WorldWideScience

Sample records for group discriminant analysis

  1. On the Variable Selection Problem in Multiple Group Discriminant Analysis.

    Science.gov (United States)

    Huberty, Carl J.

    This study was concerned with various schemes for reducing the number of variables in a multivariate analysis. Two sets of illustrative data were used; the numbers of criterion groups were 3 and 5. The proportion of correct classifications was employed as an index of discriminatory power of each subset of variables selected. Of the four procedures…

  2. Whole-genome single-nucleotide-polymorphism analysis for discrimination of Clostridium botulinum group I strains.

    Science.gov (United States)

    Gonzalez-Escalona, Narjol; Timme, Ruth; Raphael, Brian H; Zink, Donald; Sharma, Shashi K

    2014-04-01

    Clostridium botulinum is a genetically diverse Gram-positive bacterium producing extremely potent neurotoxins (botulinum neurotoxins A through G [BoNT/A-G]). The complete genome sequences of three strains harboring only the BoNT/A1 nucleotide sequence are publicly available. Although these strains contain a toxin cluster (HA(+) OrfX(-)) associated with hemagglutinin genes, little is known about the genomes of subtype A1 strains (termed HA(-) OrfX(+)) that lack hemagglutinin genes in the toxin gene cluster. We sequenced the genomes of three BoNT/A1-producing C. botulinum strains: two strains with the HA(+) OrfX(-) cluster (69A and 32A) and one strain with the HA(-) OrfX(+) cluster (CDC297). Whole-genome phylogenic single-nucleotide-polymorphism (SNP) analysis of these strains along with other publicly available C. botulinum group I strains revealed five distinct lineages. Strains 69A and 32A clustered with the C. botulinum type A1 Hall group, and strain CDC297 clustered with the C. botulinum type Ba4 strain 657. This study reports the use of whole-genome SNP sequence analysis for discrimination of C. botulinum group I strains and demonstrates the utility of this analysis in quickly differentiating C. botulinum strains harboring identical toxin gene subtypes. This analysis further supports previous work showing that strains CDC297 and 657 likely evolved from a common ancestor and independently acquired separate BoNT/A1 toxin gene clusters at distinct genomic locations.

  3. PRICE DISCRIMINATION THROUGH GROUP BUYING

    OpenAIRE

    2016-01-01

    This paper argues that when consumers are heterogeneous in group-buying costs, a monopolist seller may practice price discrimination through inducing certain consumers to participate in group buying. In contrast to the standard model, the optimal quantity/quality level for low valuation consumers without group buying is further distorted downward, whereas the levels for other consumers are socially optimal. Inducing group buying is more favorable when the proportion of high valuation consumer...

  4. Variable Selection in Discriminant Analysis.

    Science.gov (United States)

    Huberty, Carl J.; Mourad, Salah A.

    Methods for ordering and selecting variables for discriminant analysis in multiple group comparison or group prediction studies include: univariate Fs, stepwise analysis, learning discriminant function (LDF) variable correlations, communalities, LDF standardized coefficients, and weighted standardized coefficients. Five indices based on distance,…

  5. Variable Selection in Discriminant Analysis.

    Science.gov (United States)

    Huberty, Carl J.; Mourad, Salah A.

    Methods for ordering and selecting variables for discriminant analysis in multiple group comparison or group prediction studies include: univariate Fs, stepwise analysis, learning discriminant function (LDF) variable correlations, communalities, LDF standardized coefficients, and weighted standardized coefficients. Five indices based on distance,…

  6. Unsupervised Linear Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-nearest neighbor samples. The experimental results show our algorithm is effective.

  7. Discrimination of marine algal taxonomic groups based on fluorescence excitation emission matrix, parallel factor analysis and CHEMTAX

    Institute of Scientific and Technical Information of China (English)

    CHEN Xiaona; SURongguo; BAIYing; SHI Xiaoyong; YANG Rujun

    2014-01-01

    An in vivo three-dimensional fluorescence method for the determination of algae community structure was developed by parallel factor analysis (PARAFAC) and CHEMTAX. The PARAFAC model was applied to fluo-rescence excitation-emission matrix (EEM) of 60 algae species belonging to five divisions and 11 fluorescent components were identified according to the residual sum of squares and specificity of the composition profiles of fluorescent. By the 11 fluorescent components, the algae species at different growth stages were classified correctly at the division level using Bayesian discriminant analysis (BDA). Then the reference fluo-rescent component ratio matrix was constructed for CHEMTAX, and the EEM–PARAFAC–CHEMTAX method was developed to differentiate algae taxonomic groups. The correct discrimination ratios (CDRs) when the fluorometric method was used for single-species samples were 100% at the division level, except for Bacil-lariophyta with a CDR of 95.6%. The CDRs for the mixtures were above 94.0% for the dominant algae species and above 87.0% for the subdominant algae species. However, the CDRs of the subdominant algae species were too low to be unreliable when the relative abundance estimated was less than 15.0%. The fluorometric method was tested using the samples from the Jiaozhou Bay and the mesocosm experiments in the Xiaomai Island Bay in August 2007. The discrimination results of the dominant algae groups agreed with microscopy cell counts, as well as the subdominant algae groups of which the estimated relative abundance was above 15.0%. This technique would be of great aid when low-cost and rapid analysis is needed for samples in a large batch. The fluorometric technique has the ability to correctly identify dominant species with proper abundance both in vivo and in situ.

  8. Designs and Discriminations for Clinical Group Supervision in Counselling Psychology: An Analysis

    Science.gov (United States)

    Grigg, Glen

    2006-01-01

    Evidence suggests that group clinical supervision of counsellors and trainees is an effective mode of service delivery. However, clinical supervision is often understood to be concerned with teaching a generic set of skills. Without specifically labeling them as such, clinical supervision groups are implicitly identified as psycho-educational…

  9. Discriminant Incoherent Component Analysis.

    Science.gov (United States)

    Georgakis, Christos; Panagakis, Yannis; Pantic, Maja

    2016-05-01

    Face images convey rich information which can be perceived as a superposition of low-complexity components associated with attributes, such as facial identity, expressions, and activation of facial action units (AUs). For instance, low-rank components characterizing neutral facial images are associated with identity, while sparse components capturing non-rigid deformations occurring in certain face regions reveal expressions and AU activations. In this paper, the discriminant incoherent component analysis (DICA) is proposed in order to extract low-complexity components, corresponding to facial attributes, which are mutually incoherent among different classes (e.g., identity, expression, and AU activation) from training data, even in the presence of gross sparse errors. To this end, a suitable optimization problem, involving the minimization of nuclear-and l1 -norm, is solved. Having found an ensemble of class-specific incoherent components by the DICA, an unseen (test) image is expressed as a group-sparse linear combination of these components, where the non-zero coefficients reveal the class(es) of the respective facial attribute(s) that it belongs to. The performance of the DICA is experimentally assessed on both synthetic and real-world data. Emphasis is placed on face analysis tasks, namely, joint face and expression recognition, face recognition under varying percentages of training data corruption, subject-independent expression recognition, and AU detection by conducting experiments on four data sets. The proposed method outperforms all the methods that are compared with all the tasks and experimental settings.

  10. [Discrimination and depression in ethnic minority groups

    NARCIS (Netherlands)

    Ikram, U.Z.; Snijder, M.B.; Fassaert, T.J.; Schene, A.H.; Kunst, A.E.; Stronks, K.

    2015-01-01

    OBJECTIVE: To determine the contribution of perceived ethnic discrimination to depression in various ethnic minority groups in Amsterdam. DESIGN: Cross-sectional study. METHOD: We included participants aged 18-70 years of Dutch (n = 1,744), Asian Surinamese (n = 1,126), Creole Surinamese (n = 1,770)

  11. Action Recognition Using Discriminative Structured Trajectory Groups

    KAUST Repository

    Atmosukarto, Indriyati

    2015-01-06

    In this paper, we develop a novel framework for action recognition in videos. The framework is based on automatically learning the discriminative trajectory groups that are relevant to an action. Different from previous approaches, our method does not require complex computation for graph matching or complex latent models to localize the parts. We model a video as a structured bag of trajectory groups with latent class variables. We model action recognition problem in a weakly supervised setting and learn discriminative trajectory groups by employing multiple instance learning (MIL) based Support Vector Machine (SVM) using pre-computed kernels. The kernels depend on the spatio-temporal relationship between the extracted trajectory groups and their associated features. We demonstrate both quantitatively and qualitatively that the classification performance of our proposed method is superior to baselines and several state-of-the-art approaches on three challenging standard benchmark datasets.

  12. Discriminative Hash Tracking With Group Sparsity.

    Science.gov (United States)

    Du, Dandan; Zhang, Lihe; Lu, Huchuan; Mei, Xue; Li, Xiaoli

    2016-08-01

    In this paper, we propose a novel tracking framework based on discriminative supervised hashing algorithm. Different from previous methods, we treat tracking as a problem of object matching in a binary space. Using the hash functions, all target templates and candidates are mapped into compact binary codes, with which the target matching is conducted effectively. To be specific, we make full use of the label information to assign a compact and discriminative binary code for each sample. And to deal with out-of-sample case, multiple hash functions are trained to describe the learned binary codes, and group sparsity is introduced to the hash projection matrix to select the representative and discriminative features dynamically, which is crucial for the tracker to adapt to target appearance variations. The whole training problem is formulated as an optimization function where the hash codes and hash function are learned jointly. Extensive experiments on various challenging image sequences demonstrate the effectiveness and robustness of the proposed tracker.

  13. Discriminant Analysis on Land Grading

    Institute of Scientific and Technical Information of China (English)

    LIU Yaolin; HOU Yajuan

    2004-01-01

    This paper proposes the discriminant analysis on land grading after analyzing the common methods and discussing the Fisher's discriminant in detail. Actually this method deduces the dimension from multi to single, thus it makes the feature vectors in n-dimension change to a scalar, and use this scalar to classify samples. This paper illustrates the result by giving an example of the residential land grading by the discriminant analysis.

  14. Learning discriminative dictionary for group sparse representation.

    Science.gov (United States)

    Sun, Yubao; Liu, Qingshan; Tang, Jinhui; Tao, Dacheng

    2014-09-01

    In recent years, sparse representation has been widely used in object recognition applications. How to learn the dictionary is a key issue to sparse representation. A popular method is to use l1 norm as the sparsity measurement of representation coefficients for dictionary learning. However, the l1 norm treats each atom in the dictionary independently, so the learned dictionary cannot well capture the multisubspaces structural information of the data. In addition, the learned subdictionary for each class usually shares some common atoms, which weakens the discriminative ability of the reconstruction error of each subdictionary. This paper presents a new dictionary learning model to improve sparse representation for image classification, which targets at learning a class-specific subdictionary for each class and a common subdictionary shared by all classes. The model is composed of a discriminative fidelity, a weighted group sparse constraint, and a subdictionary incoherence term. The discriminative fidelity encourages each class-specific subdictionary to sparsely represent the samples in the corresponding class. The weighted group sparse constraint term aims at capturing the structural information of the data. The subdictionary incoherence term is to make all subdictionaries independent as much as possible. Because the common subdictionary represents features shared by all classes, we only use the reconstruction error of each class-specific subdictionary for classification. Extensive experiments are conducted on several public image databases, and the experimental results demonstrate the power of the proposed method, compared with the state-of-the-arts.

  15. Modeling the Effects of Person Group Factors on Discrimination

    Science.gov (United States)

    Humphry, Stephen M.

    2010-01-01

    Discrimination has traditionally been parameterized for items but not other empirical factors. Consequently, if person factors affect discrimination they cause misfit. However, by explicitly formulating the relationship between discrimination and the unit of a metric, it is possible to parameterize discrimination for person groups. This article…

  16. Efficient Global Programming Model for Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    M.ANGULAKSHMI

    2011-03-01

    Full Text Available Conventional statistical analysis includes the capacity to systematically assign individuals to groups. We suggest alternative assignment procedures, utilizing a set of interrelated goal programming formulations. This paper represents an effort to suggest ways by which the discriminant problem might reasonably be addressed via straightforward linear goal programming formulations. Simple and direct, such formulations may ultimately compete with conventional approaches - free of the classical assumptions and possessing a stronger intuitive appeal. We further demonstrate via simple illustration the potential of these procedures to play a significant part in addressing the discriminant problem, and indicate fundamental ideas that lay the foundation for other more sophisticated approaches.

  17. Comparing two methods of univariate discriminant analysis for sex discrimination in an Iberian population.

    Science.gov (United States)

    Jiménez-Arenas, Juan Manuel; Esquivel, José Antonio

    2013-05-10

    This study assesses the performance of two analytical approaches to sex discrimination based on single linear variables: discriminant analysis and the Lubischew's test. Ninety individuals from an archaeological population (La Torrecilla-Arenas del Rey, Granada, southern Spain) and 17 craniometrical variables were included in the analyses. Most craniometrical variables were higher for men. The bizygomatic breadth enabled the highest level of discrimination: 87.5% and 88.5%, using discriminant analysis and Lubischew's test, respectively. Bizygomatic breadth proved highly dimorphic in comparison to other populations reported in the literature. Lubischew's test raised the discrimination percentage in specific craniometrical variables, while others showed a superior performance by means of the discriminant analysis. The inconsistent results across statistical methods resulted from the specific formulation of each procedure. Discriminant analysis accounts both for within-group and between-group variance, while Lubischew's test emphasizes between-group variation only. Therefore, both techniques are recommended, as they provide different means of achieving optimal discrimination percentages.

  18. Investigating social discrimination of group members by laying hens.

    Science.gov (United States)

    Abeyesinghe, Siobhan M; McLeman, Morven A; Owen, Rachael C; McMahon, Claire E; Wathes, Christopher M

    2009-05-01

    Social relationships in domestic fowl are commonly assumed to rely on social recognition and its pre-requisite, discrimination of group-mates. If this is true, then the unnatural physical and social environments in which commercial laying hens are typically housed, when compared with those in which their progenitor species evolved, may compromise social function with consequent implications for welfare. Our aims were to determine whether adult hens can discriminate between unique pairs of familiar conspecifics, and to establish the most appropriate method for assessing this social discrimination. We investigated group-mate discrimination using two learning tasks in which there was bi-directional exchange of visual, auditory and olfactory information. Learning occurred in a Y-maze task (psocial discrimination or to the response task. Learning also failed to occur in this familiar/unfamiliar social discrimination task (p=0.001; n=1/10). Our findings demonstrate unequivocally that adult laying hens kept in small groups, under environmental conditions more consistent with those in which sensory capacities evolved, can discriminate group members: however, appropriate methods to demonstrate discrimination are crucial.

  19. Discriminant and Proximity Analysis in Intercultural Investigation.

    Science.gov (United States)

    Laveault, Dany

    1982-01-01

    Discriminant analysis is applied to data from previous research dealing with assessing the particularities of cognitive development in young (four to nine years old) Montagnais Indians and French Canadians. The most important future contribution of discriminant analysis to intercultural research will be its ability to conceptualize group…

  20. Semisupervised Sparse Multilinear Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    黄锴; 张丽清

    2014-01-01

    Various problems are encountered when adopting ordinary vector space algorithms for high-order tensor data input. Namely, one must overcome the Small Sample Size (SSS) and overfitting problems. In addition, the structural information of the original tensor signal is lost during the vectorization process. Therefore, comparable methods using a direct tensor input are more appropriate. In the case of electrocardiograms (ECGs), another problem must be overcome;the manual diagnosis of ECG data is expensive and time consuming, rendering it difficult to acquire data with diagnosis labels. However, when effective features for classification in the original data are very sparse, we propose a semisupervised sparse multilinear discriminant analysis (SSSMDA) method. This method uses the distribution of both the labeled and the unlabeled data together with labels discovered through a label propagation algorithm. In practice, we use 12-lead ECGs collected from a remote diagnosis system and apply a short-time-fourier transformation (STFT) to obtain third-order tensors. The experimental results highlight the sparsity of the ECG data and the ability of our method to extract sparse and effective features that can be used for classification.

  1. [Real groups in the minimal group paradigm; does the group context work as corrective or catalysing agent for social discrimination?].

    Science.gov (United States)

    Petersen, L E; Blank, H

    2001-01-01

    Studies applying the minimal group paradigm to analyze social discrimination processes have been analyzing for the most part the behavior of individuals. The present experiment extends the minimal group paradigm to the group level. The aim of the present study was to compare the decisions made by real groups (N = 3 persons) with those made by single persons. The analysis of the total points given to the in- or the outgroup as well as the strategy MIP + MDI on F revealed that groups are significantly more biased towards the ingroup than individuals. On the other hand, individuals use the strategy F on MIP + MDI significantly more than groups and thus show a greater amount of fairness. These conclusions are qualified by a new method of identifying dominant strategies which shows that the dominant strategy used by individuals and groups is fairness. A theoretical explanation of the results is offered based on social identity theory, the groupthink model and self-awareness theory.

  2. On discriminant analysis techniques and correlation structures in high dimensions

    DEFF Research Database (Denmark)

    Clemmensen, Line Katrine Harder

    This paper compares several recently proposed techniques for performing discriminant analysis in high dimensions, and illustrates that the various sparse methods dier in prediction abilities depending on their underlying assumptions about the correlation structures in the data. The techniques...... generally focus on two things: Obtaining sparsity (variable selection) and regularizing the estimate of the within-class covariance matrix. For high-dimensional data, this gives rise to increased interpretability and generalization ability over standard linear discriminant analysis. Here, we group...

  3. DISCRIMINANT ANALYSIS OF BANK PROFITABILITY LEVELS

    Directory of Open Access Journals (Sweden)

    Ante Rozga

    2013-02-01

    Full Text Available Discriminant analysis has been employed in this paper in order to identify and explain key features of bank profitability levels. Bank profitability is set up in the form of two categorical variables: profit or loss recorded and above or below average return on equity. Predictor variables are selected from various groups of financial indicators usually included in the empirical work on microeconomic determinants of bank profitability. The data from the Croatian banking sector is analyzed using the Enter method. General recommendations for a more profitable business of banking found in the bank management literature and existing empirical framework such as rationalization of overhead costs, asset growth, increase of non-interest income by expanding scale and scope of financial products proved to be important for classification of banks in different profitability levels. A higher market share may bring additional advantages. Classification results, canonical correlation and Wilks’ Lambda test confirm statistical significance of research results. Altogether, discriminant analysis turns out to be a suitable statistical method for solving presented research problem and moving forward from the bankruptcy, credit rating or default issues in finance.

  4. Discriminant analysis for repeated measures data: a review

    Directory of Open Access Journals (Sweden)

    Lisa Lix

    2010-09-01

    Full Text Available Discriminant analysis (DA encompasses procedures for classifying observations into groups (i.e., predictive discriminative analysis and describing the relative importance of variables for distinguishing amongst groups (i.e., descriptive discriminative analysis. In recent years, a number of developments have occurred in DA procedures for the analysis of data from repeated measures designs. Specifically, DA procedures have been developed for repeated measures data characterized by missing observations and/or unbalanced measurement occasions, as well as high-dimensional data in which measurements are collected repeatedly on two or more variables. This paper reviews the literature on DA procedures for univariate and multivariate repeated measures data, focusing on covariance pattern and linear mixed-effects models. A numeric example illustrates their implementation using SAS software.

  5. The relationships between major lifetime discrimination, everyday discrimination, and mental health in three racial and ethnic groups of older adults.

    Science.gov (United States)

    Ayalon, Liat; Gum, Amber M

    2011-07-01

    To evaluate the relationships between perceived exposure to major lifetime discrimination, everyday discrimination, and mental health in three racial/ethnic groups of older adults. The Health and Retirement Study is a nationally representative sample of individuals 50 years and older living in the United States. A total of 6455 Whites, 716 Latinos, and 1214 Blacks were eligible to complete a self-report psychosocial questionnaire in the year 2006. Whereas 30% of the general population reported at least one type of major lifetime discrimination, almost 45% of Black older adults reported such discrimination. Relative to the other two racial/ethnic groups (82% Whites, 82.6% Blacks), Latinos were significantly less likely to report any everyday discrimination (64.2%), whereas Blacks reported the greatest frequency of everyday discrimination. Whites reported the highest levels of life satisfaction and the lowest levels of depressive symptoms. Relative to major lifetime discrimination, everyday discrimination had a somewhat stronger correlation with mental health indicators. The relationships between discrimination and mental health outcomes were stronger for White compared to Black older adults, although everyday discrimination was still significantly associated with outcomes for Black older adults. Black older adults experience the greatest number of discriminative events, but weaker associated mental health outcomes. This could be because they have become accustomed to these experiences, benefit from social or cultural resources that serve as buffers, or selective survival, with the present sample capturing only the most resilient older adults who have learned to cope with the deleterious effects of discrimination.

  6. Differential Effects of Personal-Level vs Group-Level Racial Discrimination on Health among Black Americans.

    Science.gov (United States)

    Hagiwara, Nao; Alderson, Courtney J; Mezuk, Briana

    2016-07-21

    Racial/ethnic minorities in the United States not only experience discrimination personally but also witness or hear about fellow in-group members experiencing discrimination (ie, group-level discrimination). The objective of our study was to examine whether the effects of group-level discrimination on mental and physical health are different from those of personal-level discrimination among Black Americans by drawing upon social psychology research of the Personal/Group Discrimination Discrepancy. We conducted a secondary analysis of cross-sectional survey data from a larger study. One hundred and twenty participants, who self-identified as Black/African Americans during the laboratory sessions (57.5% women, mean age = 48.97, standard deviation = 8.58) in the parent study, were included in our analyses. Perceived personal-level discrimination was assessed with five items that were taken from two existing measures, and group-level racial discrimination was assessed with three items. Self-reported physical and mental health were assessed with a modified version of SF-8. Perceived personal-level racial discrimination was associated with worse mental health. In contrast, perceived group-level racial discrimination was associated with better mental as well as physical health. Perceived group-level racial discrimination may serve as one of several health protective factors even when individuals perceive personal-level racial discrimination. The present findings demonstrate the importance of examining both personal- and group-level experiences of racial discrimination as they independently relate to health outcomes for Black Americans.

  7. Legitimating Racial Discrimination: Emotions, Not Beliefs, Best Predict Discrimination in a Meta-Analysis

    OpenAIRE

    Talaska, Cara A.; Fiske, Susan T.; Chaiken, Shelly

    2008-01-01

    Investigations of racial bias have emphasized stereotypes and other beliefs as central explanatory mechanisms and as legitimating discrimination. In recent theory and research, emotional prejudices have emerged as another, more direct predictor of discrimination. A new comprehensive meta-analysis of 57 racial attitude-discrimination studies finds a moderate relationship between overall attitudes and discrimination. Emotional prejudices are twices as closely related to racial discrimination as...

  8. Incremental Discriminant Analysis in Tensor Space

    Science.gov (United States)

    Chang, Liu; Weidong, Zhao; Tao, Yan; Qiang, Pu; Xiaodan, Du

    2015-01-01

    To study incremental machine learning in tensor space, this paper proposes incremental tensor discriminant analysis. The algorithm employs tensor representation to carry on discriminant analysis and combine incremental learning to alleviate the computational cost. This paper proves that the algorithm can be unified into the graph framework theoretically and analyzes the time and space complexity in detail. The experiments on facial image detection have shown that the algorithm not only achieves sound performance compared with other algorithms, but also reduces the computational issues apparently. PMID:26339229

  9. DISCRIMINANT ANALYSIS IN MARKETING RESEARCH

    Directory of Open Access Journals (Sweden)

    Erika KULCSÁR

    2010-01-01

    Full Text Available This paper classifies among marketing researches aiming to study the influence that the following independent variables (explanatory have: "Estimating the expenses incurred in Centre Development Region, including country of residence" and the variable "How many days have you planned to stay in this region?" on the construction of separate groups which form the dependent variables - the type of tourist (foreign, Romanian. Thus I shall analyse the explication of the different characteristics of the groups in terms of different attributes that members of these groups have with respect to the independent variables (explanatory. Questionnaires have been distributed to hotels in Braşov, Predeal, Poiana-Braşov, Sfântu Gheorghe, Covasna, Miercurea-Ciuc, Gheorgheni, Tuşnad, Târgu-Mureş, Sighişoara, Sibiu, Alba – Iulia and other localities that have linked this route. More than 2,000 questionnaires have been distributed. The quantitative research was conducted between 15.05.2009-17.10.2009.

  10. Variable Selection Strategies in Discriminate Analysis.

    Science.gov (United States)

    Tanguma, Jesus

    This paper presents three variable selection strategies in discriminate analysis (all variables in the model, use of stepwise methods, and all possible subsets). All three methods are illustrated through examples. Although the all variables in the model and the stepwise methods are the most widely used, B. Thompson (1996) and C. Huberty (1994)…

  11. Attributions to Discrimination and Self-Esteem: The Role of Group Identification and Appraisals

    OpenAIRE

    Eccleston, Collette P.; Major, Brenda N.

    2006-01-01

    Abstract This study tested the hypothesis that appraisals of discrimination (i.e. its perceived severity, global aspects, stability, and uncontrollability) mediate the relationship between attributions to discrimination and personal self-esteem. It also tested three models of how ethnic group identification is related to discrimination attributions, discrimination appraisals, and personal self-esteem. In ...

  12. ODVBA: optimally-discriminative voxel-based analysis.

    Science.gov (United States)

    Zhang, Tianhao; Davatzikos, Christos

    2011-08-01

    Gaussian smoothing of images prior to applying voxel-based statistics is an important step in voxel-based analysis and statistical parametric mapping (VBA-SPM) and is used to account for registration errors, to Gaussianize the data and to integrate imaging signals from a region around each voxel. However, it has also become a limitation of VBA-SPM based methods, since it is often chosen empirically and lacks spatial adaptivity to the shape and spatial extent of the region of interest, such as a region of atrophy or functional activity. In this paper, we propose a new framework, named optimally-discriminative voxel-based analysis (ODVBA), for determining the optimal spatially adaptive smoothing of images, followed by applying voxel-based group analysis. In ODVBA, nonnegative discriminative projection is applied regionally to get the direction that best discriminates between two groups, e.g., patients and controls; this direction is equivalent to local filtering by an optimal kernel whose coefficients define the optimally discriminative direction. By considering all the neighborhoods that contain a given voxel, we then compose this information to produce the statistic for each voxel. Finally, permutation tests are used to obtain a statistical parametric map of group differences. ODVBA has been evaluated using simulated data in which the ground truth is known and with data from an Alzheimer's disease (AD) study. The experimental results have shown that the proposed ODVBA can precisely describe the shape and location of structural abnormality.

  13. Does personality explain in-group identification and discrimination? Evidence from the minimal group paradigm.

    Science.gov (United States)

    Reynolds, Katherine J; Turner, John C; Haslam, S Alexander; Ryan, Michelle K; Bizumic, Boris; Subasic, Emina

    2007-09-01

    The idea that a person's personality can help explain prejudice has a long history in social psychology. The classic counter-argument has been that prejudice is much more a function of people's group memberships and the nature of intergroup relations rather than individual differences. Bringing these two lines of research together, it has been suggested that personality factors may not only affect intergroup discrimination directly, but also indirectly by predisposing some individuals to identify more strongly with some relevant in-group membership. Two experiments were conducted to investigate this possibility. The participants completed various personality measures (e.g. authoritarianism, personal need for structure and ethnocentrism as well as social dominance orientation (SDO) in Experiment 2). They were then assigned to minimal groups either randomly, by choice, or (supposedly) on the basis of attitudinal similarity. In Experiment 2, the minimal group paradigm was also adapted to examine the role of SDO. Overall, there was no evidence of significant relationships between traditional personality measures and either in-group identification or discrimination. In-group identification alone emerged as the strongest predictor of discrimination. There was evidence that those participants who scored higher in SDO were more likely to act in ways that supported the creation of a power hierarchy. The implications for broader understanding of prejudice are discussed.

  14. Direct Neighborhood Discriminant Analysis for Face Recognition

    Directory of Open Access Journals (Sweden)

    Miao Cheng

    2008-01-01

    Full Text Available Face recognition is a challenging problem in computer vision and pattern recognition. Recently, many local geometrical structure-based techiniques are presented to obtain the low-dimensional representation of face images with enhanced discriminatory power. However, these methods suffer from the small simple size (SSS problem or the high computation complexity of high-dimensional data. To overcome these problems, we propose a novel local manifold structure learning method for face recognition, named direct neighborhood discriminant analysis (DNDA, which separates the nearby samples of interclass and preserves the local within-class geometry in two steps, respectively. In addition, the PCA preprocessing to reduce dimension to a large extent is not needed in DNDA avoiding loss of discriminative information. Experiments conducted on ORL, Yale, and UMIST face databases show the effectiveness of the proposed method.

  15. A Computational Discriminability Analysis on Twin Fingerprints

    Science.gov (United States)

    Liu, Yu; Srihari, Sargur N.

    Sharing similar genetic traits makes the investigation of twins an important study in forensics and biometrics. Fingerprints are one of the most commonly found types of forensic evidence. The similarity between twins’ prints is critical establish to the reliability of fingerprint identification. We present a quantitative analysis of the discriminability of twin fingerprints on a new data set (227 pairs of identical twins and fraternal twins) recently collected from a twin population using both level 1 and level 2 features. Although the patterns of minutiae among twins are more similar than in the general population, the similarity of fingerprints of twins is significantly different from that between genuine prints of the same finger. Twins fingerprints are discriminable with a 1.5%~1.7% higher EER than non-twins. And identical twins can be distinguished by examine fingerprint with a slightly higher error rate than fraternal twins.

  16. Face Recognition Using Kernel Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Linear Discrimiant Analysis (LDA) has demonstrated their success in face recognition. But LDA is difficult to handle the high nonlinear problems, such as changes of large viewpoint and illumination in face recognition. In order to overcome these problems, we investigate Kernel Discriminant Analysis (KDA) for face recognition. This approach adopts the kernel functions to replace the dot products of nonlinear mapping in the high dimensional feature space, and then the nonlinear problem can be solved in the input space conveniently without explicit mapping. Two face databases are used to test KDA approach. The results show that our approach outperforms the conventional PCA(Eigenface) and LDA(Fisherface) approaches.

  17. Multivariable Discriminant Analysis for the Differential Diagnosis of Microcytic Anemia

    Directory of Open Access Journals (Sweden)

    Eloísa Urrechaga

    2013-01-01

    Full Text Available Introduction. Iron deficiency anemia and thalassemia are the most common causes of microcytic anemia. Powerful statistical computer programming enables sensitive discriminant analyses to aid in the diagnosis. We aimed at investigating the performance of the multiple discriminant analysis (MDA to the differential diagnosis of microcytic anemia. Methods. The training group was composed of 200 β-thalassemia carriers, 65 α-thalassemia carriers, 170 iron deficiency anemia (IDA, and 45 mixed cases of thalassemia and acute phase response or iron deficiency. A set of potential predictor parameters that could detect differences among groups were selected: Red Blood Cells (RBC, hemoglobin (Hb, mean cell volume (MCV, mean cell hemoglobin (MCH, and RBC distribution width (RDW. The functions obtained with MDA analysis were applied to a set of 628 consecutive patients with microcytic anemia. Results. For classifying patients into two groups (genetic anemia and acquired anemia, only one function was needed; 87.9% β-thalassemia carriers, and 83.3% α-thalassemia carriers, and 72.1% in the mixed group were correctly classified. Conclusion. Linear discriminant functions based on hemogram data can aid in differentiating between IDA and thalassemia, so samples can be efficiently selected for further analysis to confirm the presence of genetic anemia.

  18. EM-63 Decay Curve Analysis for UXO Discrimination

    Science.gov (United States)

    2016-06-13

    EM-63 Decay Curve Analysis for UXO Discrimination ESTCP Contract # 200035 Final Report NAEVA Geophysics September 7...07 SEP 2001 2. REPORT TYPE 3. DATES COVERED 00-00-2001 to 00-00-2001 4. TITLE AND SUBTITLE EM-63 Decay Curve Analysis for UXO Discrimination ... Discrimination ESTCP Contract # 200035 Final Report NAEVA Geophysics September 7, 2001 Table Of Contents 1 Introduction

  19. Ethnic and gender differences in the association between discrimination and depressive symptoms among five immigrant groups.

    Science.gov (United States)

    Kim, Il-Ho; Noh, Samuel

    2014-12-01

    This study examines ethnic and gender differences in exposure to discrimination and its association with depressive symptoms among five immigrant groups. Data were derived from a cross-sectional survey of 900 adult immigrants (50.8% men, 49.2% women) sampled from five ethnic immigrant communities in Toronto between April and September 2001. Men reported higher levels of discrimination than women. Ethiopians had the highest perception of discrimination followed by Korean, Iranian, Vietnamese, and Irish immigrants. With regard to discrimination-related depressive symptoms, Iranian and Korean men showed a greater risk than their Irish counterparts. Among women, Vietnamese and Irish seemed to be more vulnerable to discrimination than other ethnic groups. Despite experiencing the highest level of discrimination, Ethiopian men and women showed no association between discrimination and depressive symptoms. The exposure and psychological response to discrimination vary significantly across ethnicities and gender.

  20. Forensic discrimination of dyed hair color: II. Multivariate statistical analysis.

    Science.gov (United States)

    Barrett, Julie A; Siegel, Jay A; Goodpaster, John V

    2011-01-01

    This research is intended to assess the ability of UV-visible microspectrophotometry to successfully discriminate the color of dyed hair. Fifty-five red hair dyes were analyzed and evaluated using multivariate statistical techniques including agglomerative hierarchical clustering (AHC), principal component analysis (PCA), and discriminant analysis (DA). The spectra were grouped into three classes, which were visually consistent with different shades of red. A two-dimensional PCA observations plot was constructed, describing 78.6% of the overall variance. The wavelength regions associated with the absorbance of hair and dye were highly correlated. Principal components were selected to represent 95% of the overall variance for analysis with DA. A classification accuracy of 89% was observed for the comprehensive dye set, while external validation using 20 of the dyes resulted in a prediction accuracy of 75%. Significant color loss from successive washing of hair samples was estimated to occur within 3 weeks of dye application.

  1. Discriminant analysis of bronchial asthma by linear discriminant function with parameters of flow-volumes: discriminant analysis of bronchial asthma in young male non-smokers

    Directory of Open Access Journals (Sweden)

    Meguro,Tadamichi

    1978-10-01

    Full Text Available With the parameters of a flow-volume and a volume-time curve, the discriminant analysis of bronchial asthma is described. The subjects were classified into three groups (healthy adults, mild asthmatic patients and moderates ones. The difference of the mean vectors of the parameters of the three groups was made clear by the selection methods of the discriminant analysis between any two of the groups both with 6 parameters (%FVC, FEV1.0%, peak flow rate (PF, flow rate at 50% of FVC (V50, flow rate at 25% of FVC (V25, and V50/V25 and with 8 (6 parameters mentioned above and V75, V10. Forced expiratory volume in 1 second percent (FEV1.0% or V50 was selected at the first step with 6 parameters, and V75 was selected at the first step with 8 parameters. Probabilities of misclassification with 8 parameters were lower than those with 6 ones and the probability of misclassification at the discriminant analysis between healthy adults and mild asthmatic patients with 8 parameters was 15.75% at the final step.

  2. Discriminant analysis with errors in variables

    CERN Document Server

    Loustau, Sébastien

    2012-01-01

    The effect of measurement error in discriminant analysis is investigated. Given observations $Z=X+\\epsilon$, where $\\epsilon$ denotes a random noise, the goal is to predict the density of $X$ among two possible candidates $f$ and $g$. We suppose that we have at our disposal two learning samples. The aim is to approach the best possible decision rule $G^*$ defined as a minimizer of the Bayes risk. In the free-noise case $(\\epsilon=0)$, minimax fast rates of convergence are well-known under the margin assumption in discriminant analysis (see \\cite{mammen}) or in the more general classification framework (see \\cite{tsybakov2004,AT}). In this paper we intend to establish similar results in the noisy case, i.e. when dealing with errors in variables. In particular, we discuss two possible complexity assumptions that can be set on the problem, which may alternatively concern the regularity of $f-g$ or the boundary of $G^*$. We prove minimax lower bounds for these both problems and explain how can these rates be atta...

  3. Discrimination in Recruitment: An Empirical Analysis.

    Science.gov (United States)

    Newman, Jerry M.

    1978-01-01

    To investigate whether recruitment practices of companies with affirmative action programs discriminated against Blacks or resulted in reverse discrimination, qualifications and race of fictitious job applicants were manipulated on resumes sent to a sample of employers. Responses strongly indicate discrimination, with Black applicants favored…

  4. Discrimination and content analysis of fritillaria using near infrared spectroscopy.

    Science.gov (United States)

    Meng, Yu; Wang, Shisheng; Cai, Rui; Jiang, Bohai; Zhao, Weijie

    2015-01-01

    Fritillaria is a traditional Chinese herbal medicine which can be used to moisten the lungs. The objective of this study is to develop simple, accurate, and solvent-free methods to discriminate and quantify Fritillaria herbs from seven different origins. Near infrared spectroscopy (NIRS) methods are established for the rapid discrimination of seven different Fritillaria samples and quantitative analysis of their total alkaloids. The scaling to first range method and the partial least square (PLS) method are used for the establishment of qualitative and quantitative analysis models. As a result of evaluation for the qualitative NIR model, the selectivity values between groups are always above 2, and the mistaken judgment rate of fifteen samples in prediction sets was zero. This means that the NIR model can be used to distinguish different species of Fritillaria herbs. The established quantitative NIR model can accurately predict the content of total alkaloids from Fritillaria samples.

  5. Group-based discrimination in judgments of moral purity-related behaviors: experimental and archival evidence.

    Science.gov (United States)

    Masicampo, E J; Barth, Maria; Ambady, Nalini

    2014-12-01

    Knowledge of individuals' group membership can alter moral judgments of their behavior. We found that such moral judgments were amplified when judgers learned that a person belonged to a group shown to elicit disgust in others. When a person was labeled as obese, a hippie, or "trailer trash," people judged that person's behavior differently than when such descriptors were omitted: Virtuous behaviors were more highly praised, and moral violations were more severely criticized. Such group-based discrimination in moral judgment was specific to the domain of moral purity. Members of disgust-eliciting groups but not members of other minorities were the target of harsh judgments for purity violations (e.g., lewd behavior) but not for other violations (e.g., refusing to help others). The same pattern held true for virtuous behaviors, so that members of disgust-eliciting groups were more highly praised than others but only in the purity domain. Furthermore, group-based discrimination was mediated by feelings of disgust toward the target group but not by other emotions. Last, analysis of New York Police Department officers' encounters with suspected criminals revealed a similar pattern to that found in laboratory experiments. Police officers were increasingly likely to make an arrest or issue a summons as body mass index increased (i.e., as obesity rose) among people suspected of purity crimes (e.g., prostitution) but not of other crimes (e.g., burglary). Thus, moral judgments in the lab and in the real world exhibit patterns of discrimination that are both group and behavior specific.

  6. Accounting for Ethnic Discrimination : A Discursive Study Among Minority and Majority Group Members

    NARCIS (Netherlands)

    Verkuyten, Maykel J. A. M.

    2005-01-01

    This article discusses the ways in which ethnic minority and majority group members account, in an interview context, for the existence of discrimination in Dutch society. Taking a discursive approach, the focus is on the strategies used to describe and explain discrimination. In both groups, certai

  7. Accounting for Ethnic Discrimination : A Discursive Study Among Minority and Majority Group Members

    NARCIS (Netherlands)

    Verkuyten, Maykel J. A. M.

    2005-01-01

    This article discusses the ways in which ethnic minority and majority group members account, in an interview context, for the existence of discrimination in Dutch society. Taking a discursive approach, the focus is on the strategies used to describe and explain discrimination. In both groups, certai

  8. Accounting for Ethnic Discrimination : A Discursive Study Among Minority and Majority Group Members

    NARCIS (Netherlands)

    Verkuyten, Maykel J. A. M.

    2005-01-01

    This article discusses the ways in which ethnic minority and majority group members account, in an interview context, for the existence of discrimination in Dutch society. Taking a discursive approach, the focus is on the strategies used to describe and explain discrimination. In both groups,

  9. The Discriminant Analysis Flare Forecasting System (DAFFS)

    Science.gov (United States)

    Leka, K. D.; Barnes, Graham; Wagner, Eric; Hill, Frank; Marble, Andrew R.

    2016-05-01

    The Discriminant Analysis Flare Forecasting System (DAFFS) has been developed under NOAA/Small Business Innovative Research funds to quantitatively improve upon the NOAA/SWPC flare prediction. In the Phase-I of this project, it was demonstrated that DAFFS could indeed improve by the requested 25% most of the standard flare prediction data products from NOAA/SWPC. In the Phase-II of this project, a prototype has been developed and is presently running autonomously at NWRA.DAFFS uses near-real-time data from NOAA/GOES, SDO/HMI, and the NSO/GONG network to issue both region- and full-disk forecasts of solar flares, based on multi-variable non-parametric Discriminant Analysis. Presently, DAFFS provides forecasts which match those provided by NOAA/SWPC in terms of thresholds and validity periods (including 1-, 2-, and 3- day forecasts), although issued twice daily. Of particular note regarding DAFFS capabilities are the redundant system design, automatically-generated validation statistics and the large range of customizable options available. As part of this poster, a description of the data used, algorithm, performance and customizable options will be presented, as well as a demonstration of the DAFFS prototype.DAFFS development at NWRA is supported by NOAA/SBIR contracts WC-133R-13-CN-0079 and WC-133R-14-CN-0103, with additional support from NASA contract NNH12CG10C, plus acknowledgment to the SDO/HMI and NSO/GONG facilities and NOAA/SWPC personnel for data products, support, and feedback. DAFFS is presently ready for Phase-III development.

  10. Perceived discrimination in U.S. healthcare: Charting the effects of key social characteristics within and across racial groups

    Directory of Open Access Journals (Sweden)

    Corey M. Abramson

    2015-01-01

    Full Text Available This article employs an original empirical analysis to contribute to scientific understandings of the relationship between social characteristics and perceptions of discrimination in healthcare encounters within and across racial categories in the U.S. Our analysis focuses on a diverse sample of 43,020 adults aged 18 to 85 drawn from the California Health Interview Survey (CHIS. We use a series of weighted descriptive statistics and logistic regression models to parse out factors associated with perceived discrimination and chart how they vary by race and ethnicity. Members of racial minorities were more likely to report perceptions of discrimination, and while the effect was somewhat mitigated by introducing patient and health-care system factors into our models, the race effects remained both statistically significant and of substantial magnitude (particularly for African Americans and Native Americans. Poor self-reported health and communication difficulties in the clinical encounter were associated with increased perceptions of discrimination across all groups. Further, among non-whites, increased education was associated with increased perceptions of discrimination net of other factors. These findings suggest efforts to reduce disparities in medical care should continue to focus on expanding the depth and quality of patient–provider interactions for disadvantaged racial groups, while also being attentive to other factors that affect perceived racial discrimination in healthcare encounters within and across racial groups.

  11. Links between patterns of racial socialization and discrimination experiences and psychological adjustment: a cluster analysis.

    Science.gov (United States)

    Ajayi, Alex A; Syed, Moin

    2014-10-01

    This study used a person-oriented analytic approach to identify meaningful patterns of barriers-focused racial socialization and perceived racial discrimination experiences in a sample of 295 late adolescents. Using cluster analysis, three distinct groups were identified: Low Barrier Socialization-Low Discrimination, High Barrier Socialization-Low Discrimination, and High Barrier Socialization-High Discrimination clusters. These groups were substantively unique in terms of the frequency of racial socialization messages about bias preparation and out-group mistrust its members received and their actual perceived discrimination experiences. Further, individuals in the High Barrier Socialization-High Discrimination cluster reported significantly higher depressive symptoms than those in the Low Barrier Socialization-Low Discrimination and High Barrier Socialization-Low Discrimination clusters. However, no differences in adjustment were observed between the Low Barrier Socialization-Low Discrimination and High Barrier Socialization-Low Discrimination clusters. Overall, the findings highlight important individual differences in how young people of color experience their race and how these differences have significant implications on psychological adjustment. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  12. Stochastic model updating using distance discrimination analysis

    Institute of Scientific and Technical Information of China (English)

    Deng Zhongmin; Bi Sifeng; Sez Atamturktur

    2014-01-01

    This manuscript presents a stochastic model updating method, taking both uncertainties in models and variability in testing into account. The updated finite element (FE) models obtained through the proposed technique can aid in the analysis and design of structural systems. The authors developed a stochastic model updating method integrating distance discrimination analysis (DDA) and advanced Monte Carlo (MC) technique to (1) enable more efficient MC by using a response surface model, (2) calibrate parameters with an iterative test-analysis correlation based upon DDA, and (3) utilize and compare different distance functions as correlation metrics. Using DDA, the influence of distance functions on model updating results is analyzed. The proposed sto-chastic method makes it possible to obtain a precise model updating outcome with acceptable cal-culation cost. The stochastic method is demonstrated on a helicopter case study updated using both Euclidian and Mahalanobis distance metrics. It is observed that the selected distance function influ-ences the iterative calibration process and thus, the calibration outcome, indicating that an integra-tion of different metrics might yield improved results.

  13. Anti-discrimination Analysis Using Privacy Attack Strategies

    KAUST Repository

    Ruggieri, Salvatore

    2014-09-15

    Social discrimination discovery from data is an important task to identify illegal and unethical discriminatory patterns towards protected-by-law groups, e.g., ethnic minorities. We deploy privacy attack strategies as tools for discrimination discovery under hard assumptions which have rarely tackled in the literature: indirect discrimination discovery, privacy-aware discrimination discovery, and discrimination data recovery. The intuition comes from the intriguing parallel between the role of the anti-discrimination authority in the three scenarios above and the role of an attacker in private data publishing. We design strategies and algorithms inspired/based on Frèchet bounds attacks, attribute inference attacks, and minimality attacks to the purpose of unveiling hidden discriminatory practices. Experimental results show that they can be effective tools in the hands of anti-discrimination authorities.

  14. Intergroup Discrimination in Positive and Negative Outcome Allocations: Impact of Stimulus Valence, Relative Group Status, and Relative Group Size.

    Science.gov (United States)

    Otten, Sabine; And Others

    1996-01-01

    Three studies investigated the determination of social discrimination by the valence of stimuli that are allocated between groups. The studies were based on either the minimal group paradigm or a more reality-based laboratory intergroup setting, with stimulus valence, group status, and group size as factors and with pull scores on Tajfel matrices…

  15. Intergroup discrimination in positive and negative outcome allocations : Impact of stimulus valence, relative group status, and relative group size

    NARCIS (Netherlands)

    Otten, S; Mummendey, A; Blanz, M

    1996-01-01

    Three studies investigated the determination of social discrimination by the valence of stimuli that are allocated between groups. The studies were based on either the minimal group paradigm or a more reality-based laboratory intergroup setting, with stimulus valence, group status, and group size as

  16. Age Group Differences in Perceived Age Discrimination: Associations With Self-Perceptions of Aging.

    Science.gov (United States)

    Giasson, Hannah L; Queen, Tara L; Larkina, Marina; Smith, Jacqui

    2017-08-01

    From midlife onwards, age stereotypes increasingly underlie social judgments and contribute to age-based discrimination. Whereas many studies compare differences between young and older adults in reports of age discrimination or sensitivity to age stereotypes, few consider age group differences among adults over 50. We form subgroups corresponding to social age group membership (early midlife, late midlife, young old, oldest old) and examine differences in reported experiences of everyday age discrimination and associations with self-perceptions of aging. Using cross-sectional and longitudinal data from the Health and Retirement Study (HRS: N = 15,071; M Age = 68, range 50-101), multivariate logistic regression was used to examine experiences of everyday discrimination attributed to age, and associations between age discrimination and self-perceptions of aging, in four age groups: early midlife, late midlife, young old, oldest old. People in the early midlife group (aged 50-59) reported more experiences of unfair treatment than the older age groups but were less likely to attribute their experiences to age discrimination. After controlling for covariates, individuals in all age groups who perceived their own aging positively were less likely to report experiences of age discrimination. The magnitude of this effect, however, was greatest in the early midlife group. Findings support proposals that midlife is a pivotal life period when individuals adjust to life events and social role transitions. Future longitudinal studies will provide further insight into whether positive self-perceptions of aging are especially important in this phase of the life course.

  17. Effects of a Group Psychoeducation Program on Self-Stigma, Empowerment and Perceived Discrimination of Persons with Schizophrenia.

    Science.gov (United States)

    Ivezić, Slađana Štrkalj; Sesar, Marijan Alfonso; Mužinić, Lana

    2017-03-01

    Self-stigma adversely affects recovery from schizophrenia. Analyses of self stigma reduction programs discovered that few studies have investigated the impact of education about the illness on self-stigma reduction. The objective of this study was to determine whether psychoeducation based on the principles of recovery and empowerment using therapeutic group factors assists in reduction of self-stigma, increased empowerment and reduced perception of discrimination in patients with schizophrenia. 40 patients participated in psychoeducation group program and were compared with a control group of 40 patients placed on the waiting list for the same program. A Solomon four group design was used to control the influence of the pretest. Rating scales were used to measure internalized stigma, empowerment and perception of discrimination. Two-way analysis of variance was used to determine the main effects and interaction between the treatment and pretest. Simple analysis of variance with repeated measures was used to additionally test effect of treatment onself-stigma, empowerment and perceived discrimination. The participants in the psychoeducation group had lower scores on internalized stigma (F(1,76)=8.18; pempowerment. Psychoeducation did not influence perception of discrimination. Group psychoeducation decreased the level of self stigma. This intervention can assist in recovery from schizophrenia.

  18. Fourier Analysis on Groups

    CERN Document Server

    Rudin, Walter

    2011-01-01

    In the late 1950s, many of the more refined aspects of Fourier analysis were transferred from their original settings (the unit circle, the integers, the real line) to arbitrary locally compact abelian (LCA) groups. Rudin's book, published in 1962, was the first to give a systematic account of these developments and has come to be regarded as a classic in the field. The basic facts concerning Fourier analysis and the structure of LCA groups are proved in the opening chapters, in order to make the treatment relatively self-contained.

  19. Discrimination and well-being amongst the homeless: the role of multiple group membership

    Science.gov (United States)

    Johnstone, Melissa; Jetten, Jolanda; Dingle, Genevieve A.; Parsell, Cameron; Walter, Zoe C.

    2015-01-01

    The homeless are a vulnerable population in many respects. Those experiencing homelessness not only experience personal and economic hardship they also frequently face discrimination and exclusion because of their housing status. Although past research has shown that identifying with multiple groups can buffer against the negative consequences of discrimination on well-being, it remains to be seen whether such strategies protect well-being of people who are homeless. We investigate this issue in a longitudinal study of 119 individuals who were homeless. The results showed that perceived group-based discrimination at T1 was associated with fewer group memberships, and lower subsequent well-being at T2. There was no relationship between personal discrimination at T1 on multiple group memberships at T2. The findings suggest that the experience of group-based discrimination may hinder connecting with groups in the broader social world — groups that could potentially protect the individual against the negative impact of homelessness and discrimination. PMID:26082741

  20. Verbal and Performance IQ for Discrimination Among Psychiatric Diagnostic Groups

    Science.gov (United States)

    Loro, Bert; Woodward, J. Arthur

    1976-01-01

    In view of the practical and theoretical importance of the issues involved, the current research was undertaken to investigate the diagnostic relevance of WAIS Verbal and Performance IQ in a large sample of psychiatric patients that included a variety of functional diagnostic groups as well as groups of mentally deficient and organic brain…

  1. Discrimination

    National Research Council Canada - National Science Library

    Midtbøen, Arnfinn H; Rogstad, Jon

    2012-01-01

    ... of discrimination in the labour market as well as to the mechanisms involved in discriminatory hiring practices. The design has several advantages compared to -‘single-method’ approaches and provides a more substantial understanding of the processes leading to ethnic inequality in the labour market.

  2. Racial discrimination, multiple group identities, and civic beliefs among immigrant adolescents.

    Science.gov (United States)

    Chan, Wing Yi; Latzman, Robert D

    2015-10-01

    The present study tested the independent and interactive effects of multiple group identities (i.e., American and ethnic) and racial discrimination on civic beliefs among immigrant adolescents. Seventy-seven participants completed a questionnaire during after-school programs. Ethnic identity was positively associated with civic beliefs whereas racial discrimination was negatively related to civic beliefs, and racial discrimination moderated the relationships between multiple group identities and civic beliefs. Our findings highlight the importance of studying structural and individual factors jointly in the investigation of civic beliefs among immigrant adolescents. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

  3. PRICE DISCRIMINATION AND MARKET POWER: A THEORETICAL ANALYSIS

    Directory of Open Access Journals (Sweden)

    Olga Smirnova

    2015-07-01

    Full Text Available This paper analyzes the contemporary theoretical and empirical research in the field of impact assessment of market power and conclusions about the possibilities of the company to implement price discrimination in different market structures. The results of the analysis allow to evaluate current approaches to antitrust regulation of price discrimination.

  4. Being similar versus being equal: intergroup similarity moderates the influence of in-group norms on discrimination and prejudice.

    Science.gov (United States)

    Gabarrot, Fabrice; Falomir-Pichastor, Juan M; Mugny, Gabriel

    2009-06-01

    In two studies, we examined the influence of in-group norms of anti- and pro-discrimination on prejudice and discrimination as a function of intergroup similarity (Studies 1 and 2) and in-group identification (Study 2). In a condition where there was no information about intergroup similarity (Study 1) or intergroup similarity was low (Study 2), prejudice and discrimination were lower when norms prescribe anti-discrimination compared to pro-discrimination. In contrast, when intergroup similarity was high, prejudice and discrimination were higher when the in-group norm represents anti-discrimination compared to pro-discrimination. This pattern was most apparent among highly identified in-group members (Study 2). The paradoxical effect of the anti-discrimination norm in the high similarity condition is interpreted as a response to the threat this situation introduces to in-group distinctiveness.

  5. Selective incivility: immigrant groups experience subtle workplace discrimination at different rates.

    Science.gov (United States)

    Krings, Franciska; Johnston, Claire; Binggeli, Steve; Maggiori, Christian

    2014-10-01

    Immigrants play an increasingly important role in local labor markets. Not only do they grow steadily in number but also in cultural, educational, and skill diversity, underlining the necessity to distinguish between immigrant groups when studying discrimination against immigrants. We examined immigrant employees' subtle discrimination experiences in a representative sample in Switzerland, controlling for dispositional influences. Results showed that mainly members of highly competitive immigrant groups, from immediate neighbor countries, experienced workplace incivility and that these incivility experiences were related to higher likelihoods of perceived discrimination at work. This research confirms recent accounts that successful but disliked groups are particularly likely to experience subtle interpersonal discrimination. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

  6. A Bayesian Predictive Discriminant Analysis with Screened Data

    OpenAIRE

    Hea-Jung Kim

    2015-01-01

    In the application of discriminant analysis, a situation sometimes arises where individual measurements are screened by a multidimensional screening scheme. For this situation, a discriminant analysis with screened populations is considered from a Bayesian viewpoint, and an optimal predictive rule for the analysis is proposed. In order to establish a flexible method to incorporate the prior information of the screening mechanism, we propose a hierarchical screened scale mixture of normal (HSS...

  7. Cloud type discrimination via multispectral textural analysis

    Science.gov (United States)

    Lamei, Niloufar; Crawford, Melba M.; Hutchison, Keith D.; Khazenie, Nahid

    1993-09-01

    One of the primary interests in digital image processing is the development of robust methods to perform feature detection, extraction, and classification. Until recently, classification methods for cloud discrimination were mainly based on the spectral information of the imagery. However, because of the spectral similarities of certain features (such as ice clouds and snow) and the effects of atmospheric attenuation, multi-spectral rule based classifications do not necessarily produce accurate feature discrimination. Spectral homogeneity of two different features within a scene can lead to misclassification. Furthermore, the opposite problem can occur when one feature exhibits different spectral signatures locally but is homogeneous in its cyclic spatial variation. The exploration of spatial information is often advantageous in these discrimination problems. A texture-based method for feature identification has been investigated. This method uses a set of localized spatial filters known as two dimensional Gabor functions. Gabor filters can be described as a sinusoidal plane wave within a two-dimensional Gaussian envelope. The frequency and orientation of the sine plane and the width of the Gaussian envelope are determined by the Gabor parameters. These tunable channels yield joint optimal information both in the spatial and the frequency domains. The new method has been applied to the thermal channels of the NOAA-advanced very high resolution radiometer (AVHRR) data for cloud type discrimination.

  8. Score-moment combined linear discrimination analysis (SMC-LDA) as an improved discrimination method.

    Science.gov (United States)

    Han, Jintae; Chung, Hoeil; Han, Sung-Hwan; Yoon, Moon-Young

    2007-01-01

    A new discrimination method called the score-moment combined linear discrimination analysis (SMC-LDA) has been developed and its performance has been evaluated using three practical spectroscopic datasets. The key concept of SMC-LDA was to use not only the score from principal component analysis (PCA), but also the moment of the spectrum, as inputs for LDA to improve discrimination. Along with conventional score, moment is used in spectroscopic fields as an effective alternative for spectral feature representation. Three different approaches were considered. Initially, the score generated from PCA was projected onto a two-dimensional feature space by maximizing Fisher's criterion function (conventional PCA-LDA). Next, the same procedure was performed using only moment. Finally, both score and moment were utilized simultaneously for LDA. To evaluate discrimination performances, three different spectroscopic datasets were employed: (1) infrared (IR) spectra of normal and malignant stomach tissue, (2) near-infrared (NIR) spectra of diesel and light gas oil (LGO) and (3) Raman spectra of Chinese and Korean ginseng. For each case, the best discrimination results were achieved when both score and moment were used for LDA (SMC-LDA). Since the spectral representation character of moment was different from that of score, inclusion of both score and moment for LDA provided more diversified and descriptive information.

  9. Small visible energy scalar top iterative discriminant analysis

    Indian Academy of Sciences (India)

    A Sopczak; A Finch; A Freitas; C Milsténe; M Schimtt

    2007-12-01

    Light scalar top quarks with a small mass difference with respect to the neutralino mass are of particular cosmological interest. This study uses an iterative discriminant analysis method to optimize the expected selection efficiency at the international linear collider (ILC).

  10. Discrete Discriminant analysis based on tree-structured graphical models

    DEFF Research Database (Denmark)

    Perez de la Cruz, Gonzalo; Eslava, Guillermina

    The purpose of this paper is to illustrate the potential use of discriminant analysis based on tree{structured graphical models for discrete variables. This is done by comparing its empirical performance using estimated error rates for real and simulated data. The results show that discriminant...... analysis based on tree{structured graphical models is a simple nonlinear method competitive with, and sometimes superior to, other well{known linear methods like those assuming mutual independence between variables and linear logistic regression....

  11. Discrete Discriminant analysis based on tree-structured graphical models

    DEFF Research Database (Denmark)

    Perez de la Cruz, Gonzalo; Eslava, Guillermina

    The purpose of this paper is to illustrate the potential use of discriminant analysis based on tree{structured graphical models for discrete variables. This is done by comparing its empirical performance using estimated error rates for real and simulated data. The results show that discriminant a...... analysis based on tree{structured graphical models is a simple nonlinear method competitive with, and sometimes superior to, other well{known linear methods like those assuming mutual independence between variables and linear logistic regression....

  12. Ability of heifers to discriminate between familiar herdmates and members of an unfamiliar group

    DEFF Research Database (Denmark)

    Koba, Yuki; Munksgaard, Lene; Tanida, Hajime

    2009-01-01

    Using a preference test and operant conditioning in a Y-maze, this experiment examined the ability of heifers to discriminate between their own familiar herdmates and member(s) of an unfamiliar group. Sixteen Danish Friesian heifers, eight older animals (360.6 ± 24.2 days of age) and eight younger...... individual in the Y-maze. The test was repeated 12 times, with a different unfamiliar subject for each test. In experiment 2, eight heifers were individually tested in a conditioning experiment to examine whether they could learn to discriminate between a group of their three herdmates and a group of three......, heifers did not show a preference toward familiar or unfamiliar individuals; but after conditioning, some heifers could learn to discriminate between familiar and unfamiliar groups....

  13. Face Recognition Using Double Sparse Local Fisher Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Zhan Wang

    2015-01-01

    Full Text Available Local Fisher discriminant analysis (LFDA was proposed for dealing with the multimodal problem. It not only combines the idea of locality preserving projections (LPP for preserving the local structure of the high-dimensional data but also combines the idea of Fisher discriminant analysis (FDA for obtaining the discriminant power. However, LFDA also suffers from the undersampled problem as well as many dimensionality reduction methods. Meanwhile, the projection matrix is not sparse. In this paper, we propose double sparse local Fisher discriminant analysis (DSLFDA for face recognition. The proposed method firstly constructs a sparse and data-adaptive graph with nonnegative constraint. Then, DSLFDA reformulates the objective function as a regression-type optimization problem. The undersampled problem is avoided naturally and the sparse solution can be obtained by adding the regression-type problem to a l1 penalty. Experiments on Yale, ORL, and CMU PIE face databases are implemented to demonstrate the effectiveness of the proposed method.

  14. EEG based Autism Diagnosis Using Regularized Fisher Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Mahmoud I. Kamel

    2012-04-01

    Full Text Available Diagnosis of autism is one of the difficult problems facing researchers. To reveal the discriminative pattern between autistic and normal children via electroencephalogram (EEG analysis is a big challenge. The feature extraction is averaged Fast Fourier Transform (FFT with the Regulated Fisher Linear Discriminant (RFLD classifier. Gaussinaty condition for the optimality of Regulated Fisher Linear Discriminant (RFLD has been achieved by a well-conditioned appropriate preprocessing of the data, as well as optimal shrinkage technique for the Lambda parameter. Winsorised Filtered Data gave the best result.

  15. Discrimination analysis of mass spectrometry proteomics for ovarian cancer detection

    Institute of Scientific and Technical Information of China (English)

    Yan-jun HONG; Xiao-dan WANG; David SHEN; Su ZENG

    2008-01-01

    Aim:A discrimination analysis has been explored for the probabilistic classifica-tion of healthy versus ovarian cancer serum samples using proteomics data from mass spectrometry (MS).Methods:The method employs data normalization,clustering,and a linear discriminant analysis on surface-enhanced laser desorp-tion ionization (SELDI) time-of-flight MS data.The probabilistic classification method computes the optimal linear discriminant using the complex human blood serum SELDI spectra.Cross-validation and training/testing data-split experi-ments are conducted to verify the optimal discriminant and demonstrate the accu-racy and robustness of the method.Results:The cluster discrimination method achieves excellent performance.The sensitivity,specificity,and positive predic-tive values are above 97% on ovarian cancer.The protein fraction peaks,which significantly contribute to the classification,can be available from the analysis process.Conclusion:The discrimination analysis helps the molecular identities of differentially expressed proteins and peptides between the healthy and ovarian patients.

  16. Kernel-based fisher discriminant analysis for hyperspectral target detection

    Institute of Scientific and Technical Information of China (English)

    GU Yan-feng; ZHANG Ye; YOU Di

    2007-01-01

    A new method based on kernel Fisher discriminant analysis (KFDA) is proposed for target detection of hyperspectral images. The KFDA combines kernel mapping derived from support vector machine and the classical linear Fisher discriminant analysis (LFDA), and it possesses good ability to process nonlinear data such as hyperspectral images. According to the Fisher rule that the ratio of the between-class and within-class scatters is maximized, the KFDA is used to obtain a set of optimal discriminant basis vectors in high dimensional feature space. All pixels in the hyperspectral images are projected onto the discriminant basis vectors and the target detection is performed according to the projection result. The numerical experiments are performed on hyperspectral data with 126 bands collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The experimental results show the effectiveness of the proposed detection method and prove that this method has good ability to overcome small sample size and spectral variability in the hyperspectral target detection.

  17. Do dimensions of ethnic identity mediate the association between perceived ethnic group discrimination and depressive symptoms?

