Geographical origin discrimination of lentils (Lens culinaris Medik.) using 1H NMR fingerprinting and multivariate statistical analyses.

PubMed

Longobardi, Francesco; Innamorato, Valentina; Di Gioia, Annalisa; Ventrella, Andrea; Lippolis, Vincenzo; Logrieco, Antonio F; Catucci, Lucia; Agostiano, Angela

2017-12-15

Lentil samples coming from two different countries, i.e. Italy and Canada, were analysed using untargeted 1 H NMR fingerprinting in combination with chemometrics in order to build models able to classify them according to their geographical origin. For such aim, Soft Independent Modelling of Class Analogy (SIMCA), k-Nearest Neighbor (k-NN), Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were applied to the NMR data and the results were compared. The best combination of average recognition (100%) and cross-validation prediction abilities (96.7%) was obtained for the PCA-LDA. All the statistical models were validated both by using a test set and by carrying out a Monte Carlo Cross Validation: the obtained performances were found to be satisfying for all the models, with prediction abilities higher than 95% demonstrating the suitability of the developed methods. Finally, the metabolites that mostly contributed to the lentil discrimination were indicated. Copyright © 2017 Elsevier Ltd. All rights reserved.

Water Polo Game-Related Statistics in Women’s International Championships: Differences and Discriminatory Power

PubMed Central

Escalante, Yolanda; Saavedra, Jose M.; Tella, Victor; Mansilla, Mirella; García-Hermoso, Antonio; Dominguez, Ana M.

2012-01-01

The aims of this study were (i) to compare women’s water polo game-related statistics by match outcome (winning and losing teams) and phase (preliminary, classificatory, and semi-final/bronze medal/gold medal), and (ii) identify characteristics that discriminate performances for each phase. The game-related statistics of the 124 women’s matches played in five International Championships (World and European Championships) were analyzed. Differences between winning and losing teams in each phase were determined using the chi-squared. A discriminant analysis was then performed according to context in each of the three phases. It was found that the game-related statistics differentiate the winning from the losing teams in each phase of an international championship. The differentiating variables were both offensive (centre goals, power-play goals, counterattack goal, assists, offensive fouls, steals, blocked shots, and won sprints) and defensive (goalkeeper-blocked shots, goalkeeper-blocked inferiority shots, and goalkeeper-blocked 5-m shots). The discriminant analysis showed the game-related statistics to discriminate performance in all phases: preliminary, classificatory, and final phases (92%, 90%, and 83%, respectively). Two variables were discriminatory by match outcome (winning or losing teams) in all three phases: goals and goalkeeper-blocked shots. Key pointsThe preliminary phase that more than one variable was involved in this differentiation, including both offensive and defensive aspects of the game.The game-related statistics were found to have a high discriminatory power in predicting the result of matches with shots and goalkeeper-blocked shots being discriminatory variables in all three phases.Knowledge of the characteristics of women’s water polo game-related statistics of the winning teams and their power to predict match outcomes will allow coaches to take these characteristics into account when planning training and match preparation. PMID:24149356

The Effect of Unequal Samples, Heterogeneity of Covariance Matrices, and Number of Variables on Discriminant Analysis Classification Tables and Related Statistics.

ERIC Educational Resources Information Center

Spearing, Debra; Woehlke, Paula

To assess the effect on discriminant analysis in terms of correct classification into two groups, the following parameters were systematically altered using Monte Carlo techniques: sample sizes; proportions of one group to the other; number of independent variables; and covariance matrices. The pairing of the off diagonals (or covariances) with…

Chance-corrected classification for use in discriminant analysis: Ecological applications

USGS Publications Warehouse

Titus, K.; Mosher, J.A.; Williams, B.K.

1984-01-01

A method for evaluating the classification table from a discriminant analysis is described. The statistic, kappa, is useful to ecologists in that it removes the effects of chance. It is useful even with equal group sample sizes although the need for a chance-corrected measure of prediction becomes greater with more dissimilar group sample sizes. Examples are presented.

Mathematical and Statistical Software Index.

DTIC Science & Technology

1986-08-01

geometric) mean HMEAN - harmonic mean MEDIAN - median MODE - mode QUANT - quantiles OGIVE - distribution curve IQRNG - interpercentile range RANGE ... range mutliphase pivoting algorithm cross-classification multiple discriminant analysis cross-tabul ation mul tipl e-objecti ve model curve fitting...Statistics). .. .. .... ...... ..... ...... ..... .. 21 *RANGEX (Correct Correlations for Curtailment of Range ). .. .. .... ...... ... 21 *RUMMAGE II (Analysis

Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM

ERIC Educational Resources Information Center

Warner, Rebecca M.

2007-01-01

This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…

Quantifying discrimination of Framingham risk functions with different survival C statistics.

PubMed

Pencina, Michael J; D'Agostino, Ralph B; Song, Linye

2012-07-10

Cardiovascular risk prediction functions offer an important diagnostic tool for clinicians and patients themselves. They are usually constructed with the use of parametric or semi-parametric survival regression models. It is essential to be able to evaluate the performance of these models, preferably with summaries that offer natural and intuitive interpretations. The concept of discrimination, popular in the logistic regression context, has been extended to survival analysis. However, the extension is not unique. In this paper, we define discrimination in survival analysis as the model's ability to separate those with longer event-free survival from those with shorter event-free survival within some time horizon of interest. This definition remains consistent with that used in logistic regression, in the sense that it assesses how well the model-based predictions match the observed data. Practical and conceptual examples and numerical simulations are employed to examine four C statistics proposed in the literature to evaluate the performance of survival models. We observe that they differ in the numerical values and aspects of discrimination that they capture. We conclude that the index proposed by Harrell is the most appropriate to capture discrimination described by the above definition. We suggest researchers report which C statistic they are using, provide a rationale for their selection, and be aware that comparing different indices across studies may not be meaningful. Copyright © 2012 John Wiley & Sons, Ltd.

Temporal and spatial assessment of river surface water quality using multivariate statistical techniques: a study in Can Tho City, a Mekong Delta area, Vietnam.

PubMed

Phung, Dung; Huang, Cunrui; Rutherford, Shannon; Dwirahmadi, Febi; Chu, Cordia; Wang, Xiaoming; Nguyen, Minh; Nguyen, Nga Huy; Do, Cuong Manh; Nguyen, Trung Hieu; Dinh, Tuan Anh Diep

2015-05-01

The present study is an evaluation of temporal/spatial variations of surface water quality using multivariate statistical techniques, comprising cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA). Eleven water quality parameters were monitored at 38 different sites in Can Tho City, a Mekong Delta area of Vietnam from 2008 to 2012. Hierarchical cluster analysis grouped the 38 sampling sites into three clusters, representing mixed urban-rural areas, agricultural areas and industrial zone. FA/PCA resulted in three latent factors for the entire research location, three for cluster 1, four for cluster 2, and four for cluster 3 explaining 60, 60.2, 80.9, and 70% of the total variance in the respective water quality. The varifactors from FA indicated that the parameters responsible for water quality variations are related to erosion from disturbed land or inflow of effluent from sewage plants and industry, discharges from wastewater treatment plants and domestic wastewater, agricultural activities and industrial effluents, and contamination by sewage waste with faecal coliform bacteria through sewer and septic systems. Discriminant analysis (DA) revealed that nephelometric turbidity units (NTU), chemical oxygen demand (COD) and NH₃ are the discriminating parameters in space, affording 67% correct assignation in spatial analysis; pH and NO₂ are the discriminating parameters according to season, assigning approximately 60% of cases correctly. The findings suggest a possible revised sampling strategy that can reduce the number of sampling sites and the indicator parameters responsible for large variations in water quality. This study demonstrates the usefulness of multivariate statistical techniques for evaluation of temporal/spatial variations in water quality assessment and management.

Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Manfredi, Marcello; Robotti, Elisa; Quasso, Fabio; Mazzucco, Eleonora; Calabrese, Giorgio; Marengo, Emilio

2018-01-01

The authentication and traceability of hazelnuts is very important for both the consumer and the food industry, to safeguard the protected varieties and the food quality. This study investigates the use of a portable FTIR spectrometer coupled to multivariate statistical analysis for the classification of raw hazelnuts. The method discriminates hazelnuts from different origins/cultivars based on differences of the signal intensities of their IR spectra. The multivariate classification methods, namely principal component analysis (PCA) followed by linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA), with or without variable selection, allowed a very good discrimination among the groups, with PLS-DA coupled to variable selection providing the best results. Due to the fast analysis, high sensitivity, simplicity and no sample preparation, the proposed analytical methodology could be successfully used to verify the cultivar of hazelnuts, and the analysis can be performed quickly and directly on site.

[Analysis of the speech discrimination scores of patients with congenital unilateral microtia and external auditory canal atresia in noise].

PubMed

Zhang, Y; Li, D D; Chen, X W

2017-06-20

Objective: Case-control study analysis of the speech discrimination of unilateral microtia and external auditory canal atresia patients with normal hearing subjects in quiet and noisy environment. To understand the speech recognition results of patients with unilateral external auditory canal atresia and provide scientific basis for clinical early intervention. Method: Twenty patients with unilateral congenital microtia malformation combined external auditory canal atresia, 20 age matched normal subjects as control group. All subjects used Mandarin speech audiometry material, to test the speech discrimination scores (SDS) in quiet and noisy environment in sound field. Result: There's no significant difference of speech discrimination scores under the condition of quiet between two groups. There's a statistically significant difference when the speech signal in the affected side and noise in the nomalside (single syllable, double syllable, statements; S/N=0 and S/N=-10) ( P <0.05). There's no significant difference of speech discrimination scores when the speech signal in the nomalside and noise in the affected side. There's a statistically significant difference in condition of the signal and noise in the same side when used one-syllable word recognition (S/N=0 and S/N=-5) ( P <0.05), while double syllable word and statement has no statistically significant difference ( P >0.05). Conclusion: The speech discrimination scores of unilateral congenital microtia malformation patients with external auditory canal atresia under the condition of noise is lower than the normal subjects. Copyright© by the Editorial Department of Journal of Clinical Otorhinolaryngology Head and Neck Surgery.

