; Tcuenco, Karen T; Weeks, Daniel E; DeSensi, Rebecca S; Polk, Deborah E; Wendell, Steve; Weyant, Robert J; Crout, Richard; McNeil, Daniel W; Marazita, Mary L

2012-03-09

Dental caries is the result of a complex interplay among environmental, behavioral, and genetic factors, with distinct patterns of decay likely due to specific etiologies. Therefore, global measures of decay, such as the DMFS index, may not be optimal for identifying risk factors that manifest as specific decay patterns, especially if the risk factors such as genetic susceptibility loci have small individual effects. We used two methods to extract patterns of decay from surface-level caries data in order to generate novel phenotypes with which to explore the genetic regulation of caries. The 128 tooth surfaces of the permanent dentition were scored as carious or not by intra-oral examination for 1,068 participants aged 18 to 75 years from 664 biological families. Principal components analysis (PCA) and factor analysis (FA), two methods of identifying underlying patterns without a priori surface classifications, were applied to our data. The three strongest caries patterns identified by PCA recaptured variation represented by DMFS index (correlation, r = 0.97), pit and fissure surface caries (r = 0.95), and smooth surface caries (r = 0.89). However, together, these three patterns explained only 37% of the variability in the data, indicating that a priori caries measures are insufficient for fully quantifying caries variation. In comparison, the first pattern identified by FA was strongly correlated with pit and fissure surface caries (r = 0.81), but other identified patterns, including a second pattern representing caries of the maxillary incisors, were not representative of any previously defined caries indices. Some patterns identified by PCA and FA were heritable (h(2) = 30-65%, p = 0.043-0.006), whereas other patterns were not, indicating both genetic and non-genetic etiologies of individual decay patterns. This study demonstrates the use of decay patterns as novel phenotypes to assist in understanding the multifactorial nature of dental caries.

Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines

PubMed Central

Zhang, Jing-Kui; Yan, Weizhong; Cui, De-Mi

2016-01-01

The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures. PMID:27023563

Fringe-projection profilometry based on two-dimensional empirical mode decomposition.

PubMed

Zheng, Suzhen; Cao, Yiping

2013-11-01

In 3D shape measurement, because deformed fringes often contain low-frequency information degraded with random noise and background intensity information, a new fringe-projection profilometry is proposed based on 2D empirical mode decomposition (2D-EMD). The fringe pattern is first decomposed into numbers of intrinsic mode functions by 2D-EMD. Because the method has partial noise reduction, the background components can be removed to obtain the fundamental components needed to perform Hilbert transformation to retrieve the phase information. The 2D-EMD can effectively extract the modulation phase of a single direction fringe and an inclined fringe pattern because it is a full 2D analysis method and considers the relationship between adjacent lines of a fringe patterns. In addition, as the method does not add noise repeatedly, as does ensemble EMD, the data processing time is shortened. Computer simulations and experiments prove the feasibility of this method.

Spatial arrangement of faults and opening-mode fractures

NASA Astrophysics Data System (ADS)

Laubach, S. E.; Lamarche, J.; Gauthier, B. D. M.; Dunne, W. M.; Sanderson, David J.

2018-03-01

Spatial arrangement is a fundamental characteristic of fracture arrays. The pattern of fault and opening-mode fracture positions in space defines structural heterogeneity and anisotropy in a rock volume, governs how faults and fractures affect fluid flow, and impacts our understanding of the initiation, propagation and interactions during the formation of fracture patterns. This special issue highlights recent progress with respect to characterizing and understanding the spatial arrangements of fault and fracture patterns, providing examples over a wide range of scales and structural settings. Five papers describe new methods and improvements of existing techniques to quantify spatial arrangement. One study unravels the time evolution of opening-mode fracture spatial arrangement, which are data needed to compare natural patterns with progressive fracture growth in kinematic and mechanical models. Three papers investigate the role of evolving diagenesis in localizing fractures by mechanical stratigraphy and nine discuss opening-mode fracture spatial arrangement. Two papers show the relevance of complex cluster patterns to unconventional reservoirs through examples of fractures in tight gas sandstone horizontal wells, and a study of fracture arrangement in shale. Four papers demonstrate the roles of folds in fracture localization and the development spatial patterns. One paper models along-fault friction and fluid pressure and their effects on fault-related fracture arrangement. Contributions address deformation band patterns in carbonate rocks and fault size and arrangement above a detachment fault. Three papers describe fault and fracture arrangements in basement terrains, and three document fracture patterns in shale. This collection of papers points toward improvement in field methods, continuing improvements in computer-based data analysis and creation of synthetic fracture patterns, and opportunities for further understanding fault and fracture attributes in the subsurface through coupled spatial, size, and pattern analysis.

Modeling pattern in collections of parameters

USGS Publications Warehouse

Link, W.A.

1999-01-01

Wildlife management is increasingly guided by analyses of large and complex datasets. The description of such datasets often requires a large number of parameters, among which certain patterns might be discernible. For example, one may consider a long-term study producing estimates of annual survival rates; of interest is the question whether these rates have declined through time. Several statistical methods exist for examining pattern in collections of parameters. Here, I argue for the superiority of 'random effects models' in which parameters are regarded as random variables, with distributions governed by 'hyperparameters' describing the patterns of interest. Unfortunately, implementation of random effects models is sometimes difficult. Ultrastructural models, in which the postulated pattern is built into the parameter structure of the original data analysis, are approximations to random effects models. However, this approximation is not completely satisfactory: failure to account for natural variation among parameters can lead to overstatement of the evidence for pattern among parameters. I describe quasi-likelihood methods that can be used to improve the approximation of random effects models by ultrastructural models.

Analysis of evolutionary conservation patterns and their influence on identifying protein functional sites.

PubMed

Fang, Chun; Noguchi, Tamotsu; Yamana, Hayato

2014-10-01

Evolutionary conservation information included in position-specific scoring matrix (PSSM) has been widely adopted by sequence-based methods for identifying protein functional sites, because all functional sites, whether in ordered or disordered proteins, are found to be conserved at some extent. However, different functional sites have different conservation patterns, some of them are linear contextual, some of them are mingled with highly variable residues, and some others seem to be conserved independently. Every value in PSSMs is calculated independently of each other, without carrying the contextual information of residues in the sequence. Therefore, adopting the direct output of PSSM for prediction fails to consider the relationship between conservation patterns of residues and the distribution of conservation scores in PSSMs. In order to demonstrate the importance of combining PSSMs with the specific conservation patterns of functional sites for prediction, three different PSSM-based methods for identifying three kinds of functional sites have been analyzed. Results suggest that, different PSSM-based methods differ in their capability to identify different patterns of functional sites, and better combining PSSMs with the specific conservation patterns of residues would largely facilitate the prediction.

Scaling Linguistic Characterization of Precipitation Variability

NASA Astrophysics Data System (ADS)

Primo, C.; Gutierrez, J. M.

2003-04-01

Rainfall variability is influenced by changes in the aggregation of daily rainfall. This problem is of great importance for hydrological, agricultural and ecological applications. Rainfall averages, or accumulations, are widely used as standard climatic parameters. However different aggregation schemes may lead to the same average or accumulated values. In this paper we present a fractal method to characterize different aggregation schemes. The method provides scaling exponents characterizing weekly or monthly rainfall patterns for a given station. To this aim, we establish an analogy with linguistic analysis, considering precipitation as a discrete variable (e.g., rain, no rain). Each weekly, or monthly, symbolic precipitation sequence of observed precipitation is then considered as a "word" (in this case, a binary word) which defines a specific weekly rainfall pattern. Thus, each site defines a "language" characterized by the words observed in that site during a period representative of the climatology. Then, the more variable the observed weekly precipitation sequences, the more complex the obtained language. To characterize these languages, we first applied the Zipf's method obtaining scaling histograms of rank ordered frequencies. However, to obtain significant exponents, the scaling must be maintained some orders of magnitude, requiring long sequences of daily precipitation which are not available at particular stations. Thus this analysis is not suitable for applications involving particular stations (such as regionalization). Then, we introduce an alternative fractal method applicable to data from local stations. The so-called Chaos-Game method uses Iterated Function Systems (IFS) for graphically representing rainfall languages, in a way that complex languages define complex graphical patterns. The box-counting dimension and the entropy of the resulting patterns are used as linguistic parameters to quantitatively characterize the complexity of the patterns. We illustrate the high climatological discrimination power of the linguistic parameters in the Iberian peninsula, when compared with other standard techniques (such as seasonal mean accumulated precipitation). As an example, standard and linguistic parameters are used as inputs for a clustering regionalization method, comparing the resulting clusters.

Non-stationary signal analysis based on general parameterized time-frequency transform and its application in the feature extraction of a rotary machine

NASA Astrophysics Data System (ADS)

Zhou, Peng; Peng, Zhike; Chen, Shiqian; Yang, Yang; Zhang, Wenming

2018-06-01

With the development of large rotary machines for faster and more integrated performance, the condition monitoring and fault diagnosis for them are becoming more challenging. Since the time-frequency (TF) pattern of the vibration signal from the rotary machine often contains condition information and fault feature, the methods based on TF analysis have been widely-used to solve these two problems in the industrial community. This article introduces an effective non-stationary signal analysis method based on the general parameterized time-frequency transform (GPTFT). The GPTFT is achieved by inserting a rotation operator and a shift operator in the short-time Fourier transform. This method can produce a high-concentrated TF pattern with a general kernel. A multi-component instantaneous frequency (IF) extraction method is proposed based on it. The estimation for the IF of every component is accomplished by defining a spectrum concentration index (SCI). Moreover, such an IF estimation process is iteratively operated until all the components are extracted. The tests on three simulation examples and a real vibration signal demonstrate the effectiveness and superiority of our method.

Pedigree data analysis with crossover interference.

PubMed Central

Browning, Sharon

2003-01-01

We propose a new method for calculating probabilities for pedigree genetic data that incorporates crossover interference using the chi-square models. Applications include relationship inference, genetic map construction, and linkage analysis. The method is based on importance sampling of unobserved inheritance patterns conditional on the observed genotype data and takes advantage of fast algorithms for no-interference models while using reweighting to allow for interference. We show that the method is effective for arbitrarily many markers with small pedigrees. PMID:12930760

Prospect for the development of salted egg agro industry: an analysis on marketing distribution aspect

NASA Astrophysics Data System (ADS)

Sumekar, W.; Al-Baarri, A. N.; Kurnianto, E.

2018-01-01

Marketing distribution is an important of the strategy in business development in agroindustries. The aim of the research was to introduce marketing (distribution pattern, margin and marketing efficiency) at the salted egg agro industries in Brebes Regency. Survey method had been conducted on 52 salted egg agro industries which had active PIRT certificate. The data collection was conducted by means of interview and observation. Descriptive analysis was used to determine the marketing distribution of salted eggs. Marketing efficiency was obtained by calculating marketing margin and farmer share. The results show that the salted egg agro industries implemented two marketing distribution patterns; direct marketing pattern (consumer→producers) and indirect marketing pattern (producer→retailer→consumer). The number of the salted egg agro industries which apply indirect marketing pattern is 57.69%. The implementation of direct and indirect marketing patterns was classified as efficient according to the farmer’s share values of 87.13% and 78.21%. It can be recommended the direct marketing.

Examining inter-family differences in intra-family (parent-adolescent) dynamics using grid-sequence analysis.

PubMed

Brinberg, Miriam; Fosco, Gregory M; Ram, Nilam

2017-12-01

Family systems theorists have forwarded a set of theoretical principles meant to guide family scientists and practitioners in their conceptualization of patterns of family interaction-intra-family dynamics-that, over time, give rise to family and individual dysfunction and/or adaptation. In this article, we present an analytic approach that merges state space grid methods adapted from the dynamic systems literature with sequence analysis methods adapted from molecular biology into a "grid-sequence" method for studying inter-family differences in intra-family dynamics. Using dyadic data from 86 parent-adolescent dyads who provided up to 21 daily reports about connectedness, we illustrate how grid-sequence analysis can be used to identify a typology of intrafamily dynamics and to inform theory about how specific types of intrafamily dynamics contribute to adolescent behavior problems and family members' mental health. Methodologically, grid-sequence analysis extends the toolbox of techniques for analysis of family experience sampling and daily diary data. Substantively, we identify patterns of family level microdynamics that may serve as new markers of risk/protective factors and potential points for intervention in families. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Three-dimensional profile extraction from CD-SEM image and top/bottom CD measurement by line-edge roughness analysis

NASA Astrophysics Data System (ADS)

Yamaguchi, Atsuko; Ohashi, Takeyoshi; Kawasaki, Takahiro; Inoue, Osamu; Kawada, Hiroki

2013-04-01

A new method for calculating critical dimension (CDs) at the top and bottom of three-dimensional (3D) pattern profiles from a critical-dimension scanning electron microscope (CD-SEM) image, called as "T-sigma method", is proposed and evaluated. Without preparing a library of database in advance, T-sigma can estimate a feature of a pattern sidewall. Furthermore, it supplies the optimum edge-definition (i.e., threshold level for determining edge position from a CDSEM signal) to detect the top and bottom of the pattern. This method consists of three steps. First, two components of line-edge roughness (LER); noise-induced bias (i.e., LER bias) and unbiased component (i.e., bias-free LER) are calculated with set threshold level. Second, these components are calculated with various threshold values, and the threshold-dependence of these two components, "T-sigma graph", is obtained. Finally, the optimum threshold value for the top and the bottom edge detection are given by the analysis of T-sigma graph. T-sigma was applied to CD-SEM images of three kinds of resist-pattern samples. In addition, reference metrology was performed with atomic force microscope (AFM) and scanning transmission electron microscope (STEM). Sensitivity of CD measured by T-sigma to the reference CD was higher than or equal to that measured by the conventional edge definition. Regarding the absolute measurement accuracy, T-sigma showed better results than the conventional definition. Furthermore, T-sigma graphs were calculated from CD-SEM images of two kinds of resist samples and compared with corresponding STEM observation results. Both bias-free LER and LER bias increased as the detected edge point moved from the bottom to the top of the pattern in the case that the pattern had a straight sidewall and a round top. On the other hand, they were almost constant in the case that the pattern had a re-entrant profile. T-sigma will be able to reveal a re-entrant feature. From these results, it is found that T-sigma method can provide rough cross-sectional pattern features and achieve quick, easy and accurate measurements of top and bottom CD.

Investigating Patterns of Interaction in Networked Learning and Computer-Supported Collaborative Learning: A Role for Social Network Analysis

ERIC Educational Resources Information Center

de Laat, Maarten; Lally, Vic; Lipponen, Lasse; Simons, Robert-Jan

2007-01-01

The focus of this study is to explore the advances that Social Network Analysis (SNA) can bring, in combination with other methods, when studying Networked Learning/Computer-Supported Collaborative Learning (NL/CSCL). We present a general overview of how SNA is applied in NL/CSCL research; we then go on to illustrate how this research method can…

Hearing the Signal in the Noise: A Software-Based Content Analysis of Patterns in Responses by Experts and Students to a New Venture Investment Proposal

ERIC Educational Resources Information Center

Hostager, Todd J.; Voiovich, Jason; Hughes, Raymond K.

2013-01-01

The authors apply a software-based content analysis method to uncover differences in responses by expert entrepreneurs and undergraduate entrepreneur majors to a new venture investment proposal. Data analyzed via the Leximancer software package yielded conceptual maps highlighting key differences in the nature of these responses. Study methods and…

Electropalatographic Assessment of Tongue-to-Palate Contact Patterns and Variability in Children, Adolescents, and Adults

ERIC Educational Resources Information Center

Cheng, Hei Yan; Murdoch, Bruce E.; Goozee, Justine V.; Scott, Dion

2007-01-01

Purpose: To investigate the developmental time course of tongue-to-palate contact patterns during speech from childhood to adulthood using electropalatography (EPG) and a comprehensive profile of data analysis. Method: Tongue-to-palate contacts were recorded during productions of /t/, /l/, /s/, and /k/ in 48 children, adolescents and adults (aged…

Drawing Some Evaluation Patterns Inferred from the Biblical Gideon's Passage

ERIC Educational Resources Information Center

Fernandez-Cano, Antonio

2010-01-01

This article provides an interpretation of a biblical text about the judge Gideon. It uses the hermeneutic method of research to draw some evaluation patterns from this case study relating to the analysis of needs, the declaration of aims, the context and circumstances of evaluation, the agents involved, the recruitment process, the assessment of…

Multiple Intelligences Patterns of Students at King Saud University and Its Relationship with Mathematics' Achievement

ERIC Educational Resources Information Center

Kandeel, Refat A. A.

2016-01-01

The purpose of this study was to determine the multiple intelligences patterns of students at King Saud University and its relationship with academic achievement for the courses of Mathematics. The study sample consisted of 917 students were selected a stratified random manner, the descriptive analysis method and Pearson correlation were used, the…

Investigating Relationship between Discourse Behavioral Patterns and Academic Achievements of Students in SPOC Discussion Forum

ERIC Educational Resources Information Center

Liu, Zhi; Zhang, Wenjing; Cheng, Hercy N. H.; Sun, Jianwen; Liu, Sannyuya

2018-01-01

As an overt expression of internal mental processes, discourses have become one main data source for the research of interactive learning. To deeply explore behavioral regularities among interactions, this article firstly adopts the content analysis method to summarize students' engagement patterns within a course forum in a small private online…

Combined Patterns of Risk for Problem and Obesogenic Behaviors in Adolescents: A Latent Class Analysis Approach

ERIC Educational Resources Information Center

Fleary, Sasha A.

2017-01-01

Background: Several studies have used latent class analyses to explore obesogenic behaviors and substance use in adolescents independently. We explored a variety of health risks jointly to identify distinct patterns of risk behaviors among adolescents. Methods: Latent class models were estimated using Youth Risk Behavior Surveillance System…

Patterns of Teacher's Instructional Moves: What Makes Mathematical Instructional Practices Unique?

ERIC Educational Resources Information Center

Pinter, Holly Henderson

2013-01-01

The purpose of this study was to examine patterns in fourth-grade teachers' use of instructional moves in the implementation of standards-based mathematical teaching practices. Using a mixed methods sequential explanatory design, the study consisted of two phases: quantitative selection and qualitative analysis. The first phase of the study…

Patterns and Impact of Comorbidity and Multimorbidity among Community-Resident American Indian Elders

ERIC Educational Resources Information Center

John, Robert; Kerby, Dave S.; Hennessy, Catherine Hagan

2003-01-01

Purpose: The purpose of this study is to suggest a new approach to identifying patterns of comorbidity and multimorbidity. Design and Methods: A random sample of 1,039 rural community-resident American Indian elders aged 60 years and older was surveyed. Comorbidity was investigated with four standard approaches, and with cluster analysis. Results:…

Genetic linkage analysis using pooled DNA and infrared detection of tailed STRP primer patterns

NASA Astrophysics Data System (ADS)

Oetting, William S.; Wildenberg, Scott C.; King, Richard A.

1996-04-01

The mapping of a disease locus to a specific chromosomal region is an important step in the eventual isolation and analysis of a disease causing gene. Conventional mapping methods analyze large multiplex families and/or smaller nuclear families to find linkage between the disease and a chromosome marker that maps to a known chromosomal region. This analysis is time consuming and tedious, typically requiring the determination of 30,000 genotypes or more. For appropriate populations, we have instead utilized pooled DNA samples for gene mapping which greatly reduces the amount of time necessary for an initial chromosomal screen. This technique assumes a common founder for the disease locus of interest and searches for a region of a chromosome shared between affected individuals. Our analysis involves the PCR amplification of short tandem repeat polymorphisms (STRP) to detect these shared regions. In order to reduce the cost of genotyping, we have designed unlabeled tailed PCR primers which, when combined with a labeled universal primer, provides for an alternative to synthesizing custom labeled primers. The STRP pattern is visualized with an infrared fluorescence based automated DNA sequencer and the patterns quantitated by densitometric analysis of the allele pattern. Differences in the distribution of alleles between pools of affected and unaffected individuals, including a reduction in the number of alleles in the affected pool, indicate the sharing of a region of a chromosome. We have found this method effective for markers 10 - 15 cM away from the disease locus for a recessive genetic disease.

Do suicide attempts occur more frequently in the spring too? A systematic review and rhythmic analysis.

PubMed

Coimbra, Daniel Gomes; Pereira E Silva, Aline Cristine; de Sousa-Rodrigues, Célio Fernando; Barbosa, Fabiano Timbó; de Siqueira Figueredo, Diego; Araújo Santos, José Luiz; Barbosa, Mayara Rodrigues; de Medeiros Alves, Veronica; Nardi, Antonio Egidio; de Andrade, Tiago Gomes

2016-05-15

Seasonal variations in suicides have been reported worldwide, however, there may be a different seasonal pattern in suicide attempts. The aim of this study was to perform a systematic review on seasonality of suicide attempts considering potential interfering variables, and a statistical analysis for seasonality with the collected data. Observational epidemiological studies about seasonality in suicide attempts were searched in PubMed, Web of Science, LILACS and Cochrane Library databases with terms attempted suicide, attempt and season. Monthly or seasonal data available were evaluated by rhythmic analysis softwares. Twenty-nine articles from 16 different countries were included in the final review. It was observed different patterns of seasonality, however, suicide attempts in spring and summer were the most frequent seasons reported. Eight studies indicated differences in sex and three in the method used for suicide attempts. Three articles did not find a seasonal pattern in suicide attempts. Cosinor analysis identified an overall pattern of seasonal variation with a suggested peak in spring, considering articles individually or grouped and independent of sex and method used. A restricted analysis with self-poisoning in hospital samples demonstrated the same profile. Grouping diverse populations and potential analytical bias due to lack of information are the main limitations. The identification of a seasonal profile suggests the influence of an important environmental modulator that can reverberate to suicide prevention strategies. Further studies controlling interfering variables and investigating the biological substrate for this phenomenon would be helpful to confirm our conclusion. Copyright © 2016 Elsevier B.V. All rights reserved.

A peak position comparison method for high-speed quantitative Laue microdiffraction data processing

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

Kou, Jiawei; Chen, Kai; Tamura, Nobumichi

Indexing Laue patterns of a synchrotron microdiffraction scan can take as much as ten times longer than collecting the data, impeding efficient structural analysis using this technique. Here in this paper, a novel strategy is developed. By comparing the peak positions of adjacent Laue patterns and checking the intensity sequence, grain and phase boundaries are identified, requiring only a limited number of indexing steps for each individual grain. Using this protocol, the Laue patterns can be indexed on the fly as they are taken. The validation of this method is demonstrated by analyzing the microstructure of a laser 3D printedmore » multi-phase/multi-grain Ni-based superalloy.« less

A peak position comparison method for high-speed quantitative Laue microdiffraction data processing

DOE PAGES

Kou, Jiawei; Chen, Kai; Tamura, Nobumichi

2018-09-12

Indexing Laue patterns of a synchrotron microdiffraction scan can take as much as ten times longer than collecting the data, impeding efficient structural analysis using this technique. Here in this paper, a novel strategy is developed. By comparing the peak positions of adjacent Laue patterns and checking the intensity sequence, grain and phase boundaries are identified, requiring only a limited number of indexing steps for each individual grain. Using this protocol, the Laue patterns can be indexed on the fly as they are taken. The validation of this method is demonstrated by analyzing the microstructure of a laser 3D printedmore » multi-phase/multi-grain Ni-based superalloy.« less

Application of Classification Methods for Forecasting Mid-Term Power Load Patterns

NASA Astrophysics Data System (ADS)

Piao, Minghao; Lee, Heon Gyu; Park, Jin Hyoung; Ryu, Keun Ho

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in long duration load profiles. The proposed approach in this paper consists of three stages: (i) data preprocessing: noise or outlier is removed and the continuous attribute-valued features are transformed to discrete values, (ii) cluster analysis: k-means clustering is used to create load pattern classes and the representative load profiles for each class and (iii) classification: we evaluated several supervised learning methods in order to select a suitable prediction method. According to the proposed methodology, power load measured from AMR (automatic meter reading) system, as well as customer indexes, were used as inputs for clustering. The output of clustering was the classification of representative load profiles (or classes). In order to evaluate the result of forecasting load patterns, the several classification methods were applied on a set of high voltage customers of the Korea power system and derived class labels from clustering and other features are used as input to produce classifiers. Lastly, the result of our experiments was presented.

Method translation and full metadata transfer from thermal to differential flow modulated comprehensive two dimensional gas chromatography: Profiling of suspected fragrance allergens.

PubMed

Cordero, Chiara; Rubiolo, Patrizia; Reichenbach, Stephen E; Carretta, Andrea; Cobelli, Luigi; Giardina, Matthew; Bicchi, Carlo

2017-01-13

The possibility to transfer methods from thermal to differential-flow modulated comprehensive two-dimensional gas chromatographic (GC×GC) platforms is of high interest to improve GC×GC flexibility and increase the compatibility of results from different platforms. The principles of method translation are here applied to an original method, developed for a loop-type thermal modulated GC×GC-MS/FID system, suitable for quali-quantitative screening of suspected fragrance allergens. The analysis conditions were translated to a reverse-injection differential flow modulated platform (GC×2GC-MS/FID) with a dual-parallel secondary column and dual detection. The experimental results, for a model mixture of suspected volatile allergens and for raw fragrance mixtures of different composition, confirmed the feasibility of translating methods by preserving 1 D elution order, as well as the relative alignment of resulting 2D peak patterns. A correct translation produced several benefits including an effective transfer of metadata (compound names, MS fragmentation pattern, response factors) by automatic template transformation and matching from the original/reference method to its translated counterpart. The correct translation provided: (a) 2D pattern repeatability, (b) MS fragmentation pattern reliability for identity confirmation, and (c) comparable response factors and quantitation accuracy within a concentration range of three orders of magnitude. The adoption of a narrow bore (i.e. 0.1mm d c ) first-dimension column to operate under close-to-optimal conditions with the differential-flow modulation GC×GC platform was also advantageous in halving the total analysis under the translated conditions. Copyright © 2016 Elsevier B.V. All rights reserved.

A Comparative Study of Random Patterns for Digital Image Correlation

NASA Astrophysics Data System (ADS)

Stoilov, G.; Kavardzhikov, V.; Pashkouleva, D.

2012-06-01

Digital Image Correlation (DIC) is a computer based image analysis technique utilizing random patterns, which finds applications in experimental mechanics of solids and structures. In this paper a comparative study of three simulated random patterns is done. One of them is generated according to a new algorithm, introduced by the authors. A criterion for quantitative evaluation of random patterns after the calculation of their autocorrelation functions is introduced. The patterns' deformations are simulated numerically and realized experimentally. The displacements are measured by using the DIC method. Tensile tests are performed after printing the generated random patterns on surfaces of standard iron sheet specimens. It is found that the new designed random pattern keeps relatively good quality until reaching 20% deformation.

Analysis of XFEL serial diffraction data from individual crystalline fibrils

PubMed Central

Wojtas, David H.; Ayyer, Kartik; Liang, Mengning; Mossou, Estelle; Romoli, Filippo; Seuring, Carolin; Beyerlein, Kenneth R.; Bean, Richard J.; Morgan, Andrew J.; Oberthuer, Dominik; Fleckenstein, Holger; Heymann, Michael; Gati, Cornelius; Yefanov, Oleksandr; Barthelmess, Miriam; Ornithopoulou, Eirini; Galli, Lorenzo; Xavier, P. Lourdu; Ling, Wai Li; Frank, Matthias; Yoon, Chun Hong; White, Thomas A.; Bajt, Saša; Mitraki, Anna; Boutet, Sebastien; Aquila, Andrew; Barty, Anton; Forsyth, V. Trevor; Chapman, Henry N.; Millane, Rick P.

2017-01-01

Serial diffraction data collected at the Linac Coherent Light Source from crystalline amyloid fibrils delivered in a liquid jet show that the fibrils are well oriented in the jet. At low fibril concentrations, diffraction patterns are recorded from single fibrils; these patterns are weak and contain only a few reflections. Methods are developed for determining the orientation of patterns in reciprocal space and merging them in three dimensions. This allows the individual structure amplitudes to be calculated, thus overcoming the limitations of orientation and cylindrical averaging in conventional fibre diffraction analysis. The advantages of this technique should allow structural studies of fibrous systems in biology that are inaccessible using existing techniques. PMID:29123682

Multiscale analysis of the intensity fluctuation in a time series of dynamic speckle patterns.