    Science.gov (United States)

    Brittian, Aerika S; Kim, Su Yeong; Armenta, Brian E; Lee, Richard M; Umaña-Taylor, Adriana J; Schwartz, Seth J; Villalta, Ian K; Zamboanga, Byron L; Weisskirch, Robert S; Juang, Linda P; Castillo, Linda G; Hudson, Monika L

    2015-01-01

    Ethnic group discrimination represents a notable risk factor that may contribute to mental health problems among ethnic minority college students. However, cultural resources (e.g., ethnic identity) may promote psychological adjustment in the context of group-based discriminatory experiences. In the current study, we examined the associations between perceptions of ethnic group discrimination and depressive symptoms, and explored dimensions of ethnic identity (i.e., exploration, resolution, and affirmation) as mediators of this process among 2,315 ethnic minority college students (age 18 to 30 years; 37% Black, 63% Latino). Results indicated that perceived ethnic group discrimination was associated positively with depressive symptoms among students from both ethnic groups. The relationship between perceived ethnic group discrimination and depressive symptoms was mediated by ethnic identity affirmation for Latino students, but not for Black students. Ethnic identity resolution was negatively and indirectly associated with depressive symptoms through ethnic identity affirmation for both Black and Latino students. Implications for promoting ethnic minority college students' mental health and directions for future research are discussed. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

  18. Application of Discriminant Analysis on Romanian Insurance Market

    Directory of Open Access Journals (Sweden)

    Constantin Anghelache

    2008-11-01

    Full Text Available Discriminant analysis is a supervised learning technique that can be used in order to determine which variables are the best predictors of the classification of objects belonging to a population into predetermined classes. At the same time, discriminant analysis provides a powerful tool that enables researchers to make predictions regarding the classification of new objects into predefined classes. The main goal of discriminant analysis is to determine which of the N descriptive variables have the most discriminatory power, that is, which of them are the most relevant for the classification of objects into classes. In order to classify objects, we need a mathematical model that provides the rules for optimal allocation. This is the classifier. In this paper we will discuss three of the most important models of classification: the Bayesian criterion, the Mahalanobis criterion and the Fisher criterion. In this paper, we will use discriminant analysis to classify the insurance companies that operated on the Romanian market in 2006. We have selected a number of eigth (8 relevant variables: gross written premium (GR_WRI_PRE, net mathematical reserves (NET_M_PES, gross claims paid (GR_CL_PAID, net premium reserves (NET_PRE_RES, net claim reserves (NET_CL_RES, net income (NE—_INCOME, share capital (SHARE_CAP and gross written premium ceded in Reinsurance (GR_WRI_PRE_CED. Before proceeding to discriminant analysis, we performed cluster analysis on the initial data in order to identify classes (clusters that emerge from the data.

  19. Relationships between discrimination in health care and health care outcomes among four race/ethnic groups.

    Science.gov (United States)

    Benjamins, Maureen R; Whitman, Steven

    2014-06-01

    Discrimination has been found to be detrimental to health, but less is known about the influence of discrimination in health care. To address this, the current study (1) compared levels of racial/ethnic discrimination in health care among four race/ethnic groups; (2) determined associations between this type of discrimination and health care outcomes; and (3) assessed potential mediators and moderators as suggested by previous studies. Multivariate logistic regression models were used within a population-based sample of 1,699 White, African American, Mexican, and Puerto Rican respondents. Overall, 23% of the sample reported discrimination in health care, with levels varying substantially by race/ethnicity. In adjusted models, this type of discrimination was associated with an increased likelihood of having unmet health care needs (OR = 2.48, CI = 1.57-3.90) and lower odds of perceiving excellent quality of care (OR = 0.43, CI = 0.28-0.66), but not with the use of a physician when not sick or use of alternative medicine. The mediating role of mental health factors was inconsistently observed and the relationships were not moderated by race/ethnicity. These findings expand the literature and provide preliminary evidence that can eventually inform the development of interventions and the training of health care providers.

  20. Perception of racial discrimination and psychopathology across three U.S. ethnic minority groups.

    Science.gov (United States)

    Chou, Tina; Asnaani, Anu; Hofmann, Stefan G

    2012-01-01

    To examine the association between the perception of racial discrimination and the lifetime prevalence rates of psychological disorders in the three most common ethnic minorities in the United States, we analyzed data from a sample consisting of 793 Asian Americans, 951 Hispanic Americans, and 2,795 African Americans who received the Composite International Diagnostic Interview through the Collaborative Psychiatric Epidemiology Studies. The perception of racial discrimination was associated with the endorsement of major depressive disorder, panic disorder with agoraphobia, agoraphobia without history of panic disorder, posttraumatic stress disorder, and substance use disorders in varying degrees among the three minority groups, independent of the socioeconomic status, level of education, age, and gender of participants. The results suggest that the perception of racial discrimination is associated with psychopathology in the three most common U.S. minority groups.

  1. High-Order Supervised Discriminant Analysis for Visual Data

    Institute of Scientific and Technical Information of China (English)

    Xiao-Ling Xia; Hang-Hui Huang

    2014-01-01

    In practical applications, we often have to deal with high-order data, for example, a grayscale image and a video clip are intrinsically a 2nd-order tensor and a 3rd-order tensor, respectively. In order to satisty these high-order data, it is conventional to vectorize these data in advance, which often destroys the intrinsic structures of the data and includes the curse of dimensionality. For this reason, we consider the problem of high-order data representation and classification, and propose a tensor based fisher discriminant analysis (FDA), which is a generalized version of FDA, named as GFDA. Experimental results show our GFDA outperforms the existing methods, such as the 2-directional 2-dimensional principal component analysis ((2D)2PCA), 2-directional 2-dimensional linear discriminant analysis ((2D)2LDA), and multilinear discriminant analysis (MDA), in high-order data classification under a lower compression ratio.

  2. Análisis discriminante aplicado a los grupos sexuales de Potimirim mexicana, camarón hermafrodita protándrico Discriminant analysis applied to the sexual groups of Potimirim mexicana, a protandric hermaphrodite shrimp

    Directory of Open Access Journals (Sweden)

    Ma. del Pilar Alonso-Reyes

    2010-10-01

    Full Text Available Se analizó una población del camaroncito de río Potimirim mexicana obtenida del río Máquinas en el estado de Veracruz, México. Esta especie presenta una distribución de tallas por sexo que sugiere que se trata de una especie con hermafroditismo secuencial. Se aplicó el método de análisis discriminante para establecer los subgrupos que conforman la población, dando como resultado 3 clases: organismos sexualmente indeterminados, machos y hembras. En el subgrupo de los machos se establecieron 3 conjuntos, con base en el tamaño del appendix masculina. Mediante este análisis se confirmó que P. mexicana es una especie hermafrodita secuencial.A population of the river shrimp Potimirim mexicana from the Máquinas River in Veracruz, Mexico, was analyzed. The pattern of size distribution by sex in this species suggests that it is a sequential hermaphrodite. A discriminant analysis method applied to establish the subgroups that are part or the population showed the existence of 3 classes: sexually undifferentiated organisms, males and females. Within the male subgroup 3 types or organisms were established depending on the length of the appendix masculina. With this analysis it is confirmed that P. mexicana is a species with sequential hermaphroditism.

  3. Principal Component Clustering Approach to Teaching Quality Discriminant Analysis

    Science.gov (United States)

    Xian, Sidong; Xia, Haibo; Yin, Yubo; Zhai, Zhansheng; Shang, Yan

    2016-01-01

    Teaching quality is the lifeline of the higher education. Many universities have made some effective achievement about evaluating the teaching quality. In this paper, we establish the Students' evaluation of teaching (SET) discriminant analysis model and algorithm based on principal component clustering analysis. Additionally, we classify the SET…

  4. A Bayesian Predictive Discriminant Analysis with Screened Data

    Directory of Open Access Journals (Sweden)

    Hea-Jung Kim

    2015-09-01

    Full Text Available In the application of discriminant analysis, a situation sometimes arises where individual measurements are screened by a multidimensional screening scheme. For this situation, a discriminant analysis with screened populations is considered from a Bayesian viewpoint, and an optimal predictive rule for the analysis is proposed. In order to establish a flexible method to incorporate the prior information of the screening mechanism, we propose a hierarchical screened scale mixture of normal (HSSMN model, which makes provision for flexible modeling of the screened observations. An Markov chain Monte Carlo (MCMC method using the Gibbs sampler and the Metropolis–Hastings algorithm within the Gibbs sampler is used to perform a Bayesian inference on the HSSMN models and to approximate the optimal predictive rule. A simulation study is given to demonstrate the performance of the proposed predictive discrimination procedure.

  5. Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms

    Directory of Open Access Journals (Sweden)

    Cheng-Yuan Shih

    2010-01-01

    Full Text Available This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state. The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB with effective Gabor features to recognize the facial expressions. Entropy criterion is applied to select the effective Gabor feature which is a subset of informative and nonredundant Gabor features. The proposed RDAB algorithm uses RDA as a learner in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA and quadratic discriminant analysis (QDA. It solves the small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses the particle swarm optimization (PSO algorithm to estimate optimal parameters in RDA. Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.

  6. Discrimination and common mental disorder among migrant and ethnic groups: findings from a South East London Community sample.

    Science.gov (United States)

    Hatch, S L; Gazard, B; Williams, D R; Frissa, S; Goodwin, L; Hotopf, M

    2016-05-01

    Few studies have examined discrimination and mental health in the UK, particularly by migrant status and in urban contexts with greater demographic diversity. This study aims to (1) describe the prevalence of discrimination experiences across multiple life domains; (2) to describe associations between discrimination experiences and common mental disorder (CMD); (3) to determine whether or not the relationship between discrimination and CMD varies by migrant status and ethnicity. Data on major, anticipated and everyday discrimination and CMD symptoms were collected from an ethnically diverse prospective sample of 1052 participants followed up from 2008 to 2013 in the South East London Community Health study, a population-based household survey. With few exceptions, discrimination was most prevalent among those in the Black Caribbean group. However, those in the White Other ethnic group had similar or greater reporting major and anticipated discrimination to Black or mixed ethnic minority groups. The effects of discrimination on CMD were most pronounced for individuals who had recently migrated to the UK, an ethnically heterogeneous group, and for Black and Mixed ethnic minority groups in partially adjusted models. Prior CMD accounted for differences between the Mixed and White British ethnic groups, but the strength of the association for the most recent migrant group and the Black ethnic groups remained two or more times greater than the reference groups. The strength of the relationship suggests a need for more consideration of migration status along with ethnicity in examining the impact of discrimination on mental disorder in community and clinical samples.

  7. Performance Evaluation of Discriminant Analysis and Decision Tree, for Weed Classification of Potato Fields

    Directory of Open Access Journals (Sweden)

    Farshad Vesali

    2012-09-01

    Full Text Available In present study we tried to recognizing weeds in potato fields to effective use from herbicides. As we know potato is one of the crops which is cultivated vastly all over the world and it is a major world food crop that is consumed by over one billion people world over, but it is threated by weed invade, because of row cropping system applied in potato tillage. Machine vision is used in this research for effective application of herbicides in field. About 300 color images from 3 potato farms of Qorveh city and 2 farms of Urmia University-Iran, was acquired. Images were acquired in different illumination condition from morning to evening in sunny and cloudy days. Because of overlap and shading of plants in farm condition it is hard to use morphologic parameters. In method used for classifying weeds and potato plants, primary color components of each plant were extracted and the relation between them was estimated for determining discriminant function and classifying plants using discrimination analysis. In addition the decision tree method was used to compare results with discriminant analysis. Three different classifications were applied: first, Classification was applied to discriminate potato plant from all other weeds (two groups, the rate of correct classification was 76.67% for discriminant analysis and 83.82% for decision tree; second classification was applied to discriminate potato plant from separate groups of each weed (6 groups, the rate of correct classification was 87%. And the third, Classification of potato plant versus weed species one by one. As the weeds were different, the results of classification were different in this composition. The decision tree in all conditions showed the better result than discriminant analysis.

  8. Discrimination and numerical analysis of human pathogenic ...

    African Journals Online (AJOL)

    SERVER

    2008-02-19

    Feb 19, 2008 ... Numerical analysis of whole-cell protein profiles of all strains revealed 2 .... average linkage method and correlation coefficient distance. ... distance yielded a dendrogam, consisting of two basic .... Candida glabrata: review of.

  9. Visual category recognition using Spectral Regression and Kernel Discriminant Analysis

    NARCIS (Netherlands)

    Tahir, M.A.; Kittler, J.; Mikolajczyk, K.; Yan, F.; van de Sande, K.E.A.; Gevers, T.

    2009-01-01

    Visual category recognition (VCR) is one of the most important tasks in image and video indexing. Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold learning. Recently, Spectral Regression combined with Kernel Discriminant Analysis (SR-KDA) has been s

  10. A Critical Analysis of Anti-Discrimination Law and Microaggressions in Academia

    Science.gov (United States)

    Lukes, Robin; Bangs, Joann

    2014-01-01

    This article provides a critical analysis of microaggressions and anti-discrimination law in academia. There are many challenges for faculty claiming discrimination under current civil rights laws. Examples of microaggressions that fall outside of anti-discrimination law will be provided. Traditional legal analysis of discrimination will not end…

  11. Kernel-Based Nonlinear Discriminant Analysis for Face Recognition

    Institute of Scientific and Technical Information of China (English)

    LIU QingShan (刘青山); HUANG Rui (黄锐); LU HanQing (卢汉清); MA SongDe (马颂德)

    2003-01-01

    Linear subspace analysis methods have been successfully applied to extract features for face recognition. But they are inadequate to represent the complex and nonlinear variations of real face images, such as illumination, facial expression and pose variations, because of their linear properties. In this paper, a nonlinear subspace analysis method, Kernel-based Nonlinear Discriminant Analysis (KNDA), is presented for face recognition, which combines the nonlinear kernel trick with the linear subspace analysis method - Fisher Linear Discriminant Analysis (FLDA).First, the kernel trick is used to project the input data into an implicit feature space, then FLDA is performed in this feature space. Thus nonlinear discriminant features of the input data are yielded. In addition, in order to reduce the computational complexity, a geometry-based feature vectors selection scheme is adopted. Another similar nonlinear subspace analysis is Kernel-based Principal Component Analysis (KPCA), which combines the kernel trick with linear Principal Component Analysis (PCA). Experiments are performed with the polynomial kernel, and KNDA is compared with KPCA and FLDA. Extensive experimental results show that KNDA can give a higher recognition rate than KPCA and FLDA.

  12. Variations in students' perceived reasons for, sources of, and forms of in-school discrimination: A latent class analysis.

    Science.gov (United States)

    Byrd, Christy M; Carter Andrews, Dorinda J

    2016-08-01

    Although there exists a healthy body of literature related to discrimination in schools, this research has primarily focused on racial or ethnic discrimination as perceived and experienced by students of color. Few studies examine students' perceptions of discrimination from a variety of sources, such as adults and peers, their descriptions of the discrimination, or the frequency of discrimination in the learning environment. Middle and high school students in a Midwestern school district (N=1468) completed surveys identifying whether they experienced discrimination from seven sources (e.g., peers, teachers, administrators), for seven reasons (e.g., gender, race/ethnicity, religion), and in eight forms (e.g., punished more frequently, called names, excluded from social groups). The sample was 52% White, 15% Black/African American, 14% Multiracial, and 17% Other. Latent class analysis was used to cluster individuals based on reported sources of, reasons for, and forms of discrimination. Four clusters were found, and ANOVAs were used to test for differences between clusters on perceptions of school climate, relationships with teachers, perceptions that the school was a "good school," and engagement. The Low Discrimination cluster experienced the best outcomes, whereas an intersectional cluster experienced the most discrimination and the worst outcomes. The results confirm existing research on the negative effects of discrimination. Additionally, the paper adds to the literature by highlighting the importance of an intersectional approach to examining students' perceptions of in-school discrimination. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

  13. Genetic mapping of complex discrete human diseases by discriminant analysis

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The objective of the present study is to propose and evaluate a novel multivariate approach for genetic mapping of complex categorical diseases. This approach results from an application of standard stepwise discriminant analysis to detect linkage based on the differential marker identity-by-descent (IBD) distributions among the different groups of sib pairs. Two major advantages of this method are that it allows for simultaneously testing all markers, together with other genetic and environmental factors in a single multivariate setting and it avoids explicitly modeling the complex relationship between the affection status of sib pairs and the underlying genetic determinants. The efficiency and properties of the method are demonstrated via simulations. The proposed multivariate approach has successfully located the true position(s) under various genetic scenarios. The more important finding is that using highly densely spaced markers (1~2 cM) leads to only a marginal loss of statistical efficiency of the proposed methods in terms of gene localization and statistical power. These results have well established its utility and advantages as a fine-mapping tool. A unique property of the proposed method is the ability to map multiple linked trait loci to their precise positions due to its sequential nature, as demonstrated via simulations.

  14. Harmonic Analysis and Group Representation

    CERN Document Server

    Figa-Talamanca, Alessandro

    2011-01-01

    This title includes: Lectures - A. Auslander, R. Tolimeri - Nilpotent groups and abelian varieties, M Cowling - Unitary and uniformly bounded representations of some simple Lie groups, M. Duflo - Construction de representations unitaires d'un groupe de Lie, R. Howe - On a notion of rank for unitary representations of the classical groups, V.S. Varadarajan - Eigenfunction expansions of semisimple Lie groups, and R. Zimmer - Ergodic theory, group representations and rigidity; and, Seminars - A. Koranyi - Some applications of Gelfand pairs in classical analysis.

  15. Insulation Defects Discrimination in GIS by Fisher Discriminant Analysis of Partial Discharge

    Institute of Scientific and Technical Information of China (English)

    DING Dengwei; GAO Wensheng; LIU Weidong

    2013-01-01

    To monitor the condition of online power apparatus accurately and provide appropriate guidance on their maintenance,a fundamental ultra-high frequency (UHF) database of partial discharges corresponding to different types of defects is presented for the observation of insulation state of gas insulated switchgear (GIS).In order to imitate the defects in a GIS online,five types of typical partial discharge (PD) sources (i.e.floating metal,protrusion,bouncing particles,void in spacer,and particle on the surface of solid insulation) were designed and fabricated.A wideband UHF sensor and an amplifier were applied to obtain UHF signals.Based on the characteristics of the five different defects,nine meaningful parameters which were independent of the phase of the applied voltage were extracted from the PD samples and then discussed.In this paper,Fisher discriminant analysis (FDA),a pattern recognition algorithm,was applied for the purpose of classifying the defects.In this way,there was no need to set complicated parameters and kernel function,and the total discrimination accuracy of test samples was 97.6%.It indicates that based on the nine especial parameters,the typical defects in GIS can be identified exactly by means of FDA.Hence the analysis method could be possibly regarded as a universal classification method.

  16. Multi spectral imaging analysis for meat spoilage discrimination

    DEFF Research Database (Denmark)

    Christiansen, Asger Nyman; Carstensen, Jens Michael; Papadopoulou, Olga

    ) was performed in parallel with videometer image snapshots and sensory analysis. Odour and colour characteristics of meat were determined by a test panel and attributed into three pre-characterized quality classes, namely Fresh; Semi Fresh and Spoiled during the days of its shelf life. So far, different...... classification methods: Naive Bayes Classifier as a reference model, Canonical Discriminant Analysis (CDA) and Support Vector Classification (SVC). As the final step, generalization of the models was performed using k-fold validation (k=10). Results showed that image analysis provided good discrimination of meat...... samples regarding the spoilage process as evaluated from sensory as well as from microbiological data. The support vector classification (SVC) model outperformed other models. Specifically, the misclassification error rate (MER), derived from odour characteristics, was 18% for both aerobic and MAP meat...

  17. Group analysis of differential equations

    CERN Document Server

    Ovsiannikov, L V

    1982-01-01

    Group Analysis of Differential Equations provides a systematic exposition of the theory of Lie groups and Lie algebras and its application to creating algorithms for solving the problems of the group analysis of differential equations.This text is organized into eight chapters. Chapters I to III describe the one-parameter group with its tangential field of vectors. The nonstandard treatment of the Banach Lie groups is reviewed in Chapter IV, including a discussion of the complete theory of Lie group transformations. Chapters V and VI cover the construction of partial solution classes for the g

  18. Multi spectral imaging analysis for meat spoilage discrimination

    DEFF Research Database (Denmark)

    Christiansen, Asger Nyman; Carstensen, Jens Michael; Papadopoulou, Olga

    with corresponding sensory data would be of great interest. The purpose of this research was to produce a method capable of quantifying and/or predicting the spoilage status (e.g. express in TVC counts as well as on sensory evaluation) using a multi spectral image of a meat sample and thereby avoid any time...... classification methods: Naive Bayes Classifier as a reference model, Canonical Discriminant Analysis (CDA) and Support Vector Classification (SVC). As the final step, generalization of the models was performed using k-fold validation (k=10). Results showed that image analysis provided good discrimination of meat...... samples. In the case where all data were taken together the misclassification error amounted to 16%. When spoilage status was based on visual sensory data, the model produced a MER of 22% for the combined dataset. These results suggest that it is feasible to employ a multi spectral image...

  19. Quark/gluon jet discrimination: a reproducible analysis using R

    CERN Document Server

    CERN. Geneva

    2017-01-01

    The power to discriminate between light-quark jets and gluon jets would have a huge impact on many searches for new physics at CERN and beyond. This talk will present a walk-through of the development of a prototype machine learning classifier for differentiating between quark and gluon jets at experiments like those at the Large Hadron Collider at CERN. A new fast feature selection method that combines information theory and graph analytics will be outlined. This method has found new variables that promise significant improvements in discrimination power. The prototype jet tagger is simple, interpretable, parsimonious, and computationally extremely cheap, and therefore might be suitable for use in trigger systems for real-time data processing. Nested stratified k-fold cross validation was used to generate robust estimates of model performance. The data analysis was performed entirely in the R statistical programming language, and is fully reproducible. The entire analysis workflow is data-driven, automated a...

  20. Multiple Kernel Learning in Fisher Discriminant Analysis for Face Recognition

    Directory of Open Access Journals (Sweden)

    Xiao-Zhang Liu

    2013-02-01

    Full Text Available Recent applications and developments based on support vector machines (SVMs have shown that using multiple kernels instead of a single one can enhance classifier performance. However, there are few reports on performance of the kernel‐based Fisher discriminant analysis (kernel‐based FDA method with multiple kernels. This paper proposes a multiple kernel construction method for kernel‐based FDA. The constructed kernel is a linear combination of several base kernels with a constraint on their weights. By maximizing the margin maximization criterion (MMC, we present an iterative scheme for weight optimization. The experiments on the FERET and CMU PIE face databases show that, our multiple kernel Fisher discriminant analysis (MKFD achieves high recognition performance, compared with single‐kernel‐based FDA. The experiments also show that the constructed kernel relaxes parameter selection for kernel‐based FDA to some extent.

  1. BUSINESS FAILURE PREDICTION FOR ROMANIAN SMES USING MULTIVARIATE DISCRIMINANT ANALYSIS

    OpenAIRE

    2012-01-01

    Business failure prediction is one of special importance for small and medium sized enterprises (SMEs) due to their increased vulnerability. Consequently, the purpose of this paper is to investigate the utility of financial ratios and other non-financial variables to predict business failure using a sample of Romanian SMEs and applying multiple discriminant analysis. The process that leads to failure is analyzed on a three year time horizon prior to failure and the results showed that failure...

  2. Quantum discriminant analysis for dimensionality reduction and classification

    Science.gov (United States)

    Cong, Iris; Duan, Luming

    2016-07-01

    We present quantum algorithms to efficiently perform discriminant analysis for dimensionality reduction and classification over an exponentially large input data set. Compared with the best-known classical algorithms, the quantum algorithms show an exponential speedup in both the number of training vectors M and the feature space dimension N. We generalize the previous quantum algorithm for solving systems of linear equations (2009 Phys. Rev. Lett. 103 150502) to efficiently implement a Hermitian chain product of k trace-normalized N ×N Hermitian positive-semidefinite matrices with time complexity of O({log}(N)). Using this result, we perform linear as well as nonlinear Fisher discriminant analysis for dimensionality reduction over M vectors, each in an N-dimensional feature space, in time O(p {polylog}({MN})/{ε }3), where ɛ denotes the tolerance error, and p is the number of principal projection directions desired. We also present a quantum discriminant analysis algorithm for data classification with time complexity O({log}({MN})/{ε }3).

  3. Rapid discrimination between four seagrass species using hybrid analysis.

    Science.gov (United States)

    Osathanunkul, M; Madesis, P; Ounjai, S; Suwannapoom, C; Jampeetong, A

    2015-04-27

    Biological species are traditionally identified based on their morphological features and the correct identification of species is critical in biological studies. However, some plant types, such as seagrass, are taxonomically problematic and difficult to identify. Furthermore, closely related seagrass species, such as Halophila spp, form a taxonomically unresolved complex. Although some seagrass taxa are easy to recognize, most species are difficult to identify without skilled taxonomic or molecular techniques. Barcoding coupled with High Resolution Melting analysis (BAR-HRM) offers a potentially reliable, rapid, and cost-effective method to confirm species. Here, DNA information of two chloroplast loci was used in combination with HRM analysis to discriminate four species of seagrass collected off the southern coast of Thailand. A distinct melting curve presenting one inflection point was generated for each species using rbcL primers. While the melting profiles of Cymodocea rotundata and Cymodocea serrulata were not statistically different, analysis of the normalized HRM curves produced with the rpoC primers allowed for their discrimination. The Bar-HRM technique showed promise in discriminating seagrass species and with further adaptations and improvements, could make for an effective and power tool for confirming seagrass species.

  4. Evaluation of Affirmative Action in the Context of Possible Unfair Discrimination Against Subgroups in the Designated Group

    Directory of Open Access Journals (Sweden)

    Myrone Christopher Stoffels

    2015-12-01

    Full Text Available The implementation of affirmative action measures can give rise to unfair discrimination. In cases where members of the “designated groups” compete with one another for the same position, there can be allegations of unfair discrimination. The question arises as to how the employer needs to act in order to avoid unfair discrimination in cases where more than one person from the designated group applies for the same position. The purpose of this article is to evaluate the impact of unfair discrimination on the designated group, specifically with regard to the subgroup “black people” as well as how the employer can avoid unfair discrimination in the implementation of the affirmative action measures aimed at advancing “black people” by selecting the most suitably qualified person from the sub group black people based on the national and regional demographics.

  5. Discrimination of inflammatory bowel disease using Raman spectroscopy and linear discriminant analysis methods

    Science.gov (United States)

    Ding, Hao; Cao, Ming; DuPont, Andrew W.; Scott, Larry D.; Guha, Sushovan; Singhal, Shashideep; Younes, Mamoun; Pence, Isaac; Herline, Alan; Schwartz, David; Xu, Hua; Mahadevan-Jansen, Anita; Bi, Xiaohong

    2016-03-01

    Inflammatory bowel disease (IBD) is an idiopathic disease that is typically characterized by chronic inflammation of the gastrointestinal tract. Recently much effort has been devoted to the development of novel diagnostic tools that can assist physicians for fast, accurate, and automated diagnosis of the disease. Previous research based on Raman spectroscopy has shown promising results in differentiating IBD patients from normal screening cases. In the current study, we examined IBD patients in vivo through a colonoscope-coupled Raman system. Optical diagnosis for IBD discrimination was conducted based on full-range spectra using multivariate statistical methods. Further, we incorporated several feature selection methods in machine learning into the classification model. The diagnostic performance for disease differentiation was significantly improved after feature selection. Our results showed that improved IBD diagnosis can be achieved using Raman spectroscopy in combination with multivariate analysis and feature selection.

  6. Use of discriminant analysis to identify propensity for purchasing properties

    Directory of Open Access Journals (Sweden)

    Ricardo Floriani

    2015-03-01

    Full Text Available Properties usually represent a milestone for people and families due to the high added-value when compared with family income. The objective of this study is the proposition of a discrimination model, by a discriminant analysis of people with characteristics (according to independent variables classified as potential buyers of properties, as well as to identify the interest in the use of such property, if it will be assigned to housing or leisure activities such as a cottage or beach house, and/or for investment. Thus, the following research question is proposed: What are the characteristics that better describe the profile of people which intend to acquire properties? The study justifies itself by its economic relevance in the real estate industry, as well as to the players of the real estate Market that may develop products based on the profile of potential customers. As a statistical technique, discriminant analysis was applied to the data gathered by questionnaire, which was sent via e-mail. Three hundred and thirty four responses were gathered. Based on this study, it was observed that it is possible to identify the intention for acquired properties, as well the purpose for acquiring it, for housing or investments.

  7. Discriminating dysplasia: Optical tomographic texture analysis of colorectal polyps.

    Science.gov (United States)

    Li, Wenqi; Coats, Maria; Zhang, Jianguo; McKenna, Stephen J

    2015-12-01

    Optical projection tomography enables 3-D imaging of colorectal polyps at resolutions of 5-10 µm. This paper investigates the ability of image analysis based on 3-D texture features to discriminate diagnostic levels of dysplastic change from such images, specifically, low-grade dysplasia, high-grade dysplasia and invasive cancer. We build a patch-based recognition system and evaluate both multi-class classification and ordinal regression formulations on a 90 polyp dataset. 3-D texture representations computed with a hand-crafted feature extractor, random projection, and unsupervised image filter learning are compared using a bag-of-words framework. We measure performance in terms of error rates, F-measures, and ROC surfaces. Results demonstrate that randomly projected features are effective. Discrimination was improved by carefully manipulating various important aspects of the system, including class balancing, output calibration and approximation of non-linear kernels.

  8. A Direct Estimation Approach to Sparse Linear Discriminant Analysis

    CERN Document Server

    Cai, Tony

    2011-01-01

    This paper considers sparse linear discriminant analysis of high-dimensional data. In contrast to the existing methods which are based on separate estimation of the precision matrix $\\O$ and the difference $\\de$ of the mean vectors, we introduce a simple and effective classifier by estimating the product $\\O\\de$ directly through constrained $\\ell_1$ minimization. The estimator can be implemented efficiently using linear programming and the resulting classifier is called the linear programming discriminant (LPD) rule. The LPD rule is shown to have desirable theoretical and numerical properties. It exploits the approximate sparsity of $\\O\\de$ and as a consequence allows cases where it can still perform well even when $\\O$ and/or $\\de$ cannot be estimated consistently. Asymptotic properties of the LPD rule are investigated and consistency and rate of convergence results are given. The LPD classifier has superior finite sample performance and significant computational advantages over the existing methods that req...

  9. Sex determination by discriminant function analysis of lumbar vertebrae.

    Science.gov (United States)

    Ostrofsky, Kelly R; Churchill, Steven E

    2015-01-01

    Sex determination is critical for developing the biological profile of unidentified skeletal remains. When more commonly used elements (os coxa, cranium) for sexing are not available, methods utilizing other skeletal elements are needed. This study aims to assess the degree of sexual dimorphism of the lumbar vertebrae and develop discriminant functions for sex determination from them, using a sample of South African blacks from the Raymond A. Dart Collection (47 males, 51 females). Eleven variables at each lumbar level were subjected to univariate and multivariate discriminant function analyses. Univariate equations produced classification rates ranging from 57.7% to 83.5%, with the highest accuracies associated with dimensions of the vertebral body. Multivariate stepwise analysis generated classification rates ranging from 75.9% to 88.7%. These results are comparable to other methods for sexing the skeleton and indicate that measures of the lumbar vertebrae can be used as an effective tool for sex determination.

  10. Sparse discriminant analysis for breast cancer biomarker identification and classification

    Institute of Scientific and Technical Information of China (English)

    Yu Shi; Daoqing Dai; Chaochun Liu; Hong Yan

    2009-01-01

    Biomarker identification and cancer classification are two important procedures in microarray data analysis. We propose a novel uni-fied method to carry out both tasks. We first preselect biomarker candidates by eliminating unrelated genes through the BSS/WSS ratio filter to reduce computational cost, and then use a sparse discriminant analysis method for simultaneous biomarker identification and cancer classification. Moreover, we give a mathematical justification about automatic biomarker identification. Experimental results show that the proposed method can identify key genes that have been verified in biochemical or biomedical research and classify the breast cancer type correctly.

  11. Discrimination of honeys using colorimetric sensor arrays, sensory analysis and gas chromatography techniques.

    Science.gov (United States)

    Tahir, Haroon Elrasheid; Xiaobo, Zou; Xiaowei, Huang; Jiyong, Shi; Mariod, Abdalbasit Adam

    2016-09-01

    Aroma profiles of six honey varieties of different botanical origins were investigated using colorimetric sensor array, gas chromatography-mass spectrometry (GC-MS) and descriptive sensory analysis. Fifty-eight aroma compounds were identified, including 2 norisoprenoids, 5 hydrocarbons, 4 terpenes, 6 phenols, 7 ketones, 9 acids, 12 aldehydes and 13 alcohols. Twenty abundant or active compounds were chosen as key compounds to characterize honey aroma. Discrimination of the honeys was subsequently implemented using multivariate analysis, including hierarchical clustering analysis (HCA) and principal component analysis (PCA). Honeys of the same botanical origin were grouped together in the PCA score plot and HCA dendrogram. SPME-GC/MS and colorimetric sensor array were able to discriminate the honeys effectively with the advantages of being rapid, simple and low-cost. Moreover, partial least squares regression (PLSR) was applied to indicate the relationship between sensory descriptors and aroma compounds.

  12. Over-excavation forecast of underground opening by using Bayes discriminant analysis method

    Institute of Scientific and Technical Information of China (English)

    GONG Feng-qiang; LI Xi-bing; ZHANG Wei

    2008-01-01

    A method to forecast the over-excavation of underground opening by using the Bayes discriminant analysis(BDA) theory was presented. The Bayes discriminant analysis theory was introduced. Based on an engineering example, the factors influencing the over-excavation of underground opening were taken into account to build a forecast BDA model, and the prior information about over-excavation of underground opening was also taken into consideration. Five parameters influencing the over-excavation of opening, including 2 groups of joints, 1 group of layer surface, extension and space between structure faces were selected as geometric parameters. Engineering data in an underground opening were used as the training samples. The cross-validation method was introduced to verify the stability of BDA model and the ratio of mistake-discrimination was equal to zero after the BDA model was trained. Data in an underground engineering were used to test the discriminant ability of BDA model. The results show that five forecast results are identical with the actual situation and BDA can be used in practical engineering.

  13. [Group cohesion: a concept analysis].

    Science.gov (United States)

    Lin, Yen-Ru; Chen, Yu-Jung; Tzeng, Wen-Chii; Chou, Kuei-Ru

    2007-10-01

    Group cohesion is considered an essential condition for achieving a successful treatment team. High cohesion groups more readily reach their goals, with group members also feeling more secure about their functions and contributions. In clinical practice, nurses use group teaching and group therapy to help patient and family members gain knowledge and skills related to illness treatment and recuperation. Effective group leadership helps minimize non-productive time and manpower and enhance interpersonal interaction. A further advantage of group cohesion is that the more effective administration of nursing programs that results can raise the profession level of staffs and reduce turnover. Walker and Avant (1995) employ concept analysis to use defining attributes in order to apply the same definition and communication to the same profession. The purpose of this paper was to apply this methodology to an analysis of group cohesion. Steps used include a review of the literature on conceptual definitions of group cohesion, a determination of defining attributes, model construction, identification of borderline, contrary, and related cases, and identification of antecedents and consequences and empirical tools. It is hoped that this analysis can help nursing staff to gain a better understanding of the concept of group cohesion and to apply such to clinical practice and nursing administration.

  14. Perceived Ethnic Discrimination and Problem Behaviors in Muslim Immigrant Early Adolescents : Moderating Effects of Ethnic, Religious, and National Group Identification

    NARCIS (Netherlands)

    Maes, Marlies; Stevens, Gonneke W. J. M.; Verkuijten, Maykel

    2014-01-01

    Previous research has identified ethnic group identification as a moderator in the relationship between perceived ethnic discrimination and problem behaviors in ethnic minority children. However, little is known about the influence of religious and host national identification on this relationship.

  15. Visual Tracking via Feature Tensor Multimanifold Discriminate Analysis

    Directory of Open Access Journals (Sweden)

    Ting-quan Deng

    2014-01-01

    Full Text Available In the visual tracking scenarios, if there are multiple objects, due to the interference of similar objects, tracking may fail in the progress of occlusion to separation. To address this problem, this paper proposed a visual tracking algorithm with discrimination through multimanifold learning. Color-gradient-based feature tensor was used to describe object appearance for accommodation of partial occlusion. A prior multimanifold tensor dataset is established through the template matching tracking algorithm. For the purpose of discrimination, tensor distance was defined to determine the intramanifold and intermanifold neighborhood relationship in multimanifold space. Then multimanifold discriminate analysis was employed to construct multilinear projection matrices of submanifolds. Finally, object states were obtained by combining with sequence inference. Meanwhile, the multimanifold dataset and manifold learning embedded projection should be updated online. Experiments were conducted on two real visual surveillance sequences to evaluate the proposed algorithm with three state-of-the-art tracking methods qualitatively and quantitatively. Experimental results show that the proposed algorithm can achieve effective and robust effect in multi-similar-object mutual occlusion scenarios.

  16. Discrimination in Macedonian companies: Case studies research and analysis of current discrimination grounds, forms and trends

    OpenAIRE

    Ananiev, Jovan; Poposka, Zaneta

    2013-01-01

    Discrimination which is evident in in companies in the Republic of Macedonia is mostly done by the owners or management and on the ground of personal status, gender, ethnicity and age and mostly in the form of harassment and direct discrimination.

  17. Isokinetic evaluation of knee muscles in soccer players: discriminant analysis

    Directory of Open Access Journals (Sweden)

    Bruno Fles Mazuquin

    2015-10-01

    Full Text Available ABSTRACTIntroduction:Muscle activity in soccer players can be measured by isokinetic dynamometer, which is a reliable tool for assessing human performance.Objectives:To perform isokinetic analyses and to determine which variables differentiate the under-17 (U17 soccer category from the professional (PRO.Methods:Thirty four players were assessed (n=17 for each category. The isokinetic variables used for the knee extension-flexion analysis were: peak torque (Nm, total work (J, average power (W, angle of peak torque (deg., agonist/ antagonist ratio (%, measured for three velocities (60°/s, 120°/s and 300°/s, with each series containing five repetitions. Three Wilks' Lambda discriminant analyses were performed, to identify which variables were more significant for the definition of each of the categories.Results:The discriminative variables at 60°/s in the PRO category were: extension peak torque, flexion total work, extension average power and agonist/antagonist ratio; and for the U17s were: extension total work, flexion peak torque and flexion average power. At 120°/s for the PRO category the discriminant variables were: flexion peak torque and extension average power; for the U17s they were: extension total work and flexion average power. Finally at 300°/s, the variables found in the PRO and U17 categories respectively were: extension average power and extension total work.Conclusion:Isokinetic variables for flexion and extension knee muscles were able to significantly discriminate between PRO and U17 soccer players.

  18. Discriminative Dictionary Learning With Two-Level Low Rank and Group Sparse Decomposition for Image Classification.

    Science.gov (United States)

    Wen, Zaidao; Hou, Biao; Jiao, Licheng

    2016-06-30

    Discriminative dictionary learning (DDL) framework has been widely used in image classification which aims to learn some class-specific feature vectors as well as a representative dictionary according to a set of labeled training samples. However, interclass similarities and intraclass variances among input samples and learned features will generally weaken the representability of dictionary and the discrimination of feature vectors so as to degrade the classification performance. Therefore, how to explicitly represent them becomes an important issue. In this paper, we present a novel DDL framework with two-level low rank and group sparse decomposition model. In the first level, we learn a class-shared and several class-specific dictionaries, where a low rank and a group sparse regularization are, respectively, imposed on the corresponding feature matrices. In the second level, the class-specific feature matrix will be further decomposed into a low rank and a sparse matrix so that intraclass variances can be separated to concentrate the corresponding feature vectors. Extensive experimental results demonstrate the effectiveness of our model. Compared with the other state-of-the-arts on several popular image databases, our model can achieve a competitive or better performance in terms of the classification accuracy.

  19. A Comparative Study: Globality versus Locality for Graph Construction in Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Bo Yang

    2014-01-01

    Discriminant Analysis (GmGcDA just based on globality alone, GmLcDA, and LmGcDA, we suggest that the joint of locally constructed intraclass and globally constructed interclass graphs is more discriminant.

  20. Discrimination of healthy and carious teeth using laser-induced breakdown spectroscopy and partial least square discriminant analysis.

    Science.gov (United States)

    Gazmeh, Meisam; Bahreini, Maryam; Tavassoli, Seyed Hassan

    2015-01-01

    In the laser drilling of teeth, a microplasma is generated which may be utilized for elemental analysis of ablated tissue via a laser-induced breakdown spectroscopy (LIBS) technique. In this study, LIBS is used to investigate the possibility of discrimination of healthy and carious tooth tissues. This possibility is examined using multivariate statistical analysis called partial least square discriminant analysis (PLS-DA) based on atomic and ionic emission lines of teeth LIBS spectra belonging to P, Ca, Mg, Zn, K, Sr, C, Na, H, and O elements. Results show an excellent discrimination and prediction of unknown tooth tissues. It is shown that using the PLS-DA method, the spectroscopic analysis of plasma emission during the laser drilling, would be a promising technique for caries detection.

  1. Discrimination of Chinese teas with different fermentation degrees by stepwise linear discriminant analysis (S-LDA) of the chemical compounds.

    Science.gov (United States)

    Wu, Quan-Jin; Dong, Qing-Hua; Sun, Wei-Jiang; Huang, Yan; Wang, Qiong-Qiong; Zhou, Wei-Long

    2014-09-24

    This study aimed to construct objective and accurate analytical models of tea categories based on their polyphenols and caffeine. A total of 522 tea samples of 4 commonly consumed teas with different fermentation degrees (green tea, white tea, oolong tea, and black tea) were analyzed by high-performance liquid chromatography (HPLC) coupled with spectrophotometry, utilizing ISO 14502, as analytical tools. The content of polyphenols and caffeine varied significantly according to differently fermented teas, indicating that these active constituents may discriminate fermentation degrees effectively. By principal component analysis (PCA) and stepwise linear discriminant analysis (S-LDA), the vast majority of tea samples could be successfully differentiated according to their chemical markers. This study yielded three discriminant functions with the capacity to simultaneously discriminate the four tea categories with a 97.8% correct rate. In classification of oolong and other teas, there were one discriminant function and two equations with best discriminant capacity. Furthermore, the classification of different degrees of fermentation of oolong and external validation achieved the desired results. It is suggested that polyphenols and caffeine are the distinct variables to establish internationally recognized models of teas.

  2. Fluorometric Discrimination Technique of Phytoplankton Population Based on Wavelet Analysis

    Institute of Scientific and Technical Information of China (English)

    ZHANG Shanshan; SU Rongguo; DUAN Yali; ZHANG Cui; SONG Zhijie; WANG Xiulin

    2012-01-01

    The discrete excitation-emission-matrix fluorescence spectra(EEMS)at 12 excitation wavelengths (400,430,450,460,470,490,500,510,525,550,570,and 590 nm)and emission wavelengths ranging from 600-750 nm were determined for 43 phytoplankton species.A two-rank fluorescence spectra database was established by wavelet analysis and a fluorometric discrimination technique for determining phytoplankton population was developed.For laboratory simulatively mixed samples,the samples mixed from 43 algal species(the algae of one division accounted for 25%,50%,75%,85%,and 100% of the gross biomass,respectively),the average discrimination rates at the level of division were 65.0%,87.5%,98.6%,99.0%,and 99.1%,with average relative contents of 18.9%,44.5%,68.9%,73.4%,and 82.9%,respectively;the samples mixed from 32 red tide algal species(the dominant species accounted for 60%,70%,80%,90%,and 100% of the gross biomass,respectively),the average correct discrimination rates of the dominant species at the level of genus were 63.3%,74.2%,78.8%,83.4%,and 79.4%,respectively.For the 81 laboratory mixed samples with the dominant species accounting for 75% of the gross biomass(chlorophyll),the discrimination rates of the dominant species were 95.1% and 72.8% at the level of division and genus,respectively.For the 12 samples collected from the mesocosm experiment in Maidao Bay of Qingdao in August 2007,the dominant species of the 11 samples were recognized at the division level and the dominant species of four of the five samples in which the dominant species accounted for more than 80% of the gross biomass were discriminated at the genus level;for the 12 samples obtained from Jiaozhou Bay in August 2007,the dominant species of all the 12 samples were recognized at the division level.The technique can be directly applied to fluorescence spectrophotometers and to the developing of an in situ algae fluorescence auto-analyzer for

  3. The intersectionality of discrimination attributes and bullying among youth: an applied latent class analysis.

    Science.gov (United States)

    Garnett, Bernice Raveche; Masyn, Katherine E; Austin, S Bryn; Miller, Matthew; Williams, David R; Viswanath, Kasisomayajula

    2014-08-01

    Discrimination is commonly experienced among adolescents. However, little is known about the intersection of multiple attributes of discrimination and bullying. We used a latent class analysis (LCA) to illustrate the intersections of discrimination attributes and bullying, and to assess the associations of LCA membership to depressive symptoms, deliberate self harm and suicidal ideation among a sample of ethnically diverse adolescents. The data come from the 2006 Boston Youth Survey where students were asked whether they had experienced discrimination based on four attributes: race/ethnicity, immigration status, perceived sexual orientation and weight. They were also asked whether they had been bullied or assaulted for these attributes. A total of 965 (78%) students contributed to the LCA analytic sample (45% Non-Hispanic Black, 29% Hispanic, 58% Female). The LCA revealed that a 4-class solution had adequate relative and absolute fit. The 4-classes were characterized as: low discrimination (51%); racial discrimination (33%); sexual orientation discrimination (7%); racial and weight discrimination with high bullying (intersectional class) (7%). In multivariate models, compared to the low discrimination class, individuals in the sexual orientation discrimination class and the intersectional class had higher odds of engaging in deliberate self-harm. Students in the intersectional class also had higher odds of suicidal ideation. All three discrimination latent classes had significantly higher depressive symptoms compared to the low discrimination class. Multiple attributes of discrimination and bullying co-occur among adolescents. Research should consider the co-occurrence of bullying and discrimination.

  4. Discrimination against Latina/os: A Meta-Analysis of Individual-Level Resources and Outcomes

    Science.gov (United States)

    Lee, Debbiesiu L.; Ahn, Soyeon

    2012-01-01

    This meta-analysis synthesizes the findings of 60 independent samples from 51 studies examining racial/ethnic discrimination against Latina/os in the United States. The purpose was to identify individual-level resources and outcomes that most strongly relate to discrimination. Discrimination against Latina/os significantly results in outcomes…

  5. Discrimination against Latina/os: A Meta-Analysis of Individual-Level Resources and Outcomes

    Science.gov (United States)

    Lee, Debbiesiu L.; Ahn, Soyeon

    2012-01-01

    This meta-analysis synthesizes the findings of 60 independent samples from 51 studies examining racial/ethnic discrimination against Latina/os in the United States. The purpose was to identify individual-level resources and outcomes that most strongly relate to discrimination. Discrimination against Latina/os significantly results in outcomes…

  6. Discriminant possibilities of the Hamilton depression scale: ROC analysis

    Directory of Open Access Journals (Sweden)

    Novović Zdenka

    2005-01-01

    Full Text Available The purpose of this study is to compare discrimination power of original and reconstructed version of Hamilton’s depression scale in separation of depressive vs. anxious patients and to suggest some possibilities which offer ROC analysis. The subjects of the study were 119 patients of Psychiatric clinic in Novi Sad. 67 of them were diagnosed with some of the forms of affective disorders and 52 with an anxious-phobic diagnosis. Results of ROC analysis suggest that both instruments can be used in distinguishing depressive from anxious patients, but reconstructed version shows greater sensitivity and specificity with optimal cut-off score. It also has more significant AUC, which refers to probability of prediction on the basis of the whole spectrum of the results. These data is commented in relation with current debates, between unitaristic and pluralistic oriented authors, about the nature of the anxious-depression relationship.

  7. SEM-EDS analysis and discrimination of forensic soil.

    Science.gov (United States)

    Cengiz, Salih; Cengiz Karaca, Ali; Cakir, Ismail; Bülent Uner, H; Sevindik, Aytekin

    2004-04-20

    Soils vary among different areas, and have some characteristics because of the natural effects and transfers made by human and other living beings in time. So that forensic examination of soil is not only concerned with the analysis of naturally occurring rocks, minerals, vegetation, and animal matter. It also includes the detection of such manufactured materials such as ions from synthetic fertilizers and from different environments (e.g., nitrate, phosphate, and sulfate) as environmental artifacts (e.g., lead or objects as glass, paint chips, asphalt, brick fragments, and cinders) whose presence may impart soil with characteristics that will make it unique to a particular location. Many screening and analytical methods have been applied for determining the characteristics which differentiate and discriminate the forensic soil samples but none of them easily standardized. Some of the methods that applied in forensic laboratories in forensic soil discrimination are the color comparison of the normal air-dried (dehumidified) and overheated soil samples, macroscopic observation, and low-power stereo-microscopic observation, determination of anionic composition by capillary electrophoresis (CE), and the elemental composition by scanning electron microscope (SEM)-energy dispersive X-ray spectrometer (EDS) and other high sensitivity techniques. The objective of this study was to show the effect of the application of 9 tonnes/cm2 pressure on the elemental compositions obtained by SEM-EDS technique and comparing the discrimination power of the pressed-homogenized and not homogenized forensic soil samples. For this purpose soil samples from 17 different locations of Istanbul were collected. Aliquots of the well mixed samples were dried in an oven at 110-120 degrees C and sieved by using 0.5 mm sieve and then the undersieve fraction(JEO-JSM-5600 equipped with an energy dispersive X-ray spectrometer OXFORD Link-ISIS-300. The samples from top of the sieves were examined with

  8. Magnetic resonance imaging of anterior temporal lobe cysts in children: discriminating special imaging features in a particular group of diseases

    Energy Technology Data Exchange (ETDEWEB)

    Hoffmann Nunes, Renato; Torres Pacheco, Felipe; Rocha, Antonio Jose da [Fleury Medicina e Saude, Division of Neuroradiology, Sao Paulo (Brazil); Servico de Diagnostico por Imagem, Division of Neuroradiology, Santa Casa de Misericordia de Sao Paulo Paulo, Sao Paulo (Brazil)

    2014-07-15

    We hypothesized that disorders with anterior temporal lobe (ATL) cysts might exhibit common peculiarities and distinguishable imaging features that could be useful for diagnosis. We reviewed a series of patients for neuroimaging contributions to specific diagnoses. A literature search was conducted, and institutional imaging files were reviewed to identify MR examinations with ATL cysts in children. Patients were divided according to head size, calcifications, white matter and cortical abnormalities. Unsupervised hierarchical clustering of patients on the basis of their MR and CT items was performed. We identified 23 patients in our database in whom MR revealed ATL cysts. Our series included five patients with congenital muscular dystrophy (05/23 = 21.7 %), six with megalencephalic leukoencephalopathy with subcortical cysts (06/23 = 26.1 %), three with non-megalencephalic leukoencephalopathy with subcortical cysts (03/23 = 13.1 %), seven with congenital cytomegalovirus disease (07/23 = 30.4 %) and two with Aicardi-Goutieres syndrome (02/23 = 8.7 %). After analysis, 11 clusters resulted in the highest discriminative indices. Thereafter, patients' clusters were linked to their underlying diseases. The features that best discriminated between clusters included brainstem abnormalities, cerebral calcifications and some peculiar grey and white matter abnormalities. A flow chart was drafted to guide the radiologist in these diagnoses. The authors encourage the combined interpretation of these features in the herein proposed approach that confidently predicted the final diagnosis in this particular group of disorders associated with ATL cysts. (orig.)

  9. Semi-supervised learning for ordinal Kernel Discriminant Analysis.

    Science.gov (United States)

    Pérez-Ortiz, M; Gutiérrez, P A; Carbonero-Ruz, M; Hervás-Martínez, C

    2016-12-01

    Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function.

  10. Multitask linear discriminant analysis for view invariant action recognition.

    Science.gov (United States)

    Yan, Yan; Ricci, Elisa; Subramanian, Ramanathan; Liu, Gaowen; Sebe, Nicu

    2014-12-01

    Robust action recognition under viewpoint changes has received considerable attention recently. To this end, self-similarity matrices (SSMs) have been found to be effective view-invariant action descriptors. To enhance the performance of SSM-based methods, we propose multitask linear discriminant analysis (LDA), a novel multitask learning framework for multiview action recognition that allows for the sharing of discriminative SSM features among different views (i.e., tasks). Inspired by the mathematical connection between multivariate linear regression and LDA, we model multitask multiclass LDA as a single optimization problem by choosing an appropriate class indicator matrix. In particular, we propose two variants of graph-guided multitask LDA: 1) where the graph weights specifying view dependencies are fixed a priori and 2) where graph weights are flexibly learnt from the training data. We evaluate the proposed methods extensively on multiview RGB and RGBD video data sets, and experimental results confirm that the proposed approaches compare favorably with the state-of-the-art.

  11. Discriminative Ocular Artifact Correction for Feature Learning in EEG Analysis.

    Science.gov (United States)

    Li, Xinyang; Guan, Cuntai; Zhang, Haihong; Ang, Kai Keng

    2016-11-16

    Electrooculogram (EOG) artifact contamination is a common critical issue in general electroencephalogram (EEG) studies as well as in brain computer interface (BCI) research. It is especially challenging when dedicated EOG channels are unavailable or when there are very few EEG channels available for ICA-based ocular artifact removal. It is even more challenging to avoid loss of the signal of interest during the artifact correction process, where the signal of interest can be multiple magnitudes weaker than the artifact. To address these issues, we propose a novel discriminative ocular artifact correction approach for feature learning in EEG analysis.Without extra ocular movement measurements, the artifact is extracted from raw EEG data, which is totally automatic and requires no visual inspection of artifacts. Then, artifact correction is optimized jointly with feature extraction by maximizing oscillatory correlations between trials from the same class and minimizing them between trials from different classes. We evaluate this approach on a real world EEG data set comprising 68 subjects performing cognitive tasks. The results showed that the approach is capable of not only suppressing the artifact components but also improving the discriminative power of a classifier with statistical significance. We also demonstrate that the proposed method addresses the confounding issues induced by ocular movements in cognitive EEG study.

  12. Linear discriminant analysis of character sequences using occurrences of words

    KAUST Repository

    Dutta, Subhajit

    2014-02-01

    Classification of character sequences, where the characters come from a finite set, arises in disciplines such as molecular biology and computer science. For discriminant analysis of such character sequences, the Bayes classifier based on Markov models turns out to have class boundaries defined by linear functions of occurrences of words in the sequences. It is shown that for such classifiers based on Markov models with unknown orders, if the orders are estimated from the data using cross-validation, the resulting classifier has Bayes risk consistency under suitable conditions. Even when Markov models are not valid for the data, we develop methods for constructing classifiers based on linear functions of occurrences of words, where the word length is chosen by cross-validation. Such linear classifiers are constructed using ideas of support vector machines, regression depth, and distance weighted discrimination. We show that classifiers with linear class boundaries have certain optimal properties in terms of their asymptotic misclassification probabilities. The performance of these classifiers is demonstrated in various simulated and benchmark data sets.

  13. Optimized immunohistochemistry in combination with image analysis: a reliable alternative to quantitative ELISA determination of uPA and PAI-1 for routine risk group discrimination in breast cancer.

    Science.gov (United States)

    Lang, D S; Heilenkötter, U; Schumm, W; Behrens, O; Simon, R; Vollmer, E; Goldmann, T

    2013-10-01

    The determination of the invasion markers urokinase-type plasminogen activator (uPA) and plasminogen activator inhibitor-1 (PAI-1) has further improved the possibilities for individualized therapy of breast cancer. To date, quantitative measurement by ELISA, that needs large amounts of fresh, frozen material, is the only standardized procedure for diagnostic purposes. Therefore, the aim of this study was the establishment of a reliable alternative method based on immunohistochemistry (IHC) and image analysis requiring only small amounts of fixed tumor tissue. Protein expression of uPA and PAI-1 was analyzed in HOPE-fixed tumor samples using tissue microarrays (TMAs) and semiquantitative image analysis. The results of both methods were significantly correlated and risk assessment showed an overall concordance of 78% (83/107; high- and low-risk) and of 94% (74/79) regarding only high-risk patients. The data demonstrate that optimized IHC in combination with image analysis can provide adequate clinical significance compared to ELISA-derived determination of uPA and PAI-1.

  14. gyrB as a phylogenetic discriminator for members of the Bacillus anthracis-cereus-thuringiensis group

    Science.gov (United States)

    La Duc, Myron T.; Satomi, Masataka; Agata, Norio; Venkateswaran, Kasthuri

    2004-01-01

    Bacillus anthracis, the causative agent of the human disease anthrax, Bacillus cereus, a food-borne pathogen capable of causing human illness, and Bacillus thuringiensis, a well-characterized insecticidal toxin producer, all cluster together within a very tight clade (B. cereus group) phylogenetically and are indistinguishable from one another via 16S rDNA sequence analysis. As new pathogens are continually emerging, it is imperative to devise a system capable of rapidly and accurately differentiating closely related, yet phenotypically distinct species. Although the gyrB gene has proven useful in discriminating closely related species, its sequence analysis has not yet been validated by DNA:DNA hybridization, the taxonomically accepted "gold standard". We phylogenetically characterized the gyrB sequences of various species and serotypes encompassed in the "B. cereus group," including lab strains and environmental isolates. Results were compared to those obtained from analyses of phenotypic characteristics, 16S rDNA sequence, DNA:DNA hybridization, and virulence factors. The gyrB gene proved more highly differential than 16S, while, at the same time, as analytical as costly and laborious DNA:DNA hybridization techniques in differentiating species within the B. cereus group.

  15. Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression.

    Science.gov (United States)

    Shayan, Zahra; Mohammad Gholi Mezerji, Naser; Shayan, Leila; Naseri, Parisa

    2015-11-03

    Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices. This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models. CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model. The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.

  16. Group Living Enhances Individual Resources Discrimination: The Use of Public Information by Cockroaches to Assess Shelter Quality

    Science.gov (United States)

    Canonge, Stéphane; Deneubourg, Jean-Louis; Sempo, Grégory

    2011-01-01

    In group-living organisms, consensual decision of site selection results from the interplay between individual responses to site characteristics and to group-members. Individuals independently gather personal information by exploring their environment. Through social interaction, the presence of others provides public information that could be used by individuals and modulates the individual probability of joining/leaving a site. The way that individual's information processing and the network of interactions influence the dynamics of public information (depending on population size) that in turn affect discrimination in site quality is a central question. Using binary choice between sheltering sites of different quality, we demonstrate that cockroaches in group dramatically outperform the problem-solving ability of single individual. Such use of public information allows animals to discriminate between alternatives whereas isolated individuals are ineffective (i.e. the personal discrimination efficiency is weak). Our theoretical results, obtained from a mathematical model based on behavioral rules derived from experiments, highlight that the collective discrimination emerges from competing amplification processes relying on the modulation of the individual sheltering time without shelters comparison and communication modulation. Finally, we well demonstrated here the adaptive value of such decision algorithm. Without any behavioral change, the system is able to shift to a more effective strategy when alternatives are present: the modification of the spatio-temporal distributions of individuals leading to the collective selection of the best resource. This collective discrimination implying such parsimonious and widespread mechanism must be shared by many group living-species. PMID:21701692

  17. Group living enhances individual resources discrimination: the use of public information by cockroaches to assess shelter quality.

    Science.gov (United States)

    Canonge, Stéphane; Deneubourg, Jean-Louis; Sempo, Grégory

    2011-01-01

    In group-living organisms, consensual decision of site selection results from the interplay between individual responses to site characteristics and to group-members. Individuals independently gather personal information by exploring their environment. Through social interaction, the presence of others provides public information that could be used by individuals and modulates the individual probability of joining/leaving a site. The way that individual's information processing and the network of interactions influence the dynamics of public information (depending on population size) that in turn affect discrimination in site quality is a central question. Using binary choice between sheltering sites of different quality, we demonstrate that cockroaches in group dramatically outperform the problem-solving ability of single individual. Such use of public information allows animals to discriminate between alternatives whereas isolated individuals are ineffective (i.e. the personal discrimination efficiency is weak). Our theoretical results, obtained from a mathematical model based on behavioral rules derived from experiments, highlight that the collective discrimination emerges from competing amplification processes relying on the modulation of the individual sheltering time without shelters comparison and communication modulation. Finally, we well demonstrated here the adaptive value of such decision algorithm. Without any behavioral change, the system is able to shift to a more effective strategy when alternatives are present: the modification of the spatio-temporal distributions of individuals leading to the collective selection of the best resource. This collective discrimination implying such parsimonious and widespread mechanism must be shared by many group living-species.

  18. Group living enhances individual resources discrimination: the use of public information by cockroaches to assess shelter quality.

    Directory of Open Access Journals (Sweden)

    Stéphane Canonge

    Full Text Available In group-living organisms, consensual decision of site selection results from the interplay between individual responses to site characteristics and to group-members. Individuals independently gather personal information by exploring their environment. Through social interaction, the presence of others provides public information that could be used by individuals and modulates the individual probability of joining/leaving a site. The way that individual's information processing and the network of interactions influence the dynamics of public information (depending on population size that in turn affect discrimination in site quality is a central question. Using binary choice between sheltering sites of different quality, we demonstrate that cockroaches in group dramatically outperform the problem-solving ability of single individual. Such use of public information allows animals to discriminate between alternatives whereas isolated individuals are ineffective (i.e. the personal discrimination efficiency is weak. Our theoretical results, obtained from a mathematical model based on behavioral rules derived from experiments, highlight that the collective discrimination emerges from competing amplification processes relying on the modulation of the individual sheltering time without shelters comparison and communication modulation. Finally, we well demonstrated here the adaptive value of such decision algorithm. Without any behavioral change, the system is able to shift to a more effective strategy when alternatives are present: the modification of the spatio-temporal distributions of individuals leading to the collective selection of the best resource. This collective discrimination implying such parsimonious and widespread mechanism must be shared by many group living-species.

  19. Discriminating topology in galaxy distributions using network analysis

    Science.gov (United States)

    Hong, Sungryong; Coutinho, Bruno C.; Dey, Arjun; Barabási, Albert-L.; Vogelsberger, Mark; Hernquist, Lars; Gebhardt, Karl

    2016-07-01

    The large-scale distribution of galaxies is generally analysed using the two-point correlation function. However, this statistic does not capture the topology of the distribution, and it is necessary to resort to higher order correlations to break degeneracies. We demonstrate that an alternate approach using network analysis can discriminate between topologically different distributions that have similar two-point correlations. We investigate two galaxy point distributions, one produced by a cosmological simulation and the other by a Lévy walk. For the cosmological simulation, we adopt the redshift z = 0.58 slice from Illustris and select galaxies with stellar masses greater than 108 M⊙. The two-point correlation function of these simulated galaxies follows a single power law, ξ(r) ˜ r-1.5. Then, we generate Lévy walks matching the correlation function and abundance with the simulated galaxies. We find that, while the two simulated galaxy point distributions have the same abundance and two-point correlation function, their spatial distributions are very different; most prominently, filamentary structures, absent in Lévy fractals. To quantify these missing topologies, we adopt network analysis tools and measure diameter, giant component, and transitivity from networks built by a conventional friends-of-friends recipe with various linking lengths. Unlike the abundance and two-point correlation function, these network quantities reveal a clear separation between the two simulated distributions; therefore, the galaxy distribution simulated by Illustris is not a Lévy fractal quantitatively. We find that the described network quantities offer an efficient tool for discriminating topologies and for comparing observed and theoretical distributions.

  20. DSM-5 personality traits discriminate between posttraumatic stress disorder and control groups.

    Science.gov (United States)

    James, Lisa M; Anders, Samantha L; Peterson, Carly K; Engdahl, Brian E; Krueger, Robert F; Georgopoulos, Apostolos P

    2015-07-01

    The relevance of personality traits to the study of psychopathology has long been recognized, particularly in terms of understanding patterns of comorbidity. In fact, a multidimensional personality trait model reflecting five higher-order personality dimensions-negative affect, detachment, antagonism, disinhibition, and psychoticism-is included in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and represented in the Personality Inventory for DSM-5 (PID-5). However, evaluation of these dimensions and underlying personality facets within clinical samples has been limited. In the present study, we utilized the PID-5 to evaluate the personality profile elevation and composition of 150 control veterans and 35 veterans diagnosed with posttraumatic stress disorder (PTSD). Results indicated that veterans with PTSD endorsed significantly more personality pathology than control veterans, with scores on detachment and psychoticism domains most clearly discriminating between the two groups. When personality domain scores were considered as parts of each subject's personality profile, a slightly different picture emerged. Specifically, the PTSD composition was primarily characterized by detachment and negative affect, followed by disinhibition, psychoticism, and antagonism in that order of relative importance. The profile of the control group was significantly different, mostly accounted for differences in antagonism and psychoticism. Using these complementary analytic strategies, the findings demonstrate the relevance of personality pathology to PTSD, highlight internalizing features of PTSD, and pave the way for future research aimed at evaluating the role of shared maladaptive personality traits in underlying the comorbidity of PTSD and related disorders.