Acoustic emission spectral analysis of fiber composite failure mechanisms

NASA Technical Reports Server (NTRS)

Egan, D. M.; Williams, J. H., Jr.

1978-01-01

The acoustic emission of graphite fiber polyimide composite failure mechanisms was investigated with emphasis on frequency spectrum analysis. Although visual examination of spectral densities could not distinguish among fracture sources, a paired-sample t statistical analysis of mean normalized spectral densities did provide quantitative discrimination among acoustic emissions from 10 deg, 90 deg, and plus or minus 45 deg, plus or minus 45 deg sub s specimens. Comparable discrimination was not obtained for 0 deg specimens.

Progress of statistical analysis in biomedical research through the historical review of the development of the Framingham score.

PubMed

Ignjatović, Aleksandra; Stojanović, Miodrag; Milošević, Zoran; Anđelković Apostolović, Marija

2017-12-02

The interest in developing risk models in medicine not only is appealing, but also associated with many obstacles in different aspects of predictive model development. Initially, the association of biomarkers or the association of more markers with the specific outcome was proven by statistical significance, but novel and demanding questions required the development of new and more complex statistical techniques. Progress of statistical analysis in biomedical research can be observed the best through the history of the Framingham study and development of the Framingham score. Evaluation of predictive models comes from a combination of the facts which are results of several metrics. Using logistic regression and Cox proportional hazards regression analysis, the calibration test, and the ROC curve analysis should be mandatory and eliminatory, and the central place should be taken by some new statistical techniques. In order to obtain complete information related to the new marker in the model, recently, there is a recommendation to use the reclassification tables by calculating the net reclassification index and the integrated discrimination improvement. Decision curve analysis is a novel method for evaluating the clinical usefulness of a predictive model. It may be noted that customizing and fine-tuning of the Framingham risk score initiated the development of statistical analysis. Clinically applicable predictive model should be a trade-off between all abovementioned statistical metrics, a trade-off between calibration and discrimination, accuracy and decision-making, costs and benefits, and quality and quantity of patient's life.

Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures

NASA Astrophysics Data System (ADS)

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.

Chemometric analysis for discrimination of extra virgin olive oils from whole and stoned olive pastes.

PubMed

De Luca, Michele; Restuccia, Donatella; Clodoveo, Maria Lisa; Puoci, Francesco; Ragno, Gaetano

2016-07-01

Chemometric discrimination of extra virgin olive oils (EVOO) from whole and stoned olive pastes was carried out by using Fourier transform infrared (FTIR) data and partial least squares-discriminant analysis (PLS1-DA) approach. Four Italian commercial EVOO brands, all in both whole and stoned version, were considered in this study. The adopted chemometric methodologies were able to describe the different chemical features in phenolic and volatile compounds contained in the two types of oil by using unspecific IR spectral information. Principal component analysis (PCA) was employed in cluster analysis to capture data patterns and to highlight differences between technological processes and EVOO brands. The PLS1-DA algorithm was used as supervised discriminant analysis to identify the different oil extraction procedures. Discriminant analysis was extended to the evaluation of possible adulteration by addition of aliquots of oil from whole paste to the most valuable oil from stoned olives. The statistical parameters from external validation of all the PLS models were very satisfactory, with low root mean square error of prediction (RMSEP) and relative error (RE%). Copyright © 2016 Elsevier Ltd. All rights reserved.

Predictor of increase in caregiver burden for disabled elderly at home.

PubMed

Okamoto, Kazushi; Harasawa, Yuko

2009-01-01

In order to classify the caregivers at high risk of increase in their burden early, linear discriminant analysis was performed to obtain an effective discriminant model for differentiation of the presence or absence of increase in caregiver burden. The data obtained by self-administered questionnaire from 193 caregivers of frail elderly from January to February of 2005 were used. The discriminant analysis yielded a statistically significant function explaining 35.0% (Rc=0.59; d.f.=6; p=0.0001). The configuration indicated that the psychological predictors of change in caregiver burden with much perceived stress (1.47), high caregiver burden at baseline (1.28), emotional control (0.75), effort to achieve (-0.28), symptomatic depression (0.20) and "ikigai" (purpose in life) (0.18) made statistically significant contributions to the differentiation between no increase and increase in caregiver burden. The discriminant function showed a sensitivity of 86% and specificity of 81%, and successfully classified 83% of the caregivers. The function at baseline is a simple and useful method for screening of an increase in caregiver burden among caregivers for the frail elderly at home.

Variability in source sediment contributions by applying different statistic test for a Pyrenean catchment.

PubMed

Palazón, L; Navas, A

2017-06-01

Information on sediment contribution and transport dynamics from the contributing catchments is needed to develop management plans to tackle environmental problems related with effects of fine sediment as reservoir siltation. In this respect, the fingerprinting technique is an indirect technique known to be valuable and effective for sediment source identification in river catchments. Large variability in sediment delivery was found in previous studies in the Barasona catchment (1509 km 2 , Central Spanish Pyrenees). Simulation results with SWAT and fingerprinting approaches identified badlands and agricultural uses as the main contributors to sediment supply in the reservoir. In this study the <63 μm sediment fraction from the surface reservoir sediments (2 cm) are investigated following the fingerprinting procedure to assess how the use of different statistical procedures affects the amounts of source contributions. Three optimum composite fingerprints were selected to discriminate between source contributions based in land uses/land covers from the same dataset by the application of (1) discriminant function analysis; and its combination (as second step) with (2) Kruskal-Wallis H-test and (3) principal components analysis. Source contribution results were different between assessed options with the greatest differences observed for option using #3, including the two step process: principal components analysis and discriminant function analysis. The characteristics of the solutions by the applied mixing model and the conceptual understanding of the catchment showed that the most reliable solution was achieved using #2, the two step process of Kruskal-Wallis H-test and discriminant function analysis. The assessment showed the importance of the statistical procedure used to define the optimum composite fingerprint for sediment fingerprinting applications. Copyright © 2016 Elsevier Ltd. All rights reserved.

Wavelet and receiver operating characteristic analysis of heart rate variability

NASA Astrophysics Data System (ADS)

McCaffery, G.; Griffith, T. M.; Naka, K.; Frennaux, M. P.; Matthai, C. C.

2002-02-01

Multiresolution wavelet analysis has been used to study the heart rate variability in two classes of patients with different pathological conditions. The scale dependent measure of Thurner et al. was found to be statistically significant in discriminating patients suffering from hypercardiomyopathy from a control set of normal subjects. We have performed Receiver Operating Characteristc (ROC) analysis and found the ROC area to be a useful measure by which to label the significance of the discrimination, as well as to describe the severity of heart dysfunction.

Discrimination between smiling faces: Human observers vs. automated face analysis.

PubMed

Del Líbano, Mario; Calvo, Manuel G; Fernández-Martín, Andrés; Recio, Guillermo

2018-05-11

This study investigated (a) how prototypical happy faces (with happy eyes and a smile) can be discriminated from blended expressions with a smile but non-happy eyes, depending on type and intensity of the eye expression; and (b) how smile discrimination differs for human perceivers versus automated face analysis, depending on affective valence and morphological facial features. Human observers categorized faces as happy or non-happy, or rated their valence. Automated analysis (FACET software) computed seven expressions (including joy/happiness) and 20 facial action units (AUs). Physical properties (low-level image statistics and visual saliency) of the face stimuli were controlled. Results revealed, first, that some blended expressions (especially, with angry eyes) had lower discrimination thresholds (i.e., they were identified as "non-happy" at lower non-happy eye intensities) than others (especially, with neutral eyes). Second, discrimination sensitivity was better for human perceivers than for automated FACET analysis. As an additional finding, affective valence predicted human discrimination performance, whereas morphological AUs predicted FACET discrimination. FACET can be a valid tool for categorizing prototypical expressions, but is currently more limited than human observers for discrimination of blended expressions. Configural processing facilitates detection of in/congruence(s) across regions, and thus detection of non-genuine smiling faces (due to non-happy eyes). Copyright © 2018 Elsevier B.V. All rights reserved.

Classification accuracy on the family planning participation status using kernel discriminant analysis

NASA Astrophysics Data System (ADS)

Kurniawan, Dian; Suparti; Sugito

2018-05-01

Population growth in Indonesia has increased every year. According to the population census conducted by the Central Bureau of Statistics (BPS) in 2010, the population of Indonesia has reached 237.6 million people. Therefore, to control the population growth rate, the government hold Family Planning or Keluarga Berencana (KB) program for couples of childbearing age. The purpose of this program is to improve the health of mothers and children in order to manifest prosperous society by controlling births while ensuring control of population growth. The data used in this study is the updated family data of Semarang city in 2016 that conducted by National Family Planning Coordinating Board (BKKBN). From these data, classifiers with kernel discriminant analysis will be obtained, and also classification accuracy will be obtained from that method. The result of the analysis showed that normal kernel discriminant analysis gives 71.05 % classification accuracy with 28.95 % classification error. Whereas triweight kernel discriminant analysis gives 73.68 % classification accuracy with 26.32 % classification error. Using triweight kernel discriminant for data preprocessing of family planning participation of childbearing age couples in Semarang City of 2016 can be stated better than with normal kernel discriminant.

Discrimination and prediction of cultivation age and parts of Panax ginseng by Fourier-transform infrared spectroscopy combined with multivariate statistical analysis.