PubMed

Federico, Alejandro; Kaufmann, Guillermo H

2007-04-10

We propose the application of a method based on the discrete wavelet transform to detect, identify, and measure scaling behavior in dynamic speckle. The multiscale phenomena presented by a sample and displayed by its speckle activity are analyzed by processing the time series of dynamic speckle patterns. The scaling analysis is applied to the temporal fluctuation of the speckle intensity and also to the two derived data sets generated by its magnitude and sign. The application of the method is illustrated by analyzing paint-drying processes and bruising in apples. The results are discussed taking into account the different time organizations obtained for the scaling behavior of the magnitude and the sign of the intensity fluctuation.

Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis

PubMed Central

Xu, Rui; Zhen, Zonglei; Liu, Jia

2010-01-01

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies. PMID:21152081

Landscape metrics for three-dimension urban pattern recognition

NASA Astrophysics Data System (ADS)

Liu, M.; Hu, Y.; Zhang, W.; Li, C.

2017-12-01

Understanding how landscape pattern determines population or ecosystem dynamics is crucial for managing our landscapes. Urban areas are becoming increasingly dominant social-ecological systems, so it is important to understand patterns of urbanization. Most studies of urban landscape pattern examine land-use maps in two dimensions because the acquisition of 3-dimensional information is difficult. We used Brista software based on Quickbird images and aerial photos to interpret the height of buildings, thus incorporating a 3-dimensional approach. We estimated the feasibility and accuracy of this approach. A total of 164,345 buildings in the Liaoning central urban agglomeration of China, which included seven cities, were measured. Twelve landscape metrics were proposed or chosen to describe the urban landscape patterns in 2- and 3-dimensional scales. The ecological and social meaning of landscape metrics were analyzed with multiple correlation analysis. The results showed that classification accuracy compared with field surveys was 87.6%, which means this method for interpreting building height was acceptable. The metrics effectively reflected the urban architecture in relation to number of buildings, area, height, 3-D shape and diversity aspects. We were able to describe the urban characteristics of each city with these metrics. The metrics also captured ecological and social meanings. The proposed landscape metrics provided a new method for urban landscape analysis in three dimensions.

Flow Pattern Phenomena in Two-Phase Flow in Microchannels

NASA Astrophysics Data System (ADS)

Keska, Jerry K.; Simon, William E.

2004-02-01

Space transportation systems require high-performance thermal protection and fluid management techniques for systems ranging from cryogenic fluid management devices to primary structures and propulsion systems exposed to extremely high temperatures, as well as for other space systems such as cooling or environment control for advanced space suits and integrated circuits. Although considerable developmental effort is being expended to bring potentially applicable technologies to a readiness level for practical use, new and innovative methods are still needed. One such method is the concept of Advanced Micro Cooling Modules (AMCMs), which are essentially compact two-phase heat exchangers constructed of microchannels and designed to remove large amounts of heat rapidly from critical systems by incorporating phase transition. The development of AMCMs requires fundamental technological advancement in many areas, including: (1) development of measurement methods/systems for flow-pattern measurement/identification for two-phase mixtures in microchannels; (2) development of a phenomenological model for two-phase flow which includes the quantitative measure of flow patterns; and (3) database development for multiphase heat transfer/fluid dynamics flows in microchannels. This paper focuses on the results of experimental research in the phenomena of two-phase flow in microchannels. The work encompasses both an experimental and an analytical approach to incorporating flow patterns for air-water mixtures flowing in a microchannel, which are necessary tools for the optimal design of AMCMs. Specifically, the following topics are addressed: (1) design and construction of a sensitive test system for two-phase flow in microchannels, one which measures ac and dc components of in-situ physical mixture parameters including spatial concentration using concomitant methods; (2) data acquisition and analysis in the amplitude, time, and frequency domains; and (3) analysis of results including evaluation of data acquisition techniques and their validity for application in flow pattern determination.

Diffractive elements for generating microscale laser beam patterns: a Y2K problem

NASA Astrophysics Data System (ADS)

Teiwes, Stephan; Krueger, Sven; Wernicke, Guenther K.; Ferstl, Margit

2000-03-01

Lasers are widely used in industrial fabrication for engraving, cutting and many other purposes. However, material processing at very small scales is still a matter of concern. Advances in diffractive optics could provide for laser systems that could be used for engraving or cutting of micro-scale patterns at high speeds. In our paper we focus on the design of diffractive elements which can be used for this special application. It is a common desire in material processing to apply 'discrete' as well as 'continuous' beam patterns. Especially, the latter case is difficult to handle as typical micro-scale patterns are characterized by bad band-limitation properties, and as speckles can easily occur in beam patterns. It is shown in this paper that a standard iterative design method usually fails to obtain diffractive elements that generate diffraction patterns with acceptable quality. Insights gained from an analysis of the design problems are used to optimize the iterative design method. We demonstrate applicability and success of our approach by the design of diffractive phase elements that generate a discrete and a continuous 'Y2K' pattern.

THE ROLE OF SELF-INJURY IN THE ORGANIZATION OF BEHAVIOUR

PubMed Central

Sandman, Curt A.; Kemp, Aaron S.; Mabini, Christopher; Pincus, David; Magnusson, Magnus

2012-01-01

Background Self-injuring acts are among the most dramatic behaviours exhibited by human beings. There is no known single cause and there is no universally agreed upon treatment. Sophisticated sequential and temporal analysis of behaviour has provided alternative descriptions of self-injury that provide new insights into its initiation and maintenance. Method Forty hours of observations for each of 32 participants were collected in a contiguous two-week period. Twenty categories of behavioural and environmental events were recorded electronically that captured the precise time each observation occurred. Temporal behavioural/environmental patterns associated with self-injurious events were revealed with a method (t-patterns; THEME) for detecting non-linear, real-time patterns. Results Results indicated that acts of self-injury contributed both to more patterns and to more complex patterns. Moreover, self-injury left its imprint on the organization of behaviour even when counts of self-injury were expelled from the continuous record. Conclusions Behaviour of participants was organized in a more diverse array of patterns with SIB was present. Self-injuring acts may function as singular points, increasing coherence within self-organizing patterns of behaviour. PMID:22452417

Biomechanical analysis of INFINITY rehabilitation method for treatment of low back pain

PubMed Central

Daniel, Matej; Tomanová, Michaela; Hornová, Jana; Novotná, Iva; Lhotská, Lenka

2017-01-01

[Purpose] Low back pain is a pervasive problem in modern societies. Physical rehabilitation in treatment of low back pain should reduce pain, muscle tension and restore spine stability and balance. The INFINITY® rehabilitation method that is based on a figure of eight movement pattern was proved to be effective in low back pain treatment. The aim of the paper is to estimate the effect of a figure of eight motion on the L5/S1 load and lumbar spine muscle activation in comparison to other motion patterns. [Subjects and Methods] Three-dimensional model of lumbar spine musculoskeletal system is used to simulate effect of various load motion pattern induced by displacement of the center of gravity of the upper body. Four motion patterns were examined: lateral and oblique pendulum-like motion, elliptical motion and figure of eight motion. [Results] The simple pendulum-like and elliptical-like patterns induce harmonic muscle activation and harmonic spinal load. The figure of eight motion pattern creates high-frequency spinal loading that activates remodeling of bones and tendons. The figure of eight pattern also requires muscle activity that differs from harmonic frequency and is more demanding on muscle control and could also improve muscle coordination. [Conclusion] The results of the study indicate that complex motion pattern during INFINITY® rehabilitation might enhance the spine stability by influencing its passive, active and neural components. PMID:28603355

Calculation of Debye-Scherrer diffraction patterns from highly stressed polycrystalline materials

DOE PAGES

MacDonald, M. J.; Vorberger, J.; Gamboa, E. J.; ...

2016-06-07

Calculations of Debye-Scherrer diffraction patterns from polycrystalline materials have typically been done in the limit of small deviatoric stresses. Although these methods are well suited for experiments conducted near hydrostatic conditions, more robust models are required to diagnose the large strain anisotropies present in dynamic compression experiments. A method to predict Debye-Scherrer diffraction patterns for arbitrary strains has been presented in the Voigt (iso-strain) limit. Here, we present a method to calculate Debye-Scherrer diffraction patterns from highly stressed polycrystalline samples in the Reuss (iso-stress) limit. This analysis uses elastic constants to calculate lattice strains for all initial crystallite orientations, enablingmore » elastic anisotropy and sample texture effects to be modeled directly. Furthermore, the effects of probing geometry, deviatoric stresses, and sample texture are demonstrated and compared to Voigt limit predictions. An example of shock-compressed polycrystalline diamond is presented to illustrate how this model can be applied and demonstrates the importance of including material strength when interpreting diffraction in dynamic compression experiments.« less

[Scale effect of Nanjing urban green infrastructure network pattern and connectivity analysis.

PubMed

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.

Exhaled Aerosol Pattern Discloses Lung Structural Abnormality: A Sensitivity Study Using Computational Modeling and Fractal Analysis

PubMed Central

Xi, Jinxiang; Si, Xiuhua A.; Kim, JongWon; Mckee, Edward; Lin, En-Bing

2014-01-01

Background Exhaled aerosol patterns, also called aerosol fingerprints, provide clues to the health of the lung and can be used to detect disease-modified airway structures. The key is how to decode the exhaled aerosol fingerprints and retrieve the lung structural information for a non-invasive identification of respiratory diseases. Objective and Methods In this study, a CFD-fractal analysis method was developed to quantify exhaled aerosol fingerprints and applied it to one benign and three malign conditions: a tracheal carina tumor, a bronchial tumor, and asthma. Respirations of tracer aerosols of 1 µm at a flow rate of 30 L/min were simulated, with exhaled distributions recorded at the mouth. Large eddy simulations and a Lagrangian tracking approach were used to simulate respiratory airflows and aerosol dynamics. Aerosol morphometric measures such as concentration disparity, spatial distributions, and fractal analysis were applied to distinguish various exhaled aerosol patterns. Findings Utilizing physiology-based modeling, we demonstrated substantial differences in exhaled aerosol distributions among normal and pathological airways, which were suggestive of the disease location and extent. With fractal analysis, we also demonstrated that exhaled aerosol patterns exhibited fractal behavior in both the entire image and selected regions of interest. Each exhaled aerosol fingerprint exhibited distinct pattern parameters such as spatial probability, fractal dimension, lacunarity, and multifractal spectrum. Furthermore, a correlation of the diseased location and exhaled aerosol spatial distribution was established for asthma. Conclusion Aerosol-fingerprint-based breath tests disclose clues about the site and severity of lung diseases and appear to be sensitive enough to be a practical tool for diagnosis and prognosis of respiratory diseases with structural abnormalities. PMID:25105680

Identifying functional reorganization of spelling networks: an individual peak probability comparison approach

PubMed Central

Purcell, Jeremy J.; Rapp, Brenda

2013-01-01

Previous research has shown that damage to the neural substrates of orthographic processing can lead to functional reorganization during reading (Tsapkini et al., 2011); in this research we ask if the same is true for spelling. To examine the functional reorganization of spelling networks we present a novel three-stage Individual Peak Probability Comparison (IPPC) analysis approach for comparing the activation patterns obtained during fMRI of spelling in a single brain-damaged individual with dysgraphia to those obtained in a set of non-impaired control participants. The first analysis stage characterizes the convergence in activations across non-impaired control participants by applying a technique typically used for characterizing activations across studies: Activation Likelihood Estimate (ALE) (Turkeltaub et al., 2002). This method was used to identify locations that have a high likelihood of yielding activation peaks in the non-impaired participants. The second stage provides a characterization of the degree to which the brain-damaged individual's activations correspond to the group pattern identified in Stage 1. This involves performing a Mahalanobis distance statistics analysis (Tsapkini et al., 2011) that compares each of a control group's peak activation locations to the nearest peak generated by the brain-damaged individual. The third stage evaluates the extent to which the brain-damaged individual's peaks are atypical relative to the range of individual variation among the control participants. This IPPC analysis allows for a quantifiable, statistically sound method for comparing an individual's activation pattern to the patterns observed in a control group and, thus, provides a valuable tool for identifying functional reorganization in a brain-damaged individual with impaired spelling. Furthermore, this approach can be applied more generally to compare any individual's activation pattern with that of a set of other individuals. PMID:24399981

EBIC: an evolutionary-based parallel biclustering algorithm for pattern discovery.

PubMed

Orzechowski, Patryk; Sipper, Moshe; Huang, Xiuzhen; Moore, Jason H

2018-05-22

Biclustering algorithms are commonly used for gene expression data analysis. However, accurate identification of meaningful structures is very challenging and state-of-the-art methods are incapable of discovering with high accuracy different patterns of high biological relevance. In this paper a novel biclustering algorithm based on evolutionary computation, a subfield of artificial intelligence (AI), is introduced. The method called EBIC aims to detect order-preserving patterns in complex data. EBIC is capable of discovering multiple complex patterns with unprecedented accuracy in real gene expression datasets. It is also one of the very few biclustering methods designed for parallel environments with multiple graphics processing units (GPUs). We demonstrate that EBIC greatly outperforms state-of-the-art biclustering methods, in terms of recovery and relevance, on both synthetic and genetic datasets. EBIC also yields results over 12 times faster than the most accurate reference algorithms. EBIC source code is available on GitHub at https://github.com/EpistasisLab/ebic. Correspondence and requests for materials should be addressed to P.O. (email: patryk.orzechowski@gmail.com) and J.H.M. (email: jhmoore@upenn.edu). Supplementary Data with results of analyses and additional information on the method is available at Bioinformatics online.

Individual Movement Strategies Revealed through Novel Clustering of Emergent Movement Patterns

NASA Astrophysics Data System (ADS)

Valle, Denis; Cvetojevic, Sreten; Robertson, Ellen P.; Reichert, Brian E.; Hochmair, Hartwig H.; Fletcher, Robert J.

2017-03-01

Understanding movement is critical in several disciplines but analysis methods often neglect key information by adopting each location as sampling unit, rather than each individual. We introduce a novel statistical method that, by focusing on individuals, enables better identification of temporal dynamics of connectivity, traits of individuals that explain emergent movement patterns, and sites that play a critical role in connecting subpopulations. We apply this method to two examples that span movement networks that vary considerably in size and questions: movements of an endangered raptor, the snail kite (Rostrhamus sociabilis plumbeus), and human movement in Florida inferred from Twitter. For snail kites, our method reveals substantial differences in movement strategies for different bird cohorts and temporal changes in connectivity driven by the invasion of an exotic food resource, illustrating the challenge of identifying critical connectivity sites for conservation in the presence of global change. For human movement, our method is able to reliably determine the origin of Florida visitors and identify distinct movement patterns within Florida for visitors from different places, providing near real-time information on the spatial and temporal patterns of tourists. These results emphasize the need to integrate individual variation to generate new insights when modeling movement data.

Automated quantification of neuronal networks and single-cell calcium dynamics using calcium imaging

PubMed Central

Patel, Tapan P.; Man, Karen; Firestein, Bonnie L.; Meaney, David F.

2017-01-01

Background Recent advances in genetically engineered calcium and membrane potential indicators provide the potential to estimate the activation dynamics of individual neurons within larger, mesoscale networks (100s–1000 +neurons). However, a fully integrated automated workflow for the analysis and visualization of neural microcircuits from high speed fluorescence imaging data is lacking. New method Here we introduce FluoroSNNAP, Fluorescence Single Neuron and Network Analysis Package. FluoroSNNAP is an open-source, interactive software developed in MATLAB for automated quantification of numerous biologically relevant features of both the calcium dynamics of single-cells and network activity patterns. FluoroSNNAP integrates and improves upon existing tools for spike detection, synchronization analysis, and inference of functional connectivity, making it most useful to experimentalists with little or no programming knowledge. Results We apply FluoroSNNAP to characterize the activity patterns of neuronal microcircuits undergoing developmental maturation in vitro. Separately, we highlight the utility of single-cell analysis for phenotyping a mixed population of neurons expressing a human mutant variant of the microtubule associated protein tau and wild-type tau. Comparison with existing method(s) We show the performance of semi-automated cell segmentation using spatiotemporal independent component analysis and significant improvement in detecting calcium transients using a template-based algorithm in comparison to peak-based or wavelet-based detection methods. Our software further enables automated analysis of microcircuits, which is an improvement over existing methods. Conclusions We expect the dissemination of this software will facilitate a comprehensive analysis of neuronal networks, promoting the rapid interrogation of circuits in health and disease. PMID:25629800

Advanced methods in NDE using machine learning approaches

NASA Astrophysics Data System (ADS)

Wunderlich, Christian; Tschöpe, Constanze; Duckhorn, Frank

2018-04-01

Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability prediction based on big data becomes possible, even if components are used in different versions or configurations. This is the promise behind German Industry 4.0.

Graphical methods for the sensitivity analysis in discriminant analysis

DOE PAGES

Kim, Youngil; Anderson-Cook, Christine M.; Dae-Heung, Jang

2015-09-30

Similar to regression, many measures to detect influential data points in discriminant analysis have been developed. Many follow similar principles as the diagnostic measures used in linear regression in the context of discriminant analysis. Here we focus on the impact on the predicted classification posterior probability when a data point is omitted. The new method is intuitive and easily interpretative compared to existing methods. We also propose a graphical display to show the individual movement of the posterior probability of other data points when a specific data point is omitted. This enables the summaries to capture the overall pattern ofmore » the change.« less

A prospective cohort study of dietary patterns of non-western migrants in the Netherlands in relation to risk factors for cardiovascular diseases: HELIUS-Dietary Patterns

PubMed Central

2011-01-01

Background In Western countries the prevalence of cardiovascular disease (CVD) is often higher in non-Western migrants as compared to the host population. Diet is an important modifiable determinant of CVD. Increasingly, dietary patterns rather than single nutrients are the focus of research in an attempt to account for the complexity of nutrient interactions in foods. Research on dietary patterns in non-Western migrants is limited and may be hampered by a lack of validated instruments that can be used to assess the habitual diet of non-western migrants in large scale epidemiological studies. The ultimate aims of this study are to (1) understand whether differences in dietary patterns explain differences in CVD risk between ethnic groups, by developing and validating ethnic-specific Food Frequency Questionnaires (FFQs), and (2) to investigate the determinants of these dietary patterns. This paper outlines the design and methods used in the HELIUS-Dietary Patterns study and describes a systematic approach to overcome difficulties in the assessment and analysis of dietary intake data in ethnically diverse populations. Methods/Design The HELIUS-Dietary Patterns study is embedded in the HELIUS study, a Dutch multi-ethnic cohort study. After developing ethnic-specific FFQs, we will gather data on the habitual intake of 5000 participants (18-70 years old) of ethnic Dutch, Surinamese of African and of South Asian origin, Turkish or Moroccan origin. Dietary patterns will be derived using factor analysis, but we will also evaluate diet quality using hypothesis-driven approaches. The relation between dietary patterns and CVD risk factors will be analysed using multiple linear regression analysis. Potential underlying determinants of dietary patterns like migration history, acculturation, socio-economic factors and lifestyle, will be considered. Discussion This study will allow us to investigate the contribution of the dietary patterns on CVD risk factors in a multi-ethnic population. Inclusion of five ethnic groups residing in one setting makes this study highly innovative as confounding by local environment characteristics is limited. Heterogeneity in the study population will provide variance in dietary patterns which is a great advantage when studying the link between diet and disease. PMID:21649889

Motif-Synchronization: A new method for analysis of dynamic brain networks with EEG

NASA Astrophysics Data System (ADS)

Rosário, R. S.; Cardoso, P. T.; Muñoz, M. A.; Montoya, P.; Miranda, J. G. V.

2015-12-01

The major aim of this work was to propose a new association method known as Motif-Synchronization. This method was developed to provide information about the synchronization degree and direction between two nodes of a network by counting the number of occurrences of some patterns between any two time series. The second objective of this work was to present a new methodology for the analysis of dynamic brain networks, by combining the Time-Varying Graph (TVG) method with a directional association method. We further applied the new algorithms to a set of human electroencephalogram (EEG) signals to perform a dynamic analysis of the brain functional networks (BFN).

Text Analysis of Chemistry Thesis and Dissertation Titles

ERIC Educational Resources Information Center

Scalfani, Vincent F.

2017-01-01

Programmatic text analysis can be used to understand patterns and reveal trends in data that would otherwise be difficult or impossible to uncover with manual coding methods. This work uses programmatic text analysis, specifically term frequency counts, to study nearly 10,000 chemistry thesis and dissertation titles from 1911-2015. The thesis and…

Fractal scaling in bottlenose dolphin (Tursiops truncatus) echolocation: A case study

NASA Astrophysics Data System (ADS)

Perisho, Shaun T.; Kelty-Stephen, Damian G.; Hajnal, Alen; Houser, Dorian; Kuczaj, Stan A., II

2016-02-01

Fractal scaling patterns, which entail a power-law relationship between magnitude of fluctuations in a variable and the scale at which the variable is measured, have been found in many aspects of human behavior. These findings have led to advances in behavioral models (e.g. providing empirical support for cascade-driven theories of cognition) and have had practical medical applications (e.g. providing new methods for early diagnosis of medical conditions). In the present paper, fractal analysis is used to investigate whether similar fractal scaling patterns exist in inter-click interval and peak-peak amplitude measurements of bottlenose dolphin click trains. Several echolocation recordings taken from two male bottlenose dolphins were analyzed using Detrended Fluctuation Analysis and Higuchi's (1988) method for determination of fractal dimension. Both animals were found to exhibit fractal scaling patterns near what is consistent with persistent long range correlations. These findings suggest that recent advances in human cognition and medicine may have important parallel applications to echolocation as well.

Capital Strategy in Diversification Farming Efforts Using SWOT Analysis

NASA Astrophysics Data System (ADS)

Damanhuri; Setyohadi, D. P. S.; Utami, M. M. D.; Kurnianto, M. F.; Hariono, B.

2018-01-01

Wetland farm diversification program in the district of Bojonegoro, Tulungagung, and Ponorogo can not provide an optimal contribution to the income of farmers caused because farmers are not able to cultivate high value-added commodities due to limited capital. This study aims to identify the characteristics of farming, capital pattern, stakeholder role, to analyze farming to know the pattern of planting suggestions and prospects, and to formulate capital facilitation strategy. Farming capital is obtained through loans in financial institutions with different patterns. Small farmers tend to utilize savings and credit cooperatives, microcredit, and loan sharks, while farmers with large wetland holdings tend to utilize commercial banks. P enelitian using descriptive method of farming profit analysis, and SWOT. The government through the banking institutions have provided much facilitation in the form of low-interest loans with flexible payment method. The generic strategy of selected capital facilitation is to empower farmers through farmer groups who have the capability in managing the capital needs of their members.

Restoration of out-of-focus images based on circle of confusion estimate

NASA Astrophysics Data System (ADS)

Vivirito, Paolo; Battiato, Sebastiano; Curti, Salvatore; La Cascia, M.; Pirrone, Roberto

2002-11-01

In this paper a new method for a fast out-of-focus blur estimation and restoration is proposed. It is suitable for CFA (Color Filter Array) images acquired by typical CCD/CMOS sensor. The method is based on the analysis of a single image and consists of two steps: 1) out-of-focus blur estimation via Bayer pattern analysis; 2) image restoration. Blur estimation is based on a block-wise edge detection technique. This edge detection is carried out on the green pixels of the CFA sensor image also called Bayer pattern. Once the blur level has been estimated the image is restored through the application of a new inverse filtering technique. This algorithm gives sharp images reducing ringing and crisping artifact, involving wider region of frequency. Experimental results show the effectiveness of the method, both in subjective and numerical way, by comparison with other techniques found in literature.

Quantum Associative Neural Network with Nonlinear Search Algorithm

NASA Astrophysics Data System (ADS)

Zhou, Rigui; Wang, Huian; Wu, Qian; Shi, Yang

2012-03-01

Based on analysis on properties of quantum linear superposition, to overcome the complexity of existing quantum associative memory which was proposed by Ventura, a new storage method for multiply patterns is proposed in this paper by constructing the quantum array with the binary decision diagrams. Also, the adoption of the nonlinear search algorithm increases the pattern recalling speed of this model which has multiply patterns to O( {log2}^{2^{n -t}} ) = O( n - t ) time complexity, where n is the number of quantum bit and t is the quantum information of the t quantum bit. Results of case analysis show that the associative neural network model proposed in this paper based on quantum learning is much better and optimized than other researchers' counterparts both in terms of avoiding the additional qubits or extraordinary initial operators, storing pattern and improving the recalling speed.

Coherent diffraction imaging analysis of shape-controlled nanoparticles with focused hard X-ray free-electron laser pulses.

PubMed

Takahashi, Yukio; Suzuki, Akihiro; Zettsu, Nobuyuki; Oroguchi, Tomotaka; Takayama, Yuki; Sekiguchi, Yuki; Kobayashi, Amane; Yamamoto, Masaki; Nakasako, Masayoshi

2013-01-01

We report the first demonstration of the coherent diffraction imaging analysis of nanoparticles using focused hard X-ray free-electron laser pulses, allowing us to analyze the size distribution of particles as well as the electron density projection of individual particles. We measured 1000 single-shot coherent X-ray diffraction patterns of shape-controlled Ag nanocubes and Au/Ag nanoboxes and estimated the edge length from the speckle size of the coherent diffraction patterns. We then reconstructed the two-dimensional electron density projection with sub-10 nm resolution from selected coherent diffraction patterns. This method enables the simultaneous analysis of the size distribution of synthesized nanoparticles and the structures of particles at nanoscale resolution to address correlations between individual structures of components and the statistical properties in heterogeneous systems such as nanoparticles and cells.

Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG.

PubMed

Bai, Ou; Lin, Peter; Vorbach, Sherry; Li, Jiang; Furlani, Steve; Hallett, Mark

2007-12-01

To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG). Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed. The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention. Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain-computer interface based on human natural movement, which might reduce the requirement of long-term training. Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy.

Sinusoidal modulation analysis for optical system MTF measurements.

PubMed

Boone, J M; Yu, T; Seibert, J A

1996-12-01

The modulation transfer function (MTF) is a commonly used metric for defining the spatial resolution characteristics of imaging systems. While the MTF is defined in terms of how an imaging system demodulates the amplitude of a sinusoidal input, this approach has not been in general use to measure MTFs in the medical imaging community because producing sinusoidal x-ray patterns is technically difficult. However, for optical systems such as charge coupled devices (CCD), which are rapidly becoming a part of many medical digital imaging systems, the direct measurement of modulation at discrete spatial frequencies using a sinusoidal test pattern is practical. A commercially available optical test pattern containing spatial frequencies ranging from 0.375 cycles/mm to 80 cycles/mm was sued to determine the MRF of a CCD-based optical system. These results were compared with the angulated slit method of Fujita [H. Fujita, D. Tsia, T. Itoh, K. Doi, J. Morishita, K. Ueda, and A. Ohtsuka, "A simple method for determining the modulation transfer function in digital radiography," IEEE Trans. Medical Imaging 11, 34-39 (1992)]. The use of a semiautomated profiled iterated reconstruction technique (PIRT) is introduced, where the shift factor between successive pixel rows (due to angulation) is optimized iteratively by least-squares error analysis rather than by hand measurement of the slit angle. PIRT was used to find the slit angle for the Fujita technique and to find the sine-pattern angle for the sine-pattern technique. Computer simulation of PIRT for the case of the slit image (a line spread function) demonstrated that it produced a more accurate angle determination than "hand" measurement, and there is a significant difference between the errors in the two techniques (Wilcoxon Signed Rank Test, p < 0.001). The sine-pattern method and the Fujita slit method produced comparable MTF curves for the CCD camera evaluated.

Ocean wavenumber estimation from wave-resolving time series imagery

USGS Publications Warehouse

Plant, N.G.; Holland, K.T.; Haller, M.C.