  1. Using discriminant analysis to assess polycyclic aromatic hydrocarbons contamination in Yongding New River.

    Science.gov (United States)

    Wang, Xiaojing; Zou, Zhihong; Zou, Hui

    2013-10-01

    Yongding New River has been polluted by polycyclic aromatic hydrocarbons (PAHs) which are carcinogenic and mutagenic. In three periods (the abundant water period, mean water period, dry water period), ten sites (totally 30 samples) in Yongding New River were clustered into four categories by hierarchical cluster analysis (hierarchical CA). In the same cluster, the samples had the same approximate contamination situation. In order to eliminate the dimensional differences, the data in each sample, containing 16 kinds of PAHs, were standardized with normal standardization and maximum difference standardization. According to the results of the cubic clustering criterion, pseudo F, and pseudo t (2) (PST2), the proper number of clustering for the 30 samples is 4. Before conducting hierarchical CA and K-means cluster analysis on the samples, we used principal component analysis to obtain another group data set. This data set was composed of the principal component scores which are uncorrelated variables. Hierarchical CA and K-means cluster analysis were used to classify the two data sets into four categories. With the classification results of hierarchical CA and K-means cluster analysis, discriminant analysis is applied to determine which method was better for normalization of the original data and which one was proper to cluster the samples and establish discriminant functions so that a new sample can be grouped into the right categories.

  2. How Many Genes Are Needed for a Discriminant Microarray Data Analysis ?

    CERN Document Server

    Li, W; Li, Wentian; Yang, Yaning

    2001-01-01

    The analysis of the leukemia data from Whitehead/MIT group is a discriminant analysis (also called a supervised learning). Among thousands of genes whose expression levels are measured, not all are needed for discriminant analysis: a gene may either not contribute to the separation of two types of tissues/cancers, or it may be redundant because it is highly correlated with other genes. There are two theoretical frameworks in which variable selection (or gene selection in our case) can be addressed. The first is model selection, and the second is model averaging. We have carried out model selection using Akaike information criterion and Bayesian information criterion with logistic regression (discrimination, prediction, or classification) to determine the number of genes that provide the best model. These model selection criteria set upper limits of 22-25 and 12-13 genes for this data set with 38 samples, and the best model consists of only one (no.4847, zyxin) or two genes. We have also carried out model aver...

  3. Gender, Discrimination Beliefs, Group-Based Guilt, and Responses to Affirmative Action for Australian Women

    Science.gov (United States)

    Boeckmann, Robert J.; Feather, N. T.

    2007-01-01

    Views of a selection committee's decision to promote a woman over a man on the basis of affirmative action were studied in a random sample of Australians (118 men and 111 women). The relations between perceptions of workplace gender discrimination, feelings of collective responsibility and guilt for discrimination, and judgments of entitlement to…

  4. Regularized generalized eigen-decomposition with applications to sparse supervised feature extraction and sparse discriminant analysis

    DEFF Research Database (Denmark)

    Han, Xixuan; Clemmensen, Line Katrine Harder

    2015-01-01

    techniques, for instance, 2D-Linear Discriminant Analysis (2D-LDA). Furthermore, an iterative algorithm based on the alternating direction method of multipliers is developed. The algorithm approximately solves RGED with monotonically decreasing convergence and at an acceptable speed for results of modest......We propose a general technique for obtaining sparse solutions to generalized eigenvalue problems, and call it Regularized Generalized Eigen-Decomposition (RGED). For decades, Fisher's discriminant criterion has been applied in supervised feature extraction and discriminant analysis...... accuracy. Numerical experiments based on four data sets of different types of images show that RGED has competitive classification performance with existing multidimensional and sparse techniques of discriminant analysis....

  5. Institutional Discrimination against the MinorityGroups in Bosnia and Herzegovina: Barrier to EU Membership

    Directory of Open Access Journals (Sweden)

    Bedrudin Brljavac

    2011-11-01

    Full Text Available Although over more than 10 years Bosnia and Herzegovina has been going through an extensive European Union-related reform process, the country is still facing serious democratic deficit. In particular, the post-Dayton public sphere has been dominated by ethno-nationalist political elites which are doing everything to exclude non-nationalists and members of minority groups from the decision-making process. This is a clear paradox since one of the main objectives behind the integration of the European countries into the European Community was to reduce disintegrative and dangerous influences of nationalists and establish a peaceful, prosperous, and secure community. In this article, we analyze the process of the post-Dayton ethno-nationalization resulting in a widespread discrimination against the so-called ―others‖ as they are defined in the Constitution. In the post-war BiH, democratic participation has turned into a competition between the three ethnic communities, Bosniaks, Serbs, and Croats, rather than race of equal individuals having equal right of vote. That‘s why Bosnian people are still living under the political system which is closer to ethno-democracy or ethnocracy rather than democratic regime. Under such a discriminatory regime BiH can not enter the European Union, which is a model of open and democratic society.

  6. Identification and Characterization of Forms of Discrimination against Groups at Risk for Sexually Transmitted Diseases

    Directory of Open Access Journals (Sweden)

    Elia Natividad Cabrera Álvarez

    2013-12-01

    Full Text Available Background: stigma and discrimination against men who have sex with men are among the major obstacles to preventing new infections and providing treatment to people living with human immunodeficiency virus. Objective: to identify and characterize levels and forms of discrimination against men who have sex with men. Methods: a retrospective study was conducted in six municipalities of the province of Cienfuegos during 2009-2010 as a result of the Section of stigma and discrimination included in the Survey on human immunodeficiency virus/AIDS Prevention Indicators. Sociodemographic variables, discrimination index and level of discrimination were analyzed, the latter by CONSHSH and NIVELHSH constructs, respectively. Results: acceptance of men who have sex with men predominated, although three levels of discrimination were identified in 34.5% of the respondents; medium and high levels prevailed in 98% of them. Thirty to 34% of the respondents expressed rejection, 21.5 % of them were men. Factors contributing to discrimination were related to the institutional context and the family environment. Conclusions: a slight decrease in the percentage of cases rejecting men who have sex with men was observed in 2010 compared to 2009, therefore a trend toward increasing acceptance of these people is expected, which is consistent with the results obtained at the national level.

  7. An Analysis of Discrimination by Real Estate Brokers.

    Science.gov (United States)

    Yinger, John

    This paper focuses on designing policies to eliminate discrimination in the sale of single-family houses by analyzing the behavior of the agents who actually do most of the discriminating, namely real estate agents. Discriminatory practices are said to be supported by policies of house builders, lending institutions, and government, and by the…

  8. Racial Discrimination and Asian Mental Health: A Meta-Analysis

    Science.gov (United States)

    Lee, Debbiesiu L.; Ahn, Soyeon

    2011-01-01

    Although research on racial discrimination and mental health has proliferated, findings are varied and dispersed. This study explored the critical question of how Asians, in particular, deal with discrimination and how this relates to Asian mental health. With 99 correlations from 23 independent studies, the overall relationship between racial…

  9. The Analysis of the Ethnical Discrimination on the Manpower’s Market under the Economical Crisis

    Directory of Open Access Journals (Sweden)

    Mihaela Hrisanta DOBRE

    2012-06-01

    Full Text Available Discrimination means any difference, exclusion, restriction, preference or different treatment that brings forth disadvantages for a person or a group as compared to other ones that are in similar situations. The reasons on which discrimination is based can be various, such as race, nationality, ethnics, religion, gender, sexual orientation, language, age, disabilities etc. and in this case we talk about multiple discrimination. In Romania the main forms of discrimination are linked to ethnics and to sexual appurtenance. Within this column we analysed the discrimination amongst the Romany ethnics people, according to a statistical investigation (Access onto the Labour Market – A Chance for You, the research goal being to identify the answer to the following questions: Is there any discrimination inside the Romany ethnic group? What is the correlation between their level of education and their income? What is the correlation between the level of education of the parents and the respondent’s?

  10. Discrimination of red and white rice bran from Indonesia using HPLC fingerprint analysis combined with chemometrics.

    Science.gov (United States)

    Sabir, Aryani; Rafi, Mohamad; Darusman, Latifah K

    2017-04-15

    HPLC fingerprint analysis combined with chemometrics was developed to discriminate between the red and the white rice bran grown in Indonesia. The major component in rice bran is γ-oryzanol which consisted of 4 main compounds, namely cycloartenol ferulate, cyclobranol ferulate, campesterol ferulate and β-sitosterol ferulate. Separation of these four compounds along with other compounds was performed using C18 and methanol-acetonitrile with gradient elution system. By using these intensity variations, principal component and discriminant analysis were performed to discriminate the two samples. Discriminant analysis was successfully discriminated the red from the white rice bran with predictive ability of the model showed a satisfactory classification for the test samples. The results of this study indicated that the developed method was suitable as quality control method for rice bran in terms of identification and discrimination of the red and the white rice bran.

  11. Lateral cephalometric analysis of mandibular morphology: discrimination among subjects with and without temporomandibular joint disk displacement and osteoarthrosis.

    Science.gov (United States)

    Bertram, S; Moriggl, A; Neunteufel, N; Rudisch, A; Emshoff, R

    2012-02-01

    To assess whether in patients with temporomandibular joint (TMJ) arthralgia cephalometric variables of mandibular morphology may discriminate among the magnetic resonance (MR) imaging-based TMJ groups of 'bilateral presence of disk displacement without reduction (DDwoR) and osteoarthrosis (OA)' and 'bilateral absence of bilateral DDwoR and OA'. Bilateral MR imaging of the TMJ was performed in 45 consecutive TMJ arthralgia patients to identify individuals with the specific structural characteristics of bilateral TMJ DDwoR associated with OA. Linear and angular cephalometric measurements were taken from lateral cephalograms to apply selected criteria of mandibular morphology. A discriminant function analysis was used to investigate how cephalometric parameters discriminate among the TMJ groups of 'bilateral presence of DDwoR with OA' and 'bilateral absence of DDwoR and OA'. Ramus height (Ar-Go) and effective mandibular length (Ar-Pog) produced a significant discriminant function that predicted TMJ group membership (P < 0·001). This function correctly classified 80·2% of original and cross-validated grouped cases. This study supports the concept that cephalometric variables of mandibular morphology discriminate among subjects with and without bilateral TMJ DDwoR and OA.

  12. Sparse Regression by Projection and Sparse Discriminant Analysis

    KAUST Repository

    Qi, Xin

    2015-04-03

    © 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.

  13. Skin-Color Prejudice and Within-Group Racial Discrimination: Historical and Current Impact on Latino/a Populations

    Science.gov (United States)

    Chavez-Dueñas, Nayeli Y.; Adames, Hector Y.; Organista, Kurt C.

    2014-01-01

    The psychological literature on colorism, a form of within-group racial discrimination, is sparse. In an effort to contribute to this understudied area and highlight its significance, a concise and selective review of the history of colorism in Latin America is provided. Specifically, three historical eras (i.e., conquest, colonization, and…

  14. Skin-Color Prejudice and Within-Group Racial Discrimination: Historical and Current Impact on Latino/a Populations

    Science.gov (United States)

    Chavez-Dueñas, Nayeli Y.; Adames, Hector Y.; Organista, Kurt C.

    2014-01-01

    The psychological literature on colorism, a form of within-group racial discrimination, is sparse. In an effort to contribute to this understudied area and highlight its significance, a concise and selective review of the history of colorism in Latin America is provided. Specifically, three historical eras (i.e., conquest, colonization, and…

  15. Discrimination of Klebsiella pneumoniae and Klebsiella oxytoca phylogenetic groups and other Klebsiella species by use of amplified fragment length polymorphism.

    Science.gov (United States)

    Jonas, Daniel; Spitzmüller, Bettina; Daschner, Franz D; Verhoef, Jan; Brisse, Sylvain

    2004-01-01

    Bacteria of the genus Klebsiella are opportunistic pathogens responsible for an increasing number of multiresistant infections in hospitals. The two clinically and epidemiologically most important species, Klebsiella pneumoniae and K. oxytoca, have recently been shown to be subdivided into three and two phylogenetic groups, respectively. The aim of this study was an in depth evaluation of the amplified fragment length polymorphism (AFLP) genetic characterization method for epidemiological and phylogenic analyzes of Klebsiella isolates. First, we investigated the variability of AFLP patterns for Klebsiella strains within and between different outbreaks. Second, by use of carefully characterized phylogenetically representative strains, we examined whether different Klebsiella species and phylogenetic groups can be discriminated using AFLP. Twenty-four strains originating from seven presumed outbreaks and 31 non-associated strains were investigated. The AFLP fingerprints of all epidemiologically associated strains showed three or fewer fragment differences, whereas unrelated strains differed by at least four fragments. Cluster analysis of the AFLP data revealed a very high concordance with the phylogenetic assignation of strains based on the gyrA sequence and ribotyping data. The species K. pneumoniae, K. oxytoca, K. terrigena and the possibly synonymous pair K. planticola/K. ornithinolytica each formed a separate cluster. Similarly, strains of the phylogenetic groups of K. pneumoniae and K. oxytoca fell into their corresponding clusters, with only two exceptions. This study provides a preliminary cut-off value for distinguishing epidemiologically non-related Klebsiella isolates based on AFLP data; it confirms the sharp delineation of the recently identified phylogenetic groups, and demonstrates that AFLP is suitable for identification of Klebsiella species and phylogenetic groups.

  16. Sex determination of the Acadian Flycatcher using discriminant analysis

    Science.gov (United States)

    Wilson, R.R.

    1999-01-01

    I used five morphometric variables from 114 individuals captured in Arkansas to develop a discriminant model to predict the sex of Acadian Flycatchers (Empidonax virescens). Stepwise discriminant function analyses selected wing chord and tail length as the most parsimonious subset of variables for discriminating sex. This two-variable model correctly classified 80% of females and 97% of males used to develop the model. Validation of the model using 19 individuals from Louisiana and Virginia resulted in 100% correct classification of males and females. This model provides criteria for sexing monomorphic Acadian Flycatchers during the breeding season and possibly during the winter.

  17. Discrimination of cultivation ages and cultivars of ginseng leaves using Fourier transform infrared spectroscopy combined with multivariate analysis

    Science.gov (United States)

    Kwon, Yong-Kook; Ahn, Myung Suk; Park, Jong Suk; Liu, Jang Ryol; In, Dong Su; Min, Byung Whan; Kim, Suk Weon

    2013-01-01

    To determine whether Fourier transform (FT)-IR spectral analysis combined with multivariate analysis of whole-cell extracts from ginseng leaves can be applied as a high-throughput discrimination system of cultivation ages and cultivars, a total of total 480 leaf samples belonging to 12 categories corresponding to four different cultivars (Yunpung, Kumpung, Chunpung, and an open-pollinated variety) and three different cultivation ages (1 yr, 2 yr, and 3 yr) were subjected to FT-IR. The spectral data were analyzed by principal component analysis and partial least squares-discriminant analysis. A dendrogram based on hierarchical clustering analysis of the FT-IR spectral data on ginseng leaves showed that leaf samples were initially segregated into three groups in a cultivation age-dependent manner. Then, within the same cultivation age group, leaf samples were clustered into four subgroups in a cultivar-dependent manner. The overall prediction accuracy for discrimination of cultivars and cultivation ages was 94.8% in a cross-validation test. These results clearly show that the FT-IR spectra combined with multivariate analysis from ginseng leaves can be applied as an alternative tool for discriminating of ginseng cultivars and cultivation ages. Therefore, we suggest that this result could be used as a rapid and reliable F1 hybrid seed-screening tool for accelerating the conventional breeding of ginseng. PMID:24558311

  18. Discrimination and divergence among Lactobacillus plantarum-group (LPG) isolates with reference to their probiotic functionalities from vegetable origin.

    Science.gov (United States)

    Devi, Sundru Manjulata; Aishwarya, Subramanian; Halami, Prakash M

    2016-12-01

    The present study was aimed to evaluate the diversity and probiotic properties of Lactobacillus plantarum-group cultures from vegetable origin. First, genotypic diversity of L. plantarum (n=34) was achieved by PCR of Random Amplified Polymorphic DNA and recA gene-specific multiplex PCR. The isolates were segregated into five groups namely, Lactobacillus pentosus, Lactobacillus paraplantarum, Lactobacillus arizonensis, Lactobacillus plantarum subsp. plantarum and argentoratensis. Further discrimination was achieved by restriction fragment length polymorphism of probiotic adhesion genes viz.fbp, mub and msa gene. As determined by nucleotide sequence analysis and bioinformatics Pfam database, the putative Fbp protein had only one FBP domain, whereas Mub protein had 8-10 MUB domain repeats. However, L. pentosus (except CFR MFT9), L. plantarum subsp. argentoratensis (except CFR MFT5) and L. arizonensis (except CFR MFT2) isolates gave no amplicon for the tested marker genes. Selected cultures (n=15) showed tolerance to simulated digestive fluids (20-85%), exhibited auto-aggregation (10-77%), cellular hydrophobicity (12-78%), and broad spectrum of anti-microbial activity. Concurrently, high adherence capacity to mucin was achieved for L. plantarum subsp. plantarum (MCC 2974 and CFR MFT1) and L. paraplantarum (MTCC 9483, MCC 2977, MCC 2978), which had an additional MUB domain repeat.

  19. Unbiased bootstrap error estimation for linear discriminant analysis.

    Science.gov (United States)

    Vu, Thang; Sima, Chao; Braga-Neto, Ulisses M; Dougherty, Edward R

    2014-12-01

    Convex bootstrap error estimation is a popular tool for classifier error estimation in gene expression studies. A basic question is how to determine the weight for the convex combination between the basic bootstrap estimator and the resubstitution estimator such that the resulting estimator is unbiased at finite sample sizes. The well-known 0.632 bootstrap error estimator uses asymptotic arguments to propose a fixed 0.632 weight, whereas the more recent 0.632+ bootstrap error estimator attempts to set the weight adaptively. In this paper, we study the finite sample problem in the case of linear discriminant analysis under Gaussian populations. We derive exact expressions for the weight that guarantee unbiasedness of the convex bootstrap error estimator in the univariate and multivariate cases, without making asymptotic simplifications. Using exact computation in the univariate case and an accurate approximation in the multivariate case, we obtain the required weight and show that it can deviate significantly from the constant 0.632 weight, depending on the sample size and Bayes error for the problem. The methodology is illustrated by application on data from a well-known cancer classification study.

  20. Discriminating Topology in Galaxy Distributions using Network Analysis

    CERN Document Server

    Hong, Sungryong; Dey, Arjun; Barabási, Albert -L; Vogelsberger, Mark; Hernquist, Lars; Gebhardt, Karl

    2016-01-01

    (abridged) The large-scale distribution of galaxies is generally analyzed using the two-point correlation function. However, this statistic does not capture the topology of the distribution, and it is necessary to resort to higher order correlations to break degeneracies. We demonstrate that an alternate approach using network analysis can discriminate between topologically different distributions that have similar two-point correlations. We investigate two galaxy point distributions, one produced by a cosmological simulation and the other by a L\\'evy walk. For the cosmological simulation, we adopt the redshift $z = 0.58$ slice from Illustris (Vogelsberger et al. 2014A) and select galaxies with stellar masses greater than $10^8$$M_\\odot$. The two point correlation function of these simulated galaxies follows a single power-law, $\\xi(r) \\sim r^{-1.5}$. Then, we generate L\\'evy walks matching the correlation function and abundance with the simulated galaxies. We find that, while the two simulated galaxy point d...

  1. Learning Discriminative Subspaces on Random Contrasts for Image Saliency Analysis.

    Science.gov (United States)

    Fang, Shu; Li, Jia; Tian, Yonghong; Huang, Tiejun; Chen, Xiaowu

    2017-05-01

    In visual saliency estimation, one of the most challenging tasks is to distinguish targets and distractors that share certain visual attributes. With the observation that such targets and distractors can sometimes be easily separated when projected to specific subspaces, we propose to estimate image saliency by learning a set of discriminative subspaces that perform the best in popping out targets and suppressing distractors. Toward this end, we first conduct principal component analysis on massive randomly selected image patches. The principal components, which correspond to the largest eigenvalues, are selected to construct candidate subspaces since they often demonstrate impressive abilities to separate targets and distractors. By projecting images onto various subspaces, we further characterize each image patch by its contrasts against randomly selected neighboring and peripheral regions. In this manner, the probable targets often have the highest responses, while the responses at background regions become very low. Based on such random contrasts, an optimization framework with pairwise binary terms is adopted to learn the saliency model that best separates salient targets and distractors by optimally integrating the cues from various subspaces. Experimental results on two public benchmarks show that the proposed approach outperforms 16 state-of-the-art methods in human fixation prediction.

  2. Face Recognition Using Holistic Features and Simplified Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Gou Koutaki

    2012-08-01

    Full Text Available This paper proposed an alternative approach to face recognition algorithm that is based on global/holistic features of face image and simplified Linear Discriminant Analysis (LDA. The proposed method can overcome main problems of the conventional LDA in terms of large processing time for retraining when a new class data was registered into the training data set. The holistic features of face image were proposed as dimensional reduction of raw face image. While, the simplified LDA which is the redefinition of between class scatter using constant global mean assignment was proposed to decrease time complexity of retraining process. In order to know the performance of the proposed method, several experiments were performed using several challenging face databases: ORL, YALE, ITS-Lab, INDIA, and FERET database. Furthermore, we compared the developed algorithm experimental results to the best traditional subspace methods such as DLDA, 2DLDA, (2D2DLDA, 2DPCA, and (2D22DPCA. The experimental results show that the proposed method can solve the retraining problem of the conventional LDA indicated by requiring short retraining time and stable recognition rate.

  3. Sparse linear discriminant analysis by thresholding for high dimensional data

    CERN Document Server

    Shao, Jun; Deng, Xinwei; Wang, Sijian; 10.1214/10-AOS870

    2011-01-01

    In many social, economical, biological and medical studies, one objective is to classify a subject into one of several classes based on a set of variables observed from the subject. Because the probability distribution of the variables is usually unknown, the rule of classification is constructed using a training sample. The well-known linear discriminant analysis (LDA) works well for the situation where the number of variables used for classification is much smaller than the training sample size. Because of the advance in technologies, modern statistical studies often face classification problems with the number of variables much larger than the sample size, and the LDA may perform poorly. We explore when and why the LDA has poor performance and propose a sparse LDA that is asymptotically optimal under some sparsity conditions on the unknown parameters. For illustration of application, we discuss an example of classifying human cancer into two classes of leukemia based on a set of 7,129 genes and a training ...

  4. Study of age-related changes in postural control during quiet standing through Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Andrade Adriano O

    2009-11-01

    Full Text Available Abstract Background The human body adopts a number of strategies to maintain an upright position. The analysis of the human balance allows for the understanding and identification of such strategies. The displacement of the centre of pressure (COP is a measure that has been successfully employed in studies regarding the postural control. Most of these investigations are related to the analysis of individuals suffering from neuromuscular disorders. Recent studies have shown that the elderly population is growing very fast in many countries all over the world, and therefore, researches that try to understand changes in this group are required. In this context, this study proposes the analysis of the postural control, measured by the displacement of the COP, in groups of young and elderly adults. Methods In total 59 subjects participated of this study. They were divided into seven groups according to their age. The displacement of the COP was collected for each subject standing on a force plate. Two experimental conditions, of 30 seconds each, were investigated: opened eyes and closed eyes. Traditional and recent digital signal processing tools were employed for feature computation from the displacement of the COP. Statistical analyses were carried out in order to identify significant differences between the features computed from the distinct groups that could allow for their discrimination. Results Our results showed that Linear Discrimination Analysis (LDA, which is one of the most popular feature extraction and classifier design techniques, could be successfully employed as a linear transformation, based on the linear combination of standard features for COP analysis, capable of estimating a unique feature, so-called LDA-value, from which it was possible to discriminate the investigated groups and show a high correlation between this feature and age. Conclusion These results show that the analysis of features computed from the displacement of

  5. Optimal discrimination of multiple quantum systems: controllability analysis

    Energy Technology Data Exchange (ETDEWEB)

    Turinici, Gabriel [INRIA Rocquencourt, BP 105, 78153 Le Chesnay Cedex (France); Ramakhrishna, Viswanath [Department of Mathematical Sciences and Center for Signals, Systems and Communications, University of Texas at Dallas, PO Box 830688, Richardson, TX 75083 (United States); Li Baiqing [Department of Chemistry, Princeton University, Princeton, NJ 08544 (United States); Rabitz, Herschel [Department of Chemistry, Princeton University, Princeton, NJ 08544 (United States)

    2004-01-09

    A theoretical study is presented concerning the ability to dynamically discriminate between members of a set of different (but possibly similar) quantum systems. This discrimination is analysed in terms of independently and simultaneously steering about the wavefunction of each component system to a target state of interest using a tailored control (i.e. laser) field. Controllability criteria are revealed and their applicability is demonstrated in simple cases. Discussion is also presented in some uncontrollable cases.

  6. Discriminant Analysis of a Spatially Extensive Landsliding Inventory for the Haida Gwaii, British Columbia, Canada

    Science.gov (United States)

    Sjogren, D.; Martin, Y. E.; Jagielko, L.

    2010-12-01

    Gimbarzevsky (1988) collected an exceptional landsliding inventory for the Haida Gwaii, British Columbia (formerly called the Queen Charlotte Islands). This data base includes more than 8 000 landsliding vectors, with an areal coverage of about 10 000 km2. Unfortunately, this landsliding inventory was never published in the referred literature, despite its regional significance. The data collection occurred prior to widespread use of GIS technologies in landsliding analysis, thus restricting the types of analyses that were undertaken at the time relative to what is possible today. Gimbarzevsky identified the landsliding events from 1:50 000 aerial photographs, and then transferred the landslide vectors to NTS map sheets. In this study, we digitized the landslide vectors from these original map sheets and connected each vector to a digital elevation model. Lengths of landslide vectors were then compared to results of Rood (1984), whose landsliding inventory for the Haida Gwaii relied on larger-scale aerial photographs (~ 1:13 000). A comparison of the two data bases shows that Rood’s inventory contains a more complete record of smaller landslides, whereas Gimbarzevsky’s inventory provides a much better statistical representation of less frequently occurring, medium to large landslide events. We then apply discriminant analysis to the Gimbarzevsky data base to assess which of a set of ten predictor variables, selected on the basis of mechanical theory, best predict failed vs. unfailed locations in the landscape (referred to as the grouping variable in discriminant analysis). Certain predictor variables may be cross-correlated, and any one particular variable may be related to several aspects of mechanical theory (for example, a particular variable may affect various components of shear stress and/or shear strength); it is important to recognize that the significance of particular groupings may reflect this information. Eight of the original variables were found

  7. Fluorescence spectral analysis for the discrimination of complex, similar mixtures with the aid of chemometrics.

    Science.gov (United States)

    Ni, Yongnian; Lai, Yanhua; Kokot, Serge

    2012-07-01

    An analytical method for the classification of complex real-world samples was researched and developed with the use of excitation-emission fluorescence matrix (EEFM) spectroscopy, using the medicinal herbs, Rhizoma corydalis decumbentis (RCD) and Rhizoma corydalis (RC) as example samples. The data set was obtained from various authentic RCD-A and RC-A, adulterated AD, and commercial RCD-C and RC-C samples. The spectra (range: λ(ex) = 215∼395 nm and λ(em) = 290∼560 nm), arranged in two- and three-way data matrix formats, were processed using principal component analysis (PCA) and parallel factor analysis (PARAFAC) to produce two-dimensional component-by-component plots for qualitative data classification. The RCD-A and RC-A object groups were clearly discriminated, but the AD and the RCD-C as well as RC-C samples were less well separated. PARAFAC analysis produced somewhat better discrimination, and loadings plots revealed the presence of the marker compound Protopine-a strongly fluorescing substance-as well as at least two other unidentified fluorescent components. Classification performance of the common K-nearest neighbors (KNN) and linear discrimination analysis (LDA) methods was relatively poor when compared with that of the back propagation- and radial basis function-artificial neural networks (BP-ANN and RBF-ANN) models on the basis of two- and three-way formatted data. The best results were obtained with the three-way fingerprints and the RBF-ANN model. Subsequently, the quality of the commercial samples (RCD-C and RC-C) was classified on the best optimized RBF-ANN model. Thus, EEFM spectroscopy, which provides three-way measured data, is potentially a powerful analytical technique for the analysis of complex real-world substances provided the classification is performed by the RBF-ANN or similar ANN methods.

  8. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations

    Directory of Open Access Journals (Sweden)

    Balloux François

    2010-10-01

    Full Text Available Abstract Background The dramatic progress in sequencing technologies offers unprecedented prospects for deciphering the organization of natural populations in space and time. However, the size of the datasets generated also poses some daunting challenges. In particular, Bayesian clustering algorithms based on pre-defined population genetics models such as the STRUCTURE or BAPS software may not be able to cope with this unprecedented amount of data. Thus, there is a need for less computer-intensive approaches. Multivariate analyses seem particularly appealing as they are specifically devoted to extracting information from large datasets. Unfortunately, currently available multivariate methods still lack some essential features needed to study the genetic structure of natural populations. Results We introduce the Discriminant Analysis of Principal Components (DAPC, a multivariate method designed to identify and describe clusters of genetically related individuals. When group priors are lacking, DAPC uses sequential K-means and model selection to infer genetic clusters. Our approach allows extracting rich information from genetic data, providing assignment of individuals to groups, a visual assessment of between-population differentiation, and contribution of individual alleles to population structuring. We evaluate the performance of our method using simulated data, which were also analyzed using STRUCTURE as a benchmark. Additionally, we illustrate the method by analyzing microsatellite polymorphism in worldwide human populations and hemagglutinin gene sequence variation in seasonal influenza. Conclusions Analysis of simulated data revealed that our approach performs generally better than STRUCTURE at characterizing population subdivision. The tools implemented in DAPC for the identification of clusters and graphical representation of between-group structures allow to unravel complex population structures. Our approach is also faster than

  9. A longitudinal analysis of Hispanic youth acculturation and cigarette smoking: the roles of gender, culture, family, and discrimination.

    Science.gov (United States)

    Lorenzo-Blanco, Elma I; Unger, Jennifer B; Ritt-Olson, Anamara; Soto, Daniel; Baezconde-Garbanati, Lourdes

    2013-05-01

    Risk for smoking initiation increases as Hispanic youth acculturate to U.S. society, and this association seems to be stronger for Hispanic girls than boys. To better understand the influence of culture, family, and everyday discrimination on cigarette smoking, we tested a process-oriented model of acculturation and cigarette smoking. Data came from Project RED (Reteniendo y Entendiendo Diversidad para Salud), which included 1,436 Hispanic students (54% girls) from Southern California. We used data from 9th to 11th grade (85% were 14 years old, and 86% were U.S. born) to test the influence of acculturation-related experiences on smoking over time. Multigroup structural equation analysis suggested that acculturation was associated with increased familismo and lower traditional gender roles, and enculturation was linked more with familismo and respeto. Familismo, respeto, and traditional gender roles were linked with lower family conflict and increased family cohesion, and these links were stronger for girls. Familismo and respeto were further associated with lower discrimination. Conversely, fatalismo was linked with worse family functioning (especially for boys) and increased discrimination in both the groups. Discrimination was the only predictor of smoking for boys and girls. In all, the results of the current study indicate that reducing discrimination and helping youth cope with discrimination may prevent or reduce smoking in Hispanic boys and girls. This may be achieved by promoting familismo and respeto and by discouraging fatalistic beliefs.

  10. Comparison of linear discriminant analysis methods for the classification of cancer based on gene expression data

    Directory of Open Access Journals (Sweden)

    He Miao

    2009-12-01

    Full Text Available Abstract Background More studies based on gene expression data have been reported in great detail, however, one major challenge for the methodologists is the choice of classification methods. The main purpose of this research was to compare the performance of linear discriminant analysis (LDA and its modification methods for the classification of cancer based on gene expression data. Methods The classification performance of linear discriminant analysis (LDA and its modification methods was evaluated by applying these methods to six public cancer gene expression datasets. These methods included linear discriminant analysis (LDA, prediction analysis for microarrays (PAM, shrinkage centroid regularized discriminant analysis (SCRDA, shrinkage linear discriminant analysis (SLDA and shrinkage diagonal discriminant analysis (SDDA. The procedures were performed by software R 2.80. Results PAM picked out fewer feature genes than other methods from most datasets except from Brain dataset. For the two methods of shrinkage discriminant analysis, SLDA selected more genes than SDDA from most datasets except from 2-class lung cancer dataset. When comparing SLDA with SCRDA, SLDA selected more genes than SCRDA from 2-class lung cancer, SRBCT and Brain dataset, the result was opposite for the rest datasets. The average test error of LDA modification methods was lower than LDA method. Conclusions The classification performance of LDA modification methods was superior to that of traditional LDA with respect to the average error and there was no significant difference between theses modification methods.

  11. Social behavior and kin discrimination in a mixed group of cloned and non cloned heifers (Bos taurus).

    Science.gov (United States)

    Coulon, M; Baudoin, C; Abdi, H; Heyman, Y; Deputte, B L

    2010-12-01

    For more than ten years, reproductive biotechnologies using somatic cell nuclear transfer have made possible the production of cloned animals in various domestic and laboratory species. The influence of the cloning process on offspring characteristics has been studied in various developmental aspects, however, it has not yet been documented in detail for behavioral traits. Behavioral studies of cloned animals have failed to show clear inter-individual differences associated with the cloning process. Preliminary results showed that clones favor each other's company. Preferential social interactions were observed among cloned heifers from the same donor in a mixed herd that also included cloned heifers and control heifers produced by artificial insemination (AI). These results suggest behavioral differences between cloned and non-cloned animals and similarities between clones from the same donor. The aim of the present study was to replicate and to extend these previous results and to study behavioral and cognitive mechanisms of this preferential grouping. We studied a group composed of five cloned heifers derived from the same donor cow, two cloned heifers derived from another donor cow, and AI heifers. Cloned heifers from the same donor were more spatially associated and interacted more between themselves than with heifers derived from another donor or with the AI individuals. This pattern indicates a possible kin discrimination in clones. To study this process, we performed an experiment (using an instrumental conditioning procedure with food reward) of visual discrimination between images of heads of familiar heifers, either related to the subjects or not. The results showed that all subjects (AI and cloned heifers) discriminated between images of familiar cloned heifers produced from the same donor and images of familiar unrelated heifers. Cattle discriminated well between images and used morphological similarities characteristic of cloned related heifers. Our

  12. 稀疏判别分析%Sparse discriminant analysis

    Institute of Scientific and Technical Information of China (English)

    陈小冬; 林焕祥

    2012-01-01

    Methods for manifold embedding have the following issues: on one hand, neighborhood graph is constructed in such high-dimensionality of original space that it tends to work poorly; on the other hand, appropriate values for the neighborhood size and heat kernel parameter involved in graph construction are generally difficult to be assigned. To address these problems, a new semi-supervised dimensionality reduction algorithm called SparsE Discriminant Analysis (SEDA) was proposed. Firstly, SEDA set up a sparse graph to preserve the global information and geometric structure of the data based on sparse representation. Secondly, it applied both sparse graph and Fisher criterion to seek the optimal projection. The experimental results on a broad range of data sets show that SEDA is superior to many popular dimensionality reduction methods.%针对流形嵌入降维方法中在高维空间构建近邻图无益于后续工作,以及不容易给近邻大小和热核参数赋合适值的问题,提出一种稀疏判别分析算法(SEDA).首先使用稀疏表示构建稀疏图保持数据的全局信息和几何结构,以克服流形嵌入方法的不足;其次,将稀疏保持作为正则化项使用Fisher判别准则,能够得到最优的投影.在一组高维数据集上的实验结果表明,SEDA是非常有效的半监督降维方法.

  13. Declining Bias and Gender Wage Discrimination? A Meta-Regression Analysis

    Science.gov (United States)

    Jarrell, Stephen B.; Stanley, T. D.

    2004-01-01

    The meta-regression analysis reveals that there is a strong tendency for discrimination estimates to fall and wage discrimination exist against the woman. The biasing effect of researchers' gender of not correcting for selection bias has weakened and changes in labor market have made it less important.

  14. An Application of Discriminant Analysis to Pattern Recognition of Selected Contaminated Soil Features in Thin Sections

    DEFF Research Database (Denmark)

    Ribeiro, Alexandra B.; Nielsen, Allan Aasbjerg

    1997-01-01

    qualitative microprobe results: present elements Al, Si, Cr, Fe, As (associated with others). Selected groups of calibrated images (same light conditions and magnification) submitted to discriminant analysis, in order to find a pattern of recognition in the soil features corresponding to contamination already...... in the soluble and exchangeable phase, these elements being associated primarily with amorphous-crystalline Fe-oxides, organic matter and/or resistant phases. The results obtained with sequential extraction were the prerequisite to the attempt to identify the Cr and As distribution in the solid phase. If high...... concentrations of contaminants are indicated by chemical wet analysis, these contaminants must occur directly in the solid phase. Thin sections of soil aggregates were scanned for Cu, Cr and As using an electron microprobe, and qualitative analysis was made on selected areas. Microphotographs of thin sections...

  15. EVOLUTION OF NEUROENDOCRINE CELL POPULATION AND PEPTIDERGIC INNERVATION, ASSESSED BY DISCRIMINANT ANALYSIS, DURING POSTNATAL DEVELOPMENT OF THE RAT PROSTATE

    Directory of Open Access Journals (Sweden)

    Rosario Rodríguez

    2011-05-01

    Full Text Available Serotonin immunoreactive neuroendocrine cells and peptidergic nerves (NPY and VIP could have a role in prostate growth and function. In the present study, rats grouped by stages of postnatal development (prepubertal, pubertal, young and aged adults were employed in order to ascertain whether age causes changes in the number of serotoninergic neuroendocrine cells and the length of NPY and VIP fibres. Discriminant analysis was performed in order to ascertain the classificatory power of stereologic variables (absolute and relative measurements of cell number and fibre length on age groups. The following conclusions were drawn: a discriminant analysis confirms the androgen-dependence of both neuroendocrine cells and NPYVIP innervation during the postnatal development of the rat prostate; b periglandular innervation has more relevance than interglandular innervation in classifying the rats in age groups; and c peptidergic nerves from ventral, ampullar and periductal regions were more age-dependent than nerves from the dorso-lateral region.

  16. Optimal measurement position estimation by discriminant analysis based on Wilks' lambda for myoelectric hand control.

    Science.gov (United States)

    Kiso, Atsushi; Taniguchi, Yu; Seki, Hirokazu

    2011-01-01

    This paper describes an optimal measurement position estimation by the discriminant analysis based on Wilks' lambda for the myoelectric hand control. In the past studies, the myoelectric signals were measured from the same positions for the motions discrimination. However, the optimal measurement positions of the myoelectric signals for the motion discrimination are different according to the remaining muscle situation of amputees. Therefore the purpose of this study is to estimate the optimal and fewer measurement positions for the precise motion discrimination of the human forearm. This study proposes the estimation method of the optimal measurement positions by the discriminant analysis based on Wilks' lambda among the myoelectric signal measured from multiple positions. Some experiments on the myoelectric hand simulator show the effectiveness of the proposed optimal measurement position estimation method.

  17. EFFECTS OF A GROUP PSYCHOEDUCATION PROGRAM ON SELF-STIGMA, EMPOWERMENT AND PERCEIVED DISCRIMINATION OF PERSONS WITH SCHIZOPHRENIA

    OpenAIRE

    Štrkalj Ivezić, Slađana; Alfonso Sesar, Marijan; Mužinić, Lana

    2017-01-01

    Background: Self-stigma adversely affects recovery from schizophrenia. Analyses of self stigma reduction programs discovered that few studies have investigated the impact of education about the illness on self-stigma reduction. The objective of this study was to determine whether psychoeducation based on the principles of recovery and empowerment using therapeutic group factors assists in reduction of self-stigma, increased empowerment and reduced perception of discrimination in pati...

  18. Sex determination using discriminant analysis of the medial and lateral condyles of the femur in Koreans.

    Science.gov (United States)

    Kim, Deog-Im; Kwak, Dai-Soon; Han, Seung-Ho

    2013-12-10

    The proximal and distal parts of the femur show the differences between the sexes. Head diameter and the breadth of the epicondyle of the femur are known to distinguish males from females. The proximal end of the femur is studied to determine sex using discriminant analysis but; its distal end is not done. This study aims to develop an equation specific to Koreans by using the medial and lateral condyles of the femur, and to demonstrate the usefulness of equations for specific population groups. We used three-dimensional images from 202 Korean femurs. Twelve variables were measured with a computer program after the femurs were in alignment. Eleven variables showed a statistically significant difference between the sexes (Psex determination in situations where the skull and pelvis are missing and part of the femur is available. The study also demonstrates the need for different equations for different population groups.

  19. Kernel Model Applied in Kernel Direct Discriminant Analysis for the Recognition of Face with Nonlinear Variations

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A kernel-based discriminant analysis method called kernel direct discriminant analysis is employed, which combines the merit of direct linear discriminant analysis with that of kernel trick. In order to demonstrate its better robustness to the complex and nonlinear variations of real face images, such as illumination, facial expression, scale and pose variations, experiments are carried out on the Olivetti Research Laboratory, Yale and self-built face databases. The results indicate that in contrast to kernel principal component analysis and kernel linear discriminant analysis, the method can achieve lower (7%) error rate using only a very small set of features. Furthermore, a new corrected kernel model is proposed to improve the recognition performance. Experimental results confirm its superiority (1% in terms of recognition rate) to other polynomial kernel models.

  20. An approach for mechanical fault classification based on generalized discriminant analysis

    Institute of Scientific and Technical Information of China (English)

    LI Wei-hua; SHI Tie-lin; YANG Shu-zi

    2006-01-01

    To deal with pattern classification of complicated mechanical faults,an approach to multi-faults classification based on generalized discriminant analysis is presented.Compared with linear discriminant analysis (LDA),generalized discriminant analysis (GDA),one of nonlinear discriminant analysis methods,is more suitable for classifying the linear non-separable problem.The connection and difference between KPCA (Kernel Principal Component Analysis) and GDA is discussed.KPCA is good at detection of machine abnormality while GDA performs well in multi-faults classification based on the collection of historical faults symptoms.When the proposed method is applied to air compressor condition classification and gear fault classification,an excellent performance in complicated multi-faults classification is presented.

  1. Block-diagonal discriminant analysis and its bias-corrected rules.

    Science.gov (United States)

    Pang, Herbert; Tong, Tiejun; Ng, Michael

    2013-06-01

    High-throughput expression profiling allows simultaneous measure of tens of thousands of genes at once. These data have motivated the development of reliable biomarkers for disease subtypes identification and diagnosis. Many methods have been developed in the literature for analyzing these data, such as diagonal discriminant analysis, support vector machines, and k-nearest neighbor methods. The diagonal discriminant methods have been shown to perform well for high-dimensional data with small sample sizes. Despite its popularity, the independence assumption is unlikely to be true in practice. Recently, a gene module based linear discriminant analysis strategy has been proposed by utilizing the correlation among genes in discriminant analysis. However, the approach can be underpowered when the samples of the two classes are unbalanced. In this paper, we propose to correct the biases in the discriminant scores of block-diagonal discriminant analysis. In simulation studies, our proposed method outperforms other approaches in various settings. We also illustrate our proposed discriminant analysis method for analyzing microarray data studies.

  2. Cloud-type discrimination via multispectral textural analysis

    Science.gov (United States)

    Lamei, Niloufar; Hutchison, Keith D.; Crawford, Melba M.; Khazenie, Nahid

    1994-04-01

    In recent years, with the development of satellite and computer technology, Earth observation and atmospheric research have become highly dependent on digital imagery. One of the primary interests in digital image processing is the development of robust methods to perform feature detection, extraction, and classification. Until recently, classification methods for cloud discrimination were mainly based on the spectral information of the imagery. However, because of the spectral similarities of certain features (such as ice clouds and snow) and the effects of atmospheric attenuation, multispectral rule-based classifications do not necessarily produce accurate feature discrimination. Spectral homogeneity of two different features within a scene can lead to misclassification. Furthermore, the opposite problem can occur when one feature exhibits different spectral signatures locally but is homogeneous in its cyclic spatial variation. The exploration of spatial information is often advantageous in these discrimination problems. A texture- based method for feature identification has been investigated. This method uses a set of localized spatial filters known as 2-D Gabor functions. Gabor filters can be described as a sinusoidal plane wave within a 2-D Gaussian envelope. The frequency and orientation of the sine plane and the width of the Gaussian envelope are determine by the Gabor parameters. These tunable channels yield joint optimal information both in the spatial and the frequency domains. The new method has been applied to the thermal channels of the NOAA Advanced Very High Resolution Radiometer data for cloud-type discrimination. Results show that additional texture information improves discrimination between cloud types (especially thin cirrus).

  3. The effect of personal and group discrimination on the subjective well-being of people with mental illness: the role of internalized stigma and collective action intention.

    Science.gov (United States)

    Pérez-Garín, Daniel; Molero, Fernando; Bos, Arjan E R

    2017-04-01

    The goal of this study is to test a model in which personal discrimination predicts internalized stigma, while group discrimination predicts a greater willingness to engage in collective action. Internalized stigma and collective action, in turn, are associated to positive and negative affect. A cross-sectional study with 213 people with mental illness was conducted. The model was tested using path analysis. Although the data supported the model, its fit was not sufficiently good. A respecified model, in which a direct path from collective action to internalized stigma was added, showed a good fit. Personal and group discrimination appear to impact subjective well-being through two different paths: the internalization of stigma and collective action intentions, respectively. These two paths, however, are not completely independent, as collective action predicts a lower internalization of stigma. Thus, collective action appears as an important tool to reduce internalized stigma and improve subjective well-being. Future interventions to reduce the impact of stigma should fight the internalization of stigma and promote collective action are suggested.

  4. Combining pharmacophore fingerprints and PLS-discriminant analysis for virtual screening and SAR elucidation

    DEFF Research Database (Denmark)

    Askjær, Sune; Langgård, Morten

    2008-01-01

    fingerprints proved superior to the TGT and TGD fingerprints. Examples of SAR elucidation based on PLS-DA model interpretation of model coefficients using a reversible pharmacophore fingerprint are given. In addition, we tested the hypothesis that feature combinations coming from the analysis of two...... the lead optimization toward a final drug candidate. This paper presents a combined approach to solving these two problems of ligand-based virtual screening and elucidation of SAR based on interplay between pharmacophore fingerprints and interpretation of PLS-discriminant analysis (PLS-DA) models....... The virtual screening capability of the PLS-DA method is compared to group fusion maximum similarity searching in a test using four graph-based pharmacophore fingerprints over a range of 10 diverse targets. The PLS-DA method was generally found to do better than the Smax method. The GpiDAPH3 and PCH...

  5. Assessing Credit Default using Logistic Regression and Multiple Discriminant Analysis: Empirical Evidence from Bosnia and Herzegovina

    Directory of Open Access Journals (Sweden)

    Deni Memić

    2015-01-01

    Full Text Available This article has an aim to assess credit default prediction on the banking market in Bosnia and Herzegovina nationwide as well as on its constitutional entities (Federation of Bosnia and Herzegovina and Republika Srpska. Ability to classify companies info different predefined groups or finding an appropriate tool which would replace human assessment in classifying companies into good and bad buckets has been one of the main interests on risk management researchers for a long time. We investigated the possibility and accuracy of default prediction using traditional statistical methods logistic regression (logit and multiple discriminant analysis (MDA and compared their predictive abilities. The results show that the created models have high predictive ability. For logit models, some variables are more influential on the default prediction than the others. Return on assets (ROA is statistically significant in all four periods prior to default, having very high regression coefficients, or high impact on the model's ability to predict default. Similar results are obtained for MDA models. It is also found that predictive ability differs between logistic regression and multiple discriminant analysis.

  6. Correlation analysis of proprioceptive acuity in ipsilateral position-matching and velocity-discrimination.

    Science.gov (United States)

    Djupsjöbacka, Mats; Domkin, Dmitry

    2005-01-01

    In order to plan and control movements the central nervous system (CNS) needs to continuously keep track of the state of the musculoskeletal system. Therefore the CNS constantly uses sensory input from mechanoreceptors in muscles, joints and skin to update information about body configuration on different levels of the CNS. On the conscious level, such representations constitute proprioception. Different tests for assessment of proprioceptive acuity have been described. However, it is unclear if the proprioceptive acuity measurements in these tests correlate within subjects. By using both uni- and multivariate analysis we compared proprioceptive acuity in different variants of ipsilateral active and passive limb position-matching and ipsilateral passive limb movement velocity-discrimination in a group of healthy subjects. The analysis of the position-matching data revealed a higher acuity of matching for active movements in comparison to passive ones. The acuity of matching was negatively correlated to movement extent. There was a lack of correlation between proprioceptive acuity measurements in position-matching and velocity-discrimination.

  7. Multiple Subject Barycentric Discriminant Analysis (MUSUBADA: How to Assign Scans to Categories without Using Spatial Normalization

    Directory of Open Access Journals (Sweden)

    Hervé Abdi

    2012-01-01

    Full Text Available We present a new discriminant analysis (DA method called Multiple Subject Barycentric Discriminant Analysis (MUSUBADA suited for analyzing fMRI data because it handles datasets with multiple participants that each provides different number of variables (i.e., voxels that are themselves grouped into regions of interest (ROIs. Like DA, MUSUBADA (1 assigns observations to predefined categories, (2 gives factorial maps displaying observations and categories, and (3 optimally assigns observations to categories. MUSUBADA handles cases with more variables than observations and can project portions of the data table (e.g., subtables, which can represent participants or ROIs on the factorial maps. Therefore MUSUBADA can analyze datasets with different voxel numbers per participant and, so does not require spatial normalization. MUSUBADA statistical inferences are implemented with cross-validation techniques (e.g., jackknife and bootstrap, its performance is evaluated with confusion matrices (for fixed and random models and represented with prediction, tolerance, and confidence intervals. We present an example where we predict the image categories (houses, shoes, chairs, and human, monkey, dog, faces, of images watched by participants whose brains were scanned. This example corresponds to a DA question in which the data table is made of subtables (one per subject and with more variables than observations.

  8. The differentiation of camel breeds based on meat measurements using discriminant analysis.

    Science.gov (United States)

    Al-Atiyat, Raed Mahmoud; Suliman, Gamal; AlSuhaibani, Entissar; El-Waziry, Ahmad; Al-Owaimer, Abdullah; Basmaeil, Saeid

    2016-06-01

    The meat productivity of camel in the tropics is still under investigation for identification of better meat breed or type. Therefore, four one-humped Saudi Arabian (SA) camel breeds, Majaheem, Maghateer, Hamrah, and Safrah were experimented in order to differentiate them from each other based on meat measurements. The measurements were biometrical meat traits measured on six intact males from each breed. The results showed higher values of the Majaheem breed than that obtained for the other breeds except few cases such dressing percentage and rib-eye area. In differentiation analysis, the most discriminating meat variables were myofibrillar protein index, meat color components (L* and a*, b*), and cooking loss. Consequently, the Safrah and the Majaheem breeds presented the largest dissimilarity as evidenced by their multivariate means. The canonical discriminant analysis allowed an additional understanding of the differentiation between breeds. Furthermore, two large clusters, one formed by Hamrah and Maghateer in one group along with Safrah. These classifications may assign each breed into one cluster considering they are better as meat producers. The Majaheem was clustered alone in another cluster that might be a result of being better as milk producers. Nevertheless, the productivity type of the camel breeds of SA needs further morphology and genetic descriptions.

  9. Discrimination, attribution, and racial group identification: implications for psychological distress among Black Americans in the National Survey of American Life (2001-2003).

    Science.gov (United States)

    Chae, David H; Lincoln, Karen D; Jackson, James S

    2011-10-01

    There is increasing evidence that experiencing discrimination may contribute to poor mental health among Black Americans. However, few studies have distinguished between discrimination attributed to race versus other forms of discrimination or have compared differences in their psychological implications. Using nationally representative data on 5,191 Black Americans in the National Survey of American Life (NSAL; 2001-2003), this study examined serious psychological distress (SPD) in relation to discrimination attributed to racial versus nonracial causes and also investigated whether racial group identification may be a buffer. We found that discrimination was associated with greater odds of SPD, regardless of attribution. Racial attributions were associated with higher odds of SPD compared with attributions to nonracial causes for each level of discrimination. High racial group identification buffered the negative effect of moderate levels of both racially and nonracially attributed discrimination. Our results provide evidence for the negative influence of discrimination on SPD among Black Americans and indicate that high racial group identification may somewhat mitigate their negative mental health effects. Our study suggests that discrimination and racial group identification should be addressed to protect against psychological distress among Black Americans. © 2011 American Orthopsychiatric Association.

  10. Differential diagnosis of posterior fossa brain tumors: Multiple discriminant analysis of Tl-SPECT and FDG-PET.

    Science.gov (United States)

    Yamauchi, Moritaka; Okada, Tomohisa; Okada, Tsutomu; Yamamoto, Akira; Fushimi, Yasutaka; Arakawa, Yoshiki; Miyamoto, Susumu; Togashi, Kaori

    2017-08-01

    This study investigated the combined capability of thallium-201 (Tl)-SPECT and fluorine-18-fluoro-deoxy-glucose (FDG)-PET for differential diagnosis of posterior fossa brain tumors using multiple discriminant analysis.This retrospective study was conducted under approval of the institutional review board. In the hospital information system, 27 patients with posterior fossa intra-axial tumor between January 2009 and June 2015 were enrolled and grouped as the following 7 entities: low grade glioma (LGG) 6, anaplastic astrocytoma (AA) 2, glioblastoma (GBM) 3, medulloblastoma (MB) 3, hemangioblastoma (HB) 6, metastatic tumor (Mets) 3, and malignant lymphoma (ML) 4. Tl and FDG uptakes were measured at the tumors and control areas, and several indexes were derived. Using indexes selected by the stepwise method, discriminant analysis was conducted with leave-one-out cross-validation.The predicted accuracy for tumor classification was 70.4% at initial analysis and 55.6% at cross-validation to differentiate 7 tumor entities. HB, LGG, and ML were well-discriminated, but AA was located next to LGG. GBM, MB, and Mets largely overlapped and could not be well distinguished even applying multiple discriminant analysis. Correct classification in the original and cross-validation analyses was 44.4% and 33.3% for Tl-SPECT and 55.6% and 48.1% for FDG-PET.

  11. An Analysis of Visible Racial Discrimination In Invisible Man

    Institute of Scientific and Technical Information of China (English)

    LUO Hui

    2016-01-01

    Invisible Man by Ralph Ellison is regarded as one of the classical works in contemporary Black American Literature. By briefly analyzing the causes and effects of racial discrimination, the study aims to explore the blacks’responses to it. It is concluded that the hero, as an“invisible man”, should accept his black identity, admitting the blacks’tradition and cultural iden-tity, then he can find his self-belonging in the black community.

  12. Do experiences of racial discrimination predict cardiovascular disease among African American Men? The moderating role of internalized negative racial group attitudes

    OpenAIRE

    Chae, David H.; Lincoln, Karen D.; Adler, Nancy E.; Syme, S. Leonard

    2010-01-01

    Studies examining associations between racial discrimination and cardiovascular health outcomes have been inconsistent, with some studies finding the highest risk of hypertension among African Americans who report no discrimination. A potential explanation of the latter is that hypertension and other cardiovascular problems are fostered by internalization and denial of racial discrimination. To explore this hypothesis, the current study examines the role of internalized negative racial group ...

  13. Do experiences of racial discrimination predict cardiovascular disease among African American men? The moderating role of internalized negative racial group attitudes.

    Science.gov (United States)

    Chae, David H; Lincoln, Karen D; Adler, Nancy E; Syme, S Leonard

    2010-09-01

    Studies examining associations between racial discrimination and cardiovascular health outcomes have been inconsistent, with some studies finding the highest risk of hypertension among African Americans who report no discrimination. A potential explanation of the latter is that hypertension and other cardiovascular problems are fostered by internalization and denial of racial discrimination. To explore this hypothesis, the current study examines the role of internalized negative racial group attitudes in linking experiences of racial discrimination and history of cardiovascular disease among African American men. We predicted a significant interaction between reported discrimination and internalized negative racial group attitudes in predicting cardiovascular disease. Weighted logistic regression analyses were conducted among 1216 African American men from the National Survey of American Life (NSAL; 2001-2003). We found no main effect of racial discrimination in predicting history of cardiovascular disease. However, agreeing with negative beliefs about Blacks was positively associated with cardiovascular disease history, and also moderated the effect of racial discrimination. Reporting racial discrimination was associated with higher risk of cardiovascular disease among African American men who disagreed with negative beliefs about Blacks. However, among African American men who endorsed negative beliefs about Blacks, the risk of cardiovascular disease was greatest among those reporting no discrimination. Findings suggest that racial discrimination and the internalization of negative racial group attitudes are both risk factors for cardiovascular disease among African American men. Furthermore, the combination of internalizing negative beliefs about Blacks and the absence of reported racial discrimination appear to be associated with particularly poor cardiovascular health. Steps to address racial discrimination as well as programs aimed at developing a positive

  14. Analysis of Financial Ratio to Distinguish Indonesia Joint Venture General Insurance Company Performance using Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Subiakto Soekarno

    2012-01-01

    Full Text Available Insurance industry stands as a service business that plays a significant role in Indonesiaeconomical condition. The development of insurance industry in Indonesia, both of generalinsurance and life insurance, has increased very fast. The general insurance industry itselfdivided into two major players which are local private company and Joint Venture Company.Lately, the use of statistical techniques and financial ratios models to asses financial institutionsuch as insurance company have been used as one of the appropriate combination inpredicting the performance of an industry. This research aims to distinguish between JointVenture General Insurance Companies that have a good performance and those who are lessperforming well using Discriminant Analysis. Further, the findings led that DiscriminantAnalysis is able to distinguish Joint Venture General Insurance Companies that have a goodperformance and those who are not performing well. There are also six ratios which are RBC,Technical Reserve to Investment Ratio, Debt Ratio, Return on Equity, Loss Ratio, and ExpenseRatio that stand as the most influential ratios to distinguish the performance of joint venturegeneral insurance companies. In addition, the result suggest business people to be concernedtoward those six ratios, to increase their companies’ performance.Key words: general insurance, financial ratio, discriminant analysis

  15. Analysis of Financial Ratio to Distinguish Indonesia Joint Venture General Insurance Company Performance using Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Subiakto Soekarno

    2012-01-01

    Full Text Available Insurance industry stands as a service business that plays a significant role in Indonesiaeconomical condition. The development of insurance industry in Indonesia, both of generalinsurance and life insurance, has increased very fast. The general insurance industry itselfdivided into two major players which are local private company and Joint Venture Company.Lately, the use of statistical techniques and financial ratios models to asses financial institutionsuch as insurance company have been used as one of the appropriate combination inpredicting the performance of an industry. This research aims to distinguish between JointVenture General Insurance Companies that have a good performance and those who are lessperforming well using Discriminant Analysis. Further, the findings led that DiscriminantAnalysis is able to distinguish Joint Venture General Insurance Companies that have a goodperformance and those who are not performing well. There are also six ratios which are RBC,Technical Reserve to Investment Ratio, Debt Ratio, Return on Equity, Loss Ratio, and ExpenseRatio that stand as the most influential ratios to distinguish the performance of joint venturegeneral insurance companies. In addition, the result suggest business people to be concernedtoward those six ratios, to increase their companies’ performance.Key words: general insurance, financial ratio, discriminant analysis

  16. Free choice profiling sensory analysis to discriminate coffees

    Directory of Open Access Journals (Sweden)

    Cíntia Sorane Good Kitzberger

    2016-12-01

    Full Text Available Sensory attributes were evaluated from Arabica coffee genotypes growing in two places in Brazil, Mandaguari and Londrina. Post-harvest and roasted process was standardized. Free choice profiling sensory analysis was apply to investigate the influence of genetic variability and local cultivation (Londrina and Mandaguari, Brazil on the sensory characteristics of coffee genotypes. A sensory panel evaluated coffees from Mandaguari in two groups: one (Sarchimor derived, IPR100, IPR102, IPR105, IPR106 characterized by transparency, coffee colour, green aroma, taste (green, bitter, fermented, astringent and a watery texture, another group (Catuaí, Sarchimor derived, IPR101, IPR103 was characterized by coffee colour, brightness, aroma (coffee, acid, sweet, chocolate, acidity, bitterness, burnt aroma, sweetness and full-bodied. Coffees from Londrina presented brightness, coffee colour, sweet, green, burnt aroma, astringent, bitter, fermented, green taste; and watery texture (Catuaí, IPR97, IPR98, IPR100. Another group (Sarchimor derived, IPR101, IPR102, IPR103, IPR105, IPR106 were associated with turbidity, aroma (green, coffee, sweet, acidity, astringency, bitterness, sweetness and full-bodied. Catuaí, Iapar59, IPR99, IPR101, IPR103 and IPR108 exhibited positive attributes when grown in either locale. Edaphoclimatic conditions play a major role in the sensory profiles of coffee.

  17. Mu opioid mediated discriminative-stimulus effects of tramadol: an individual subjects analysis.

    Science.gov (United States)

    Strickland, Justin C; Rush, Craig R; Stoops, William W

    2015-03-01

    Drug discrimination procedures use dose-dependent generalization, substitution, and pretreatment with selective agonists and antagonists to evaluate receptor systems mediating interoceptive effects of drugs. Despite the extensive use of these techniques in the nonhuman animal literature, few studies have used human participants. Specifically, human studies have not routinely used antagonist administration as a pharmacological tool to elucidate the mechanisms mediating the discriminative stimulus effects of drugs. This study evaluated the discriminative-stimulus effects of tramadol, an atypical analgesic with monoamine and mu opioid activity. Three human participants first learned to discriminate 100 mg tramadol from placebo. A range of tramadol doses (25 to 150 mg) and hydromorphone (4 mg) with and without naltrexone pretreatment (50 mg) were then administered to participants after they acquired the discrimination. Tramadol produced dose-dependent increases in drug-appropriate responding and hydromorphone partially or fully substituted for tramadol in all participants. These effects were attenuated by naltrexone. Individual participant records indicated a relationship between mu opioid activity (i.e., miosis) and drug discrimination performance. Our findings indicate that mu opioid activity may mediate the discriminative-stimulus effects of tramadol in humans. The correspondence of generalization, substitution, and pretreatment findings with the animal literature supports the neuropharmacological specificity of the drug discrimination procedure. © Society for the Experimental Analysis of Behavior.

  18. Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures

    Science.gov (United States)

    Li, Quanbao; Wei, Fajie; Zhou, Shenghan

    2017-05-01

    The linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.

  19. The discrimination of honey origin using melissopalynology and Raman spectroscopy techniques coupled with multivariate analysis.

    Science.gov (United States)

    Corvucci, Francesca; Nobili, Lara; Melucci, Dora; Grillenzoni, Francesca-Vittoria

    2015-02-15

    Honey traceability to food quality is required by consumers and food control institutions. Melissopalynologists traditionally use percentages of nectariferous pollens to discriminate the botanical origin and the entire pollen spectrum (presence/absence, type and quantities and association of some pollen types) to determinate the geographical origin of honeys. To improve melissopalynological routine analysis, principal components analysis (PCA) was used. A remarkable and innovative result was that the most significant pollens for the traditional discrimination of the botanical and geographical origin of honeys were the same as those individuated with the chemometric model. The reliability of assignments of samples to honey classes was estimated through explained variance (85%). This confirms that the chemometric model properly describes the melissopalynological data. With the aim to improve honey discrimination, FT-microRaman spectrography and multivariate analysis were also applied. Well performing PCA models and good agreement with known classes were achieved. Encouraging results were obtained for botanical discrimination.