PubMed

Lee, Byeong-Ju; Kim, Hye-Youn; Lim, Sa Rang; Huang, Linfang; Choi, Hyung-Kyoon

2017-01-01

Panax ginseng C.A. Meyer is a herb used for medicinal purposes, and its discrimination according to cultivation age has been an important and practical issue. This study employed Fourier-transform infrared (FT-IR) spectroscopy with multivariate statistical analysis to obtain a prediction model for discriminating cultivation ages (5 and 6 years) and three different parts (rhizome, tap root, and lateral root) of P. ginseng. The optimal partial-least-squares regression (PLSR) models for discriminating ginseng samples were determined by selecting normalization methods, number of partial-least-squares (PLS) components, and variable influence on projection (VIP) cutoff values. The best prediction model for discriminating 5- and 6-year-old ginseng was developed using tap root, vector normalization applied after the second differentiation, one PLS component, and a VIP cutoff of 1.0 (based on the lowest root-mean-square error of prediction value). In addition, for discriminating among the three parts of P. ginseng, optimized PLSR models were established using data sets obtained from vector normalization, two PLS components, and VIP cutoff values of 1.5 (for 5-year-old ginseng) and 1.3 (for 6-year-old ginseng). To our knowledge, this is the first study to provide a novel strategy for rapidly discriminating the cultivation ages and parts of P. ginseng using FT-IR by selected normalization methods, number of PLS components, and VIP cutoff values.

Discrimination and prediction of cultivation age and parts of Panax ginseng by Fourier-transform infrared spectroscopy combined with multivariate statistical analysis

PubMed Central

Lim, Sa Rang; Huang, Linfang

2017-01-01

Panax ginseng C.A. Meyer is a herb used for medicinal purposes, and its discrimination according to cultivation age has been an important and practical issue. This study employed Fourier-transform infrared (FT-IR) spectroscopy with multivariate statistical analysis to obtain a prediction model for discriminating cultivation ages (5 and 6 years) and three different parts (rhizome, tap root, and lateral root) of P. ginseng. The optimal partial-least-squares regression (PLSR) models for discriminating ginseng samples were determined by selecting normalization methods, number of partial-least-squares (PLS) components, and variable influence on projection (VIP) cutoff values. The best prediction model for discriminating 5- and 6-year-old ginseng was developed using tap root, vector normalization applied after the second differentiation, one PLS component, and a VIP cutoff of 1.0 (based on the lowest root-mean-square error of prediction value). In addition, for discriminating among the three parts of P. ginseng, optimized PLSR models were established using data sets obtained from vector normalization, two PLS components, and VIP cutoff values of 1.5 (for 5-year-old ginseng) and 1.3 (for 6-year-old ginseng). To our knowledge, this is the first study to provide a novel strategy for rapidly discriminating the cultivation ages and parts of P. ginseng using FT-IR by selected normalization methods, number of PLS components, and VIP cutoff values. PMID:29049369

Detection of cervical lesions by multivariate analysis of diffuse reflectance spectra: a clinical study.

PubMed

Prabitha, Vasumathi Gopala; Suchetha, Sambasivan; Jayanthi, Jayaraj Lalitha; Baiju, Kamalasanan Vijayakumary; Rema, Prabhakaran; Anuraj, Koyippurath; Mathews, Anita; Sebastian, Paul; Subhash, Narayanan

2016-01-01

Diffuse reflectance (DR) spectroscopy is a non-invasive, real-time, and cost-effective tool for early detection of malignant changes in squamous epithelial tissues. The present study aims to evaluate the diagnostic power of diffuse reflectance spectroscopy for non-invasive discrimination of cervical lesions in vivo. A clinical trial was carried out on 48 sites in 34 patients by recording DR spectra using a point-monitoring device with white light illumination. The acquired data were analyzed and classified using multivariate statistical analysis based on principal component analysis (PCA) and linear discriminant analysis (LDA). Diagnostic accuracies were validated using random number generators. The receiver operating characteristic (ROC) curves were plotted for evaluating the discriminating power of the proposed statistical technique. An algorithm was developed and used to classify non-diseased (normal) from diseased sites (abnormal) with a sensitivity of 72 % and specificity of 87 %. While low-grade squamous intraepithelial lesion (LSIL) could be discriminated from normal with a sensitivity of 56 % and specificity of 80 %, and high-grade squamous intraepithelial lesion (HSIL) from normal with a sensitivity of 89 % and specificity of 97 %, LSIL could be discriminated from HSIL with 100 % sensitivity and specificity. The areas under the ROC curves were 0.993 (95 % confidence interval (CI) 0.0 to 1) and 1 (95 % CI 1) for the discrimination of HSIL from normal and HSIL from LSIL, respectively. The results of the study show that DR spectroscopy could be used along with multivariate analytical techniques as a non-invasive technique to monitor cervical disease status in real time.

Some observations on the use of discriminant analysis in ecology

USGS Publications Warehouse

Williams, B.K.

1983-01-01

The application of discriminant analysis in ecological investigations is discussed. The appropriate statistical assumptions for discriminant analysis are illustrated, and both classification and group separation approaches are outlined. Three assumptions that are crucial in ecological studies are discussed at length, and the consequences of their violation are developed. These assumptions are: equality of dispersions, identifiability of prior probabilities, and precise and accurate estimation of means and dispersions. The use of discriminant functions for purposes of interpreting ecological relationships is also discussed. It is suggested that the common practice of imputing ecological 'meaning' to the signs and magnitudes of coefficients be replaced by an assessment of 'structure coefficients.' Finally, the potential and limitations of representation of data in canonical space are considered, and some cautionary points are made concerning ecological interpretation of patterns in canonical space.

[The application of the multidimensional statistical methods in the evaluation of the influence of atmospheric pollution on the population's health].

PubMed

Surzhikov, V D; Surzhikov, D V

2014-01-01

The search and measurement of causal relationships between exposure to air pollution and health state of the population is based on the system analysis and risk assessment to improve the quality of research. With this purpose there is applied the modern statistical analysis with the use of criteria of independence, principal component analysis and discriminate function analysis. As a result of analysis out of all atmospheric pollutants there were separated four main components: for diseases of the circulatory system main principal component is implied with concentrations of suspended solids, nitrogen dioxide, carbon monoxide, hydrogen fluoride, for the respiratory diseases the main c principal component is closely associated with suspended solids, sulfur dioxide and nitrogen dioxide, charcoal black. The discriminant function was shown to be used as a measure of the level of air pollution.

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

PubMed

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.

Atrial Electrogram Fractionation Distribution before and after Pulmonary Vein Isolation in Human Persistent Atrial Fibrillation-A Retrospective Multivariate Statistical Analysis.

PubMed

Almeida, Tiago P; Chu, Gavin S; Li, Xin; Dastagir, Nawshin; Tuan, Jiun H; Stafford, Peter J; Schlindwein, Fernando S; Ng, G André

2017-01-01

Purpose: Complex fractionated atrial electrograms (CFAE)-guided ablation after pulmonary vein isolation (PVI) has been used for persistent atrial fibrillation (persAF) therapy. This strategy has shown suboptimal outcomes due to, among other factors, undetected changes in the atrial tissue following PVI. In the present work, we investigate CFAE distribution before and after PVI in patients with persAF using a multivariate statistical model. Methods: 207 pairs of atrial electrograms (AEGs) were collected before and after PVI respectively, from corresponding LA regions in 18 persAF patients. Twelve attributes were measured from the AEGs, before and after PVI. Statistical models based on multivariate analysis of variance (MANOVA) and linear discriminant analysis (LDA) have been used to characterize the atrial regions and AEGs. Results: PVI significantly reduced CFAEs in the LA (70 vs. 40%; P < 0.0001). Four types of LA regions were identified, based on the AEGs characteristics: (i) fractionated before PVI that remained fractionated after PVI (31% of the collected points); (ii) fractionated that converted to normal (39%); (iii) normal prior to PVI that became fractionated (9%) and; (iv) normal that remained normal (21%). Individually, the attributes failed to distinguish these LA regions, but multivariate statistical models were effective in their discrimination ( P < 0.0001). Conclusion: Our results have unveiled that there are LA regions resistant to PVI, while others are affected by it. Although, traditional methods were unable to identify these different regions, the proposed multivariate statistical model discriminated LA regions resistant to PVI from those affected by it without prior ablation information.

Statistically Correct Methodology for Compositional Data in New Discriminant Function Tectonomagmatic Diagrams and Application to Ophiolite Origin

NASA Astrophysics Data System (ADS)

Verma, Surendra P.; Pandarinath, Kailasa; Verma, Sanjeet K.

2011-07-01

In the lead presentation (invited talk) of Session SE05 (Frontiers in Geochemistry with Reference to Lithospheric Evolution and Metallogeny) of AOGS2010, we have highlighted the requirement of correct statistical treatment of geochemical data. In most diagrams used for interpreting compositional data, the basic statistical assumption of open space for all variables is violated. Among these graphic tools, discrimination diagrams have been in use for nearly 40 years to decipher tectonic setting. The newer set of five tectonomagmatic discrimination diagrams published in 2006 (based on major-elements) and two sets made available in 2008 and 2011 (both based on immobile elements) fulfill all statistical requirements for correct handling of compositional data, including the multivariate nature of compositional variables, representative sampling, and probability-based tectonic field boundaries. Additionally in the most recent proposal of 2011, samples having normally distributed, discordant-outlier free, log-ratio variables were used in linear discriminant analysis. In these three sets of five diagrams each, discrimination was successfully documented for four tectonic settings (island arc, continental rift, ocean-island, and mid-ocean ridge). The discrimination diagrams have been extensively evaluated for their performance by different workers. We exemplify these two sets of new diagrams (one set based on major-elements and the other on immobile elements) using ophiolites from Boso Peninsula, Japan. This example is included for illustration purposes only and is not meant for testing of these newer diagrams. Their evaluation and comparison with older, conventional bivariate or ternary diagrams have been reported in other papers.