2008-01-01

We review several approaches that have been used to estimate ocean surface gravity wavenumbers from wave-resolving remotely sensed image sequences. Two fundamentally different approaches that utilize these data exist. A power spectral density approach identifies wavenumbers where image intensity variance is maximized. Alternatively, a cross-spectral correlation approach identifies wavenumbers where intensity coherence is maximized. We develop a solution to the latter approach based on a tomographic analysis that utilizes a nonlinear inverse method. The solution is tolerant to noise and other forms of sampling deficiency and can be applied to arbitrary sampling patterns, as well as to full-frame imagery. The solution includes error predictions that can be used for data retrieval quality control and for evaluating sample designs. A quantitative analysis of the intrinsic resolution of the method indicates that the cross-spectral correlation fitting improves resolution by a factor of about ten times as compared to the power spectral density fitting approach. The resolution analysis also provides a rule of thumb for nearshore bathymetry retrievals-short-scale cross-shore patterns may be resolved if they are about ten times longer than the average water depth over the pattern. This guidance can be applied to sample design to constrain both the sensor array (image resolution) and the analysis array (tomographic resolution). ?? 2008 IEEE.

A polarized digital shearing speckle pattern interferometry system based on temporal wavelet transformation.

PubMed

Feng, Ziang; Gao, Zhan; Zhang, Xiaoqiong; Wang, Shengjia; Yang, Dong; Yuan, Hao; Qin, Jie

2015-09-01

Digital shearing speckle pattern interferometry (DSSPI) has been recognized as a practical tool in testing strain. The DSSPI system which is based on temporal analysis is attractive because of its ability to measure strain dynamically. In this paper, such a DSSPI system with Wollaston prism has been built. The principles and system arrangement are described and the preliminary experimental result of the displacement-derivative test of an aluminum plate is shown with the wavelet transformation method and the Fourier transformation method. The simulations have been conducted with the finite element method. The comparison of the results shows that quantitative measurement of displacement-derivative has been realized.

A Peptide-Based Method for 13C Metabolic Flux Analysis in Microbial Communities

PubMed Central

Ghosh, Amit; Nilmeier, Jerome; Weaver, Daniel; Adams, Paul D.; Keasling, Jay D.; Mukhopadhyay, Aindrila; Petzold, Christopher J.; Martín, Héctor García

2014-01-01

The study of intracellular metabolic fluxes and inter-species metabolite exchange for microbial communities is of crucial importance to understand and predict their behaviour. The most authoritative method of measuring intracellular fluxes, 13C Metabolic Flux Analysis (13C MFA), uses the labeling pattern obtained from metabolites (typically amino acids) during 13C labeling experiments to derive intracellular fluxes. However, these metabolite labeling patterns cannot easily be obtained for each of the members of the community. Here we propose a new type of 13C MFA that infers fluxes based on peptide labeling, instead of amino acid labeling. The advantage of this method resides in the fact that the peptide sequence can be used to identify the microbial species it originates from and, simultaneously, the peptide labeling can be used to infer intracellular metabolic fluxes. Peptide identity and labeling patterns can be obtained in a high-throughput manner from modern proteomics techniques. We show that, using this method, it is theoretically possible to recover intracellular metabolic fluxes in the same way as through the standard amino acid based 13C MFA, and quantify the amount of information lost as a consequence of using peptides instead of amino acids. We show that by using a relatively small number of peptides we can counter this information loss. We computationally tested this method with a well-characterized simple microbial community consisting of two species. PMID:25188426

An investigation on thermal patterns in Iran based on spatial autocorrelation

NASA Astrophysics Data System (ADS)

Fallah Ghalhari, Gholamabbas; Dadashi Roudbari, Abbasali

2018-02-01

The present study aimed at investigating temporal-spatial patterns and monthly patterns of temperature in Iran using new spatial statistical methods such as cluster and outlier analysis, and hotspot analysis. To do so, climatic parameters, monthly average temperature of 122 synoptic stations, were assessed. Statistical analysis showed that January with 120.75% had the most fluctuation among the studied months. Global Moran's Index revealed that yearly changes of temperature in Iran followed a strong spatially clustered pattern. Findings showed that the biggest thermal cluster pattern in Iran, 0.975388, occurred in May. Cluster and outlier analyses showed that thermal homogeneity in Iran decreases in cold months, while it increases in warm months. This is due to the radiation angle and synoptic systems which strongly influence thermal order in Iran. The elevations, however, have the most notable part proved by Geographically weighted regression model. Iran's thermal analysis through hotspot showed that hot thermal patterns (very hot, hot, and semi-hot) were dominant in the South, covering an area of 33.5% (about 552,145.3 km2). Regions such as mountain foot and low lands lack any significant spatial autocorrelation, 25.2% covering about 415,345.1 km2. The last is the cold thermal area (very cold, cold, and semi-cold) with about 25.2% covering about 552,145.3 km2 of the whole area of Iran.

Fluid dynamic instabilities: theory and application to pattern forming in complex media

PubMed Central

Brun, P.-T.

2017-01-01

In this review article, we exemplify the use of stability analysis tools to rationalize pattern formation in complex media. Specifically, we focus on fluid flows, and show how the destabilization of their interface sets the blueprint of the patterns they eventually form. We review the potential use and limitations of the theoretical methods at the end, in terms of their applications to practical settings, e.g. as guidelines to design and fabricate structures while harnessing instabilities. This article is part of the themed issue ‘Patterning through instabilities in complex media: theory and applications’. PMID:28373378

Palmar dermatoglyphic patterns in twins.

PubMed

Jacques, S M; Salzano, F M; Penña, H F

1977-01-01

The role of genetic factors in the determination of palmar dermatoglyphic patterns was investigated in a series of 49 MZ and 51 DZ twins, using Spearman's rank correlation and analysis of variance. Both methods indicated that the genetic effect in the distribution of patterns is highest in the interdigital III and lowest in the interdigital IV regions, the hypothenar and thenar showing intermediate values. As for interdigital II, no evaluation of genetic effects was possible using the nonparametric test, but the estimates of genetic variance indicate that inherited factors may play a relatively minor role in the pattern distribution of this area.

Identifying autism from neural representations of social interactions: neurocognitive markers of autism.

PubMed

Just, Marcel Adam; Cherkassky, Vladimir L; Buchweitz, Augusto; Keller, Timothy A; Mitchell, Tom M

2014-01-01

Autism is a psychiatric/neurological condition in which alterations in social interaction (among other symptoms) are diagnosed by behavioral psychiatric methods. The main goal of this study was to determine how the neural representations and meanings of social concepts (such as to insult) are altered in autism. A second goal was to determine whether these alterations can serve as neurocognitive markers of autism. The approach is based on previous advances in fMRI analysis methods that permit (a) the identification of a concept, such as the thought of a physical object, from its fMRI pattern, and (b) the ability to assess the semantic content of a concept from its fMRI pattern. These factor analysis and machine learning methods were applied to the fMRI activation patterns of 17 adults with high-functioning autism and matched controls, scanned while thinking about 16 social interactions. One prominent neural representation factor that emerged (manifested mainly in posterior midline regions) was related to self-representation, but this factor was present only for the control participants, and was near-absent in the autism group. Moreover, machine learning algorithms classified individuals as autistic or control with 97% accuracy from their fMRI neurocognitive markers. The findings suggest that psychiatric alterations of thought can begin to be biologically understood by assessing the form and content of the altered thought's underlying brain activation patterns.

Identifying Autism from Neural Representations of Social Interactions: Neurocognitive Markers of Autism

PubMed Central

Just, Marcel Adam; Cherkassky, Vladimir L.; Buchweitz, Augusto; Keller, Timothy A.; Mitchell, Tom M.

2014-01-01

Autism is a psychiatric/neurological condition in which alterations in social interaction (among other symptoms) are diagnosed by behavioral psychiatric methods. The main goal of this study was to determine how the neural representations and meanings of social concepts (such as to insult) are altered in autism. A second goal was to determine whether these alterations can serve as neurocognitive markers of autism. The approach is based on previous advances in fMRI analysis methods that permit (a) the identification of a concept, such as the thought of a physical object, from its fMRI pattern, and (b) the ability to assess the semantic content of a concept from its fMRI pattern. These factor analysis and machine learning methods were applied to the fMRI activation patterns of 17 adults with high-functioning autism and matched controls, scanned while thinking about 16 social interactions. One prominent neural representation factor that emerged (manifested mainly in posterior midline regions) was related to self-representation, but this factor was present only for the control participants, and was near-absent in the autism group. Moreover, machine learning algorithms classified individuals as autistic or control with 97% accuracy from their fMRI neurocognitive markers. The findings suggest that psychiatric alterations of thought can begin to be biologically understood by assessing the form and content of the altered thought’s underlying brain activation patterns. PMID:25461818

Social Network Analysis: A New Methodology for Counseling Research.

ERIC Educational Resources Information Center

Koehly, Laura M.; Shivy, Victoria A.

1998-01-01

Social network analysis (SNA) uses indices of relatedness among individuals to produce representations of social structures and positions inherent in dyads or groups. SNA methods provide quantitative representations of ongoing transactional patterns in a given social environment. Methodological issues, applications and resources are discussed…

Green Curriculum Analysis in Technological Education

ERIC Educational Resources Information Center

Chakraborty, Arpita; Singh, Manvendra Pratap; Roy, Mousumi

2018-01-01

With rapid industrialization and technological development, India is facing adverse affects of unsustainable pattern of production and consumption. Education for sustainable development has been widely recognized to reduce the threat of environmental degradation and resource depletion. This paper used the content analysis method to explore the…

Rapid determination of floral aroma compounds of lilac blossom by fast gas chromatography combined with surface acoustic wave sensor.

PubMed

Oh, Se Yeon; Shin, Hyun Du; Kim, Sung Jean; Hong, Jongki

2008-03-07

A novel analytical method using fast gas chromatography combined with surface acoustic wave sensor (GC/SAW) has been developed for the detection of volatile aroma compounds emanated from lilac blossom (Syringa species: Syringa vulgaris variginata and Syringa dilatata). GC/SAW could detect and quantify various fragrance emitted from lilac blossom, enabling to provide fragrance pattern analysis results. The fragrance pattern analysis could easily characterize the delicate differences in aromas caused by the substantial difference of chemical composition according to different color and shape of petals. Moreover, the method validation of GC/SAW was performed for the purpose of volatile floral actual aroma analysis, achieving a high reproducibility and excellent sensitivity. From the validation results, GC/SAW could serve as an alternative analytical technique for the analysis of volatile floral actual aroma of lilac. In addition, headspace solid-phase microextraction (HS-SPME) GC-MS was employed to further confirm the identification of fragrances emitted from lilac blossom and compared to GC/SAW.

Targeted and untargeted-metabolite profiling to track the compositional integrity of ginger during processing using digitally-enhanced HPTLC pattern recognition analysis.

PubMed

Ibrahim, Reham S; Fathy, Hoda

2018-03-30

Tracking the impact of commonly applied post-harvesting and industrial processing practices on the compositional integrity of ginger rhizome was implemented in this work. Untargeted metabolite profiling was performed using digitally-enhanced HPTLC method where the chromatographic fingerprints were extracted using ImageJ software then analysed with multivariate Principal Component Analysis (PCA) for pattern recognition. A targeted approach was applied using a new, validated, simple and fast HPTLC image analysis method for simultaneous quantification of the officially recognized markers 6-, 8-, 10-gingerol and 6-shogaol in conjunction with chemometric Hierarchical Clustering Analysis (HCA). The results of both targeted and untargeted metabolite profiling revealed that peeling, drying in addition to storage employed during processing have a great influence on ginger chemo-profile, the different forms of processed ginger shouldn't be used interchangeably. Moreover, it deemed necessary to consider the holistic metabolic profile for comprehensive evaluation of ginger during processing. Copyright © 2018. Published by Elsevier B.V.

Polychromatic spectral pattern analysis of ultra-weak photon emissions from a human body.

PubMed

Kobayashi, Masaki; Iwasa, Torai; Tada, Mika

2016-06-01

Ultra-weak photon emission (UPE), often designated as biophoton emission, is generally observed in a wide range of living organisms, including human beings. This phenomenon is closely associated with reactive oxygen species (ROS) generated during normal metabolic processes and pathological states induced by oxidative stress. Application of UPE extracting the pathophysiological information has long been anticipated because of its potential non-invasiveness, facilitating its diagnostic use. Nevertheless, its weak intensity and UPE mechanism complexity hinder its use for practical applications. Spectroscopy is crucially important for UPE analysis. However, filter-type spectroscopy technique, used as a conventional method for UPE analysis, intrinsically limits its performance because of its monochromatic scheme. To overcome the shortcomings of conventional methods, the authors developed a polychromatic spectroscopy system for UPE spectral pattern analysis. It is based on a highly efficient lens systems and a transmission-type diffraction grating with a highly sensitive, cooled, charge-coupled-device (CCD) camera. Spectral pattern analysis of the human body was done for a fingertip using the developed system. The UPE spectrum covers the spectral range of 450-750nm, with a dominant emission region of 570-670nm. The primary peak is located in the 600-650nm region. Furthermore, application of UPE source exploration was demonstrated with the chemiluminescence spectrum of melanin and coexistence with oxidized linoleic acid. Copyright © 2016 Elsevier B.V. All rights reserved.

Sketching Muslims: A Corpus Driven Analysis of Representations around the Word "Muslim" in the British Press 1998-2009

ERIC Educational Resources Information Center

Baker, Paul; Gabrielatos, Costas; McEnery, Tony

2013-01-01

This article uses methods from corpus linguistics and critical discourse analysis to examine patterns of representation around the word "Muslim" in a 143 million word corpus of British newspaper articles published between 1998 and 2009. Using the analysis tool Sketch Engine, an analysis of noun collocates of "Muslim" found that the following…

Suitability of the molecular subtyping methods intergenic spacer region, direct genome restriction analysis, and pulsed-field gel electrophoresis for clinical and environmental Vibrio parahaemolyticus isolates.

PubMed

Lüdeke, Catharina H M; Fischer, Markus; LaFon, Patti; Cooper, Kara; Jones, Jessica L

2014-07-01

Vibrio parahaemolyticus is the leading cause of infectious illness associated with seafood consumption in the United States. Molecular fingerprinting of strains has become a valuable research tool for understanding this pathogen. However, there are many subtyping methods available and little information on how they compare to one another. For this study, a collection of 67 oyster and 77 clinical V. parahaemolyticus isolates were analyzed by three subtyping methods--intergenic spacer region (ISR-1), direct genome restriction analysis (DGREA), and pulsed-field gel electrophoresis (PFGE)--to determine the utility of these methods for discriminatory subtyping. ISR-1 analysis, run as previously described, provided the lowest discrimination of all the methods (discriminatory index [DI]=0.8665). However, using a broader analytical range than previously reported, ISR-1 clustered isolates based on origin (oyster versus clinical) and had a DI=0.9986. DGREA provided a DI=0.9993-0.9995, but did not consistently cluster the isolates by any identifiable characteristics (origin, serotype, or virulence genotype) and ∼ 15% of isolates were untypeable by this method. PFGE provided a DI=0.9998 when using the combined pattern analysis of both restriction enzymes, SfiI and NotI. This analysis was more discriminatory than using either enzyme pattern alone and primarily grouped isolates by serotype, regardless of strain origin (clinical or oyster) or presence of currently accepted virulence markers. These results indicate that PFGE and ISR-1 are more reliable methods for subtyping V. parahemolyticus, rather than DGREA. Additionally, ISR-1 may provide an indication of pathogenic potential; however, more detailed studies are needed. These data highlight the diversity within V. parahaemolyticus and the need for appropriate selection of subtyping methods depending on the study objectives.

[Optimization of cluster analysis based on drug resistance profiles of MRSA isolates].

PubMed

Tani, Hiroya; Kishi, Takahiko; Gotoh, Minehiro; Yamagishi, Yuka; Mikamo, Hiroshige

2015-12-01

We examined 402 methicillin-resistant Staphylococcus aureus (MRSA) strains isolated from clinical specimens in our hospital between November 19, 2010 and December 27, 2011 to evaluate the similarity between cluster analysis of drug susceptibility tests and pulsed-field gel electrophoresis (PFGE). The results showed that the 402 strains tested were classified into 27 PFGE patterns (151 subtypes of patterns). Cluster analyses of drug susceptibility tests with the cut-off distance yielding a similar classification capability showed favorable results--when the MIC method was used, and minimum inhibitory concentration (MIC) values were used directly in the method, the level of agreement with PFGE was 74.2% when 15 drugs were tested. The Unweighted Pair Group Method with Arithmetic mean (UPGMA) method was effective when the cut-off distance was 16. Using the SIR method in which susceptible (S), intermediate (I), and resistant (R) were coded as 0, 2, and 3, respectively, according to the Clinical and Laboratory Standards Institute (CLSI) criteria, the level of agreement with PFGE was 75.9% when the number of drugs tested was 17, the method used for clustering was the UPGMA, and the cut-off distance was 3.6. In addition, to assess the reproducibility of the results, 10 strains were randomly sampled from the overall test and subjected to cluster analysis. This was repeated 100 times under the same conditions. The results indicated good reproducibility of the results, with the level of agreement with PFGE showing a mean of 82.0%, standard deviation of 12.1%, and mode of 90.0% for the MIC method and a mean of 80.0%, standard deviation of 13.4%, and mode of 90.0% for the SIR method. In summary, cluster analysis for drug susceptibility tests is useful for the epidemiological analysis of MRSA.

Definition of spatial patterns of bark beetle Ips typographus (L.) outbreak spreading in Tatra Mountains (Central Europe), using GIS

Treesearch

Rastislav Jakus; Wojciech Grodzki; Marek Jezik; Marcin Jachym

2003-01-01

The spread of bark beetle outbreaks in the Tatra Mountains was explored by using both terrestrial and remote sensing techniques. Both approaches have proven to be useful for studying spatial patterns of bark beetle population dynamics. The terrestrial methods were applied on existing forestry databases. Vegetation change analysis (image differentiation), digital...

Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation/Completion of Episodic Information.

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

Aimone, James Bradley; Betty, Rita

Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation/Completion of Episodic Information - Sandia researchers developed novel methods and metrics for studying the computational function of neurogenesis, thus generating substantial impact to the neuroscience and neural computing communities. This work could benefit applications in machine learning and other analysis activities.

Network-constrained spatio-temporal clustering analysis of traffic collisions in Jianghan District of Wuhan, China

PubMed Central

Fan, Yaxin; Zhu, Xinyan; Guo, Wei; Guo, Tao

2018-01-01

The analysis of traffic collisions is essential for urban safety and the sustainable development of the urban environment. Reducing the road traffic injuries and the financial losses caused by collisions is the most important goal of traffic management. In addition, traffic collisions are a major cause of traffic congestion, which is a serious issue that affects everyone in the society. Therefore, traffic collision analysis is essential for all parties, including drivers, pedestrians, and traffic officers, to understand the road risks at a finer spatio-temporal scale. However, traffic collisions in the urban context are dynamic and complex. Thus, it is important to detect how the collision hotspots evolve over time through spatio-temporal clustering analysis. In addition, traffic collisions are not isolated events in space. The characteristics of the traffic collisions and their surrounding locations also present an influence of the clusters. This work tries to explore the spatio-temporal clustering patterns of traffic collisions by combining a set of network-constrained methods. These methods were tested using the traffic collision data in Jianghan District of Wuhan, China. The results demonstrated that these methods offer different perspectives of the spatio-temporal clustering patterns. The weighted network kernel density estimation provides an intuitive way to incorporate attribute information. The network cross K-function shows that there are varying clustering tendencies between traffic collisions and different types of POIs. The proposed network differential Local Moran’s I and network local indicators of mobility association provide straightforward and quantitative measures of the hotspot changes. This case study shows that these methods could help researchers, practitioners, and policy-makers to better understand the spatio-temporal clustering patterns of traffic collisions. PMID:29672551

Measurement of trained speech patterns in stuttering: interjudge and intrajudge agreement of experts by means of modified time-interval analysis.

PubMed

Alpermann, Anke; Huber, Walter; Natke, Ulrich; Willmes, Klaus

2010-09-01

Improved fluency after stuttering therapy is usually measured by the percentage of stuttered syllables. However, outcome studies rarely evaluate the use of trained speech patterns that speakers use to manage stuttering. This study investigated whether the modified time interval analysis can distinguish between trained speech patterns, fluent speech, and stuttered speech. Seventeen German experts on stuttering judged a speech sample on two occasions. Speakers of the sample were stuttering adults, who were not undergoing therapy, as well as participants in a fluency shaping and a stuttering modification therapy. Results showed satisfactory inter-judge and intra-judge agreement above 80%. Intervals with trained speech patterns were identified as consistently as stuttered and fluent intervals. We discuss limitations of the study, as well as implications of our findings for the development of training for identification of trained speech patterns and future outcome studies. The reader will be able to (a) explain different methods to measure the use of trained speech patterns, (b) evaluate whether German experts are able to discriminate intervals with trained speech patterns reliably from fluent and stuttered intervals and (c) describe how the measurement of trained speech patterns can contribute to outcome studies.

[Development and application of morphological analysis method in Aspergillus niger fermentation].

PubMed

Tang, Wenjun; Xia, Jianye; Chu, Ju; Zhuang, Yingping; Zhang, Siliang

2015-02-01

Filamentous fungi are widely used in industrial fermentation. Particular fungal morphology acts as a critical index for a successful fermentation. To break the bottleneck of morphological analysis, we have developed a reliable method for fungal morphological analysis. By this method, we can prepare hundreds of pellet samples simultaneously and obtain quantitative morphological information at large scale quickly. This method can largely increase the accuracy and reliability of morphological analysis result. Based on that, the studies of Aspergillus niger morphology under different oxygen supply conditions and shear rate conditions were carried out. As a result, the morphological responding patterns of A. niger morphology to these conditions were quantitatively demonstrated, which laid a solid foundation for the further scale-up.

Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.

PubMed

Zafar, Raheel; Kamel, Nidal; Naufal, Mohamad; Malik, Aamir Saeed; Dass, Sarat C; Ahmad, Rana Fayyaz; Abdullah, Jafri M; Reza, Faruque

2017-01-01

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

Clustering of change patterns using Fourier coefficients.

PubMed

Kim, Jaehee; Kim, Haseong

2008-01-15

To understand the behavior of genes, it is important to explore how the patterns of gene expression change over a time period because biologically related gene groups can share the same change patterns. Many clustering algorithms have been proposed to group observation data. However, because of the complexity of the underlying functions there have not been many studies on grouping data based on change patterns. In this study, the problem of finding similar change patterns is induced to clustering with the derivative Fourier coefficients. The sample Fourier coefficients not only provide information about the underlying functions, but also reduce the dimension. In addition, as their limiting distribution is a multivariate normal, a model-based clustering method incorporating statistical properties would be appropriate. This work is aimed at discovering gene groups with similar change patterns that share similar biological properties. We developed a statistical model using derivative Fourier coefficients to identify similar change patterns of gene expression. We used a model-based method to cluster the Fourier series estimation of derivatives. The model-based method is advantageous over other methods in our proposed model because the sample Fourier coefficients asymptotically follow the multivariate normal distribution. Change patterns are automatically estimated with the Fourier representation in our model. Our model was tested in simulations and on real gene data sets. The simulation results showed that the model-based clustering method with the sample Fourier coefficients has a lower clustering error rate than K-means clustering. Even when the number of repeated time points was small, the same results were obtained. We also applied our model to cluster change patterns of yeast cell cycle microarray expression data with alpha-factor synchronization. It showed that, as the method clusters with the probability-neighboring data, the model-based clustering with our proposed model yielded biologically interpretable results. We expect that our proposed Fourier analysis with suitably chosen smoothing parameters could serve as a useful tool in classifying genes and interpreting possible biological change patterns. The R program is available upon the request.

Sensitivity Analysis for Probabilistic Neural Network Structure Reduction.

PubMed

Kowalski, Piotr A; Kusy, Maciej

2018-05-01

In this paper, we propose the use of local sensitivity analysis (LSA) for the structure simplification of the probabilistic neural network (PNN). Three algorithms are introduced. The first algorithm applies LSA to the PNN input layer reduction by selecting significant features of input patterns. The second algorithm utilizes LSA to remove redundant pattern neurons of the network. The third algorithm combines the proposed two and constitutes the solution of how they can work together. PNN with a product kernel estimator is used, where each multiplicand computes a one-dimensional Cauchy function. Therefore, the smoothing parameter is separately calculated for each dimension by means of the plug-in method. The classification qualities of the reduced and full structure PNN are compared. Furthermore, we evaluate the performance of PNN, for which global sensitivity analysis (GSA) and the common reduction methods are applied, both in the input layer and the pattern layer. The models are tested on the classification problems of eight repository data sets. A 10-fold cross validation procedure is used to determine the prediction ability of the networks. Based on the obtained results, it is shown that the LSA can be used as an alternative PNN reduction approach.

Multivariate Analysis as a Method for Evaluating the Conceptual Perceptions of Korean Medicine Students regarding Phlegm Pattern

PubMed Central

Kim, Hyungsuk; Park, Young-Jae; Park, Young-Bae

2013-01-01

Individuals may perceive the concepts in Korean medicine pattern classification differently because it is performed according to the integration of a variety of information. Therefore, analysis about individual perspective is very important for examining the cross-sectional perspective state of Korean medicine concepts and developing both the clinical guideline including diagnosis and the curriculum of Korean medicine colleges. Moreover, because this conceptual difference is thought to begin with college education, it is worthwhile to observe students' viewpoints. So, we suggested multivariate analysis to explore the dimensional structure of Korean medicine students' conceptual perceptions regarding phlegm pattern. We surveyed 326 students divided into 5 groups based on their year of study. Data were analyzed using multidimensional scaling and factor analysis. Within-group difference was the smallest for third-year students, who have received Korean medicine education in full for the first time. With the exception of first-year students, the conceptual map revealed that each group's mean perceptions of phlegm pattern were distributed in almost linear fashion. To determine the effect of education, we investigated the preference rankings and scores of each symptom. We also extracted factors to identify latent variables and to compare the between-group conceptual characteristics regarding phlegm pattern. PMID:24062789

Suicide methods in children and adolescents.

PubMed

Kõlves, Kairi; de Leo, Diego

2017-02-01

There are notable differences in suicide methods between countries. The aim of this paper is to analyse and describe suicide methods in children and adolescents aged 10-19 years in different countries/territories worldwide. Suicide data by ICD-10 X codes were obtained from the WHO Mortality Database and population data from the World Bank. In total, 101 countries or territories, have data at least for 5 years in 2000-2009. Cluster analysis by suicide methods was performed for countries/territories with at least 10 suicide cases separately by gender (74 for males and 71 for females) in 2000-2009. The most frequent suicide method was hanging, followed by poisoning by pesticides for females and firearms for males. Cluster analyses of similarities in the country/territory level suicide method patterns by gender identified four clusters for both gender. Hanging and poisoning by pesticides defined the clusters of countries/territories by their suicide patterns in youth for both genders. In addition, a mixed method and a jumping from height cluster were identified for females and two mixed method clusters for males. A number of geographical similarities were observed. Overall, the patterns of suicide methods in children and adolescents reflect lethality, availability and acceptability of suicide means similarly to country specific patterns of all ages. Means restriction has very good potential in preventing youth suicides in different countries. It is also crucial to consider cognitive availability influenced by sensationalised media reporting and/or provision of technical details about specific methods.

[Identification of mycobacteria by matrix-assisted laser desorption-ionization time-of-flight mass spectrometry--using reference strains and clinical isolates of Mycobacterium].