  20. Learning to learn during visual discrimination in group housed dwarf goats (Capra hircus).

    Science.gov (United States)

    Langbein, Jan; Siebert, Katrin; Nürnberg, Gerd; Manteuffel, Gerhard

    2007-11-01

    Using an automated learning device, we investigated "learning to learn" by dwarf goats (Capra hircus) in what was for them a familiar environment and normal social settings. Nine problems, each consisting of four discriminable black symbols, each with one S-super+ and three different S-super(-), were presented on a computer screen. Mean daily learning success improved over the course of the first four problems, and the improvement was maintained throughout the remaining five problems. The number of trials to reach the learning criterion decreased significantly beginning with problem four. Such results may be interpreted as evidence that the goats were developing a learning set. In the present case, the learning set appeared to have two components. One involved gaining familiarity and apparent understanding of the learning device and the basic requirements of the discrimination task. The second component involved learning potential error factors to be ignored, as well as learning commonalities that carried over from one problem to the next. Among the error factors, evidence of apparent preferences for specific symbols was seen, which had a predictable effect on performances.

  1. Evaluation of sensory panels of consumers of specialty coffee beverages using the boosting method in discriminant analysis

    Directory of Open Access Journals (Sweden)

    Gilberto Rodrigues Liska

    2015-12-01

    Full Text Available Automatic classification methods have been widely used in numerous situations and the boosting method has become known for use of a classification algorithm, which considers a set of training data and, from that set, constructs a classifier with reweighted versions of the training set. Given this characteristic, the aim of this study is to assess a sensory experiment related to acceptance tests with specialty coffees, with reference to both trained and untrained consumer groups. For the consumer group, four sensory characteristics were evaluated, such as aroma, body, sweetness, and final score, attributed to four types of specialty coffees. In order to obtain a classification rule that discriminates trained and untrained tasters, we used the conventional Fisher’s Linear Discriminant Analysis (LDA and discriminant analysis via boosting algorithm (AdaBoost. The criteria used in the comparison of the two approaches were sensitivity, specificity, false positive rate, false negative rate, and accuracy of classification methods. Additionally, to evaluate the performance of the classifiers, the success rates and error rates were obtained by Monte Carlo simulation, considering 100 replicas of a random partition of 70% for the training set, and the remaining for the test set. It was concluded that the boosting method applied to discriminant analysis yielded a higher sensitivity rate in regard to the trained panel, at a value of 80.63% and, hence, reduction in the rate of false negatives, at 19.37%. Thus, the boosting method may be used as a means of improving the LDA classifier for discrimination of trained tasters.

  2. Assessing Reclamation Levels of Coastal Saline Lands with Integrated Stepwise Discriminant Analysis and Laboratory Hyperspectral Data

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    At different times over the past 30 years in Zhejiang Province, China, the coastal tidelands have been successively enclosed and reclaimed for agricultural land use. The purpose of this work was to evaluate whether laboratory hyperspectral data might be used to estimate the physicochemical characteristics of these reclaimed saline soils. A coastal region of Shangyu City (Zhejiang Province), which was grouped into four subzones according to reclamation history, was used as the study area, and soil samples were collected in each subzone. Physicochemical analyses showed that the soils were characterized by high electrical conductivity and sand content with low organic matter; the longer the saline lands had been reclaimed, the lower were the electrical conductivity and sand content and the higher the organic matter content.These changing trends of soil chemical and physical properties were found in laboratory reflectance spectra of soil samples and their first-order derivative curves. Stepwise discriminant analysis (SDA) identified six salient spectral bands at 488,530, 670, 880, 1 400, and 1 900 nm. Using derived discriminant functions for saline lands with different historical years of reclamation, classification revealed an overall accuracy from a self-test of 86.6% and from cross-validation of 89.3%.Therefore, as opposed to time-consuming field investigations, this study suggested that remotely sensed hyperspectral data could serve as a promising measure to assess the reclamation levels of coastal saline lands.

  3. Lameness detection challenges in automated milking systems addressed with partial least squares discriminant analysis

    DEFF Research Database (Denmark)

    Garcia, Emanuel; Klaas, Ilka Christine; Amigo Rubio, Jose Manuel;

    2014-01-01

    . Eighty variables retrieved from AMS were summarized week-wise and used to predict 2 defined classes: nonlame and clinically lame cows. Variables were represented with 2 transformations of the week summarized variables, using 2-wk data blocks before gait scoring, totaling 320 variables (2 × 2 × 80......). The reference gait scoring error was estimated in the first week of the study and was, on average, 15%. Two partial least squares discriminant analysis models were fitted to parity 1 and parity 2 groups, respectively, to assign the lameness class according to the predicted probability of being lame (score 3......Lameness is prevalent in dairy herds. It causes decreased animal welfare and leads to higher production costs. This study explored data from an automatic milking system (AMS) to model on-farm gait scoring from a commercial farm. A total of 88 cows were gait scored once per week, for 2 5-wk periods...

  4. Lameness detection challenges in automated milking systems addressed with partial least squares discriminant analysis

    DEFF Research Database (Denmark)

    Garcia, Emanuel; Klaas, Ilka Christine; Amigo Rubio, Jose Manuel;

    2014-01-01

    Lameness is prevalent in dairy herds. It causes decreased animal welfare and leads to higher production costs. This study explored data from an automatic milking system (AMS) to model on-farm gait scoring from a commercial farm. A total of 88 cows were gait scored once per week, for 2 5-wk periods....... Eighty variables retrieved from AMS were summarized week-wise and used to predict 2 defined classes: nonlame and clinically lame cows. Variables were represented with 2 transformations of the week summarized variables, using 2-wk data blocks before gait scoring, totaling 320 variables (2 × 2 × 80......). The reference gait scoring error was estimated in the first week of the study and was, on average, 15%. Two partial least squares discriminant analysis models were fitted to parity 1 and parity 2 groups, respectively, to assign the lameness class according to the predicted probability of being lame (score 3...

  5. Gas Classification Using Combined Features Based on a Discriminant Analysis for an Electronic Nose

    Directory of Open Access Journals (Sweden)

    Sang-Il Choi

    2016-01-01

    Full Text Available This paper proposes a gas classification method for an electronic nose (e-nose system, for which combined features that have been configured through discriminant analysis are used. First, each global feature is extracted from the entire measurement section of the data samples, while the same process is applied to the local features of the section that corresponds to the stabilization, exposure, and purge stages. The discriminative information amounts in the individual features are then measured based on the discriminant analysis, and the combined features are subsequently composed by selecting the features that have a large amount of discriminative information. Regarding a variety of volatile organic compound data, the results of the experiment show that, in a noisy environment, the proposed method exhibits classification performance that is relatively excellent compared to the other feature types.

  6. Discrimination of handlebar grip samples by fourier transform infrared microspectroscopy analysis and statistics

    Directory of Open Access Journals (Sweden)

    Zeyu Lin

    2017-01-01

    Full Text Available In this paper, the authors presented a study on the discrimination of handlebar grip samples, to provide effective forensic science service for hit and run traffic cases. 50 bicycle handlebar grip samples, 49 electric bike handlebar grip samples, and 96 motorcycle handlebar grip samples have been randomly collected by the local police in Beijing (China. Fourier transform infrared microspectroscopy (FTIR was utilized as analytical technology. Then, target absorption selection, data pretreatment, and discrimination of linked samples and unlinked samples were chosen as three steps to improve the discrimination of FTIR spectrums collected from different handlebar grip samples. Principal component analysis and receiver operating characteristic curve were utilized to evaluate different data selection methods and different data pretreatment methods, respectively. It is possible to explore the evidential value of handlebar grip residue evidence through instrumental analysis and statistical treatments. It will provide a universal discrimination method for other forensic science samples as well.

  7. Application of Discriminant Analysis for Studying the Source Rock Potential of Probable Formations in the Lorestan Basin, Iran

    Directory of Open Access Journals (Sweden)

    Amir Negahdari

    2014-06-01

    Full Text Available Understanding the performance and role of each formation in a petroleum play is crucial for the efficient and precise exploration and exploitation of trapped hydrocarbons in a sedimentary basin. The Lorestan basin is one of the most important hydrocarbon basins of Iran, and it includes various oil-prone potential source rocks and reservoir rocks. Previous geochemical studies of the basin were not accurate and there remain various uncertainties about the potential of the probable source rocks of the basin. In the present research, the geochemical characteristics of four probable source rocks of the Lorestan basin are studied using Rock-Eval pyrolysis and discriminant analysis. In achieving this goal, several discriminant functions are defined to evaluate the discriminant factor for the division of samples into two groups. The function with the highest discriminant factor was selected for the classification of probable source rocks into two groups: weak and strong. Among the studied formations, Garau and Pabdeh had the richest and poorest source rocks of the Lorestan basin, respectively. The comparison of the obtained results with the previous literature shows that the proposed model is more reliable for the recognition of the richness of source rock in the area.

  8. Renormalization group analysis of turbulence

    Science.gov (United States)

    Smith, Leslie M.

    1989-01-01

    The objective is to understand and extend a recent theory of turbulence based on dynamic renormalization group (RNG) techniques. The application of RNG methods to hydrodynamic turbulence was explored most extensively by Yakhot and Orszag (1986). An eddy viscosity was calculated which was consistent with the Kolmogorov inertial range by systematic elimination of the small scales in the flow. Further, assumed smallness of the nonlinear terms in the redefined equations for the large scales results in predictions for important flow constants such as the Kolmogorov constant. It is emphasized that no adjustable parameters are needed. The parameterization of the small scales in a self-consistent manner has important implications for sub-grid modeling.

  9. A nutritional risk screening model for patients with liver cirrhosis established using discriminant analysis

    Directory of Open Access Journals (Sweden)

    ZHU Binghua

    2017-06-01

    Full Text Available ObjectiveTo establish a nutritional risk screening model for patients with liver cirrhosis using discriminant analysis. MethodsThe clinical data of 273 patients with liver cirrhosis who were admitted to Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from August 2015 to March 2016 were collected. Body height, body weight, upper arm circumference, triceps skinfold thickness, subscapular skinfold thickness, and hand grip strength were measured and recorded, and then body mass index (BMI and upper arm muscle circumference were calculated. Laboratory markers including liver function parameters, renal function parameters, and vitamins were measured. The patients were asked to complete Nutritional Risk Screening 2002 and Malnutrition Universal Screening Tool (MUST, and a self-developed nutritional risk screening pathway was used for nutritional risk classification. Observation scales of the four diagnostic methods in traditional Chinese medicine were used to collect patients′ symptoms and signs. Continuous data were expressed as mean±SD (x±s; an analysis of variance was used for comparison between multiple groups, and the least significant difference t-test was used for further comparison between two groups. Discriminant analysis was used for model establishment, and cross validation was used for model verification. ResultsThe nutritional risk screening pathway for patients with liver cirrhosis was used for the screening of respondents, and there were 49 patients (17.95% in non-risk group, 49 (17.95% in possible-risk group, and 175 (64.10% in risk group. The distance criterion function was used to establish the nutritional risk screening model for patients with liver cirrhosis: D1=-11.885+0.310×BMI+0150×MAC+0.005×P-Alb-0.001×Vit B12+0.103×Vit D-0.89×ascites-0.404×weakness-0.560×hypochondriac pain+0035×dysphoria with feverish sensation (note: if a patient has ascites, weakness, hypochondriac pain

  10. Issues in Predictive Discriminant Analysis: Using and Interpreting the Leave-One-Out Jackknife Method and the Improvement-Over-Change "I" Index Effect Size.

    Science.gov (United States)

    Hwang, Dae-Yeop

    Prediction of group membership is the goal of predictive discriminant analysis (PDA) and the accuracy of group classification is the focus of PDA. The purpose of this paper is to provide an overview of how PDA works and how it can be used to answer a variety of research questions. The paper explains what PDA is and why it is important, and it…

  11. Discrimination of Bacillus anthracis from closely related microorganisms by analysis of 16S and 23S rRNA with oligonucleotide microchips

    Science.gov (United States)

    Bavykin, Sergei G.; Mirzabekov, Andrei D.

    2007-10-30

    The present invention is directed to a novel method of discriminating a highly infectious bacterium Bacillus anthracis from a group of closely related microorganisms. Sequence variations in the 16S and 23S rRNA of the B. cereus subgroup including B. anthracis are utilized to construct an array that can detect these sequence variations through selective hybridizations. The identification and analysis of these sequence variations enables positive discrimination of isolates of the B. cereus group that includes B. anthracis. Discrimination of single base differences in rRNA was achieved with a microchip during analysis of B. cereus group isolates from both single and in mixed probes, as well as identification of polymorphic sites. Successful use of a microchip to determine the appropriate subgroup classification using eight reference microorganisms from the B. cereus group as a study set, was demonstrated.

  12. Group theory analysis of braided geometry structures

    Institute of Scientific and Technical Information of China (English)

    FENG Wei; MA Wensuo

    2005-01-01

    The braided geometry structures are analyzed with point groups and space groups for which the continuous yarn of the braided preforms is segmented and expressed in some special symbols. All structures of braided material are described and classified with group theory, and new braiding methods are found. The group theory analysis lays the theoretical foundation for optimizing material performance.

  13. DISCRIMINATIVE ANALYSIS OF MORPHOLOGIC AND MOTORIC PARAMETER TO JUDO AND KARATE SPORTIEST BOYS

    Directory of Open Access Journals (Sweden)

    Lulzim Ibri

    2013-07-01

    Full Text Available In sample from 160 boys from secondary schools of Prizren 16-17 age, separated in two groups were implicated 18 tests, from them 10 test for valuation morphologic characteristic and 8 test, for valuation motoric abilities. Group (A is component from 80 judo athletes’ boys and group (B from 80 karate athletes’ boys. Purpose of this investigation is to verify changes between judo and karate athletes’ boys in morphologic characteristic and motoric abilities. The problem of investigation was to investigate if there are changes between judo and karate athletes’ boys in morphologic characteristic that represent longitudinal dimensionality, body measure and adipose tissue, and in motoric abilities (used is eurofit battery tests. For global analysis of dimension to some changes and variable system (which contribute in changes between judo and karate athletes’ boys were implicated t-test for small independent sample and, canonic discriminative analysis. The results of this study show that judo and karate athletes significantly differ among themselves in motoric abilities, judo athletes are better in the tests: long jump from place (LOJU, squeeze palm (SQPA and support the knuckle (SUKN, while the karate athletes are better in the tests: taping for hands (TAHE, reach sitting down position (RSDP and run there-hire 10x5 meters (R10x5M, but these changes were not noticed and morphological variables.

  14. Groupes de r\\'eflexion, g\\'eom\\'etrie du discriminant et partitions non-crois\\'ees

    CERN Document Server

    Ripoll, Vivien

    2010-01-01

    Reflection groups, geometry of the discriminant and noncrossing partitions. When W is a well-generated complex reflection group, the noncrossing partition lattice NCP_W of type W is a very rich combinatorial object, extending the notion of noncrossing partitions of an n-gon. This structure appears in several algebraic setups (dual braid monoid, cluster algebras...). Many combinatorial properties of NCP_W are proved case-by-case, using the classification of reflection groups. It is the case for Chapoton's formula, expressing the number of multichains of a given length in the lattice NCP_W, in terms of the invariant degrees of W. This thesis work is motivated by the search for a geometric explanation of this formula, which could lead to a uniform understanding of the connections between the combinatorics of NCP_W and the invariant theory of W. The starting point is to use the Lyashko-Looijenga covering (LL), based on the geometry of the discriminant of W. In the first chapter, some topological constructions of ...

  15. Comparison of discriminant analysis methods: Application to occupational exposure to particulate matter

    Science.gov (United States)

    Ramos, M. Rosário; Carolino, E.; Viegas, Carla; Viegas, Sandra

    2016-06-01

    Health effects associated with occupational exposure to particulate matter have been studied by several authors. In this study were selected six industries of five different areas: Cork company 1, Cork company 2, poultry, slaughterhouse for cattle, riding arena and production of animal feed. The measurements tool was a portable device for direct reading. This tool provides information on the particle number concentration for six different diameters, namely 0.3 µm, 0.5 µm, 1 µm, 2.5 µm, 5 µm and 10 µm. The focus on these features is because they might be more closely related with adverse health effects. The aim is to identify the particles that better discriminate the industries, with the ultimate goal of classifying industries regarding potential negative effects on workers' health. Several methods of discriminant analysis were applied to data of occupational exposure to particulate matter and compared with respect to classification accuracy. The selected methods were linear discriminant analyses (LDA); linear quadratic discriminant analysis (QDA), robust linear discriminant analysis with selected estimators (MLE (Maximum Likelihood Estimators), MVE (Minimum Volume Elipsoid), "t", MCD (Minimum Covariance Determinant), MCD-A, MCD-B), multinomial logistic regression and artificial neural networks (ANN). The predictive accuracy of the methods was accessed through a simulation study. ANN yielded the highest rate of classification accuracy in the data set under study. Results indicate that the particle number concentration of diameter size 0.5 µm is the parameter that better discriminates industries.

  16. Mixture subclass discriminant analysis link to restricted Gaussian model and other generalizations.

    Science.gov (United States)

    Gkalelis, Nikolaos; Mezaris, Vasileios; Kompatsiaris, Ioannis; Stathaki, Tania

    2013-01-01

    In this paper, a theoretical link between mixture subclass discriminant analysis (MSDA) and a restricted Gaussian model is first presented. Then, two further discriminant analysis (DA) methods, i.e., fractional step MSDA (FSMSDA) and kernel MSDA (KMSDA) are proposed. Linking MSDA to an appropriate Gaussian model allows the derivation of a new DA method under the expectation maximization (EM) framework (EM-MSDA), which simultaneously derives the discriminant subspace and the maximum likelihood estimates. The two other proposed methods generalize MSDA in order to solve problems inherited from conventional DA. FSMSDA solves the subclass separation problem, that is, the situation in which the dimensionality of the discriminant subspace is strictly smaller than the rank of the inter-between-subclass scatter matrix. This is done by an appropriate weighting scheme and the utilization of an iterative algorithm for preserving useful discriminant directions. On the other hand, KMSDA uses the kernel trick to separate data with nonlinearly separable subclass structure. Extensive experimentation shows that the proposed methods outperform conventional MSDA and other linear discriminant analysis variants.

  17. Discrimination of gastric cancer from normal by serum RNA based on surface-enhanced Raman spectroscopy (SERS) and multivariate analysis

    Science.gov (United States)

    Chen, Yanping; Chen, Gang; Zheng, Xiongwei; He, Cheng; Feng, Shangyuan; Chen, Yan; Lin, Xiaoqian; Chen, Rong; Zeng, Haisan

    2012-01-01

    Purpose: Here, the authors explore the feasibility of discriminating cancer patients from healthy controls by serum RNA detection based on surface-enhanced Raman spectroscopy (SERS) and multivariate analysis. Methods: MgSO4-aggregated silver nanoparticles (Ag NP) as the SERS-active substrate presented strong SERS signals to RNA. SERS measurements were performed on two groups of serum RNA samples: one group from patients (n = 31) with gastric cancer and the other group from healthy volunteers (n = 34). Results: Tentative assignments of the Raman bands in the normalized SERS spectra demonstrated that there are differential expressions of circulating RNA between the gastric cancer group and the control group. Principal component analysis (PCA) combined with linear discriminate analysis (LDA) was introduced to differentiate gastric cancer from normal and achieved sensitivity of 100% and specificity of 94.1%. Conclusions: This exploratory study demonstrated potential for developing serum RNA SERS analysis into a useful clinical tool for noninvasive screening and detection of cancer. PMID:22957632

  18. Chemical discrimination of lubricant marketing types using direct analysis in real time time-of-flight mass spectrometry.

    Science.gov (United States)

    Maric, Mark; Harvey, Lauren; Tomcsak, Maren; Solano, Angelique; Bridge, Candice

    2017-06-30

    In comparison to other violent crimes, sexual assaults suffer from very low prosecution and conviction rates especially in the absence of DNA evidence. As a result, the forensic community needs to utilize other forms of trace contact evidence, like lubricant evidence, in order to provide a link between the victim and the assailant. In this study, 90 personal bottled and condom lubricants from the three main marketing types, silicone-based, water-based and condoms, were characterized by direct analysis in real time time of flight mass spectrometry (DART-TOFMS). The instrumental data was analyzed by multivariate statistics including hierarchal cluster analysis, principal component analysis, and linear discriminant analysis. By interpreting the mass spectral data with multivariate statistics, 12 discrete groupings were identified, indicating inherent chemical diversity not only between but within the three main marketing groups. A number of unique chemical markers, both major and minor, were identified, other than the three main chemical components (i.e. PEG, PDMS and nonoxynol-9) currently used for lubricant classification. The data was validated by a stratified 20% withheld cross-validation which demonstrated that there was minimal overlap between the groupings. Based on the groupings identified and unique features of each group, a highly discriminating statistical model was then developed that aims to provide the foundation for the development of a forensic lubricant database that may eventually be applied to casework. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  19. Electron - nuclear recoil discrimination by pulse shape analysis

    CERN Document Server

    Elbs, J; Collin, E; Godfrin, H; Suvorova, O

    2007-01-01

    In the framework of the ``ULTIMA'' project, we use ultra cold superfluid 3He bolometers for the direct detection of single particle events, aimed for a future use as a dark matter detector. One parameter of the pulse shape observed after such an event is the thermalization time constant. Until now it was believed that this parameter only depends on geometrical factors and superfluid 3He properties, and that it is independent of the nature of the incident particles. In this report we show new results which demonstrate that a difference for muon- and neutron events, as well as events simulated by heater pulses exist. The possibility to use this difference for event discrimination in a future dark matter detector will be discussed.

  20. Justifying direct discrimination: an analysis of the scope for a general justification defence in cases of direct sex discrimination

    OpenAIRE

    Moran, E. R.

    2000-01-01

    The prospect of a justification defence in cases of direct sex discrimination is universally criticised by academic commentators on the ground that it would subvert the goal of equality that underlies sex discrimination and equal treatment legislation. At the outset the thesis examines the differences between the sexes, how these differences can be used to explain the distinction between direct and indirect sex discrimination and considers various concepts of equality. Building...

  1. Multivariate analysis of dermatoglyphics of severe mental retardates: an application of the constellation graphical method for discriminant analysis.

    Directory of Open Access Journals (Sweden)

    Wakita,Yoshiharu

    1988-06-01

    Full Text Available We studied the dermatoglyphics of 353 severe mental retardates (excluding those with chromosomal abnormalities and major limb malformations, using multivariate analysis, to determine how early intrauterine factors are related to the etiology of mental retardation. First, dermatoglyphics were compared between 140 individuals with undefined prenatal factors and 700 normal controls. After 6 and 9 dermatoglyphic traits were chosen as discriminative variables for males and females, respectively, the data were subjected separately for each sex to the constellation graphical method for discriminant analysis. The same formula as obtained in the idiopathic group was subsequently applied to data from cases in other etiological categories. When the misclassification rate was 0.03, the rates of correct classification of the male patients into the etiological categories of undefined prenatal, defined prenatal, perinatal, postnatal and unknown (no anamnestic data available categories were 19.7% (13/66, 20.0% (3/15, 8.8% (5/57, 5.0% (1/20 and 7.7% (2/26, while the correct classification rates of females were 24.3% (18/74, 42.1% (8/19, 18.9% (7/37, 5.1% (1/16 and 13.0% (3/23, respectively. The results suggest that early intrauterine factors such as those producing dermatoglyphic deviations may contribute to the pathogenesis of severe mental retardation not only in patients with undefined prenatal etiological factors but also in those with perinatal factors, especially those of the female sex.

  2. Stability classification model of mine-lane surrounding rock based on distance discriminant analysis method

    Institute of Scientific and Technical Information of China (English)

    ZHANG Wei; LI Xi-bing; GONG Feng-qiang

    2008-01-01

    Based on the principle of Mahalanobis distance discriminant analysis (DDA) theory, a stability classification model for mine-lane surrounding rock was established, including six indexes of discriminant factors that reflect the engineering quality of surrounding rock: lane depth below surface, span of lane, ratio of directly top layer thickness to coal thickness, uniaxial comprehensive strength of surrounding rock, development degree coefficient of surrounding rock joint and range of broken surrounding rock zone. A DDA model was obtained through training 15 practical measuring samples. The re-substitution method was introduced to verify the stability of DDA model and the ratio of mis-discrimination is zero. The DDA model was used to discriminate3 new samples and the results are identical with actual rock kind. Compared with the artificial neural network method and support vector mechanic method, the results show that this model has high prediction accuracy and can be used in practical engineering.

  3. Baseline drift effect on the performance of neutron and gamma ray discrimination using frequency gradient analysis

    CERN Document Server

    Liu, Guofu; Yang, Jun; Lin, Cunbao; Hu, Qingqing; Peng, Jinxian

    2013-01-01

    Frequency gradient analysis (FGA) effectively discriminates neutrons and gamma rays by examining the frequency-domain features of the photomultiplier tube anode signal. This approach is insensitive to noise but is inevitably affected by the baseline drift, similar to other pulse shape discrimination methods. The baseline drift effect is attributed to the factors such as power line fluctuation, dark current, noise disturbances, hum, and pulse tail in front-end electronics. This effect needs to be elucidated and quantified before the baseline shift can be estimated and removed from the captured signal. Therefore, the effect of baseline shift on the discrimination performance of neutrons and gamma rays with organic scintillation detectors using FGA is investigated in this paper. The relationship between the baseline shift and discrimination parameters of FGA is derived and verified by an experimental system consisting of an americium-beryllium source, a BC501A liquid scintillator detector, and a 5 GSPS 8-bit osc...

  4. Combined cluster and discriminant analysis: An efficient chemometric approach in diesel fuel characterization.

    Science.gov (United States)

    Novák, Márton; Palya, Dóra; Bodai, Zsolt; Nyiri, Zoltán; Magyar, Norbert; Kovács, József; Eke, Zsuzsanna

    2017-01-01

    Combined cluster and discriminant analysis (CCDA) as a chemometric tool in compound specific isotope analysis of diesel fuels was studied. The stable carbon isotope ratios (δ(13)C) of n-alkanes in diesel fuel can be used to characterize or differentiate diesels originating from different sources. We investigated 25 diesel fuel samples representing 20 different brands. The samples were collected from 25 different service stations in 11 European countries over a 2 year period. The n-alkane fraction of diesel fuels was separated using solid-state urea clathrate formation combined with silica gel fractionation. The stable carbon isotope ratios of C10-C24 n-alkanes were measured with gas chromatography-isotope ratio mass spectrometry (GC-IRMS) using perdeuterated n-alkanes as internal standards. Beside the 25 samples one additional diesel fuel was prepared and measured three times to get totally homogenous samples in order to test the performance of our analytical and statistical routine. Stable isotope ratio data were evaluated with hierarchical cluster analysis (HCA), principal component analysis (PCA) and CCDA. CCDA combines two multivariate data analysis methods hierarchical cluster analysis with linear discriminant analysis (LDA). The main idea behind CCDA is to compare the goodness of preconceived (based on the sample origins) and random groupings. In CCDA all the samples were compared pairwise. The results for the parallel sample preparations showed that the analytical procedure does not have any significant effect on the δ(13)C values of n-alkanes. The three parallels proved to be totally homogenous with CCDA. HCA and PCA can be useful tools when the examining of the relationship among several samples is in question. However, these two techniques cannot be always decisive on the origin of similar samples. The initial hypothesis that all diesel fuel samples are considered chemically unique was verified by CCDA. The main advantage of CCDA is that it gives an

  5. Black Women, Work, Stress, and Perceived Discrimination: The Focused Support Group Model as an Intervention for Stress Reduction

    Science.gov (United States)

    MAYS, VICKIE M.

    2013-01-01

    This exploratory study examined the use of two components (small and large groups) of a community-based intervention, the Focused Support Group (FSG) model, to alleviate employment-related stressors in Black women. Participants were assigned to small groups based on occupational status. Groups met for five weekly 3-hr sessions in didactic or small- and large-group formats. Two evaluations following the didactic session and the small and large group sessions elicited information on satisfaction with each of the formats, self-reported change in stress, awareness of interpersonal and sociopolitical issues affecting Black women in the labor force, assessing support networks, and usefulness of specific discussion topics to stress reduction. Results indicated the usefulness of the small- and large-group formats in reduction of self-reported stress and increases in personal and professional sources of support. Discussions on race and sex discrimination in the workplace were effective in overall stress reduction. The study highlights labor force participation as a potential source of stress for Black women, and supports the development of culture- and gender-appropriate community interventions as viable and cost-effective methods for stress reduction. PMID:9225548

  6. Determination of sex by discriminant function analysis of mandibles from a Central Indian population

    Directory of Open Access Journals (Sweden)

    Kanchankumar P Wankhede

    2015-01-01

    Full Text Available Context: Identification of sex from skeletal remains is one of the important forensic considerations. Discriminant function analysis is increasingly used to determine the sex from skeleton. Aims: To develop discriminant function to determine sex from mandible in a Central Indian population. Settings and Design: This was a prospective study done at the Department of Anatomy. Materials and Methods: The mandibles used in the present study were from the museum specimens. Only 82 adult mandibles (55 male and 27 female that had been preserved were selected. Ten mandibular parameters were measured. Statistical Analysis Used: Statistical analysis was conducted using Statistical Package for Social Sciences (SPSS for Windows, version 16. The level of statistical significance was set at P < 0.05. Results: Using stepwise discriminant function analysis, only six variables were selected as the best discriminant between sexes, with the projection length of corpus mandibulae being the most dimorphic. It was observed that sex classification accuracy of the discriminant functions ranged from 57.3 to 80.5% for the individual variables, 81.7% for the stepwise method, and 85.4% for the direct method. Conclusion: The results of the study show that mandibles can be used for determining sex and the results are comparable with other similar studies. The studied mandibular variables showed sexual dimorphism with an accuracy comparable with other skeletal remains, next to cranium and pelvis.

  7. ALE meta-analysis reveals dissociable networks for affective and discriminative aspects of touch.

    Science.gov (United States)

    Morrison, India

    2016-04-01

    Emotionally-laden tactile stimulation-such as a caress on the skin or the feel of velvet-may represent a functionally distinct domain of touch, underpinned by specific cortical pathways. In order to determine whether, and to what extent, cortical functional neuroanatomy supports a distinction between affective and discriminative touch, an activation likelihood estimate (ALE) meta-analysis was performed. This meta-analysis statistically mapped reported functional magnetic resonance imaging (fMRI) activations from 17 published affective touch studies in which tactile stimulation was associated with positive subjective evaluation (n = 291, 34 experimental contrasts). A separate ALE meta-analysis mapped regions most likely to be activated by tactile stimulation during detection and discrimination tasks (n = 1,075, 91 experimental contrasts). These meta-analyses revealed dissociable regions for affective and discriminative touch, with posterior insula (PI) more likely to be activated for affective touch, and primary somatosensory cortices (SI) more likely to be activated for discriminative touch. Secondary somatosensory cortex had a high likelihood of engagement by both affective and discriminative touch. Further, meta-analytic connectivity (MCAM) analyses investigated network-level co-activation likelihoods independent of task or stimulus, across a range of domains and paradigms. Affective-related PI and discriminative-related SI regions co-activated with different networks, implicated in dissociable functions, but sharing somatosensory co-activations. Taken together, these meta-analytic findings suggest that affective and discriminative touch are dissociable both on the regional and network levels. However, their degree of shared activation likelihood in somatosensory cortices indicates that this dissociation reflects functional biases within tactile processing networks, rather than functionally and anatomically distinct pathways.

  8. Racial/ethnic differences in responses to the everyday discrimination scale: a differential item functioning analysis.

    Science.gov (United States)

    Lewis, Tené T; Yang, Frances M; Jacobs, Elizabeth A; Fitchett, George

    2012-03-01

    The authors examined the impact of race/ethnicity on responses to the Everyday Discrimination Scale, one of the most widely used discrimination scales in epidemiologic and public health research. Participants were 3,295 middle-aged US women (African-American, Caucasian, Chinese, Hispanic, and Japanese) from the Study of Women's Health Across the Nation (SWAN) baseline examination (1996-1997). Multiple-indicator, multiple-cause models were used to examine differential item functioning (DIF) on the Everyday Discrimination Scale by race/ethnicity. After adjustment for age, education, and language of interview, meaningful DIF was observed for 3 (out of 10) items: "receiving poorer service in restaurants or stores," "being treated as if you are dishonest," and "being treated with less courtesy than other people" (all P's discrimination differed slightly for women of different racial/ethnic groups, with certain "public" experiences appearing to have more salience for African-American and Chinese women and "dishonesty" having more salience for racial/ethnic minority women overall. "Courtesy" appeared to have more salience for Hispanic women only in comparison with African-American women. Findings suggest that the Everyday Discrimination Scale could potentially be used across racial/ethnic groups as originally intended. However, researchers should use caution with items that demonstrated DIF.

  9. Discriminant analysis of milk adulteration based on near-infrared spectroscopy and pattern recognition

    Science.gov (United States)

    Liu, Rong; Lv, Guorong; He, Bin; Xu, Kexin

    2011-03-01

    Since the beginning of the 21st century, the issue of food safety is becoming a global concern. It is very important to develop a rapid, cost-effective, and widely available method for food adulteration detection. In this paper, near-infrared spectroscopy techniques and pattern recognition were applied to study the qualitative discriminant analysis method. The samples were prepared and adulterated with one of the three adulterants, urea, glucose and melamine with different concentrations. First, the spectral characteristics of milk and adulterant samples were analyzed. Then, pattern recognition methods were used for qualitative discriminant analysis of milk adulteration. Soft independent modeling of class analogy and partial least squares discriminant analysis (PLSDA) were used to construct discriminant models, respectively. Furthermore, the optimization method of the model was studied. The best spectral pretreatment methods and the optimal band were determined. In the optimal conditions, PLSDA models were constructed respectively for each type of adulterated sample sets (urea, melamine and glucose) and all the three types of adulterated sample sets. Results showed that, the discrimination accuracy of model achieved 93.2% in the classification of different adulterated and unadulterated milk samples. Thus, it can be concluded that near-infrared spectroscopy and PLSDA can be used to identify whether the milk has been adulterated or not and the type of adulterant used.

  10. Chemical Discrimination of Cortex Phellodendri amurensis and Cortex Phellodendri chinensis by Multivariate Analysis Approach.

    Science.gov (United States)

    Sun, Hui; Wang, Huiyu; Zhang, Aihua; Yan, Guangli; Han, Ying; Li, Yuan; Wu, Xiuhong; Meng, Xiangcai; Wang, Xijun

    2016-01-01

    As herbal medicines have an important position in health care systems worldwide, their current assessment, and quality control are a major bottleneck. Cortex Phellodendri chinensis (CPC) and Cortex Phellodendri amurensis (CPA) are widely used in China, however, how to identify species of CPA and CPC has become urgent. In this study, multivariate analysis approach was performed to the investigation of chemical discrimination of CPA and CPC. Principal component analysis showed that two herbs could be separated clearly. The chemical markers such as berberine, palmatine, phellodendrine, magnoflorine, obacunone, and obaculactone were identified through the orthogonal partial least squared discriminant analysis, and were identified tentatively by the accurate mass of quadruple-time-of-flight mass spectrometry. A total of 29 components can be used as the chemical markers for discrimination of CPA and CPC. Of them, phellodenrine is significantly higher in CPC than that of CPA, whereas obacunone and obaculactone are significantly higher in CPA than that of CPC. The present study proves that multivariate analysis approach based chemical analysis greatly contributes to the investigation of CPA and CPC, and showed that the identified chemical markers as a whole should be used to discriminate the two herbal medicines, and simultaneously the results also provided chemical information for their quality assessment. Multivariate analysis approach was performed to the investigate the herbal medicineThe chemical markers were identified through multivariate analysis approachA total of 29 components can be used as the chemical markers. UPLC-Q/TOF-MS-based multivariate analysis method for the herbal medicine samples Abbreviations used: CPC: Cortex Phellodendri chinensis, CPA: Cortex Phellodendri amurensis, PCA: Principal component analysis, OPLS-DA: Orthogonal partial least squares discriminant analysis, BPI: Base peaks ion intensity.

  11. Discriminant analysis of some east Tennessee forest herb niches. Environmental Sciences Division Publication No. 752

    Energy Technology Data Exchange (ETDEWEB)

    Mann, L.K.; Shugart, H.H.; Kitchings, J.T.

    1978-03-01

    The purpose of this study was to evaluate the effectiveness of using discriminant analysis in assessing plant niches. As a component of research by the Environmental Research Park Project at Oak Ridge, Tennessee, five sites were inventoried for herbaceous species. From this inventory, four sympatric species of Galium and seventeen co-occurring herbaceous species were selected for discriminant analysis. The four species of Galium were treated as two data sets: one was composed of information collected at one site (a mesic hardwood area) and the other contained data from two cedar sites of shallow soil over limestone bedrock. The seventeen herbaceous species all occurred in the mesic hardwood area.

  12. A Qualitative Analysis of Multiracial Students' Experiences with Prejudice and Discrimination in College

    Science.gov (United States)

    Museus, Samuel D.; Lambe Sariñana, Susan A.; Yee, April L.; Robinson, Thomas E.

    2016-01-01

    Mixed-race persons constitute a substantial and growing population in the United States. We examined multiracial college students' experiences with prejudice and discrimination in college with conducted focus group interviews with 12 mixed-race participants and individual interviews with 22 mixed-race undergraduates to understand how they…

  13. A Qualitative Analysis of Multiracial Students' Experiences with Prejudice and Discrimination in College

    Science.gov (United States)

    Museus, Samuel D.; Lambe Sariñana, Susan A.; Yee, April L.; Robinson, Thomas E.

    2016-01-01

    Mixed-race persons constitute a substantial and growing population in the United States. We examined multiracial college students' experiences with prejudice and discrimination in college with conducted focus group interviews with 12 mixed-race participants and individual interviews with 22 mixed-race undergraduates to understand how they…

  14. DISCRIMINATOR CREDIBILITY DIMENSIONS OF AN ONLINE ACQUISITION WEBSITE – AN ANALYSIS OF AN INTERNATIONAL CONSTRUCT ON A SPECIFIC ROMANIAN TARGET

    Directory of Open Access Journals (Sweden)

    Oana TUGULEA

    2014-09-01

    Full Text Available The purpose of this study is to identify credibility dimensions that predict the level of credibility of an online sales clothes Website in Romania. The objectives of this research are: (1 identifying the online sales clothes Websites’ credibility dimensions that better discriminate between students to evaluate an online sales clothes Website to be credible or not credible; (2 creating a discriminant function to predict to which of the two analysed groups one user better fits; (3 identifying dimensions that discriminate students between the two groups for two specific situation: second year of study students and third year of study students. For the overall group, the most important discriminator dimension is real world feel. For second year of study students, the only discriminator is trustworthiness. Among the most important discriminating dimensions for third year of study students are real world feel and ease of use.

  15. Discrimination of whisky brands and counterfeit identification by UV-Vis spectroscopy and multivariate data analysis.

    Science.gov (United States)

    Martins, Angélica Rocha; Talhavini, Márcio; Vieira, Maurício Leite; Zacca, Jorge Jardim; Braga, Jez Willian Batista

    2017-08-15

    The discrimination of whisky brands and counterfeit identification were performed by UV-Vis spectroscopy combined with partial least squares for discriminant analysis (PLS-DA). In the proposed method all spectra were obtained with no sample preparation. The discrimination models were built with the employment of seven whisky brands: Red Label, Black Label, White Horse, Chivas Regal (12years), Ballantine's Finest, Old Parr and Natu Nobilis. The method was validated with an independent test set of authentic samples belonging to the seven selected brands and another eleven brands not included in the training samples. Furthermore, seventy-three counterfeit samples were also used to validate the method. Results showed correct classification rates for genuine and false samples over 98.6% and 93.1%, respectively, indicating that the method can be helpful for the forensic analysis of whisky samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Use of correspondence discriminant analysis to predict the subcellular location of bacterial proteins.

    Science.gov (United States)

    Perrière, Guy; Thioulouse, Jean

    2003-02-01

    Correspondence discriminant analysis (CDA) is a multivariate statistical method derived from discriminant analysis which can be used on contingency tables. We have used CDA to separate Gram negative bacteria proteins according to their subcellular location. The high resolution of the discrimination obtained makes this method a good tool to predict subcellular location when this information is not known. The main advantage of this technique is its simplicity. Indeed, by computing two linear formulae on amino acid composition, it is possible to classify a protein into one of the three classes of subcellular location we have defined. The CDA itself can be computed with the ADE-4 software package that can be downloaded, as well as the data set used in this study, from the Pôle Bio-Informatique Lyonnais (PBIL) server at http://pbil.univ-lyon1.fr.

  17. Group adaptation, formal darwinism and contextual analysis.

    Science.gov (United States)

    Okasha, S; Paternotte, C

    2012-06-01

    We consider the question: under what circumstances can the concept of adaptation be applied to groups, rather than individuals? Gardner and Grafen (2009, J. Evol. Biol.22: 659-671) develop a novel approach to this question, building on Grafen's 'formal Darwinism' project, which defines adaptation in terms of links between evolutionary dynamics and optimization. They conclude that only clonal groups, and to a lesser extent groups in which reproductive competition is repressed, can be considered as adaptive units. We re-examine the conditions under which the selection-optimization links hold at the group level. We focus on an important distinction between two ways of understanding the links, which have different implications regarding group adaptationism. We show how the formal Darwinism approach can be reconciled with G.C. Williams' famous analysis of group adaptation, and we consider the relationships between group adaptation, the Price equation approach to multi-level selection, and the alternative approach based on contextual analysis.

  18. Selection of effective maximal expiratory parameters to differentiate asthmatic patients from healthy adults by discriminant analysis using all possible selection procedure.

    Directory of Open Access Journals (Sweden)

    Meguro,Tadamichi

    1986-08-01

    Full Text Available Maximal expiratory volume-time and flow-volume (MEVT and MEFV curves were drawn for young male nonsmoking healthy adults and for young male nonsmoking asthmatic patients. Eleven parameters, two MEVT (%FVC and FEV1.0%, six MEFV (PFR, V75, V50, V25, V10 and V50/V25, and three MTC parameters (MTC75-50, MTC50-25 and MTC25-RV were used for the multivariate analysis. The multivariate analysis in this study consisted of correlation coefficient matrix computation, the test for mean values in the multivariates, and the linear discriminant analysis using the all possible selection procedure (APSP. Correlation coefficients among flow rate parameters and flow rate related parameters in high lung volumes were different between the two groups. In the eleven-parameter discriminant analysis by APSP using single parameters, PFR, V75 (flow rate at 75% of forced vital capacity, and FEV1.0% were considered to be the effective parameters. In the seven-parameter discriminant analysis using the parameter groups, the group of all parameters and the %FVC and flow rate-related parameter group were considered to be the effective numerical alternatives to MEFV curves discriminating between healthy adults and asthmatic patients.

  19. Contributions to sensitivity analysis and generalized discriminant analysis; Contributions a l'analyse de sensibilite et a l'analyse discriminante generalisee

    Energy Technology Data Exchange (ETDEWEB)

    Jacques, J

    2005-12-15

    Two topics are studied in this thesis: sensitivity analysis and generalized discriminant analysis. Global sensitivity analysis of a mathematical model studies how the output variables of this last react to variations of its inputs. The methods based on the study of the variance quantify the part of variance of the response of the model due to each input variable and each subset of input variables. The first subject of this thesis is the impact of a model uncertainty on results of a sensitivity analysis. Two particular forms of uncertainty are studied: that due to a change of the model of reference, and that due to the use of a simplified model with the place of the model of reference. A second problem was studied during this thesis, that of models with correlated inputs. Indeed, classical sensitivity indices not having significance (from an interpretation point of view) in the presence of correlation of the inputs, we propose a multidimensional approach consisting in expressing the sensitivity of the output of the model to groups of correlated variables. Applications in the field of nuclear engineering illustrate this work. Generalized discriminant analysis consists in classifying the individuals of a test sample in groups, by using information contained in a training sample, when these two samples do not come from the same population. This work extends existing methods in a Gaussian context to the case of binary data. An application in public health illustrates the utility of generalized discrimination models thus defined. (author)

  20. Renormalization group analysis of the gluon mass equation

    CERN Document Server

    Aguilar, A C; Papavassiliou, J

    2014-01-01

    In the present work we carry out a systematic study of the renormalization properties of the integral equation that determines the momentum evolution of the effective gluon mass. A detailed, all-order analysis of the complete kernel appearing in this particular equation reveals that the renormalization procedure may be accomplished through the sole use of ingredients known from the standard perturbative treatment of the theory, with no additional assumptions. However, the subtle interplay of terms operating at the level of the exact equation gets distorted by the approximations usually employed when evaluating the aforementioned kernel. This fact is reflected in the form of the obtained solutions, whose deviations from the correct behavior are best quantified by resorting to appropriately defined renormalization-group invariant quantities. This analysis, in turn, provides a solid guiding principle for improving the form of the kernel, and furnishes a well-defined criterion for discriminating between various p...

  1. Racial discrimination mediates race differences in sleep problems: A longitudinal analysis.

    Science.gov (United States)

    Fuller-Rowell, Thomas E; Curtis, David S; El-Sheikh, Mona; Duke, Adrienne M; Ryff, Carol D; Zgierska, Aleksandra E

    2017-04-01

    To examine changes in sleep problems over a 1.5-year period among Black or African American (AA) and White or European American (EA) college students and to consider the role of racial discrimination as a mediator of race differences in sleep problems over time. Students attending a large, predominantly White university (N = 133, 41% AA, 57% female, mean age = 18.8, SD = .90) reported on habitual sleep characteristics and experiences of racial discrimination at baseline and follow-up assessments. A latent variable for sleep problems was assessed from reports of sleep latency, duration, efficiency, and quality. Longitudinal models were used to examine race differences in sleep problems over time and the mediating role of perceived discrimination. Covariates included age, gender, parent education, parent income, body mass index, self-rated physical health, and depressive symptoms. Each of the individual sleep measures was also examined separately, and sensitivity analyses were conducted using alternative formulations of the sleep problems measure. AAs had greater increases in sleep problems than EAs. Perceived discrimination was also associated with increases in sleep problems over time and mediated racial disparities in sleep. This pattern of findings was similar when each of the sleep indicators was considered separately and held with alternative sleep problems measures. The findings highlight the importance of racial disparities in sleep across the college years and suggest that experiences of discrimination contribute to group disparities. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  2. Combined approach based on principal component analysis and canonical discriminant analysis for investigating hyperspectral plant response

    Directory of Open Access Journals (Sweden)

    Anna Maria Stellacci

    2012-07-01

    Full Text Available Hyperspectral (HS data represents an extremely powerful means for rapidly detecting crop stress and then aiding in the rational management of natural resources in agriculture. However, large volume of data poses a challenge for data processing and extracting crucial information. Multivariate statistical techniques can play a key role in the analysis of HS data, as they may allow to both eliminate redundant information and identify synthetic indices which maximize differences among levels of stress. In this paper we propose an integrated approach, based on the combined use of Principal Component Analysis (PCA and Canonical Discriminant Analysis (CDA, to investigate HS plant response and discriminate plant status. The approach was preliminary evaluated on a data set collected on durum wheat plants grown under different nitrogen (N stress levels. Hyperspectral measurements were performed at anthesis through a high resolution field spectroradiometer, ASD FieldSpec HandHeld, covering the 325-1075 nm region. Reflectance data were first restricted to the interval 510-1000 nm and then divided into five bands of the electromagnetic spectrum [green: 510-580 nm; yellow: 581-630 nm; red: 631-690 nm; red-edge: 705-770 nm; near-infrared (NIR: 771-1000 nm]. PCA was applied to each spectral interval. CDA was performed on the extracted components to identify the factors maximizing the differences among plants fertilised with increasing N rates. Within the intervals of green, yellow and red only the first principal component (PC had an eigenvalue greater than 1 and explained more than 95% of total variance; within the ranges of red-edge and NIR, the first two PCs had an eigenvalue higher than 1. Two canonical variables explained cumulatively more than 81% of total variance and the first was able to discriminate wheat plants differently fertilised, as confirmed also by the significant correlation with aboveground biomass and grain yield parameters. The combined

  3. Analysis of roles and groups in blogosphere

    OpenAIRE

    Gliwa, Bogdan; Zygmunt, Anna; Koźlak, Jarosław

    2013-01-01

    In the paper different roles of users in social media, taking into consideration their strength of influence and different degrees of cooperativeness, are introduced. Such identified roles are used for the analysis of characteristics of groups of strongly connected entities. The different classes of groups, considering the distribution of roles of users belonging to them, are presented and discussed.

  4. ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R

    Directory of Open Access Journals (Sweden)

    Tarn Duong

    2007-09-01

    Full Text Available Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing. Currently it contains functionality for kernel density estimation and kernel discriminant analysis. It is a comprehensive package for bandwidth matrix selection, implementing a wide range of data-driven diagonal and unconstrained bandwidth selectors.

  5. Optimal Class Separation in Hyperspectral Image Data: Iterated Canonical Discriminant Analysis

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Müller, Andreas

    This paper describes canonical discriminant analysis and sketches an iterative version which is then applied to obtain optimal separation between a region, here examplified by either “water” or “wood/trees” and the rest of a HyMap image. We show that the iterative version greatly enhances the sep...

  6. Discrimination of bromodeoxyuridine labelled and unlabelled mitotic cells in flow cytometric bromodeoxyuridine/DNA analysis

    DEFF Research Database (Denmark)

    Jensen, P O; Larsen, J K; Christensen, I J

    1994-01-01

    Bromodeoxyuridine (BrdUrd) labelled and unlabelled mitotic cells, respectively, can be discriminated from interphase cells using a new method, based on immunocytochemical staining of BrdUrd and flow cytometric four-parameter analysis of DNA content, BrdUrd incorporation, and forward and orthogona...

  7. A Research Project Using the Safran Student Interest Inventory (SSII): Discriminant Analysis of University Majors.

    Science.gov (United States)

    Nichols, K. E.

    Safran Student Interest Inventory (SSII) data was gathered on 135 university students registered in five different faculties. A discriminant analysis of the data indicated that the SSII was a good test for separating students into faculties and therefore would make a good counselling instrument. Some results are also present using Differential…

  8. Cloud mask via cumulative discriminant analysis applied to satellite infrared observations: scientific basis and initial evaluation

    Directory of Open Access Journals (Sweden)

    U. Amato

    2014-06-01

    Full Text Available We introduce a classification method (Cumulative Discriminant Analysis of the Discriminant Analysis type to discriminate between cloudy and clear sky satellite observations in the thermal infrared. The tool is intended for the high spectral resolution infrared sounder (IRS planned for the geostationary METEOSAT (Meteorological Satellite Third Generation platform and uses IASI (Infrared Atmospheric Sounding Interferometer data as a proxy. The Cumulative Discriminant Analysis does not introduce biases intrinsic with the approximation of the probability density functions and is flexible enough to adapt to different strategies to optimize the cloud mask. The methodology is based on nine statistics computed from IASI spectral radiances, which exploit the high spectral resolution of the instrument and which effectively summarize information contained within the IASI spectrum. A Principal Component Analysis prior step is also introduced which makes the problem more consistent with the statistical assumptions of the methodology. An initial assessment of the scheme is performed based on global and regional IASI real data sets and cloud masks obtained from AVHRR (Advanced Very High Resolution Radiometer and SEVIRI (Spinning Enhanced Visible and Infrared Imager imagers. The agreement with these independent cloud masks is generally well above 80%, except at high latitudes in their winter seasons.

  9. Cloud mask via cumulative discriminant analysis applied to satellite infrared observations: scientific basis and initial evaluation

    Science.gov (United States)

    Amato, U.; Lavanant, L.; Liuzzi, G.; Masiello, G.; Serio, C.; Stuhlmann, R.; Tjemkes, S. A.

    2014-10-01

    We introduce a classification method (cumulative discriminant analysis) of the discriminant analysis type to discriminate between cloudy and clear-sky satellite observations in the thermal infrared. The tool is intended for the high-spectral-resolution infrared sounder (IRS) planned for the geostationary METEOSAT (Meteorological Satellite) Third Generation platform and uses IASI (Infrared Atmospheric Sounding Interferometer) data as a proxy. The cumulative discriminant analysis does not introduce biases intrinsic with the approximation of the probability density functions and is flexible enough to adapt to different strategies to optimize the cloud mask. The methodology is based on nine statistics computed from IASI spectral radiances, which exploit the high spectral resolution of the instrument and which effectively summarize information contained within the IASI spectrum. A principal component analysis prior step is also introduced, which makes the problem more consistent with the statistical assumptions of the methodology. An initial assessment of the scheme is performed based on global and regional IASI real data sets and cloud masks obtained from AVHRR (Advanced Very High Resolution Radiometer) and SEVIRI (Spinning Enhanced Visible and Infrared Imager) imagers. The agreement with these independent cloud masks is generally well above 80 %, except at high latitudes in the winter seasons.

  10. Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

    Directory of Open Access Journals (Sweden)

    Shengwen Guo

    2017-05-01

    Full Text Available Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI. Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI, the converted MCI (cMCI, and the normal control (NC groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM. An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and

  11. Comparative analysis of pulse shape discrimination methods in a {sup 6}Li loaded plastic scintillator

    Energy Technology Data Exchange (ETDEWEB)

    Balmer, Matthew J.I., E-mail: m.balmer@lancaster.ac.uk [Department of Engineering, Lancaster University, LA1 4YR (United Kingdom); Gamage, Kelum A.A. [Department of Engineering, Lancaster University, LA1 4YR (United Kingdom); Taylor, Graeme C. [Neutron Metrology Group, National Physical Laboratory, Teddington, TW11 0LW (United Kingdom)

    2015-07-11

    Three algorithms for discriminating between fast neutrons, thermal neutrons and gamma rays in a {sup 6}Li loaded plastic scintillator have been compared. Following a literature review of existing pulse shape discrimination techniques, the performance of the charge comparison method, triangular filtering and frequency gradient analysis were investigated in this work. The scintillator was exposed to three different mixed gamma/neutron radiation fields. The figure of merit of neutron/gamma separation was investigated over a broad energy range, as well as for the neutron capture energy region. After optimisation, all three methods were found to perform similarly in terms of neutron/gamma separation.

  12. 24 Y-chromosomal STR haplotypic polymorphisms for Chinese Uygur ethnic group and its phylogenic analysis with other Chinese groups.

    Science.gov (United States)

    Liu, Wen-Juan; Pu, Hong-Wei; Yang, Chun-Hua; Meng, Hao-Tian; Zhang, Yu-Dang; Zhang, Li-Ping; Yan, Jiang-Wei; Wang, Hong-Dan; Ren, Jian-Wen; Sun, Jun-Yi; Liu, Chao; Wang, Hui; Zhu, Bo-Feng

    2015-02-01

    The Uygur ethnic minority is the largest ethnic group in the Xinjiang Uygur Autonomous Region of China, and is a precious resource for the study of ethnogeny and forensic biology. Previous studies have focused on the genetic background of the Uygur group, however, the patrilineal descent of the group is still unclear. In this study, we investigated the genetic diversity of 24 Y-STR loci in the Uygur group and analyzed the population differentiations as well as the genetic relationships between the Uygur group and other previously reported populations using 17 Y-filer loci. According to haplotypic analysis of the 24 Y-STR loci in 109 Uygur individuals, 104 different haplotypes were obtained, 99 of which were unique. The haplotypic diversity and discrimination capacity of these 24 Y-STR loci in Uygur group were 0.9992 and 0.9541, respectively. An additional 7 loci (DYS388, DYS444, DYS447, DYS449, DYS522, and DYS527a,b) showed high genetic diversity and improved the overall discrimination capacity of the 24 Y-STR system. Pairwise Fst and neighbor-joining analysis showed that the Uygur group was genetically close to the Han populations from different regions. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  13. Social status and the pursuit of positive social identity: Systematic domains of intergroup differentiation and discrimination for high- and low- status groups.

    Science.gov (United States)

    Oldmeadow, Julian A; Fiske, Susan T

    2010-07-01

    Research on intergroup discrimination has focused on the cognitive and motivational mechanisms involved, but the role of stereotype content has been neglected. Drawing on social identity theory and stereotype content research, the current studies investigated the role of stereotype content in intergroup differentiation and discrimination. Across two studies, students from high- and low-status groups differentiated themselves positively on stereotypes of competence and warmth respectively, and in allocations of resources in domains relevant to competence (academics, research) and warmth (sports, community outreach). Furthermore, there was evidence that discrimination by high- and low-status groups was driven by their respective stereotypes of competence and warmth. It is argued that stereotypes of competence and warmth, derived from status and power relations between groups, define the domains in which groups pursue positively distinct identities.

  14. Regularized discriminant analysis for multi-sensor decision fusion and damage detection with Lamb waves

    Science.gov (United States)

    Mishra, Spandan; Vanli, O. Arda; Huffer, Fred W.; Jung, Sungmoon

    2016-04-01

    In this study we propose a regularized linear discriminant analysis approach for damage detection which does not require an intermediate feature extraction step and therefore more efficient in handling data with high-dimensionality. A robust discriminant model is obtained by shrinking of the covariance matrix to a diagonal matrix and thresholding redundant predictors without hurting the predictive power of the model. The shrinking and threshold parameters of the discriminant function (decision boundary) are estimated to minimize the classification error. Furthermore, it is shown how the damage classification achieved by the proposed method can be extended to multiple sensors by following a Bayesian decision-fusion formulation. The detection probability of each sensor is used as a prior condition to estimate the posterior detection probability of the entire network and the posterior detection probability is used as a quantitative basis to make the final decision about the damage.

  15. Shrinkage-based diagonal discriminant analysis and its applications in high-dimensional data.

    Science.gov (United States)

    Pang, Herbert; Tong, Tiejun; Zhao, Hongyu

    2009-12-01

    High-dimensional data such as microarrays have brought us new statistical challenges. For example, using a large number of genes to classify samples based on a small number of microarrays remains a difficult problem. Diagonal discriminant analysis, support vector machines, and k-nearest neighbor have been suggested as among the best methods for small sample size situations, but none was found to be superior to others. In this article, we propose an improved diagonal discriminant approach through shrinkage and regularization of the variances. The performance of our new approach along with the existing methods is studied through simulations and applications to real data. These studies show that the proposed shrinkage-based and regularization diagonal discriminant methods have lower misclassification rates than existing methods in many cases.

  16. A Clinical Analysis of 293 FUO Patients, A Diagnostic Model Discriminating infectious Diseases from Non-infectious Diseases

    Institute of Scientific and Technical Information of China (English)

    2014-01-01

    Objective A diagnostic model was established to discriminate infectious diseases from non-infectious diseases. Methods The clinical data of patients with fever of unknown origin (FUO) hospitalized in Xiangya Hospital Central South University, from January, 2006 to April, 2011 were retrospectively analyzed. Patients enrolled were divided into two groups. The ifrst group was used to develop a diagnostic model: independent variables were recorded and considered in a logistic regression analysis to identify infectious and non-infectious diseases (αin= 0.05, αout= 0.10). The second group was used to evaluate the diagnostic model and make ROC analysis. Results The diagnostic rate of 143 patients in the ifrst group was 87.4%, the diagnosis included infectious disease (52.4%), connective tissue diseases (16.8%), neoplastic disease (16.1%) and miscellaneous (2.1%). The diagnostic rate of 168 patients in the second group was 88.4%, and the diagnosis was similar to the ifrst group. Logistic regression analysis showed that decreased white blood cell count (WBC 320 U/L) and lymphadenectasis were independent risk factors associated with non-infectious diseases. The odds ratios were 14.74, 5.84 and 5.11 (P≤ 0.01) , respectively. In ROC analysis, the sensitivity and speciifcity of the positive predictive values was 62.1% and 89.1%, respectively, while that of negative predicting values were 75% and 81.7%, respectively (AUC = 0.76,P = 0.00). Conclusions The combination of WBC 320 U/L and lymphadenectasis may be useful in discriminating infectious diseases from non-infectious diseases in patients hospitalized as FUO.

  17. Chinese Migrant Adolescents' Perceived Discrimination and Psychological Well-Being: The Moderating Roles of Group Identity and the Type of School.

    Directory of Open Access Journals (Sweden)

    Xia Liu

    Full Text Available Perceived discrimination can be harmful to migrant adolescents in China. However, little is known about the processes through which discrimination may be linked to decreased well-being in Chinese migrant adolescents. This study examined the relationship between perceived discrimination and three indices of psychological well-being (self-esteem, life satisfaction, collective self-esteem in 798 Chinese migrant adolescents (49.4% from public schools. Group identity affirmation and belonging (GIAB was examined as a protective factor that was expected to alleviate the negative effects of perceived discrimination on well-being, and the type of school was investigated as a potential moderator of the associations of interest. The results indicate that perceived discrimination was negatively linked to the three indices of psychological well-being and that the negative effects of perceived discrimination on psychological well-being were particularly salient for migrant adolescents attending public schools. Additionally, GIAB emerged as a protective buffer against perceived discrimination's negative effects on collective well-being.

  18. Revealing discriminating power of the elements in edible sea salts: Line-intensity correlation analysis from laser-induced plasma emission spectra

    Science.gov (United States)

    Lee, Yonghoon; Ham, Kyung-Sik; Han, Song-Hee; Yoo, Jonghyun; Jeong, Sungho

    2014-11-01

    We have investigated the discriminating power of the elements in edible sea salts using Laser-Induced Breakdown Spectroscopy (LIBS). For the ten different sea salts from South Korea, China, Japan, France, Mexico and New Zealand, LIBS spectra were recorded in the spectral range between 190 and 1040 nm, identifying the presence of Na, Cl, K, Ca, Mg, Li, Sr, Al, Si, Ti, Fe, C, O, N, and H. Intensity correlation analysis of the observed emission lines provided a valuable insight into the discriminating power of the different elements in the sea salts. The correlation analysis suggests that the elements with independent discrimination power can be categorized into three groups; those that represent dissolved ions in seawater (K, Li, and Mg), those that are associated with calcified particles (Ca and Sr), and those that are present in soils contained in the sea salts (Al, Si, Ti, and Fe). Classification models using a few emission lines selected based on the results from intensity correlation analysis and full broadband LIBS spectra were developed based on Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) and their performances were compared. Our results indicate that effective combination of a few emission lines can provide a dependable model for discriminating the edible sea salts and the performance is not much degraded from that based on the full broadband spectra. This can be rationalized by the intensity correlation results.

  19. Detection of Helicobacter pylori carriers by discriminant analysis of urea and pH levels in gastric juices.

    Science.gov (United States)

    Ameglio, F; Abbolito, M R; Giannarelli, D; Citarda, F; Grassi, A; Gandolfo, G M; Casale, V

    1991-08-01

    An alternative approach to the problems inherent in current methods for detecting Helicobacter pylori carriers--that of being generally time-consuming, expensive, and not sufficiently sensitive--was devised by using the urea concentration and pH levels of gastric juices. A linear discriminant analysis of these variables, measured in 54 patients submitted to digestive endoscopy for gastritis, provided a mathematical formula for assigning the subjects (previously classified by other standard methods) to groups of either positive or negative H pylori carriers. The results obtained showed a correct classification in 52 out of 54 cases with only one false negative and one false positive case.

  20. Characterization of local complex structures in a recurrence plot to improve nonlinear dynamic discriminant analysis.

    Science.gov (United States)

    Ding, Hang

    2014-01-01

    Structures in recurrence plots (RPs), preserving the rich information of nonlinear invariants and trajectory characteristics, have been increasingly analyzed in dynamic discrimination studies. The conventional analysis of RPs is mainly focused on quantifying the overall diagonal and vertical line structures through a method, called recurrence quantification analysis (RQA). This study extensively explores the information in RPs by quantifying local complex RP structures. To do this, an approach was developed to analyze the combination of three major RQA variables: determinism, laminarity, and recurrence rate (DLR) in a metawindow moving over a RP. It was then evaluated in two experiments discriminating (1) ideal nonlinear dynamic series emulated from the Lorenz system with different control parameters and (2) data sets of human heart rate regulations with normal sinus rhythms (n = 18) and congestive heart failure (n = 29). Finally, the DLR was compared with seven major RQA variables in terms of discriminatory power, measured by standardized mean difference (DSMD). In the two experiments, DLR resulted in the highest discriminatory power with DSMD = 2.53 and 0.98, respectively, which were 7.41 and 2.09 times the best performance from RQA. The study also revealed that the optimal RP structures for the discriminations were neither typical diagonal structures nor vertical structures. These findings indicate that local complex RP structures contain some rich information unexploited by RQA. Therefore, future research to extensively analyze complex RP structures would potentially improve the effectiveness of the RP analysis in dynamic discrimination studies.