Game Related Statistics Discriminating Between Starters and Nonstarters Players in Women’S National Basketball Association League (WNBA)

PubMed Central

Gòmez, Miguel-Ángel; Lorenzo, Alberto; Ortega, Enrique; Sampaio, Jaime; Ibàñez, Sergio-José

2009-01-01

The aim of the present study was to identify the game-related statistics that allow discriminating between starters and nonstarter players in women’s basketball when related to winning or losing games and best or worst teams. The sample comprised all 216 regular season games from the 2005 Women’s National Basketball Association League (WNBA). The game-related statistics included were 2- and 3- point field-goals (both successful and unsuccessful), free-throws (both successful and unsuccessful), defensive and offensive rebounds, assists, blocks, fouls, steals, turnovers and minutes played. Results from multivariate analysis showed that when best teams won, the discriminant game-related statistics were successful 2-point field-goals (SC = 0.47), successful free-throws (SC = 0.44), fouls (SC = -0.41), assists (SC = 0.37), and defensive rebounds (SC = 0.37). When the worst teams won, the discriminant game-related statistics were successful 2-point field- goals (SC = 0.37), successful free-throws (SC = 0.45), assists (SC = 0.58), and steals (SC = 0.35). The results showed that the successful 2-point field-goals, successful free-throws and the assists were the most powerful variables discriminating between starters and nonstarters. These specific characteristics helped to point out the importance of starters’ players shooting and passing ability during competitions. Key points The players’ game-related statistical profile varied according to team status, game outcome and team quality in women’s basketball. The results of this work help to point out the different player’s performance described in women’s basketball compared with men’s basketball. The results obtained enhance the importance of starters and nonstarters contribution to team’s performance in different game contexts. Results showed the power of successful 2-point field-goals, successful free-throws and assists discriminating between starters and nonstarters in all the analyses. PMID:24149538

Game Related Statistics Which Discriminate Between Winning and Losing Under-16 Male Basketball Games

PubMed Central

Lorenzo, Alberto; Gómez, Miguel Ángel; Ortega, Enrique; Ibáñez, Sergio José; Sampaio, Jaime

2010-01-01

The aim of the present study was to identify the game-related statistics which discriminate between winning and losing teams in under-16 years old male basketball games. The sample gathered all 122 games in the 2004 and 2005 Under-16 European Championships. The game-related statistics analysed were the free-throws (both successful and unsuccessful), 2- and 3-points field-goals (both successful and unsuccessful) offensive and defensive rebounds, blocks, assists, fouls, turnovers and steals. The winning teams exhibited lower ball possessions per game and better offensive and defensive efficacy coefficients than the losing teams. Results from discriminant analysis were statistically significant and allowed to emphasize several structure coefficients (SC). In close games (final score differences below 9 points), the discriminant variables were the turnovers (SC = -0.47) and the assists (SC = 0.33). In balanced games (final score differences between 10 and 29 points), the variables that discriminated between the groups were the successful 2-point field-goals (SC = -0.34) and defensive rebounds (SC = -0. 36); and in unbalanced games (final score differences above 30 points) the variables that best discriminated both groups were the successful 2-point field-goals (SC = 0.37). These results allowed understanding that these players' specific characteristics result in a different game-related statistical profile and helped to point out the importance of the perceptive and decision making process in practice and in competition. Key points The players' game-related statistical profile varied according to game type, game outcome and in formative categories in basketball. The results of this work help to point out the different player's performance described in U-16 men's basketball teams compared with senior and professional men's basketball teams. The results obtained enhance the importance of the perceptive and decision making process in practice and in competition. PMID:24149794

Discrimination of soft tissues using laser-induced breakdown spectroscopy in combination with k nearest neighbors (kNN) and support vector machine (SVM) classifiers

NASA Astrophysics Data System (ADS)

Li, Xiaohui; Yang, Sibo; Fan, Rongwei; Yu, Xin; Chen, Deying

2018-06-01

In this paper, discrimination of soft tissues using laser-induced breakdown spectroscopy (LIBS) in combination with multivariate statistical methods is presented. Fresh pork fat, skin, ham, loin and tenderloin muscle tissues are manually cut into slices and ablated using a 1064 nm pulsed Nd:YAG laser. Discrimination analyses between fat, skin and muscle tissues, and further between highly similar ham, loin and tenderloin muscle tissues, are performed based on the LIBS spectra in combination with multivariate statistical methods, including principal component analysis (PCA), k nearest neighbors (kNN) classification, and support vector machine (SVM) classification. Performances of the discrimination models, including accuracy, sensitivity and specificity, are evaluated using 10-fold cross validation. The classification models are optimized to achieve best discrimination performances. The fat, skin and muscle tissues can be definitely discriminated using both kNN and SVM classifiers, with accuracy of over 99.83%, sensitivity of over 0.995 and specificity of over 0.998. The highly similar ham, loin and tenderloin muscle tissues can also be discriminated with acceptable performances. The best performances are achieved with SVM classifier using Gaussian kernel function, with accuracy of 76.84%, sensitivity of over 0.742 and specificity of over 0.869. The results show that the LIBS technique assisted with multivariate statistical methods could be a powerful tool for online discrimination of soft tissues, even for tissues of high similarity, such as muscles from different parts of the animal body. This technique could be used for discrimination of tissues suffering minor clinical changes, thus may advance the diagnosis of early lesions and abnormalities.

DEVELOPMENT AND PSYCHOMETRIC TESTING OF A MULTIDIMENSIONAL INSTRUMENT OF PERCEIVED DISCRIMINATION AMONG AFRICAN AMERICANS IN THE JACKSON HEART STUDY

PubMed Central

Sims, Mario; Wyatt, Sharon B.; Gutierrez, Mary Lou; Taylor, Herman A.; Williams, David R.

2009-01-01

Objective Assessing the discrimination-health disparities hypothesis requires psychometrically sound, multidimensional measures of discrimination. Among the available discrimination measures, few are multidimensional and none have adequate psychometric testing in a large, African American sample. We report the development and psychometric testing of the multidimensional Jackson Heart Study Discrimination (JHSDIS) Instrument. Methods A multidimensional measure assessing the occurrence, frequency, attribution, and coping responses to perceived everyday and lifetime discrimination; lifetime burden of discrimination; and effect of skin color was developed and tested in the 5302-member cohort of the Jackson Heart Study. Internal consistency was calculated by using Cronbach α. coefficient. Confirmatory factor analysis established the dimensions, and intercorrelation coefficients assessed the discriminant validity of the instrument. Setting Tri-county area of the Jackson, MS metropolitan statistical area. Results The JHSDIS was psychometrically sound (overall α=.78, .84 and .77, respectively, for the everyday and lifetime subscales). Confirmatory factor analysis yielded 11 factors, which confirmed the a priori dimensions represented. Conclusions The JHSDIS combined three scales into a single multidimensional instrument with good psychometric properties in a large sample of African Americans. This analysis lays the foundation for using this instrument in research that will examine the association between perceived discrimination and CVD among African Americans. PMID:19341164

A Comparison of Analytical and Data Preprocessing Methods for Spectral Fingerprinting

PubMed Central

LUTHRIA, DEVANAND L.; MUKHOPADHYAY, SUDARSAN; LIN, LONG-ZE; HARNLY, JAMES M.

2013-01-01

Spectral fingerprinting, as a method of discriminating between plant cultivars and growing treatments for a common set of broccoli samples, was compared for six analytical instruments. Spectra were acquired for finely powdered solid samples using Fourier transform infrared (FT-IR) and Fourier transform near-infrared (NIR) spectrometry. Spectra were also acquired for unfractionated aqueous methanol extracts of the powders using molecular absorption in the ultraviolet (UV) and visible (VIS) regions and mass spectrometry with negative (MS−) and positive (MS+) ionization. The spectra were analyzed using nested one-way analysis of variance (ANOVA) and principal component analysis (PCA) to statistically evaluate the quality of discrimination. All six methods showed statistically significant differences between the cultivars and treatments. The significance of the statistical tests was improved by the judicious selection of spectral regions (IR and NIR), masses (MS+ and MS−), and derivatives (IR, NIR, UV, and VIS). PMID:21352644

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

PubMed

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.

Alteration mapping at Goldfield, Nevada, by cluster and discriminant analysis of LANDSAT digital data

NASA Technical Reports Server (NTRS)

Ballew, G.

1977-01-01

The ability of Landsat multispectral digital data to differentiate among 62 combinations of rock and alteration types at the Goldfield mining district of Western Nevada was investigated by using statistical techniques of cluster and discriminant analysis. Multivariate discriminant analysis was not effective in classifying each of the 62 groups, with classification results essentially the same whether data of four channels alone or combined with six ratios of channels were used. Bivariate plots of group means revealed a cluster of three groups including mill tailings, basalt and all other rock and alteration types. Automatic hierarchical clustering based on the fourth dimensional Mahalanobis distance between group means of 30 groups having five or more samples was performed. The results of the cluster analysis revealed hierarchies of mill tailings vs. natural materials, basalt vs. non-basalt, highly reflectant rocks vs. other rocks and exclusively unaltered rocks vs. predominantly altered rocks. The hierarchies were used to determine the order in which sets of multiple discriminant analyses were to be performed and the resulting discriminant functions were used to produce a map of geology and alteration which has an overall accuracy of 70 percent for discriminating exclusively altered rocks from predominantly altered rocks.