PubMed

Niitsuma, Katsunao; Saito, Miwako; Koshiba, Shizuko; Kaneko, Michiyo

2014-05-01

Matrix-assisted laser desorption-ionization time-of-flight mass spectrometry (MALDI-TOF MS) method is being played an important role for the inspection of clinical microorganism as a rapid and the price reduction. Mass spectra obtained by measuring become points of identification whether the peak pattern match any species mass spectral pattern. We currently use MALDI-TOF MS for rapid and accurate diagnosis of inactivated reference and clinical isolates of Mycobacterium because of the improved pretreatment techniques compared with former inspection methods that pose a higher risk of infection to the operator. The identification matching rate of score value (SV) peak pattern spectra was compared with that of conventional methods such as strain diffusion/amplification. Also, cultures were examined after a fixed number of days. Compared with the initial inspection technique, the pretreatment stage of current MALDI-TOF MS inspection techniques can improve the analysis of inactivated acid-fast bacteria that are often used as inspection criteria strains of clinical isolates. Next, we compared the concordance rate for identification between MALDI-TOF MS and conventional methods such as diffusion/amplification by comparison of peak pattern spectra and evaluated SV spectra to identify differences in the culture media after the retention period. In examination of 158 strains of clinical isolated Mycobacterium tuberculosis complex (MTC), the identification coincidence rate in the genus level in a matching pattern was 99.4%, when the species level was included 94.9%. About 37 strains of nontuberculous mycobacteria (NTM), the identification coincidence rate in the genus level was 94.6%. M. bovis BCG (Tokyo strain) in the reference strain was judged by the matching pattern to be MTC, and it suggested that they are M. tuberculosis and affinity species with high DNA homology. Nontuberculous mycobacterial M. gordonae strain JATA 33-01 shared peak pattern spectra, excluding the isolates, with each clinically isolated strain. However, the mass spectra of six M. gordonae clinical isolates suggested polymorphisms with similar mass-to-charge ratios compared with those of the reference strains. The peak pattern spectra of the clinical isolates and reference strains, excluding the NTM M. gordonae strain JATA33-01, were consistent with the peak pattern characteristics of each isolate. However, a comparison between the peak patterns of the reference strains and those of the six clinically isolated M. gordonae strains revealed a similar mass-to-charge ratio, which may indicate few polymorphisms. The SV spectrum of the improved inspection technique showed no fidelity, but it was acceptable after days of culture as indicated by the decrease in SV (0.3 degree). Also, the reproducibility of this method was good, but no difference was observed from the SV of the improved inspection technique, which decreased by approximately 0.3 because of the number of days of culture storage. In addition, expansion of the database and dissemination of regional specificity by genotype analysis of clinical isolates was relevant to the accumulated data, as expected. In future studies, the relevance and regional specificity of clinical isolates by genotype analysis can be determined by stacking the solid media and database penetration.

Three-dimensional MRI perfusion maps: a step beyond volumetric analysis in mental disorders

PubMed Central

Fabene, Paolo F; Farace, Paolo; Brambilla, Paolo; Andreone, Nicola; Cerini, Roberto; Pelizza, Luisa; Versace, Amelia; Rambaldelli, Gianluca; Birbaumer, Niels; Tansella, Michele; Sbarbati, Andrea

2007-01-01

A new type of magnetic resonance imaging analysis, based on fusion of three-dimensional reconstructions of time-to-peak parametric maps and high-resolution T1-weighted images, is proposed in order to evaluate the perfusion of selected volumes of interest. Because in recent years a wealth of data have suggested the crucial involvement of vascular alterations in mental diseases, we tested our new method on a restricted sample of schizophrenic patients and matched healthy controls. The perfusion of the whole brain was compared with that of the caudate nucleus by means of intrasubject analysis. As expected, owing to the encephalic vascular pattern, a significantly lower time-to-peak was observed in the caudate nucleus than in the whole brain in all healthy controls, indicating that the suggested method has enough sensitivity to detect subtle perfusion changes even in small volumes of interest. Interestingly, a less uniform pattern was observed in the schizophrenic patients. The latter finding needs to be replicated in an adequate number of subjects. In summary, the three-dimensional analysis method we propose has been shown to be a feasible tool for revealing subtle vascular changes both in normal subjects and in pathological conditions. PMID:17229290

Combining self-organizing mapping and supervised affinity propagation clustering approach to investigate functional brain networks involved in motor imagery and execution with fMRI measurements.

PubMed

Zhang, Jiang; Liu, Qi; Chen, Huafu; Yuan, Zhen; Huang, Jin; Deng, Lihua; Lu, Fengmei; Zhang, Junpeng; Wang, Yuqing; Wang, Mingwen; Chen, Liangyin

2015-01-01

Clustering analysis methods have been widely applied to identifying the functional brain networks of a multitask paradigm. However, the previously used clustering analysis techniques are computationally expensive and thus impractical for clinical applications. In this study a novel method, called SOM-SAPC that combines self-organizing mapping (SOM) and supervised affinity propagation clustering (SAPC), is proposed and implemented to identify the motor execution (ME) and motor imagery (MI) networks. In SOM-SAPC, SOM was first performed to process fMRI data and SAPC is further utilized for clustering the patterns of functional networks. As a result, SOM-SAPC is able to significantly reduce the computational cost for brain network analysis. Simulation and clinical tests involving ME and MI were conducted based on SOM-SAPC, and the analysis results indicated that functional brain networks were clearly identified with different response patterns and reduced computational cost. In particular, three activation clusters were clearly revealed, which include parts of the visual, ME and MI functional networks. These findings validated that SOM-SAPC is an effective and robust method to analyze the fMRI data with multitasks.

Spatial analysis of sunshine duration by combination of satellite and station data

NASA Astrophysics Data System (ADS)

Frei, C.; Stöckli, R.; Dürr, B.

2009-09-01

Sunshine duration can exhibit rich fine scale patterns associated with special meteorological phenomena, such as fog layers and topographically triggered clouds. Networks of climate stations are mostly too coarse and poorly representative to resolve these patterns explicitly. We present a method which combines station observations with satellite-derived cloud-cover data to produce km-scale fields of sunshine duration. The method is not relying on contemporous satellite information, hence it can be applied over climatological time scales. We apply and evaluate the combination method over the territory of Switzerland. The combination method is based on Universal Kriging. First, the satellite data (a Heliosat clear sky index from MSG, extending over a 5 year preiod) is subjected to a S-mode Principal Component (PC) Analysis. Second, a set of leading PC loadings (seasonally stratified) is introduced as external drift covariates and their optimal linear combination is estimated from the station data (70 stations). Finally, the stochastic component is an autocorrelated field with an exponential variogram, estimated climatologically for each calendar month. For Switzerland the leading PCs of the clear sky index depict familiar patterns of cloud variability which are inhereted in the combination process. The resulting sunshine duration fields exhibit fine-scale structures that are physically plausible, linked to the topography and characteristic of the regional climate. These patterns could not be inferred from station data and/or topographic predictors alone. A cross-validation reveals that the combination method explains between 80-90% of the spatial variance in winter and autumn months. In spring and summer the relative performance is lower (60-75% explained spatial variance) but absolute errors are smaller. Our presentation will also discuss some results from a climatology of the derived sunshine duration fields.

Automated pattern analysis: A newsilent partner in insect acoustic detection studies

USDA-ARS?s Scientific Manuscript database

This seminar reviews methods that have been developed for automated analysis of field-collected sounds used to estimate pest populations and guide insect pest management decisions. Several examples are presented of successful usage of acoustic technology to map insect distributions in field environ...

Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning.

PubMed

Davatzikos, Christos

2016-10-01

The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. Copyright © 2016. Published by Elsevier B.V.

Techniques for generation of control and guidance signals derived from optical fields, part 2

NASA Technical Reports Server (NTRS)

Hemami, H.; Mcghee, R. B.; Gardner, S. R.

1971-01-01

The development is reported of a high resolution technique for the detection and identification of landmarks from spacecraft optical fields. By making use of nonlinear regression analysis, a method is presented whereby a sequence of synthetic images produced by a digital computer can be automatically adjusted to provide a least squares approximation to a real image. The convergence of the method is demonstrated by means of a computer simulation for both elliptical and rectangular patterns. Statistical simulation studies with elliptical and rectangular patterns show that the computational techniques developed are able to at least match human pattern recognition capabilities, even in the presence of large amounts of noise. Unlike most pattern recognition techniques, this ability is unaffected by arbitrary pattern rotation, translation, and scale change. Further development of the basic approach may eventually allow a spacecraft or robot vehicle to be provided with an ability to very accurately determine its spatial relationship to arbitrary known objects within its optical field of view.

Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning

PubMed Central

Davatzikos, Christos

2017-01-01

The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. PMID:27514582

Quantitative study on spatio-temporal change of urban landscape pattern based on RS/GIS: a case of Xi'an metropolitan area in China

NASA Astrophysics Data System (ADS)

Chen, Meiwu; Zong, Yueguang; Ma, Qiang; Li, Jian

2007-06-01

The study on landscape pattern is an important field of urban land use and ecological change. Since 1990s, the widely accepted Patch-Corridor-Matrix model is generally used in qualitative description of landscape pattern. In recent years, quantitative evaluation on urban landscape dynamics is becoming hot in research. By making a critical review on existing research methods of landscape pattern, a new approach based on RS/GIS is put forward in this paper, comprising three steps, "General pattern characteristics - Gradient differentiation feature- Directional signature of the landscape", and we call it GGD. This method is applied to the case study of Xi'an metropolitan area in China. The result shows that the method is effective on quantitative study of urban landscape. The preparation of the method GGD is setting up research platform based on RS and GIS. By using the software of Geographical Information System (Arcgis9.0 & Erdas), the authors got the interpretation of remote sensing images of different years, and carried on the division of the landscape type of the research region. By calculating various index of landscape level with software Fragstats3.3 as an assistant tool and adopting three steps of GGD combined with landscape index, this paper can assesses the landscape spatial pattern of urban area: 1) General pattern characteristics analysis is to get transition probability of various landscape through Markov chain and to predict the landscape transformation by introducing CA model. The analysis emphasizes on total landscape structure and its change over time; 2) Gradient characteristic analysis, which makes gradient zone by taking city as a center outwardly with certain distance and contrastively analyzes the landscape index of each subarea, stresses the spatial character of landscape pattern, verifies urban morphology theories and provides the quantitative warranty for establishment of urban modality. Therefore, the analysis is useful for supervising urban expanding speed; 3)Direction characteristic analysis, which is setting up radiate strip on west-east, south-north, southwest-northeast and northwest-southeast and form certain width on each direction, can precisely and quantitatively indicate different characteristic of urban landscape at each development direction, and by combined with gradient analysis it is highly advantageous to the examination and planning of urban expanding direction. In the case study on Xi'an metropolitan area, remote sensing images of 1988 and 2005 Landsat-TM were handled, and the division of the landscape type of the region was also carried on. According to the above approach, the result was got and some valuable information was showed as follows: 1) The diversity of overall landscape of Xi'an metropolitan area tends to increase and the degree of fragmentation tends to deepen. With the increase of urbanization level, the visual component of landscape is more and more diversified, the shapes of landscape is more and more complicated and ecologically more and more fragmented. In the region where urbanization level is low, natural landscape is the main component of the landscape, the diversity of the landscape is low. And because landscape is seriously disturbed by human activities with urbanization, fragmentation of the landscape emerges periodically. 2) In the process of transect gradient analysis, the landscape pattern index can explore the urbanization gradient, and its trend to reduce gradually towards the suburban. The landscape of area with a high urbanization level is mainly man-created, and its patches show large number, small area, simple shape and higher landscape heterogeneity. The transect gradient analysis on different time series indicates the relationship between urbanization level and landscape pattern. The landscape of urban area suffers intensely from human being, and its pattern component and spatial collocation depends on the interference intensity to a large degree. In the area with a high urbanization level, its pattern component is more man-created and less natural landscape. The landscape collocation characteristic of its patches takes on a large number, little average area, simple shape and low polymerization degree. 3) Analysis of direction and gradient of Xi'an metropolitan area can quantitatively reflect influence of urbanization and characteristics of urban landscape in the main development axes of north-south and east. Result shows that the degree of internal integration between Xi'an city and Xianyang city is gradually enhanced with the quickly urbanization course in China.

A Tutorial on Multiblock Discriminant Correspondence Analysis (MUDICA): A New Method for Analyzing Discourse Data from Clinical Populations

ERIC Educational Resources Information Center

Williams, Lynne J.; Abdi, Herve; French, Rebecca; Orange, Joseph B.

2010-01-01

Purpose: In communication disorders research, clinical groups are frequently described based on patterns of performance, but researchers often study only a few participants described by many quantitative and qualitative variables. These data are difficult to handle with standard inferential tools (e.g., analysis of variance or factor analysis)…

Analysis of XFEL serial diffraction data from individual crystalline fibrils

DOE PAGES

Wojtas, David H.; Ayyer, Kartik; Liang, Mengning; ...

2017-10-20

Serial diffraction data collected at the Linac Coherent Light Source from crystalline amyloid fibrils delivered in a liquid jet show that the fibrils are well oriented in the jet. At low fibril concentrations, diffraction patterns are recorded from single fibrils; these patterns are weak and contain only a few reflections. Methods are developed for determining the orientation of patterns in reciprocal space and merging them in three dimensions. This allows the individual structure amplitudes to be calculated, thus overcoming the limitations of orientation and cylindrical averaging in conventional fibre diffraction analysis. In conclusion, the advantages of this technique should allowmore » structural studies of fibrous systems in biology that are inaccessible using existing techniques.« less

Analysis of XFEL serial diffraction data from individual crystalline fibrils

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

Wojtas, David H.; Ayyer, Kartik; Liang, Mengning

Serial diffraction data collected at the Linac Coherent Light Source from crystalline amyloid fibrils delivered in a liquid jet show that the fibrils are well oriented in the jet. At low fibril concentrations, diffraction patterns are recorded from single fibrils; these patterns are weak and contain only a few reflections. Methods are developed for determining the orientation of patterns in reciprocal space and merging them in three dimensions. This allows the individual structure amplitudes to be calculated, thus overcoming the limitations of orientation and cylindrical averaging in conventional fibre diffraction analysis. In conclusion, the advantages of this technique should allowmore » structural studies of fibrous systems in biology that are inaccessible using existing techniques.« less

Analysis of gait patterns pre- and post- Single Event Multilevel Surgery in children with Cerebral Palsy by means of Offset-Wise Movement Analysis Profile and Linear Fit Method.

PubMed

Ancillao, Andrea; van der Krogt, Marjolein M; Buizer, Annemieke I; Witbreuk, Melinda M; Cappa, Paolo; Harlaar, Jaap

2017-10-01

Gait analysis is used for the assessment of walking ability of children with cerebral palsy (CP), to inform clinical decision making and to quantify changes after treatment. To simplify gait analysis interpretation and to quantify deviations from normality, some quantitative synthetic descriptors were developed over the years, such as the Movement Analysis Profile (MAP) and the Linear Fit Method (LFM), but their interpretation is not always straightforward. The aims of this work were to: (i) study gait changes, by means of synthetic descriptors, in children with CP that underwent Single Event Multilevel Surgery; (ii) compare the MAP and the LFM on these patients; (iii) design a new index that may overcome the limitations of the previous methods, i.e. the lack of information about the direction of deviation or its source. Gait analysis exams of 10 children with CP, pre- and post-surgery, were collected and MAP and LFM were computed. A new index was designed asa modified version of the MAP by separating out changes in offset (named OC-MAP). MAP documented an improvement in the gait pattern after surgery. The highest effect was observed for the knee flexion/extension angle. However, a worsening was observed as an increase in anterior pelvic tilt. An important source of gait deviation was recognized in the offset between observed tracks and reference. OC-MAP allowed the assessment of the offset component versus the shape component of deviation. LFM provided results similar to OC-MAP offset analysis but could not be considered reliable due to intrinsic limitations. As offset in gait features played an important role in gait deviation, OC-MAP synthetic analysis was proposed as a novel approach to a meaningful parameterisation of global deviations in gait patterns of subjects with CP and gait changes after treatment. Copyright © 2017 Elsevier B.V. All rights reserved.

Striking the balance: Privacy and spatial pattern preservation in masked GPS data

NASA Astrophysics Data System (ADS)

Seidl, Dara E.

Volunteered location and trajectory data are increasingly collected and applied in analysis for a variety of academic fields and recreational pursuits. As access to personal location data increases, issues of privacy arise as individuals become identifiable and linked to other repositories of information. While the quality and precision of data are essential to accurate analysis, there is a tradeoff between privacy and access to data. Obfuscation of point data is a solution that aims to protect privacy and maximize preservation of spatial pattern. This study explores two methods of location obfuscation for volunteered GPS data: grid masking and random perturbation. These methods are applied to travel survey GPS data in the greater metropolitan regions of Chicago and Atlanta in the first large-scale GPS masking study of its kind.

Principal component analysis vs. self-organizing maps combined with hierarchical clustering for pattern recognition in volcano seismic spectra

NASA Astrophysics Data System (ADS)

Unglert, K.; Radić, V.; Jellinek, A. M.

2016-06-01

Variations in the spectral content of volcano seismicity related to changes in volcanic activity are commonly identified manually in spectrograms. However, long time series of monitoring data at volcano observatories require tools to facilitate automated and rapid processing. Techniques such as self-organizing maps (SOM) and principal component analysis (PCA) can help to quickly and automatically identify important patterns related to impending eruptions. For the first time, we evaluate the performance of SOM and PCA on synthetic volcano seismic spectra constructed from observations during two well-studied eruptions at Klauea Volcano, Hawai'i, that include features observed in many volcanic settings. In particular, our objective is to test which of the techniques can best retrieve a set of three spectral patterns that we used to compose a synthetic spectrogram. We find that, without a priori knowledge of the given set of patterns, neither SOM nor PCA can directly recover the spectra. We thus test hierarchical clustering, a commonly used method, to investigate whether clustering in the space of the principal components and on the SOM, respectively, can retrieve the known patterns. Our clustering method applied to the SOM fails to detect the correct number and shape of the known input spectra. In contrast, clustering of the data reconstructed by the first three PCA modes reproduces these patterns and their occurrence in time more consistently. This result suggests that PCA in combination with hierarchical clustering is a powerful practical tool for automated identification of characteristic patterns in volcano seismic spectra. Our results indicate that, in contrast to PCA, common clustering algorithms may not be ideal to group patterns on the SOM and that it is crucial to evaluate the performance of these tools on a control dataset prior to their application to real data.

Environmental drivers of heterogeneity in the trophic-functional structure of protozoan communities during an annual cycle in a coastal ecosystem.

PubMed

Xu, Guangjian; Yang, Eun Jin; Xu, Henglong

2017-08-15

Trophic-functional groupings are an important biological trait to summarize community structure in functional space. The heterogeneity of the tropic-functional pattern of protozoan communities and its environmental drivers were studied in coastal waters of the Yellow Sea during a 1-year cycle. Samples were collected using the glass slide method at four stations within a water pollution gradient. A second-stage matrix-based analysis was used to summarize spatial variation in the annual pattern of the functional structure. A clustering analysis revealed significant variability in the trophic-functional pattern among the four stations during the 1-year cycle. The heterogeneity in the trophic-functional pattern of the communities was significantly related to changes in environmental variables, particularly ammonium-nitrogen and nitrates, alone or in combination with dissolved oxygen. These results suggest that the heterogeneity in annual patterns of protozoan trophic-functional structure may reflect water quality status in coastal ecosystems. Copyright © 2017. Published by Elsevier Ltd.

Palatal rugae pattern: An aid for sex identification

PubMed Central

Gadicherla, Prahlad; Saini, Divya; Bhaskar, Milana

2017-01-01

Background: Palatal rugoscopy, or palatoscopy, is the process by which human identification can be obtained by inspecting the transverse palatal rugae inside the mouth. Aim: The aim of the study is to investigate the potential of using palatal rugae as an aid for sex identification in Bengaluru population. Materials and Methods: One hundred plaster casts equally distributed between males and females belonging to age range of 4–16 years were examined for different rugae patterns. Thomas and Kotze classification was adopted for identification of these rugae patterns. Statistical Analysis: The data obtained were subjected to discriminant function analysis to determine the applicability of palatal rugae pattern as an aid for sex identification. Results: Difference in unification patterns among males and females was found to be statistically significant. No significant difference was found between males and females in terms of number of rugae. Overall, wavy and curvy were the most predominant type of rugae seen. Discriminant function analysis enabled sex identification with an accuracy of 80%. Conclusion: This preliminary study undertaken showed the existence of a distinct pattern of distribution of palatal rugae between males and females of Bengaluru population. This study opens scope for further research with a larger sample size to establish palatal rugae as a valuable tool for sex identification for forensic purposes. PMID:28584485

A biologically relevant method for considering patterns of oceanic retention in the Southern Ocean

NASA Astrophysics Data System (ADS)

Mori, Mao; Corney, Stuart P.; Melbourne-Thomas, Jessica; Klocker, Andreas; Sumner, Michael; Constable, Andrew

2017-12-01

Many marine species have planktonic forms - either during a larval stage or throughout their lifecycle - that move passively or are strongly influenced by ocean currents. Understanding these patterns of movement is important for informing marine ecosystem management and for understanding ecological processes generally. Retention of biological particles in a particular area due to ocean currents has received less attention than transport pathways, particularly for the Southern Ocean. We present a method for modelling retention time, based on the half-life for particles in a particular region, that is relevant for biological processes. This method uses geostrophic velocities at the ocean surface, derived from 23 years of satellite altimetry data (1993-2016), to simulate the advection of passive particles during the Southern Hemisphere summer season (from December to March). We assess spatial patterns in the retention time of passive particles and evaluate the processes affecting these patterns for the Indian sector of the Southern Ocean. Our results indicate that the distribution of retention time is related to bathymetric features and the resulting ocean dynamics. Our analysis also reveals a moderate level of consistency between spatial patterns of retention time and observations of Antarctic krill (Euphausia superba) distribution.

Faithful replication of grating patterns in polymer through electrohydrodynamic instabilities

NASA Astrophysics Data System (ADS)

Li, H.; Yu, W.; Wang, T.; Zhang, H.; Cao, Y.; Abraham, E.; Desmulliez, M. P. Y.

2014-07-01

Electrohydrodynamic instability patterning (EHDIP) as an alternative patterning method has attracted a great deal of attention over the past decade. This article demonstrates the faithful transfer of patterns with a high aspect ratio onto a polymer film via electrohydrodynamic instabilities for a given patterned grating mask. We perform a simple mathematical analysis to determine the influence of process parameters on the pressure difference ▵P. Through numerical simulation, it is demonstrated that thick films subject to large electric fields are essential to realize this faithful replication. In particular, the influence of the material properties of the polymer on pattern replication is discussed in detail. It is found that, to achieve the smaller periodic patterns with a higher resolution, film with a larger value of the dielectric constant and smaller value of the surface tension should be chosen. In addition, an ideal replication of the mask pattern with a short evolution time is possible by reducing the viscosity of the polymer liquid. Finally, the experiments of the pattern replication with and without defects are demonstrated to compare with the numerical simulation results. It is found that experiments are in good agreement with the simulation results and prove that the numerical simulation method provides an effective way to predict faithful replication.

[Spatial distribution pattern of Chilo suppressalis analyzed by classical method and geostatistics].

PubMed

Yuan, Zheming; Fu, Wei; Li, Fangyi

2004-04-01

Two original samples of Chilo suppressalis and their grid, random and sequence samples were analyzed by classical method and geostatistics to characterize the spatial distribution pattern of C. suppressalis. The limitations of spatial distribution analysis with classical method, especially influenced by the original position of grid, were summarized rather completely. On the contrary, geostatistics characterized well the spatial distribution pattern, congregation intensity and spatial heterogeneity of C. suppressalis. According to geostatistics, the population was up to Poisson distribution in low density. As for higher density population, its distribution was up to aggregative, and the aggregation intensity and dependence range were 0.1056 and 193 cm, respectively. Spatial heterogeneity was also found in the higher density population. Its spatial correlativity in line direction was more closely than that in row direction, and the dependence ranges in line and row direction were 115 and 264 cm, respectively.

Fringe image processing based on structured light series

NASA Astrophysics Data System (ADS)

Gai, Shaoyan; Da, Feipeng; Li, Hongyan

2009-11-01

The code analysis of the fringe image is playing a vital role in the data acquisition of structured light systems, which affects precision, computational speed and reliability of the measurement processing. According to the self-normalizing characteristic, a fringe image processing method based on structured light is proposed. In this method, a series of projective patterns is used when detecting the fringe order of the image pixels. The structured light system geometry is presented, which consist of a white light projector and a digital camera, the former projects sinusoidal fringe patterns upon the object, and the latter acquires the fringe patterns that are deformed by the object's shape. Then the binary images with distinct white and black strips can be obtained and the ability to resist image noise is improved greatly. The proposed method can be implemented easily and applied for profile measurement based on special binary code in a wide field.

A spot pattern test chart technique for measurement of geometric aberrations caused by an intervening medium—a novel method

NASA Astrophysics Data System (ADS)

Ganesan, A. R.; Arulmozhivarman, P.; Jesson, M.

2005-12-01

Accurate surface metrology and transmission characteristics measurements have become vital to certify the manufacturing excellence in the field of glass visors, windshields, menu boards and transportation industries. We report a simple, cost-effective and novel technique for the measurement of geometric aberrations in transparent materials such as glass sheets, Perspex, etc. The technique makes use of an array of spot pattern, we call the spot pattern test chart technique, in the diffraction limited imaging position having large field of view. Performance features include variable angular dynamic range and angular sensitivity. Transparent sheets as the intervening medium introduced in the line of sight, causing aberrations, are estimated in real time using the Zernike reconstruction method. Quantitative comparative analysis between a Shack-Hartmann wavefront sensor and the proposed new method is presented and the results are discussed.

Detection of sunn pest-damaged wheat samples using visible/near-infrared spectroscopy based on pattern recognition.

PubMed

Basati, Zahra; Jamshidi, Bahareh; Rasekh, Mansour; Abbaspour-Gilandeh, Yousef

2018-05-30

The presence of sunn pest-damaged grains in wheat mass reduces the quality of flour and bread produced from it. Therefore, it is essential to assess the quality of the samples in collecting and storage centers of wheat and flour mills. In this research, the capability of visible/near-infrared (Vis/NIR) spectroscopy combined with pattern recognition methods was investigated for discrimination of wheat samples with different percentages of sunn pest-damaged. To this end, various samples belonging to five classes (healthy and 5%, 10%, 15% and 20% unhealthy) were analyzed using Vis/NIR spectroscopy (wavelength range of 350-1000 nm) based on both supervised and unsupervised pattern recognition methods. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) as the unsupervised techniques and soft independent modeling of class analogies (SIMCA) and partial least squares-discriminant analysis (PLS-DA) as supervised methods were used. The results showed that Vis/NIR spectra of healthy samples were correctly clustered using both PCA and HCA. Due to the high overlapping between the four unhealthy classes (5%, 10%, 15% and 20%), it was not possible to discriminate all the unhealthy samples in individual classes. However, when considering only the two main categories of healthy and unhealthy, an acceptable degree of separation between the classes can be obtained after classification with supervised pattern recognition methods of SIMCA and PLS-DA. SIMCA based on PCA modeling correctly classified samples in two classes of healthy and unhealthy with classification accuracy of 100%. Moreover, the power of the wavelengths of 839 nm, 918 nm and 995 nm were more than other wavelengths to discriminate two classes of healthy and unhealthy. It was also concluded that PLS-DA provides excellent classification results of healthy and unhealthy samples (R 2  = 0.973 and RMSECV = 0.057). Therefore, Vis/NIR spectroscopy based on pattern recognition techniques can be useful for rapid distinguishing the healthy wheat samples from those damaged by sunn pest in the maintenance and processing centers. Copyright © 2018 Elsevier B.V. All rights reserved.

A median-Gaussian filtering framework for Moiré pattern noise removal from X-ray microscopy image.

PubMed

Wei, Zhouping; Wang, Jian; Nichol, Helen; Wiebe, Sheldon; Chapman, Dean

2012-02-01

Moiré pattern noise in Scanning Transmission X-ray Microscopy (STXM) imaging introduces significant errors in qualitative and quantitative image analysis. Due to the complex origin of the noise, it is difficult to avoid Moiré pattern noise during the image data acquisition stage. In this paper, we introduce a post-processing method for filtering Moiré pattern noise from STXM images. This method includes a semi-automatic detection of the spectral peaks in the Fourier amplitude spectrum by using a local median filter, and elimination of the spectral noise peaks using a Gaussian notch filter. The proposed median-Gaussian filtering framework shows good results for STXM images with the size of power of two, if such parameters as threshold, sizes of the median and Gaussian filters, and size of the low frequency window, have been properly selected. Copyright © 2011 Elsevier Ltd. All rights reserved.