  1. Generalized Discriminant Analysis algorithm for feature reduction in Cyber Attack Detection System

    Directory of Open Access Journals (Sweden)

    Shailendra Singh

    2009-10-01

    Full Text Available This Generalized Discriminant Analysis (GDA has provided an extremely powerful approach to extracting non-linear features. The network traffic data provided for the design of intrusion detection system always are large with ineffective information, thus we need to remove the worthless information from the original high dimensional database. To improve the generalization ability, we usually generate a small set of features from the original input variables by feature extraction. The conventional Linear Discriminant Analysis (LDA feature reduction technique has its limitations. It is not suitable for non-linear dataset. Thus we propose an efficient algorithm based on the Generalized Discriminant Analysis (GDA feature reduction technique which is novel approach used in the area of cyber attack detection. This not only reduces the number of the input features but also increases the classification accuracy and reduces the training and testing time of the classifiers by selecting most discriminating features. We use Artificial Neural Network (ANN and C4.5 classifiers to compare the performance of the proposed technique. The result indicates the superiority of algorithm.

  2. Characterization of local complex structures in a recurrence plot to improve nonlinear dynamic discriminant analysis

    Science.gov (United States)

    Ding, Hang

    2014-01-01

    Structures in recurrence plots (RPs), preserving the rich information of nonlinear invariants and trajectory characteristics, have been increasingly analyzed in dynamic discrimination studies. The conventional analysis of RPs is mainly focused on quantifying the overall diagonal and vertical line structures through a method, called recurrence quantification analysis (RQA). This study extensively explores the information in RPs by quantifying local complex RP structures. To do this, an approach was developed to analyze the combination of three major RQA variables: determinism, laminarity, and recurrence rate (DLR) in a metawindow moving over a RP. It was then evaluated in two experiments discriminating (1) ideal nonlinear dynamic series emulated from the Lorenz system with different control parameters and (2) data sets of human heart rate regulations with normal sinus rhythms (n = 18) and congestive heart failure (n = 29). Finally, the DLR was compared with seven major RQA variables in terms of discriminatory power, measured by standardized mean difference (DSMD). In the two experiments, DLR resulted in the highest discriminatory power with DSMD = 2.53 and 0.98, respectively, which were 7.41 and 2.09 times the best performance from RQA. The study also revealed that the optimal RP structures for the discriminations were neither typical diagonal structures nor vertical structures. These findings indicate that local complex RP structures contain some rich information unexploited by RQA. Therefore, future research to extensively analyze complex RP structures would potentially improve the effectiveness of the RP analysis in dynamic discrimination studies.

  3. The prevalence of discrimination across racial groups in contemporary America: Results from a nationally representative sample of adults.

    Science.gov (United States)

    Boutwell, Brian B; Nedelec, Joseph L; Winegard, Bo; Shackelford, Todd; Beaver, Kevin M; Vaughn, Michael; Barnes, J C; Wright, John P

    2017-01-01

    A large body of social science research is devoted to understanding the causes and correlates of discrimination. Comparatively less effort has been aimed at providing a general prevalence estimate of discrimination using a nationally representative sample. The current study is intended to offer such an estimate using a large sample of American respondents (N = 14,793) while also exploring perceptions regarding why respondents felt they were discriminated against. The results provide a broad estimate of self-reported discrimination experiences-an event that was only reported by about one-quarter of all sample members-across racial and ethnic categories.

  4. The prevalence of discrimination across racial groups in contemporary America: Results from a nationally representative sample of adults

    Science.gov (United States)

    Nedelec, Joseph L.; Winegard, Bo; Shackelford, Todd; Beaver, Kevin M.; Vaughn, Michael; Barnes, J. C.; Wright, John P.

    2017-01-01

    A large body of social science research is devoted to understanding the causes and correlates of discrimination. Comparatively less effort has been aimed at providing a general prevalence estimate of discrimination using a nationally representative sample. The current study is intended to offer such an estimate using a large sample of American respondents (N = 14,793) while also exploring perceptions regarding why respondents felt they were discriminated against. The results provide a broad estimate of self-reported discrimination experiences—an event that was only reported by about one-quarter of all sample members—across racial and ethnic categories. PMID:28837680

  5. Discrimination of Anemonefish Species by PCR-RFLP Analysis of Mitochondrial Gene Fragments

    Directory of Open Access Journals (Sweden)

    Chuta Boonphakdee

    2008-01-01

    Full Text Available A means of discriminating among species of clown anemonefishes, based on restriction enzyme analysis of partial mitochondrial DNA sequences, was investigated. Mitochondrial 16S rRNA and cytochrome b genes from 6 species (7 strains of anemonefish (Premnas biculeatus, Amphiprion polymnus, A. sandaracinos, A. perideraion, A. ocellaris, A. ocellaris var. and A. percula were PCR-amplified. A 623-bp portion of 16S rRNA gene was obtained from different fishes using the same pair of primers. Further investigation of this 16S rRNA fragment, by restriction endonuclease digestion with BfuCI and RsaI, was not able to distinguish all fishes studied, but did yield 3 different digestion patterns. The first was specific to P. biculaetus, the sole member of the genus Premnas, while the remaining two separated the Amphiprion species into 2 groups: 1 A. polymnas, A. sandaracinos and A. perideraion, and 2 A. ocellaris, A. ocellaris var. and A. percula. In contrast to this, restriction endonuclease digestion of a 786-bp fragment of the cytochrome b gene with HinfI and RsaI, was able to differentiate different 7 anemonefishes. This utility marker is valuable for unambiguous species/strain identification of juvenile anemonefishes.

  6. Analysing breast cancer microarrays from African Americans using shrinkage-based discriminant analysis

    Directory of Open Access Journals (Sweden)

    Pang Herbert

    2010-10-01

    Full Text Available Abstract Breast cancer tumours among African Americans are usually more aggressive than those found in Caucasian populations. African-American patients with breast cancer also have higher mortality rates than Caucasian women. A better understanding of the disease aetiology of these breast cancers can help to improve and develop new methods for cancer prevention, diagnosis and treatment. The main goal of this project was to identify genes that help differentiate between oestrogen receptor-positive and -negative samples among a small group of African-American patients with breast cancer. Breast cancer microarrays from one of the largest genomic consortiums were analysed using 13 African-American and 201 Caucasian samples with oestrogen receptor status. We used a shrinkage-based classification method to identify genes that were informative in discriminating between oestrogen receptor-positive and -negative samples. Subset analysis and permutation were performed to obtain a set of genes unique to the African-American population. We identified a set of 156 probe sets, which gave a misclassification rate of 0.16 in distinguishing between oestrogen receptor-positive and -negative patients. The biological relevance of our findings was explored through literature-mining techniques and pathway mapping. An independent dataset was used to validate our findings and we found that the top ten genes mapped onto this dataset gave a misclassification rate of 0.15. The described method allows us best to utilise the information available from small sample size microarray data in the context of ethnic minorities.

  7. Morphological evaluation of common bean diversity in Bosnia and Herzegovina using the discriminant analysis of principal components (DAPC multivariate method

    Directory of Open Access Journals (Sweden)

    Grahić Jasmin

    2013-01-01

    Full Text Available In order to analyze morphological characteristics of locally cultivated common bean landraces from Bosnia and Herzegovina (B&H, thirteen quantitative and qualitative traits of 40 P. vulgaris accessions, collected from four geographical regions (Northwest B&H, Northeast B&H, Central B&H and Sarajevo and maintained at the Gene bank of the Faculty of Agriculture and Food Sciences in Sarajevo, were examined. Principal component analysis (PCA showed that the proportion of variance retained in the first two principal components was 54.35%. The first principal component had high contributing factor loadings from seed width, seed height and seed weight, whilst the second principal component had high contributing factor loadings from the analyzed traits seed per pod and pod length. PCA plot, based on the first two principal components, displayed a high level of variability among the analyzed material. The discriminant analysis of principal components (DAPC created 3 discriminant functions (DF, whereby the first two discriminant functions accounted for 90.4% of the variance retained. Based on the retained DFs, DAPC provided group membership probabilities which showed that 70% of the accessions examined were correctly classified between the geographically defined groups. Based on the taxonomic distance, 40 common bean accessions analyzed in this study formed two major clusters, whereas two accessions Acc304 and Acc307 didn’t group in any of those. Acc360 and Acc362, as well as Acc324 and Acc371 displayed a high level of similarity and are probably the same landrace. The present diversity of Bosnia and Herzegovina’s common been landraces could be useful in future breeding programs.

  8. Multivariate Analysis of Laser-Induced Breakdown Spectroscopy for Discrimination between Explosives and Plastics

    Institute of Scientific and Technical Information of China (English)

    WANG Qian-Qian; LIU Kai; ZHAO Hua

    2012-01-01

    A method to distinguish explosives from plastics using laser-induced breakdown spectroscopy is discussed. A model for classification with cross-validation theory is built based on the partial least-square discriminant analysis method. Seven types of plastics and one explosive are used as samples to test the model. The experimental results demonstrate that laser-induced breakdown spectroscopy has the capacity to discriminate explosives from plastics combined with chemometrics methods. The results could be useful for prospective research of laser-induced breakdown spectroscopy on the differentiation of explosives and other materials.%A method to distinguish explosives from plastics using laser-induced breakdown spectroscopy is discussed.A model for classification with cross-validation theory is built based on the partial least-square discriminant analysis method.Seven types of plastics and one explosive are used as samples to test the model.The experimental results demonstrate that laser-induced breakdown spectroscopy has the capacity to discriminate explosives from plastics combined with chemometrics methods.The results could be useful for prospective research of laser-induced breakdown spectroscopy on the differentiation of explosives and other materials.

  9. Comparative analysis of cyanobacterial superoxide dismutases to discriminate canonical forms

    Directory of Open Access Journals (Sweden)

    Prabaharan Dharmar

    2007-11-01

    Full Text Available Abstract Background Superoxide dismutases (SOD are ubiquitous metalloenzymes that catalyze the disproportion of superoxide to peroxide and molecular oxygen through alternate oxidation and reduction of their metal ions. In general, SODs are classified into four forms by their catalytic metals namely; FeSOD, MnSOD, Cu/ZnSOD and NiSOD. In addition, a cambialistic form that uses Fe/Mn in its active site also exists. Cyanobacteria, the oxygen evolving photosynthetic prokaryotes, produce reactive oxygen species that can damage cellular components leading to cell death. Thus, the co-evolution of an antioxidant system was necessary for the survival of photosynthetic organisms with SOD as the initial enzyme evolved to alleviate the toxic effect. Cyanobacteria represent the first oxygenic photoautotrophs and their SOD sequences available in the databases lack clear annotation. Hence, the present study focuses on structure and sequence pattern of subsets of cyanobacterial superoxide dismutases. Result The sequence conservation and structural analysis of Fe (Thermosynechococcus elongatus BP1 and MnSOD (Anabaena sp. PCC7120 reveal the sharing of N and C terminal domains. At the C terminal domain, the metal binding motif in cyanoprokaryotes is DVWEHAYY while it is D-X-[WF]-E-H-[STA]-[FY]-[FY] in other pro- and eukaryotes. The cyanobacterial FeSOD differs from MnSOD at least in three ways viz. (i FeSOD has a metal specific signature F184X3A188Q189.......T280......F/Y303 while, in Mn it is R184X3G188G189......G280......W303, (ii aspartate ligand forms a hydrogen bond from the active site with the outer sphere residue of W243 in Fe where as it is Q262 in MnSOD; and (iii two unique lysine residues at positions 201 and 255 with a photosynthetic role, found only in FeSOD. Further, most of the cyanobacterial Mn metalloforms have a specific transmembrane hydrophobic pocket that distinguishes FeSOD from Mn isoform. Cyanobacterial Cu/ZnSOD has a copper domain and two

  10. Dimensional analysis and group theory in astrophysics

    CERN Document Server

    Kurth, Rudolf

    2013-01-01

    Dimensional Analysis and Group Theory in Astrophysics describes how dimensional analysis, refined by mathematical regularity hypotheses, can be applied to purely qualitative physical assumptions. The book focuses on the continuous spectral of the stars and the mass-luminosity relationship. The text discusses the technique of dimensional analysis, covering both relativistic phenomena and the stellar systems. The book also explains the fundamental conclusion of dimensional analysis, wherein the unknown functions shall be given certain specified forms. The Wien and Stefan-Boltzmann Laws can be si

  11. Thermal oxidation process accelerates degradation of the olive oil mixed with sunflower oil and enables its discrimination using synchronous fluorescence spectroscopy and chemometric analysis

    Science.gov (United States)

    Mabood, Fazal; Boqué, Ricard; Folcarelli, Rita; Busto, Olga; Al-Harrasi, Ahmed; Hussain, Javid

    2015-05-01

    We have investigated the effect of thermal treatment on the discrimination of pure extra virgin olive oil (EVOO) samples from EVOO samples adulterated with sunflower oil. Two groups of samples were used. One group was analyzed at room temperature (25 °C) and the other group was thermally treated in a thermostatic water bath at 75 °C for 8 h, in contact with air and with light exposure, to favor oxidation. All samples were then measured with synchronous fluorescence spectroscopy. Fluorescence spectra were acquired by varying the excitation wavelength in the region from 250 to 720 nm. In order to optimize the differences between excitation and emission wavelengths, four constant differential wavelengths, i.e., 20 nm, 40 nm, 60 nm and 80 nm, were tried. Partial least-squares discriminant analysis (PLS-DA) was used to discriminate between pure and adulterated oils. It was found that the 20 nm difference was the optimal, at which the discrimination models showed the best results. The best PLS-DA models were those built with the difference spectra (75-25 °C), which were able to discriminate pure from adulterated oils at a 2% level of adulteration. Furthermore, PLS regression models were built to quantify the level of adulteration. Again, the best model was the one built with the difference spectra, with a prediction error of 1.75% of adulteration.

  12. Revealing discriminating power of the elements in edible sea salts: Line-intensity correlation analysis from laser-induced plasma emission spectra

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Yonghoon, E-mail: yhlee@mokpo.ac.kr [Department of Chemistry, Mokpo National University, Jeonnam 534-729 (Korea, Republic of); Ham, Kyung-Sik [Department of Food Engineering, Mokpo National University, Jeonnam 534-729 (Korea, Republic of); Han, Song-Hee [Division of Maritime Transportation System, Mokpo National Maritime University, Jeonnam 530-729 (Korea, Republic of); Yoo, Jonghyun, E-mail: jyoo@appliedspectra.com [Applied Spectra, Inc., 46665 Fremont Boulevard, Fremont, CA 94538 (United States); Jeong, Sungho [School of Mechatronics, Gwangju Institute of Science and Technology, Gwangju 500-712 (Korea, Republic of)

    2014-11-01

    We have investigated the discriminating power of the elements in edible sea salts using Laser-Induced Breakdown Spectroscopy (LIBS). For the ten different sea salts from South Korea, China, Japan, France, Mexico and New Zealand, LIBS spectra were recorded in the spectral range between 190 and 1040 nm, identifying the presence of Na, Cl, K, Ca, Mg, Li, Sr, Al, Si, Ti, Fe, C, O, N, and H. Intensity correlation analysis of the observed emission lines provided a valuable insight into the discriminating power of the different elements in the sea salts. The correlation analysis suggests that the elements with independent discrimination power can be categorized into three groups; those that represent dissolved ions in seawater (K, Li, and Mg), those that are associated with calcified particles (Ca and Sr), and those that are present in soils contained in the sea salts (Al, Si, Ti, and Fe). Classification models using a few emission lines selected based on the results from intensity correlation analysis and full broadband LIBS spectra were developed based on Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) and their performances were compared. Our results indicate that effective combination of a few emission lines can provide a dependable model for discriminating the edible sea salts and the performance is not much degraded from that based on the full broadband spectra. This can be rationalized by the intensity correlation results. - Highlights: • Broadband LIBS spectra of various edible sea salts were obtained. • Intensity correlation of emission lines of the elements in edible sea salts was analyzed. • The elements were categorized into three groups with independent discriminating power. • The effective combination of a few lines can provide dependable classification models.

  13. Responding to group-based discrimination : The impact of social structure on willingness to engage in mentoring

    NARCIS (Netherlands)

    Hersby, Mette D.; Jetten, Jolanda; Ryan, Michelle K.; Schmitt, Michael T.

    2011-01-01

    In two studies we examined women's willingness to engage in mentoring as a function of the perceived pervasiveness of gender discrimination and the appraised legitimacy of discrimination. In line with predictions, and confirming predictions from social identity theory, we found that perceiving discr

  14. Responding to group-based discrimination : The impact of social structure on willingness to engage in mentoring

    NARCIS (Netherlands)

    Hersby, Mette D.; Jetten, Jolanda; Ryan, Michelle K.; Schmitt, Michael T.

    2011-01-01

    In two studies we examined women's willingness to engage in mentoring as a function of the perceived pervasiveness of gender discrimination and the appraised legitimacy of discrimination. In line with predictions, and confirming predictions from social identity theory, we found that perceiving discr

  15. Two functions of verbal intergroup discrimination : Identity and instrumental motives as a result of group identification and threat

    NARCIS (Netherlands)

    Scheepers, D; Spears, R; Doosje, B; Manstead, ASR

    2003-01-01

    In two studies, the authors examined the circumstances under which discrimination has an identity confirmation function or an instrumental function (instigating collective action). In Study 1, participants (N = 601) described a situation in which they had discriminated and then completed measures of

  16. Responding to group-based discrimination : The impact of social structure on willingness to engage in mentoring

    NARCIS (Netherlands)

    Hersby, Mette D.; Jetten, Jolanda; Ryan, Michelle K.; Schmitt, Michael T.

    In two studies we examined women's willingness to engage in mentoring as a function of the perceived pervasiveness of gender discrimination and the appraised legitimacy of discrimination. In line with predictions, and confirming predictions from social identity theory, we found that perceiving

  17. Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification

    Science.gov (United States)

    Wen, Zaidao; Hou, Biao; Jiao, Licheng

    2017-07-01

    Linear synthesis model based dictionary learning framework has achieved remarkable performances in image classification in the last decade. Behaved as a generative feature model, it however suffers from some intrinsic deficiencies. In this paper, we propose a novel parametric nonlinear analysis cosparse model (NACM) with which a unique feature vector will be much more efficiently extracted. Additionally, we derive a deep insight to demonstrate that NACM is capable of simultaneously learning the task adapted feature transformation and regularization to encode our preferences, domain prior knowledge and task oriented supervised information into the features. The proposed NACM is devoted to the classification task as a discriminative feature model and yield a novel discriminative nonlinear analysis operator learning framework (DNAOL). The theoretical analysis and experimental performances clearly demonstrate that DNAOL will not only achieve the better or at least competitive classification accuracies than the state-of-the-art algorithms but it can also dramatically reduce the time complexities in both training and testing phases.

  18. Digital discrimination of neutrons and gamma-rays in organic scintillation detectors using moment analysis

    Science.gov (United States)

    Xie, Xufei; Zhang, Xing; Yuan, Xi; Chen, Jinxiang; Li, Xiangqing; Zhang, Guohui; Fan, Tieshuan; Yuan, Guoliang; Yang, Jinwei; Yang, Qingwei

    2012-09-01

    Digital discrimination of neutron and gamma-ray events in an organic scintillator has been investigated by moment analysis. Signals induced by an americium-beryllium (Am/Be) isotropic neutron source in a stilbene crystal detector have been sampled with a flash analogue-to-digital converter (ADC) of 1 GSamples/s sampling rate and 10-bit vertical resolution. Neutrons and gamma-rays have been successfully discriminated with a threshold corresponding to gamma-ray energy about 217 keV. Moment analysis has also been verified against the results assessed by a time-of-flight (TOF) measurement. It is shown that the classification of neutrons and gamma-rays afforded by moment analysis is consistent with that achieved by digital TOF measurement. This method has been applied to analyze the data acquired from the stilbene crystal detector in mixed radiation field of the HL-2A tokamak deuterium plasma discharges and the results are described.

  19. Social Status, Discrimination, and Minority Individuals' Mental Health: a Secondary Analysis of US National Surveys.

    Science.gov (United States)

    Lo, Celia C; Cheng, Tyrone C

    2017-08-15

    Our study measured minority individuals' social status factors and frequency of discrimination experiences, in order to delineate social mechanisms linking race/ethnicity to mental status (specifically, to current mood/anxiety disorder and self-rated mental health). In this nationally representative secondary research, our data analyses drew on the cross-sectional "Collaborative Psychiatric Epidemiology Surveys," dating 2001-2003. The sample for the final model numbered 9368 respondents (2016 Asians, 2676 Latinos, 4676 blacks). Across races/ethnicities, better mental health was associated with male gender, higher income, marriage, more education, and less-frequent discrimination experiences; discrimination experiences could impair health, especially among blacks. Marriage's strong contribution to Asians' mental health did not hold among blacks; education's contribution to Latinos' mental health did not hold among blacks either. Blacks' mental health was unaffected by immigration status, but Asian and Latino immigrants showed less-robust mental health than native-born counterparts. Across the three racial/ethnic groups studied, differences were noted in relationships between self-reported mental health status and the employed social status and discrimination factors.

  20. The Control Group and Meta-Analysis

    Directory of Open Access Journals (Sweden)

    John E Hunter

    2014-09-01

    Full Text Available Social scientists use a mixture of different methodologies, which creates problems for researchers attempting to review the cumulative results of all studies.  Standard practice for review studies using meta-analysis is to adjust the findings of all studies that use control groups and to include studies not having control groups without adjustment for extraneous effects, or to not use studies that lack a control group, which could produce an erroneous result.  Our study develops a novel meta-analytic procedure that combines the evidence on control group change with evidence on change from the intervention, making it possible to adjust for the effects of extraneous factors in all studies and bridges the gap between control group studies and other types of studies. DOI: 10.2458/azu_jmmss.v5i1.18302

  1. Chinese Migrant Adolescents’ Perceived Discrimination and Psychological Well-Being: The Moderating Roles of Group Identity and the Type of School

    Science.gov (United States)

    Liu, Xia; Zhao, Jingxin

    2016-01-01

    Perceived discrimination can be harmful to migrant adolescents in China. However, little is known about the processes through which discrimination may be linked to decreased well-being in Chinese migrant adolescents. This study examined the relationship between perceived discrimination and three indices of psychological well-being (self-esteem, life satisfaction, collective self-esteem) in 798 Chinese migrant adolescents (49.4% from public schools). Group identity affirmation and belonging (GIAB) was examined as a protective factor that was expected to alleviate the negative effects of perceived discrimination on well-being, and the type of school was investigated as a potential moderator of the associations of interest. The results indicate that perceived discrimination was negatively linked to the three indices of psychological well-being and that the negative effects of perceived discrimination on psychological well-being were particularly salient for migrant adolescents attending public schools. Additionally, GIAB emerged as a protective buffer against perceived discrimination’s negative effects on collective well-being. PMID:26731529

  2. Differentiation of walnut wood species and steam treatment using ATR-FTIR and partial least squares discriminant analysis (PLS-DA).

    Science.gov (United States)

    Hobro, Alison J; Kuligowski, Julia; Döll, Markus; Lendl, Bernhard

    2010-11-01

    Wood is a ubiquitous material used in everyday life. Accurate identification of species can be of importance in a historical context enabling appropriate conservation treatment and adequate choice of material to be applied to historic wooden objects, and in a more modern context, in the identification of forgeries. Wood is also often treated to improve certain physical characteristics, often strength and durability. However, determination of whether or not a piece of wood has been treated can be very difficult. Infrared spectroscopy has previously been applied to differentiate between different wood species or between treated and untreated wood, often in conjunction with chemometric analysis techniques. Here, we report the use of mid-IR spectroscopy, coupled with partial least squares discriminant analysis for the discrimination between two walnut wood species and to differentiate between steam-treated and untreated samples of each of these wood species. We show that the discrimination between species and between steam-treated and non-steam-treated wood from Juglans nigra is very clear and, while analysis of the quality of the discrimination between steam-treated and non-steam-treated J. regia samples is not as good, it is, nevertheless, sufficient for discrimination between the two groups with a statistical significance of P < 0.0001.

  3. Forensic discrimination of blue ballpoint pen inks based on thin layer chromatography and image analysis.

    Science.gov (United States)

    Djozan, Djavanshir; Baheri, Tahmineh; Karimian, Ghader; Shahidi, Masomeh

    2008-08-06

    This article aims to provide a new and fast method for differentiation of inks on a questioned document. The data acquisition was carried out by designing specific image analysis software for evaluating thin layer chromatograms (TLC-IA). The ink spot was extracted from the document using methanol and separated by TLC using plastic sheet silica gel 60 without fluorescent indicator, and a mixture of ethyl acetate, ethanol, and water (70:35:30, v/v/v) as mobile phase. To discriminate between different pen inks, new software was designed on the basis of intensity profile of red, green, and blue (RGB) characteristic. In practice, after development of chromatogram, the chromatograms were scanned by ordinary office scanner, intensity profiles of RGB characteristics on the development straight of each sample were produced and compared with the mentioned software. RGB profiles of ballpoint inks from various manufacturers showed that the patterns in most cases were distinctly different from each other. This new method allowed discriminating among different pen inks with a high reliability and the discriminating power of 92.8%. Blue ballpoint pen inks of 41 different samples available on the local market were successfully analyzed and discriminated.

  4. Optical spectroscopic analysis for the discrimination of extra-virgin olive-oil (Conference Presentation)

    Science.gov (United States)

    McReynolds, Naomi; Auñón Garcia, Juan M.; Guengerich, Zoe; Smith, Terry K.; Dholakia, Kishan

    2017-02-01

    We present an optical spectroscopic technique, making use of both Raman signals and fluorescence spectroscopy, for the identification of five brands of commercially available extra-virgin olive-oil (EVOO). We demonstrate our technique on both a `bulk-optics' free-space system and a compact device. Using the compact device, which is capable of recording both Raman and fluorescence signals, we achieved an average sensitivity and specificity of 98.4% and 99.6% for discrimination, respectively. Our approach demonstrates that both Raman and fluorescence spectroscopy can be used for portable discrimination of EVOOs which obviates the need to use centralised laboratories and opens up the prospect of in-field testing. This technique may enable detection of EVOO that has undergone counterfeiting or adulteration. One of the main challenges facing Raman spectroscopy for use in quality control of EVOOs is that the oxidation of EVOO, which naturally occurs due to aging, causes shifts in Raman spectra with time, which implies regular retraining would be necessary. We present a potential method of analysis to minimize the effect that aging has on discrimination efficiency; we show that by discarding the first principal component, which contains information on the variations due to oxidation, we can improve discrimination efficiency thus improving the robustness of our technique.

  5. Process Analysis of the CV Group's Operation

    CERN Document Server

    Wilhelmsson, M

    2000-01-01

    This report will give an explanation of the internal reorganization that has been done because of the necessity to optimize operation in the cooling and ventilation group. The basic structure for the group was defined at the end of 1998. We understood then that change was needed to accommodate the increased workload due to the LHC project. In addition, we face a relatively large turnover of personnel (retirements and some recruitment) with related integration issues to consider. We would also like to implement new approaches in the management of both operations and maintenance. After some running-in problems during the first half of 1999, we realized that much more could be gained with the analysis and the definition and documenting of each single function and generic activity within the group. The authors will explain how this analysis was carried out and give some feedback of the outcome, so far.

  6. Harmonic analysis on exponential solvable Lie groups

    CERN Document Server

    Fujiwara, Hidenori

    2015-01-01

    This book is the first one that brings together recent results on the harmonic analysis of exponential solvable Lie groups. There still are many interesting open problems, and the book contributes to the future progress of this research field. As well, various related topics are presented to motivate young researchers. The orbit method invented by Kirillov is applied to study basic problems in the analysis on exponential solvable Lie groups. This method tells us that the unitary dual of these groups is realized as the space of their coadjoint orbits. This fact is established using the Mackey theory for induced representations, and that mechanism is explained first. One of the fundamental problems in the representation theory is the irreducible decomposition of induced or restricted representations. Therefore, these decompositions are studied in detail before proceeding to various related problems: the multiplicity formula, Plancherel formulas, intertwining operators, Frobenius reciprocity, and associated alge...

  7. Methylation Linear Discriminant Analysis (MLDA for identifying differentially methylated CpG islands

    Directory of Open Access Journals (Sweden)

    Vass J Keith

    2008-08-01

    Full Text Available Abstract Background Hypermethylation of promoter CpG islands is strongly correlated to transcriptional gene silencing and epigenetic maintenance of the silenced state. As well as its role in tumor development, CpG island methylation contributes to the acquisition of resistance to chemotherapy. Differential Methylation Hybridisation (DMH is one technique used for genome-wide DNA methylation analysis. The study of such microarray data sets should ideally account for the specific biological features of DNA methylation and the non-symmetrical distribution of the ratios of unmethylated and methylated sequences hybridised on the array. We have therefore developed a novel algorithm tailored to this type of data, Methylation Linear Discriminant Analysis (MLDA. Results MLDA was programmed in R (version 2.7.0 and the package is available at CRAN 1. This approach utilizes linear regression models of non-normalised hybridisation data to define methylation status. Log-transformed signal intensities of unmethylated controls on the microarray are used as a reference. The signal intensities of DNA samples digested with methylation sensitive restriction enzymes and mock digested are then transformed to the likelihood of a locus being methylated using this reference. We tested the ability of MLDA to identify loci differentially methylated as analysed by DMH between cisplatin sensitive and resistant ovarian cancer cell lines. MLDA identified 115 differentially methylated loci and 23 out of 26 of these loci have been independently validated by Methylation Specific PCR and/or bisulphite pyrosequencing. Conclusion MLDA has advantages for analyzing methylation data from CpG island microarrays, since there is a clear rational for the definition of methylation status, it uses DMH data without between-group normalisation and is less influenced by cross-hybridisation of loci. The MLDA algorithm successfully identified differentially methylated loci between two classes of

  8. Multilinear Biased Discriminant Analysis: A Novel Method for Facial Action Unit Representation

    CERN Document Server

    Khademi, Mahmoud; Manzuri-Shalmani, Mohammad T

    2010-01-01

    In this paper a novel efficient method for representation of facial action units by encoding an image sequence as a fourth-order tensor is presented. The multilinear tensor-based extension of the biased discriminant analysis (BDA) algorithm, called multilinear biased discriminant analysis (MBDA), is first proposed. Then, we apply the MBDA and two-dimensional BDA (2DBDA) algorithms, as the dimensionality reduction techniques, to Gabor representations and the geometric features of the input image sequence respectively. The proposed scheme can deal with the asymmetry between positive and negative samples as well as curse of dimensionality dilemma. Extensive experiments on Cohn-Kanade database show the superiority of the proposed method for representation of the subtle changes and the temporal information involved in formation of the facial expressions. As an accurate tool, this representation can be applied to many areas such as recognition of spontaneous and deliberate facial expressions, multi modal/media huma...

  9. Z-score linear discriminant analysis for EEG based brain-computer interfaces.

    Directory of Open Access Journals (Sweden)

    Rui Zhang

    Full Text Available Linear discriminant analysis (LDA is one of the most popular classification algorithms for brain-computer interfaces (BCI. LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enhanced version of LDA, namely z-score linear discriminant analysis (Z-LDA, which introduces a new decision boundary definition strategy to handle with the heteroscedastic class distributions. Z-LDA defines decision boundary through z-score utilizing both mean and standard deviation information of the projected data, which can adaptively adjust the decision boundary to fit for heteroscedastic distribution situation. Results derived from both simulation dataset and two actual BCI datasets consistently show that Z-LDA achieves significantly higher average classification accuracies than conventional LDA, indicating the superiority of the new proposed decision boundary definition strategy.

  10. Facial Expression Representation Using Heteroscedastic Linear Discriminant Analysis and Gabor Wavelets

    CERN Document Server

    Khademi, Mahmoud; Manzuri, Mohammad T

    2010-01-01

    In this paper, a novel representation for facial expressions in two-dimensional image sequences is presented. We apply a variation of two-dimensional heteroscedastic linear discriminant analysis (2DHLDA) algorithm, as an efficient dimensionality reduction technique, to Gabor representation of the input sequence. 2DHLDA is an extension of the two-dimensional linear discriminant analysis (2DLDA) approach and removes the equal within-class covariance. By applying 2DHLDA in two directions, we eliminate the correlations between both image columns and image rows. Then, we perform a one-dimensional LDA on the new features. This combined method can alleviate the small sample size problem and instability encountered by HLDA. The proposed method is robust to illumination changes and can represent temporal information as well as subtle changes in facial muscles properly. Also, employing both geometric and appearance features and using support vector machines (SVMs) classifier, we provide experiments on Cohn-Kanade datab...

  11. Sparse dimensionality reduction of hyperspectral image based on semi-supervised local Fisher discriminant analysis

    Science.gov (United States)

    Shao, Zhenfeng; Zhang, Lei

    2014-09-01

    This paper presents a novel sparse dimensionality reduction method of hyperspectral image based on semi-supervised local Fisher discriminant analysis (SELF). The proposed method is designed to be especially effective for dealing with the out-of-sample extrapolation to realize advantageous complementarities between SELF and sparsity preserving projections (SPP). Compared to SELF and SPP, the method proposed herein offers highly discriminative ability and produces an explicit nonlinear feature mapping for the out-of-sample extrapolation. This is due to the fact that the proposed method can get an explicit feature mapping for dimensionality reduction and improve the classification performance of classifiers by performing dimensionality reduction. Experimental analysis on the sparsity and efficacy of low dimensional outputs shows that, sparse dimensionality reduction based on SELF can yield good classification results and interpretability in the field of hyperspectral remote sensing.

  12. Z-score linear discriminant analysis for EEG based brain-computer interfaces.

    Science.gov (United States)

    Zhang, Rui; Xu, Peng; Guo, Lanjin; Zhang, Yangsong; Li, Peiyang; Yao, Dezhong

    2013-01-01

    Linear discriminant analysis (LDA) is one of the most popular classification algorithms for brain-computer interfaces (BCI). LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enhanced version of LDA, namely z-score linear discriminant analysis (Z-LDA), which introduces a new decision boundary definition strategy to handle with the heteroscedastic class distributions. Z-LDA defines decision boundary through z-score utilizing both mean and standard deviation information of the projected data, which can adaptively adjust the decision boundary to fit for heteroscedastic distribution situation. Results derived from both simulation dataset and two actual BCI datasets consistently show that Z-LDA achieves significantly higher average classification accuracies than conventional LDA, indicating the superiority of the new proposed decision boundary definition strategy.

  13. Using discriminant analysis to detect intrusions in external communication for self-driving vehicles

    Directory of Open Access Journals (Sweden)

    Khattab M.Ali Alheeti

    2017-08-01

    Full Text Available Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoc networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System (IDS to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service (DoS and black hole attacks on vehicular ad hoc networks (VANETs. The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis (LDA and Quadratic Discriminant Analysis (QDA which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection.

  14. Fisher's Discriminant and Relevant Component Analysis for static facial expression classification

    OpenAIRE

    Sorci, Matteo; Antonini, Gianluca; Thiran, Jean-Philippe

    2007-01-01

    This paper addresses the issue of automatic classification of the six universal emotional categories (joy, surprise, fear, anger, disgust, sadness) in the case of static images. Appearance parameters are extracted by an active appearance model(AAM) representing the input for the classification step. We show how Relevant Component Analysis (RCA) in combination with Fisher's Linear Discriminant (FLD) provides a good "plug-\\&-play" classifier in the context of facial expression recognitio...

  15. Origin of uncontrolled water emissions in Alicante: use of discriminant analysis

    OpenAIRE

    Chinchón Payá, Servando; Blas Bravo, Isabel de; Nueda Roldán, María José; García Andreu, Francisco

    2010-01-01

    A model has been developed to predict the origin of uncontrolled water flows. For this purpose, we analysed a total of 52 elements using ICP techniques on 72 water samples in all: 40 of them from leaks in the drinking water supply network and the remaining 32 from groundwater outcrops. The study focused on the cases registered within the Alicante town limits (Spain). The use of multivariate statistical classification tools such as discriminant analysis made it possible not only to reduce the ...

  16. 2D Face Recognition System Based on Selected Gabor Filters and Linear Discriminant Analysis LDA

    OpenAIRE

    Hafez, Samir F.; Selim, Mazen M.; Hala H. Zayed

    2015-01-01

    We present a new approach for face recognition system. The method is based on 2D face image features using subset of non-correlated and Orthogonal Gabor Filters instead of using the whole Gabor Filter Bank, then compressing the output feature vector using Linear Discriminant Analysis (LDA). The face image has been enhanced using multi stage image processing technique to normalize it and compensate for illumination variation. Experimental results show that the proposed system is effective for ...

  17. Machinery fault diagnosis using joint global and local/nonlocal discriminant analysis with selective ensemble learning

    Science.gov (United States)

    Yu, Jianbo

    2016-11-01

    The vibration signals of faulty machine are generally non-stationary and nonlinear under those complicated working conditions. Thus, it is a big challenge to extract and select the effective features from vibration signals for machinery fault diagnosis. This paper proposes a new manifold learning algorithm, joint global and local/nonlocal discriminant analysis (GLNDA), which aims to extract effective intrinsic geometrical information from the given vibration data. Comparisons with other regular methods, principal component analysis (PCA), local preserving projection (LPP), linear discriminant analysis (LDA) and local LDA (LLDA), illustrate the superiority of GLNDA in machinery fault diagnosis. Based on the extracted information by GLNDA, a GLNDA-based Fisher discriminant rule (FDR) is put forward and applied to machinery fault diagnosis without additional recognizer construction procedure. By importing Bagging into GLNDA score-based feature selection and FDR, a novel manifold ensemble method (selective GLNDA ensemble, SE-GLNDA) is investigated for machinery fault diagnosis. The motivation for developing ensemble of manifold learning components is that it can achieve higher accuracy and applicability than single component in machinery fault diagnosis. The effectiveness of the SE-GLNDA-based fault diagnosis method has been verified by experimental results from bearing full life testers.

  18. Spike detection, characterization, and discrimination using feature analysis software written in LabVIEW.

    Science.gov (United States)

    Stewart, C M; Newlands, S D; Perachio, A A

    2004-12-01

    Rapid and accurate discrimination of single units from extracellular recordings is a fundamental process for the analysis and interpretation of electrophysiological recordings. We present an algorithm that performs detection, characterization, discrimination, and analysis of action potentials from extracellular recording sessions. The program was entirely written in LabVIEW (National Instruments), and requires no external hardware devices or a priori information about action potential shapes. Waveform events are detected by scanning the digital record for voltages that exceed a user-adjustable trigger. Detected events are characterized to determine nine different time and voltage levels for each event. Various algebraic combinations of these waveform features are used as axis choices for 2-D Cartesian plots of events. The user selects axis choices that generate distinct clusters. Multiple clusters may be defined as action potentials by manually generating boundaries of arbitrary shape. Events defined as action potentials are validated by visual inspection of overlain waveforms. Stimulus-response relationships may be identified by selecting any recorded channel for comparison to continuous and average cycle histograms of binned unit data. The algorithm includes novel aspects of feature analysis and acquisition, including higher acquisition rates for electrophysiological data compared to other channels. The program confirms that electrophysiological data may be discriminated with high-speed and efficiency using algebraic combinations of waveform features derived from high-speed digital records.

  19. Discrimination of wild Paris based on near infrared spectroscopy and high performance liquid chromatography combined with multivariate analysis.

    Directory of Open Access Journals (Sweden)

    Yanli Zhao

    Full Text Available Different geographical origins and species of Paris obtained from southwestern China were discriminated by near infrared (NIR spectroscopy and high performance liquid chromatography (HPLC combined with multivariate analysis. The NIR parameter settings were scanning (64 times, resolution (4 cm(-1, scanning range (10,000 cm(-1∼4000 cm(-1 and parallel collection (3 times. NIR spectrum was optimized by TQ 8.6 software, and the ranges 7455∼6852 cm(-1 and 5973∼4007 cm(-1 were selected according to the spectrum standard deviation. The contents of polyphyllin I, polyphyllin II, polyphyllin VI, and polyphyllin VII and total steroid saponins were detected by HPLC. The contents of chemical components data matrix and spectrum data matrix were integrated and analyzed by partial least squares discriminant analysis (PLS-DA. From the PLS-DA model of NIR spectrum, Paris samples were separated into three groups according to the different geographical origins. The R(2X and Q(2Y described accumulative contribution rates were 99.50% and 94.03% of the total variance, respectively. The PLS-DA model according to 12 species of Paris described 99.62% of the variation in X and predicted 95.23% in Y. The results of the contents of chemical components described differences among collections quantitatively. A multivariate statistical model of PLS-DA showed geographical origins of Paris had a much greater influence on Paris compared with species. NIR and HPLC combined with multivariate analysis could discriminate different geographical origins and different species. The quality of Paris showed regional dependence.

  20. Discrimination of Wild Paris Based on Near Infrared Spectroscopy and High Performance Liquid Chromatography Combined with Multivariate Analysis

    Science.gov (United States)

    Zhao, Yanli; Zhang, Ji; Yuan, Tianjun; Shen, Tao; Li, Wei; Yang, Shihua; Hou, Ying; Wang, Yuanzhong; Jin, Hang

    2014-01-01

    Different geographical origins and species of Paris obtained from southwestern China were discriminated by near infrared (NIR) spectroscopy and high performance liquid chromatography (HPLC) combined with multivariate analysis. The NIR parameter settings were scanning (64 times), resolution (4 cm−1), scanning range (10000 cm−1∼4000 cm−1) and parallel collection (3 times). NIR spectrum was optimized by TQ 8.6 software, and the ranges 7455∼6852 cm−1 and 5973∼4007 cm−1 were selected according to the spectrum standard deviation. The contents of polyphyllin I, polyphyllin II, polyphyllin VI, and polyphyllin VII and total steroid saponins were detected by HPLC. The contents of chemical components data matrix and spectrum data matrix were integrated and analyzed by partial least squares discriminant analysis (PLS-DA). From the PLS-DA model of NIR spectrum, Paris samples were separated into three groups according to the different geographical origins. The R2X and Q2Y described accumulative contribution rates were 99.50% and 94.03% of the total variance, respectively. The PLS-DA model according to 12 species of Paris described 99.62% of the variation in X and predicted 95.23% in Y. The results of the contents of chemical components described differences among collections quantitatively. A multivariate statistical model of PLS-DA showed geographical origins of Paris had a much greater influence on Paris compared with species. NIR and HPLC combined with multivariate analysis could discriminate different geographical origins and different species. The quality of Paris showed regional dependence. PMID:24558477

  1. Discrimination of wild Paris based on near infrared spectroscopy and high performance liquid chromatography combined with multivariate analysis.

    Science.gov (United States)

    Zhao, Yanli; Zhang, Ji; Yuan, Tianjun; Shen, Tao; Li, Wei; Yang, Shihua; Hou, Ying; Wang, Yuanzhong; Jin, Hang

    2014-01-01

    Different geographical origins and species of Paris obtained from southwestern China were discriminated by near infrared (NIR) spectroscopy and high performance liquid chromatography (HPLC) combined with multivariate analysis. The NIR parameter settings were scanning (64 times), resolution (4 cm(-1)), scanning range (10,000 cm(-1)∼4000 cm(-1)) and parallel collection (3 times). NIR spectrum was optimized by TQ 8.6 software, and the ranges 7455∼6852 cm(-1) and 5973∼4007 cm(-1) were selected according to the spectrum standard deviation. The contents of polyphyllin I, polyphyllin II, polyphyllin VI, and polyphyllin VII and total steroid saponins were detected by HPLC. The contents of chemical components data matrix and spectrum data matrix were integrated and analyzed by partial least squares discriminant analysis (PLS-DA). From the PLS-DA model of NIR spectrum, Paris samples were separated into three groups according to the different geographical origins. The R(2)X and Q(2)Y described accumulative contribution rates were 99.50% and 94.03% of the total variance, respectively. The PLS-DA model according to 12 species of Paris described 99.62% of the variation in X and predicted 95.23% in Y. The results of the contents of chemical components described differences among collections quantitatively. A multivariate statistical model of PLS-DA showed geographical origins of Paris had a much greater influence on Paris compared with species. NIR and HPLC combined with multivariate analysis could discriminate different geographical origins and different species. The quality of Paris showed regional dependence.

  2. Discrimination and characterization of breath from smokers and non-smokers via electronic nose and GC/MS analysis.

    Science.gov (United States)

    Witt, Katharina; Reulecke, Sina; Voss, Andreas

    2011-01-01

    The objective of this study was to prove the general applicability of an electronic nose for analyzing exhaled breath considering the dependency on smoking. At first, odor compounds from spices (n=6) were detected via the electronic nose and further characterized and classified with gas chromatography/ mass spectrometry to demonstrate the principle ability of the electronic nose. Then, the exhaled breath from smokers and non-smokers were analyzed to prove the influence of smoking on breath analyses with the electronic nose. The exhaled breath was sampled from 11 smokers and 11 non-smokers in a special sampling bag with the mounted sensor chip of the electronic nose. Additionally, solid phase micro-extraction (SPME) technique was established for detection of the specific chemical compounds with gas chromatography and mass spectrometry (GC/MS). For analyses of the sensor signals the principle component analysis (PCA) was applied and the groups were differentiated by linear discriminant function analysis. In accordance to the discrimination between the different spices and between smokers and non-smokers the PCA analysis leads to an optimum accuracy of 100%. The results of this study show that an electronic nose has the ability to detect different changes of odor components and provides separation of smoking side effects in smelling different diseases.

  3. The Effects of a Discriminative Stimulus, Paired with Individual and Group Reward Contingencies, on the Decibel Levels in an Elementary School Lunch Room.

    Science.gov (United States)

    Davey, Bryan; Alexander, Melina; Edmonson, Claudia; Stenhoff, Donald; West, Richard P.

    A study examined the effects of using a musical clocklight as discriminative stimulus, paired with individual and group contingency rewards, on the decibel level in an elementary school lunchroom. Subjects were 256 students aged 5-12, who ate lunch in two sessions for younger and older students. The musical clocklight (MCL) apparatus consisted of…

  4. Underreporting Discrimination among Arab American and Muslim American Community College Students: Using Focus Groups to Unravel the Ambiguities within the Survey Data

    Science.gov (United States)

    Shammas, Diane

    2017-01-01

    Using a mixed methods approach, the researcher gathered a set of narrative responses from focus groups that supported the claim of underreporting campus discrimination on a survey. Multiple studies have shown that underrepresented minority students are likely to bond with same-ethnic peers in a racially tense campus climate. This mixed method is a…

  5. Underreporting Discrimination among Arab American and Muslim American Community College Students: Using Focus Groups to Unravel the Ambiguities within the Survey Data

    Science.gov (United States)

    Shammas, Diane

    2017-01-01

    Using a mixed methods approach, the researcher gathered a set of narrative responses from focus groups that supported the claim of underreporting campus discrimination on a survey. Multiple studies have shown that underrepresented minority students are likely to bond with same-ethnic peers in a racially tense campus climate. This mixed method is a…

  6. Estimation of Optimal Measurement Position of Human Forearm EMG Signal by Discriminant Analysis Based on Wilks' lambda

    Science.gov (United States)

    Kiso, Atsushi; Taniguchi, Yu; Seki, Hirokazu

    This paper describes the estimation of the optimal measurement position by discriminant analysis based on Wilks' lambda for myoelectric hand control. In previous studies, for motion discrimination, the myoelectric signals were measured at the same positions. However, the optimal measurement positions of the myoelectric signals for motion discrimination differ depending on the remaining muscles of amputees. Therefore, the purpose of this study is to estimate the optimal and fewer measurement positions for precise motion discrimination of a human forearm. This study proposes a method for estimating the optimal measurement positions by discriminant analysis based on Wilks' lambda, using the myoelectric signals measured at multiple positions. The results of some experiments on the myoelectric hand simulator show the effectiveness of the proposed optimal measurement position estimation method.

  7. Scalable group level probabilistic sparse factor analysis

    DEFF Research Database (Denmark)

    Hinrich, Jesper Løve; Nielsen, Søren Føns Vind; Riis, Nicolai Andre Brogaard

    2017-01-01

    Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component...... pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise modeling. For task-based and resting state fMRI, we show that the sparsity constraint gives rise to components similar to those obtained by group independent component analysis. The noise modeling...... shows that noise is reduced in areas typically associated with activation by the experimental design. The psFA model identifies sparse components and the probabilistic setting provides a natural way to handle parameter uncertainties. The variational Bayesian framework easily extends to more complex...

  8. Principal component analysis for neural electron/jet discrimination in highly segmented calorimeters

    CERN Document Server

    Vassali, M R

    2001-01-01

    A neural electron/jet discriminator based on calorimetry is developed for the second-level trigger system of the ATLAS detector. As preprocessing of the calorimeter information, a principal component analysis is performed on each segment of the two sections (electromagnetic and hadronic) of the calorimeter system, in order to reduce significantly the dimension of the input data space and fully explore the detailed energy deposition profile, which is provided by the highly-segmented calorimeter system. It is shown that projecting calorimeter data onto 33 segmented principal components, the discrimination efficiency of the neural classifier reaches 98.9% for electrons (with only 1% of false alarm probability). Furthermore, restricting data projection onto only 9 components, an electron efficiency of 99.1% is achieved (with 3% of false alarm), which confirms that a fast triggering system may be designed using few components. (6 refs).

  9. UV-vis absorption spectroscopy and multivariate analysis as a method to discriminate tequila

    Science.gov (United States)

    Barbosa-García, O.; Ramos-Ortíz, G.; Maldonado, J. L.; Pichardo-Molina, J. L.; Meneses-Nava, M. A.; Landgrave, J. E. A.; Cervantes-Martínez, J.

    2007-01-01

    Based on the UV-vis absorption spectra of commercially bottled tequilas, and with the aid of multivariate analysis, it is proved that different brands of white tequila can be identified from such spectra, and that 100% agave and mixed tequilas can be discriminated as well. Our study was done with 60 tequilas, 58 of them purchased at liquor stores in various Mexican cities, and two directly acquired from a distillery. All the tequilas were of the "white" type, that is, no aged spirits were considered. For the purposes of discrimination and quality control of tequilas, the spectroscopic method that we present here offers an attractive alternative to the traditional methods, like gas chromatography, which is expensive and time-consuming.

  10. Discriminant analysis in career studying "decision/indecision": the Career Factors Inventory (CFI) as a diagnostic measure.

    Science.gov (United States)

    Ferreira, Ana Sousa; Lima, Rosário

    2010-11-01

    Literature has shown that, nowadays, a multidimensional approach to decision-making has become prioritized. The Careers Factor Inventory (CFI) is, in fact, a multidimensional measurement instrument for evaluating career indecision, which may be useful in the diagnosis of adaptation behaviors in terms of career decision versus indecision. This study emerges as a follow-up to a previous study which used the CFI on a sample of university students in which this measurement instrument was found to be capable of distinguishing Low decided vs. Highly decided groups and to evaluate the discriminatory capacity of the CFI scales. It is the aim, here, to further analyse the results obtained in such study with a view to grounding the importance of the use of this Inventory as an instrument for distinguishing people who present different decision levels in relation to their careers. In this study, 494 university students from a number of higher education establishments and courses are part of the afore mentioned Low decided and Highly decided groups. The collected data were analysed by means of Discrete Discriminant Analysis models and corroborate the discriminant power of the Inventory and its use as a diagnostic instrument in the psychological intervention of career counseling and development.

  11. Theoretical analysis and experimental research on port/starboard discrimination in towed line array

    Institute of Scientific and Technical Information of China (English)

    DU Xuanmin; ZHU Daizhu; ZHAO Rongrong; YAO Lan

    2001-01-01

    The theoretical analysis and experimental research on Port/Starboard (P/S) discrimination in towed line array are proposed. Two methods resolving the P/S ambiguity with hydrophone triplets are introduced. By processing experimental data, the theoretical analysis is verified. The processing algorithm is extended to broadband signal. The research results show that the method based on optimum beamforming with triplets can be used to remove the port/starboard ambiguity. Also because of the simplicity of the method, it is expected to be implemented in practical towed line array sonar.

  12. Independent component feature-based human activity recognition via Linear Discriminant Analysis and Hidden Markov Model.

    Science.gov (United States)

    Uddin, Md; Lee, J J; Kim, T S

    2008-01-01

    In proactive computing, human activity recognition from image sequences is an active research area. This paper presents a novel approach of human activity recognition based on Linear Discriminant Analysis (LDA) of Independent Component (IC) features from shape information. With extracted features, Hidden Markov Model (HMM) is applied for training and recognition. The recognition performance using LDA of IC features has been compared to other approaches including Principle Component Analysis (PCA), LDA of PC, and ICA. The preliminary results show much improved performance in the recognition rate with our proposed method.

  13. Discriminating between cultivars and treatments of broccoli using mass spectral fingerprinting and analysis of variance-principal component analysis.

    Science.gov (United States)

    Luthria, Devanand L; Lin, Long-Ze; Robbins, Rebecca J; Finley, John W; Banuelos, Gary S; Harnly, James M

    2008-11-12

    Metabolite fingerprints, obtained with direct injection mass spectrometry (MS) with both positive and negative ionization, were used with analysis of variance-principal components analysis (ANOVA-PCA) to discriminate between cultivars and growing treatments of broccoli. The sample set consisted of two cultivars of broccoli, Majestic and Legacy, the first grown with four different levels of Se and the second grown organically and conventionally with two rates of irrigation. Chemical composition differences in the two cultivars and seven treatments produced patterns that were visually and statistically distinguishable using ANOVA-PCA. PCA loadings allowed identification of the molecular and fragment ions that provided the most significant chemical differences. A standardized profiling method for phenolic compounds showed that important discriminating ions were not phenolic compounds. The elution times of the discriminating ions and previous results suggest that they were common sugars and organic acids. ANOVA calculations of the positive and negative ionization MS fingerprints showed that 33% of the variance came from the cultivar, 59% from the growing treatment, and 8% from analytical uncertainty. Although the positive and negative ionization fingerprints differed significantly, there was no difference in the distribution of variance. High variance of individual masses with cultivars or growing treatment was correlated with high PCA loadings. The ANOVA data suggest that only variables with high variance for analytical uncertainty should be deleted. All other variables represent discriminating masses that allow separation of the samples with respect to cultivar and treatment.

  14. Characteristic fingerprint based on gingerol derivative analysis for discrimination of ginger (Zingiber officinale) according to geographical origin using HPLC-DAD combined with chemometrics.

    Science.gov (United States)

    Yudthavorasit, Soparat; Wongravee, Kanet; Leepipatpiboon, Natchanun

    2014-09-01

    Chromatographic fingerprints of gingers from five different ginger-producing countries (China, India, Malaysia, Thailand and Vietnam) were newly established to discriminate the origin of ginger. The pungent bioactive principles of ginger, gingerols and six other gingerol-related compounds were determined and identified. Their variations in HPLC profiles create the characteristic pattern of each origin by employing similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) and linear discriminant analysis (LDA). As results, the ginger profiles tended to be grouped and separated on the basis of the geographical closeness of the countries of origin. An effective mathematical model with high predictive ability was obtained and chemical markers for each origin were also identified as the characteristic active compounds to differentiate the ginger origin. The proposed method is useful for quality control of ginger in case of origin labelling and to assess food authenticity issues.

  15. Gender identification of Caspian Terns using external morphology and discriminant function analysis

    Science.gov (United States)

    Ackerman, J.T.; Takekawa, J.Y.; Bluso, J.D.; Yee, J.L.; Eagles-Smith, C. A.

    2008-01-01

    Caspian Tern (Sterna caspia) plumage characteristics are sexually monochromatic and gender cannot easily be distinguished in the field without extensive behavioral observations. We assessed sexual size dimorphism and developed a discriminant function to assign gender in Caspian Terns based on external morphology. We collected and measured Caspian Terns in San Francisco Bay, California, and confirmed their gender based on necropsy and genetic analysis. Of the eight morphological measurements we examined, only bill depth at the gonys and head plus bill length differed between males and females with males being larger than females. A discriminant function using both bill depth at the gonys and head plus bill length accurately assigned gender of 83% of terns for which gender was known. We improved the accuracy of our discriminant function to 90% by excluding individuals that had less than a 75% posterior probability of correctly being assigned to gender. Caspian Terns showed little sexual size dimorphism in many morphometries, but our results indicate they can be reliably assigned to gender in the field using two morphological measurements.

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

    Science.gov (United States)

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

    2010-05-01

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

  17. Using confirmatory factor analysis to manage discriminant validity issues in social pharmacy research.

    Science.gov (United States)

    Carter, Stephen R

    2016-06-01

    Background Confirmatory factory analysis (CFA) and structural equation modelling (SEM) are increasingly used in social pharmacy research. One of the key benefits of CFA is that it allows researchers to provide evidence for the validity of internal factor structure of measurement scales. In particular, CFA can be used to provide evidence for the validity of the assertion that a hypothesized multi-dimensional scale discriminates between sub-scales. Aim This manuscript aims to provide guidance for researchers who wish to use CFA to provide evidence for the internal factor structure of measurement scales. Methods The manuscript places discriminant validity in the context of providing overall validity evidence for measurement scales. Four examples from the recent social pharmacy literature are used to critically examine the various methods which are used to establish discriminant validity. Using a hypothetical scenario, the manuscript demonstrates how commonly used output from CFA computer programs can be used to provide evidence for separateness of sub-scales within a multi-dimensional scale. Conclusion The manuscript concludes with recommendations for the conduct and reporting of studies which use CFA to provide evidence of internal factor structure of measurement scales.

  18. Internet cancer support groups: a feminist analysis.

    Science.gov (United States)

    Im, Eun-Ok; Chee, Wonshik; Tsai, Hsiu-Min; Lin, Li-Chen; Cheng, Ching-Yu

    2005-01-01

    Internet Cancer Support Groups (ICSGs) are an emerging form of support group on Internet specifically for cancer patients. Previous studies have indicated the effectiveness of ICSGs as a research setting or a data-collection method. Yet recent studies have also indicated that ICSGs tend to serve highly educated, high-income White males who tend to be at an early stage of cancer. In this article, a total of 317 general ICSGs and 229 ethnic-specific ICSGs searched through Google.com, Yahoo.com, Msn.com, AOL.com, and ACOR.org are analyzed from a feminist perspective. The written records of group discussions and written memos by the research staff members were also analyzed using content analysis. The idea categories that emerged about these groups include (a) authenticity issues; (b) ethnicity and gender issues; (c) intersubjectivity issues; and (d) potential ethical issues. The findings suggest that (a) researchers adopt multiple recruitment strategies through various Internet sites and/or real settings; (b) researchers raise their own awareness of the potential influences of the health-related resources provided by ICSGs and regularly update their knowledge related to the federal and state standards and/or policies related to ICSGs; and (c) researchers consider adopting a quota-sampling method.

  19. Morphological Discrimination of Greek Honey Bee Populations Based on Geometric Morphometrics Analysis of Wing Shape

    Directory of Open Access Journals (Sweden)

    Charistos Leonidas

    2014-06-01

    Full Text Available Honey bees collected from 32 different localities in Greece were studied based on the geometric morphometrics approach using the coordinates of 19 landmarks located at wing vein intersections. Procrustes analysis, principal component analysis, and Canonical variate analysis (CVA detected population variability among the studied samples. According to the Principal component analysis (PCA of pooled data from each locality, the most differentiated populations were the populations from the Aegean island localities Astypalaia, Chios, and Kythira. However, the populations with the most distant according to the canonical variate analysis performed on all measurements were the populations from Heraklion and Chania (both from Crete island. These results can be used as a starting point for the use of geometric morphometrics in the discrimination of honey bee populations in Greece and the establishment of conservation areas for local honey bee populations.

  20. Rapid discrimination of plastic packaging materials using MIR spectroscopy coupled with independent components analysis (ICA).

    Science.gov (United States)

    Kassouf, Amine; Maalouly, Jacqueline; Rutledge, Douglas N; Chebib, Hanna; Ducruet, Violette

    2014-11-01

    Plastic packaging wastes increased considerably in recent decades, raising a major and serious public concern on political, economical and environmental levels. Dealing with this kind of problems is generally done by landfilling and energy recovery. However, these two methods are becoming more and more expensive, hazardous to the public health and the environment. Therefore, recycling is gaining worldwide consideration as a solution to decrease the growing volume of plastic packaging wastes and simultaneously reduce the consumption of oil required to produce virgin resin. Nevertheless, a major shortage is encountered in recycling which is related to the sorting of plastic wastes. In this paper, a feasibility study was performed in order to test the potential of an innovative approach combining mid infrared (MIR) spectroscopy with independent components analysis (ICA), as a simple and fast approach which could achieve high separation rates. This approach (MIR-ICA) gave 100% discrimination rates in the separation of all studied plastics: polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), polystyrene (PS) and polylactide (PLA). In addition, some more specific discriminations were obtained separating plastic materials belonging to the same polymer family e.g. high density polyethylene (HDPE) from low density polyethylene (LDPE). High discrimination rates were obtained despite the heterogeneity among samples especially differences in colors, thicknesses and surface textures. The reproducibility of the proposed approach was also tested using two spectrometers with considerable differences in their sensitivities. Discrimination rates were not affected proving that the developed approach could be extrapolated to different spectrometers. MIR combined with ICA is a promising tool for plastic waste separation that can help improve performance in this field; however further technological improvements and developments are required before it can be applied

  1. An intercomparison of different topography effects on discrimination performance of fuzzy change vector analysis algorithm

    Science.gov (United States)

    Singh, Sartajvir; Talwar, Rajneesh

    2016-12-01

    Detection of snow cover changes is vital for avalanche hazard analysis and flood flashes that arise due to variation in temperature. Hence, multitemporal change detection is one of the practical mean to estimate the snow cover changes over larger area using remotely sensed data. There have been some previous studies that examined how accuracy of change detection analysis is affected by different topography effects over Northwestern Indian Himalayas. The present work emphases on the intercomparison of different topography effects on discrimination performance of fuzzy based change vector analysis (FCVA) as change detection algorithm that includes extraction of change-magnitude and change-direction from a specific pixel belongs multiple or partial membership. The qualitative and quantitative analysis of the proposed FCVA algorithm is performed under topographic conditions and topographic correction conditions. The experimental outcomes confirmed that in change category discrimination procedure, FCVA with topographic correction achieved 86.8% overall accuracy and 4.8% decay (82% of overall accuracy) is found in FCVA without topographic correction. This study suggests that by incorporating the topographic correction model over mountainous region satellite imagery, performance of FCVA algorithm can be significantly improved up to great extent in terms of determining actual change categories.

  2. Intergroup and Within-Group Perceived Discrimination among U.S.-Born and Foreign-Born Latino Youth

    Science.gov (United States)

    Cordova, David, Jr.; Cervantes, Richard C.

    2010-01-01

    Despite the deleterious mental health and health consequences experiences of perceived discrimination can have on ethnic and racial minorities in the United States, a dearth of qualitative studies exist to develop a better understanding of such experiences. As part of a larger study examining psychosocial stress events, and in an effort to fill…

  3. Perceived Ethnic Discrimination and the Metabolic Syndrome in Ethnic Minority Groups: The Healthy Life in an Urban Setting Study

    NARCIS (Netherlands)

    Ikram, U.Z.; Snijder, M.B.; Agyemang, C.; Schene, A.H.; Peters, R.J.; Stronks, K.; Kunst, A.E.

    2017-01-01

    OBJECTIVE: Ethnic differences in the metabolic syndrome could be explained by perceived ethnic discrimination (PED). It is unclear whether PED is associated with the metabolic syndrome. We assessed this association and quantified the contribution of PED to the metabolic syndrome. METHODS: Baseline d

  4. Discriminated Dimensional Analysis Versus Classical Dimensional Analysis and Applications to Heat Transfer and Fluid Dynamics%区别对待的因次分析方法与传统因次分析方法的比较及其在传热和流体力学中的应用

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In contrast to classical dimensional analysis, discriminated dimensional analysis assumes that spatial coordinates are dimensionally independent of each other and allows other types of geometrical quantity to be used in the dimensional basis, such as surfaces and angles. As a consequence, discriminated dimensional analysis leads to a lower number of dimensional groups, which makes the solution more precise. Besides, these discriminated groups have a clear physical meaning in terms of force and energy balances. The paper introduces this technique and provides dimensional equations for the main quantities and physical parameters of the heat transfer and fluid flow fields. Two applications are presented to demonstrate the efficiency of this method.