Assay based on electrical impedance spectroscopy to discriminate between normal and cancerous mammalian cells

NASA Astrophysics Data System (ADS)

Giana, Fabián Eduardo; Bonetto, Fabián José; Bellotti, Mariela Inés

2018-03-01

In this work we present an assay to discriminate between normal and cancerous cells. The method is based on the measurement of electrical impedance spectra of in vitro cell cultures. We developed a protocol consisting on four consecutive measurement phases, each of them designed to obtain different information about the cell cultures. Through the analysis of the measured data, 26 characteristic features were obtained for both cell types. From the complete set of features, we selected the most relevant in terms of their discriminant capacity by means of conventional statistical tests. A linear discriminant analysis was then carried out on the selected features, allowing the classification of the samples in normal or cancerous with 4.5% of false positives and no false negatives.

Assay based on electrical impedance spectroscopy to discriminate between normal and cancerous mammalian cells.

PubMed

Giana, Fabián Eduardo; Bonetto, Fabián José; Bellotti, Mariela Inés

2018-03-01

In this work we present an assay to discriminate between normal and cancerous cells. The method is based on the measurement of electrical impedance spectra of in vitro cell cultures. We developed a protocol consisting on four consecutive measurement phases, each of them designed to obtain different information about the cell cultures. Through the analysis of the measured data, 26 characteristic features were obtained for both cell types. From the complete set of features, we selected the most relevant in terms of their discriminant capacity by means of conventional statistical tests. A linear discriminant analysis was then carried out on the selected features, allowing the classification of the samples in normal or cancerous with 4.5% of false positives and no false negatives.

The discrimination of sea ice types using SAR backscatter statistics

NASA Technical Reports Server (NTRS)

Shuchman, Robert A.; Wackerman, Christopher C.; Maffett, Andrew L.; Onstott, Robert G.; Sutherland, Laura L.

1989-01-01

X-band (HH) synthetic aperture radar (SAR) data of sea ice collected during the Marginal Ice Zone Experiment in March and April of 1987 was statistically analyzed with respect to discriminating open water, first-year ice, multiyear ice, and Odden. Odden are large expanses of nilas ice that rapidly form in the Greenland Sea and transform into pancake ice. A first-order statistical analysis indicated that mean versus variance can segment out open water and first-year ice, and skewness versus modified skewness can segment the Odden and multilayer categories. In additions to first-order statistics, a model has been generated for the distribution function of the SAR ice data. Segmentation of ice types was also attempted using textural measurements. In this case, the general co-occurency matrix was evaluated. The textural method did not generate better results than the first-order statistical approach.

Gender discrimination and prediction on the basis of facial metric information.

PubMed

Fellous, J M

1997-07-01

Horizontal and vertical facial measurements are statistically independent. Discriminant analysis shows that five of such normalized distances explain over 95% of the gender differences of "training" samples and predict the gender of 90% novel test faces exhibiting various facial expressions. The robustness of the method and its results are assessed. It is argued that these distances (termed fiducial) are compatible with those found experimentally by psychophysical and neurophysiological studies. In consequence, partial explanations for the effects observed in these experiments can be found in the intrinsic statistical nature of the facial stimuli used.

Effects of Consecutive Basketball Games on the Game-Related Statistics that Discriminate Winner and Losing Teams

PubMed Central

Ibáñez, Sergio J.; García, Javier; Feu, Sebastian; Lorenzo, Alberto; Sampaio, Jaime

2009-01-01

The aim of the present study was to identify the game-related statistics that discriminated basketball winning and losing teams in each of the three consecutive games played in a condensed tournament format. The data were obtained from the Spanish Basketball Federation and included game-related statistics from the Under-20 league (2005-2006 and 2006-2007 seasons). A total of 223 games were analyzed with the following game-related statistics: two and three-point field goal (made and missed), free-throws (made and missed), offensive and defensive rebounds, assists, steals, turnovers, blocks (made and received), fouls committed, ball possessions and offensive rating. Results showed that winning teams in this competition had better values in all game-related statistics, with the exception of three point field goals made, free-throws missed and turnovers (p ≥ 0.05). The main effect of game number was only identified in turnovers, with a statistical significant decrease between the second and third game. No interaction was found in the analysed variables. A discriminant analysis allowed identifying the two-point field goals made, the defensive rebounds and the assists as discriminators between winning and losing teams in all three games. Additionally to these, only the three-point field goals made contributed to discriminate teams in game three, suggesting a moderate effect of fatigue. Coaches may benefit from being aware of this variation in game determinant related statistics and, also, from using offensive and defensive strategies in the third game, allowing to explore or hide the three point field-goals performance. Key points Overall team performances along the three consecutive games were very similar, not confirming an accumulated fatigue effect. The results from the three-point field goals in the third game suggested that winning teams were able to shoot better from longer distances and this could be the result of exhibiting higher conditioning status and/or the losing teams’ exhibiting low conditioning in defense. PMID:24150011

Spectral discrimination of macrophyte species during different seasons in a tropical wetland using in-situ hyperspectral remote sensing

NASA Astrophysics Data System (ADS)

Saluja, Ridhi; Garg, J. K.

2017-10-01

Wetlands, one of the most productive ecosystems on Earth, perform myriad ecological functions and provide a host of ecological services. Despite their ecological and economic values, wetlands have experienced significant degradation during the last century and the trend continues. Hyperspectral sensors provide opportunities to map and monitor macrophyte species within wetlands for their management and conservation. In this study, an attempt has been made to evaluate the potential of narrowband spectroradiometer data in discriminating wetland macrophytes during different seasons. main objectives of the research were (1) to determine whether macrophyte species could be discriminated based on in-situ hyperspectral reflectance collected over different seasons and at each measured waveband (400-950nm), (2) to compare the effectiveness of spectral reflectance and spectral indices in discriminating macrophyte species, and (3) to identify spectral wavelengths that are most sensitive in discriminating macrophyte species. Spectral characteristics of dominant wetland macrophyte species were collected seasonally using SVC GER 1500 portable spectroradiometer over the 400 to 1050nm spectral range at 1.5nm interval, at the Bhindawas wetland in the state of Haryana, India. Hyperspectral observations were pre-processed and subjected to statistical analysis, which involved a two-step approach including feature selection (ANOVA and KW test) and feature extraction (LDA and PCA). Statistical analysis revealed that the most influential wavelengths for discrimination were distributed along the spectral profile from visible to the near-infrared regions. The results suggest that hyperspectral data can be used discriminate wetland macrophyte species working as an effective tool for advanced mapping and monitoring of wetlands.

A Comparison of Two-Group Classification Methods

ERIC Educational Resources Information Center

Holden, Jocelyn E.; Finch, W. Holmes; Kelley, Ken

2011-01-01

The statistical classification of "N" individuals into "G" mutually exclusive groups when the actual group membership is unknown is common in the social and behavioral sciences. The results of such classification methods often have important consequences. Among the most common methods of statistical classification are linear discriminant analysis,…

Discrimination and prediction of the origin of Chinese and Korean soybeans using Fourier transform infrared spectrometry (FT-IR) with multivariate statistical analysis

PubMed Central

Lee, Byeong-Ju; Zhou, Yaoyao; Lee, Jae Soung; Shin, Byeung Kon; Seo, Jeong-Ah; Lee, Doyup; Kim, Young-Suk

2018-01-01

The ability to determine the origin of soybeans is an important issue following the inclusion of this information in the labeling of agricultural food products becoming mandatory in South Korea in 2017. This study was carried out to construct a prediction model for discriminating Chinese and Korean soybeans using Fourier-transform infrared (FT-IR) spectroscopy and multivariate statistical analysis. The optimal prediction models for discriminating soybean samples were obtained by selecting appropriate scaling methods, normalization methods, variable influence on projection (VIP) cutoff values, and wave-number regions. The factors for constructing the optimal partial-least-squares regression (PLSR) prediction model were using second derivatives, vector normalization, unit variance scaling, and the 4000–400 cm–1 region (excluding water vapor and carbon dioxide). The PLSR model for discriminating Chinese and Korean soybean samples had the best predictability when a VIP cutoff value was not applied. When Chinese soybean samples were identified, a PLSR model that has the lowest root-mean-square error of the prediction value was obtained using a VIP cutoff value of 1.5. The optimal PLSR prediction model for discriminating Korean soybean samples was also obtained using a VIP cutoff value of 1.5. This is the first study that has combined FT-IR spectroscopy with normalization methods, VIP cutoff values, and selected wave-number regions for discriminating Chinese and Korean soybeans. PMID:29689113

Classification of edible oils by employing 31P and 1H NMR spectroscopy in combination with multivariate statistical analysis. A proposal for the detection of seed oil adulteration in virgin olive oils.

PubMed

Vigli, Georgia; Philippidis, Angelos; Spyros, Apostolos; Dais, Photis

2003-09-10

A combination of (1)H NMR and (31)P NMR spectroscopy and multivariate statistical analysis was used to classify 192 samples from 13 types of vegetable oils, namely, hazelnut, sunflower, corn, soybean, sesame, walnut, rapeseed, almond, palm, groundnut, safflower, coconut, and virgin olive oils from various regions of Greece. 1,2-Diglycerides, 1,3-diglycerides, the ratio of 1,2-diglycerides to total diglycerides, acidity, iodine value, and fatty acid composition determined upon analysis of the respective (1)H NMR and (31)P NMR spectra were selected as variables to establish a classification/prediction model by employing discriminant analysis. This model, obtained from the training set of 128 samples, resulted in a significant discrimination among the different classes of oils, whereas 100% of correct validated assignments for 64 samples were obtained. Different artificial mixtures of olive-hazelnut, olive-corn, olive-sunflower, and olive-soybean oils were prepared and analyzed by (1)H NMR and (31)P NMR spectroscopy. Subsequent discriminant analysis of the data allowed detection of adulteration as low as 5% w/w, provided that fresh virgin olive oil samples were used, as reflected by their high 1,2-diglycerides to total diglycerides ratio (D > or = 0.90).