A Single-Wing Removal Method to Assess Correspondence Between Gene Expression and Phenotype in Butterflies: The Case of Distal-less.

PubMed

Adhikari, Kiran; Otaki, Joji M

2016-02-01

It is often desirable but difficult to retrieve information on the mature phenotype of an immature tissue sample that has been subjected to gene expression analysis. This problem cannot be ignored when individual variation within a species is large. To circumvent this problem in the butterfly wing system, we developed a new surgical method for removing a single forewing from a pupa using Junonia orithya; the operated pupa was left to develop to an adult without eclosion. The removed right forewing was subjected to gene expression analysis, whereas the non-removed left forewing was examined for color patterns. As a test case, we focused on Distal-less (Dll), which likely plays an active role in inducing elemental patterns, including eyespots. The Dll expression level in forewings was paired with eyespot size data from the same individual. One third of the operated pupae survived and developed wing color patterns. Dll expression levels were significantly higher in males than in females, although male eyespots were smaller in size than female eyespots. Eyespot size data showed weak but significant correlations with the Dll expression level in females. These results demonstrate that a single-wing removal method was successfully applied to the butterfly wing system and suggest the weak and non-exclusive contribution of Dll to eyespot size determination in this butterfly. Our novel methodology for establishing correspondence between gene expression and phenotype can be applied to other candidate genes for color pattern development in butterflies. Conceptually similar methods may also be applicable in other developmental systems.

DNA Extraction from Soils: Old Bias for New Microbial Diversity Analysis Methods

PubMed Central

Martin-Laurent, F.; Philippot, L.; Hallet, S.; Chaussod, R.; Germon, J. C.; Soulas, G.; Catroux, G.

2001-01-01

The impact of three different soil DNA extraction methods on bacterial diversity was evaluated using PCR-based 16S ribosomal DNA analysis. DNA extracted directly from three soils showing contrasting physicochemical properties was subjected to amplified ribosomal DNA restriction analysis and ribosomal intergenic spacer analysis (RISA). The obtained RISA patterns revealed clearly that both the phylotype abundance and the composition of the indigenous bacterial community are dependent on the DNA recovery method used. In addition, this effect was also shown in the context of an experimental study aiming to estimate the impact on soil biodiversity of the application of farmyard manure or sewage sludge onto a monoculture of maize for 15 years. PMID:11319122

Evolutionary Influenced Interaction Pattern as Indicator for the Investigation of Natural Variants Causing Nephrogenic Diabetes Insipidus

PubMed Central

Labudde, Dirk

2015-01-01

The importance of short membrane sequence motifs has been shown in many works and emphasizes the related sequence motif analysis. Together with specific transmembrane helix-helix interactions, the analysis of interacting sequence parts is helpful for understanding the process during membrane protein folding and in retaining the three-dimensional fold. Here we present a simple high-throughput analysis method for deriving mutational information of interacting sequence parts. Applied on aquaporin water channel proteins, our approach supports the analysis of mutational variants within different interacting subsequences and finally the investigation of natural variants which cause diseases like, for example, nephrogenic diabetes insipidus. In this work we demonstrate a simple method for massive membrane protein data analysis. As shown, the presented in silico analyses provide information about interacting sequence parts which are constrained by protein evolution. We present a simple graphical visualization medium for the representation of evolutionary influenced interaction pattern pairs (EIPPs) adapted to mutagen investigations of aquaporin-2, a protein whose mutants are involved in the rare endocrine disorder known as nephrogenic diabetes insipidus, and membrane proteins in general. Furthermore, we present a new method to derive new evolutionary variations within EIPPs which can be used for further mutagen laboratory investigations. PMID:26180540

Evolutionary Influenced Interaction Pattern as Indicator for the Investigation of Natural Variants Causing Nephrogenic Diabetes Insipidus.

PubMed

Grunert, Steffen; Labudde, Dirk

2015-01-01

The importance of short membrane sequence motifs has been shown in many works and emphasizes the related sequence motif analysis. Together with specific transmembrane helix-helix interactions, the analysis of interacting sequence parts is helpful for understanding the process during membrane protein folding and in retaining the three-dimensional fold. Here we present a simple high-throughput analysis method for deriving mutational information of interacting sequence parts. Applied on aquaporin water channel proteins, our approach supports the analysis of mutational variants within different interacting subsequences and finally the investigation of natural variants which cause diseases like, for example, nephrogenic diabetes insipidus. In this work we demonstrate a simple method for massive membrane protein data analysis. As shown, the presented in silico analyses provide information about interacting sequence parts which are constrained by protein evolution. We present a simple graphical visualization medium for the representation of evolutionary influenced interaction pattern pairs (EIPPs) adapted to mutagen investigations of aquaporin-2, a protein whose mutants are involved in the rare endocrine disorder known as nephrogenic diabetes insipidus, and membrane proteins in general. Furthermore, we present a new method to derive new evolutionary variations within EIPPs which can be used for further mutagen laboratory investigations.

Demographic, socioeconomic, and behavioral factors affecting patterns of tooth decay in the permanent dentition: Principal components and factor analyses

PubMed Central

Shaffer, John R.; Polk, Deborah E.; Feingold, Eleanor; Wang, Xiaojing; Cuenco, Karen T.; Weeks, Daniel E.; DeSensi, Rebecca S.; Weyant, Robert J.; Crout, Richard; McNeil, Daniel W.; Marazita, Mary L.

2012-01-01

Objectives Dental caries of the permanent dentition is a multi-factorial disease resulting from the complex interplay of endogenous and environmental risk factors. The disease is not easily quantified due to the innumerable possible combinations of carious lesions across individual tooth surfaces of the permanent dentition. Global measures of decay, such as the DMFS index (which was developed for surveillance applications), may not be optimal for studying the epidemiology of dental caries because they ignore the distinct patterns of decay across the dentition. We hypothesize that specific risk factors may manifest their effects on specific tooth surfaces leading to patterns of decay that can be identified and studied. In this study we utilized two statistical methods of extracting patterns of decay from surface-level caries data in order to create novel phenotypes with which to study the risk factors affecting dental caries. Methods Intra-oral dental examinations were performed on 1,068 participants aged 18 to 75 years to assess dental caries. The 128 tooth surfaces of the permanent dentition were scored as carious or not and used as input for principal components analysis (PCA) and factor analysis (FA), two methods of identifying underlying patterns without a priori knowledge of the patterns. Demographic (age, sex, birth year, race/ethnicity, and educational attainment), anthropometric (height, body mass index, waist circumference), endogenous (saliva flow), and environmental (tooth brushing frequency, home water source, and home water fluoride) risk factors were tested for association with the caries patterns identified by PCA and FA, as well as DMFS, for comparison. The ten strongest patterns (i.e., those that explain the most variation in the data set) extracted by PCA and FA were considered. Results The three strongest patterns identified by PCA reflected (i) global extent of decay (i.e., comparable to DMFS index), (ii) pit and fissure surface caries, and (iii) smooth surface caries, respectively. The two strongest patterns identified by FA corresponded to (i) pit and fissure surface caries and (ii) maxillary incisor caries. Age and birth year were significantly associated with several patterns of decay, including global decay/DMFS index. Sex, race, educational attainment, and tooth brushing were each associated with specific patterns of decay, but not with global decay/DMFS index. Conclusions Taken together, these results support the notion that caries experience is separable into patterns attributable to distinct risk factors. This study demonstrates the utility of such novel caries patterns as new outcomes for exploring the complex, multifactorial nature of dental caries. PMID:23106439

A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences.

PubMed

Xue, Yun; Liao, Zhengling; Li, Meihang; Luo, Jie; Kuang, Qiuhua; Hu, Xiaohui; Li, Tiechen

2015-01-01

Order-preserving submatrices (OPSMs) have been applied in many fields, such as DNA microarray data analysis, automatic recommendation systems, and target marketing systems, as an important unsupervised learning model. Unfortunately, most existing methods are heuristic algorithms which are unable to reveal OPSMs entirely in NP-complete problem. In particular, deep OPSMs, corresponding to long patterns with few supporting sequences, incur explosive computational costs and are completely pruned by most popular methods. In this paper, we propose an exact method to discover all OPSMs based on frequent sequential pattern mining. First, an existing algorithm was adjusted to disclose all common subsequence (ACS) between every two row sequences, and therefore all deep OPSMs will not be missed. Then, an improved data structure for prefix tree was used to store and traverse ACS, and Apriori principle was employed to efficiently mine the frequent sequential pattern. Finally, experiments were implemented on gene and synthetic datasets. Results demonstrated the effectiveness and efficiency of this method.

Uniform Local Binary Pattern Based Texture-Edge Feature for 3D Human Behavior Recognition.

PubMed

Ming, Yue; Wang, Guangchao; Fan, Chunxiao

2015-01-01

With the rapid development of 3D somatosensory technology, human behavior recognition has become an important research field. Human behavior feature analysis has evolved from traditional 2D features to 3D features. In order to improve the performance of human activity recognition, a human behavior recognition method is proposed, which is based on a hybrid texture-edge local pattern coding feature extraction and integration of RGB and depth videos information. The paper mainly focuses on background subtraction on RGB and depth video sequences of behaviors, extracting and integrating historical images of the behavior outlines, feature extraction and classification. The new method of 3D human behavior recognition has achieved the rapid and efficient recognition of behavior videos. A large number of experiments show that the proposed method has faster speed and higher recognition rate. The recognition method has good robustness for different environmental colors, lightings and other factors. Meanwhile, the feature of mixed texture-edge uniform local binary pattern can be used in most 3D behavior recognition.

Multicolor microcontact printing of proteins on nanoporous surface for patterned immunoassay

NASA Astrophysics Data System (ADS)

Ng, Elaine; Gopal, Ashwini; Hoshino, Kazunori; Zhang, Xiaojing

2011-07-01

The large scale patterning of therapeutic proteins is a key to the efficient design, characterization, and production of biologics for cost effective, high throughput, and point-of-care detection and analysis system. We demonstrate an efficient method for protein deposition and adsorption on nanoporous silica substrates in specific patterns using a method called "micro-contact printing". Multiple color-tagged proteins can be printed through sequential application of such micro-patterning technique. Two groups of experiments were performed. In the first group, the protein stamp was aligned precisely with the printing sites, where the stamp was applied multiple times. Optimal conditions were identified for protein transfer and adsorption using the pore size of 4 nm and thickness of 30 nm porous silica thin film. In the second group, we demonstrate the patterning of two-color rabbit immunoglobin labeled with fluorescein isothiocyanate and tetramethyl rhodamine iso-thiocyanate on porous silica substrates that have a pore size 4 nm, porosity 57% and thickness of the porous layer 30 nm. A pair of protein stamps, with corresponding alignment markings and coupled patterns, were aligned and used to produce a two-colored stamp pattern of proteins on porous silica. Different colored proteins can be applied to exemplify the diverse protein composition within a sample. This method of multicolor microcontact printing can be used to perform a fluorescence-based patterned enzyme-linked immunosorbent assay to detect the presence of various proteins within a sample.

A system for learning statistical motion patterns.

PubMed

Hu, Weiming; Xiao, Xuejuan; Fu, Zhouyu; Xie, Dan; Tan, Tieniu; Maybank, Steve

2006-09-01

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.

Two-dimensional wavelet transform for reliability-guided phase unwrapping in optical fringe pattern analysis.

PubMed

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.

On the multiple imputation variance estimator for control-based and delta-adjusted pattern mixture models.

PubMed

Tang, Yongqiang

2017-12-01

Control-based pattern mixture models (PMM) and delta-adjusted PMMs are commonly used as sensitivity analyses in clinical trials with non-ignorable dropout. These PMMs assume that the statistical behavior of outcomes varies by pattern in the experimental arm in the imputation procedure, but the imputed data are typically analyzed by a standard method such as the primary analysis model. In the multiple imputation (MI) inference, Rubin's variance estimator is generally biased when the imputation and analysis models are uncongenial. One objective of the article is to quantify the bias of Rubin's variance estimator in the control-based and delta-adjusted PMMs for longitudinal continuous outcomes. These PMMs assume the same observed data distribution as the mixed effects model for repeated measures (MMRM). We derive analytic expressions for the MI treatment effect estimator and the associated Rubin's variance in these PMMs and MMRM as functions of the maximum likelihood estimator from the MMRM analysis and the observed proportion of subjects in each dropout pattern when the number of imputations is infinite. The asymptotic bias is generally small or negligible in the delta-adjusted PMM, but can be sizable in the control-based PMM. This indicates that the inference based on Rubin's rule is approximately valid in the delta-adjusted PMM. A simple variance estimator is proposed to ensure asymptotically valid MI inferences in these PMMs, and compared with the bootstrap variance. The proposed method is illustrated by the analysis of an antidepressant trial, and its performance is further evaluated via a simulation study. © 2017, The International Biometric Society.

[Sociodemographic context of homicide in Mexico City: a spatial analysis].

PubMed

Fuentes Flores, César; Sánchez Salinas, Omar

2015-12-01

Investigate the spatial distribution pattern of the homicide rate and its relation to sociodemographic features in the Benito Juárez, Coyoacán, and Cuauhtémoc districts of Mexico City in 2010. Inferential cross-sectional study that uses spatial analysis methods to study the spatial association of the homicide rate and demographic features. Spatial association was determined through the location quotient, multiple regression analysis, and the use of geographically weighted regression. Homicides show a heterogeneous location pattern with high rates in areas with non-residential land use, low population density, and low marginalization. Spatial analysis tools are powerful instruments for the design of prevention- and recreation-focused public safety policies that aim to reduce mortality from external causes such as homicides.

Comparisons between real and complex Gauss wavelet transform methods of three-dimensional shape reconstruction

NASA Astrophysics Data System (ADS)

Xu, Luopeng; Dan, Youquan; Wang, Qingyuan

2015-10-01

The continuous wavelet transform (CWT) introduces an expandable spatial and frequency window which can overcome the inferiority of localization characteristic in Fourier transform and windowed Fourier transform. The CWT method is widely applied in the non-stationary signal analysis field including optical 3D shape reconstruction with remarkable performance. In optical 3D surface measurement, the performance of CWT for optical fringe pattern phase reconstruction usually depends on the choice of wavelet function. A large kind of wavelet functions of CWT, such as Mexican Hat wavelet, Morlet wavelet, DOG wavelet, Gabor wavelet and so on, can be generated from Gauss wavelet function. However, so far, application of the Gauss wavelet transform (GWT) method (i.e. CWT with Gauss wavelet function) in optical profilometry is few reported. In this paper, the method using GWT for optical fringe pattern phase reconstruction is presented first and the comparisons between real and complex GWT methods are discussed in detail. The examples of numerical simulations are also given and analyzed. The results show that both the real GWT method along with a Hilbert transform and the complex GWT method can realize three-dimensional surface reconstruction; and the performance of reconstruction generally depends on the frequency domain appearance of Gauss wavelet functions. For the case of optical fringe pattern of large phase variation with position, the performance of real GWT is better than that of complex one due to complex Gauss series wavelets existing frequency sidelobes. Finally, the experiments are carried out and the experimental results agree well with our theoretical analysis.

Fusion Bead Procedure for Nuclear Forensics Employing Synthetic Enstatite to Dissolve Uraniferous and Other Challenging Materials Prior to Laser Ablation Inductively Coupled Plasma Mass Spectrometry.

PubMed

Reading, David G; Croudace, Ian W; Warwick, Phillip E

2017-06-06

There is an increasing demand for rapid and effective analytical tools to support nuclear forensic investigations of seized or suspect materials. Some methods are simply adapted from other scientific disciplines and can effectively be used to rapidly prepare complex materials for subsequent analysis. A novel sample fusion method is developed, tested, and validated to produce homogeneous, flux-free glass beads of geochemical reference materials (GRMs), uranium ores, and uranium ore concentrates (UOC) prior to the analysis of 14 rare earth elements (REE) via laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The novelty of the procedure is the production of glass beads using 9 parts high purity synthetic enstatite (MgSiO 3 ) as the glass former with 1 part of sample (sample mass ∼1.5 mg). The beads are rapidly prepared (∼10 min overall time) by fusing the blended mixture on an iridium strip resistance heater in an argon-purged chamber. Many elements can be measured in the glass bead, but the rare earth group in particular is a valuable series in nuclear forensic studies and is well-determined using LA-ICP-MS. The REE data obtained from the GRMs, presented as chondrite normalized patterns, are in very good agreement with consensus patterns. The UOCs have comparable patterns to solution ICP-MS methods and published data. The attractions of the current development are its conservation of sample, speed of preparation, and suitability for microbeam analysis, all of which are favorable for nuclear forensics practitioners and geochemists requiring REE patterns from scarce or valuable samples.

Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma

PubMed Central

Akbari, Hamed; Macyszyn, Luke; Da, Xiao; Bilello, Michel; Wolf, Ronald L.; Martinez-Lage, Maria; Biros, George; Alonso-Basanta, Michelle; O’Rourke, Donald M.; Davatzikos, Christos

2016-01-01

Background Glioblastoma is an aggressive and highly infiltrative brain cancer. Standard surgical resection is guided by enhancement on postcontrast T1-weighted (T1) magnetic resonance imaging (MRI), which is insufficient for delineating surrounding infiltrating tumor. Objective To develop imaging biomarkers that delineate areas of tumor infiltration and predict early recurrence in peritumoral tissue. Such markers would enable intensive, yet targeted, surgery and radiotherapy, thereby potentially delaying recurrence and prolonging survival. Methods Preoperative multiparametric MRIs (T1, T1-Gad, T2-weighted [T2], T2-fluid-attenuated inversion recovery [FLAIR], diffusion tensor imaging (DTI), and dynamic susceptibility contrast-enhanced [DSC]-MRI) from 31 patients were combined using machine learning methods, thereby creating predictive spatial maps of infiltrated peritumoral tissue. Cross validation was used in the retrospective cohort to achieve generalizable biomarkers. Subsequently, the imaging signatures learned from the retrospective study were used in a replication cohort of 34 new patients. Spatial maps representing likelihood of tumor infiltration and future early recurrence were compared to regions of recurrence on postresection follow-up studies with pathology confirmation. Results This technique produced predictions of early recurrence with a mean area under the curve (AUC) of 0.84, sensitivity of 91%, specificity of 93%, and odds ratio estimates of 9.29 (99% CI, 8.95–9.65) for tissue predicted to be heavily infiltrated in the replication study. Regions of tumor recurrence were found to have subtle, yet fairly distinctive multiparametric imaging signatures when analyzed quantitatively by pattern analysis and machine learning. Conclusion Visually imperceptible imaging patterns discovered via multiparametric pattern analysis methods were found to estimate the extent of infiltration and location of future tumor recurrence, paving the way for improved targeted treatment. PMID:26813856

Well pattern optimization in a low permeability sandstone reservoir: a case study from Erlian Basin in China

NASA Astrophysics Data System (ADS)

Wang, Xia; Fu, Lixia; Yan, Aihua; Guo, Fajun; Wu, Cong; Chen, Hong; Wang, Xinying; Lu, Ming

2018-02-01

Study on optimization of development well patterns is the core content of oilfield development and is a prerequisite for rational and effective development of oilfield. The study on well pattern optimization mainly includes types of well patterns and density of well patterns. This paper takes the Aer-3 fault block as an example. Firstly, models were built for diamond-shaped inverted 9-spot patterns, rectangular 5-spot patterns, square inverted 9-spot patterns and inverted 7-spot patterns under the same well pattern density to correlate the effect of different well patterns on development; secondly, comprehensive analysis was conducted to well pattern density in terms of economy and technology using such methods as oil reservoir engineering, numerical simulation, economic limits and economic rationality. Finally, the development mode of vertical well + horizontal well was presented according to the characteristics of oil reservoirs in some well blocks, which has realized efficient development of this fault block.

Imaging and quantitative methods for studying cytoskeletal rearrangements during root development and gravitropism.

PubMed

Jacques, Eveline; Wells, Darren M; Bennett, Malcolm J; Vissenberg, Kris

2015-01-01

High-resolution imaging of cytoskeletal structures paves the way for standardized methods to quantify cytoskeletal organization. Here we provide a detailed description of the analysis performed to determine the microtubule patterns in gravistimulated roots, using the recently developed software tool MicroFilament Analyzer.

Fast alignment-free sequence comparison using spaced-word frequencies.

PubMed

Leimeister, Chris-Andre; Boden, Marcus; Horwege, Sebastian; Lindner, Sebastian; Morgenstern, Burkhard

2014-07-15

Alignment-free methods for sequence comparison are increasingly used for genome analysis and phylogeny reconstruction; they circumvent various difficulties of traditional alignment-based approaches. In particular, alignment-free methods are much faster than pairwise or multiple alignments. They are, however, less accurate than methods based on sequence alignment. Most alignment-free approaches work by comparing the word composition of sequences. A well-known problem with these methods is that neighbouring word matches are far from independent. To reduce the statistical dependency between adjacent word matches, we propose to use 'spaced words', defined by patterns of 'match' and 'don't care' positions, for alignment-free sequence comparison. We describe a fast implementation of this approach using recursive hashing and bit operations, and we show that further improvements can be achieved by using multiple patterns instead of single patterns. To evaluate our approach, we use spaced-word frequencies as a basis for fast phylogeny reconstruction. Using real-world and simulated sequence data, we demonstrate that our multiple-pattern approach produces better phylogenies than approaches relying on contiguous words. Our program is freely available at http://spaced.gobics.de/. © The Author 2014. Published by Oxford University Press.

[Emergy analysis on different planting patterns of typical watersheds in Loess Plateau.

PubMed

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.

Driver behavior in car-to-pedestrian incidents: An application of the Driving Reliability and Error Analysis Method (DREAM).

PubMed

Habibovic, Azra; Tivesten, Emma; Uchida, Nobuyuki; Bärgman, Jonas; Ljung Aust, Mikael

2013-01-01

To develop relevant road safety countermeasures, it is necessary to first obtain an in-depth understanding of how and why safety-critical situations such as incidents, near-crashes, and crashes occur. Video-recordings from naturalistic driving studies provide detailed information on events and circumstances prior to such situations that is difficult to obtain from traditional crash investigations, at least when it comes to the observable driver behavior. This study analyzed causation in 90 video-recordings of car-to-pedestrian incidents captured by onboard cameras in a naturalistic driving study in Japan. The Driving Reliability and Error Analysis Method (DREAM) was modified and used to identify contributing factors and causation patterns in these incidents. Two main causation patterns were found. In intersections, drivers failed to recognize the presence of the conflict pedestrian due to visual obstructions and/or because their attention was allocated towards something other than the conflict pedestrian. In incidents away from intersections, this pattern reoccurred along with another pattern showing that pedestrians often behaved in unexpected ways. These patterns indicate that an interactive advanced driver assistance system (ADAS) able to redirect the driver's attention could have averted many of the intersection incidents, while autonomous systems may be needed away from intersections. Cooperative ADAS may be needed to address issues raised by visual obstructions. Copyright © 2012 Elsevier Ltd. All rights reserved.

Modularity in protein structures: study on all-alpha proteins.

PubMed

Khan, Taushif; Ghosh, Indira

2015-01-01

Modularity is known as one of the most important features of protein's robust and efficient design. The architecture and topology of proteins play a vital role by providing necessary robust scaffolds to support organism's growth and survival in constant evolutionary pressure. These complex biomolecules can be represented by several layers of modular architecture, but it is pivotal to understand and explore the smallest biologically relevant structural component. In the present study, we have developed a component-based method, using protein's secondary structures and their arrangements (i.e. patterns) in order to investigate its structural space. Our result on all-alpha protein shows that the known structural space is highly populated with limited set of structural patterns. We have also noticed that these frequently observed structural patterns are present as modules or "building blocks" in large proteins (i.e. higher secondary structure content). From structural descriptor analysis, observed patterns are found to be within similar deviation; however, frequent patterns are found to be distinctly occurring in diverse functions e.g. in enzymatic classes and reactions. In this study, we are introducing a simple approach to explore protein structural space using combinatorial- and graph-based geometry methods, which can be used to describe modularity in protein structures. Moreover, analysis indicates that protein function seems to be the driving force that shapes the known structure space.

Application of Remote-Sensing Observations for Detecting Patterns of Localization of Cu-Ni Mineralization of the Norilsk Ore Region

NASA Astrophysics Data System (ADS)

Milovsky, G. A.; Ishmukhametova, V. T.; Shemyakina, E. M.

2017-12-01

The methods of a complex analysis of materials of space, gravimetric, and magnetometric surveys were developed on the basis of a study of reference fields of the Norilsk ore region (Imangda, etc.) for detection patterns of the localization of Cu-Ni (with PGMs) mineralization in intrusive complexes of the northwestern frame of the Siberian Platform.

Spatiotemporal Data Mining, Analysis, and Visualization of Human Activity Data

ERIC Educational Resources Information Center

Li, Xun

2012-01-01

This dissertation addresses the research challenge of developing efficient new methods for discovering useful patterns and knowledge in large volumes of electronically collected spatiotemporal activity data. I propose to analyze three types of such spatiotemporal activity data in a methodological framework that integrates spatial analysis, data…

Permutation entropy analysis of heart rate variability for the assessment of cardiovascular autonomic neuropathy in type 1 diabetes mellitus.

PubMed

Carricarte Naranjo, Claudia; Sanchez-Rodriguez, Lazaro M; Brown Martínez, Marta; Estévez Báez, Mario; Machado García, Andrés

2017-07-01

Heart rate variability (HRV) analysis is a relevant tool for the diagnosis of cardiovascular autonomic neuropathy (CAN). To our knowledge, no previous investigation on CAN has assessed the complexity of HRV from an ordinal perspective. Therefore, the aim of this work is to explore the potential of permutation entropy (PE) analysis of HRV complexity for the assessment of CAN. For this purpose, we performed a short-term PE analysis of HRV in healthy subjects and type 1 diabetes mellitus patients, including patients with CAN. Standard HRV indicators were also calculated in the control group. A discriminant analysis was used to select the variables combination with best discriminative power between control and CAN patients groups, as well as for classifying cases. We found that for some specific temporal scales, PE indicators were significantly lower in CAN patients than those calculated for controls. In such cases, there were ordinal patterns with high probabilities of occurrence, while others were hardly found. We posit this behavior occurs due to a decrease of HRV complexity in the diseased system. Discriminant functions based on PE measures or probabilities of occurrence of ordinal patterns provided an average of 75% and 96% classification accuracy. Correlations of PE and HRV measures showed to depend only on temporal scale, regardless of pattern length. PE analysis at some specific temporal scales, seem to provide additional information to that obtained with traditional HRV methods. We concluded that PE analysis of HRV is a promising method for the assessment of CAN. Copyright © 2017 Elsevier Ltd. All rights reserved.

Movement pattern recognition in basketball free-throw shooting.

PubMed

Schmidt, Andrea

2012-04-01

The purpose of the present study was to analyze the movement patterns of free-throw shooters in basketball at different skill levels. There were two points of interest. First, to explore what information can be drawn from the movement pattern and second, to examine the methodological possibilities of pattern analysis. To this end, several qualitative and quantitative methods were employed. The resulting data were converged in a triangulation. Using a special kind of ANN named Dynamically Controlled Networks (DyCoN), a 'complex feature' consisting of several isolated features (angle displacements and velocities of the articulations of the kinematic chain) was calculated. This 'complex feature' was displayed by a trajectory combining several neurons of the network, reflecting the devolution of the twelve angle measures over the time course of each shooting action. In further network analyses individual characteristics were detected, as well as movement phases. Throwing patterns were successfully classified and the stability and variability of the realized pattern were established. The movement patterns found were clearly individually shaped as well as formed by the skill level. The triangulation confirmed the individual movement organizations. Finally, a high stability of the network methods was documented. Copyright © 2012. Published by Elsevier B.V.

Differentiation of organic and non-organic winter wheat cultivars from a controlled field trial by crystallization patterns.