  5. Rapid discrimination of bergamot essential oil by paper spray mass spectrometry and chemometric analysis.

    Science.gov (United States)

    Taverna, Domenico; Di Donna, Leonardo; Mazzotti, Fabio; Tagarelli, Antonio; Napoli, Anna; Furia, Emilia; Sindona, Giovanni

    2016-09-01

    A novel approach for the rapid discrimination of bergamot essential oil from other citrus fruits oils is presented. The method was developed using paper spray mass spectrometry (PS-MS) allowing for a rapid molecular profiling coupled with a statistic tool for a precise and reliable discrimination between the bergamot complex matrix and other similar matrices, commonly used for its reconstitution. Ambient mass spectrometry possesses the ability to record mass spectra of ordinary samples, in their native environment, without sample preparation or pre-separation by creating ions outside the instrument. The present study reports a PS-MS method for the determination of oxygen heterocyclic compounds such as furocoumarins, psoralens and flavonoids present in the non-volatile fraction of citrus fruits essential oils followed by chemometric analysis. The volatile fraction of Bergamot is one of the most known and fashionable natural products, which found applications in flavoring industry as ingredient in beverages and flavored foodstuff. The development of the presented method employed bergamot, sweet orange, orange, cedar, grapefruit and mandarin essential oils. PS-MS measurements were carried out in full scan mode for a total run time of 2 min. The capability of PS-MS profiling to act as marker for the classification of bergamot essential oils was evaluated by using multivariate statistical analysis. Two pattern recognition techniques, linear discriminant analysis and soft independent modeling of class analogy, were applied to MS data. The cross-validation procedure has shown excellent results in terms of the prediction ability because both models have correctly classified all samples for each category. Copyright © 2016 John Wiley & Sons, Ltd.

  6. Compact Groups analysis using weak gravitational lensing

    Science.gov (United States)

    Chalela, Martín; Johana Gonzalez, Elizabeth; Garcia Lambas, Diego; Foëx, Gael

    2017-01-01

    We present a weak lensing analysis of a sample of SDSS Compact Groups (CGs). Using the measured radial density contrast profile, we derive the average masses under the assumption of spherical symmetry, obtaining a velocity dispersion for the Singular Isothermal Spherical model, σV = 270 ± 40 km s-1, and for the NFW model, R_{200}=0.53± 0.10 h_{70}^{-1}Mpc. We test three different definitions of CGs centres to identify which best traces the true dark matter halo centre, concluding that a luminosity weighted centre is the most suitable choice. We also study the lensing signal dependence on CGs physical radius, group surface brightness, and morphological mixing. We find that groups with more concentrated galaxy members show steeper mass profiles and larger velocity dispersions. We argue that both, a possible lower fraction of interloper and a true steeper profile, could be playing a role in this effect. Straightforward velocity dispersion estimates from member spectroscopy yields σV ≈ 230 km s-1 in agreement with our lensing results.

  7. Classification of Error-Diffused Halftone Images Based on Spectral Regression Kernel Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Zhigao Zeng

    2016-01-01

    Full Text Available This paper proposes a novel algorithm to solve the challenging problem of classifying error-diffused halftone images. We firstly design the class feature matrices, after extracting the image patches according to their statistics characteristics, to classify the error-diffused halftone images. Then, the spectral regression kernel discriminant analysis is used for feature dimension reduction. The error-diffused halftone images are finally classified using an idea similar to the nearest centroids classifier. As demonstrated by the experimental results, our method is fast and can achieve a high classification accuracy rate with an added benefit of robustness in tackling noise.

  8. Discourse analysis of gender equality and non-discrimination laws and strategies

    Directory of Open Access Journals (Sweden)

    Antonijević Zorana

    2017-01-01

    Full Text Available Based on the contemporary research on gender and language, using the method of discourse analysis applied to the laws and policies, this article explains how certain linguistic practice, in the context of the administrative discourse, produces meaning that may or may not contribute to its better understanding and more efficient implementation. Through discourse analysis of gender equality and non-discrimination laws and strategies in Serbia, it has been shown how and with what consequences the socio-political and academic elites affect defining and promoting certain concepts (gender, sex, gender equality, discrimination in one social and historical moment. The paper is placed in the theoretical framework of three visions of gender equality: perspective of equal treatment, women‘s perspectives and gender perspective (Booth, Bennett 2002, that are corresponding to the three strategies for achieving gender equality: equal treatment, specific policy of gender equality and gender mainstreaming (Verloo, 2001. The discourse analysis of the Law on Gender Equality (2009, the National Strategy for the Improvement of the Position of Women and Advancement of Gender Equality (2009, the Law on Prohibition of Discrimination (2009 and the Strategy for Prevention and Protection against Discrimination (2013, has shown the context of use and meaning of terms gender and sex, as well as implications it has on their potential to change the existing paradigms and understanding of gender equality, and the implementation of policies in Serbia. Analysis of the use of terms sex and gender in the most important legal and strategic documents for achieving gender equality, showed that the choice of certain categories and terms is always a political choice. The authors show how these documents are written in the key of two gender perspectives and strategies: equal treatment and the specific policy of gender equality, while the third - introduction of a gender perspective

  9. Estimating the causes of traffic accidents using logistic regression and discriminant analysis.

    Science.gov (United States)

    Karacasu, Murat; Ergül, Barış; Altin Yavuz, Arzu

    2014-01-01

    Factors that affect traffic accidents have been analysed in various ways. In this study, we use the methods of logistic regression and discriminant analysis to determine the damages due to injury and non-injury accidents in the Eskisehir Province. Data were obtained from the accident reports of the General Directorate of Security in Eskisehir; 2552 traffic accidents between January and December 2009 were investigated regarding whether they resulted in injury. According to the results, the effects of traffic accidents were reflected in the variables. These results provide a wealth of information that may aid future measures toward the prevention of undesired results.

  10. The specter of discrimination: Fear of interpersonal racial discrimination among adolescents in Chicago.

    Science.gov (United States)

    Herda, Daniel

    2016-01-01

    This analysis examines fear of interpersonal racial discrimination among Black, Hispanic, and White adolescents. The extent and correlates of these concerns are examined using survey data from the Project for Human Development in Chicago Neighborhoods. Borrowing from the fear-of-crime literature, the contact hypothesis, and group threat theory, several hypotheses are developed linking discrimination fear to direct personal experience with discrimination, indirect or vicarious experience, and environmental signals of discrimination. Results show that about half of Blacks and Hispanics have feared discrimination in the past year. Multivariate results indicate that fear is most likely if one has experienced victimization first-hand and when one's parent is affected by discrimination. Further, a larger presence neighborhood outgroups produces greater fear. Overall, discrimination fear constitutes an additional obstacle for minority adolescents as they transition to adulthood. The phenomenon warrants increased scholarly attention and represents a fruitful avenue for future research.

  11. Rapid discrimination of plastic packaging materials using MIR spectroscopy coupled with independent components analysis (ICA)

    Energy Technology Data Exchange (ETDEWEB)

    Kassouf, Amine, E-mail: amine.kassouf@agroparistech.fr [ER004 “Lebanese Food Packaging”, Faculty of Sciences II, Lebanese University, 90656 Jdeideth El Matn, Fanar (Lebanon); INRA, UMR1145 Ingénierie Procédés Aliments, 1 Avenue des Olympiades, 91300 Massy (France); AgroParisTech, UMR1145 Ingénierie Procédés Aliments, 16 rue Claude Bernard, 75005 Paris (France); Maalouly, Jacqueline, E-mail: j_maalouly@hotmail.com [ER004 “Lebanese Food Packaging”, Faculty of Sciences II, Lebanese University, 90656 Jdeideth El Matn, Fanar (Lebanon); Rutledge, Douglas N., E-mail: douglas.rutledge@agroparistech.fr [INRA, UMR1145 Ingénierie Procédés Aliments, 1 Avenue des Olympiades, 91300 Massy (France); AgroParisTech, UMR1145 Ingénierie Procédés Aliments, 16 rue Claude Bernard, 75005 Paris (France); Chebib, Hanna, E-mail: hchebib@hotmail.com [ER004 “Lebanese Food Packaging”, Faculty of Sciences II, Lebanese University, 90656 Jdeideth El Matn, Fanar (Lebanon); Ducruet, Violette, E-mail: violette.ducruet@agroparistech.fr [INRA, UMR1145 Ingénierie Procédés Aliments, 1 Avenue des Olympiades, 91300 Massy (France); AgroParisTech, UMR1145 Ingénierie Procédés Aliments, 16 rue Claude Bernard, 75005 Paris (France)

    2014-11-15

    Highlights: • An innovative technique, MIR-ICA, was applied to plastic packaging separation. • This study was carried out on PE, PP, PS, PET and PLA plastic packaging materials. • ICA was applied to discriminate plastics and 100% separation rates were obtained. • Analyses performed on two spectrometers proved the reproducibility of the method. • MIR-ICA is a simple and fast technique allowing plastic identification/classification. - Abstract: Plastic packaging wastes increased considerably in recent decades, raising a major and serious public concern on political, economical and environmental levels. Dealing with this kind of problems is generally done by landfilling and energy recovery. However, these two methods are becoming more and more expensive, hazardous to the public health and the environment. Therefore, recycling is gaining worldwide consideration as a solution to decrease the growing volume of plastic packaging wastes and simultaneously reduce the consumption of oil required to produce virgin resin. Nevertheless, a major shortage is encountered in recycling which is related to the sorting of plastic wastes. In this paper, a feasibility study was performed in order to test the potential of an innovative approach combining mid infrared (MIR) spectroscopy with independent components analysis (ICA), as a simple and fast approach which could achieve high separation rates. This approach (MIR-ICA) gave 100% discrimination rates in the separation of all studied plastics: polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), polystyrene (PS) and polylactide (PLA). In addition, some more specific discriminations were obtained separating plastic materials belonging to the same polymer family e.g. high density polyethylene (HDPE) from low density polyethylene (LDPE). High discrimination rates were obtained despite the heterogeneity among samples especially differences in colors, thicknesses and surface textures. The reproducibility of

  12. Discriminant analysis of pulmonary function parameters. Healthy adults versus mild asthmatics and moderate asthmatics.

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    Meguro,Tadamichi

    1982-08-01

    Full Text Available Volume-time (V-T and flow-volume (F-V curves were measured in all the subjects of nonsmoking young males (mean value 26.3 yrs. of age, healthy and asthmatics. Eleven parameters of pulmonary function tests composed of two V-T, six F-V, and three mean time constant (MTC parameters, were calculated from the curves. These parameters were used in the two analyses through the all possible selection procedure (APSP discriminating between healthy adults and mild asthmatics and also between healthy and moderate. Flow rate at 75% of FVC (V75 proved to be the most useful parameter and V50 the next best in both analyses. The probability of misclassification using all eleven parameters was 19.64% in the analysis of healthy adults and mild asthmatics, and 4.29% in the analysis of healthy adults and moderate asthmatics. There was a little difference in the parameters selected at every step. The discriminant analysis proved that the flow-volume patterns were different according to the severity of bronchial asthma. Thus flow-volume recognition was considered to be important in analyzing the severity of bronchial asthma.

  13. Predicting groundwater redox status on a regional scale using linear discriminant analysis

    Science.gov (United States)

    Close, M. E.; Abraham, P.; Humphries, B.; Lilburne, L.; Cuthill, T.; Wilson, S.

    2016-08-01

    Reducing conditions are necessary for denitrification, thus the groundwater redox status can be used to identify subsurface zones where potentially significant nitrate reduction can occur. Groundwater chemistry in two contrasting regions of New Zealand was classified with respect to redox status and related to mappable factors, such as geology, topography and soil characteristics using discriminant analysis. Redox assignment was carried out for water sampled from 568 and 2223 wells in the Waikato and Canterbury regions, respectively. For the Waikato region 64% of wells sampled indicated oxic conditions in the water; 18% indicated reduced conditions and 18% had attributes indicating both reducing and oxic conditions termed "mixed". In Canterbury 84% of wells indicated oxic conditions; 10% were mixed; and only 5% indicated reduced conditions. The analysis was performed over three different well depths, 100 m. For both regions, the percentage of oxidised groundwater decreased with increasing well depth. Linear discriminant analysis was used to develop models to differentiate between the three redox states. Models were derived for each depth and region using 67% of the data, and then subsequently validated on the remaining 33%. The average agreement between predicted and measured redox status was 63% and 70% for the Waikato and Canterbury regions, respectively. The models were incorporated into GIS and the prediction of redox status was extended over the whole region, excluding mountainous land. This knowledge improves spatial prediction of reduced groundwater zones, and therefore, when combined with groundwater flow paths, improves estimates of denitrification.

  14. Classification tree analysis for the discrimination of pleural exudates and transudates.

    Science.gov (United States)

    Esquerda, Aureli; Trujillano, Javier; López de Ullibarri, Ignacio; Bielsa, Silvia; Madroñero, Ana B; Porcel, José M

    2007-01-01

    Classification and regression tree (CART) analysis is a non-parametric technique suitable for the generation of clinical decision rules. We have studied the performance of CART analysis in the separation of pleural exudates and transudates. Basic demographic, radiologic and laboratory data were retrospectively evaluated in 1257 pleural effusions (204 transudates and 1053 exudates, according to standard clinical criteria) and submitted for CART analysis. The model's discriminative ability was compared with that of Light's criteria, in both the original formulation and an abbreviated version, i.e., deleting the pleural fluid (PF)/serum lactate dehydrogenase (LDH) ratio from the triad. A first CART model built starting from all available data identified PF/serum protein ratio and PF LDH ratios as the two best discriminatory parameters. This algorithm achieved a sensitivity of 96.8%, slightly lower than that of classical Light's criteria (98.5%) and comparable to that of the abbreviated Light's criteria (97.0%), and significantly better specificity (85.3%) compared to both classical (74.0%) and abbreviated (79.4%) Light's criteria. A second CART model developed after excluding serum measurements selected PF protein and PF LDH as the most discriminatory variables, and correctly classified 97.2% of exudates and 77.0% of transudates. CART-based algorithms can efficiently discriminate between pleural exudates and transudates.

  15. Rapid discrimination of the geographical origins of an oolong tea (anxi-tieguanyin) by near-infrared spectroscopy and partial least squares discriminant analysis.

    Science.gov (United States)

    Yan, Si-Min; Liu, Jun-Ping; Xu, Lu; Fu, Xian-Shu; Cui, Hai-Feng; Yun, Zhen-Yu; Yu, Xiao-Ping; Ye, Zi-Hong

    2014-01-01

    This paper focuses on a rapid and nondestructive way to discriminate the geographical origin of Anxi-Tieguanyin tea by near-infrared (NIR) spectroscopy and chemometrics. 450 representative samples were collected from Anxi County, the original producing area of Tieguanyin tea, and another 120 Tieguanyin samples with similar appearance were collected from unprotected producing areas in China. All these samples were measured by NIR. The Stahel-Donoho estimates (SDE) outlyingness diagnosis was used to remove the outliers. Partial least squares discriminant analysis (PLSDA) was performed to develop a classification model and predict the authenticity of unknown objects. To improve the sensitivity and specificity of classification, the raw data was preprocessed to reduce unwanted spectral variations by standard normal variate (SNV) transformation, taking second-order derivatives (D2) spectra, and smoothing. As the best model, the sensitivity and specificity reached 0.931 and 1.000 with SNV spectra. Combination of NIR spectrometry and statistical model selection can provide an effective and rapid method to discriminate the geographical producing area of Anxi-Tieguanyin.

  16. Rapid Discrimination of the Geographical Origins of an Oolong Tea (Anxi-Tieguanyin by Near-Infrared Spectroscopy and Partial Least Squares Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Si-Min Yan

    2014-01-01

    Full Text Available This paper focuses on a rapid and nondestructive way to discriminate the geographical origin of Anxi-Tieguanyin tea by near-infrared (NIR spectroscopy and chemometrics. 450 representative samples were collected from Anxi County, the original producing area of Tieguanyin tea, and another 120 Tieguanyin samples with similar appearance were collected from unprotected producing areas in China. All these samples were measured by NIR. The Stahel-Donoho estimates (SDE outlyingness diagnosis was used to remove the outliers. Partial least squares discriminant analysis (PLSDA was performed to develop a classification model and predict the authenticity of unknown objects. To improve the sensitivity and specificity of classification, the raw data was preprocessed to reduce unwanted spectral variations by standard normal variate (SNV transformation, taking second-order derivatives (D2 spectra, and smoothing. As the best model, the sensitivity and specificity reached 0.931 and 1.000 with SNV spectra. Combination of NIR spectrometry and statistical model selection can provide an effective and rapid method to discriminate the geographical producing area of Anxi-Tieguanyin.

  17. A qualitative analysis of hate speech reported to the Romanian National Council for Combating Discrimination (2003‑2015

    Directory of Open Access Journals (Sweden)

    Adriana Iordache

    2015-12-01

    Full Text Available The article analyzes the specificities of Romanian hate speech over a period of twelve years through a qualitative analysis of 384 Decisions of the National Council for Combating Discrimination. The study employs a coding methodology which allows one to separate decisions according to the group that was the victim of hate speech. The article finds that stereotypes employed are similar to those encountered in the international literature. The main target of hate speech is the Roma, who are seen as „dirty“, „uncivilized“ and a threat to Romania’s image abroad. Other stereotypes encountered were that of the „disloyal“ Hungarian and of the sexually promiscuous woman. Moreover, women are seen as unfit for management positions. The article also discusses stereotypes about homosexuals, who are seen as „sick“ and about non-orthodox religions, portrayed as „sectarian“.

  18. Technical, perceptual and motor skills in novice-expert water polo players: an individual discriminant analysis for talent development.

    Science.gov (United States)

    DʼErcole, Alessandro A; DʼErcole, Cristina; Gobbi, Massimo; Gobbi, Fabio

    2013-12-01

    The 4 tasks (A, B, C, and Y) have the characteristic of containing one more element than the task performed before it. In fact, task B introduces the slalom which is not present in task A. Task C introduces the ball control that are not present in tasks A and B, whereas task Y introduces the slalom and ball control in a visual dual task situation developed in horizontal swimming over a distance of 20 m at maximum speed. This exercise not included in task C. These tasks were performed by a group of pre-adolescent players and national under 18 water polo players. The novice players showed that tasks B and C are predictors of task Y. Such characteristics were not present in the expert players. The novice players also had difficulty in performing task Y because of the visual-attention overload, a difficulty that was not present in the expert players. To improve the 4 skills, the coach of the novice players developed a technical-didactic program, which was checked 6 months after the pretest. The posttest was not significantly different from the pretest while the individual discriminant analysis identified the improvements in some novice players, which on elaboration proved significant, enabling us to distinguish 2 subgroups, one with higher learning rates and the other with lower learning rates. In the practical applications, we describe the didactic tools (task analysis) and the different levels of development of technical skills in water polo. Improvements in these skills are explained through computational models like the HMOSAIC (Hierarchical, Modular, Selection and Identification for Control) while the individual discriminant analysis enables us to do a longitudinal analysis that is not possible with cross-sectional models.

  19. Penalized discriminant analysis for the detection of wild-grown and cultivated Ganoderma lucidum using Fourier transform infrared spectroscopy

    Science.gov (United States)

    Zhu, Ying; Tan, Tuck Lee

    2016-04-01

    An effective and simple analytical method using Fourier transform infrared (FTIR) spectroscopy to distinguish wild-grown high-quality Ganoderma lucidum (G. lucidum) from cultivated one is of essential importance for its quality assurance and medicinal value estimation. Commonly used chemical and analytical methods using full spectrum are not so effective for the detection and interpretation due to the complex system of the herbal medicine. In this study, two penalized discriminant analysis models, penalized linear discriminant analysis (PLDA) and elastic net (Elnet),using FTIR spectroscopy have been explored for the purpose of discrimination and interpretation. The classification performances of the two penalized models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The Elnet model involving a combination of L1 and L2 norm penalties enabled an automatic selection of a small number of informative spectral absorption bands and gave an excellent classification accuracy of 99% for discrimination between spectra of wild-grown and cultivated G. lucidum. Its classification performance was superior to that of the PLDA model in a pure L1 setting and outperformed the PCDA and PLSDA models using full wavelength. The well-performed selection of informative spectral features leads to substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of G. lucidum regarding its anti-cancer effects.

  20. Penalized discriminant analysis for the detection of wild-grown and cultivated Ganoderma lucidum using Fourier transform infrared spectroscopy.

    Science.gov (United States)

    Zhu, Ying; Tan, Tuck Lee

    2016-04-15

    An effective and simple analytical method using Fourier transform infrared (FTIR) spectroscopy to distinguish wild-grown high-quality Ganoderma lucidum (G. lucidum) from cultivated one is of essential importance for its quality assurance and medicinal value estimation. Commonly used chemical and analytical methods using full spectrum are not so effective for the detection and interpretation due to the complex system of the herbal medicine. In this study, two penalized discriminant analysis models, penalized linear discriminant analysis (PLDA) and elastic net (Elnet),using FTIR spectroscopy have been explored for the purpose of discrimination and interpretation. The classification performances of the two penalized models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The Elnet model involving a combination of L1 and L2 norm penalties enabled an automatic selection of a small number of informative spectral absorption bands and gave an excellent classification accuracy of 99% for discrimination between spectra of wild-grown and cultivated G. lucidum. Its classification performance was superior to that of the PLDA model in a pure L1 setting and outperformed the PCDA and PLSDA models using full wavelength. The well-performed selection of informative spectral features leads to substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of G. lucidum regarding its anti-cancer effects.

  1. Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu-Natal, South Africa

    Science.gov (United States)

    Peerbhay, Kabir Yunus; Mutanga, Onisimo; Ismail, Riyad

    2013-05-01

    Discriminating commercial tree species using hyperspectral remote sensing techniques is critical in monitoring the spatial distributions and compositions of commercial forests. However, issues related to data dimensionality and multicollinearity limit the successful application of the technology. The aim of this study was to examine the utility of the partial least squares discriminant analysis (PLS-DA) technique in accurately classifying six exotic commercial forest species (Eucalyptus grandis, Eucalyptus nitens, Eucalyptus smithii, Pinus patula, Pinus elliotii and Acacia mearnsii) using airborne AISA Eagle hyperspectral imagery (393-900 nm). Additionally, the variable importance in the projection (VIP) method was used to identify subsets of bands that could successfully discriminate the forest species. Results indicated that the PLS-DA model that used all the AISA Eagle bands (n = 230) produced an overall accuracy of 80.61% and a kappa value of 0.77, with user's and producer's accuracies ranging from 50% to 100%. In comparison, incorporating the optimal subset of VIP selected wavebands (n = 78) in the PLS-DA model resulted in an improved overall accuracy of 88.78% and a kappa value of 0.87, with user's and producer's accuracies ranging from 70% to 100%. Bands located predominantly within the visible region of the electromagnetic spectrum (393-723 nm) showed the most capability in terms of discriminating between the six commercial forest species. Overall, the research has demonstrated the potential of using PLS-DA for reducing the dimensionality of hyperspectral datasets as well as determining the optimal subset of bands to produce the highest classification accuracies.

  2. Local classification: Locally weighted-partial least squares-discriminant analysis (LW-PLS-DA).

    Science.gov (United States)

    Bevilacqua, Marta; Marini, Federico

    2014-08-01

    The possibility of devising a simple, flexible and accurate non-linear classification method, by extending the locally weighted partial least squares (LW-PLS) approach to the cases where the algorithm is used in a discriminant way (partial least squares discriminant analysis, PLS-DA), is presented. In particular, to assess which category an unknown sample belongs to, the proposed algorithm operates by identifying which training objects are most similar to the one to be predicted and building a PLS-DA model using these calibration samples only. Moreover, the influence of the selected training samples on the local model can be further modulated by adopting a not uniform distance-based weighting scheme which allows the farthest calibration objects to have less impact than the closest ones. The performances of the proposed locally weighted-partial least squares-discriminant analysis (LW-PLS-DA) algorithm have been tested on three simulated data sets characterized by a varying degree of non-linearity: in all cases, a classification accuracy higher than 99% on external validation samples was achieved. Moreover, when also applied to a real data set (classification of rice varieties), characterized by a high extent of non-linearity, the proposed method provided an average correct classification rate of about 93% on the test set. By the preliminary results, showed in this paper, the performances of the proposed LW-PLS-DA approach have proved to be comparable and in some cases better than those obtained by other non-linear methods (k nearest neighbors, kernel-PLS-DA and, in the case of rice, counterpropagation neural networks).

  3. Renormalization group analysis of the gluon mass equation

    Science.gov (United States)

    Aguilar, A. C.; Binosi, D.; Papavassiliou, J.

    2014-04-01

    We carry out a systematic study of the renormalization properties of the integral equation that determines the momentum evolution of the effective gluon mass in pure Yang-Mills theory, without quark effects taken into account. A detailed, all-order analysis of the complete kernel appearing in this particular equation, derived in the Landau gauge, reveals that the renormalization procedure may be accomplished through the sole use of ingredients known from the standard perturbative treatment of the theory, with no additional assumptions. However, the subtle interplay of terms operating at the level of the exact equation gets distorted by the approximations usually employed when evaluating the aforementioned kernel. This fact is reflected in the form of the obtained solutions, for which the deviations from the correct behavior are best quantified by resorting to appropriately defined renormalization-group invariant quantities. This analysis, in turn, provides a solid guiding principle for improving the form of the kernel, and furnishes a well-defined criterion for discriminating between various possibilities. Certain renormalization-group inspired Ansätze for the kernel are then proposed, and their numerical implications are explored in detail. One of the solutions obtained fulfills the theoretical expectations to a high degree of accuracy, yielding a gluon mass that is positive definite throughout the entire range of physical momenta, and displays in the ultraviolet the so-called "power-law" running, in agreement with standard arguments based on the operator product expansion. Some of the technical difficulties thwarting a more rigorous determination of the kernel are discussed, and possible future directions are briefly mentioned.

  4. Exclusively visual analysis of classroom group interactions

    Science.gov (United States)

    Tucker, Laura; Scherr, Rachel E.; Zickler, Todd; Mazur, Eric

    2016-12-01

    Large-scale audiovisual data that measure group learning are time consuming to collect and analyze. As an initial step towards scaling qualitative classroom observation, we qualitatively coded classroom video using an established coding scheme with and without its audio cues. We find that interrater reliability is as high when using visual data only—without audio—as when using both visual and audio data to code. Also, interrater reliability is high when comparing use of visual and audio data to visual-only data. We see a small bias to code interactions as group discussion when visual and audio data are used compared with video-only data. This work establishes that meaningful educational observation can be made through visual information alone. Further, it suggests that after initial work to create a coding scheme and validate it in each environment, computer-automated visual coding could drastically increase the breadth of qualitative studies and allow for meaningful educational analysis on a far greater scale.

  5. Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis

    Directory of Open Access Journals (Sweden)

    A.V. Faria

    2011-02-01

    Full Text Available High resolution proton nuclear magnetic resonance spectroscopy (¹H MRS can be used to detect biochemical changes in vitro caused by distinct pathologies. It can reveal distinct metabolic profiles of brain tumors although the accurate analysis and classification of different spectra remains a challenge. In this study, the pattern recognition method partial least squares discriminant analysis (PLS-DA was used to classify 11.7 T ¹H MRS spectra of brain tissue extracts from patients with brain tumors into four classes (high-grade neuroglial, low-grade neuroglial, non-neuroglial, and metastasis and a group of control brain tissue. PLS-DA revealed 9 metabolites as the most important in group differentiation: γ-aminobutyric acid, acetoacetate, alanine, creatine, glutamate/glutamine, glycine, myo-inositol, N-acetylaspartate, and choline compounds. Leave-one-out cross-validation showed that PLS-DA was efficient in group characterization. The metabolic patterns detected can be explained on the basis of previous multimodal studies of tumor metabolism and are consistent with neoplastic cell abnormalities possibly related to high turnover, resistance to apoptosis, osmotic stress and tumor tendency to use alternative energetic pathways such as glycolysis and ketogenesis.

  6. Predicting ethnic and racial discrimination: a meta-analysis of IAT criterion studies.

    Science.gov (United States)

    Oswald, Frederick L; Mitchell, Gregory; Blanton, Hart; Jaccard, James; Tetlock, Philip E

    2013-08-01

    This article reports a meta-analysis of studies examining the predictive validity of the Implicit Association Test (IAT) and explicit measures of bias for a wide range of criterion measures of discrimination. The meta-analysis estimates the heterogeneity of effects within and across 2 domains of intergroup bias (interracial and interethnic), 6 criterion categories (interpersonal behavior, person perception, policy preference, microbehavior, response time, and brain activity), 2 versions of the IAT (stereotype and attitude IATs), 3 strategies for measuring explicit bias (feeling thermometers, multi-item explicit measures such as the Modern Racism Scale, and ad hoc measures of intergroup attitudes and stereotypes), and 4 criterion-scoring methods (computed majority-minority difference scores, relative majority-minority ratings, minority-only ratings, and majority-only ratings). IATs were poor predictors of every criterion category other than brain activity, and the IATs performed no better than simple explicit measures. These results have important implications for the construct validity of IATs, for competing theories of prejudice and attitude-behavior relations, and for measuring and modeling prejudice and discrimination.

  7. Prostate lesion detection and localization based on locality alignment discriminant analysis

    Science.gov (United States)

    Lin, Mingquan; Chen, Weifu; Zhao, Mingbo; Gibson, Eli; Bastian-Jordan, Matthew; Cool, Derek W.; Kassam, Zahra; Chow, Tommy W. S.; Ward, Aaron; Chiu, Bernard

    2017-03-01

    Prostatic adenocarcinoma is one of the most commonly occurring cancers among men in the world, and it also the most curable cancer when it is detected early. Multiparametric MRI (mpMRI) combines anatomic and functional prostate imaging techniques, which have been shown to produce high sensitivity and specificity in cancer localization, which is important in planning biopsies and focal therapies. However, in previous investigations, lesion localization was achieved mainly by manual segmentation, which is time-consuming and prone to observer variability. Here, we developed an algorithm based on locality alignment discriminant analysis (LADA) technique, which can be considered as a version of linear discriminant analysis (LDA) localized to patches in the feature space. Sensitivity, specificity and accuracy generated by the proposed algorithm in five prostates by LADA were 52.2%, 89.1% and 85.1% respectively, compared to 31.3%, 85.3% and 80.9% generated by LDA. The delineation accuracy attainable by this tool has a potential in increasing the cancer detection rate in biopsies and in minimizing collateral damage of surrounding tissues in focal therapies.

  8. An item factor analysis and item response theory-based revision of the Everyday Discrimination Scale.

    Science.gov (United States)

    Stucky, Brian D; Gottfredson, Nisha C; Panter, A T; Daye, Charles E; Allen, Walter R; Wightman, Linda F

    2011-04-01

    The Everyday Discrimination Scale (EDS), a widely used measure of daily perceived discrimination, is purported to be unidimensional, to function well among African Americans, and to have adequate construct validity. Two separate studies and data sources were used to examine and cross-validate the psychometric properties of the EDS. In Study 1, an exploratory factor analysis was conducted on a sample of African American law students (N = 589), providing strong evidence of local dependence, or nuisance multidimensionality within the EDS. In Study 2, a separate nationally representative community sample (N = 3,527) was used to model the identified local dependence in an item factor analysis (i.e., bifactor model). Next, item response theory (IRT) calibrations were conducted to obtain item parameters. A five-item, revised-EDS was then tested for gender differential item functioning (in an IRT framework). Based on these analyses, a summed score to IRT-scaled score translation table is provided for the revised-EDS. Our results indicate that the revised-EDS is unidimensional, with minimal differential item functioning, and retains predictive validity consistent with the original scale.

  9. Forensic analysis of Salvia divinorum using multivariate statistical procedures. Part I: discrimination from related Salvia species.

    Science.gov (United States)

    Willard, Melissa A Bodnar; McGuffin, Victoria L; Smith, Ruth Waddell

    2012-01-01

    Salvia divinorum is a hallucinogenic herb that is internationally regulated. In this study, salvinorin A, the active compound in S. divinorum, was extracted from S. divinorum plant leaves using a 5-min extraction with dichloromethane. Four additional Salvia species (Salvia officinalis, Salvia guaranitica, Salvia splendens, and Salvia nemorosa) were extracted using this procedure, and all extracts were analyzed by gas chromatography-mass spectrometry. Differentiation of S. divinorum from other Salvia species was successful based on visual assessment of the resulting chromatograms. To provide a more objective comparison, the total ion chromatograms (TICs) were subjected to principal components analysis (PCA). Prior to PCA, the TICs were subjected to a series of data pretreatment procedures to minimize non-chemical sources of variance in the data set. Successful discrimination of S. divinorum from the other four Salvia species was possible based on visual assessment of the PCA scores plot. To provide a numerical assessment of the discrimination, a series of statistical procedures such as Euclidean distance measurement, hierarchical cluster analysis, Student's t tests, Wilcoxon rank-sum tests, and Pearson product moment correlation were also applied to the PCA scores. The statistical procedures were then compared to determine the advantages and disadvantages for forensic applications.

  10. Semi-automated porosity identification from thin section images using image analysis and intelligent discriminant classifiers

    Science.gov (United States)

    Ghiasi-Freez, Javad; Soleimanpour, Iman; Kadkhodaie-Ilkhchi, Ali; Ziaii, Mansur; Sedighi, Mahdi; Hatampour, Amir

    2012-08-01

    Identification of different types of porosity within a reservoir rock is a functional parameter for reservoir characterization since various pore types play different roles in fluid transport and also, the pore spaces determine the fluid storage capacity of the reservoir. The present paper introduces a model for semi-automatic identification of porosity types within thin section images. To get this goal, a pattern recognition algorithm is followed. Firstly, six geometrical shape parameters of sixteen largest pores of each image are extracted using image analysis techniques. The extracted parameters and their corresponding pore types of 294 pores are used for training two intelligent discriminant classifiers, namely linear and quadratic discriminant analysis. The trained classifiers take the geometrical features of the pores to identify the type and percentage of five types of porosity, including interparticle, intraparticle, oomoldic, biomoldic, and vuggy in each image. The accuracy of classifiers is determined from two standpoints. Firstly, the predicted and measured percentages of each type of porosity are compared with each other. The results indicate reliable performance for predicting percentage of each type of porosity. In the second step, the precisions of classifiers for categorizing the pore spaces are analyzed. The classifiers also took a high acceptance score when used for individual recognition of pore spaces. The proposed methodology is a further promising application for petroleum geologists allowing statistical study of pore types in a rapid and accurate way.

  11. Classification of hand preshaping in persons with stroke using Linear Discriminant Analysis.

    Science.gov (United States)

    Puthenveettil, Saumya; Fluet, Gerard; Qiu, Qinyin; Adamovich, Sergei

    2012-01-01

    This study describes the analysis of hand preshaping using Linear Discriminant Analysis (LDA) to predict hand formation during reaching and grasping tasks of the hemiparetic hand, following a series of upper extremity motor intervention treatments. The purpose of this study is to use classification of hand posture as an additional tool for evaluating the effectiveness of therapies for upper extremity rehabilitation such as virtual reality (VR) therapy and conventional physical therapy. Classification error for discriminating between two objects during hand preshaping is obtained for the hemiparetic and unimpaired hands pre and post training. Eight subjects post stroke participated in a two-week training session consisting of upper extremity motor training. Four subjects trained with interactive VR computer games and four subjects trained with clinical physical therapy procedures of similar intensity. Subjects' finger joint angles were measured during a kinematic reach to grasp test using CyberGlove® and arm joint angles were measured using the trackSTAR™ system prior to training and after training. The unimpaired hand of subjects preshape into the target object with greater accuracy than the hemiparetic hand as indicated by lower classification errors. Hemiparetic hand improved in preshaping accuracy and time to reach minimum error. Classification of hand preshaping may provide insight into improvements in motor performance elicited by robotically facilitated virtually simulated training sessions or conventional physical therapy.

  12. Shape-based discriminative analysis of combined bilateral hippocampi using multiple object alignment

    Science.gov (United States)

    Shen, Li; Makedon, Fillia; Saykin, Andrew

    2004-05-01

    Shape analysis of hippocampi in schizophrenia has been preformed previously using the spherical harmonic SPHARM description. In these studies, the left and right hippocampi are aligned independently and the spatial relation between them is not explored. This paper presents a new SPHARM-based technique which examines not only the individual shape information of the two hippocampi but also the spatial relation between them. The left and right hippocampi are treated as a single shape configuration. A ploy-shape alignment algorithm is developed for aligning configurations of multiple SPHARM surfaces as follows: (1) the total volume is normalized; (2) the parameter space is aligned for creating the surface correspondence; (3) landmarks are created by a uniform sampling of multiple surfaces for each configuration; (4) a quaternion-based algorithm is employed to align each landmark representation to the mean configuration through the least square rotation and translation iteratively until the mean converges. After applying the poly-shape alignment algorithm, a point distribution model is applied to aligned landmarks for feature extraction. Classification is performed using Fisher's linear discriminant with an effective feature selection scheme. Applying the above procedure to our hippocampal data (14 controls versus 25 schizophrenics, all right-handed males), we achieve the best cross-validation accuracy of 92%, supporting the idea that the whole shape configuration of the two hippocampi provides valuable information in detecting schizophrenia. The results of an ROC analysis and a visualization of discriminative patterns are also included.

  13. A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

    Science.gov (United States)

    Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

    2015-12-01

    This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

  14. The role of critical ethnic awareness and social support in the discrimination-depression relationship among Asian Americans: path analysis.

    Science.gov (United States)

    Kim, Isok

    2014-01-01

    This study used a path analytic technique to examine associations among critical ethnic awareness, racial discrimination, social support, and depressive symptoms. Using a convenience sample from online survey of Asian American adults (N = 405), the study tested 2 main hypotheses: First, based on the empowerment theory, critical ethnic awareness would be positively associated with racial discrimination experience; and second, based on the social support deterioration model, social support would partially mediate the relationship between racial discrimination and depressive symptoms. The result of the path analysis model showed that the proposed path model was a good fit based on global fit indices, χ²(2) = 4.70, p = .10; root mean square error of approximation = 0.06; comparative fit index = 0.97; Tucker-Lewis index = 0.92; and standardized root mean square residual = 0.03. The examinations of study hypotheses demonstrated that critical ethnic awareness was directly associated (b = .11, p racial discrimination experience, whereas social support had a significant indirect effect (b = .48; bias-corrected 95% confidence interval [0.02, 1.26]) between the racial discrimination experience and depressive symptoms. The proposed path model illustrated that both critical ethnic awareness and social support are important mechanisms for explaining the relationship between racial discrimination and depressive symptoms among this sample of Asian Americans. This study highlights the usefulness of the critical ethnic awareness concept as a way to better understand how Asian Americans might perceive and recognize racial discrimination experiences in relation to its mental health consequences.

  15. Rapid direct analysis to discriminate geographic origin of extra virgin olive oils by flash gas chromatography electronic nose and chemometrics.

    Science.gov (United States)

    Melucci, Dora; Bendini, Alessandra; Tesini, Federica; Barbieri, Sara; Zappi, Alessandro; Vichi, Stefania; Conte, Lanfranco; Gallina Toschi, Tullia

    2016-08-01

    At present, the geographical origin of extra virgin olive oils can be ensured by documented traceability, although chemical analysis may add information that is useful for possible confirmation. This preliminary study investigated the effectiveness of flash gas chromatography electronic nose and multivariate data analysis to perform rapid screening of commercial extra virgin olive oils characterized by a different geographical origin declared in the label. A comparison with solid phase micro extraction coupled to gas chromatography mass spectrometry was also performed. The new method is suitable to verify the geographic origin of extra virgin olive oils based on principal components analysis and discriminant analysis applied to the volatile profile of the headspace as a fingerprint. The selected variables were suitable in discriminating between "100% Italian" and "non-100% Italian" oils. Partial least squares discriminant analysis also allowed prediction of the degree of membership of unknown samples to the classes examined.

  16. Is it really organic?--multi-isotopic analysis as a tool to discriminate between organic and conventional plants.

    Science.gov (United States)

    Laursen, K H; Mihailova, A; Kelly, S D; Epov, V N; Bérail, S; Schjoerring, J K; Donard, O F X; Larsen, E H; Pedentchouk, N; Marca-Bell, A D; Halekoh, U; Olesen, J E; Husted, S

    2013-12-01

    Novel procedures for analytical authentication of organic plant products are urgently needed. Here we present the first study encompassing stable isotopes of hydrogen, carbon, nitrogen, oxygen, magnesium and sulphur as well as compound-specific nitrogen and oxygen isotope analysis of nitrate for discrimination of organically and conventionally grown plants. The study was based on wheat, barley, faba bean and potato produced in rigorously controlled long-term field trials comprising 144 experimental plots. Nitrogen isotope analysis revealed the use of animal manure, but was unable to discriminate between plants that were fertilised with synthetic nitrogen fertilisers or green manures from atmospheric nitrogen fixing legumes. This limitation was bypassed using oxygen isotope analysis of nitrate in potato tubers, while hydrogen isotope analysis allowed complete discrimination of organic and conventional wheat and barley grains. It is concluded, that multi-isotopic analysis has the potential to disclose fraudulent substitutions of organic with conventionally cultivated plants.

  17. Laws' masks descriptors applied to bone texture analysis: an innovative and discriminant tool in osteoporosis

    Energy Technology Data Exchange (ETDEWEB)

    Rachidi, M. [Orleans Hospital, INSERM Unit U658, Orleans (France); INSERM-U658. IPROS Hopital Porte Madeleine, Orleans (France); Marchadier, A. [Orleans Hospital, IPROS, Orleans (France); Gadois, C. [D3A Medical Systems, Orleans (France); Lespessailles, E. [Ipros-service de Rhumatologie CHR d' Orleans, Orleans (France); Chappard, C.; Benhamou, C.L. [Orleans Hospital, INSERM Unit U658, Orleans (France)

    2008-06-15

    The objective of this study was to explore Laws' masks analysis to describe structural variations of trabecular bone due to osteoporosis on high-resolution digital radiographs and to check its dependence on the spatial resolution. Laws' masks are well established as one of the best methods for texture analysis in image processing and are used in various applications, but not in bone tissue characterisation. This method is based on masks that aim to filter the images. From each mask, five classical statistical parameters can be calculated. The study was performed on 182 healthy postmenopausal women with no fractures and 114 age-matched women with fractures [26 hip fractures (HFs), 29 vertebrae fractures (VFs), 29 wrist fractures (WFs) and 30 other fractures (OFs)]. For all subjects radiographs were obtained of the calcaneus with a new high-resolution X-ray device with direct digitisation (BMA, D3A, France). The lumbar spine, femoral neck, and total hip bone mineral density (BMD) were assessed by dual-energy X-ray absorptiometry. In terms of reproducibility, the best results were obtained with the TR{sub E5E5} mask, especially for three parameters: 'mean', 'standard deviation' and 'entropy' with, respectively, in vivo mid-term root mean square average coefficient of variation (RMSCV)%=1.79, 4.24 and 2.05. The 'mean' and 'entropy' parameters had a better reproducibility but 'standard deviation' showed a better discriminant power. Thus, for univariate analysis, the difference between subjects with fractures and controls was significant (P<10{sup -3}) and significant for each fracture group independently (P<10{sup -4} for HF, P=0.025 for VF and P < 10{sup -3} for OF). After multivariate analysis with adjustment for age and total hip BMD, the difference concerning the 'standard deviation' parameter remained statistically significant between the control group and the HF and VF groups (P<5 x 10

  18. 41 CFR 60-2.12 - Job group analysis.

    Science.gov (United States)

    2010-07-01

    ... 41 Public Contracts and Property Management 1 2010-07-01 2010-07-01 true Job group analysis. 60-2... group analysis. (a) Purpose: A job group analysis is a method of combining job titles within the... employed. (b) In the job group analysis, jobs at the establishment with similar content, wage rates,...

  19. The role of discriminant analysis in the refinement of customer satisfaction assessment

    Directory of Open Access Journals (Sweden)

    Verdessi BD

    2000-01-01

    Full Text Available OBJECTIVE: To test discriminant analysis as a method of turning the information of a routine customer satisfaction survey (CSS into a more accurate decision-making tool. METHODS: A 7-question, 10-multiple choice, self-applied questionnaire was used to study a sample of patients seen in two outpatient care units in Valparaíso, Chile, one of primary care (n=100 and the other of secondary care (n=249. Two cutting points were considered in the dependent variable (final satisfaction score: satisfied versus unsatisfied, and very satisfied versus all others. Results were compared with empirical measures (proportion of satisfied individuals, proportion of unsatisfied individuals and size of the median. RESULTS: The response rate was very high, over 97.0% in both units. A new variable, medical attention, was revealed, as explaining satisfaction at the primary care unit. The proportion of the total variability explained by the model was very high (over 99.4% in both units, when comparing satisfied with unsatisfied customers. In the analysis of very satisfied versus all other customers, significant relationship was identified only in the case of the primary care unit, which explained a small proportion of the variability (41.9%. CONCLUSIONS: Discriminant analysis identified relationships not revealed by the previous analysis. It provided information about the proportion of the variability explained by the model. It identified non-significant relationships suggested by empirical analysis (e.g. the case of the relation very satisfied versus others in the secondary care unit. It measured the contribution of each independent variable to the explanation of the variation of the dependent one.

  20. The role of discriminant analysis in the refinement of customer satisfaction assessment

    Directory of Open Access Journals (Sweden)

    BD Verdessi

    2000-12-01

    Full Text Available OBJECTIVE: To test discriminant analysis as a method of turning the information of a routine customer satisfaction survey (CSS into a more accurate decision-making tool. METHODS: A 7-question, 10-multiple choice, self-applied questionnaire was used to study a sample of patients seen in two outpatient care units in Valparaíso, Chile, one of primary care (n=100 and the other of secondary care (n=249. Two cutting points were considered in the dependent variable (final satisfaction score: satisfied versus unsatisfied, and very satisfied versus all others. Results were compared with empirical measures (proportion of satisfied individuals, proportion of unsatisfied individuals and size of the median. RESULTS: The response rate was very high, over 97.0% in both units. A new variable, medical attention, was revealed, as explaining satisfaction at the primary care unit. The proportion of the total variability explained by the model was very high (over 99.4% in both units, when comparing satisfied with unsatisfied customers. In the analysis of very satisfied versus all other customers, significant relationship was identified only in the case of the primary care unit, which explained a small proportion of the variability (41.9%. CONCLUSIONS: Discriminant analysis identified relationships not revealed by the previous analysis. It provided information about the proportion of the variability explained by the model. It identified non-significant relationships suggested by empirical analysis (e.g. the case of the relation very satisfied versus others in the secondary care unit. It measured the contribution of each independent variable to the explanation of the variation of the dependent one.

  1. Discrimination Against State and Local Government LGBT Employees: An Analysis of Administrative Complaints

    OpenAIRE

    Mallory, Christy; Sears, Brad

    2014-01-01

    This article documents evidence of recent discrimination against lesbian, gay, bisexual, and transgender (LGBT) public sector workers by analyzing employment discrimination complaints filed with state and local administrative agencies. We present information about 589 complaints of sexual orientation and gender identity discrimination filed by public sector workers in 123 jurisdictions. We find that discrimination against LGBT people in the public sector is pervasive and occurs nearly as freq...

  2. Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

    Directory of Open Access Journals (Sweden)

    I. Crawford

    2015-07-01

    Full Text Available In this paper we present improved methods for discriminating and quantifying Primary Biological Aerosol Particles (PBAP by applying hierarchical agglomerative cluster analysis to multi-parameter ultra violet-light induced fluorescence (UV-LIF spectrometer data. The methods employed in this study can be applied to data sets in excess of 1×106 points on a desktop computer, allowing for each fluorescent particle in a dataset to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient dataset. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4 where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best performing methods were applied to the BEACHON-RoMBAS ambient dataset where it was found that the z-score and range normalisation methods yield similar results with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the underestimation of bacterial aerosol concentration by a factor of 5. We suggest that this likely due to errors arising from misatrribution

  3. Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

    Directory of Open Access Journals (Sweden)

    I. Crawford

    2015-11-01

    Full Text Available In this paper we present improved methods for discriminating and quantifying primary biological aerosol particles (PBAPs by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF spectrometer data. The methods employed in this study can be applied to data sets in excess of 1 × 106 points on a desktop computer, allowing for each fluorescent particle in a data set to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4 where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best-performing methods were applied to the BEACHON-RoMBAS (Bio–hydro–atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen–Rocky Mountain Biogenic Aerosol Study ambient data set, where it was found that the z-score and range normalisation methods yield similar results, with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the

  4. Discrimination of Aurantii Fructus Immaturus and Fructus Poniciri Trifoliatae Immaturus by Flow Injection UV Spectroscopy (FIUV) and 1H NMR using Partial Least-squares Discriminant Analysis (PLS-DA)

    Science.gov (United States)

    Two simple fingerprinting methods, flow-injection UV spectroscopy (FIUV) and 1H nuclear magnetic resonance (NMR), for discrimination of Aurantii FructusImmaturus and Fructus Poniciri TrifoliataeImmaturususing were described. Both methods were combined with partial least-squares discriminant analysis...

  5. Ensemble regularized linear discriminant analysis classifier for P300-based brain-computer interface.

    Science.gov (United States)

    Onishi, Akinari; Natsume, Kiyohisa

    2013-01-01

    This paper demonstrates a better classification performance of an ensemble classifier using a regularized linear discriminant analysis (LDA) for P300-based brain-computer interface (BCI). The ensemble classifier with an LDA is sensitive to the lack of training data because covariance matrices are estimated imprecisely. One of the solution against the lack of training data is to employ a regularized LDA. Thus we employed the regularized LDA for the ensemble classifier of the P300-based BCI. The principal component analysis (PCA) was used for the dimension reduction. As a result, an ensemble regularized LDA classifier showed significantly better classification performance than an ensemble un-regularized LDA classifier. Therefore the proposed ensemble regularized LDA classifier is robust against the lack of training data.

  6. Colored inks analysis and differentiation: A first step in artistic contemporary prints discrimination

    Energy Technology Data Exchange (ETDEWEB)

    Vila, Anna [Department de Pintura, Conservacio-Restauracio, Facultat de Belles Arts, Universitat de Barcelona, C/Pau Gargallo 4, 08028 Barcelona (Spain)]. E-mail: avila@sct.ub.es; Ferrer, Nuria [Serveis Cientificotecnics, Universitat de Barcelona, C/Lluis Sole i Sabaris 1, 08028 Barcelona (Spain)]. E-mail: nferrer@sctub.es; Garcia, Jose F. [Department de Pintura, Conservacio-Restauracio, Facultat de Belles Arts, Universitat de Barcelona, C/Pau Gargallo 4, 08028 Barcelona (Spain)]. E-mail: ifgarcia@ub.edu

    2007-04-04

    Prints are the most popular artistic technique. Due to their manufacturing procedure, they are also one of the most frequently falsified types of artwork. In terms of their economic and historic value, the chemical analysis and characterisation of coloured inks and their principal constituent materials (pigments), together with the historical and aesthetic information available in the Catalogues Raisonees, are important tools in distinguishing originals from non-original prints. The chemical characterisation and discrimination of coloured inks has test in this study. Analysis using Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM) and X-ray diffraction (XRD) has been done on blue pigments and inks, due to this colour is one of the most representative for the presence of organic and inorganic materials in their composition. Conclusion obtained for this colour would demonstrate the capability of the approach when it is applied to any other coloured set of inks.

  7. Bioelectric signal classification using a recurrent probabilistic neural network with time-series discriminant component analysis.

    Science.gov (United States)

    Hayashi, Hideaki; Shima, Keisuke; Shibanoki, Taro; Kurita, Yuichi; Tsuji, Toshio

    2013-01-01

    This paper outlines a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower-dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model that incorporates a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into a neural network so that parameters can be obtained appropriately as network coefficients according to backpropagation-through-time-based training algorithm. The network is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. In the experiments conducted during the study, the validity of the proposed network was demonstrated for EEG signals.

  8. A Unified Factors Analysis Framework for Discriminative Feature Extraction and Object Recognition

    Directory of Open Access Journals (Sweden)

    Ningbo Hao

    2016-01-01

    Full Text Available Various methods for feature extraction and dimensionality reduction have been proposed in recent decades, including supervised and unsupervised methods and linear and nonlinear methods. Despite the different motivations of these methods, we present in this paper a general formulation known as factor analysis to unify them within a common framework. During factor analysis, an object can be seen as being comprised of content and style factors, and the objective of feature extraction and dimensionality reduction is to obtain the content factor without style factor. There are two vital steps in factor analysis framework; one is the design of factor separating objective function, including the design of partition and weight matrix, and the other is the design of space mapping function. In this paper, classical Linear Discriminant Analysis (LDA and Locality Preserving Projection (LPP algorithms are improved based on factor analysis framework, and LDA based on factor analysis (FA-LDA and LPP based on factor analysis (FA-LPP are proposed. Experimental results show the superiority of our proposed approach in classification performance compared to classical LDA and LPP algorithms.

  9. Mass discrimination in elastic recoil detection analysis and its application to Al2O3 on MoS2

    Science.gov (United States)

    Laricchiuta, G.; Vandervorst, W.; Meersschaut, J.

    2017-09-01

    A time of flight-energy (TOF-E) telescope is often used to detect the scattered and recoiled atoms in elastic recoil detection analysis. The experimental two-dimensional TOF-E histogram may be numerically transformed into a time of flight-mass (TOF-M) histogram. The limited mass resolution in the TOF-M histogram, which results from the limited energy resolution of the energy detector, makes it sometimes difficult to discriminate elements with a small difference in atomic mass. We describe a mass discrimination procedure to numerically discriminate the elements in the TOF-M histogram. The procedure is illustrated on a sample consisting of an Al and a Si layer deposited on a MgO substrate. Besides, we apply the procedure to discriminate Al and Si in a sample consisting of Al2O3 deposited on MoS2/SiO2/Si.

  10. Is it really organic? – Multi-isotopic analysis as a tool to discriminate between organic and conventional plants

    DEFF Research Database (Denmark)

    Laursen, K.H.; Mihailova, A.; Kelly, S.D.

    2013-01-01

    for discrimination of organically and conventionally grown plants. The study was based on wheat, barley, faba bean and potato produced in rigorously controlled long-term field trials comprising 144 experimental plots. Nitrogen isotope analysis revealed the use of animal manure, but was unable to discriminate between......Novel procedures for analytical authentication of organic plant products are urgently needed. Here we present the first study encompassing stable isotopes of hydrogen, carbon, nitrogen, oxygen, magnesium and sulphur as well as compound-specific nitrogen and oxygen isotope analysis of nitrate...... plants that were fertilised with synthetic nitrogen fertilisers or green manures from atmospheric nitrogen fixing legumes. This limitation was bypassed using oxygen isotope analysis of nitrate in potato tubers, while hydrogen isotope analysis allowed complete discrimination of organic and conventional...

  11. Discrimination of Semen cassiae from two related species based on the multivariate analysis of high-performance liquid chromatography fingerprints.

    Science.gov (United States)

    Tang, Liying; Wu, Hongwei; Zhou, Xidan; Xu, Yilong; Zhou, Guohong; Wang, Ting; Kou, Zhenzhen; Wang, Zhuju

    2015-07-01

    A simple and efficient high-performance liquid chromatography fingerprint method was developed to discriminate Semen cassiae from two related species: Cassia obtusifolia L. (CO) and Cassia tora L. (CT), the seeds of which are abbreviated as COS and CTS, respectively. 22 major bioactive ingredients in 42 samples (20 COS and 22 CTS) collected from different provinces of China were identified. The statistical methods included similarity analysis and partial least-squares discriminant analysis. The pattern analysis method was specific and could be readily used for the comprehensive evaluation of Semen cassiae samples. Therefore, high-performance liquid chromatography fingerprint in combination with pattern analysis provided a simple and reliable method for discriminating between COS and CTS. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Genetic Discrimination

    Science.gov (United States)

    ... in Genetics Archive Regulation of Genetic Tests Genetic Discrimination Overview Many Americans fear that participating in research ... I) and employment (Title II). Read more Genetic Discrimination and Other Laws Genetic Discrimination and Other Laws ...

  13. Discrimination of bacillus anthracis and closely related microorganisms by analysis of 16S and 23S rRNA with oligonucleotide microarray.

    Energy Technology Data Exchange (ETDEWEB)

    Bavykin, S. G.; Mikhailovich, V. M.; Zakharyev, V. M.; Lysov, Y. P.; Kelly, J. J.; Alferov, O. S.; Jackman, J.; Stahl, D. A.; Mirzabekov, A. D.; Gavin, I. M.; Kukhtin, A. V.; Chandler, D. (Biochip Technology Center); (Engelhardt Inst. of Molecular Biology); (Northwestern Univ.); (Georgetown Univ.)

    2008-01-30

    Analysis of 16S rRNA sequences is a commonly used method for the identification and discrimination of microorganisms. However, the high similarity of 16S and 23S rRNA sequences of Bacillus cereus group organisms (up to 99-100%) and repeatedly failed attempts to develop molecular typing systems that would use DNA sequences to discriminate between species within this group have resulted in several suggestions to consider B. cereus and B. thuringiensis, or these two species together with B. anthracis, as one species. Recently, we divided the B. cereus group into seven subgroups, Anthracis, Cereus A and B, Thuringiensis A and B, and Mycoides A and B, based on 16S rRNA, 23S rRNA and gyrB gene sequences and identified subgroup-specific makers in each of these three genes. Here we for the first time demonstrated discrimination of these seven subgroups, including subgroup Anthracis, with a 3D gel element microarray of oligonucleotide probes targeting 16S and 23S rRNA markers. This is the first microarray enabled identification of B. anthracis and discrimination of these seven subgroups in pure cell cultures and in environmental samples using rRNA sequences. The microarray bearing perfect match/mismatch (p/mm) probe pairs was specific enough to discriminate single nucleotide polymorphisms (SNPs) and was able to identify targeted organisms in 5 min. We also demonstrated the ability of the microarray to determine subgroup affiliations for B. cereus group isolates without rRNA sequencing. Correlation of these seven subgroups with groupings based on multilocus sequence typing (MLST), fluorescent amplified fragment length polymorphism analysis (AFLP) and multilocus enzyme electrophoresis (MME) analysis of a wide spectrum of different genes, and the demonstration of subgroup-specific differences in toxin profiles, psychrotolerance, and the ability to harbor some plasmids, suggest that these seven subgroups are not based solely on neutral genomic polymorphisms, but instead reflect

  14. Promises of silent salesman to the FMCG industry: an investigation using linear discriminant analysis approach

    Directory of Open Access Journals (Sweden)

    Shekhar Suraj Kushe

    2015-12-01

    Full Text Available Packaging which is often called as the ‘silent salesman’ is an important component of marketing. Today the importance of packaging has risen to such an extent that product packaging is rightly called as the fifth ‘P’ of marketing mix. FMCG are products which are utilized by large number of people. The present study examined the discriminating power of five selected FMCG packaging variables namely ‘picture’, ‘colour’, ‘size’, ‘shape’ and ‘material’ amidst those who purchased FMCG based on these packaging variables and for those who purchased FMCG not based on these packaging variables. Descriptive research was carried out in the study. Respondents (students were asked to rate four packaging variable on a five point Likert’s scale. Discriminant analysis showed that only two variables namely ‘Colour’ (.706 and ‘Shape’ (–.527 were good predictors. Variables ‘Picture’, ‘size’ and ‘material’ were considered as poor predictors as far as the student communities were considered. The cross validated classification showed that out of the 240 samples drawn, 91.8% of the cases were correctly classified.

  15. Sex determination from the talus in a contemporary Greek population using discriminant function analysis.

    Science.gov (United States)

    Peckmann, Tanya R; Orr, Kayla; Meek, Susan; Manolis, Sotiris K

    2015-07-01

    The determination of sex is an important part of building the biological profile for unknown human remains. Many of the bones traditionally used for the determination of sex are often found fragmented or incomplete in forensic and archaeological cases. The goal of the present research was to derive discriminant function equations from the talus, a preservationally favoured bone, for sexing skeletons from a contemporary Greek population. Nine parameters were measured on 182 individuals (96 males and 86 females) from the University of Athens Human Skeletal Reference Collection. The individuals ranged in age from 20 to 99 years old. The statistical analyses showed that all measured parameters were sexually dimorphic. Discriminant function score equations were generated for use in sex determination. The average accuracy of sex classification ranged from 65.2% to 93.4% for the univariate analysis, 90%-96.5% for the direct method and 86.7% for the stepwise method. Comparisons to other populations were made. Overall, the cross-validated accuracies ranged from 65.5% to 83.2% and males were most often correctly identified. The talus was shown to be useful for sex determination in the modern Greek population.

  16. Sex determination from the calcaneus in a 20th century Greek population using discriminant function analysis.

    Science.gov (United States)

    Peckmann, Tanya R; Orr, Kayla; Meek, Susan; Manolis, Sotiris K

    2015-12-01

    The skull and post-cranium have been used for the determination of sex for unknown human remains. However, in forensic cases where skeletal remains often exhibit postmortem damage and taphonomic changes the calcaneus may be used for the determination of sex as it is a preservationally favored bone. The goal of the present research was to derive discriminant function equations from the calcaneus for estimation of sex from a contemporary Greek population. Nine parameters were measured on 198 individuals (103 males and 95 females), ranging in age from 20 to 99 years old, from the University of Athens Human Skeletal Reference Collection. The statistical analyses showed that all variables were sexually dimorphic. Discriminant function score equations were generated for use in sex determination. The average accuracy of sex classification ranged from 70% to 90% for the univariate analysis, 82.9% to 87.5% for the direct method, and 86.2% for the stepwise method. Comparisons to other populations were made. Overall, the cross-validated accuracies ranged from 48.6% to 56.1% with males most often identified correctly and females most often misidentified. The calcaneus was shown to be useful for sex determination in the twentieth century Greek population.

  17. Wave Mode Discrimination of Coded Ultrasonic Guided Waves Using Two-Dimensional Compressed Pulse Analysis.

    Science.gov (United States)

    Malo, Sergio; Fateri, Sina; Livadas, Makis; Mares, Cristinel; Gan, Tat-Hean

    2017-07-01

    Ultrasonic guided waves testing is a technique successfully used in many industrial scenarios worldwide. For many complex applications, the dispersive nature and multimode behavior of the technique still poses a challenge for correct defect detection capabilities. In order to improve the performance of the guided waves, a 2-D compressed pulse analysis is presented in this paper. This novel technique combines the use of pulse compression and dispersion compensation in order to improve the signal-to-noise ratio (SNR) and temporal-spatial resolution of the signals. The ability of the technique to discriminate different wave modes is also highlighted. In addition, an iterative algorithm is developed to identify the wave modes of interest using adaptive peak detection to enable automatic wave mode discrimination. The employed algorithm is developed in order to pave the way for further in situ applications. The performance of Barker-coded and chirp waveforms is studied in a multimodal scenario where longitudinal and flexural wave packets are superposed. The technique is tested in both synthetic and experimental conditions. The enhancements in SNR and temporal resolution are quantified as well as their ability to accurately calculate the propagation distance for different wave modes.

  18. A New Method for Improving the Discrimination Power and Weights Dispersion in the Data Envelopment Analysis

    Directory of Open Access Journals (Sweden)

    S. Kordrostami

    2013-06-01

    Full Text Available The appropriate choice of input-output weights is necessary to have a successful DEA model. Generally, if the number of DMUs i.e., n, is less than number of inputs and outputs i.e., m+s, then many of DMUs are introduced as efficient then the discrimination between DMUs is not possible. Besides, DEA models are free to choose the best weights. For resolving the problems that are resulted from freedom of weights, some constraints are set on the input-output weights. Symmetric weight constraints are a kind of weight constrains. In this paper, we represent a new model based on a multi-criterion data envelopment analysis (MCDEA are developed to moderate the homogeneity of weights distribution by using symmetric weight constrains.Consequently, we show that the improvement of the dispersal of unrealistic input-output weights and the increasing discrimination power for our suggested models. Finally, as an application of the new model, we use this model to evaluate and ranking guilan selected hospitals.