Discrimination of common Mediterranean plant species using field spectroradiometry

NASA Astrophysics Data System (ADS)

Manevski, Kiril; Manakos, Ioannis; Petropoulos, George P.; Kalaitzidis, Chariton

2011-12-01

Field spectroradiometry of land surface objects supports remote sensing analysis, facilitates the discrimination of vegetation species, and enhances the mapping efficiency. Especially in the Mediterranean, spectral discrimination of common vegetation types, such as phrygana and maquis species, remains a challenge. Both phrygana and maquis may be used as a direct indicator for grazing management, fire history and severity, and the state of the wider ecosystem equilibrium. This study aims to investigate the capability of field spectroradiometry supporting remote sensing analysis of the land cover of a characteristic Mediterranean area. Five common Mediterranean maquis and phrygana species were examined. Spectra acquisition was performed during an intensive field campaign deployed in spring 2010, supported by a novel platform MUFSPEM@MED (Mobile Unit for Field SPEctral Measurements at the MEDiterranean) for high canopy measurements. Parametric and non-parametric statistical tests have been applied to the continuum-removed reflectance of the species in the visible to shortwave infrared spectral range. Interpretation of the results indicated distinct discrimination between the studied species at specific spectral regions. Statistically significant wavelengths were principally found in both the visible and the near infrared regions of the electromagnetic spectrum. Spectral bands in the shortwave infrared demonstrated significant discrimination features for the examined species adapted to Mediterranean drought. All in all, results confirmed the prospect for a more accurate mapping of the species spatial distribution using remote sensing imagery coupled with in situ spectral information.

The Use of Match Statistics that Discriminate Between Successful and Unsuccessful Soccer Teams

PubMed Central

Castellano, Julen; Casamichana, David; Lago, Carlos

2012-01-01

Three soccer World Cups were analysed with the aim of identifying the match statistics which best discriminated between winning, drawing and losing teams. The analysis was based on 177 matches played during the three most recent World Cup tournaments: Korea/Japan 2002 (59), Germany 2006 (59) and South Africa 2010 (59). Two categories of variables were studied: 1) those related to attacking play: goals scored, total shots, shots on target, shots off target, ball possession, number of off-sides committed, fouls received and corners; and 2) those related to defence: total shots received, shots on target received, shots off target received, off-sides received, fouls committed, corners against, yellow cards and red cards. Discriminant analysis of these matches revealed the following: (a) the variables related to attacking play that best differentiated between winning, drawing and losing teams were total shots, shots on target and ball possession; and (b) the most discriminating variables related to defence were total shots received and shots on target received. These results suggest that winning, drawing and losing national teams may be discriminated from one another on the basis of variables such as ball possession and the effectiveness of their attacking play. This information may be of benefit to both coaches and players, adding to their knowledge about soccer performance indicators and helping to guide the training process. PMID:23487020

Comparative analysis of targeted metabolomics: dominance-based rough set approach versus orthogonal partial least square-discriminant analysis.

PubMed

Blasco, H; Błaszczyński, J; Billaut, J C; Nadal-Desbarats, L; Pradat, P F; Devos, D; Moreau, C; Andres, C R; Emond, P; Corcia, P; Słowiński, R

2015-02-01

Metabolomics is an emerging field that includes ascertaining a metabolic profile from a combination of small molecules, and which has health applications. Metabolomic methods are currently applied to discover diagnostic biomarkers and to identify pathophysiological pathways involved in pathology. However, metabolomic data are complex and are usually analyzed by statistical methods. Although the methods have been widely described, most have not been either standardized or validated. Data analysis is the foundation of a robust methodology, so new mathematical methods need to be developed to assess and complement current methods. We therefore applied, for the first time, the dominance-based rough set approach (DRSA) to metabolomics data; we also assessed the complementarity of this method with standard statistical methods. Some attributes were transformed in a way allowing us to discover global and local monotonic relationships between condition and decision attributes. We used previously published metabolomics data (18 variables) for amyotrophic lateral sclerosis (ALS) and non-ALS patients. Principal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA) allowed satisfactory discrimination (72.7%) between ALS and non-ALS patients. Some discriminant metabolites were identified: acetate, acetone, pyruvate and glutamine. The concentrations of acetate and pyruvate were also identified by univariate analysis as significantly different between ALS and non-ALS patients. DRSA correctly classified 68.7% of the cases and established rules involving some of the metabolites highlighted by OPLS-DA (acetate and acetone). Some rules identified potential biomarkers not revealed by OPLS-DA (beta-hydroxybutyrate). We also found a large number of common discriminating metabolites after Bayesian confirmation measures, particularly acetate, pyruvate, acetone and ascorbate, consistent with the pathophysiological pathways involved in ALS. DRSA provides a complementary method for improving the predictive performance of the multivariate data analysis usually used in metabolomics. This method could help in the identification of metabolites involved in disease pathogenesis. Interestingly, these different strategies mostly identified the same metabolites as being discriminant. The selection of strong decision rules with high value of Bayesian confirmation provides useful information about relevant condition-decision relationships not otherwise revealed in metabolomics data. Copyright © 2014 Elsevier Inc. All rights reserved.

Persistence of discrimination: Revisiting Axtell, Epstein and Young

NASA Astrophysics Data System (ADS)

Weisbuch, Gérard

2018-02-01

We reformulate an earlier model of the "Emergence of classes..." proposed by Axtell et al. (2001) using more elaborate cognitive processes allowing a statistical physics approach. The thorough analysis of the phase space and of the basins of attraction leads to a reconsideration of the previous social interpretations: our model predicts the reinforcement of discrimination biases and their long term stability rather than the emergence of classes.

Examining the effectiveness of discriminant function analysis and cluster analysis in species identification of male field crickets based on their calling songs.

PubMed

Jaiswara, Ranjana; Nandi, Diptarup; Balakrishnan, Rohini

2013-01-01

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 classification and identification.

Multi-element fingerprinting as a tool in origin authentication of four east China marine species.

PubMed

Guo, Lipan; Gong, Like; Yu, Yanlei; Zhang, Hong

2013-12-01

The contents of 25 elements in 4 types of commercial marine species from the East China Sea were determined by inductively coupled plasma mass spectrometry and atomic absorption spectrometry. The elemental composition was used to differentiate marine species according to geographical origin by multivariate statistical analysis. The results showed that principal component analysis could distinguish samples from different areas and reveal the elements which played the most important role in origin diversity. The established models by partial least squares discriminant analysis (PLS-DA) and by probabilistic neural network (PNN) can both precisely predict the origin of the marine species. Further study indicated that PLS-DA and PNN were efficacious in regional discrimination. The models from these 2 statistical methods, with an accuracy of 97.92% and 100%, respectively, could both distinguish samples from different areas without the need for species differentiation. © 2013 Institute of Food Technologists®

Employer Learning and the Signaling Value of Education. National Longitudinal Surveys Discussion Paper.

ERIC Educational Resources Information Center

Altonji, Joseph G.; Pierret, Charles R.

A statistical analysis was performed to test the hypothesis that, if profit-maximizing firms have limited information about the general productivity of new workers, they may choose to use easily observable characteristics such as years of education to discriminate statistically among workers. Information about employer learning was obtained by…

Spectral discrimination of serum from liver cancer and liver cirrhosis using Raman spectroscopy

NASA Astrophysics Data System (ADS)

Yang, Tianyue; Li, Xiaozhou; Yu, Ting; Sun, Ruomin; Li, Siqi

2011-07-01

In this paper, Raman spectra of human serum were measured using Raman spectroscopy, then the spectra was analyzed by multivariate statistical methods of principal component analysis (PCA). Then linear discriminant analysis (LDA) was utilized to differentiate the loading score of different diseases as the diagnosing algorithm. Artificial neural network (ANN) was used for cross-validation. The diagnosis sensitivity and specificity by PCA-LDA are 88% and 79%, while that of the PCA-ANN are 89% and 95%. It can be seen that modern analyzing method is a useful tool for the analysis of serum spectra for diagnosing diseases.

Intraoperative optical biopsy for brain tumors using spectro-lifetime properties of intrinsic fluorophores

NASA Astrophysics Data System (ADS)

Vasefi, Fartash; Kittle, David S.; Nie, Zhaojun; Falcone, Christina; Patil, Chirag G.; Chu, Ray M.; Mamelak, Adam N.; Black, Keith L.; Butte, Pramod V.

2016-04-01

We have developed and tested a system for real-time intra-operative optical identification and classification of brain tissues using time-resolved fluorescence spectroscopy (TRFS). A supervised learning algorithm using linear discriminant analysis (LDA) employing selected intrinsic fluorescence decay temporal points in 6 spectral bands was employed to maximize statistical significance difference between training groups. The linear discriminant analysis on in vivo human tissues obtained by TRFS measurements (N = 35) were validated by histopathologic analysis and neuronavigation correlation to pre-operative MRI images. These results demonstrate that TRFS can differentiate between normal cortex, white matter and glioma.

Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques

NASA Astrophysics Data System (ADS)

Gulgundi, Mohammad Shahid; Shetty, Amba

2018-03-01

Groundwater quality deterioration due to anthropogenic activities has become a subject of prime concern. The objective of the study was to assess the spatial and temporal variations in groundwater quality and to identify the sources in the western half of the Bengaluru city using multivariate statistical techniques. Water quality index rating was calculated for pre and post monsoon seasons to quantify overall water quality for human consumption. The post-monsoon samples show signs of poor quality in drinking purpose compared to pre-monsoon. Cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the groundwater quality data measured on 14 parameters from 67 sites distributed across the city. Hierarchical cluster analysis (CA) grouped the 67 sampling stations into two groups, cluster 1 having high pollution and cluster 2 having lesser pollution. Discriminant analysis (DA) was applied to delineate the most meaningful parameters accounting for temporal and spatial variations in groundwater quality of the study area. Temporal DA identified pH as the most important parameter, which discriminates between water quality in the pre-monsoon and post-monsoon seasons and accounts for 72% seasonal assignation of cases. Spatial DA identified Mg, Cl and NO3 as the three most important parameters discriminating between two clusters and accounting for 89% spatial assignation of cases. Principal component analysis was applied to the dataset obtained from the two clusters, which evolved three factors in each cluster, explaining 85.4 and 84% of the total variance, respectively. Varifactors obtained from principal component analysis showed that groundwater quality variation is mainly explained by dissolution of minerals from rock water interactions in the aquifer, effect of anthropogenic activities and ion exchange processes in water.

Alteration mapping at Goldfield, Nevada, by cluster and discriminant analysis of Landsat digital data. [mapping of hydrothermally altered volcanic rocks

NASA Technical Reports Server (NTRS)

Ballew, G.

1977-01-01

The ability of Landsat multispectral digital data to differentiate among 62 combinations of rock and alteration types at the Goldfield mining district of Western Nevada was investigated by using statistical techniques of cluster and discriminant analysis. Multivariate discriminant analysis was not effective in classifying each of the 62 groups, with classification results essentially the same whether data of four channels alone or combined with six ratios of channels were used. Bivariate plots of group means revealed a cluster of three groups including mill tailings, basalt and all other rock and alteration types. Automatic hierarchical clustering based on the fourth dimensional Mahalanobis distance between group means of 30 groups having five or more samples was performed using Johnson's HICLUS program. The results of the cluster analysis revealed hierarchies of mill tailings vs. natural materials, basalt vs. non-basalt, highly reflectant rocks vs. other rocks and exclusively unaltered rocks vs. predominantly altered rocks. The hierarchies were used to determine the order in which sets of multiple discriminant analyses were to be performed and the resulting discriminant functions were used to produce a map of geology and alteration which has an overall accuracy of 70 percent for discriminating exclusively altered rocks from predominantly altered rocks.

The use of multicomponent statistical analysis in hydrogeological environmental research.

PubMed

Lambrakis, Nicolaos; Antonakos, Andreas; Panagopoulos, George

2004-04-01

The present article examines the possibilities of investigating NO(3)(-) spread in aquifers by applying multicomponent statistical methods (factor, cluster and discriminant analysis) on hydrogeological, hydrochemical, and environmental parameters. A 4-R-Mode factor model determined from the analysis showed its useful role in investigating hydrogeological parameters affecting NO(3)(-) concentration, such as its dilution by upcoming groundwater of the recharge areas. The relationship between NO(3)(-) concentration and agricultural activities can be determined sufficiently by the first factor which relies on NO(3)(-) and SO(4)(2-) of the same origin-that of agricultural fertilizers. The other three factors of R-Mode analysis are not connected directly to the NO(3)(-) problem. They do however, by extracting the role of the unsaturated zone, show an interesting relationship between organic matter content, thickness and saturated hydraulic conductivity. The application of Hirerarchical Cluster Analysis, based on all possible combinations of classification method, showed two main groups of samples. The first group comprises samples from the edges and the second from the central part of the study area. By the application of Discriminant Analysis it was shown that NO(3)(-) and SO(4)(2-) ions are the most significant variables in the discriminant function. Therefore, the first group is considered to comprise all samples from areas not influenced by fertilizers lying on the edges of contaminating activities such as crop cultivation, while the second comprises all the other samples.

Discriminatory power of water polo game-related statistics at the 2008 Olympic Games.

PubMed

Escalante, Yolanda; Saavedra, Jose M; Mansilla, Mirella; Tella, Victor

2011-02-01

The aims of this study were (1) to compare water polo game-related statistics by context (winning and losing teams) and sex (men and women), and (2) to identify characteristics discriminating the performances for each sex. The game-related statistics of the 64 matches (44 men's and 20 women's) played in the final phase of the Olympic Games held in Beijing in 2008 were analysed. Unpaired t-tests compared winners and losers and men and women, and confidence intervals and effect sizes of the differences were calculated. The results were subjected to a discriminant analysis to identify the differentiating game-related statistics of the winning and losing teams. The results showed the differences between winning and losing men's teams to be in both defence and offence, whereas in women's teams they were only in offence. In men's games, passing (assists), aggressive play (exclusions), centre position effectiveness (centre shots), and goalkeeper defence (goalkeeper-blocked 5-m shots) predominated, whereas in women's games the play was more dynamic (possessions). The variable that most discriminated performance in men was goalkeeper-blocked shots, and in women shooting effectiveness (shots). These results should help coaches when planning training and competition.

Research of facial feature extraction based on MMC

NASA Astrophysics Data System (ADS)

Xue, Donglin; Zhao, Jiufen; Tang, Qinhong; Shi, Shaokun

2017-07-01

Based on the maximum margin criterion (MMC), a new algorithm of statistically uncorrelated optimal discriminant vectors and a new algorithm of orthogonal optimal discriminant vectors for feature extraction were proposed. The purpose of the maximum margin criterion is to maximize the inter-class scatter while simultaneously minimizing the intra-class scatter after the projection. Compared with original MMC method and principal component analysis (PCA) method, the proposed methods are better in terms of reducing or eliminating the statistically correlation between features and improving recognition rate. The experiment results on Olivetti Research Laboratory (ORL) face database shows that the new feature extraction method of statistically uncorrelated maximum margin criterion (SUMMC) are better in terms of recognition rate and stability. Besides, the relations between maximum margin criterion and Fisher criterion for feature extraction were revealed.

Women cannot discriminate between different paracervical block techniques applied to opposite sides of the cervix.

PubMed

Grossman, R A

1995-09-01

The purpose of this study was to determine whether women can discriminate better from less effective paracervical block techniques applied to opposite sides of the cervix. If this discrimination could be made, it would be possible to compare different techniques and thus improve the quality of paracervical anesthesia. Two milliliters of local anesthetic was applied to one side and 6 ml to the other side of volunteers' cervices before cervical dilation. Statistical examination was by sequential analysis. The study was stopped after 47 subjects had entered, when sequential analysis found that there was no significant difference in women's perception of pain. Nine women reported more pain on the side with more anesthesia and eight reported more pain on the side with less anesthesia. Because the amount of anesthesia did not make a difference, the null hypothesis (that women cannot discriminate between different anesthetic techniques) was accepted. Women are not able to discriminate different doses of local anesthetic when applied to opposite sides of the cervix.

Discrimination of premalignant lesions and cancer tissues from normal gastric tissues using Raman spectroscopy

NASA Astrophysics Data System (ADS)

Luo, Shuwen; Chen, Changshui; Mao, Hua; Jin, Shaoqin

2013-06-01

The feasibility of early detection of gastric cancer using near-infrared (NIR) Raman spectroscopy (RS) by distinguishing premalignant lesions (adenomatous polyp, n=27) and cancer tissues (adenocarcinoma, n=33) from normal gastric tissues (n=45) is evaluated. Significant differences in Raman spectra are observed among the normal, adenomatous polyp, and adenocarcinoma gastric tissues at 936, 1003, 1032, 1174, 1208, 1323, 1335, 1450, and 1655 cm-1. Diverse statistical methods are employed to develop effective diagnostic algorithms for classifying the Raman spectra of different types of ex vivo gastric tissues, including principal component analysis (PCA), linear discriminant analysis (LDA), and naive Bayesian classifier (NBC) techniques. Compared with PCA-LDA algorithms, PCA-NBC techniques together with leave-one-out, cross-validation method provide better discriminative results of normal, adenomatous polyp, and adenocarcinoma gastric tissues, resulting in superior sensitivities of 96.3%, 96.9%, and 96.9%, and specificities of 93%, 100%, and 95.2%, respectively. Therefore, NIR RS associated with multivariate statistical algorithms has the potential for early diagnosis of gastric premalignant lesions and cancer tissues in molecular level.

Comparative genome analysis of a large Dutch Legionella pneumophila strain collection identifies five markers highly correlated with clinical strains

PubMed Central

2010-01-01

Background Discrimination between clinical and environmental strains within many bacterial species is currently underexplored. Genomic analyses have clearly shown the enormous variability in genome composition between different strains of a bacterial species. In this study we have used Legionella pneumophila, the causative agent of Legionnaire's disease, to search for genomic markers related to pathogenicity. During a large surveillance study in The Netherlands well-characterized patient-derived strains and environmental strains were collected. We have used a mixed-genome microarray to perform comparative-genome analysis of 257 strains from this collection. Results Microarray analysis indicated that 480 DNA markers (out of in total 3360 markers) showed clear variation in presence between individual strains and these were therefore selected for further analysis. Unsupervised statistical analysis of these markers showed the enormous genomic variation within the species but did not show any correlation with a pathogenic phenotype. We therefore used supervised statistical analysis to identify discriminating markers. Genetic programming was used both to identify predictive markers and to define their interrelationships. A model consisting of five markers was developed that together correctly predicted 100% of the clinical strains and 69% of the environmental strains. Conclusions A novel approach for identifying predictive markers enabling discrimination between clinical and environmental isolates of L. pneumophila is presented. Out of over 3000 possible markers, five were selected that together enabled correct prediction of all the clinical strains included in this study. This novel approach for identifying predictive markers can be applied to all bacterial species, allowing for better discrimination between strains well equipped to cause human disease and relatively harmless strains. PMID:20630115

Summary statistics in auditory perception.