PubMed

Kahl, Johannes; Busscher, Nicolaas; Mergardt, Gaby; Mäder, Paul; Torp, Torfinn; Ploeger, Angelika

2015-01-01

There is a need for authentication tools in order to verify the existing certification system. Recently, markers for analytical authentication of organic products were evaluated. Herein, crystallization with additives was described as an interesting fingerprint approach which needs further evidence, based on a standardized method and well-documented sample origin. The fingerprint of wheat cultivars from a controlled field trial is generated from structure analysis variables of crystal patterns. Method performance was tested on factors such as crystallization chamber, day of experiment and region of interest of the patterns. Two different organic treatments and two different treatments of the non-organic regime can be grouped together in each of three consecutive seasons. When the k-nearest-neighbor classification method was applied, approximately 84% of Runal samples and 95% of Titlis samples were classified correctly into organic and non-organic origin using cross-validation. Crystallization with additive offers an interesting complementary fingerprint method for organic wheat samples. When the method is applied to winter wheat from the DOK trial, organic and non-organic treated samples can be differentiated significantly based on pattern recognition. Therefore crystallization with additives seems to be a promising tool in organic wheat authentication. © 2014 Society of Chemical Industry.

Traceable Mueller polarimetry and scatterometry for shape reconstruction of grating structures

NASA Astrophysics Data System (ADS)

Hansen, Poul-Erik; Madsen, Morten H.; Lehtolahti, Joonas; Nielsen, Lars

2017-11-01

Dimensional measurements of multi-patterned transmission gratings with a mixture of long and small periods are great challenges for optical metrology today. It is a further challenge when the aspect ratio of the structures is high, that is, when the height of structures is larger than the pitch. Here we consider a double patterned transmission grating with pitches of 500 nm and 20 000 nm. For measuring the geometrical properties of double patterned transmission grating we use a combined spectroscopic Mueller polarimetry and scatterometry setup. For modelling the experimentally obtained data we rigorously compute the scattering signal by solving Maxwell's equations using the RCWA method on a supercell structure. We also present a new method for analyzing the Mueller polarimetry parameters that performs the analysis in the measured variables. This new inversion method for finding the best fit between measured and calculated values are tested on silicon gratings with periods from 300 to 600 nm. The method is shown to give results within the expanded uncertainty of reference AFM measurements. The application of the new inversion method and the supercell structure to the double patterned transmission grating gives best estimates of dimensional quantities that are in fair agreement with those derived from local AFM measurements

Dietary Pattern and Its Association with the Prevalence of Obesity and Related Cardiometabolic Risk Factors among Chinese Children

PubMed Central

Shang, Xianwen; Li, Yanping; Liu, Ailing; Zhang, Qian; Hu, Xiaoqi; Du, Songming; Ma, Guansheng

2012-01-01

Background The association of dietary pattern with chronic diseases has been investigated widely in western countries. However, information is quite limited among children in China. Our study is aimed to identify the dietary patterns of Chinese children and examine their association with obesity and related cardiometabolic risk factors. Methods A total of 5267 children were selected using multistage random sampling from 30 primary schools of 5 provincial capital cities in China. Dietary intake was derived from 24 hour dietary recall for three consecutive days. Anthropometric measurements, glucose and lipid profiles were obtained. Factor analysis combined with cluster analysis was used for identifying major dietary patterns. The associations of dietary patterns with obesity and related cardiometabolic risk factors were examined by logistic regression analysis. Results Three mutually exclusive dietary patterns were identified, which were labeled as the healthy dietary pattern, the transitive dietary pattern, and the Western dietary pattern. Compared with children of the healthy dietary pattern, the multiple-adjusted odds ratios (95% confidence interval (CI)) of obesity were 1.11 (0.89–1.38) for children with the transitive dietary pattern and 1.80 (1.15–2.81) for children with the Western dietary pattern, which was 1.31 (95%CI 1.09–1.56) and 1.71 (95%CI: 1.13–2.56), respectively, for abdominal obesity. The Western dietary pattern was associated with significantly higher concentrations of low-density lipoprotein cholesterol (P<.001), triglycerides (P<.001), systolic blood pressure (P = 0.0435) and fasting glucose (P = 0.0082) and a lower concentration of high-density lipoprotein cholesterol (P = 0.0023), as compared with the healthy dietary pattern. Conclusions The Western dietary pattern characterized by red meat, eggs, refined grain and products, was positively associated with odds of obesity, the levels of plasma glucose, low-density lipoprotein cholesterol and triglycerides, and was inversely associated with the level of high-density lipoprotein cholesterol. PMID:22905228

A weak pattern random creation and scoring method for lithography process tuning

NASA Astrophysics Data System (ADS)

Zhang, Meili; Deng, Guogui; Wang, Mudan; Yu, Shirui; Hu, Xinyi; Du, Chunshan; Wan, Qijian; Liu, Zhengfang; Gao, Gensheng; Kabeel, Aliaa; Madkour, Kareem; ElManhawy, Wael; Kwan, Joe

2018-03-01

As the IC technology node moves forward, critical dimension becomes smaller and smaller, which brings huge challenge to IC manufacturing. Lithography is one of the most important steps during the whole manufacturing process and litho hotspots become a big source of yield detractors. Thus tuning lithographic recipes to cover a big range of litho hotspots is very essential to yield enhancing. During early technology developing stage, foundries only have limited customer layout data for recipe tuning. So collecting enough patterns is significant for process optimization. After accumulating enough patterns, a general way to treat them is not precise and applicable. Instead, an approach to scoring these patterns could provide a priority and reference to address different patterns more effectively. For example, the weakest group of patterns could be applied the most limited specs to ensure process robustness. This paper presents a new method of creation of real design alike patterns of multiple layers based on design rules using Layout Schema Generator (LSG) utility and a pattern scoring flow using Litho-friendly Design (LFD) and Pattern Matching. Through LSG, plenty of new unknown patterns could be created for further exploration. Then, litho simulation through LFD and topological matches by using Pattern Matching is applied on the output patterns of LSG. Finally, lithographical severity, printability properties and topological distribution of every pattern are collected. After a statistical analysis of pattern data, every pattern is given a relative score representing the pattern's yield detracting level. By sorting the output pattern score tables, weak patterns could be filtered out for further research and process tuning. This pattern generation and scoring flow is demonstrated on 28nm logic technology node. A weak pattern library is created and scored to help improve recipe coverage of litho hotspots and enhance the reliability of process.

A New Classification of Diabetic Gait Pattern Based on Cluster Analysis of Biomechanical Data

PubMed Central

Sawacha, Zimi; Guarneri, Gabriella; Avogaro, Angelo; Cobelli, Claudio

2010-01-01

Background The diabetic foot, one of the most serious complications of diabetes mellitus and a major risk factor for plantar ulceration, is determined mainly by peripheral neuropathy. Neuropathic patients exhibit decreased stability while standing as well as during dynamic conditions. A new methodology for diabetic gait pattern classification based on cluster analysis has been proposed that aims to identify groups of subjects with similar patterns of gait and verify if three-dimensional gait data are able to distinguish diabetic gait patterns from one of the control subjects. Method The gait of 20 nondiabetic individuals and 46 diabetes patients with and without peripheral neuropathy was analyzed [mean age 59.0 (2.9) and 61.1(4.4) years, mean body mass index (BMI) 24.0 (2.8), and 26.3 (2.0)]. K-means cluster analysis was applied to classify the subjects' gait patterns through the analysis of their ground reaction forces, joints and segments (trunk, hip, knee, ankle) angles, and moments. Results Cluster analysis classification led to definition of four well-separated clusters: one aggregating just neuropathic subjects, one aggregating both neuropathics and non-neuropathics, one including only diabetes patients, and one including either controls or diabetic and neuropathic subjects. Conclusions Cluster analysis was useful in grouping subjects with similar gait patterns and provided evidence that there were subgroups that might otherwise not be observed if a group ensemble was presented for any specific variable. In particular, we observed the presence of neuropathic subjects with a gait similar to the controls and diabetes patients with a long disease duration with a gait as altered as the neuropathic one. PMID:20920432

Analysis of sea use landscape pattern based on GIS: a case study in Huludao, China.

PubMed

Suo, Anning; Wang, Chen; Zhang, Minghui

2016-01-01

This study aims to analyse sea use landscape patterns on a regional scale based on methods of landscape ecology integrated with sea use spatial characteristics. Several landscape-level analysis indices, such as the dominance index, complex index, intensivity index, diversity index and sea congruency index, were established using Geographic Information System (GIS) and applied in Huludao, China. The results indicated that sea use landscape analysis indices, which were created based on the characteristics of sea use spatial patterns using GIS, are suitable to quantitatively describe the landscape patterns of sea use. They are operable tools for the landscape analysis of sea use. The sea use landscape in Huludao was dominated by fishing use with a landscape dominance index of 0.724. The sea use landscape is a complex mosaic with high diversity and plenty of fishing areas, as shown by the landscape complex index of 27.21 and the landscape diversity index of 1.25. Most sea use patches correspond to the marine functional zonation plan and the sea use congruency index is 0.89 in the fishing zone and 0.92 in the transportation zone.

Application of pattern mixture models to address missing data in longitudinal data analysis using SPSS.

PubMed

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.

Group-level spatio-temporal pattern recovery in MEG decoding using multi-task joint feature learning.

PubMed

Kia, Seyed Mostafa; Pedregosa, Fabian; Blumenthal, Anna; Passerini, Andrea

2017-06-15

The use of machine learning models to discriminate between patterns of neural activity has become in recent years a standard analysis approach in neuroimaging studies. Whenever these models are linear, the estimated parameters can be visualized in the form of brain maps which can aid in understanding how brain activity in space and time underlies a cognitive function. However, the recovered brain maps often suffer from lack of interpretability, especially in group analysis of multi-subject data. To facilitate the application of brain decoding in group-level analysis, we present an application of multi-task joint feature learning for group-level multivariate pattern recovery in single-trial magnetoencephalography (MEG) decoding. The proposed method allows for recovering sparse yet consistent patterns across different subjects, and therefore enhances the interpretability of the decoding model. Our experimental results demonstrate that the mutli-task joint feature learning framework is capable of recovering more meaningful patterns of varying spatio-temporally distributed brain activity across individuals while still maintaining excellent generalization performance. We compare the performance of the multi-task joint feature learning in terms of generalization, reproducibility, and quality of pattern recovery against traditional single-subject and pooling approaches on both simulated and real MEG datasets. These results can facilitate the usage of brain decoding for the characterization of fine-level distinctive patterns in group-level inference. Considering the importance of group-level analysis, the proposed approach can provide a methodological shift towards more interpretable brain decoding models. Copyright © 2017 Elsevier B.V. All rights reserved.

Transportation forecasting : analysis and quantitative methods

DOT National Transportation Integrated Search

1983-01-01

This Record contains the following papers: Development of Survey Instruments Suitable for Determining Non-Home Activity Patterns; Sequential, History-Dependent Approach to Trip-Chaining Behavior; Identifying Time and History Dependencies of Activity ...

Computer-assisted techniques to evaluate fringe patterns

NASA Astrophysics Data System (ADS)

Sciammarella, Cesar A.; Bhat, Gopalakrishna K.

1992-01-01

Strain measurement using interferometry requires an efficient way to extract the desired information from interferometric fringes. Availability of digital image processing systems makes it possible to use digital techniques for the analysis of fringes. In the past, there have been several developments in the area of one dimensional and two dimensional fringe analysis techniques, including the carrier fringe method (spatial heterodyning) and the phase stepping (quasi-heterodyning) technique. This paper presents some new developments in the area of two dimensional fringe analysis, including a phase stepping technique supplemented by the carrier fringe method and a two dimensional Fourier transform method to obtain the strain directly from the discontinuous phase contour map.

Identification and reproducibility of dietary patterns in a Danish cohort: the Inter99 study.

PubMed

Lau, Cathrine; Glümer, Charlotte; Toft, Ulla; Tetens, Inge; Carstensen, Bendix; Jørgensen, Torben; Borch-Johnsen, Knut

2008-05-01

We aimed to identify dietary patterns in a Danish adult population and assess the reproducibility of the dietary patterns identified. Baseline data of 3,372 women and 3,191 men (30-60 years old) from the population-based survey Inter99 was used. Food intake, assessed by a FFQ, was aggregated into thirty-four separate food groups. Dietary patterns were identified by principal component analysis. Confirmatory factor analysis and Bland Altman plots were used to assess the reproducibility of the dietary patterns identified. The Bland Altman plots were used as an alternative and new method. Two factors were retained for both women and men, which accounted for 15.1-17.4 % of the total variation. The 'Traditional' pattern was characterised by high loadings ( > or = 0.40) on paté or high-fat meat for sandwiches, mayonnaise salads, red meat, potatoes, butter and lard, low-fat fish, low-fat meat for sandwiches, and sauces. The 'Modern' pattern was characterised by high loadings on vegetables, fruit, mixed vegetable dishes, vegetable oil and vinegar dressing, poultry, and pasta, rice and wheat kernels. Small differences were observed between patterns identified for women and men. The root mean square error approximation from the confirmatory factor analysis was 0.08. The variation observed from the Bland Altman plots of factors from explorative v. confirmative analyses and explorative analyses from two sub-samples was between 18.8 and 47.7 %. Pearson's correlation was >0.89 (P < 0.0001). The reproducibility was better for women than for men. We conclude that the 'Traditional' and 'Modern' dietary patterns identified were reproducible.

Automatic Generation of English-Japanese Translation Pattern Utilizing Genetic Programming Technique

NASA Astrophysics Data System (ADS)

Matsumura, Koki; Tamekuni, Yuji; Kimura, Shuhei

There are a lot of constructional differences in an English-Japanese phrase template, and that often makes the act of translation difficult. Moreover, there exist various and tremendous phrase templates and sentence to be refered to. It is not easy to prepare the corpus that covers the all. Therefore, it is very significant to generate the translation pattern of the sentence pattern automatically from a viewpoint of the translation success rate and the capacity of the pattern dictionary. Then, for the purpose of realizing the automatic generation of the translation pattern, this paper proposed the new method for the generation of the translation pattern by using the genetic programming technique (GP). The technique tries to generate the translation pattern of various sentences which are not registered in the phrase template dictionary automatically by giving the genetic operation to the parsing tree of a basic pattern. The tree consists of the pair of the English-Japanese sentence generated as the first stage population. The analysis tree data base with 50,100,150,200 pairs was prepared as the first stage population. And this system was applied and executed for an English input of 1,555 sentences. As a result, the analysis tree increases from 200 to 517, and the accuracy rate of the translation pattern has improved from 42.57% to 70.10%. And, 86.71% of the generated translations was successfully done, whose meanings are enough acceptable and understandable. It seemed that this proposal technique became a clue to raise the translation success rate, and to find the possibility of the reduction of the analysis tree data base.

Striving for Discussion: An Analysis of a Teacher Educator's Comments in Whole-Class Conversation

ERIC Educational Resources Information Center

Reynolds, Todd

2016-01-01

During my English Language Arts methods class, I noticed that my discussion patterns were teacher-focused and in an Initiation-Response-Evaluation format. Because I wanted to model dialogic methods of whole-class discussions for my preservice teachers, I recoiled from this finding and began a self-study using an action research method to examine…

QR-on-a-chip: a computer-recognizable micro-pattern engraved microfluidic device for high-throughput image acquisition.

PubMed

Yun, Kyungwon; Lee, Hyunjae; Bang, Hyunwoo; Jeon, Noo Li

2016-02-21

This study proposes a novel way to achieve high-throughput image acquisition based on a computer-recognizable micro-pattern implemented on a microfluidic device. We integrated the QR code, a two-dimensional barcode system, onto the microfluidic device to simplify imaging of multiple ROIs (regions of interest). A standard QR code pattern was modified to arrays of cylindrical structures of polydimethylsiloxane (PDMS). Utilizing the recognition of the micro-pattern, the proposed system enables: (1) device identification, which allows referencing additional information of the device, such as device imaging sequences or the ROIs and (2) composing a coordinate system for an arbitrarily located microfluidic device with respect to the stage. Based on these functionalities, the proposed method performs one-step high-throughput imaging for data acquisition in microfluidic devices without further manual exploration and locating of the desired ROIs. In our experience, the proposed method significantly reduced the time for the preparation of an acquisition. We expect that the method will innovatively improve the prototype device data acquisition and analysis.

Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation

PubMed Central

Lashkari, Danial; Sridharan, Ramesh; Vul, Edward; Hsieh, Po-Jang; Kanwisher, Nancy; Golland, Polina

2011-01-01

We develop a method for unsupervised analysis of functional brain images that learns group-level patterns of functional response. Our algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary activation variable. At the next level, we define a prior over the sets of activation variables in all subjects. We use a Hierarchical Dirichlet Process as the prior in order to simultaneously learn the patterns of response that are shared across the group, and to estimate the number of these patterns supported by data. Inference based on this model enables automatic discovery and characterization of salient and consistent patterns in functional signals. We apply our method to data from a study that explores the response of the visual cortex to a collection of images. The discovered profiles of activation correspond to selectivity to a number of image categories such as faces, bodies, and scenes. More generally, our results appear superior to the results of alternative data-driven methods in capturing the category structure in the space of stimuli. PMID:21841977

Calculation of Debye-Scherrer diffraction patterns from highly stressed polycrystalline materials

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

MacDonald, M. J., E-mail: macdonm@umich.edu; SLAC National Accelerator Laboratory, Menlo Park, California 94025; Vorberger, J.

2016-06-07

Calculations of Debye-Scherrer diffraction patterns from polycrystalline materials have typically been done in the limit of small deviatoric stresses. Although these methods are well suited for experiments conducted near hydrostatic conditions, more robust models are required to diagnose the large strain anisotropies present in dynamic compression experiments. A method to predict Debye-Scherrer diffraction patterns for arbitrary strains has been presented in the Voigt (iso-strain) limit [Higginbotham, J. Appl. Phys. 115, 174906 (2014)]. Here, we present a method to calculate Debye-Scherrer diffraction patterns from highly stressed polycrystalline samples in the Reuss (iso-stress) limit. This analysis uses elastic constants to calculate latticemore » strains for all initial crystallite orientations, enabling elastic anisotropy and sample texture effects to be modeled directly. The effects of probing geometry, deviatoric stresses, and sample texture are demonstrated and compared to Voigt limit predictions. An example of shock-compressed polycrystalline diamond is presented to illustrate how this model can be applied and demonstrates the importance of including material strength when interpreting diffraction in dynamic compression experiments.« less

Routing performance analysis and optimization within a massively parallel computer

DOEpatents

Archer, Charles Jens; Peters, Amanda; Pinnow, Kurt Walter; Swartz, Brent Allen

2013-04-16

An apparatus, program product and method optimize the operation of a massively parallel computer system by, in part, receiving actual performance data concerning an application executed by the plurality of interconnected nodes, and analyzing the actual performance data to identify an actual performance pattern. A desired performance pattern may be determined for the application, and an algorithm may be selected from among a plurality of algorithms stored within a memory, the algorithm being configured to achieve the desired performance pattern based on the actual performance data.

Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis

PubMed Central

Hirayama, Jun-ichiro; Hyvärinen, Aapo; Kiviniemi, Vesa; Kawanabe, Motoaki; Yamashita, Okito

2016-01-01

Characterizing the variability of resting-state functional brain connectivity across subjects and/or over time has recently attracted much attention. Principal component analysis (PCA) serves as a fundamental statistical technique for such analyses. However, performing PCA on high-dimensional connectivity matrices yields complicated “eigenconnectivity” patterns, for which systematic interpretation is a challenging issue. Here, we overcome this issue with a novel constrained PCA method for connectivity matrices by extending the idea of the previously proposed orthogonal connectivity factorization method. Our new method, modular connectivity factorization (MCF), explicitly introduces the modularity of brain networks as a parametric constraint on eigenconnectivity matrices. In particular, MCF analyzes the variability in both intra- and inter-module connectivities, simultaneously finding network modules in a principled, data-driven manner. The parametric constraint provides a compact module-based visualization scheme with which the result can be intuitively interpreted. We develop an optimization algorithm to solve the constrained PCA problem and validate our method in simulation studies and with a resting-state functional connectivity MRI dataset of 986 subjects. The results show that the proposed MCF method successfully reveals the underlying modular eigenconnectivity patterns in more general situations and is a promising alternative to existing methods. PMID:28002474

Analysis of 3-D Tongue Motion From Tagged and Cine Magnetic Resonance Images

PubMed Central

Woo, Jonghye; Lee, Junghoon; Murano, Emi Z.; Stone, Maureen; Prince, Jerry L.

2016-01-01

Purpose Measuring tongue deformation and internal muscle motion during speech has been a challenging task because the tongue deforms in 3 dimensions, contains interdigitated muscles, and is largely hidden within the vocal tract. In this article, a new method is proposed to analyze tagged and cine magnetic resonance images of the tongue during speech in order to estimate 3-dimensional tissue displacement and deformation over time. Method The method involves computing 2-dimensional motion components using a standard tag-processing method called harmonic phase, constructing superresolution tongue volumes using cine magnetic resonance images, segmenting the tongue region using a random-walker algorithm, and estimating 3-dimensional tongue motion using an incompressible deformation estimation algorithm. Results Evaluation of the method is presented with a control group and a group of people who had received a glossectomy carrying out a speech task. A 2-step principal-components analysis is then used to reveal the unique motion patterns of the subjects. Azimuth motion angles and motion on the mirrored hemi-tongues are analyzed. Conclusion Tests of the method with a various collection of subjects show its capability of capturing patient motion patterns and indicate its potential value in future speech studies. PMID:27295428

Large-Scale Circulation and Climate Variability. Chapter 5

NASA Technical Reports Server (NTRS)

Perlwitz, J.; Knutson, T.; Kossin, J. P.; LeGrande, A. N.

2017-01-01

The causes of regional climate trends cannot be understood without considering the impact of variations in large-scale atmospheric circulation and an assessment of the role of internally generated climate variability. There are contributions to regional climate trends from changes in large-scale latitudinal circulation, which is generally organized into three cells in each hemisphere-Hadley cell, Ferrell cell and Polar cell-and which determines the location of subtropical dry zones and midlatitude jet streams. These circulation cells are expected to shift poleward during warmer periods, which could result in poleward shifts in precipitation patterns, affecting natural ecosystems, agriculture, and water resources. In addition, regional climate can be strongly affected by non-local responses to recurring patterns (or modes) of variability of the atmospheric circulation or the coupled atmosphere-ocean system. These modes of variability represent preferred spatial patterns and their temporal variation. They account for gross features in variance and for teleconnections which describe climate links between geographically separated regions. Modes of variability are often described as a product of a spatial climate pattern and an associated climate index time series that are identified based on statistical methods like Principal Component Analysis (PC analysis), which is also called Empirical Orthogonal Function Analysis (EOF analysis), and cluster analysis.

Typology of Alcohol Users Based on Longitudinal Patterns of Drinking

PubMed Central

Harrington, Magdalena; Velicer, Wayne F.; Ramsey, Susan

2014-01-01

Objective Worldwide, alcohol is the most commonly used psychoactive substance. However, heterogeneity among alcohol users has been widely recognized. This paper presents a typology of alcohol users based on an implementation of idiographic methodology to examine longitudinal daily and cyclic (weekly) patterns of alcohol use at the individual level. Method A secondary data analysis was performed on the pre-intervention data from a large randomized control trial. A time series analysis was performed at the individual level, and a dynamic cluster analysis was employed to identify homogenous longitudinal patterns of drinking behavior at the group level. The analysis employed 180 daily observations of alcohol use in a sample of 177 alcohol users. Results The first order autocorrelations ranged from −.76 to .72, and seventh order autocorrelations ranged from −.27 to .79. Eight distinct profiles of alcohol users were identified, each characterized by a unique configuration of first and seventh autoregressive terms and longitudinal trajectories of alcohol use. External validity of the profiles confirmed the theoretical relevance of different patterns of alcohol use. Significant differences among the eight subtypes were found on gender, marital status, frequency of drug use, lifetime alcohol dependence, family history of alcohol use and the Short Index of Problems. Conclusions Our findings demonstrate that individuals can have very different temporal patterns of drinking behavior. The daily and cyclic patterns of alcohol use may be important for designing tailored interventions for problem drinkers. PMID:24333036

3-D surface profilometry based on modulation measurement by applying wavelet transform method

NASA Astrophysics Data System (ADS)

Zhong, Min; Chen, Feng; Xiao, Chao; Wei, Yongchao

2017-01-01

A new analysis of 3-D surface profilometry based on modulation measurement technique by the application of Wavelet Transform method is proposed. As a tool excelling for its multi-resolution and localization in the time and frequency domains, Wavelet Transform method with good localized time-frequency analysis ability and effective de-noizing capacity can extract the modulation distribution more accurately than Fourier Transform method. Especially for the analysis of complex object, more details of the measured object can be well remained. In this paper, the theoretical derivation of Wavelet Transform method that obtains the modulation values from a captured fringe pattern is given. Both computer simulation and elementary experiment are used to show the validity of the proposed method by making a comparison with the results of Fourier Transform method. The results show that the Wavelet Transform method has a better performance than the Fourier Transform method in modulation values retrieval.

Porphyromonas endodontalis: prevalence and distribution of restriction enzyme patterns in families.

PubMed

Petit, M D; van Winkelhoff, A J; van Steenbergen, T J; de Graaff, J

1993-08-01

In this study we determined the prevalence and distribution of Porphyromonas endodontalis in 26 families consisting of 107 subjects. P. endodontalis was present in 24% of the investigated subjects and was recovered most often from the dorsum of the tongue (50%). Isolation was also possible from the tonsils, the buccal mucosa, the saliva and the periodontal pocket. The usefulness of restriction endonuclease analysis as a typing method for this particular species was investigated by typing 19 isolates from unrelated individuals. All these isolates had unique restriction endonuclease patterns. The observed heterogeneity indicates that restriction endonuclease analysis is a sensitive measure of genetic dissimilarity between P. endodontalis isolates and is able to characterize individual isolates. Application of restriction endonuclease analysis to the obtained clinical isolates in this study shows the possibility of the presence of multiple clonal types within one subject. The DNA patterns of all P. endodontalis isolates from unrelated individuals were found to be distinct. In 3 families the DNA patterns of isolates from the mother and her child were indistinguishable. These data indicate the possibility of intrafamilial transmission of P. endodontalis.

Automated image based prominent nucleoli detection

PubMed Central

Yap, Choon K.; Kalaw, Emarene M.; Singh, Malay; Chong, Kian T.; Giron, Danilo M.; Huang, Chao-Hui; Cheng, Li; Law, Yan N.; Lee, Hwee Kuan

2015-01-01

Introduction: Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Materials and Methods: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. Results: The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Conclusions: Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings. PMID:26167383

FracPaQ: a MATLAB™ Toolbox for the Quantification of Fracture Patterns

NASA Astrophysics Data System (ADS)

Healy, D.; Rizzo, R. E.; Cornwell, D. G.; Timms, N.; Farrell, N. J.; Watkins, H.; Gomez-Rivas, E.; Smith, M.

2016-12-01

The patterns of fractures in deformed rocks are rarely uniform or random. Fracture orientations, sizes, shapes and spatial distributions often exhibit some kind of order. In detail, there may be relationships among the different fracture attributes e.g. small fractures dominated by one orientation, larger fractures by another. These relationships are important because the mechanical (e.g. strength, anisotropy) and transport (e.g. fluids, heat) properties of rock depend on these fracture patterns and fracture attributes. This presentation describes an open source toolbox to quantify fracture patterns, including distributions in fracture attributes and their spatial variation. Software has been developed to quantify fracture patterns from 2-D digital images, such as thin section micrographs, geological maps, outcrop or aerial photographs or satellite images. The toolbox comprises a suite of MATLAB™ scripts based on published quantitative methods for the analysis of fracture attributes: orientations, lengths, intensity, density and connectivity. An estimate of permeability in 2-D is made using a parallel plate model. The software provides an objective and consistent methodology for quantifying fracture patterns and their variations in 2-D across a wide range of length scales. Our current focus for the application of the software is on quantifying the fracture patterns in and around fault zones. There is a large body of published work on the quantification of relatively simple joint patterns, but fault zones present a bigger, and arguably more important, challenge. The method presented is inherently scale independent, and a key task will be to analyse and integrate quantitative fracture pattern data from micro- to macro-scales. Planned future releases will incorporate multi-scale analyses based on a wavelet method to look for scale transitions, and combining fracture traces from multiple 2-D images to derive the statistically equivalent 3-D fracture pattern.