  19. Image analysis of food particles can discriminate deficient mastication of mixed foodstuffs simulating daily meal.

    Science.gov (United States)

    Sugimoto, K; Hashimoto, Y; Fukuike, C; Kodama, N; Minagi, S

    2014-03-01

    Because food texture is regarded as an important factor for smooth deglutition, identification of objective parameters that could provide a basis for food texture selection for elderly or dysphagic patients is of great importance. We aimed to develop an objective evaluation method of mastication using a mixed test food comprising foodstuffs, simulating daily dietary life. The particle size distribution (>2 mm in diameter) in a bolus was analysed using a digital image under dark-field illumination. Ten female participants (mean age ± s.d., 27·6 ± 2·6 years) masticated a mixed test food comprising prescribed amounts of rice, sausage, hard omelette, raw cabbage and raw cucumber with 100%, 75%, 50% and 25% of the number of their masticatory strokes. A single set of coefficient thresholds of 0·10 for the homogeneity index and 1·62 for the particle size index showed excellent discrimination of deficient masticatory conditions with high sensitivity (0·90) and specificity (0·77). Based on the results of this study, normal mastication was discriminated from deficient masticatory conditions using a large particle analysis of mixed foodstuffs, thus showing the possibility of future application of this method for objective decision-making regarding the properties of meals served to dysphagic patients.

  20. Discriminative analysis of brain functional connectivity patterns for mental fatigue classification.

    Science.gov (United States)

    Sun, Yu; Lim, Julian; Meng, Jianjun; Kwok, Kenneth; Thakor, Nitish; Bezerianos, Anastasios

    2014-10-01

    Mental fatigue is a commonly experienced state that can be induced by placing heavy demands on cognitive systems. This often leads to lowered productivity and increased safety risks. In this study, we developed a functional-connectivity based mental fatigue monitoring method. Twenty-six subjects underwent a 20-min mentally demanding test of sustained attention with high-resolution EEG monitoring. Functional connectivity patterns were obtained on the cortical surface via source localization of cortical activities in the first and last 5-min quartiles of the experiment. Multivariate pattern analysis was then adopted to extract the highly discriminative functional connectivity information. The algorithm used in the present study demonstrated an overall accuracy of 81.5% (p fatigue classification through leave-one-out cross validation. Moreover, we found that the most discriminative connectivity features were located in or across middle frontal gyrus and several motor areas, in agreement with the important role that these cortical regions play in the maintenance of sustained attention. This work therefore demonstrates the feasibility of a functional-connectivity-based mental fatigue assessment method, opening up a new avenue for modeling natural brain dynamics under different mental states. Our method has potential applications in several domains, including traffic and industrial safety.

  1. Group Counseling with United States Racial Minority Groups: A 25-Year Content Analysis

    Science.gov (United States)

    Stark-Rose, Rose M.; Livingston-Sacin, Tina M.; Merchant, Niloufer; Finley, Amanda C.

    2012-01-01

    A 25-year content analysis was conducted of published group work articles that focused on 5 racial groups (African American, Asian American/Pacific Islander, Latino/a, Native American, and Intercultural group). Articles were included if they described an intervention or conceptual model with 1 of the racial groups. The analysis revealed 15 content…

  2. Group Counseling with United States Racial Minority Groups: A 25-Year Content Analysis

    Science.gov (United States)

    Stark-Rose, Rose M.; Livingston-Sacin, Tina M.; Merchant, Niloufer; Finley, Amanda C.

    2012-01-01

    A 25-year content analysis was conducted of published group work articles that focused on 5 racial groups (African American, Asian American/Pacific Islander, Latino/a, Native American, and Intercultural group). Articles were included if they described an intervention or conceptual model with 1 of the racial groups. The analysis revealed 15 content…

  3. [Discriminant analysis of raw milk adulterated with botanical filling material using near infrared spectroscopy].

    Science.gov (United States)

    Li, Liang; Ding, Wu

    2010-05-01

    In order to find out a fast measure method of adulterated milk based on near infrared spectroscopy, milk adulterated with plant butter, vegetable protein and starch was collected respectively. Using Fourier transform near infrared spectroscopy to scan the samples, the spectrum data were obtained. The samples were scanned in the spectral region between 4 000 and 12 000 cm(-1) by FT-NIR spectrometer with an optic fiber of 2 mm path-length and an InGaAs detector. Then all data were analyzed by principal component analysis combined with Fisher line discriminant analysis (FLDA) and partial least squares (PLS). Results show that the accumulative reliabilities of the first six components were more than 99%, so the first six components were applied as FLDA inputs and the values of the type of milk were applied as the outputs. An adulterated milk qualitative discriminant model based on Fisher line discriminant analysis was developed finally. The result indicated that the accuracy of detection of calibration samples is 97.78%. The unknown test samples were tested by this model and the correct identification rate is 94.44%. Partial least square models for detecting the content of material added to raw milk were set up with good veracity. The predictive correlation coefficient (R2) of calibration sets of milk adulterated with plant butter, vegetable protein and starch are 99.08%, 99.96% and 99.39%, respectively, while the root mean square errors of cross validation (RMSECV) of the three calibration sets are 0.304%, 0.013 5% and 0.060%, respectively. The R2 of validation sets of the three kinds of adulterated milk are 98.50%, 99.94% and 98.50%, respectively, while the root mean square errors of prediction (RMSEP) of the three validation sets are 0.323%, 0.028 8% and 0.068%, respectively. All of these suggested that near infrared spectroscopy has good potential for rapid qualitative and quantitative detection of milk adulterated with botanical filling material.

  4. Mutual Group Hypnosis: A Social Interaction Analysis.

    Science.gov (United States)

    Sanders, Shirley

    Mutual Group Hypnosis is discussed in terms of its similarity to group dynamics in general and in terms of its similarity to a social interaction program (Role Modeling) designed to foster the expression of warmth and acceptance among group members. Hypnosis also fosters a regression to prelogical thought processes in the service of the ego. Group…

  5. Mutual Group Hypnosis: A Social Interaction Analysis.

    Science.gov (United States)

    Sanders, Shirley

    Mutual Group Hypnosis is discussed in terms of its similarity to group dynamics in general and in terms of its similarity to a social interaction program (Role Modeling) designed to foster the expression of warmth and acceptance among group members. Hypnosis also fosters a regression to prelogical thought processes in the service of the ego. Group…

  6. Variable Selection and Updating In Model-Based Discriminant Analysis for High Dimensional Data with Food Authenticity Applications.

    Science.gov (United States)

    Murphy, Thomas Brendan; Dean, Nema; Raftery, Adrian E

    2010-03-01

    Food authenticity studies are concerned with determining if food samples have been correctly labelled or not. Discriminant analysis methods are an integral part of the methodology for food authentication. Motivated by food authenticity applications, a model-based discriminant analysis method that includes variable selection is presented. The discriminant analysis model is fitted in a semi-supervised manner using both labeled and unlabeled data. The method is shown to give excellent classification performance on several high-dimensional multiclass food authenticity datasets with more variables than observations. The variables selected by the proposed method provide information about which variables are meaningful for classification purposes. A headlong search strategy for variable selection is shown to be efficient in terms of computation and achieves excellent classification performance. In applications to several food authenticity datasets, our proposed method outperformed default implementations of Random Forests, AdaBoost, transductive SVMs and Bayesian Multinomial Regression by substantial margins.

  7. A discrimination technique for extensive air showers based on multiscale, lacunarity and neural network analysis

    Science.gov (United States)

    Pagliaro, Antonio; D'Alí Staiti, G.; D'Anna, F.

    2011-03-01

    We present a new method for the identification of extensive air showers initiated by different primaries. The method uses the multiscale concept and is based on the analysis of multifractal behaviour and lacunarity of secondary particle distributions together with a properly designed and trained artificial neural network. In the present work the method is discussed and applied to a set of fully simulated vertical showers, in the experimental framework of ARGO-YBJ, to obtain hadron to gamma primary separation. We show that the presented approach gives very good results, leading, in the 1-10 TeV energy range, to a clear improvement of the discrimination power with respect to the existing figures for extended shower detectors.

  8. A discrimination technique for extensive air showers based on multiscale, lacunarity and neural network analysis

    Energy Technology Data Exchange (ETDEWEB)

    Pagliaro, Antonio, E-mail: pagliaro@ifc.inaf.it [Istituto di Astrofisica Spaziale e Fisica Cosmica di Palermo - Istituto Nazionale di Astrofisica, Via Ugo La Malfa 153, 90146 Palermo (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Catania, Viale A. Doria 6, 95125 Catania (Italy); D' Ali Staiti, G. [Universita degli Studi di Palermo, Dipartimento di Fisica e Tecnologie Relative, Viale delle Scienze, Edificio 18, 90128 Palermo (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Catania, Viale A. Doria 6, 95125 Catania (Italy); D' Anna, F. [Istituto di Astrofisica Spaziale e Fisica Cosmica di Palermo - Istituto Nazionale di Astrofisica, Via Ugo La Malfa 153, 90146 Palermo (Italy)

    2011-03-15

    We present a new method for the identification of extensive air showers initiated by different primaries. The method uses the multiscale concept and is based on the analysis of multifractal behaviour and lacunarity of secondary particle distributions together with a properly designed and trained artificial neural network. In the present work the method is discussed and applied to a set of fully simulated vertical showers, in the experimental framework of ARGO-YBJ, to obtain hadron to gamma primary separation. We show that the presented approach gives very good results, leading, in the 1-10 TeV energy range, to a clear improvement of the discrimination power with respect to the existing figures for extended shower detectors.

  9. GSK-3β polymorphism discriminates bipolar disorder and schizophrenia: a systematic meta-analysis.

    Science.gov (United States)

    Tang, Hui; Shen, Na; Jin, Huijuan; Liu, Dan; Miao, Xiaoping; Zhu, Ling-Qiang

    2013-12-01

    Glycogen synthase kinase 3 (GSK-3) is a well-known conserved and ubiquitous protein kinase and playing a pivotal role in neurodevelopment, neurogenesis, learning/memory, and neuronal cell death. Dysfunction of GSK-3 had been seen in multiple neurodegenerative and psychiatric diseases. Bipolar disorder and schizophrenia are two common psychiatric diseases first occur in adolescence or young adulthood. They share similar risk genes as well as clinical symptoms, which make it is difficult to be discriminated from each other. Here, by using meta-analysis we reported that glycogen synthase kinase 3β promoter inactive mutant rs334558 may contribute to the development of schizophrenia not bipolar disorder. This might be used to distinguish these two diseases.

  10. A Multimodal Biometric System Using Linear Discriminant Analysis For Improved Performance

    CERN Document Server

    Khan, Aamir; Khurshid, Aasim; Akram, Adeel

    2012-01-01

    Essentially a biometric system is a pattern recognition system which recognizes a user by determining the authenticity of a specific anatomical or behavioral characteristic possessed by the user. With the ever increasing integration of computers and Internet into daily life style, it has become necessary to protect sensitive and personal data. This paper proposes a multimodal biometric system which incorporates more than one biometric trait to attain higher security and to handle failure to enroll situations for some users. This paper is aimed at investigating a multimodal biometric identity system using Linear Discriminant Analysis as backbone to both facial and speech recognition and implementing such system in real-time using SignalWAVE.

  11. External Defect classification of Citrus Fruit Images using Linear Discriminant Analysis Clustering and ANN classifiers

    Directory of Open Access Journals (Sweden)

    K.Vijayarekha

    2012-12-01

    Full Text Available Linear Discriminant Analysis (LDA is one technique for transforming raw data into a new feature space in which classification can be carried out more robustly. It is useful where the within-class frequencies are unequal. This method maximizes the ratio of between-class variance to the within-class variance in any particular data set and the maximal separability is guaranteed. LDA clustering models are used to classify object into different category. This study makes use of LDA for clustering the features obtained for the citrus fruit images taken in five different domains. Sub-windows of size 40x40 are cropped from the citrus fruit images having defects such as pitting, splitting and stem end rot. Features are extracted in four domains such as statistical features, fourier transform based features, discrete wavelet transform based features and stationary wavelet transform based features. The results of clustering and classification using LDA and ANN classifiers are reported

  12. Efficient Discriminate Component Analysis using Support Vector Machine Classifier on Invariant Pose and Illumination Face Images

    Directory of Open Access Journals (Sweden)

    R. Rajalakshmi

    2015-03-01

    Full Text Available Face recognition is the process of categorizing a person in an image by evaluating with a known face image library. The pose and illumination variations are two main practical confronts for an automatic face recognition system. This study proposes a novel face recognition algorithm known as Efficient Discriminant Component Analysis (EDCA for face recognition under varying poses and illumination conditions. This EDCA algorithm overcomes the high dimensionality problem in the feature space by extracting features from the low dimensional frequency band of the image. It combines the features of both LDA and PCA algorithms and these features are used in the training set and is classified using Support Vector Machine classifier. The experiments were performed on the CMU-PIE datasets. The experimental results show that the proposed algorithm produces a higher recognition rate than the existing LDA and PCA based face recognition techniques.

  13. On Kolmogorov asymptotics of estimators of the misclassification error rate in linear discriminant analysis

    KAUST Repository

    Zollanvari, Amin

    2013-05-24

    We provide a fundamental theorem that can be used in conjunction with Kolmogorov asymptotic conditions to derive the first moments of well-known estimators of the actual error rate in linear discriminant analysis of a multivariate Gaussian model under the assumption of a common known covariance matrix. The estimators studied in this paper are plug-in and smoothed resubstitution error estimators, both of which have not been studied before under Kolmogorov asymptotic conditions. As a result of this work, we present an optimal smoothing parameter that makes the smoothed resubstitution an unbiased estimator of the true error. For the sake of completeness, we further show how to utilize the presented fundamental theorem to achieve several previously reported results, namely the first moment of the resubstitution estimator and the actual error rate. We provide numerical examples to show the accuracy of the succeeding finite sample approximations in situations where the number of dimensions is comparable or even larger than the sample size.

  14. On Kolmogorov Asymptotics of Estimators of the Misclassification Error Rate in Linear Discriminant Analysis.

    Science.gov (United States)

    Zollanvari, Amin; Genton, Marc G

    2013-08-01

    We provide a fundamental theorem that can be used in conjunction with Kolmogorov asymptotic conditions to derive the first moments of well-known estimators of the actual error rate in linear discriminant analysis of a multivariate Gaussian model under the assumption of a common known covariance matrix. The estimators studied in this paper are plug-in and smoothed resubstitution error estimators, both of which have not been studied before under Kolmogorov asymptotic conditions. As a result of this work, we present an optimal smoothing parameter that makes the smoothed resubstitution an unbiased estimator of the true error. For the sake of completeness, we further show how to utilize the presented fundamental theorem to achieve several previously reported results, namely the first moment of the resubstitution estimator and the actual error rate. We provide numerical examples to show the accuracy of the succeeding finite sample approximations in situations where the number of dimensions is comparable or even larger than the sample size.

  15. Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    CHEN Xinyi; YAN Xuefeng

    2013-01-01

    Fault diagnosis and monitoring are very important for complex chemical process.There are numerous methods that have been studied in this field,in which the effective visualization method is still challenging.In order to get a better visualization effect,a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed.FDA can reduce the dimension of the data in terms of maximizing the separability of the classes.After feature extraction by FDA,SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states.Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method.The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.

  16. Study of major volatiles in wines and discriminant analysis applied to classification according to region.

    Science.gov (United States)

    Huerta-Díaz-Regañon, M D; Salinas Fernández, M R; Masoud, T

    1997-12-01

    The major volatiles of eighty eight wines (white, rosè and red) from Madrid were studied. The samples came from the three districts forming the "Vinos de Madrid" DO (Denominación de Origen) region: Arganda, Navalcarnero and San Martín, and were analyzed by gas chromatography. The resulting data were treated by Stepwise Discriminant Analysis (SDA) in order to ascertain the efficacity of these compounds in classifying the wines according to their geographical origin. The results confirm that the above components were of little use in classifying the red and white wines and, although a correct classification percentage of 90.91% was obtained for the rosés when all the variables were used, this too was considered unsatisfactory.

  17. The Importance of the Discriminant Analysis for the Evolution of the Equity Prices

    Directory of Open Access Journals (Sweden)

    Dinca G.

    2014-12-01

    Full Text Available This paper aims to show the correlation between the results obtained using the discriminant analysis and the evolution of stock prices of listed Romanian companies. For this purpose, we have carried out the research on a sample of 32 issuers from categories I and II of the Bucharest Stock Exchange, pertaining to nine economic sectors, for the period 2010- 2012. Our study is based on the Anghel prediction model of bankruptcy, using the stock prices of the 32 listed companies from the first and the last day of trading for each year examined. The results obtained by applying the prediction model allow the classification of issuers into potential bankrupt and non-bankrupt firms and help investors take appropriate decisions on the stock market.

  18. Quantitative Classification of Quartz by Laser Induced Breakdown Spectroscopy in Conjunction with Discriminant Function Analysis

    Directory of Open Access Journals (Sweden)

    A. Ali

    2016-01-01

    Full Text Available A responsive laser induced breakdown spectroscopic system was developed and improved for utilizing it as a sensor for the classification of quartz samples on the basis of trace elements present in the acquired samples. Laser induced breakdown spectroscopy (LIBS in conjunction with discriminant function analysis (DFA was applied for the classification of five different types of quartz samples. The quartz plasmas were produced at ambient pressure using Nd:YAG laser at fundamental harmonic mode (1064 nm. We optimized the detection system by finding the suitable delay time of the laser excitation. This is the first study, where the developed technique (LIBS+DFA was successfully employed to probe and confirm the elemental composition of quartz samples.

  19. Cross View Gait Recognition Using Joint-Direct Linear Discriminant Analysis

    Science.gov (United States)

    Portillo-Portillo, Jose; Leyva, Roberto; Sanchez, Victor; Sanchez-Perez, Gabriel; Perez-Meana, Hector; Olivares-Mercado, Jesus; Toscano-Medina, Karina; Nakano-Miyatake, Mariko

    2016-01-01

    This paper proposes a view-invariant gait recognition framework that employs a unique view invariant model that profits from the dimensionality reduction provided by Direct Linear Discriminant Analysis (DLDA). The framework, which employs gait energy images (GEIs), creates a single joint model that accurately classifies GEIs captured at different angles. Moreover, the proposed framework also helps to reduce the under-sampling problem (USP) that usually appears when the number of training samples is much smaller than the dimension of the feature space. Evaluation experiments compare the proposed framework’s computational complexity and recognition accuracy against those of other view-invariant methods. Results show improvements in both computational complexity and recognition accuracy. PMID:28025484

  20. Cross View Gait Recognition Using Joint-Direct Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Jose Portillo-Portillo

    2016-12-01

    Full Text Available This paper proposes a view-invariant gait recognition framework that employs a unique view invariant model that profits from the dimensionality reduction provided by Direct Linear Discriminant Analysis (DLDA. The framework, which employs gait energy images (GEIs, creates a single joint model that accurately classifies GEIs captured at different angles. Moreover, the proposed framework also helps to reduce the under-sampling problem (USP that usually appears when the number of training samples is much smaller than the dimension of the feature space. Evaluation experiments compare the proposed framework’s computational complexity and recognition accuracy against those of other view-invariant methods. Results show improvements in both computational complexity and recognition accuracy.

  1. A Multimodal Biometric System Using Linear Discriminant Analysis For Improved Performance

    Directory of Open Access Journals (Sweden)

    Aamir Khan

    2011-11-01

    Full Text Available Essentially a biometric system is a pattern recognition system which recognizes a user by determining the authenticity of a specific anatomical or behavioral characteristic possessed by the user. With the ever increasing integration of computers and Internet into daily life style, it has become necessary to protect sensitive and personal data. This paper proposes a multimodal biometric system which incorporates more than one biometric trait to attain higher security and to handle failure to enroll situations for some users. This paper is aimed at investigating a multimodal biometric identity system using Linear Discriminant Analysis as backbone to both facial and speech recognition and implementing such system in real-time using SignalWAVE.

  2. Multiple binary classifications via linear discriminant analysis for improved controllability of a powered prosthesis.

    Science.gov (United States)

    Hargrove, Levi J; Scheme, Erik J; Englehart, Kevin B; Hudgins, Bernard S

    2010-02-01

    This paper describes a novel pattern recognition based myoelectric control system that uses parallel binary classification and class specific thresholds. The system was designed with an intuitive configuration interface, similar to existing conventional myoelectric control systems. The system was assessed quantitatively with a classification error metric and functionally with a clothespin test implemented in a virtual environment. For each case, the proposed system was compared to a state-of-the-art pattern recognition system based on linear discriminant analysis and a conventional myoelectric control scheme with mode switching. These assessments showed that the proposed control system had a higher classification error ( p myoelectric control system ( p myoelectric control system which is robust, easily configured, and highly usable.

  3. Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis.

    Science.gov (United States)

    Acquah, Gifty E; Via, Brian K; Billor, Nedret; Fasina, Oladiran O; Eckhardt, Lori G

    2016-08-27

    As new markets, technologies and economies evolve in the low carbon bioeconomy, forest logging residue, a largely untapped renewable resource will play a vital role. The feedstock can however be variable depending on plant species and plant part component. This heterogeneity can influence the physical, chemical and thermochemical properties of the material, and thus the final yield and quality of products. Although it is challenging to control compositional variability of a batch of feedstock, it is feasible to monitor this heterogeneity and make the necessary changes in process parameters. Such a system will be a first step towards optimization, quality assurance and cost-effectiveness of processes in the emerging biofuel/chemical industry. The objective of this study was therefore to qualitatively classify forest logging residue made up of different plant parts using both near infrared spectroscopy (NIRS) and Fourier transform infrared spectroscopy (FTIRS) together with linear discriminant analysis (LDA). Forest logging residue harvested from several Pinus taeda (loblolly pine) plantations in Alabama, USA, were classified into three plant part components: clean wood, wood and bark and slash (i.e., limbs and foliage). Five-fold cross-validated linear discriminant functions had classification accuracies of over 96% for both NIRS and FTIRS based models. An extra factor/principal component (PC) was however needed to achieve this in FTIRS modeling. Analysis of factor loadings of both NIR and FTIR spectra showed that, the statistically different amount of cellulose in the three plant part components of logging residue contributed to their initial separation. This study demonstrated that NIR or FTIR spectroscopy coupled with PCA and LDA has the potential to be used as a high throughput tool in classifying the plant part makeup of a batch of forest logging residue feedstock. Thus, NIR/FTIR could be employed as a tool to rapidly probe/monitor the variability of forest

  4. Neighbor Class Linear Discriminate Analysis%近邻类鉴别分析方法

    Institute of Scientific and Technical Information of China (English)

    王言伟; 丁晓青; 刘长松

    2012-01-01

    提出一种近邻类鉴别分析方法,线性鉴别分析是该方法的一个特例.线性鉴别分析通过最大化类间散度同时最小化类内散度寻找最佳投影,其中类间散度是所有类之间散度的总体平均;而近邻类鉴别分析中类间散度定义为各个类与其k个近邻类之间的平均散度.该方法通过选取适当的近邻类数,能够缓解线性鉴别降维后造成的部分类的重叠.实验结果表明近邻类鉴别分析方法性能稳定且优于传统的线性鉴别分析.%A method of neighbor class linear discriminant analysis (NCLDA) is proposed. Linear discriminant analysis ( LDA) is a special case of this method. LDA finds the optimal projections by maximum between-class scatter while by minimum within-class scatter. The between-class scatter is an average over divergences among all classes. In NCLDA, between-class scatter is defined as average divergences between one class and its k nearest neighbor classes. By selecting proper numbers of neighbor class, NCLDA alleviates overlaps among classes caused by LDA. The experimental results show that the proposed NCLDA is robust and outperforms LDA.

  5. Discrimination in relation to parenthood reported by community psychiatric service users in the UK: a framework analysis.

    Science.gov (United States)

    Jeffery, Debra; Clement, Sarah; Corker, Elizabeth; Howard, Louise M; Murray, Joanna; Thornicroft, Graham

    2013-04-20

    Experienced discrimination refers to an individual's perception that they have been treated unfairly due to an attribute and is an important recent focus within stigma research. A significant proportion of mental health service users report experiencing mental illness-based discrimination in relation to parenthood. Existing studies in this area have not gone beyond prevalence, therefore little is known about the nature of experienced discrimination in relation to parenthood, and how is it constituted. This study aims to generate a typology of community psychiatric service users' reports of mental illness-based discrimination in relation to becoming or being a parent. A secondary aim is to assess the prevalence of these types of experienced discrimination. In a telephone survey 2026 community psychiatric service users in ten UK Mental Health service provider organisations (Trusts) were asked about discrimination experienced in the previous 12 months using the Discrimination and Stigma Scale (DISC). The sample were asked if, due to their mental health problem, they had been treated unfairly in starting a family, or in their role as a parent, and gave examples of this. Prevalence is reported and the examples of experienced discrimination in relation to parenthood were analysed using the framework method of qualitative analysis. Three hundred and four participants (73% female) reported experienced discrimination, with prevalences of 22.5% and 28.3% for starting a family and for the parenting role respectively. Participants gave 89 examples of discrimination about starting a family and 228 about parenting, and these occurred in social and professional contexts. Ten themes were identified. These related to being seen as an unfit parent; people not being understanding; being stopped from having children; not being allowed to see their children; not getting the support needed; children being affected; children avoiding their parents; children's difficulties being blamed

  6. Mid infrared and fluorescence spectroscopies coupled with factorial discriminant analysis technique to identify sheep milk from different feeding systems

    OpenAIRE

    Karoui, Romdhane; Hammami, Moncef; Rouissi, Hamadi; Blecker, Christophe

    2011-01-01

    Mid infrared spectroscopy (MIR) combined with multivariate data analysis was used to discriminate between ewes milk samples according to their feeding systems (controls, ewes fed scotch bean and ewes fed soybean). The MIR spectra were scanned throughout the first 11 weeks of the lactation stage. When factorial discriminant analysis (FDA) with leave one-out cross-validation was applied, separately, to the three spectral regions in the MIR (i.e. 3000-2800, 1700-1500 and 1500-900 cm(-1)), the cl...

  7. Statistical analysis of Thematic Mapper Simulator data for the geobotanical discrimination of rock types in southwest Oregon

    Science.gov (United States)

    Morrissey, L. A.; Weinstock, K. J.; Mouat, D. A.; Card, D. H.

    1984-01-01

    An evaluation of Thematic Mapper Simulator (TMS) data for the geobotanical discrimination of rock types based on vegetative cover characteristics is addressed in this research. A methodology for accomplishing this evaluation utilizing univariate and multivariate techniques is presented. TMS data acquired with a Daedalus DEI-1260 multispectral scanner were integrated with vegetation and geologic information for subsequent statistical analyses, which included a chi-square test, an analysis of variance, stepwise discriminant analysis, and Duncan's multiple range test. Results indicate that ultramafic rock types are spectrally separable from nonultramafics based on vegetative cover through the use of statistical analyses.

  8. Three applications of functional analysis with group dynamic cognitive behavioral group therapy.

    Science.gov (United States)

    Scharwächter, Peter

    2008-01-01

    Case illustrations from group dynamic cognitive behavioral group therapy are presented to demonstrate three applications of functional analysis and the resulting cognitive behavioral interventions. The principles of group dynamic cognitive behavioral group therapy are explained. A functional analysis is applied first to the problem behavior of an individual group member. A clinical case illustrates how the group members help to change this individual group member's behavior from a learning theory perspective. Next, the circular interactional problem behavior between two group members is reduced to the individual functional analysis of each of the two member's problem behaviors. It is then illustrated how the two group member's problem behaviors, as well as feedback from others, contribute toward helping to change each others behavior. The paper concludes that functional analysis and ensuing behavioral interventions can be also applied to group as a whole behavior.

  9. Expert group formation using facility location analysis

    NARCIS (Netherlands)

    Neshati, Mahmood; Beigy, Hamid; Hiemstra, Djoerd

    2014-01-01

    In this paper, we propose an optimization framework to retrieve an optimal group of experts to perform a multi-aspect task. While a diverse set of skills are needed to perform a multi-aspect task, the group of assigned experts should be able to collectively cover all these required skills. We consid

  10. Rapid discrimination of Salmonella isolates by single-strand conformation polymorphism analysis.

    Science.gov (United States)

    Al-Adhami, Batol H; Huby-Chilton, Florence; Blais, Burton W; Martinez-Perez, Amalia; Chilton, Neil B; Gajadhar, Alvin A

    2008-10-01

    A molecular typing technique was developed for the differentiation of Salmonella isolates based on single-strand conformation polymorphism (SSCP) analysis of amplicons generated by PCR. Amplicons from parts of the fimA (both the 5' and 3' ends), mdh, invA, and atpD genes were generated separately from a panel of Salmonella strains representing Salmonella bongori, and four subspecies and 17 serovars of Salmonella enterica. These amplicons were subjected to SSCP analysis for differentiation of the salmonellae on the basis of different conformational forms arising due to nucleotide sequence variations in the target genes. Several distinct SSCP banding patterns (a maximum of 14 each for atpD and fimA 3' end) were observed with this panel of Salmonella strains for amplicons generated from each target gene. The best discrimination of Salmonella subspecies and serovar was achieved from the SSCP analysis of a combination of at least three gene targets: atpD, invA, and either mdh or fimA 3' end. This demonstrates the applicability of SSCP analysis as an important additional method to classical typing approaches for the differentiation of foodborne Salmonella isolates. SSCP is simple to perform and should be readily transferable to food microbiology laboratories with basic PCR capability.

  11. Analysis of transference in Gestalt group psychotherapy.

    Science.gov (United States)

    Frew, J E

    1990-04-01

    In Gestalt therapy, transference is viewed as a contact boundary disturbance which impairs the patient's ability to accurately perceive the present therapy situation. The boundary disturbances in Gestalt therapy most closely related to the analytic notion of transference are projection, introjection, and confluence. In Gestalt group psychotherapy, group members interfere with the process of need identification and satisfaction by distorting their contact with each other through projecting, introjecting, and being confluent. The Gestalt group therapist uses interventions directed to individuals and to the group to increase participants' awareness of these boundary disturbances and of the present contact opportunities available to them when these disturbances are resolved. In formulating interventions, the leader is mindful of the function of boundary disturbances to the group-as-a-whole as well as to individuals.

  12. Demographic Consequences of Gender Discrimination in China: Simulation Analysis of Policy Options

    OpenAIRE

    Quanbao, Jiang; Shuzhuo, Li; Marcus W., Feldman

    2011-01-01

    The large number of missing females in China, a consequence of gender discrimination, is having and will continue to have a profound effect on the country's population development. In this paper, we analyze the causes of this gender discrimination in terms of institutions, culture and, economy, and suggest public policies that might help eliminate gender discrimination. Using a population simulation model, we study the effect of public policies on the sex ratio at birth and excess female chil...

  13. The convergent and discriminant validity of burnout measures in sport: a multi-trait/multi-method analysis.

    Science.gov (United States)

    Cresswell, Scott L; Eklund, Robert C

    2006-02-01

    Athlete burnout research has been hampered by the lack of an adequate measurement tool. The Athlete Burnout Questionnaire (ABQ) and the Maslach Burnout Inventory General Survey (MBI-GS) are two recently developed self-report instruments designed to assess burnout. The convergent and discriminant validity of the ABQ and MBI-GS were assessed through multi-trait/multi-method analysis with a sporting population. Overall, the ABQ and the MBI-GS displayed acceptable convergent validity with matching subscales highly correlated, and satisfactory internal discriminant validity with lower correlations between non-matching subscales. Both scales also indicated an adequate discrimination between the concepts of burnout and depression. These findings add support to previous findings in non-sporting populations that depression and burnout are separate constructs. Based on the psychometric results, construct validity analysis and practical considerations, the results support the use of the ABQ to assess athlete burnout.

  14. A Brief Analysis of Discrimination in Language Classroom--from the Perspecti ve of Soci oli ngui sti cs

    Institute of Scientific and Technical Information of China (English)

    曹志勇

    2014-01-01

    It is widely acknowledged that sociolinguistics is the study of language in certain context concerned with our soci-ety. Sociolinguistics and linguistics are intrinsically related to each other, but there has been difference as well. Linguistics research deals with language system itself, which belongs to the micro lev-el on the one hand; many phenomena reflect discrimination in language classroom, these discrimination are caused by social fac-tors to a certain degree. This paper makes a brief analysis of dis-crimination in language classroom from the perspective of socio-linguistics, which deals with many issues such as depiction of lan-guage discrimination、analysis of phenomenon and accordingly-solved measures.

  15. Classification of the long-QT syndrome based on discriminant analysis of T-wave morphology

    DEFF Research Database (Denmark)

    Struijk, Johannes; Kanters, J.K.; Andersen, Mads Peter

    2006-01-01

    been shown to be useful discriminators, but no single ECG parameter has been sufficient to solve the diagnostic problem. In this study we present a method for discrimination among persons with a normal genotype and those with mutations in the KCNQ1 (KvLQT1 or LQT1) and KCNH2 (HERG or LQT2) genes...... on the basis of parameters describing T-wave morphology in terms of duration, asymmetry, flatness and amplitude. Discriminant analyses based on 4 or 5 parameters both resulted in perfect discrimination in a learning set of 36 subjects. In both cases cross-validation of the resulting classifiers showed...

  16. Discriminant analysis to classify glioma grading using dynamic contrast-enhanced MRI and immunohistochemical markers

    Energy Technology Data Exchange (ETDEWEB)

    Awasthi, Rishi [Sanjay Gandhi Post Graduate Institute of Medical Sciences, Department of Radiodiagnosis, Lucknow (India); Rathore, Ram K.S.; Sahoo, Prativa [Indian Institute of Technology, Department of Mathematics and Statistics, Kanpur (India); Soni, Priyanka; Husain, Nuzhat [Chhatrapati Sahuji Maharj Medical University, Department of Pathology, Lucknow (India); Awasthi, Ashish; Pandey, Chandra M. [Sanjay Gandhi Post Graduate Institute of Medical Sciences, Department of Biostatistics, Lucknow (India); Behari, Sanjay; Singh, Rohit K. [Sanjay Gandhi Post Graduate Institute of Medical Sciences, Department of Neurosurgery, Lucknow (India); Gupta, Rakesh K. [Sanjay Gandhi Post Graduate Institute of Medical Sciences, MR Section, Department of Radiodiagnosis, Lucknow, UP (India)

    2012-03-15

    The purpose of the present study was to look for the possible predictors which might discriminate between high- and low-grade gliomas by pooling dynamic contrast-enhanced (DCE)-perfusion derived indices and immunohistochemical markers. DCE-MRI was performed in 76 patients with different grades of gliomas. Perfusion indices, i.e., relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), permeability (k{sup trans} and k{sub ep}), and leakage (v{sub e}) were quantified. MMP-9-, PRL-3-, HIF-1{alpha}-, and VEGF-expressing cells were quantified from the excised tumor tissues. Discriminant function analysis using these markers was used to identify discriminatory variables using a stepwise procedure. To look for correlations between immunohistochemical parameters and DCE metrics, Pearson's correlation coefficient was also used. A discriminant function for differentiating between high- and low-grade tumors was constructed using DCE-MRI-derived rCBV, k{sub ep}, and v{sub e}. The form of the functions estimated are ''D{sub 1} = 0.642 x rCBV + 0.591 x k{sub ep} - 1.501 x v{sub e} - 1.550'' and ''D{sub 2} = 1.608 x rCBV + 3.033 x k{sub ep} + 5.508 x v{sub e} - 8.784'' for low- and high-grade tumors, respectively. This function classified overall 92.1% of the cases correctly (89.1% high-grade tumors and 100% low-grade tumors). In addition, VEGF expression correlated with rCBV and rCBF, whereas MMP-9 expression correlated with k{sub ep}. A significant positive correlation of HIF-1{alpha} with rCBV and VEGF expression was also found. DCE-MRI may be used to differentiate between high-grade and low-grade brain tumors non-invasively, which may be helpful in appropriate treatment planning and management of these patients. The correlation of its indices with immunohistochemical markers suggests that this imaging technique is useful in tissue characterization of gliomas. (orig.)

  17. Examining the effectiveness of discriminant function analysis and cluster analysis in species identification of male field crickets based on their calling songs.

    Directory of Open Access Journals (Sweden)

    Ranjana Jaiswara

    Full Text Available Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species

  18. Multi-class ERP-based BCI data analysis using a discriminant space self-organizing map.

    Science.gov (United States)

    Onishi, Akinari; Natsume, Kiyohisa

    2014-01-01

    Emotional or non-emotional image stimulus is recently applied to event-related potential (ERP) based brain computer interfaces (BCI). Though the classification performance is over 80% in a single trial, a discrimination between those ERPs has not been considered. In this research we tried to clarify the discriminability of four-class ERP-based BCI target data elicited by desk, seal, spider images and letter intensifications. A conventional self organizing map (SOM) and newly proposed discriminant space SOM (ds-SOM) were applied, then the discriminabilites were visualized. We also classify all pairs of those ERPs by stepwise linear discriminant analysis (SWLDA) and verify the visualization of discriminabilities. As a result, the ds-SOM showed understandable visualization of the data with a shorter computational time than the traditional SOM. We also confirmed the clear boundary between the letter cluster and the other clusters. The result was coherent with the classification performances by SWLDA. The method might be helpful not only for developing a new BCI paradigm, but also for the big data analysis.

  19. Surface Water Quality Assessment of Wular Lake, A Ramsar Site in Kashmir Himalaya, Using Discriminant Analysis and WQI

    Directory of Open Access Journals (Sweden)

    Salim Aijaz Bhat

    2014-01-01

    Full Text Available Multivariate techniques, discriminant analysis, and WQI were applied to analyze a water quality data set including 27 parameters at 5 sites of the Lake Wular in Kashmir Himalaya from 2011 to 2013 to investigate spatiotemporal variations and identify potential pollution sources. Spatial and temporal variations in water quality parameters were evaluated through stepwise discriminant analysis (DA. The first spatial discriminant function (DF accounted for 76.5% of the total spatial variance, and the second DF accounted for 19.1%. The mean values of water temperature, EC, total-N, K, and silicate showed a strong contribution to discriminate the five sampling sites. The mean concentration of NO2-N, total-N, and sulphate showed a strong contribution to discriminate the four sampling seasons and accounted for most of the expected seasonal variations. The order of major cations and anions was Ca2+>Mg2+> Na+>K+ and Cl->SO42->SiO22- respectively. The results of water quality index, employing thirteen core parameters vital for drinking water purposes, showed values of 49.2, 46.5, 47.3, 40.6, and 37.1 for sites I, II, III, IV, and V, respectively. These index values reflect that the water of lake is in good condition for different purposes but increased values alarm us about future repercussions.

  20. An Improved Method for Discriminating ECG Signals using Typical Nonlinear Dynamic Parameters and Recurrence Quantification Analysis in Cardiac Disease Therapy.

    Science.gov (United States)

    Tang, M; Chang, C Q; Fung, P C W; Chau, K T; Chan, F H Y

    2005-01-01

    The discrimination of ECG signals using nonlinear dynamic parameters is of crucial importance in the cardiac disease therapy and chaos control for arrhythmia defibrillation in the cardiac system. However, the discrimination results of previous studies using features such as maximal Lyapunov exponent (λmax) and correlation dimension (D2) alone are somewhat limited in recognition rate. In this paper, improved methods for computing λmaxand D2are purposed. Another parameter from recurrence quantification analysis is incorporated to the new multi-feature Bayesian classifier with λmaxand D2so as to improve the discrimination power. Experimental results have verified the prediction using Fisher discriminant that the maximal vertical line length (Vmax) from recurrence quantification analysis is the best to distinguish different ECG classes. Experimental results using the MIT-BIH Arrhythmia Database show improved and excellent overall accuracy (96.3%), average sensitivity (96.3%) and average specificity (98.15%) for discriminating sinus, premature ventricular contraction and ventricular flutter signals.

  1. Racial Discrimination and Racial Socialization as Predictors of African American Adolescents’ Racial Identity Development using Latent Transition Analysis

    Science.gov (United States)

    Seaton, Eleanor K.; Yip, Tiffany; Morgan-Lopez, Antonio; Sellers, Robert M.

    2013-01-01

    The current study examined perceptions of racial discrimination and racial socialization on racial identity development among 566 African American adolescents over three years. Latent class analyses were used to estimate identity statuses (Diffuse, Foreclosed, Moratorium and Achieved). The probabilities of transitioning from one stage to another were examined with latent transition analyses to determine the likelihood of youth progressing, regressing or remaining constant. Racial socialization and perceptions of racial discrimination were examined as covariates to assess the association with changes in racial identity status. The results indicated that perceptions of racial discrimination were not linked to any changes in racial identity. Youth who reported higher levels of racial socialization were less likely to be in Diffuse or Foreclosed compared to the Achieved group. PMID:21875184

  2. Racial discrimination and racial socialization as predictors of African American adolescents' racial identity development using latent transition analysis.

    Science.gov (United States)

    Seaton, Eleanor K; Yip, Tiffany; Morgan-Lopez, Antonio; Sellers, Robert M

    2012-03-01

    The present study examined perceptions of racial discrimination and racial socialization on racial identity development among 566 African American adolescents over 3 years. Latent class analyses were used to estimate identity statuses (Diffuse, Foreclosed, Moratorium, and Achieved). The probabilities of transitioning from one stage to another were examined with latent transition analyses to determine the likelihood of youth progressing, regressing, or remaining constant. Racial socialization and perceptions of racial discrimination were examined as covariates to assess the association with changes in racial identity status. The results indicated that perceptions of racial discrimination were not linked to any changes in racial identity. Youth who reported higher levels of racial socialization were less likely to be in Diffuse or Foreclosed compared with the Achieved group. PsycINFO Database Record (c) 2012 APA, all rights reserved.

  3. A Differential Item Functional Analysis by Age of Perceived Interpersonal Discrimination in a Multi-racial/ethnic Sample of Adults.

    Science.gov (United States)

    Owens, Sherry; Kristjansson, Alfgeir L; Hunte, Haslyn E R

    2015-11-05

    We investigated whether individual items on the nine item William's Perceived Everyday Discrimination Scale (EDS) functioned differently by age (racial groups in the United States: Asians (n=2,017); Hispanics (n=2,688); Black Caribbeans (n=1,377); African Americans (n=3,434); and Whites (n=854). We used data from the 2001-2003 National Survey of American Lives and the 2001-2003 National Latino and Asian Studies. Multiple-indicator, multiple-cause models (MIMIC) were used to examine differential item functioning (DIF) on the EDS by age within each racial/ethnic group. Overall, Asian and Hispanic respondents reported less discrimination than Whites; on the other hand, African Americans and Black Caribbeans reported more discrimination than Whites. Regardless of race/ethnicity, the younger respondents (aged discrimination than the older respondents (aged ≥ 45 years). In terms of age by race/ethnicity, the results were mixed for 19 out of 45 tests of DIF (40%). No differences in item function were observed among Black Caribbeans. "Being called names or insulted" and others acting as "if they are afraid" of the respondents were the only two items that did not exhibit differential item functioning by age across all racial/ethnic groups. Overall, our findings suggest that the EDS scale should be used with caution in multi-age multi-racial/ethnic samples.

  4. Where intersubjectivity and group analysis meet.

    Science.gov (United States)

    Aviv, Alex

    2010-01-01

    Intersubjectivity can be defined as the union or contact of the subjectivities (Gordon, 1991). In therapy, it refers to the interaction between therapist and patient, and the processes that affect and are affected by that interaction. The essence of these interactions in the setting of group therapy, the obstacles that may arise because of them, and ways in which the therapist may identify and facilitate intersubjectivity in order to promote discourse that will enrich the sessions are discussed. I briefly touch upon earlier theories of psychoanalysis, demonstrating how changes in approaches influenced perceptions of what occurs in therapy. The unique place of intersubjectivity within the group analytic setting is emphasized using several vignettes from sessions I have led as a group analyst candidate to better illustrate how intersubjectivity can be observed in vivo.

  5. A quantitative analysis on the sources of dune sand in the Hulun Buir Sandy Land:application of stepwise discriminant analysis (SDA) to the granulometric data

    Institute of Scientific and Technical Information of China (English)

    HANGuang; ZHANGGuifang; YANGWenbin

    2004-01-01

    Quantltatively determining the sources of dune sand uis one of the problems necessarily and urgently to be solved in aeolian landforms and desertification research. Based on the granulometric data of sand materials from the Hulun Buir Sandy Land, the paper employs the stepwise discriminant analysis technique (SDA) for two groups to select the principal factors determining the differences between surface loose sediments. The extent of similarity between two statistical populations can be described quantitatively by three factors such as the number of principal variables, Mahalanobis distance D2 and confidence level α for F-test. Results reveal that: 1) Aeolian dune sand in the region mainly derives from Hailar Formation (Q3), while fluvial sand and palaeosol also supply partially source sand for dunes; and 2) in the vicinity of Cuogang Town and west of the broad valley of the lower reaches of Hailar River, fluvial sand can naturally become principal supplier for dune sand.

  6. Perceived Discrimination among African American Adolescents and Allostatic Load: A Longitudinal Analysis with Buffering Effects

    Science.gov (United States)

    Brody, Gene H.; Lei, Man-Kit; Chae, David H.; Yu, Tianyi; Kogan, Steven M.; Beach, Steven R. H.

    2014-01-01

    This study was designed to examine the prospective relations of perceived racial discrimination with allostatic load (AL), along with a possible buffer of the association. A sample of 331 African Americans in the rural South provided assessments of perceived discrimination from ages 16 to 18 years. When youth were 18 years, caregivers reported…

  7. Perceived Discrimination among African American Adolescents and Allostatic Load: A Longitudinal Analysis with Buffering Effects

    Science.gov (United States)

    Brody, Gene H.; Lei, Man-Kit; Chae, David H.; Yu, Tianyi; Kogan, Steven M.; Beach, Steven R. H.

    2014-01-01

    This study was designed to examine the prospective relations of perceived racial discrimination with allostatic load (AL), along with a possible buffer of the association. A sample of 331 African Americans in the rural South provided assessments of perceived discrimination from ages 16 to 18 years. When youth were 18 years, caregivers reported…

  8. Qualitative analysis of mental health service users' reported experiences of discrimination.

    Science.gov (United States)

    Hamilton, S; Pinfold, V; Cotney, J; Couperthwaite, L; Matthews, J; Barret, K; Warren, S; Corker, E; Rose, D; Thornicroft, G; Henderson, C

    2016-08-01

    To better understand mental health service users' experiences of stigma and discrimination in different settings. An annual telephone survey of people with a mental health diagnosis conducted to evaluate the Time to Change antistigma campaign in England. Of 985 people who participated in 2013, 84 took part in a qualitative interview which was audio recorded. Of these, 50 interviews were transcribed and thematically analysed to explore accounts of discrimination. We analysed common types of behaviour; motivations ascribed to the discriminators; expectations of what fair treatment would have been; and the impact of discrimination on participants. Discrimination was most common in five contexts: welfare benefits, mental health care, physical health care, family and friends. Participants often found it hard to assess whether a behaviour was discriminatory or not. Lack of support, whether by public services or by friends and family, was often experienced as discrimination, reflecting an expectation that positive behaviours and reasonable adjustments should be offered in response to mental health needs. The impact of discrimination across different settings was often perceived by participants as aggravating their mental health, and there is thus a need to treat discrimination as a health issue, not just a social justice issue. © 2016 The Authors. Acta Psychiatrica Scandinavica Published by John Wiley & Sons Ltd.

  9. Lacunarity analysis of spaceborne radar image texture for rock unit discrimination

    Science.gov (United States)

    Dong, Pinliang

    Fractal geometry has led to new understanding of many natural objects and phenomena. As a scale-dependent measure, lacunarity can be used to discriminate different textures that may not be differentiated by fractal dimension. Based on a differential box counting method and a gliding-box algorithm, a new lacunarity estimation method is developed for texture analysis of digital images, and a "Lacunarity Analysis" extension built for ArcView (ESRI) geographical information system software. To reveal the directional properties of textures, the directionality of lacunarity is also defined. The new lacunarity measure is evaluated through quantitative comparison with the Voss lacunarity, the binary lacunarity, the grey level cooccurrence matrix (GLCM) based texture measures (homogeneity, contrast, dissimilarity, entropy), the fractal dimension, and the min-max operator using Brodatz textures. The results from Brodatz textures suggest that the new lacunarity estimation method for grey-scale images provides more accurate texture measurements than the above-mentioned fractal-based and statistical texture measures. In comparison with the Voss lacunarity, the fractal dimension, and the GLCM-based texture measures, the new lacunarity measure is then applied to dual-band (L and C) and dual-polarization (HH and HV) Shuttle Imaging Radar (SIR-C), and C-band HH polarization Radarsat images of two imaging modes for rock unit discrimination in a study area between California and Arizona, USA. Using textural analysis of 36 SIR-C and Radarsat sub-images and classification accuracy assessment of the combined Landsat Thematic Mapper (TM) images and spaceborne radar textural feature images, it has been demonstrated that the new lacunarity measure outperformed other texture measures in comparison, and the L-band HH polarization SIR-C image provides more textural information of the rock units compared with the Radarsat and other SIR-C radar images used in this study. The study shows that

  10. Regularized discriminate analysis for breast mass detection on full field digital mammograms

    Science.gov (United States)

    Wei, Jun; Sahiner, Berkman; Zhang, Yiheng; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Zhou, Chuan; Ge, Jun; Wu, Yi-Ta

    2006-03-01

    In computer-aided detection (CAD) applications, an important step is to design a classifier for the differentiation of the abnormal from the normal structures. We have previously developed a stepwise linear discriminant analysis (LDA) method with simplex optimization for this purpose. In this study, our goal was to investigate the performance of a regularized discriminant analysis (RDA) classifier in combination with a feature selection method for classification of the masses and normal tissues detected on full field digital mammograms (FFDM). The feature selection scheme combined a forward stepwise feature selection process and a backward stepwise feature elimination process to obtain the best feature subset. An RDA classifier and an LDA classifier in combination with this new feature selection method were compared to an LDA classifier with stepwise feature selection. A data set of 130 patients containing 260 mammograms with 130 biopsy-proven masses was used. All cases had two mammographic views. The true locations of the masses were identified by experienced radiologists. To evaluate the performance of the classifiers, we randomly divided the data set into two independent sets of approximately equal size for training and testing. The training and testing were performed using the 2-fold cross validation method. The detection performance of the CAD system was assessed by free response receiver operating characteristic (FROC) analysis. The average test FROC curve was obtained by averaging the FP rates at the same sensitivity along the two corresponding test FROC curves from the 2-fold cross validation. At the case-based sensitivities of 90%, 80% and 70% on the test set, our RDA classifier with the new feature selection scheme achieved an FP rate of 1.8, 1.1, and 0.6 FPs/image, respectively, compared to 2.1, 1.4, and 0.8 FPs/image with stepwise LDA with simplex optimization. Our results indicate that RDA in combination with the sequential forward inclusion

  11. An economic analysis of price discrimination among telecom operators——a case study of group users%电信业网间价格歧视的经济学分析——以“集团用户”为例

    Institute of Scientific and Technical Information of China (English)

    姚惠泽; 石磊; 侯瑾

    2012-01-01

    Our research found that, because network scale effect exists, the business of the "group users" , "affection user number" makes the "big nets" absorb customer for "small nets" . The group's internal initial market share to operators have a more important influence, this leads to more intense competition between the mobile operators. We also points out that the "group users" launched by operators for the main purpose of absorb the opponents' customers, but virtually reduced the customers on the use of fixed net. Fixed net became the victim of the mobile network competition, lead to the substitution of mobile to fixed net more grievous. The two respects leads to the market structure of Chinese telecoms industry more imbalances.%本文的研究发现,由于网络规模效应的存在,各运营商推行的“集团用户”、“亲情号码”业务使得集团内“大网”对于“小网”客户的吸附作用较为明显,集团内部初始市场份额对运营商有着较为重要的影响,这导致了移动运营商之间的竞争更加激烈;同时指出“集团用户”推出的主要目的是移动运营商争取对方客户,但无形中减少了客户对固定电话的使用,固话成了移动网络竞争的牺牲品,导致移动对固话的替代作用更加明显,以上两方面加剧了中国电信业市场结构的失衡.

  12. Exclusively Visual Analysis of Classroom Group Interactions

    Science.gov (United States)

    Tucker, Laura; Scherr, Rachel E.; Zickler, Todd; Mazur, Eric

    2016-01-01

    Large-scale audiovisual data that measure group learning are time consuming to collect and analyze. As an initial step towards scaling qualitative classroom observation, we qualitatively coded classroom video using an established coding scheme with and without its audio cues. We find that interrater reliability is as high when using visual data…

  13. Improvement Distance Discriminant Analysis Method%改进的距离判别分析法

    Institute of Scientific and Technical Information of China (English)

    黄利文

    2011-01-01

    在不对判别变量进行处理的条件下,对传统的距离判别方法进行改进,提出一种新的判别方法,试图解决复杂球形数据的判别问题,以提高判别的正确率.通过实例表明,该方法的判别效果良好,能较好地处理复杂球形数据的判别问题.%This paper presents a new discriminant method through improving the traditional distance discriminant method and tries to improve the accuracy of discriminant problems of complex spherical data in condition of not reducing variables. The example shows that this method has good discriminant effect, and can effectively deal with the discriminant problems of complex spherical data.

  14. The Effectiveness of the Discriminant Analysis Models. Study Based on the Selected Polish Companies Quoted on the Warsaw Stock Exchange

    Directory of Open Access Journals (Sweden)

    Grzegorz Gołębiowski

    2008-07-01

    Full Text Available The article presents the result of research on en effectiveness of discriminant models on the example of selected Polish joint-stock companies which declared bankruptcy. Aside from general results describing an effectiveness of discriminant models on the base of the own research of the authors, a comparison of the received findings with others economists results of research was made. Moreover, an analysis how the relation of an enterprise with the economic situation impacts on an effectiveness of considered models concerning the forecast of a risk of bankruptcy was carried out.

  15. Discriminant Context Information Analysis for Post-Ranking Person Re-Identification.

    Science.gov (United States)

    Garcia, Jorge; Martinel, Niki; Gardel, Alfredo; Bravo, Ignacio; Foresti, Gian Luca; Micheloni, Christian

    2017-01-16

    Existing approaches for person re-identification are mainly based on creating distinctive representations or on learning optimal metrics. The achieved results are then provided in form of a list of ranked matching persons. It often happens that the true match is not ranked first but it is in the first positions. This is mostly due to the visual ambiguities shared between the true match and other "similar" persons. At the current state, there is a lack of a study of such visual ambiguities which limit the re-identification performance within the first ranks. We believe that an analysis of the similar appearances of the first ranks can be helpful in detecting, hence removing, such visual ambiguities. We propose to achieve such a goal by introducing an unsupervised post-ranking framework. Once the initial ranking is available, content and context sets are extracted. Then, these are exploited to remove the visual ambiguities and to obtain the discriminant feature space which is finally exploited to compute the new ranking. An in-depth analysis of the performance achieved on three public benchmark datasets support our believes. For every dataset, the proposed method remarkably improves the first ranks results and outperforms state-of-the-art approaches.

  16. Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies

    Science.gov (United States)

    Ciucci, Sara; Ge, Yan; Durán, Claudio; Palladini, Alessandra; Jiménez-Jiménez, Víctor; Martínez-Sánchez, Luisa María; Wang, Yuting; Sales, Susanne; Shevchenko, Andrej; Poser, Steven W.; Herbig, Maik; Otto, Oliver; Androutsellis-Theotokis, Andreas; Guck, Jochen; Gerl, Mathias J.; Cannistraci, Carlo Vittorio

    2017-01-01

    Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics. PMID:28287094

  17. Ultrasonic analysis to discriminate bread dough of different types of flour

    Science.gov (United States)

    García-Álvarez, J.; Rosell, C. M.; García-Hernández, M. J.; Chávez, J. A.; Turó, A.; Salazar, J.

    2012-12-01

    Many varieties of bread are prepared using flour coming from wheat. However, there are other types of flours milled from rice, legumes and some fruits and vegetables that are also suitable for baking purposes, used alone or in combination with wheat flour. The type of flour employed strongly influences the dough consistency, which is a relevant property for determining the dough potential for breadmaking purposes. Traditional methods for dough testing are relatively expensive, time-consuming, off-line and often require skilled operators. In this work, ultrasonic analysis are performed in order to obtain acoustic properties of bread dough samples prepared using two different types of flour, wheat flour and rice flour. The dough acoustic properties can be related to its viscoelastic characteristics, which in turn determine the dough feasibility for baking. The main advantages of the ultrasonic dough testing can be, among others, its low cost, fast, hygienic and on-line performance. The obtained results point out the potential of the ultrasonic analysis to discriminate doughs of different types of flour.

  18. HOLOMORPHIC HARMONIC ANALYSIS ON COMPLEX REDUCTIVE GROUPS

    Institute of Scientific and Technical Information of China (English)

    An Jinpeng; Qian Min; Wang Zhengdong

    2008-01-01

    The authors define the holomorphic Fourier transform of holomorphic func-tions on complex reductive groups, prove some properties such as the Fourier inversion formula, and give some applications. The definition of the holomorphic Fourier transform makes use of the notion of K-admissible measures. The authors prove that K-admissible measures are abundant, and the definition of holomorphic Fourier transform is independent of the choice of K-admissible measures.

  19. Multivariate analysis of microarray data by principal component discriminant analysis: Prioritizing relevant transcripts linked to the degradation of different carbohydrates in Pseudomonas putida S12

    NARCIS (Netherlands)

    Werf, M.J. van der; Pieterse, B.; Luijk, N. van; Schuren, F.; Werff van der - Vat, B. van der; Overkamp, K.; Jellema, R.H.

    2006-01-01

    The value of the multivariate data analysis tools principal component analysis (PCA) and principal component discriminant analysis (PCDA) for prioritizing leads generated by microarrays was evaluated. To this end, Pseudomonas putida S12 was grown in independent triplicate fermentations on four

  20. Multivariate analysis of microarray data by principal component discriminant analysis: Prioritizing relevant transcripts linked to the degradation of different carbohydrates in Pseudomonas putida S12

    NARCIS (Netherlands)

    Werf, M.J. van der; Pieterse, B.; Luijk, N. van; Schuren, F.; Werff van der - Vat, B. van der; Overkamp, K.; Jellema, R.H.

    2006-01-01

    The value of the multivariate data analysis tools principal component analysis (PCA) and principal component discriminant analysis (PCDA) for prioritizing leads generated by microarrays was evaluated. To this end, Pseudomonas putida S12 was grown in independent triplicate fermentations on four diffe

  1. Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Gifty E. Acquah

    2016-08-01

    Full Text Available As new markets, technologies and economies evolve in the low carbon bioeconomy, forest logging residue, a largely untapped renewable resource will play a vital role. The feedstock can however be variable depending on plant species and plant part component. This heterogeneity can influence the physical, chemical and thermochemical properties of the material, and thus the final yield and quality of products. Although it is challenging to control compositional variability of a batch of feedstock, it is feasible to monitor this heterogeneity and make the necessary changes in process parameters. Such a system will be a first step towards optimization, quality assurance and cost-effectiveness of processes in the emerging biofuel/chemical industry. The objective of this study was therefore to qualitatively classify forest logging residue made up of different plant parts using both near infrared spectroscopy (NIRS and Fourier transform infrared spectroscopy (FTIRS together with linear discriminant analysis (LDA. Forest logging residue harvested from several Pinus taeda (loblolly pine plantations in Alabama, USA, were classified into three plant part components: clean wood, wood and bark and slash (i.e., limbs and foliage. Five-fold cross-validated linear discriminant functions had classification accuracies of over 96% for both NIRS and FTIRS based models. An extra factor/principal component (PC was however needed to achieve this in FTIRS modeling. Analysis of factor loadings of both NIR and FTIR spectra showed that, the statistically different amount of cellulose in the three plant part components of logging residue contributed to their initial separation. This study demonstrated that NIR or FTIR spectroscopy coupled with PCA and LDA has the potential to be used as a high throughput tool in classifying the plant part makeup of a batch of forest logging residue feedstock. Thus, NIR/FTIR could be employed as a tool to rapidly probe/monitor the variability

  2. Pitch discrimination associated with phonological awareness: Evidence from congenital amusia.

    Science.gov (United States)

    Sun, Yanan; Lu, Xuejing; Ho, Hao Tam; Thompson, William Forde

    2017-03-13

    Research suggests that musical skills are associated with phonological abilities. To further investigate this association, we examined whether phonological impairments are evident in individuals with poor music abilities. Twenty individuals with congenital amusia and 20 matched controls were assessed on a pure-tone pitch discrimination task, a rhythm discrimination task, and four phonological tests. Amusic participants showed deficits in discriminating pitch and discriminating rhythmic patterns that involve a regular beat. At a group level, these individuals performed similarly to controls on all phonological tests. However, eight amusics with severe pitch impairment, as identified by the pitch discrimination task, exhibited significantly worse performance than all other participants in phonological awareness. A hierarchical regression analysis indicated that pitch discrimination thresholds predicted phonological awareness beyond that predicted by phonological short-term memory and rhythm discrimination. In contrast, our rhythm discrimination task did not predict phonological awareness beyond that predicted by pitch discrimination thresholds. These findings suggest that accurate pitch discrimination is critical for phonological processing. We propose that deficits in early-stage pitch discrimination may be associated with impaired phonological awareness and we discuss the shared role of pitch discrimination for processing music and speech.

  3. Flash-Type Discrimination

    Science.gov (United States)

    Koshak, William J.

    2010-01-01

    This viewgraph presentation describes the significant progress made in the flash-type discrimination algorithm development. The contents include: 1) Highlights of Progress for GLM-R3 Flash-Type discrimination Algorithm Development; 2) Maximum Group Area (MGA) Data; 3) Retrieval Errors from Simulations; and 4) Preliminary Global-scale Retrieval.

  4. European Bone Marrow Working Group trial on reproducibility of World Health Organization criteria to discriminate essential thrombocythemia from prefibrotic primary myelofibrosis.

    NARCIS (Netherlands)

    Buhr, T.; Hebeda, K.M.; Kaloutsi, V.; Porwit, A.; Walt, J. Van der; Kreipe, H.

    2012-01-01

    BACKGROUND: The World Health Organization classification of myeloproliferative neoplasms discriminates between essential thrombocythemia and the prefibrotic phase of primary myelofibrosis. This discrimination is clinically relevant because essential thrombocythemia is associated with a favorable pro

  5. European Bone Marrow Working Group trial on reproducibility of World Health Organization criteria to discriminate essential thrombocythemia from prefibrotic primary myelofibrosis

    NARCIS (Netherlands)

    Buhr, Thomas; Hebeda, Konnie; Kaloutsi, Vassiliki; Porwit, Anna; Van der Walt, Jon; Kreipe, Hans

    2012-01-01

    Background The World Health Organization classification of myeloproliferative neoplasms discriminates between essential thrombocythemia and the prefibrotic phase of primary myelofibrosis. This discrimination is clinically relevant because essential thrombocythemia is associated with a favorable prog

  6. Likelihood Analysis of the Local Group Acceleration

    CERN Document Server

    Schmoldt, I M; Teodoro, L; Efstathiou, G P; Frenk, C S; Keeble, O; Maddox, S J; Oliver, S; Rowan-Robinson, M; Saunders, W J; Sutherland, W; Tadros, H; White, S D M

    1999-01-01

    We compute the acceleration on the Local Group using 11206 IRAS galaxies from the recently completed all-sky PSCz redshift survey. Measuring the acceleration vector in redshift space generates systematic uncertainties due to the redshift space distortions in the density field. We therefore assign galaxies to their real space positions by adopting a non-parametric model for the velocity field that solely relies on the linear gravitational instability and linear biasing hypotheses. Remaining systematic contributions to the measured acceleration vector are corrected for by using PSCz mock catalogues from N-body experiments. The resulting acceleration vector points approx. 15 degrees away from the CMB dipole apex, with a remarkable alignment between small and large scale contributions. A considerable fraction of the measured acceleration is generated within 40 h-1 Mpc with a non-negligible contribution from scales between 90 and 140 h-1 Mpc after which the acceleration amplitude seems to have converged. The local...

  7. Development of renormalization group analysis of turbulence

    Science.gov (United States)

    Smith, L. M.