Sensing Urban Land-Use Patterns by Integrating Google Tensorflow and Scene-Classification Models

NASA Astrophysics Data System (ADS)

Yao, Y.; Liang, H.; Li, X.; Zhang, J.; He, J.

2017-09-01

With the rapid progress of China's urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model's ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

Functional network mediates age-related differences in reaction time: a replication and extension study

PubMed Central

Gazes, Yunglin; Habeck, Christian; O'Shea, Deirdre; Razlighi, Qolamreza R; Steffener, Jason; Stern, Yaakov

2015-01-01

Introduction A functional activation (i.e., ordinal trend) pattern was previously identified in both young and older adults during task-switching performance, the expression of which correlated with reaction time. The current study aimed to (1) replicate this functional activation pattern in a new group of fMRI activation data, and (2) extend the previous study by specifically examining whether the effect of aging on reaction time can be explained by differences in the activation of the functional activation pattern. Method A total of 47 young and 50 older participants were included in the extension analysis. Participants performed task-switching as the activation task and were cued by the color of the stimulus for the task to be performed in each block. To test for replication, two approaches were implemented. The first approach tested the replicability of the predictive power of the previously identified functional activation pattern by forward applying the pattern to the Study II data and the second approach was rederivation of the activation pattern in the Study II data. Results Both approaches showed successful replication in the new data set. Using mediation analysis, expression of the pattern from the first approach was found to partially mediate age-related effects on reaction time such that older age was associated with greater activation of the brain pattern and longer reaction time, suggesting that brain activation efficiency (defined as “the rate of activation increase with increasing task difficulty” in Neuropsychologia 47, 2009, 2015) of the regions in the Ordinal trend pattern directly accounts for age-related differences in task performance. Discussion The successful replication of the functional activation pattern demonstrates the versatility of the Ordinal Trend Canonical Variates Analysis, and the ability to summarize each participant's brain activation map into one number provides a useful metric in multimodal analysis as well as cross-study comparisons. PMID:25874162

Reconstruction of human brain spontaneous activity based on frequency-pattern analysis of magnetoencephalography data

PubMed Central

Llinás, Rodolfo R.; Ustinin, Mikhail N.; Rykunov, Stanislav D.; Boyko, Anna I.; Sychev, Vyacheslav V.; Walton, Kerry D.; Rabello, Guilherme M.; Garcia, John

2015-01-01

A new method for the analysis and localization of brain activity has been developed, based on multichannel magnetic field recordings, over minutes, superimposed on the MRI of the individual. Here, a high resolution Fourier Transform is obtained over the entire recording period, leading to a detailed multi-frequency spectrum. Further analysis implements a total decomposition of the frequency components into functionally invariant entities, each having an invariant field pattern localizable in recording space. The method, addressed as functional tomography, makes it possible to find the distribution of magnetic field sources in space. Here, the method is applied to the analysis of simulated data, to oscillating signals activating a physical current dipoles phantom, and to recordings of spontaneous brain activity in 10 healthy adults. In the analysis of simulated data, 61 dipoles are localized with 0.7 mm precision. Concerning the physical phantom the method is able to localize three simultaneously activated current dipoles with 1 mm precision. Spatial resolution 3 mm was attained when localizing spontaneous alpha rhythm activity in 10 healthy adults, where the alpha peak was specified for each subject individually. Co-registration of the functional tomograms with each subject's head MRI localized alpha range activity to the occipital and/or posterior parietal brain region. This is the first application of this new functional tomography to human brain activity. The method successfully provides an overall view of brain electrical activity, a detailed spectral description and, combined with MRI, the localization of sources in anatomical brain space. PMID:26528119

Comparison of tests for spatial heterogeneity on data with global clustering patterns and outliers

PubMed Central

Jackson, Monica C; Huang, Lan; Luo, Jun; Hachey, Mark; Feuer, Eric

2009-01-01

Background The ability to evaluate geographic heterogeneity of cancer incidence and mortality is important in cancer surveillance. Many statistical methods for evaluating global clustering and local cluster patterns are developed and have been examined by many simulation studies. However, the performance of these methods on two extreme cases (global clustering evaluation and local anomaly (outlier) detection) has not been thoroughly investigated. Methods We compare methods for global clustering evaluation including Tango's Index, Moran's I, and Oden's I*pop; and cluster detection methods such as local Moran's I and SaTScan elliptic version on simulated count data that mimic global clustering patterns and outliers for cancer cases in the continental United States. We examine the power and precision of the selected methods in the purely spatial analysis. We illustrate Tango's MEET and SaTScan elliptic version on a 1987-2004 HIV and a 1950-1969 lung cancer mortality data in the United States. Results For simulated data with outlier patterns, Tango's MEET, Moran's I and I*pop had powers less than 0.2, and SaTScan had powers around 0.97. For simulated data with global clustering patterns, Tango's MEET and I*pop (with 50% of total population as the maximum search window) had powers close to 1. SaTScan had powers around 0.7-0.8 and Moran's I has powers around 0.2-0.3. In the real data example, Tango's MEET indicated the existence of global clustering patterns in both the HIV and lung cancer mortality data. SaTScan found a large cluster for HIV mortality rates, which is consistent with the finding from Tango's MEET. SaTScan also found clusters and outliers in the lung cancer mortality data. Conclusion SaTScan elliptic version is more efficient for outlier detection compared with the other methods evaluated in this article. Tango's MEET and Oden's I*pop perform best in global clustering scenarios among the selected methods. The use of SaTScan for data with global clustering patterns should be used with caution since SatScan may reveal an incorrect spatial pattern even though it has enough power to reject a null hypothesis of homogeneous relative risk. Tango's method should be used for global clustering evaluation instead of SaTScan. PMID:19822013

Assessing prescription drug abuse using functional principal component analysis (FPCA) of wastewater data.

PubMed

Salvatore, Stefania; Røislien, Jo; Baz-Lomba, Jose A; Bramness, Jørgen G

2017-03-01

Wastewater-based epidemiology is an alternative method for estimating the collective drug use in a community. We applied functional data analysis, a statistical framework developed for analysing curve data, to investigate weekly temporal patterns in wastewater measurements of three prescription drugs with known abuse potential: methadone, oxazepam and methylphenidate, comparing them to positive and negative control drugs. Sewage samples were collected in February 2014 from a wastewater treatment plant in Oslo, Norway. The weekly pattern of each drug was extracted by fitting of generalized additive models, using trigonometric functions to model the cyclic behaviour. From the weekly component, the main temporal features were then extracted using functional principal component analysis. Results are presented through the functional principal components (FPCs) and corresponding FPC scores. Clinically, the most important weekly feature of the wastewater-based epidemiology data was the second FPC, representing the difference between average midweek level and a peak during the weekend, representing possible recreational use of a drug in the weekend. Estimated scores on this FPC indicated recreational use of methylphenidate, with a high weekend peak, but not for methadone and oxazepam. The functional principal component analysis uncovered clinically important temporal features of the weekly patterns of the use of prescription drugs detected from wastewater analysis. This may be used as a post-marketing surveillance method to monitor prescription drugs with abuse potential. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Lack of maintenance of gait pattern as measured by instrumental methods suggests psychogenic gait.

PubMed

Merello, Marcelo; Ballesteros, Diego; Rossi, Malco; Arena, Julieta; Crespo, Marcos; Cervio, Andres; Cuello Oderiz, Carolina; Rivero, Alberto; Cerquetti, Daniel; Risk, Marcelo; Balej, Jorge

2012-01-01

Fluctuation is a common feature of all psychogenic gait disorder (PGD) patterns. Whether this fluctuation involves only the degree of impairment or whether it affects the gait pattern itself remains an interesting question. We hypothesize that, on repeated measurements, both normal and abnormal gait may present quantitative differences while maintaining their basic underlying pattern; conversely, in psychogenic gait, the basic pattern appears not to be preserved. Using an optoelectronic system, data acquired from 19 normal subjects and 66 patients were applied to train a neural network (NN) and subsequently classify gait patterns into four different groups (normal, ataxic, spastic-paraparetic and parkinsonian). Five patients who fulfilled clinical criteria for psychogenic gait and six controls were then prospectively evaluated on two separate occasions, three months apart. Normal controls and ataxic, parkinsonian or spastic patients were correctly identified by the NN, and categorized within the corresponding groups at baseline as well as at a three-month follow-up evaluation. NN analysis showed that after three months, no PGD patient preserved the gait pattern detected at baseline, even though this finding was not clinically apparent. Modification of gait pattern detected by repeated kinematic measurement and NN analysis could suggest the presence of PGD, particularly in difficult-to-diagnose cases.

Evaluation of a New Digital Automated Glycemic Pattern Detection Tool

PubMed Central

Albiñana, Emma; Artes, Maite; Corcoy, Rosa; Fernández-García, Diego; García-Alemán, Jorge; García-Cuartero, Beatriz; González, Cintia; Rivero, María Teresa; Casamira, Núria; Weissmann, Jörg

2017-01-01

Abstract Background: Blood glucose meters are reliable devices for data collection, providing electronic logs of historical data easier to interpret than handwritten logbooks. Automated tools to analyze these data are necessary to facilitate glucose pattern detection and support treatment adjustment. These tools emerge in a broad variety in a more or less nonevaluated manner. The aim of this study was to compare eDetecta, a new automated pattern detection tool, to nonautomated pattern analysis in terms of time investment, data interpretation, and clinical utility, with the overarching goal to identify early in development and implementation of tool areas of improvement and potential safety risks. Methods: Multicenter web-based evaluation in which 37 endocrinologists were asked to assess glycemic patterns of 4 real reports (2 continuous subcutaneous insulin infusion [CSII] and 2 multiple daily injection [MDI]). Endocrinologist and eDetecta analyses were compared on time spent to analyze each report and agreement on the presence or absence of defined patterns. Results: eDetecta module markedly reduced the time taken to analyze each case on the basis of the emminens eConecta reports (CSII: 18 min; MDI: 12.5), compared to the automatic eDetecta analysis. Agreement between endocrinologists and eDetecta varied depending on the patterns, with high level of agreement in patterns of glycemic variability. Further analysis of low level of agreement led to identifying areas where algorithms used could be improved to optimize trend pattern identification. Conclusion: eDetecta was a useful tool for glycemic pattern detection, helping clinicians to reduce time required to review emminens eConecta glycemic reports. No safety risks were identified during the study. PMID:29091477

Advanced image based methods for structural integrity monitoring: Review and prospects

NASA Astrophysics Data System (ADS)

Farahani, Behzad V.; Sousa, Pedro José; Barros, Francisco; Tavares, Paulo J.; Moreira, Pedro M. G. P.

2018-02-01

There is a growing trend in engineering to develop methods for structural integrity monitoring and characterization of in-service mechanical behaviour of components. The fast growth in recent years of image processing techniques and image-based sensing for experimental mechanics, brought about a paradigm change in phenomena sensing. Hence, several widely applicable optical approaches are playing a significant role in support of experiment. The current review manuscript describes advanced image based methods for structural integrity monitoring, and focuses on methods such as Digital Image Correlation (DIC), Thermoelastic Stress Analysis (TSA), Electronic Speckle Pattern Interferometry (ESPI) and Speckle Pattern Shearing Interferometry (Shearography). These non-contact full-field techniques rely on intensive image processing methods to measure mechanical behaviour, and evolve even as reviews such as this are being written, which justifies a special effort to keep abreast of this progress.

Analysis of signals propagating in a phononic crystal PZT layer deposited on a silicon substrate.

PubMed

Hladky-Hennion, Anne-Christine; Vasseur, Jérôme; Dubus, Bertrand; Morvan, Bruno; Wilkie-Chancellier, Nicolas; Martinez, Loïc

2013-12-01

The design of a stop-band filter constituted by a periodically patterned lead zirconate titanate (PZT) layer, polarized along its thickness, deposited on a silicon substrate and sandwiched between interdigitated electrodes for emission/reception of guided elastic waves, is investigated. The filter characteristics are theoretically evaluated by using finite element simulations: dispersion curves of a patterned PZT layer with a specific pattern geometry deposited on a silicon substrate present an absolute stop band. The whole structure is modeled with realistic conditions, including appropriate interdigitated electrodes to propagate a guided mode in the piezoelectric layer. A robust method for signal analysis based on the Gabor transform is applied to treat transmitted signals; extract attenuation, group delays, and wave number variations versus frequency; and identify stop-band filter characteristics.

Assessment of the information content of patterns: an algorithm

NASA Astrophysics Data System (ADS)

Daemi, M. Farhang; Beurle, R. L.

1991-12-01

A preliminary investigation confirmed the possibility of assessing the translational and rotational information content of simple artificial images. The calculation is tedious, and for more realistic patterns it is essential to implement the method on a computer. This paper describes an algorithm developed for this purpose which confirms the results of the preliminary investigation. Use of the algorithm facilitates much more comprehensive analysis of the combined effect of continuous rotation and fine translation, and paves the way for analysis of more realistic patterns. Owing to the volume of calculation involved in these algorithms, extensive computing facilities were necessary. The major part of the work was carried out using an ICL 3900 series mainframe computer as well as other powerful workstations such as a RISC architecture MIPS machine.

Simultaneous overlay and CD measurement for double patterning: scatterometry and RCWA approach

NASA Astrophysics Data System (ADS)

Li, Jie; Liu, Zhuan; Rabello, Silvio; Dasari, Prasad; Kritsun, Oleg; Volkman, Catherine; Park, Jungchul; Singh, Lovejeet

2009-03-01

As optical lithography advances to 32 nm technology node and beyond, double patterning technology (DPT) has emerged as an attractive solution to circumvent the fundamental optical limitations. DPT poses unique demands on critical dimension (CD) uniformity and overlay control, making the tolerance decrease much faster than the rate at which critical dimension shrinks. This, in turn, makes metrology even more challenging. In the past, multi-pad diffractionbased overlay (DBO) using empirical approach has been shown to be an effective approach to measure overlay error associated with double patterning [1]. In this method, registration errors for double patterning were extracted from specially designed diffraction targets (three or four pads for each direction); CD variation is assumed negligible within each group of adjacent pads and not addressed in the measurement. In another paper, encouraging results were reported with a first attempt at simultaneously extracting overlay and CD parameters using scatterometry [2]. In this work, we apply scatterometry with a rigorous coupled wave analysis (RCWA) approach to characterize two double-patterning processes: litho-etch-litho-etch (LELE) and litho-freeze-litho-etch (LFLE). The advantage of performing rigorous modeling is to reduce the number of pads within each measurement target, thus reducing space requirement and improving throughput, and simultaneously extract CD and overlay information. This method measures overlay errors and CDs by fitting the optical signals with spectra calculated from a model of the targets. Good correlation is obtained between the results from this method and that of several reference techniques, including empirical multi-pad DBO, CD-SEM, and IBO. We also perform total measurement uncertainty (TMU) analysis to evaluate the overall performance. We demonstrate that scatterometry provides a promising solution to meet the challenging overlay metrology requirement in DPT.

Digital Image Correlation of 2D X-ray Powder Diffraction Data for Lattice Strain Evaluation

PubMed Central

Zhang, Hongjia; Sui, Tan; Daisenberger, Dominik; Fong, Kai Soon

2018-01-01

High energy 2D X-ray powder diffraction experiments are widely used for lattice strain measurement. The 2D to 1D conversion of diffraction patterns is a necessary step used to prepare the data for full pattern refinement, but is inefficient when only peak centre position information is required for lattice strain evaluation. The multi-step conversion process is likely to lead to increased errors associated with the ‘caking’ (radial binning) or fitting procedures. A new method is proposed here that relies on direct Digital Image Correlation analysis of 2D X-ray powder diffraction patterns (XRD-DIC, for short). As an example of using XRD-DIC, residual strain values along the central line in a Mg AZ31B alloy bar after 3-point bending are calculated by using both XRD-DIC and the conventional ‘caking’ with fitting procedures. Comparison of the results for strain values in different azimuthal angles demonstrates excellent agreement between the two methods. The principal strains and directions are calculated using multiple direction strain data, leading to full in-plane strain evaluation. It is therefore concluded that XRD-DIC provides a reliable and robust method for strain evaluation from 2D powder diffraction data. The XRD-DIC approach simplifies the analysis process by skipping 2D to 1D conversion, and opens new possibilities for robust 2D powder diffraction data analysis for full in-plane strain evaluation. PMID:29543728

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

PubMed

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 < 0.0001), 0.062 for SD v (AUC: 0.847, p < 0.0001), 0.117 for A 1 (AUC: 0.876, p < 0.0001), and 0.349 for MUD-MDD (AUC: 0.948, p < 0.0001). This is the first study to analyze multiple aspects of respiration using various mathematical constructs and provides quantitative indices of respiratory stability and determining quantitative cutoff value for differentiating regular and irregular respiration.

Classification of neck movement patterns related to whiplash-associated disorders using neural networks.

PubMed

Grip, Helena; Ohberg, Fredrik; Wiklund, Urban; Sterner, Ylva; Karlsson, J Stefan; Gerdle, Björn

2003-12-01

This paper presents a new method for classification of neck movement patterns related to Whiplash-associated disorders (WAD) using a resilient backpropagation neural network (BPNN). WAD are a common diagnosis after neck trauma, typically caused by rear-end car accidents. Since physical injuries seldom are found with present imaging techniques, the diagnosis can be difficult to make. The active range of the neck is often visually inspected in patients with neck pain, but this is a subjective measure, and a more objective decision support system, that gives a reliable and more detailed analysis of neck movement pattern, is needed. The objective of this study was to evaluate the predictive ability of a BPNN, using neck movement variables as input. Three-dimensional (3-D) neck movement data from 59 subjects with WAD and 56 control subjects were collected with a ProReflex system. Rotation angle and angle velocity were calculated using the instantaneous helical axis method and motion variables were extracted. A principal component analysis was performed in order to reduce data and improve the BPNN performance. BPNNs with six hidden nodes had a predictivity of 0.89, a sensitivity of 0.90 and a specificity of 0.88, which are very promising results. This shows that neck movement analysis combined with a neural network could build the basis of a decision support system for classifying suspected WAD, even though further evaluation of the method is needed.

Comparison of two occurrence risk assessment methods for collapse gully erosion ——A case study in Guangdong province

NASA Astrophysics Data System (ADS)

Sun, K.; Cheng, D. B.; He, J. J.; Zhao, Y. L.

2018-02-01

Collapse gully erosion is a specific type of soil erosion in the red soil region of southern China, and early warning and prevention of the occurrence of collapse gully erosion is very important. Based on the idea of risk assessment, this research, taking Guangdong province as an example, adopt the information acquisition analysis and the logistic regression analysis, to discuss the feasibility for collapse gully erosion risk assessment in regional scale, and compare the applicability of the different risk assessment methods. The results show that in the Guangdong province, the risk degree of collapse gully erosion occurrence is high in northeastern and western area, and relatively low in southwestern and central part. The comparing analysis of the different risk assessment methods on collapse gully also indicated that the risk distribution patterns from the different methods were basically consistent. However, the accuracy of risk map from the information acquisition analysis method was slightly better than that from the logistic regression analysis method.

The time course of individual face recognition: A pattern analysis of ERP signals.

PubMed

Nemrodov, Dan; Niemeier, Matthias; Mok, Jenkin Ngo Yin; Nestor, Adrian

2016-05-15

An extensive body of work documents the time course of neural face processing in the human visual cortex. However, the majority of this work has focused on specific temporal landmarks, such as N170 and N250 components, derived through univariate analyses of EEG data. Here, we take on a broader evaluation of ERP signals related to individual face recognition as we attempt to move beyond the leading theoretical and methodological framework through the application of pattern analysis to ERP data. Specifically, we investigate the spatiotemporal profile of identity recognition across variation in emotional expression. To this end, we apply pattern classification to ERP signals both in time, for any single electrode, and in space, across multiple electrodes. Our results confirm the significance of traditional ERP components in face processing. At the same time though, they support the idea that the temporal profile of face recognition is incompletely described by such components. First, we show that signals associated with different facial identities can be discriminated from each other outside the scope of these components, as early as 70ms following stimulus presentation. Next, electrodes associated with traditional ERP components as well as, critically, those not associated with such components are shown to contribute information to stimulus discriminability. And last, the levels of ERP-based pattern discrimination are found to correlate with recognition accuracy across subjects confirming the relevance of these methods for bridging brain and behavior data. Altogether, the current results shed new light on the fine-grained time course of neural face processing and showcase the value of novel methods for pattern analysis to investigating fundamental aspects of visual recognition. Copyright © 2016 Elsevier Inc. All rights reserved.

Understanding and Managing Causality of Change in Socio-Technical Systems 3

DTIC Science & Technology

2012-01-06

influence, and (4) management and control. The questions are listed below. Dynamics and Context  What can be learned from patterns of causal...has proven to be an insufficient method to determine existence of behavioral and performance patterns . Cognitive work analysis, on the other hand...to provide a point of comparison, including Victorian bushfires, Queensland and Victorian floods, and the mine collapse in Chile. Privacy, Threats

Novel Perspectives on the Characterization of Species-Dependent Optical Signatures of Bacterial Colonies by Digital Holography.

PubMed

Buzalewicz, Igor; Kujawińska, Małgorzata; Krauze, Wojciech; Podbielska, Halina

2016-01-01

The use of light diffraction for the microbiological diagnosis of bacterial colonies was a significant breakthrough with widespread implications for the food industry and clinical practice. We previously confirmed that optical sensors for bacterial colony light diffraction can be used for bacterial identification. This paper is focused on the novel perspectives of this method based on digital in-line holography (DIH), which is able to reconstruct the amplitude and phase properties of examined objects, as well as the amplitude and phase patterns of the optical field scattered/diffracted by the bacterial colony in any chosen observation plane behind the object from single digital hologram. Analysis of the amplitude and phase patterns inside a colony revealed its unique optical properties, which are associated with the internal structure and geometry of the bacterial colony. Moreover, on a computational level, it is possible to select the desired scattered/diffracted pattern within the entire observation volume that exhibits the largest amount of unique, differentiating bacterial features. These properties distinguish this method from the already proposed sensing techniques based on light diffraction/scattering of bacterial colonies. The reconstructed diffraction patterns have a similar spatial distribution as the recorded Fresnel patterns, previously applied for bacterial identification with over 98% accuracy, but they are characterized by both intensity and phase distributions. Our results using digital holography provide new optical discriminators of bacterial species revealed in one single step in form of new optical signatures of bacterial colonies: digital holograms, reconstructed amplitude and phase patterns, as well as diffraction patterns from all observation space, which exhibit species-dependent features. To the best of our knowledge, this is the first report on bacterial colony analysis via digital holography and our study represents an innovative approach to the subject.

Ionospheric irregularity characteristics from quasiperiodic structure in the radio wave scintillation

NASA Astrophysics Data System (ADS)

Chen, K. Y.; Su, S. Y.; Liu, C. H.; Basu, S.

2005-06-01

Quasiperiodic (QP) diffraction pattern in scintillation patches has been known to highly correlate with the edge structures of a plasma bubble (Franke et al., 1984). A new time-frequency analysis method of Hilbert-Huang transform (HHT) has been applied to analyze the scintillation data taken at Ascension Island to understand the characteristics of corresponding ionosphere irregularities. The HHT method enables us to extract the quasiperiodic diffraction signals embedded inside the scintillation data and to obtain the characteristics of such diffraction signals. The cross correlation of the two sets of diffraction signals received by two stations at each end of Ascension Island indicates that the density irregularity pattern that causes the diffraction pattern should have an eastward drift velocity of ˜130 m/s. The HHT analysis of the instantaneous frequency in the QP diffraction patterns also reveals some frequency shifts in their peak frequencies. For the QP diffraction pattern caused by the leading edge of the large density gradient at the east wall of a structured bubble, an ascending note in the peak frequency is observed, and for the trailing edge a descending note is observed. The linear change in the transient of the peak frequency in the QP diffraction pattern is consistent with the theory and the simulation result of Franke et al. Estimate of the slope in the transient frequency provides us the information that allows us to identify the locations of plasma walls, and the east-west scale of the irregularity can be estimated. In our case we obtain about 24 km in the east-west scale. Furthermore, the height location of density irregularities that cause the diffraction pattern is estimated to be between 310 and 330 km, that is, around the F peak during observation.

Novel Perspectives on the Characterization of Species-Dependent Optical Signatures of Bacterial Colonies by Digital Holography

PubMed Central

Buzalewicz, Igor; Kujawińska, Małgorzata; Krauze, Wojciech; Podbielska, Halina

2016-01-01

The use of light diffraction for the microbiological diagnosis of bacterial colonies was a significant breakthrough with widespread implications for the food industry and clinical practice. We previously confirmed that optical sensors for bacterial colony light diffraction can be used for bacterial identification. This paper is focused on the novel perspectives of this method based on digital in-line holography (DIH), which is able to reconstruct the amplitude and phase properties of examined objects, as well as the amplitude and phase patterns of the optical field scattered/diffracted by the bacterial colony in any chosen observation plane behind the object from single digital hologram. Analysis of the amplitude and phase patterns inside a colony revealed its unique optical properties, which are associated with the internal structure and geometry of the bacterial colony. Moreover, on a computational level, it is possible to select the desired scattered/diffracted pattern within the entire observation volume that exhibits the largest amount of unique, differentiating bacterial features. These properties distinguish this method from the already proposed sensing techniques based on light diffraction/scattering of bacterial colonies. The reconstructed diffraction patterns have a similar spatial distribution as the recorded Fresnel patterns, previously applied for bacterial identification with over 98% accuracy, but they are characterized by both intensity and phase distributions. Our results using digital holography provide new optical discriminators of bacterial species revealed in one single step in form of new optical signatures of bacterial colonies: digital holograms, reconstructed amplitude and phase patterns, as well as diffraction patterns from all observation space, which exhibit species-dependent features. To the best of our knowledge, this is the first report on bacterial colony analysis via digital holography and our study represents an innovative approach to the subject. PMID:26943121

Local Context Finder (LCF) reveals multidimensional relationships among mRNA expression profiles of Arabidopsis responding to pathogen infection

PubMed Central

Katagiri, Fumiaki; Glazebrook, Jane

2003-01-01

A major task in computational analysis of mRNA expression profiles is definition of relationships among profiles on the basis of similarities among them. This is generally achieved by pattern recognition in the distribution of data points representing each profile in a high-dimensional space. Some drawbacks of commonly used pattern recognition algorithms stem from their use of a globally linear space and/or limited degrees of freedom. A pattern recognition method called Local Context Finder (LCF) is described here. LCF uses nonlinear dimensionality reduction for pattern recognition. Then it builds a network of profiles based on the nonlinear dimensionality reduction results. LCF was used to analyze mRNA expression profiles of the plant host Arabidopsis interacting with the bacterial pathogen Pseudomonas syringae. In one case, LCF revealed two dimensions essential to explain the effects of the NahG transgene and the ndr1 mutation on resistant and susceptible responses. In another case, plant mutants deficient in responses to pathogen infection were classified on the basis of LCF analysis of their profiles. The classification by LCF was consistent with the results of biological characterization of the mutants. Thus, LCF is a powerful method for extracting information from expression profile data. PMID:12960373

Replicability of time-varying connectivity patterns in large resting state fMRI samples.

PubMed

Abrol, Anees; Damaraju, Eswar; Miller, Robyn L; Stephen, Julia M; Claus, Eric D; Mayer, Andrew R; Calhoun, Vince D

2017-12-01

The past few years have seen an emergence of approaches that leverage temporal changes in whole-brain patterns of functional connectivity (the chronnectome). In this chronnectome study, we investigate the replicability of the human brain's inter-regional coupling dynamics during rest by evaluating two different dynamic functional network connectivity (dFNC) analysis frameworks using 7 500 functional magnetic resonance imaging (fMRI) datasets. To quantify the extent to which the emergent functional connectivity (FC) patterns are reproducible, we characterize the temporal dynamics by deriving several summary measures across multiple large, independent age-matched samples. Reproducibility was demonstrated through the existence of basic connectivity patterns (FC states) amidst an ensemble of inter-regional connections. Furthermore, application of the methods to conservatively configured (statistically stationary, linear and Gaussian) surrogate datasets revealed that some of the studied state summary measures were indeed statistically significant and also suggested that this class of null model did not explain the fMRI data fully. This extensive testing of reproducibility of similarity statistics also suggests that the estimated FC states are robust against variation in data quality, analysis, grouping, and decomposition methods. We conclude that future investigations probing the functional and neurophysiological relevance of time-varying connectivity assume critical importance. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Replicability of time-varying connectivity patterns in large resting state fMRI samples

PubMed Central

Abrol, Anees; Damaraju, Eswar; Miller, Robyn L.; Stephen, Julia M.; Claus, Eric D.; Mayer, Andrew R.; Calhoun, Vince D.