    1990-01-01

    The renormalization group (RG) procedure for nonlinear, dissipative systems is now quite standard, and its applications to the problem of hydrodynamic turbulence are becoming well known. In summary, the RG method isolates self similar behavior and provides a systematic procedure to describe scale invariant dynamics in terms of large scale variables only. The parameterization of the small scales in a self consistent manner has important implications for sub-grid modeling. This paper develops the homogeneous, isotropic turbulence and addresses the meaning and consequence of epsilon-expansion. The theory is then extended to include a weak mean flow and application of the RG method to a sequence of models is shown to converge to the Navier-Stokes equations.

  8. Development of renormalization group analysis of turbulence

    Science.gov (United States)

    Smith, L. M.

    1990-01-01

    The renormalization group (RG) procedure for nonlinear, dissipative systems is now quite standard, and its applications to the problem of hydrodynamic turbulence are becoming well known. In summary, the RG method isolates self similar behavior and provides a systematic procedure to describe scale invariant dynamics in terms of large scale variables only. The parameterization of the small scales in a self consistent manner has important implications for sub-grid modeling. This paper develops the homogeneous, isotropic turbulence and addresses the meaning and consequence of epsilon-expansion. The theory is then extended to include a weak mean flow and application of the RG method to a sequence of models is shown to converge to the Navier-Stokes equations.

  9. Price Discrimination

    OpenAIRE

    Armstrong, Mark

    2008-01-01

    This paper surveys recent economic research on price discrimination, both in monopoly and oligopoly markets. Topics include static and dynamic forms of price discrimination, and both final and input markets are considered. Potential antitrust aspects of price discrimination are highlighted throughout the paper. The paper argues that the informational requirements to make accurate policy are very great, and with most forms of price discrimination a laissez-faire policy may be the best availabl...

  10. Epileptic Seizure Detection Using Lacunarity and Bayesian Linear Discriminant Analysis in Intracranial EEG.

    Science.gov (United States)

    Zhou, Weidong; Liu, Yinxia; Yuan, Qi; Li, Xueli

    2013-12-01

    Automatic seizure detection plays an important role in long-term epilepsy monitoring, and seizure detection algorithms have been intensively investigated over the years. This paper proposes an algorithm for seizure detection using lacunarity and Bayesian linear discriminant analysis (BLDA) in long-term intracranial EEG. Lacunarity is a measure of heterogeneity for a fractal. The proposed method first conducts wavelet decomposition on EEGs with five scales, and selects the wavelet coefficients at scale 3, 4, and 5 for subsequent processing. Effective features including lacunarity and fluctuation index are extracted from the selected three scales, and then sent into the BLDA for training and classification. Finally, postprocessing which includes smoothing, threshold judgment, multichannels integration, and collar technique is applied to obtain high sensitivity and low false detection rate. The proposed algorithm is evaluated on 289.14 h intracranial EEG data from 21-patient Freiburg dataset and yields a sensitivity of 96.25% and a false detection rate of 0.13/h with a mean delay time of 13.8 s.

  11. On-line Batch Process Monitoring and Diagnosing Based on Fisher Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    ZHAO Xu; SHAO Hui-he

    2006-01-01

    A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis (FDA) was proposed. This method does not need to predict the future observations of variables, so it is more sensitive to fault detection and stronger implement for monitoring. In order to improve the monitoring performance,the variables trajectories of batch process are separated into several blocks. The key to the proposed approach for on-line monitoring is to calculate the distance of block data that project to low-dimension Fisher space between new batch and reference batch. Comparing the distance with the predefine threshold, it can be considered whether the batch process is normal or abnormal. Fault diagnosis is performed based on the weights in fault direction calculated by FDA. The proposed method was applied to the simulation model of fed-batch penicillin fermentation and the results were compared with those obtained using MPCA. The simulation results clearly show that the on-line monitoring method based on FDA is more efficient than the MPCA.

  12. PENDETEKSIAN KANKER PARU–PARU DENGAN MENGGUNAKAN TRANSFORMASI WAVELET DAN METODE LINEAR DISCRIMINANT ANALYSIS

    Directory of Open Access Journals (Sweden)

    Hanung Tyas Saksono

    2010-07-01

    Full Text Available Kanker merupakan pertumbuhan dan penyebaran sel-sel abnormal yang memiliki karakteristik yang khas. Kanker yang sudah menyebar dan tidak dapat terkontrol lagi, biasanya akan menyebabkan kematian. Kanker paru-paru lebih sering menyebabkan pria meninggal dibanding kanker lain, dimana yang sering menjadi penyebab kanker paru-paru adalah merokok. Cara yang digunakan untuk mendeteksi kanker paru-paru ialah melalui pemeriksaan hasil foto rontgen dada. Penelitian ini bertujuan untuk menghasilkan suatu sistem aplikasi yang dapat mendiagnosa citra paru-parudan mengklasifikasikan paru-paruke dalam tipe kanker, normal atau efusi serta menganalisa performansi sistem yang digunakan dalam proses pengenalan citra paru-paru. Proses pendeteksian diawali dengan pemrosesan awal pada citra paru-paru, proses ekstraksi ciri menggunakan Transformasi Wavelet, dan proses klasifikasi menggunakan Linear Discriminant Analysis (LDA. Pemrosesan awal dilakukan untuk membuang informasi yang tidak dibutuhkan dalam pengolahan citra. Proses ekstraksi ciri dilakukan dengan cara mengurangi dimensi citra paru- paru yang akan menjadi masukan pada proses pengenalan menggunakan LDA. Pada penelitian ini citra latih yang digunakan adalah 60 buah citra, yang terdiri dari 20 kelas citra kondisi normal, 20 kelas citra kondisi kanker, dan 20 kelas citra kondisi efusi. Citra uji yang akan digunakan juga berjumlah 60 buah citra, yang tediri dari 20 citra untuk masing-masing kelas. Akurasi yang dihasilkan sistem pada pendeteksian kanker paru-paru ini sebesar 100% untuk citra latih dan 95% untuk citra uji.

  13. Discriminating lava flows of different age within Nyamuragira's volcanic field using spectral mixture analysis

    Science.gov (United States)

    Li, Long; Canters, Frank; Solana, Carmen; Ma, Weiwei; Chen, Longqian; Kervyn, Matthieu

    2015-08-01

    In this study, linear spectral mixture analysis (LSMA) is used to characterize the spectral heterogeneity of lava flows from Nyamuragira volcano, Democratic Republic of Congo, where vegetation and lava are the two main land covers. In order to estimate fractions of vegetation and lava through satellite remote sensing, we made use of 30 m resolution Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Land Imager (ALI) imagery. 2 m Pleiades data was used for validation. From the results, we conclude that (1) LSMA is capable of characterizing volcanic fields and discriminating between different types of lava surfaces; (2) three lava endmembers can be identified as lava of old, intermediate and young age, corresponding to different stages in lichen growth and chemical weathering; (3) a strong relationship is observed between vegetation fraction and lava age, where vegetation at Nyamuragira starts to significantly colonize lava flows ∼15 years after eruption and occupies over 50% of the lava surfaces ∼40 years after eruption. Our study demonstrates the capability of spectral unmixing to characterize lava surfaces and vegetation colonization over time, which is particularly useful for poorly known volcanoes or those not accessible for physical or political reasons.

  14. Recognition for avian influenza virus proteins based on support vector machine and linear discriminant analysis

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples. Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA). The results obtained from LDA are as follows: the identification accuracy (Ria) for training samples is 99.8% and Ria by leave one out cross validation is 99.5%. Both Ria of 99.8% for training samples and Ria of 99.3% by leave one out cross validation are obtained using SVM model, respectively. External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model. The external Ria for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA.

  15. Comparing discriminant analysis and neural network for the determination of sex using femur head measurements.

    Science.gov (United States)

    Alunni, Véronique; Jardin, Philippe du; Nogueira, Luisa; Buchet, Luc; Quatrehomme, Gérald

    2015-08-01

    The measurement of the femoral head is usually considered an interesting variable for the sex determination of skeletal remains. To date, there are few published reference measurements of the femoral head in a modern European population for the purpose of sex determination. In this study, 116 femurs from 58 individuals of the South of France (Nice Bone Collection, Nice, France) were studied. Three measurements of the femoral head were taken: the vertical head diameter (VHD), the transversal head diameter (THD) and the head circumference (HC). The results show that: (i) there is no statistical difference between the right and left femurs for each of the three measurements (VHD, THD and HC). Therefore we arbitrarily chose to use the measures from the right femurs (N=58) to pursue our experiments; (ii) the measurements of the femoral head are similar to those of contemporary American populations; (iii) the dimensions of the femoral head place the measurements of the French population somewhere between Germany or Croatia, and Spain; (iv) there is no significant secular trend (in contrast with the femoral neck diameter); (v) the femoral head measurement as a single variable is useful for sex determination: a 96.5% rate of accuracy was obtained using THD and HC measurements with the artificial neural network; and a 94.8% rate of accuracy using VHD, both with the discriminant analysis and the neural network.

  16. Partial Least Square Discriminant Analysis Discovered a Dietary Pattern Inversely Associated with Nasopharyngeal Carcinoma Risk.

    Science.gov (United States)

    Lo, Yen-Li; Pan, Wen-Harn; Hsu, Wan-Lun; Chien, Yin-Chu; Chen, Jen-Yang; Hsu, Mow-Ming; Lou, Pei-Jen; Chen, I-How; Hildesheim, Allan; Chen, Chien-Jen

    2016-01-01

    Evidence on the association between dietary component, dietary pattern and nasopharyngeal carcinoma (NPC) is scarce. A major challenge is the high degree of correlation among dietary constituents. We aimed to identify dietary pattern associated with NPC and to illustrate the dose-response relationship between the identified dietary pattern scores and the risk of NPC. Taking advantage of a matched NPC case-control study, data from a total of 319 incident cases and 319 matched controls were analyzed. Dietary pattern was derived employing partial least square discriminant analysis (PLS-DA) performed on energy-adjusted food frequencies derived from a 66-item food-frequency questionnaire. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated with multiple conditional logistic regression models, linking pattern scores and NPC risk. A high score of the PLS-DA derived pattern was characterized by high intakes of fruits, milk, fresh fish, vegetables, tea, and eggs ordered by loading values. We observed that one unit increase in the scores was associated with a significantly lower risk of NPC (ORadj = 0.73, 95% CI = 0.60-0.88) after controlling for potential confounders. Similar results were observed among Epstein-Barr virus seropositive subjects. An NPC protective diet is indicated with more phytonutrient-rich plant foods (fruits, vegetables), milk, other protein-rich foods (in particular fresh fish and eggs), and tea. This information may be used to design potential dietary regimen for NPC prevention.

  17. Differentiation of Pueraria lobata and Pueraria thomsonii using partial least square discriminant analysis (PLS-DA).

    Science.gov (United States)

    Wong, Ka H; Razmovski-Naumovski, Valentina; Li, Kong M; Li, George Q; Chan, Kelvin

    2013-10-01

    The aims of the study were to differentiate Pueraria lobata from its related species Pueraria thomsonii and to examine the raw herbal material used in manufacturing kudzu root granules using partial least square discriminant analysis (PLS-DA). Sixty-four raw materials of P. lobata and P. thomsonii and kudzu root-labelled granules were analysed by ultra performance liquid chromatography. To differentiate P. lobata from P. thomsonii, PLS-DA models using the variables selected from the entire chromatograms, genetic algorithm (GA), successive projection algorithm (SPA), puerarin alone and six selected peaks were employed. The models constructed by GA and SPA demonstrated superior classification ability and lower model's complexity as compared to the model based on the entire chromatographic matrix, whilst the model constructed by the six selected peaks was comparable to the entire chromatographic model. The model established by puerarin alone showed inferior classification ability. In addition, the PLS-DA models constructed by the entire chromatographic matrix, GA, SPA and the six selected peaks showed that four brands out of seventeen granules were mislabelled as P. lobata. In conclusion, PLS-DA is a promising procedure for differentiating Pueraria species and determining raw material used in commercial products.

  18. Material decomposition with the multi-energy attenuation coefficient ratio by using a multiple discriminant analysis

    Science.gov (United States)

    Lee, Woo-Jin; Kang, Se-Ryong; Choi, Soon-Chul; Lee, Sam-Sun; Heo, Min-Suk; Huh, Kyung-Hoe; Yi, Won-Jin

    2016-07-01

    The objective of this study was to develop a spectral CT system using a photon counting detector and to decompose materials by applying a multiple discriminant analysis (MDA) to the energy-dependent attenuation coefficient ratios. We imaged cylindrical phantoms of Polymethyl methacrylate (PMMA) with four holes filled with calcium chloride, iodine, and gold nanoparticle contrast agents. The attenuation coefficients were measured via reconstructed multi-energy images, and the linear attenuation ratio was used for material identification. The MDA projection matrix, determined from training phantoms, was used to identify the four materials in the testing phantoms. For quantification purposes, the relationships between the attenuation coefficients at multiple energy bins and the concentrations were characterized by using the least-squares method for each material. The mean identification accuracy for each of the three materials were 0.94 ± 0.09 for iodine, 0.96 ± 0.07 for gold nanoparticles, and 0.92 ± 0.05 for calcium chloride. The mean quantification errors were 1.90 ± 1.58% for iodine, 3.85 ± 3.13% for gold nanoparticle, and 3.40 ± 2.62% for calcium chloride. The developed multi-energy CT system based on the photon-counting detector with MDA can precisely decompose the four materials.

  19. Recognition for avian influenza virus proteins based on support vector machine and linear discriminant analysis

    Institute of Scientific and Technical Information of China (English)

    LIANG GuiZhao; LIAO ChunYang; WU ShiRong; LI GenRong; HE Liu; GAO JianKun; Gan MengYu; LI DeJing; CHEN GuoPing; WANG GuiXue; LONG Sha; CHEN ZeCong; JING JuHua; ZHENG XiaoLin; ZENG Hui; ZHANG QiaoXia; ZHANG MengJun; YANG Qi; TIAN FeiFei; TONG JianBo; WANG JiaoNa; LIU YongHong; YANG ShanBin; LI Bo; QIU LiangJia; CAI ShaoXi; ZHAO Na; YANG Yan; SU XiaLi; SONG Jian; CHEN MeiXia; ZHANG XueJiao; SUN JiaYing; MEI Hu; LI JingWei; CHEN GuoHua; CHEN Gang; DENG Jie; PENG ChuanYou; ZHU WanPing; XU LuoNan; WU YuQuan; LIAO LiMin; LI Zhi; ZHOU Yuan; LI Jun; LU DaJun; SU QinLiang; HUANG ZhengHu; ZHOU Ping; LI ZhiLiang; YANG Li; ZHOU Peng; YANG ShengXi; SHU Mao

    2008-01-01

    Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples.Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA).The results obtained from LDA are as follows: the identification accuracy (Ria) for training samples is 99.8% and Ria by leave one out cross validation is 99.5%.Both Ria of 99.8% for training samples and Ria of 99.3% by leave one out cross validation are obtained using SVM model, respectively.External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model.The external Ria for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA.

  20. A comparison of independent component analysis algorithms and measures to discriminate between EEG and artifact components.

    Science.gov (United States)

    Dharmaprani, Dhani; Nguyen, Hoang K; Lewis, Trent W; DeLosAngeles, Dylan; Willoughby, John O; Pope, Kenneth J

    2016-08-01

    Independent Component Analysis (ICA) is a powerful statistical tool capable of separating multivariate scalp electrical signals into their additive independent or source components, specifically EEG or electroencephalogram and artifacts. Although ICA is a widely accepted EEG signal processing technique, classification of the recovered independent components (ICs) is still flawed, as current practice still requires subjective human decisions. Here we build on the results from Fitzgibbon et al. [1] to compare three measures and three ICA algorithms. Using EEG data acquired during neuromuscular paralysis, we tested the ability of the measures (spectral slope, peripherality and spatial smoothness) and algorithms (FastICA, Infomax and JADE) to identify components containing EMG. Spatial smoothness showed differentiation between paralysis and pre-paralysis ICs comparable to spectral slope, whereas peripherality showed less differentiation. A combination of the measures showed better differentiation than any measure alone. Furthermore, FastICA provided the best discrimination between muscle-free and muscle-contaminated recordings in the shortest time, suggesting it may be the most suited to EEG applications of the considered algorithms. Spatial smoothness results suggest that a significant number of ICs are mixed, i.e. contain signals from more than one biological source, and so the development of an ICA algorithm that is optimised to produce ICs that are easily classifiable is warranted.

  1. The LDA beamformer: Optimal estimation of ERP source time series using linear discriminant analysis.

    Science.gov (United States)

    Treder, Matthias S; Porbadnigk, Anne K; Shahbazi Avarvand, Forooz; Müller, Klaus-Robert; Blankertz, Benjamin

    2016-04-01

    We introduce a novel beamforming approach for estimating event-related potential (ERP) source time series based on regularized linear discriminant analysis (LDA). The optimization problems in LDA and linearly-constrained minimum-variance (LCMV) beamformers are formally equivalent. The approaches differ in that, in LCMV beamformers, the spatial patterns are derived from a source model, whereas in an LDA beamformer the spatial patterns are derived directly from the data (i.e., the ERP peak). Using a formal proof and MEG simulations, we show that the LDA beamformer is robust to correlated sources and offers a higher signal-to-noise ratio than the LCMV beamformer and PCA. As an application, we use EEG data from an oddball experiment to show how the LDA beamformer can be harnessed to detect single-trial ERP latencies and estimate connectivity between ERP sources. Concluding, the LDA beamformer optimally reconstructs ERP sources by maximizing the ERP signal-to-noise ratio. Hence, it is a highly suited tool for analyzing ERP source time series, particularly in EEG/MEG studies wherein a source model is not available.

  2. The possibility of discriminating within the Bacillus cereus group using gyrB sequencing and PCR-RFLP

    DEFF Research Database (Denmark)

    Jensen, Gert B; Fisker, Niels; Sparsø, Thomas;

    2005-01-01

    Based on a combination of PCR and restriction endonuclease (RE) digestion (PCR-RE digestion), we have examined the possibility of differentiating members of the Bacillus cereus group. Fragments of the gyrB gene (362 bp) from pure cultures of 12 B. cereus, 25 B. thuringiensis, 25 B. mycoides and two...

  3. Mathematical Analysis of Piaget's Grouping Concept. Papy's Minicomputer as a Grouping

    Science.gov (United States)

    Steiner, H. G.

    1974-01-01

    Through a mathematical analysis, Piaget's grouping concept can be formally interpreted as being a hybrid between the mathematical concepts of a group and a lattice. Some relevant pedagogical models are presented. Activities with Cuisenaire Rods, Dienes Blocks, and Papy's Minicomputer are shown to take place in groupings. (LS)

  4. UV-visible microscope spectrophotometric polarization and dichroism with increased discrimination power in forensic analysis

    Science.gov (United States)

    Purcell, Dale Kevin

    Microanalysis of transfer (Trace) evidence is the application of a microscope and microscopical techniques for the collection, observation, documentation, examination, identification, and discrimination of micrometer sized particles or domains. Microscope spectrophotometry is the union of microscopy and spectroscopy for microanalysis. Analytical microspectroscopy is the science of studying the emission, reflection, transmission, and absorption of electromagnetic radiation to determine the structure or chemical composition of microscopic-size materials. Microscope spectrophotometry instrument designs have evolved from monochromatic illumination which transmitted through the microscope and sample and then is detected by a photometer detector (photomultiplier tube) to systems in which broad-band (white light) illumination falls incident upon a sample followed by a non-scanning grating spectrometer equipped with a solid-state multi-element detector. Most of these small modern spectrometers are configured with either silicon based charged-couple device detectors (200-950 nm) or InGaAs based diode array detectors (850-2300 nm) with computerized data acquisition and signal processing being common. A focus of this research was to evaluate the performance characteristics of various modern forensic (UV-Vis) microscope photometer systems as well as review early model instrumental designs. An important focus of this research was to efficiently measure ultraviolet-visible spectra of microscopically small specimens for classification, differentiation, and possibly individualization. The first stage of the project consisted of the preparation of microscope slides containing neutral density filter reference materials, molecular fluorescence reference materials, and dichroic reference materials. Upon completion of these standard slide preparations analysis began with measurements in order to evaluate figures of merit for comparison of the instruments investigated. The figures of

  5. The use of discriminant analysis for evaluation of early-response multiple biomarkers of radiation exposure using non-human primate 6-Gy whole-body radiation model

    Energy Technology Data Exchange (ETDEWEB)

    Ossetrova, N.I. [Armed Forces Radiobiology Research Institute, 8901 Wisconsin Avenue, Bethesda, MD 20889-5603 (United States)], E-mail: ossetrova@afrri.usuhs.mil; Farese, A.M.; MacVittie, T.J. [Marlene and Stewart Greenebaum Cancer Center, Bressler Research Building, Room 7-039, University of Maryland-Baltimore, 655 West Baltimore Street, Baltimore, MD 21201 (United States); Manglapus, G.L.; Blakely, W.F. [Armed Forces Radiobiology Research Institute, 8901 Wisconsin Avenue, Bethesda, MD 20889-5603 (United States)

    2007-07-15

    The present need to rapidly identify severely irradiated individuals in mass-casualty and population-monitoring scenarios prompted an evaluation of potential protein biomarkers to provide early diagnostic information after exposure. The level of specific proteins measured using immunodiagnostic technologies may be useful as protein biomarkers to provide early diagnostic information for acute radiation exposures. Herein we present results from on-going studies using a non-human primate (NHP) 6-Gy X-rays ( 0.13Gymin{sup -1}) whole-body radiation model. Protein targets were measured by enzyme-linked immunosorbent assay (ELISA) in blood plasma before, 1, and 2 days after exposure. Exposure of 10 NHPs to 6 Gy resulted in the up-regulation of plasma levels of (a) p21 WAF1/CIP1, (b) interleukin 6 (IL-6), (c) tissue enzyme salivary {alpha}-amylase, and (d) C-reactive protein. Data presented show the potential utility of protein biomarkers selected from distinctly different pathways to detect radiation exposure. A correlation analysis demonstrated strong correlations among different combinations of four candidate radiation-responsive blood protein biomarkers. Data analyzed with use of multivariate discriminant analysis established very successful separation of NHP groups: 100% discrimination power for animals with correct classification for separation between groups before and 1 day after irradiation, and 95% discrimination power for separation between groups before and 2 days after irradiation. These results also demonstrate proof-in-concept that multiple protein biomarkers provide early diagnostic information to the medical community, along with classical biodosimetric methodologies, to effectively manage radiation casualty incidents.

  6. Discrimination and chemical phylogenetic study of seven species of Dendrobium using infrared spectroscopy combined with cluster analysis

    Science.gov (United States)

    Luo, Congpei; He, Tao; Chun, Ze

    2013-04-01

    Dendrobium is a commonly used and precious herb in Traditional Chinese Medicine. The high biodiversity of Dendrobium and the therapeutic needs require tools for the correct and fast discrimination of different Dendrobium species. This study investigates Fourier transform infrared spectroscopy followed by cluster analysis for discrimination and chemical phylogenetic study of seven Dendrobium species. Despite the general pattern of the IR spectra, different intensities, shapes, peak positions were found in the IR spectra of these samples, especially in the range of 1800-800 cm-1. The second derivative transformation and alcoholic extracting procedure obviously enlarged the tiny spectral differences among these samples. The results indicated each Dendrobium species had a characteristic IR spectra profile, which could be used to discriminate them. The similarity coefficients among the samples were analyzed based on their second derivative IR spectra, which ranged from 0.7632 to 0.9700, among the seven Dendrobium species, and from 0.5163 to 0.9615, among the ethanol extracts. A dendrogram was constructed based on cluster analysis the IR spectra for studying the chemical phylogenetic relationships among the samples. The results indicated that D. denneanum and D. crepidatum could be the alternative resources to substitute D. chrysotoxum, D. officinale and D. nobile which were officially recorded in Chinese Pharmacopoeia. In conclusion, with the advantages of high resolution, speediness and convenience, the experimental approach can successfully discriminate and construct the chemical phylogenetic relationships of the seven Dendrobium species.

  7. Discriminant analysis for characterization of hydrochemistry of two mountain river basins of contrasting climates in the southern Western Ghats, India.

    Science.gov (United States)

    Thomas, Jobin; Joseph, Sabu; Thrivikramji, K P

    2015-06-01

    Discriminant analysis (DA) was performed on river hydrochemistry data for three seasons (i.e., monsoon (MON), post-monsoon (POM), and pre-monsoon (PRM)) to examine the spatio-temporal hydrochemical variability of two mountain river basins (Muthirapuzha River Basin (MRB) and Pambar River Basin (PRB)) of the southern Western Ghats, India. Although the river basins drain tropical mountainous terrain, climate and degree of anthropogenic disturbances show significant differences (i.e., humid, more disturbed MRB vs semiarid, less disturbed PRB). In MRB, TDS, Na(+), pH, Mg(2+), and K(+) are the attributes responsible for significant hydrochemical variations between the seasons, while Cl(-), TH, and Na(+) are the predictors in PRB. The temporal discriminant models imply the importance of rainfall pattern, relative contribution of groundwater toward stream discharge and farming activities in hydrochemistry between the seasons. Inclusion of hydrochemical attributes (in the temporal discriminant functions) that can be derived from both natural and anthropogenic sources suggests that ionic enrichment strongly depends on the seasons, and is mainly due to the variability in the intensity of anthropogenic activities as well as fluctuations in river discharge. In spatial discriminant models, Cl(-) is the only variable responsible for hydrochemical variations between the basins (during MON), whereas Si discriminates during POM and PRM, implying the role of atmospheric supply, anthropogenic modifications as well as intensity of weathering. In the spatial discrimination models, misclassification of hydrochemistry data between MRB and PRB can be attributed to the overlapping effect of humid climate of MRB extending toward the upstream of (semiarid) PRB. This study underscores the versatility of DA in deciphering the significance of climatic controls on hydrochemical composition of tropical mountain rivers.

  8. Patterns of Discrimination, Grievances and Political Activity Among Europe's Roma: A Cross-Sectional Analysis

    Directory of Open Access Journals (Sweden)

    Jonathan Fox

    2001-01-01

    Full Text Available The purpose of this study is to analyse in a large-n cross-sectional format the patterns of discrimination, grievances and political activity among European Roma (Gypsies using data from the Minority at Risk project. The model tested here is a two-step model positing that discrimination leads to grievance formation which in turn leads to protest and rebellion. The results show that the Roma, in general, conform to this model but differ in some important specifics.

  9. Coarse-Grained Multifractality Analysis Based on Structure Function Measurements to Discriminate Healthy from Distressed Foetuses

    Directory of Open Access Journals (Sweden)

    Souad Oudjemia

    2013-01-01

    Full Text Available This paper proposes a combined coarse-grained multifractal method to discriminate between distressed and normal foetuses. The coarse-graining operation was performed by means of a coarse-grained procedure and the multifractal operation was based on a structure function. The proposed method was evaluated by one hundred recordings including eighty normal foetuses and twenty distressed foetuses. We found that it was possible to discriminate between distressed and normal foetuses using the Hurst exponent, singularity, and Holder spectra.

  10. Genetic analysis of 19 X chromosome STR loci for forensic purposes in four Chinese ethnic groups

    Science.gov (United States)

    Yang, Xingyi; Zhang, Xiaofang; Zhu, Junyong; Chen, Linli; Liu, Changhui; Feng, Xingling; Chen, Ling; Wang, Huijun; Liu, Chao

    2017-01-01

    A new 19 X- short tandem repeat (STR) multiplex PCR system has recently been developed, though its applicability in forensic studies has not been thoroughly assessed. In this study, 932 unrelated individuals from four Chinese ethnic groups (Han, Tibet, Uighur and Hui) were successfully genotyped using this new multiplex PCR system. Our results showed significant linkage disequilibrium between markers DXS10103 and DXS10101 in all four ethnic groups; markers DXS10159 and DXS10162, DXS6809 and DXS6789, and HPRTB and DXS10101 in Tibetan populations; and markers DXS10074 and DXS10075 in Uighur populations. The combined powers of discrimination in males and females were calculated according to haplotype frequencies from allele distributions rather than haplotype counts in the relevant population and were high in four ethnic groups. The cumulative powers of discrimination of the tested X-STR loci were 1.000000000000000 and 0.999999999997940 in females and males, respectively. All 19 X-STR loci are highly polymorphic. The highest Reynolds genetic distances were observed for the Tibet-Uighur pairwise comparisons. This study represents an extensive report on X-STR marker variation in minor Chinese populations and a comprehensive analysis of the diversity of these 19 X STR markers in four Chinese ethnic groups. PMID:28211539

  11. Genetic analysis of 19 X chromosome STR loci for forensic purposes in four Chinese ethnic groups.

    Science.gov (United States)

    Yang, Xingyi; Zhang, Xiaofang; Zhu, Junyong; Chen, Linli; Liu, Changhui; Feng, Xingling; Chen, Ling; Wang, Huijun; Liu, Chao

    2017-02-17

    A new 19 X- short tandem repeat (STR) multiplex PCR system has recently been developed, though its applicability in forensic studies has not been thoroughly assessed. In this study, 932 unrelated individuals from four Chinese ethnic groups (Han, Tibet, Uighur and Hui) were successfully genotyped using this new multiplex PCR system. Our results showed significant linkage disequilibrium between markers DXS10103 and DXS10101 in all four ethnic groups; markers DXS10159 and DXS10162, DXS6809 and DXS6789, and HPRTB and DXS10101 in Tibetan populations; and markers DXS10074 and DXS10075 in Uighur populations. The combined powers of discrimination in males and females were calculated according to haplotype frequencies from allele distributions rather than haplotype counts in the relevant population and were high in four ethnic groups. The cumulative powers of discrimination of the tested X-STR loci were 1.000000000000000 and 0.999999999997940 in females and males, respectively. All 19 X-STR loci are highly polymorphic. The highest Reynolds genetic distances were observed for the Tibet-Uighur pairwise comparisons. This study represents an extensive report on X-STR marker variation in minor Chinese populations and a comprehensive analysis of the diversity of these 19 X STR markers in four Chinese ethnic groups.

  12. In Vitro Cell Death Discrimination and Screening Method by Simple and Cost-Effective Viability Analysis.

    Science.gov (United States)

    Helm, Katharina; Beyreis, Marlena; Mayr, Christian; Ritter, Markus; Jakab, Martin; Kiesslich, Tobias; Plaetzer, Kristjan

    2017-01-01

    For in vitro cytotoxicity testing, discrimination of apoptosis and necrosis represents valuable information. Viability analysis performed at two different time points post treatment could serve such a purpose because the dynamics of metabolic activity of apoptotic and necrotic cells is different, i.e. a more rapid decline of cellular metabolism during necrosis whereas cellular metabolism is maintained during the entire execution phase of apoptosis. This study describes a straightforward approach to distinguish apoptosis and necrosis. A431 human epidermoid carcinoma cells were treated with different concentrations/doses of actinomycin D (Act-D), 4,5,6,7-tetrabromo-2-azabenzimidazole (TBB), Ro 31-8220, H2O2 and photodynamic treatment (PDT). The resazurin viability signal was recorded at 2 and 24 hrs post treatment. Apoptosis and necrosis were verified by measuring caspase 3/7 and membrane integrity. Calculation of the difference curve between the 2 and 24 hrs resazurin signals yields the following information: a positive difference signal indicates apoptosis (i.e. high metabolic activity at early time points and low signal at 24 hrs post treatment) while an early reduction of the viability signal indicates necrosis. For all treatments, this dose-dependent sequence of cellular responses could be confirmed by independent assays. Simple and cost-effective viability analysis provides reliable information about the dose ranges of a cytotoxic agent where apoptosis or necrosis occurs. This may serve as a starting point for further in-depth characterisation of cytotoxic treatments. © 2017 The Author(s)Published by S. Karger AG, Basel.

  13. Does discrimination breed grievances - and do grievances breed violence? : New evidence from an analysis of religious minorities in developing countries

    OpenAIRE

    Basedau, Matthias; Fox, Jonathan; Pierskalla, Jan H.; Strüver, Georg; Vüllers, Johannes

    2017-01-01

    Since Ted Gurr’s Why Men Rebel it has become conventional wisdom that (relative) deprivation creates grievances and that these grievances in turn lead to intergroup violence. Recently, studies have yielded evidence that the exclusion of ethnic groups is a substantial conflict risk. From a theoretical angle, the relationship is straightforward and is likely to unfold as a causal chain that runs from objective discrimination to (subjective) grievances and then to violence. We test this proposit...

  14. Patterns and correlates of self-reported racial discrimination among Australian Aboriginal and Torres Strait Islander adults, 2008-09: analysis of national survey data.

    Science.gov (United States)

    Cunningham, Joan; Paradies, Yin C

    2013-07-01

    There is now considerable evidence that racism is a pernicious and enduring social problem with a wide range of detrimental outcomes for individuals, communities and societies. Although indigenous people worldwide are subjected to high levels of racism, there is a paucity of population-based, quantitative data about the factors associated with their reporting of racial discrimination, about the settings in which such discrimination takes place, and about the frequency with which it is experienced. Such information is essential in efforts to reduce both exposure to racism among indigenous people and the harms associated with such exposure. Weighted data on self-reported racial discrimination from over 7,000 Indigenous Australian adults participating in the 2008-09 National Aboriginal and Torres Strait Islander Survey, a nationally representative survey conducted by the Australian Bureau of Statistics, were analysed by socioeconomic, demographic and cultural factors. More than one in four respondents (27%) reported experiencing racial discrimination in the past year. Racial discrimination was most commonly reported in public (41% of those reporting any racial discrimination), legal (40%) and work (30%) settings. Among those reporting any racial discrimination, about 40% experienced this discrimination most or all of the time (as opposed to a little or some of the time) in at least one setting. Reporting of racial discrimination peaked in the 35-44 year age group and then declined. Higher reporting of racial discrimination was associated with removal from family, low trust, unemployment, having a university degree, and indicators of cultural identity and participation. Lower reporting of racial discrimination was associated with home ownership, remote residence and having relatively few Indigenous friends. These data indicate that racial discrimination is commonly experienced across a wide variety of settings, with public, legal and work settings identified as

  15. Structural Discrimination

    DEFF Research Database (Denmark)

    Thorsen, Mira Skadegård

    In this article, I discuss structural discrimination, an underrepresented area of study in Danish discrimination and intercultural research. It is defined here as discursive and constitutive, and presented as a central element of my analytical approach. This notion is employed in the with which...... to understand and identify aspects of power and asymmetry in communication and interactions. With this as a defining term, I address how exclusion and discrimination exist, while also being indiscernible, within widely accepted societal norms. I introduce the concepts of microdiscrimination and benevolent...... discrimination as two ways of articulating particular, opaque forms of racial discrimination that occur in everyday Danish (and other) contexts, and have therefore become normalized. I present and discuss discrimination as it surfaces in data from my empirical studies of discrimination in Danish contexts...

  16. Functional MRI Representational Similarity Analysis Reveals a Dissociation between Discriminative and Relative Location Information in the Human Visual System

    Directory of Open Access Journals (Sweden)

    Zvi N Roth

    2016-03-01

    Full Text Available Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e. discriminative information may be less relevant than information regarding the relative location of two objects (i.e. relative information. For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action.We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in early visual cortex, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS, but not from EVC, where we find the spatial representation to be warped.We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model’s receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC.These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representation into relative spatial representation along the visual stream.

  17. Functional MRI Representational Similarity Analysis Reveals a Dissociation between Discriminative and Relative Location Information in the Human Visual System.

    Science.gov (United States)

    Roth, Zvi N

    2016-01-01

    Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC) activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e., discriminative information) may be less relevant than information regarding the relative location of two objects (i.e., relative information). For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action. We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in EVC, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS), but not from EVC, where we find the spatial representation to be warped. We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model's receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC. These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representations into relative spatial representations along the visual stream.

  18. Functional MRI Representational Similarity Analysis Reveals a Dissociation between Discriminative and Relative Location Information in the Human Visual System

    Science.gov (United States)

    Roth, Zvi N.

    2016-01-01

    Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC) activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e., discriminative information) may be less relevant than information regarding the relative location of two objects (i.e., relative information). For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action. We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in EVC, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS), but not from EVC, where we find the spatial representation to be warped. We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model's receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC. These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representations into relative spatial representations along the visual stream. PMID:27242455

  19. Evaluation of volatile metabolites as markers in Lycopersicon esculentum L. cultivars discrimination by multivariate analysis of headspace solid phase microextraction and mass spectrometry data.

    Science.gov (United States)

    Figueira, José; Câmara, Hugo; Pereira, Jorge; Câmara, José S

    2014-02-15

    To gain insights on the effects of cultivar on the volatile metabolomic expression of different tomato (Lycopersicon esculentum L.) cultivars--Plum, Campari, Grape, Cherry and Regional, cultivated under similar edafoclimatic conditions, and to identify the most discriminate volatile marker metabolites related to the cultivar, the chromatographic profiles resulting from headspace solid phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-qMS) analysis, combined with multivariate analysis were investigated. The data set composed by the 77 volatile metabolites identified in the target tomato cultivars, 5 of which (2,2,6-trimethylcyclohexanone, 2-methyl-6-methyleneoctan-2-ol, 4-octadecyl-morpholine, (Z)-methyl-3-hexenoate and 3-octanone) are reported for the first time in tomato volatile metabolomic composition, was evaluated by chemometrics. Firstly, principal component analysis was carried out in order to visualise data trends and clusters, and then, linear discriminant analysis in order to detect the set of volatile metabolites able to differentiate groups according to tomato cultivars. The results obtained revealed a perfect discrimination between the different Lycopersicon esculentum L. cultivars considered. The assignment success rate was 100% in classification and 80% in prediction ability by using "leave-one-out" cross-validation procedure. The volatile profile was able to differentiate all five cultivars and revealed complex interactions between them including the participation in the same biosynthetic pathway. The volatile metabolomic platform for tomato samples obtained by HS-SPME/GC-qMS here described, and the interrelationship detected among the volatile metabolites can be used as a roadmap for biotechnological applications, namely to improve tomato aroma and their acceptance in the final consumer, and for traceability studies. Copyright © 2013 Elsevier Ltd. All rights reserved.

  20. A novel multiplex PCR discriminates Bacillus anthracis and its genetically related strains from other Bacillus cereus group species.

    Directory of Open Access Journals (Sweden)

    Hirohito Ogawa

    Full Text Available Anthrax is an important zoonotic disease worldwide that is caused by Bacillus anthracis, a spore-forming pathogenic bacterium. A rapid and sensitive method to detect B. anthracis is important for anthrax risk management and control in animal cases to address public health issues. However, it has recently become difficult to identify B. anthracis by using previously reported molecular-based methods because of the emergence of B. cereus, which causes severe extra-intestinal infection, as well as the human pathogenic B. thuringiensis, both of which are genetically related to B. anthracis. The close genetic relation of chromosomal backgrounds has led to complexity of molecular-based diagnosis. In this study, we established a B. anthracis multiplex PCR that can screen for the presence of B. anthracis virulent plasmids and differentiate B. anthracis and its genetically related strains from other B. cereus group species. Six sets of primers targeting a chromosome of B. anthracis and B. anthracis-like strains, two virulent plasmids, pXO1 and pXO2, a bacterial gene, 16S rRNA gene, and a mammalian gene, actin-beta gene, were designed. The multiplex PCR detected approximately 3.0 CFU of B. anthracis DNA per PCR reaction and was sensitive to B. anthracis. The internal control primers also detected all bacterial and mammalian DNAs examined, indicating the practical applicability of this assay as it enables monitoring of appropriate amplification. The assay was also applied for detection of clinical strains genetically related to B. anthracis, which were B. cereus strains isolated from outbreaks of hospital infections in Japan, and field strains isolated in Zambia, and the assay differentiated B. anthracis and its genetically related strains from other B. cereus group strains. Taken together, the results indicate that the newly developed multiplex PCR is a sensitive and practical method for detecting B. anthracis.

  1. A novel multiplex PCR discriminates Bacillus anthracis and its genetically related strains from other Bacillus cereus group species.

    Science.gov (United States)

    Ogawa, Hirohito; Fujikura, Daisuke; Ohnuma, Miyuki; Ohnishi, Naomi; Hang'ombe, Bernard M; Mimuro, Hitomi; Ezaki, Takayuki; Mweene, Aaron S; Higashi, Hideaki

    2015-01-01

    Anthrax is an important zoonotic disease worldwide that is caused by Bacillus anthracis, a spore-forming pathogenic bacterium. A rapid and sensitive method to detect B. anthracis is important for anthrax risk management and control in animal cases to address public health issues. However, it has recently become difficult to identify B. anthracis by using previously reported molecular-based methods because of the emergence of B. cereus, which causes severe extra-intestinal infection, as well as the human pathogenic B. thuringiensis, both of which are genetically related to B. anthracis. The close genetic relation of chromosomal backgrounds has led to complexity of molecular-based diagnosis. In this study, we established a B. anthracis multiplex PCR that can screen for the presence of B. anthracis virulent plasmids and differentiate B. anthracis and its genetically related strains from other B. cereus group species. Six sets of primers targeting a chromosome of B. anthracis and B. anthracis-like strains, two virulent plasmids, pXO1 and pXO2, a bacterial gene, 16S rRNA gene, and a mammalian gene, actin-beta gene, were designed. The multiplex PCR detected approximately 3.0 CFU of B. anthracis DNA per PCR reaction and was sensitive to B. anthracis. The internal control primers also detected all bacterial and mammalian DNAs examined, indicating the practical applicability of this assay as it enables monitoring of appropriate amplification. The assay was also applied for detection of clinical strains genetically related to B. anthracis, which were B. cereus strains isolated from outbreaks of hospital infections in Japan, and field strains isolated in Zambia, and the assay differentiated B. anthracis and its genetically related strains from other B. cereus group strains. Taken together, the results indicate that the newly developed multiplex PCR is a sensitive and practical method for detecting B. anthracis.

  2. Immunophenotypic analysis of erythroid dysplasia in myelodysplastic syndromes. A report from the IMDSFlow working group

    Science.gov (United States)

    Westers, Theresia M.; Cremers, Eline M.P.; Oelschlaegel, Uta; Johansson, Ulrika; Bettelheim, Peter; Matarraz, Sergio; Orfao, Alberto; Moshaver, Bijan; Brodersen, Lisa Eidenschink; Loken, Michael R.; Wells, Denise A.; Subirá, Dolores; Cullen, Matthew; te Marvelde, Jeroen G.; van der Velden, Vincent H.J.; Preijers, Frank W.M.B.; Chu, Sung-Chao; Feuillard, Jean; Guérin, Estelle; Psarra, Katherina; Porwit, Anna; Saft, Leonie; Ireland, Robin; Milne, Timothy; Béné, Marie C.; Witte, Birgit I.; Della Porta, Matteo G.; Kern, Wolfgang; van de Loosdrecht, Arjan A.

    2017-01-01

    Current recommendations for diagnosing myelodysplastic syndromes endorse flow cytometry as an informative tool. Most flow cytometry protocols focus on the analysis of progenitor cells and the evaluation of the maturing myelomonocytic lineage. However, one of the most frequently observed features of myelodysplastic syndromes is anemia, which may be associated with dyserythropoiesis. Therefore, analysis of changes in flow cytometry features of nucleated erythroid cells may complement current flow cytometry tools. The multicenter study within the IMDSFlow Working Group, reported herein, focused on defining flow cytometry parameters that enable discrimination of dyserythropoiesis associated with myelodysplastic syndromes from non-clonal cytopenias. Data from a learning cohort were compared between myelodysplasia and controls, and results were validated in a separate cohort. The learning cohort comprised 245 myelodysplasia cases, 290 pathological, and 142 normal controls; the validation cohort comprised 129 myelodysplasia cases, 153 pathological, and 49 normal controls. Multivariate logistic regression analysis performed in the learning cohort revealed that analysis of expression of CD36 and CD71 (expressed as coefficient of variation), in combination with CD71 fluorescence intensity and the percentage of CD117+ erythroid progenitors provided the best discrimination between myelodysplastic syndromes and non-clonal cytopenias (specificity 90%; 95% confidence interval: 84–94%). The high specificity of this marker set was confirmed in the validation cohort (92%; 95% confidence interval: 86–97%). This erythroid flow cytometry marker combination may improve the evaluation of cytopenic cases with suspected myelodysplasia, particularly when combined with flow cytometry assessment of the myelomonocytic lineage. PMID:27758818

  3. Early discrimination of nasopharyngeal carcinoma based on tissue deoxyribose nucleic acid surface-enhanced Raman spectroscopy analysis

    Science.gov (United States)

    Qiu, Sufang; Li, Chao; Lin, Jinyong; Xu, Yuanji; Lu, Jun; Huang, Qingting; Zou, Changyan; Chen, Chao; Xiao, Nanyang; Lin, Duo; Chen, Rong; Pan, Jianji; Feng, Shangyuan

    2016-12-01

    Surface-enhanced Raman spectroscopy (SERS) was employed to detect deoxyribose nucleic acid (DNA) variations associated with the development of nasopharyngeal carcinoma (NPC). Significant SERS spectral differences between the DNA extracted from early NPC, advanced NPC, and normal nasopharyngeal tissue specimens were observed at 678, 729, 788, 1337, 1421, 1506, and 1573 cm-1, which reflects the genetic variations in NPC. Principal component analysis combined with discriminant function analysis for early NPC discrimination yielded a diagnostic accuracy of 86.8%, 92.3%, and 87.9% for early NPC, advanced NPC, and normal nasopharyngeal tissue DNA, respectively. In this exploratory study, we demonstrated the potential of SERS for early detection of NPC based on the DNA molecular study of biopsy tissues.

  4. Towards Maximum Spanning Tree Model in Web 3.0 Design and Development for Students using Discriminant Analysis

    CERN Document Server

    Padma, S

    2012-01-01

    Web 3.0 is an evolving extension of the web 2.0 scenario. The perceptions regarding web 3.0 is different from person to person . Web 3.0 Architecture supports ubiquitous connectivity, network computing, open identity, intelligent web, distributed databases and intelligent applications. Some of the technologies which lead to the design and development of web 3.0 applications are Artificial intelligence, Automated reasoning, Cognitive architecture, Semantic web . An attempt is made to capture the requirements of Students inline with web 3.0 so as to bridge the gap between the design and development of web 3.0 applications and requirements among Students. Maximum Spanning Tree modeling of the requirements facilitate the identification of key areas and key attributes in the design and development of software products for Students in Web 3.0 using Discriminant analysis. Keywords : Web 3.0, Discriminant analysis, Design and Development, Model, Maximum Spanning Tree 1.

  5. Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals.

    Directory of Open Access Journals (Sweden)

    Guan Yu

    Full Text Available Accurately identifying mild cognitive impairment (MCI individuals who will progress to Alzheimer's disease (AD is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI and fluorodeoxyglucose positron emission tomography (FDG-PET. However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI subjects and 226 stable MCI (sMCI subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images and also the single-task classification method (using only MRI or only subjects with both MRI and

  6. The use of kernel local Fisher discriminant analysis for the channelization of the Hotelling model observer

    Science.gov (United States)

    Wen, Gezheng; Markey, Mia K.

    2015-03-01

    It is resource-intensive to conduct human studies for task-based assessment of medical image quality and system optimization. Thus, numerical model observers have been developed as a surrogate for human observers. The Hotelling observer (HO) is the optimal linear observer for signal-detection tasks, but the high dimensionality of imaging data results in a heavy computational burden. Channelization is often used to approximate the HO through a dimensionality reduction step, but how to produce channelized images without losing significant image information remains a key challenge. Kernel local Fisher discriminant analysis (KLFDA) uses kernel techniques to perform supervised dimensionality reduction, which finds an embedding transformation that maximizes betweenclass separability and preserves within-class local structure in the low-dimensional manifold. It is powerful for classification tasks, especially when the distribution of a class is multimodal. Such multimodality could be observed in many practical clinical tasks. For example, primary and metastatic lesions may both appear in medical imaging studies, but the distributions of their typical characteristics (e.g., size) may be very different. In this study, we propose to use KLFDA as a novel channelization method. The dimension of the embedded manifold (i.e., the result of KLFDA) is a counterpart to the number of channels in the state-of-art linear channelization. We present a simulation study to demonstrate the potential usefulness of KLFDA for building the channelized HOs (CHOs) and generating reliable decision statistics for clinical tasks. We show that the performance of the CHO with KLFDA channels is comparable to that of the benchmark CHOs.

  7. Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals.

    Science.gov (United States)

    Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang

    2014-01-01

    Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images

  8. Studies in genetic discrimination. Final progress report

    Energy Technology Data Exchange (ETDEWEB)

    1994-06-01

    We have screened 1006 respondents in a study of genetic discrimination. Analysis of these responses has produced evidence of the range of institutions engaged in genetic discrimination and demonstrates the impact of this discrimination on the respondents to the study. We have found that both ignorance and policy underlie genetic discrimination and that anti-discrimination laws are being violated.

  9. Sex determination from the mandibular ramus flexure of Koreans by discrimination function analysis using three-dimensional mandible models.

    Science.gov (United States)

    Lin, Chenghe; Jiao, Benzheng; Liu, Shanshan; Guan, Feng; Chung, Nak-Eun; Han, Seung-Ho; Lee, U-Young

    2014-03-01

    It has been known that mandible ramus flexure is an important morphologic trait for sex determination. However, it will be unavailable when mandible is incomplete or fragmented. Therefore, the anthropometric analysis on incomplete or fragmented mandible becomes more important. The aim of this study is to investigate the sex-discriminant potential of mandible ramus flexure on the Korean three-dimensional (3D) mandible models with anthropometric analysis. The sample consists of 240 three dimensional mandibular models obtained from Korean population (M:F; 120:120, mean age 46.2 y), collected by The Catholic Institute for Applied Anatomy, The Catholic University of Korea. Anthropometric information about 11 metric was taken with Mimics, anthropometry libraries toolkit. These parameters were subjected to different discriminant function analyses using SPSS 17.0. Univariate analyses showed that the resubstitution accuracies for sex determination range from 50.4 to 77.1%. Mandibular flexure upper border (MFUB), maximum ramus vertical height (MRVH), and upper ramus vertical height (URVH) expressed the greatest dimorphism, 72.1 to 77.1%. Bivariate analyses indicated that the combination of MFUB and MRVH hold even higher resubstitution accuracy of 81.7%. Furthermore, the direct and stepwise discriminant analyses with the variables on the upper ramus above flexure could predict sex in 83.3 and 85.0%, respectively. When all variables of mandibular ramus flexure were input in stepwise discriminant analysis, the resubstitution accuracy arrived as high as 88.8%. Therefore, we concluded that the upper ramus above flexure hold the larger potentials than the mandibular ramus flexure itself to predict sexes, and that the equations in bivariate and multivariate analysis from our study will be helpful for sex determination on Korean population in forensic science and law.

  10. Functional connectivity in tactile object discrimination: a principal component analysis of an event related fMRI-Study.

    Directory of Open Access Journals (Sweden)

    Susanne Hartmann

    Full Text Available BACKGROUND: Tactile object discrimination is an essential human skill that relies on functional connectivity between the neural substrates of motor, somatosensory and supramodal areas. From a theoretical point of view, such distributed networks elude categorical analysis because subtraction methods are univariate. Thus, the aim of this study was to identify the neural networks involved in somatosensory object discrimination using a voxel-based principal component analysis (PCA of event-related functional magnetic resonance images. METHODOLOGY/PRINCIPAL FINDINGS: Seven healthy, right-handed subjects aged between 22 and 44 years were required to discriminate with their dominant hand the length differences between otherwise identical parallelepipeds in a two-alternative forced-choice paradigm. Of the 34 principal components retained for analysis according to the 'bootstrapped' Kaiser-Guttman criterion, t-tests applied to the subject-condition expression coefficients showed significant mean differences between the object presentation and inter-stimulus phases in PC 1, 3, 26 and 32. Specifically, PC 1 reflected object exploration or manipulation, PC 3 somatosensory and short-term memory processes. PC 26 evinced the perception that certain parallelepipeds could not be distinguished, while PC 32 emerged in those choices when they could be. Among the cerebral regions evident in the PCs are the left posterior parietal lobe and premotor cortex in PC 1, the left superior parietal lobule (SPL and the right cuneus in PC 3, the medial frontal and orbitofrontal cortex bilaterally in PC 26, and the right intraparietal sulcus, anterior SPL and dorsolateral prefrontal cortex in PC 32. CONCLUSIONS/SIGNIFICANCE: The analysis provides evidence for the concerted action of large-scale cortico-subcortical networks mediating tactile object discrimination. Parallel to activity in nodes processing object-related impulses we found activity in key cerebral regions

  11. [Study on the genuineness and producing area of Panax notoginseng based on infrared spectroscopy combined with discriminant analysis].

    Science.gov (United States)

    Liu, Fei; Wang, Yuan-zhong; Yang, Chun-yan; Jin, Hang

    2015-01-01

    The genuineness and producing area of Panax notoginseng were studied based on infrared spectroscopy combined with discriminant analysis. The infrared spectra of 136 taproots of P. notoginseng from 13 planting point in 11 counties were collected and the second derivate spectra were calculated by Omnic 8. 0 software. The infrared spectra and their second derivate spectra in the range 1 800 - 700 cm-1 were used to build model by stepwise discriminant analysis, which was in order to distinguish study on the genuineness of P. notoginseng. The model built based on the second derivate spectra showed the better recognition effect for the genuineness of P. notoginseng. The correct rate of returned classification reached to 100%, and the prediction accuracy was 93. 4%. The stability of model was tested by cross validation and the method was performed extrapolation validation. The second derivate spectra combined with the same discriminant analysis method were used to distinguish the producing area of P. notoginseng. The recognition effect of models built based on different range of spectrum and different numbers of samples were compared and found that when the model was built by collecting 8 samples from each planting point as training sample and the spectrum in the range 1 500 - 1 200 cm-1 , the recognition effect was better, with the correct rate of returned classification reached to 99. 0%, and the prediction accuracy was 76. 5%. The results indicated that infrared spectroscopy combined with discriminant analysis showed good recognition effect for the genuineness of P. notoginseng. The method might be a hopeful new method for identification of genuineness of P. notoginseng in practice. The method could recognize the producing area of P. notoginseng to some extent and could be a new thought for identification of the producing area of P. natoginseng.

  12. Effects of measurement errors on psychometric measurements in ergonomics studies: Implications for correlations, ANOVA, linear regression, factor analysis, and linear discriminant analysis.

    Science.gov (United States)

    Liu, Yan; Salvendy, Gavriel

    2009-05-01

    This paper aims to demonstrate the effects of measurement errors on psychometric measurements in ergonomics studies. A variety of sources can cause random measurement errors in ergonomics studies and these errors can distort virtually every statistic computed and lead investigators to erroneous conclusions. The effects of measurement errors on five most widely used statistical analysis tools have been discussed and illustrated: correlation; ANOVA; linear regression; factor analysis; linear discriminant analysis. It has been shown that measurement errors can greatly attenuate correlations between variables, reduce statistical power of ANOVA, distort (overestimate, underestimate or even change the sign of) regression coefficients, underrate the explanation contributions of the most important factors in factor analysis and depreciate the significance of discriminant function and discrimination abilities of individual variables in discrimination analysis. The discussions will be restricted to subjective scales and survey methods and their reliability estimates. Other methods applied in ergonomics research, such as physical and electrophysiological measurements and chemical and biomedical analysis methods, also have issues of measurement errors, but they are beyond the scope of this paper. As there has been increasing interest in the development and testing of theories in ergonomics research, it has become very important for ergonomics researchers to understand the effects of measurement errors on their experiment results, which the authors believe is very critical to research progress in theory development and cumulative knowledge in the ergonomics field.

  13. Characterization of Fatty Acid Profile of Argan Oil and Other Edible Vegetable Oils by Gas Chromatography and Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Ascensión Rueda

    2014-01-01

    Full Text Available Virgin argan oil is an emergent oil that is being introduced into specialized international markets as a healthy and luxury food. In order to compare the fatty acid composition of argan oil with that of the eleven other vegetable edible oils, a combination of gas chromatography as analytical technique and multivariate discriminant analysis was applied. This analysis takes into account the conjoint effect of all the variables analyzed in the discrimination between oils and also shows the contribution of each variable to oils characterization. The model correctly classified 100% oil samples. According to the fatty acid composition, argan oil showed closest similarity firstly with sesame oil and secondly with high oleic sunflower oil. Olive oil was close to avocado oil and almond oil, followed by argan oil. Thus, similarities and differences between vegetable oils based on their fatty acid profile were established by the application of multivariate discriminant analysis. This method was proven to be a useful tool to study the relationships between oils according to the fat composition and to determine the importance of the fatty acid variables on the oils classification.

  14. [Discriminant analysis of near infrared diffuse reflectance spectra to detect adulteration of non-ruminant meat and bone meal].

    Science.gov (United States)

    Li, Qiong-Fei; Yang, Zeng-Ling; Han, Lu-Jia

    2008-03-01

    In order to study the feasibility of using near infrared (NIR) diffuse reflectance spectroscopy to discriminate adultera tion of non-ruminant meat and bone meal (MBM) with ruminant MBM, a total of 39 MBM samples made up of 15 from pig, 15 from poultry, 5 from cattle and 4 from sheep produced in different areas in China were chosen. The MBM samples were ground with 0. 5 mm sieve. 252 specimens were prepared by non-ruminant MBM deliberately adulterated with different proportion of ruminant MBM. The specimens were scanned by FOSS NIRSystem 6500. A calibration set of 180 specimens and an independent validation set of 72 specimens were randomly selected by the WINISI software. Discriminant analysis model was developed by partial least squares (PLS) on the calibration set and validated with independent validation set. The best discriminant model was obtained using standard normal variate and detrend (SNVD) and second derivative for spectrum pretreatment; this model had a coefficient of determination (R2(CV)) of 0.83 and a standard error of cross-validation (SECV) of 0. 147 1. For the independent validation set, the correct classification rate is 90%. There were a false negative specimen (0.5%) and two uncertain specimens (1%, 1.5%) in validation set. Results showed that it is feasible to use NIR diffuse reflectance spectroscopy to discriminate adulteration of non-ruminant MBM with ruminant MBM, but for specimens adulterated with ruminant MBM at less than 2%, the accuracy of calibration model needs to be improved. NIR was a rapid and non-destructive approach to discriminating adulteration of non-ruminant MBM with ruminant MIBM.

  15. Predicting Bank Financial Failures Using Discriminant Analysis And Support Vector Machines Methods A Comparative Analysis In Commercial Banks In Sudan 2006-2014

    Directory of Open Access Journals (Sweden)

    Mohammed A. SirElkhatim

    2015-08-01

    Full Text Available Bank failures threaten the economic system as a whole. Therefore predicting bank financial failures is crucial to prevent andor lessen its negative effects on the economic system. Financial crises affecting both emerging markets and advanced countries over the centuries have severe economic consequences but they can be hard to prevent and predict identifying financial crises causes remains both science and art said Stijn Claessens assistant director of the International Monetary Fund. While it would be better to mitigate risks financial crises will recur often in waves and better crisis management is therefore important. Analyses of recurrent causes suggest that to prevent crises governments should consider reforms in many underlying areas. That includes developing prudent fiscal and monetary policies better regulating the financial sector including reducing the problem of too-big-to-fail banks and developing effective macro-prudential policies. Despite new regulations and better supervision crises are likely to recur in part because they can reflect deeper problems related to income inequality the political economy and common human behavior. As such improvements in crisis management are also needed. This is originally a classification problem to categorize banks as healthy or non-healthy ones. This study aims to apply Discriminant analysis and Support Vector Machines methods to the bank failure prediction problem in a Sudanese case and to present a comprehensive computational comparison of the classification performances of the techniques tested. Eleven financial and non-financial ratios with six feature groups including capital adequacy asset quality Earning and liquidity CAMELS are selected as predictor variables in the study. Credit risk also been evaluated using logistic analysis to study the effect of Islamic finance modes sectors and payment types used by Sudanese banks with regard to their possibilities of failure. Experimental results

  16. Multiple Group Analysis in Multilevel Structural Equation Model Across Level 1 Groups.

    Science.gov (United States)

    Ryu, Ehri

    2015-01-01

    This article introduces and evaluates a procedure for conducting multiple group analysis in multilevel structural equation model across Level 1 groups (MG1-MSEM; Ryu, 2014). When group membership is at Level 1, multiple group analysis raises two issues that cannot be solved by a simple extension of the standard multiple group analysis in single-level structural equation model. First, the Level 2 data are not independent between Level 1 groups. Second, the standard procedure fails to take into account the dependency between members of different Level 1 groups within the same cluster. The MG1-MSEM approach provides solutions to these problems. In MG1-MSEM, the Level 1 mean structure is necessary to represent the differences between Level 1 groups within clusters. The Level 2 model is the same regardless of Level 1 group membership. A simulation study examined the performance of MUML (Muthén's maximum likelihood) estimation in MG1-MSEM. The MG1-MSEM approach is illustrated for both a multilevel path model and a multilevel factor model using empirical data sets.

  17. Sensitization to group direction in the postgraduate training on Group-Analysis

    Directory of Open Access Journals (Sweden)

    Simone Bruschetta

    2014-09-01

    Full Text Available The psychodynamic training group here introduced is a part of the General Training on Group Analysis of the Centre of Palermo of COIRAG Postgraduate School on Analytic Psychotherapy. The training project’s aim, built for the class of the third year, develops a sensitization device which provide a unique set of aquarium. The aim of that methodological artifice is not to engage students on specific group management techniques, but to allow the whole class group to bring into play the complexity of relations, of which is necessary to have awareness in order to lead a group within an institutional context: The main clinical referents that we chose to monitor in this experience are the relationship between conductors and participants and the relationship between group, task and setting. The brief description of this methodology is also including the reporting of two "cases" treated in the course of training. Keywords: Group leadership, Founding dimension, Cultural themes 

  18. Analysis of gene expression using gene sets discriminates cancer patients with and without late radiation toxicity

    NARCIS (Netherlands)

    J.P. Svensson; L.J.A. Stalpers; R.E.E. Esveldt-van Lange; N.A.P. Franken; J. Haveman; B. Klein; I. Turesson; H. Vrieling; M. Giphart-Gassler

    2006-01-01

    Background Radiation is an effective anti-cancer therapy but leads to severe late radiation toxicity in 5%-10% of patients. Assuming that genetic susceptibility impacts this risk, we hypothesized that the cellular response of normal tissue to X-rays could discriminate patients with and without late

  19. Otolith shape analysis for stock discrimination of two Collichthys genus croaker (Pieces: Sciaenidae,) from the northern Chinese coast

    Science.gov (United States)

    Zhao, Bo; Liu, Jinhu; Song, Junjie; Cao, Liang; Dou, Shuozeng

    2017-08-01

    The otolith morphology of two croaker species (Collichthys lucidus and Collichthys niveatus) from three areas (Liaodong Bay, LD; Huanghe (Yellow) River estuary, HRE; Jiaozhou Bay, JZ) along the northern Chinese coast were investigated for species identification and stock discrimination. The otolith contour shape described by elliptic Fourier coefficients (EFC) were analysed using principal components analysis (PCA) and stepwise canonical discriminant analysis (CDA) to identify species and stocks. The two species were well differentiated, with an overall classification success rate of 97.8%. And variations in the otolith shapes were significant enough to discriminate among the three geographical samples of C. lucidus (67.7%) or C. niveatus (65.2%). Relatively high mis-assignment occurred between the geographically adjacent LD and HRE samples, which implied that individual mixing may exist between the two samples. This study yielded information complementary to that derived from genetic studies and provided information for assessing the stock structure of C. lucidus and C. niveatus in the Bohai Sea and the Yellow Sea.

  20. 四组易混淆中药的辨析%Discrimination of 4 groups easy-mislead Chinese traditional medicine

    Institute of Scientific and Technical Information of China (English)

    孙保明; 周红超

    2011-01-01

    Traditional Chinese medicines of 4 groups are discriminated and distinguished in order to guarantee the clinical medicines quality. The authors distinguish the 4 groups carefully by the shapes, appearance features and the microscope appearance. Approximate name medicines have diversity on the clinical effective, doctors can judge properly by the dis -criminating main points. Doctors can distinguish from the traditional medicines' appearance and microscope features in orver si tyon the clinical effective,doctors.%鉴别区分四组中药材,保证临床用药质量,笔者根据四组药材的外观性状特征、显微特征,仔细进行鉴别区分.名称相近的品种在临床功效上存在差异,广大医药工作者可以根据鉴别要点做出正确的判断.为确保临床疗效,在鉴别药材和分类时可从外观形状特征和显微特征入手,仔细区分辨别.