2018-01-01

The past few years have seen an emergence of approaches that leverage temporal changes in whole-brain patterns of functional connectivity (the chronnectome). In this chronnectome study, we investigate the replicability of the human brain’s inter-regional coupling dynamics during rest by evaluating two different dynamic functional network connectivity (dFNC) analysis frameworks using 7 500 functional magnetic resonance imaging (fMRI) datasets. To quantify the extent to which the emergent functional connectivity (FC) patterns are reproducible, we characterize the temporal dynamics by deriving several summary measures across multiple large, independent age-matched samples. Reproducibility was demonstrated through the existence of basic connectivity patterns (FC states) amidst an ensemble of inter-regional connections. Furthermore, application of the methods to conservatively configured (statistically stationary, linear and Gaussian) surrogate datasets revealed that some of the studied state summary measures were indeed statistically significant and also suggested that this class of null model did not explain the fMRI data fully. This extensive testing of reproducibility of similarity statistics also suggests that the estimated FC states are robust against variation in data quality, analysis, grouping, and decomposition methods. We conclude that future investigations probing the functional and neurophysiological relevance of time-varying connectivity assume critical importance. PMID:28916181

Linguistic Pattern Analysis of Misspellings of Typically Developing Writers in Grades 1 to 9

PubMed Central

Bahr, Ruth Huntley; Silliman, Elaine R.; Berninger, Virginia W.; Dow, Michael

2012-01-01

Purpose A mixed methods approach, evaluating triple word form theory, was used to describe linguistic patterns of misspellings. Method Spelling errors were taken from narrative and expository writing samples provided by 888 typically developing students in grades 1–9. Errors were coded by category (phonological, orthographic, and morphological) and specific linguistic feature affected. Grade level effects were analyzed with trend analysis. Qualitative analyses determined frequent error types and how use of specific linguistic features varied across grades. Results Phonological, orthographic, and morphological errors were noted across all grades, but orthographic errors predominated. Linear trends revealed developmental shifts in error proportions for the orthographic and morphological categories between grades 4–5. Similar error types were noted across age groups but the nature of linguistic feature error changed with age. Conclusions Triple word-form theory was supported. By grade 1, orthographic errors predominated and phonological and morphological error patterns were evident. Morphological errors increased in relative frequency in older students, probably due to a combination of word-formation issues and vocabulary growth. These patterns suggest that normal spelling development reflects non-linear growth and that it takes a long time to develop a robust orthographic lexicon that coordinates phonology, orthography, and morphology and supports word-specific, conventional spelling. PMID:22473834

Dynamics of Disagreement: Large-Scale Temporal Network Analysis Reveals Negative Interactions in Online Collaboration

NASA Astrophysics Data System (ADS)

Tsvetkova, Milena; García-Gavilanes, Ruth; Yasseri, Taha

2016-11-01

Disagreement and conflict are a fact of social life. However, negative interactions are rarely explicitly declared and recorded and this makes them hard for scientists to study. In an attempt to understand the structural and temporal features of negative interactions in the community, we use complex network methods to analyze patterns in the timing and configuration of reverts of article edits to Wikipedia. We investigate how often and how fast pairs of reverts occur compared to a null model in order to control for patterns that are natural to the content production or are due to the internal rules of Wikipedia. Our results suggest that Wikipedia editors systematically revert the same person, revert back their reverter, and come to defend a reverted editor. We further relate these interactions to the status of the involved editors. Even though the individual reverts might not necessarily be negative social interactions, our analysis points to the existence of certain patterns of negative social dynamics within the community of editors. Some of these patterns have not been previously explored and carry implications for the knowledge collection practice conducted on Wikipedia. Our method can be applied to other large-scale temporal collaboration networks to identify the existence of negative social interactions and other social processes.

Monogamy on the Street: A Mixed Methods Study of Homeless Men

ERIC Educational Resources Information Center

Brown, Ryan A.; Kennedy, David P.; Tucker, Joan S.; Golinelli, Daniela; Wenzel, Suzanne L.

2013-01-01

In this study, we used a mixed methods approach to explore the determinants of relationship patterns and risky sex among homeless men living in downtown Los Angeles. This involved analysis of qualitative interviews focused on gender ideology and sexual events ("n" = 30) as well as structured interviews ("n" = 305) focused on…

Clinical Practice Patterns and Beliefs in the Management of Hamstrings Strain Injuries.

PubMed

Di Trani Lobacz, Andrea; Glutting, Joseph; Kaminski, Thomas W

2016-02-01

Hamstrings strain injuries (HSIs) are among the most commonly occurring injuries in sport and are top causes of missed playing time. Lingering symptoms, prolonged recovery, and a high reinjury rate (12%-34%) make HSI management a frustrating and challenging process for the athletic trainer (AT). The clinical practice patterns and opinions of ATs regarding HSI treatment and rehabilitation are unknown. To examine the frequency of method use and opinions about current HSI management among ATs. Cross-sectional study. Survey administered to registrants at the 2013 National Athletic Trainers' Association Clinical Symposia and AT Expo. A total of 1356 certified ATs (691 men, 665 women; age = 35.4 ± 10.5 years, time certified = 11.92 ± 9.75 years). A survey was distributed electronically to 7272 registrants and on paper to another 700 attendees. Validity and reliability were established before distribution. Participants reported demographic information and rated their frequency of treatment and rehabilitation method use and agreement with questions assessing confidence, satisfaction, and desire for better clinical practice guidelines. Exploratory factor analysis and principal axis factor analysis were used. We also calculated descriptive statistics and χ(2) tests to assess practice patterns. The response rate was 17% (n = 1356). A 2-factor solution was accepted for factor analysis (r = 0.76, r = 0.70), indicating that ATs follow either a contemporary or traditional management style. Various practice patterns were evident across employment settings and years of clinical experience. Satisfaction with the current HSI management plan was high (73.6%), whereas confidence in returning an athlete to play was lower (62.0%). Rates of use were associated with belief in effectiveness for all methods assessed (P < .001). Higher confidence levels were associated with high use of several methods; we observed increased satisfaction (χ(2)2 = 22.5, P = .002) but not increased confidence levels in more experienced ATs. Our study demonstrated the lack of consensus in HSI treatment and rehabilitation and the ATs' desire for better clinical practice guidelines. Future research in which multimodal strategies, including both traditional and contemporary methods, are studied is warranted for effective management of HSI.

Dietary pattern analysis: a comparison between matched vegetarian and omnivorous subjects.

PubMed

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.

A new method for locating changes in a tree reveals distinct nucleotide polymorphism vs. divergence patterns in mouse mitochondrial control region.

PubMed

Galtier, N; Boursot, P

2000-03-01

A new, model-based method was devised to locate nucleotide changes in a given phylogenetic tree. For each site, the posterior probability of any possible change in each branch of the tree is computed. This probabilistic method is a valuable alternative to the maximum parsimony method when base composition is skewed (i.e., different from 25% A, 25% C, 25% G, 25% T): computer simulations showed that parsimony misses more rare --> common than common --> rare changes, resulting in biased inferred change matrices, whereas the new method appeared unbiased. The probabilistic method was applied to the analysis of the mutation and substitution processes in the mitochondrial control region of mouse. Distinct change patterns were found at the polymorphism (within species) and divergence (between species) levels, rejecting the hypothesis of a neutral evolution of base composition in mitochondrial DNA.

Infant feeding patterns over the first year of life: influence of family characteristics

PubMed Central

Betoko, Aisha; Charles, Marie-Aline; Hankard, Régis; Forhan, Anne; Bonet, Mercedes; Saurel-Cubizolles, Marie-Josephe; Heude, Barbara; De Lauzon-Guillain, Blandine

2013-01-01

Background/Objectives Early eating patterns and behaviors can determine later eating habits and food preferences and they have been related to the development of childhood overweight and obesity. We aimed to identify patterns of feeding in the first year of life and to examine their associations with family characteristics. Subjects/Methods Our analysis included 1004 infants from the EDEN mother-child cohort. Feeding practices were assessed through maternal self-report at birth, 4, 8 and 12 months. Principal component analysis was applied to derive patterns from breastfeeding duration, age at complementary food (CF) introduction and type of food used at 1y. Associations between patterns and family characteristics were analyzed by linear regressions. Results The main source of variability in infant feeding was characterized by a pattern labeled ‘Late CF introduction and use of ready-prepared baby foods’. Older, more educated, primiparous women with high monthly income ranked high on this pattern. The second pattern, labeled ‘Longer breastfeeding, late CF introduction and use of home-made foods’ was the closest to infant feeding guidelines. Mothers ranking high on this pattern were older and more educated. The third pattern, labeled ‘Use of adults’ foods’ suggests a less age-specific diet for the infants. Mothers ranking high on this pattern were often younger and multiparous. Recruitment center was related to all patterns. Conclusion Not only maternal education level and age but also parity and region are important contributors to the variability in patterns. Further studies are needed to describe associations between these patterns and infant growth and later food preferences. PMID:23299715

Analysis of cylindrical wrap-around and doubly conformal patch antennas by way of the finite element-artificial absorber method

NASA Technical Reports Server (NTRS)

Volakis, J. L.; Kempel, L. C.; Sliva, R.; Wang, H. T. G.; Woo, A. G.

1994-01-01

The goal of this project was to develop analysis codes for computing the scattering and radiation of antennas on cylindrically and doubly conformal platforms. The finite element-boundary integral (FE-BI) method has been shown to accurately model the scattering and radiation of cavity-backed patch antennas. Unfortunately extension of this rigorous technique to coated or doubly curved platforms is cumbersome and inefficient. An alternative approximate approach is to employ an absorbing boundary condition (ABC) for terminating the finite element mesh thus avoiding use of a Green's function. A FE-ABC method is used to calculate the radar cross section (RCS) and radiation pattern of a cavity-backed patch antenna which is recessed within a metallic surface. It is shown that this approach is accurate for RCS and antenna pattern calculations with an ABC surface displaced as little as 0.3 lambda from the cavity aperture. These patch antennas may have a dielectric overlay which may also be modeled with this technique.

Joint mapping of genes and conditions via multidimensional unfolding analysis

PubMed Central

Van Deun, Katrijn; Marchal, Kathleen; Heiser, Willem J; Engelen, Kristof; Van Mechelen, Iven

2007-01-01

Background Microarray compendia profile the expression of genes in a number of experimental conditions. Such data compendia are useful not only to group genes and conditions based on their similarity in overall expression over profiles but also to gain information on more subtle relations between genes and conditions. Getting a clear visual overview of all these patterns in a single easy-to-grasp representation is a useful preliminary analysis step: We propose to use for this purpose an advanced exploratory method, called multidimensional unfolding. Results We present a novel algorithm for multidimensional unfolding that overcomes both general problems and problems that are specific for the analysis of gene expression data sets. Applying the algorithm to two publicly available microarray compendia illustrates its power as a tool for exploratory data analysis: The unfolding analysis of a first data set resulted in a two-dimensional representation which clearly reveals temporal regulation patterns for the genes and a meaningful structure for the time points, while the analysis of a second data set showed the algorithm's ability to go beyond a mere identification of those genes that discriminate between different patient or tissue types. Conclusion Multidimensional unfolding offers a useful tool for preliminary explorations of microarray data: By relying on an easy-to-grasp low-dimensional geometric framework, relations among genes, among conditions and between genes and conditions are simultaneously represented in an accessible way which may reveal interesting patterns in the data. An additional advantage of the method is that it can be applied to the raw data without necessitating the choice of suitable genewise transformations of the data. PMID:17550582

Preprocessing with image denoising and histogram equalization for endoscopy image analysis using texture analysis.

PubMed

Hiroyasu, Tomoyuki; Hayashinuma, Katsutoshi; Ichikawa, Hiroshi; Yagi, Nobuaki

2015-08-01

A preprocessing method for endoscopy image analysis using texture analysis is proposed. In a previous study, we proposed a feature value that combines a co-occurrence matrix and a run-length matrix to analyze the extent of early gastric cancer from images taken with narrow-band imaging endoscopy. However, the obtained feature value does not identify lesion zones correctly due to the influence of noise and halation. Therefore, we propose a new preprocessing method with a non-local means filter for de-noising and contrast limited adaptive histogram equalization. We have confirmed that the pattern of gastric mucosa in images can be improved by the proposed method. Furthermore, the lesion zone is shown more correctly by the obtained color map.

Bilinear Time-frequency Analysis for Lamb Wave Signal Detected by Electromagnetic Acoustic Transducer

NASA Astrophysics Data System (ADS)

Sun, Wenxiu; Liu, Guoqiang; Xia, Hui; Xia, Zhengwu

2018-03-01

Accurate acquisition of the detection signal travel time plays a very important role in cross-hole tomography. The experimental platform of aluminum plate under the perpendicular magnetic field is established and the bilinear time-frequency analysis methods, Wigner-Ville Distribution (WVD) and the pseudo-Wigner-Ville distribution (PWVD), are applied to analyse the Lamb wave signals detected by electromagnetic acoustic transducer (EMAT). By extracting the same frequency component of the time-frequency spectrum as the excitation frequency, the travel time information can be obtained. In comparison with traditional linear time-frequency analysis method such as short-time Fourier transform (STFT), the bilinear time-frequency analysis method PWVD is more appropriate in extracting travel time and recognizing patterns of Lamb wave.

Extended quantification of the generalized recurrence plot

NASA Astrophysics Data System (ADS)

Riedl, Maik; Marwan, Norbert; Kurths, Jürgen

2016-04-01

The generalized recurrence plot is a modern tool for quantification of complex spatial patterns. Its application spans the analysis of trabecular bone structures, Turing structures, turbulent spatial plankton patterns, and fractals. But, it is also successfully applied to the description of spatio-temporal dynamics and the detection of regime shifts, such as in the complex Ginzburg-Landau- equation. The recurrence plot based determinism is a central measure in this framework quantifying the level of regularities in temporal and spatial structures. We extend this measure for the generalized recurrence plot considering additional operations of symmetry than the simple translation. It is tested not only on two-dimensional regular patterns and noise but also on complex spatial patterns reconstructing the parameter space of the complex Ginzburg-Landau-equation. The extended version of the determinism resulted in values which are consistent to the original recurrence plot approach. Furthermore, the proposed method allows a split of the determinism into parts which based on laminar and non-laminar regions of the two-dimensional pattern of the complex Ginzburg-Landau-equation. A comparison of these parts with a standard method of image classification, the co-occurrence matrix approach, shows differences especially in the description of patterns associated with turbulence. In that case, it seems that the extended version of the determinism allows a distinction of phase turbulence and defect turbulence by means of their spatial patterns. This ability of the proposed method promise new insights in other systems with turbulent dynamics coming from climatology, biology, ecology, and social sciences, for example.

Using RSAT oligo-analysis and dyad-analysis tools to discover regulatory signals in nucleic sequences.

PubMed

Defrance, Matthieu; Janky, Rekin's; Sand, Olivier; van Helden, Jacques

2008-01-01

This protocol explains how to discover functional signals in genomic sequences by detecting over- or under-represented oligonucleotides (words) or spaced pairs thereof (dyads) with the Regulatory Sequence Analysis Tools (http://rsat.ulb.ac.be/rsat/). Two typical applications are presented: (i) predicting transcription factor-binding motifs in promoters of coregulated genes and (ii) discovering phylogenetic footprints in promoters of orthologous genes. The steps of this protocol include purging genomic sequences to discard redundant fragments, discovering over-represented patterns and assembling them to obtain degenerate motifs, scanning sequences and drawing feature maps. The main strength of the method is its statistical ground: the binomial significance provides an efficient control on the rate of false positives. In contrast with optimization-based pattern discovery algorithms, the method supports the detection of under- as well as over-represented motifs. Computation times vary from seconds (gene clusters) to minutes (whole genomes). The execution of the whole protocol should take approximately 1 h.

Video tracking analysis of behavioral patterns during estrus in goats

PubMed Central

ENDO, Natsumi; RAHAYU, Larasati Puji; ARAKAWA, Toshiya; TANAKA, Tomomi

2015-01-01

Here, we report a new method for measuring behavioral patterns during estrus in goats based on video tracking analysis. Data were collected from cycling goats, which were in estrus (n = 8) or not in estrus (n = 8). An observation pen (2.5 m × 2.5 m) was set up in the corner of the female paddock with one side adjacent to a male paddock. The positions and movements of goats were tracked every 0.5 sec for 10 min by using a video tracking software, and the trajectory data were used for the analysis. There were no significant differences in the durations of standing and walking or the total length of movement. However, the number of approaches to a male and the duration of staying near the male were higher in goats in estrus than in goats not in estrus. The proposed evaluation method may be suitable for detailed monitoring of behavioral changes during estrus in goats. PMID:26560676

Error Analysis in Mathematics. Technical Report #1012

ERIC Educational Resources Information Center

Lai, Cheng-Fei

2012-01-01

Error analysis is a method commonly used to identify the cause of student errors when they make consistent mistakes. It is a process of reviewing a student's work and then looking for patterns of misunderstanding. Errors in mathematics can be factual, procedural, or conceptual, and may occur for a number of reasons. Reasons why students make…

LGBTQ Studies and Interdisciplinarity: A Citation Analysis of Master's Theses

ERIC Educational Resources Information Center

Graziano, Vince

2018-01-01

Emergent programs or newly established areas of study are often viewed as interdisciplinary. But how is interdisciplinarity defined or measured? The identification of research methods and the selection of objects of inquiry are significant elements in this definition. Citation analysis, however, also plays a role. Citation patterns in master's…

An Economic Analysis of College Scholarship Policy.

ERIC Educational Resources Information Center

Owen, John D.

A national scholarship policy based on a cost-benefit analysis of the social value of education is proposed as one method for improving current patterns of allocating US college scholarships and tuition funds. A central college subsidy agency, operating on a limited budget, would be required to allocate funds according to the maximum overall…

Automatic classification of acetowhite temporal patterns to identify precursor lesions of cervical cancer

NASA Astrophysics Data System (ADS)

Gutiérrez-Fragoso, K.; Acosta-Mesa, H. G.; Cruz-Ramírez, N.; Hernández-Jiménez, R.

2013-12-01

Cervical cancer has remained, until now, as a serious public health problem in developing countries. The most common method of screening is the Pap test or cytology. When abnormalities are reported in the result, the patient is referred to a dysplasia clinic for colposcopy. During this test, a solution of acetic acid is applied, which produces a color change in the tissue and is known as acetowhitening phenomenon. This reaction aims to obtaining a sample of tissue and its histological analysis let to establish a final diagnosis. During the colposcopy test, digital images can be acquired to analyze the behavior of the acetowhitening reaction from a temporal approach. In this way, we try to identify precursor lesions of cervical cancer through a process of automatic classification of acetowhite temporal patterns. In this paper, we present the performance analysis of three classification methods: kNN, Naïve Bayes and C4.5. The results showed that there is similarity between some acetowhite temporal patterns of normal and abnormal tissues. Therefore we conclude that it is not sufficient to only consider the temporal dynamic of the acetowhitening reaction to establish a diagnosis by an automatic method. Information from cytologic, colposcopic and histopathologic disciplines should be integrated as well.

Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis.

PubMed

Christodoulidis, Stergios; Anthimopoulos, Marios; Ebner, Lukas; Christe, Andreas; Mougiakakou, Stavroula

2017-01-01

Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns, and generates a map of pathologies. In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem. In this study, we present an improved method for training the proposed network by transferring knowledge from the similar domain of general texture classification. Six publicly available texture databases are used to pretrain networks with the proposed architecture, which are then fine-tuned on the lung tissue data. The resulting CNNs are combined in an ensemble and their fused knowledge is compressed back to a network with the original architecture. The proposed approach resulted in an absolute increase of about 2% in the performance of the proposed CNN. The results demonstrate the potential of transfer learning in the field of medical image analysis, indicate the textural nature of the problem and show that the method used for training a network can be as important as designing its architecture.

Dietary patterns in pregnancy and birth weight.

PubMed

Coelho, Natália de Lima Pereira; Cunha, Diana Barbosa; Esteves, Ana Paula Pereira; Lacerda, Elisa Maria de Aquino; Theme Filha, Mariza Miranda

2015-01-01

OBJECTIVE To analyze if dietary patterns during the third gestational trimester are associated with birth weight.METHODS Longitudinal study conducted in the cities of Petropolis and Queimados, Rio de Janeiro (RJ), Southeastern Brazil, between 2007 and 2008. We analyzed data from the first and second follow-up wave of a prospective cohort. Food consumption of 1,298 pregnant women was assessed using a semi-quantitative questionnaire about food frequency. Dietary patterns were obtained by exploratory factor analysis, using the Varimax rotation method. We also applied the multivariate linear regression model to estimate the association between food consumption patterns and birth weight.RESULTS Four patterns of consumption - which explain 36.4% of the variability - were identified and divided as follows: (1) prudent pattern (milk, yogurt, cheese, fruit and fresh-fruit juice, cracker, and chicken/beef/fish/liver), which explained 14.9% of the consumption; (2) traditional pattern, consisting of beans, rice, vegetables, breads, butter/margarine and sugar, which explained 8.8% of the variation in consumption; (3) Western pattern (potato/cassava/yams, macaroni, flour/farofa/grits, pizza/hamburger/deep fried pastries, soft drinks/cool drinks and pork/sausages/egg), which accounts for 6.9% of the variance; and (4) snack pattern (sandwich cookie, salty snacks, chocolate, and chocolate drink mix), which explains 5.7% of the consumption variability. The snack dietary pattern was positively associated with birth weight (β = 56.64; p = 0.04) in pregnant adolescents.CONCLUSIONS For pregnant adolescents, the greater the adherence to snack pattern during pregnancy, the greater the baby's birth weight.

The analysis of image feature robustness using cometcloud

PubMed Central

Qi, Xin; Kim, Hyunjoo; Xing, Fuyong; Parashar, Manish; Foran, David J.; Yang, Lin

2012-01-01

The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval. PMID:23248759

Non-random nature of spontaneous mIPSCs in mouse auditory brainstem neurons revealed by recurrence quantification analysis

PubMed Central

Leao, Richardson N; Leao, Fabricio N; Walmsley, Bruce

2005-01-01

A change in the spontaneous release of neurotransmitter is a useful indicator of processes occurring within presynaptic terminals. Linear techniques (e.g. Fourier transform) have been used to analyse spontaneous synaptic events in previous studies, but such methods are inappropriate if the timing pattern is complex. We have investigated spontaneous glycinergic miniature synaptic currents (mIPSCs) in principal cells of the medial nucleus of the trapezoid body. The random versus deterministic (or periodic) nature of mIPSCs was assessed using recurrence quantification analysis. Nonlinear methods were then used to quantify any detected determinism in spontaneous release, and to test for chaotic or fractal patterns. Modelling demonstrated that this procedure is much more sensitive in detecting periodicities than conventional techniques. mIPSCs were found to exhibit periodicities that were abolished by blockade of internal calcium stores with ryanodine, suggesting calcium oscillations in the presynaptic inhibitory terminals. Analysis indicated that mIPSC occurrences were chaotic in nature. Furthermore, periodicities were less evident in congenitally deaf mice than in normal mice, indicating that appropriate neural activity during development is necessary for the expression of deterministic chaos in mIPSC patterns. We suggest that chaotic oscillations of mIPSC occurrences play a physiological role in signal processing in the auditory brainstem. PMID:16271982

A shape-preserving oriented partial differential equation based on a new fidelity term for electronic speckle pattern interferometry fringe patterns denoising

NASA Astrophysics Data System (ADS)

Xu, Wenjun; Tang, Chen; Zheng, Tingyue; Qiu, Yue

2018-07-01

Oriented partial differential equations (OPDEs) have been demonstrated to be a powerful tool for preserving the integrity of fringes while filtering electronic speckle pattern interferometry (ESPI) fringe patterns. However, the main drawback of OPDEs-based methods is that many iterations are often needed, which causes the change in the shape of fringes. Change in the shape of fringes will affect the accuracy of subsequent fringe analysis. In this paper, we focus on preserving the shape of fringes while filtering, suggested here for the first time. We propose a shape-preserving OPDE for ESPI fringe patterns denoising by introducing a new fidelity term to the previous second-order single oriented PDE (SOOPDE). In our proposed fidelity term, the evolution image is subtracted from the shrinkage result of original noisy image by shearlet transform. Our proposed shape-preserving OPDE is capable of eliminating noise effectively, keeping the integrity of fringes, and more importantly, preserving the shape of fringes. We test the proposed shape-preserving OPDE on three computer-simulated and three experimentally obtained ESPI fringe patterns with poor quality. Furthermore, we compare our model with three representative filtering methods, including the widely used SOOPDE, shearlet transform and coherence-enhancing diffusion (CED). We also compare our proposed fidelity term with the traditional fidelity term. Experimental results show that the proposed shape-preserving OPDE not only yields filtered images with visual quality on par with those by CED which is the state-of-the-art method for ESPI fringe patterns denoising, but also keeps the shape of ESPI fringe patterns.

Necessary Sequencing Depth and Clustering Method to Obtain Relatively Stable Diversity Patterns in Studying Fish Gut Microbiota.

PubMed

Xiao, Fanshu; Yu, Yuhe; Li, Jinjin; Juneau, Philippe; Yan, Qingyun

2018-05-25

The 16S rRNA gene is one of the most commonly used molecular markers for estimating bacterial diversity during the past decades. However, there is no consistency about the sequencing depth (from thousand to millions of sequences per sample), and the clustering methods used to generate OTUs may also be different among studies. These inconsistent premises make effective comparisons among studies difficult or unreliable. This study aims to examine the necessary sequencing depth and clustering method that would be needed to ensure a stable diversity patterns for studying fish gut microbiota. A total number of 42 samples dataset of Siniperca chuatsi (carnivorous fish) gut microbiota were used to test how the sequencing depth and clustering may affect the alpha and beta diversity patterns of fish intestinal microbiota. Interestingly, we found that the sequencing depth (resampling 1000-11,000 per sample) and the clustering methods (UPARSE and UCLUST) did not bias the estimates of the diversity patterns during the fish development from larva to adult. Although we should acknowledge that a suitable sequencing depth may differ case by case, our finding indicates that a shallow sequencing such as 1000 sequences per sample may be also enough to reflect the general diversity patterns of fish gut microbiota. However, we have shown in the present study that strict pre-processing of the original sequences is required to ensure reliable results. This study provides evidences to help making a strong scientific choice of the sequencing depth and clustering method for future studies on fish gut microbiota patterns, but at the same time reducing as much as possible the costs related to the analysis.

Visual analysis and exploration of complex corporate shareholder networks

NASA Astrophysics Data System (ADS)

Tekušová, Tatiana; Kohlhammer, Jörn

2008-01-01

The analysis of large corporate shareholder network structures is an important task in corporate governance, in financing, and in financial investment domains. In a modern economy, large structures of cross-corporation, cross-border shareholder relationships exist, forming complex networks. These networks are often difficult to analyze with traditional approaches. An efficient visualization of the networks helps to reveal the interdependent shareholding formations and the controlling patterns. In this paper, we propose an effective visualization tool that supports the financial analyst in understanding complex shareholding networks. We develop an interactive visual analysis system by combining state-of-the-art visualization technologies with economic analysis methods. Our system is capable to reveal patterns in large corporate shareholder networks, allows the visual identification of the ultimate shareholders, and supports the visual analysis of integrated cash flow and control rights. We apply our system on an extensive real-world database of shareholder relationships, showing its usefulness for effective visual analysis.

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705