PubMed

McDermott, Josh H; Schemitsch, Michael; Simoncelli, Eero P

2013-04-01

Sensory signals are transduced at high resolution, but their structure must be stored in a more compact format. Here we provide evidence that the auditory system summarizes the temporal details of sounds using time-averaged statistics. We measured discrimination of 'sound textures' that were characterized by particular statistical properties, as normally result from the superposition of many acoustic features in auditory scenes. When listeners discriminated examples of different textures, performance improved with excerpt duration. In contrast, when listeners discriminated different examples of the same texture, performance declined with duration, a paradoxical result given that the information available for discrimination grows with duration. These results indicate that once these sounds are of moderate length, the brain's representation is limited to time-averaged statistics, which, for different examples of the same texture, converge to the same values with increasing duration. Such statistical representations produce good categorical discrimination, but limit the ability to discern temporal detail.

Hiring a Gay Man, Taking a Risk?: A Lab Experiment on Employment Discrimination and Risk Aversion.

PubMed

Baert, Stijn

2018-01-01

We investigate risk aversion as a driver of labor market discrimination against homosexual men. We show that more hiring discrimination by more risk-averse employers is consistent with taste-based and statistical discrimination. To test this hypothesis we conduct a scenario experiment in which experimental employers take a fictitious hiring decision concerning a heterosexual or homosexual male job candidate. In addition, participants are surveyed on their risk aversion and other characteristics that might correlate with this risk aversion. Analysis of the (post-)experimental data confirms our hypothesis. The likelihood of a beneficial hiring decision for homosexual male candidates decreases by 31.7% when employers are a standard deviation more risk-averse.

A simple and fast representation space for classifying complex time series

NASA Astrophysics Data System (ADS)

Zunino, Luciano; Olivares, Felipe; Bariviera, Aurelio F.; Rosso, Osvaldo A.

2017-03-01

In the context of time series analysis considerable effort has been directed towards the implementation of efficient discriminating statistical quantifiers. Very recently, a simple and fast representation space has been introduced, namely the number of turning points versus the Abbe value. It is able to separate time series from stationary and non-stationary processes with long-range dependences. In this work we show that this bidimensional approach is useful for distinguishing complex time series: different sets of financial and physiological data are efficiently discriminated. Additionally, a multiscale generalization that takes into account the multiple time scales often involved in complex systems has been also proposed. This multiscale analysis is essential to reach a higher discriminative power between physiological time series in health and disease.

Assessment of sampling stability in ecological applications of discriminant analysis

USGS Publications Warehouse

Williams, B.K.; Titus, K.

1988-01-01

A simulation study was undertaken to assess the sampling stability of the variable loadings in linear discriminant function analysis. A factorial design was used for the factors of multivariate dimensionality, dispersion structure, configuration of group means, and sample size. A total of 32,400 discriminant analyses were conducted, based on data from simulated populations with appropriate underlying statistical distributions. A review of 60 published studies and 142 individual analyses indicated that sample sizes in ecological studies often have met that requirement. However, individual group sample sizes frequently were very unequal, and checks of assumptions usually were not reported. The authors recommend that ecologists obtain group sample sizes that are at least three times as large as the number of variables measured.

Nonlinear Statistical Estimation with Numerical Maximum Likelihood

DTIC Science & Technology

1974-10-01

probably most directly attributable to the speed, precision and compactness of the linear programming algorithm exercised ; the mutual primal-dual...discriminant analysis is to classify the individual as a member of T# or IT, 1 2 according to the relative...Introduction to the Dissertation 1 Introduction to Statistical Estimation Theory 3 Choice of Estimator.. .Density Functions 12 Choice of Estimator

Testing alternative ground water models using cross-validation and other methods

USGS Publications Warehouse

Foglia, L.; Mehl, S.W.; Hill, M.C.; Perona, P.; Burlando, P.

2007-01-01

Many methods can be used to test alternative ground water models. Of concern in this work are methods able to (1) rank alternative models (also called model discrimination) and (2) identify observations important to parameter estimates and predictions (equivalent to the purpose served by some types of sensitivity analysis). Some of the measures investigated are computationally efficient; others are computationally demanding. The latter are generally needed to account for model nonlinearity. The efficient model discrimination methods investigated include the information criteria: the corrected Akaike information criterion, Bayesian information criterion, and generalized cross-validation. The efficient sensitivity analysis measures used are dimensionless scaled sensitivity (DSS), composite scaled sensitivity, and parameter correlation coefficient (PCC); the other statistics are DFBETAS, Cook's D, and observation-prediction statistic. Acronyms are explained in the introduction. Cross-validation (CV) is a computationally intensive nonlinear method that is used for both model discrimination and sensitivity analysis. The methods are tested using up to five alternative parsimoniously constructed models of the ground water system of the Maggia Valley in southern Switzerland. The alternative models differ in their representation of hydraulic conductivity. A new method for graphically representing CV and sensitivity analysis results for complex models is presented and used to evaluate the utility of the efficient statistics. The results indicate that for model selection, the information criteria produce similar results at much smaller computational cost than CV. For identifying important observations, the only obviously inferior linear measure is DSS; the poor performance was expected because DSS does not include the effects of parameter correlation and PCC reveals large parameter correlations. ?? 2007 National Ground Water Association.

Statistical analysis of hail characteristics in the hail-protected western part of Croatia using data from hail suppression stations

NASA Astrophysics Data System (ADS)

Počakal, Damir; Štalec, Janez

In the continental part of Croatia, operational hail suppression has been conducted for more than 30 years. The current protected area is 25,177 km 2 and has about 492 hail suppression stations which are managed with eight weather radar centres. This paper present a statistical analysis of parameters connected with hail occurrence on hail suppression stations in the western part of protected area in 1981-2000 period. This analysis compares data of two periods with different intensity of hail suppression activity and is made as a part of a project for assessment of hail suppression efficiency in Croatia. Because of disruption in hail suppression system during the independence war in Croatia (1991-1995), lack of rockets and other objective circumstances, it is considered that in the 1991-2000 period, hail suppression system could not act properly. Because of that, a comparison of hail suppression data for two periods was made. The first period (1981-1990), which is characterised with full application of hail suppression technology is compared with the second period (1991-2000). The protected area is divided into quadrants (9×9 km), such that every quadrant has at least one hail suppression station and intercomparison is more precise. Discriminant analysis was performed for the yearly values of each quadrant. These values included number of cases with solid precipitation, hail damage, heavy hail damage, number of active hail suppression stations, number of days with solid precipitation, solid precipitation damage, heavy solid precipitation damage and the number and duration of air traffic control bans. The discriminant analysis shows that there is a significant difference between the two periods. Average values of observed periods on isolated discriminant function 1 are for the first period (1981-1990) -0.36 and for the second period +0.23 standard deviation of all observations. The analysis for all eight variables shows statistically substantial differences in the number of hail suppression stations (which have a positive correlation) and in the number of cases with air traffic control ban, which have, like all other variables, a negative correlation. Results of statistical analysis for two periods show positive influence of hail suppression system. The discriminant analysis made for three periods shows that these three periods can not be compared because of the short time period, the difference in hail suppression technology, working conditions and possible differences in meteorological conditions. Therefore, neither the effectiveness nor ineffectiveness of hail suppression operations nor their efficiency can be statistically proven. For an exact assessment of hail suppression effectiveness, it is necessary to develop a project, which would take into consideration all the parameters used in such previous projects around the world—a hailpad polygon.

Identification of discriminant proteins through antibody profiling, methods and apparatus for identifying an individual

DOEpatents

Apel, William A.; Thompson, Vicki S; Lacey, Jeffrey A.; Gentillon, Cynthia A.

2016-08-09

A method for determining a plurality of proteins for discriminating and positively identifying an individual based from a biological sample. The method may include profiling a biological sample from a plurality of individuals against a protein array including a plurality of proteins. The protein array may include proteins attached to a support in a preselected pattern such that locations of the proteins are known. The biological sample may be contacted with the protein array such that a portion of antibodies in the biological sample reacts with and binds to the proteins forming immune complexes. A statistical analysis method, such as discriminant analysis, may be performed to determine discriminating proteins for distinguishing individuals. Proteins of interest may be used to form a protein array. Such a protein array may be used, for example, to compare a forensic sample from an unknown source with a sample from a known source.

Identification of discriminant proteins through antibody profiling, methods and apparatus for identifying an individual

DOEpatents

Thompson, Vicki S; Lacey, Jeffrey A; Gentillon, Cynthia A; Apel, William A

2015-03-03

A method for determining a plurality of proteins for discriminating and positively identifying an individual based from a biological sample. The method may include profiling a biological sample from a plurality of individuals against a protein array including a plurality of proteins. The protein array may include proteins attached to a support in a preselected pattern such that locations of the proteins are known. The biological sample may be contacted with the protein array such that a portion of antibodies in the biological sample reacts with and binds to the proteins forming immune complexes. A statistical analysis method, such as discriminant analysis, may be performed to determine discriminating proteins for distinguishing individuals. Proteins of interest may be used to form a protein array. Such a protein array may be used, for example, to compare a forensic sample from an unknown source with a sample from a known source.

Characterizing chaotic melodies in automatic music composition

NASA Astrophysics Data System (ADS)

Coca, Andrés E.; Tost, Gerard O.; Zhao, Liang

2010-09-01

In this paper, we initially present an algorithm for automatic composition of melodies using chaotic dynamical systems. Afterward, we characterize chaotic music in a comprehensive way as comprising three perspectives: musical discrimination, dynamical influence on musical features, and musical perception. With respect to the first perspective, the coherence between generated chaotic melodies (continuous as well as discrete chaotic melodies) and a set of classical reference melodies is characterized by statistical descriptors and melodic measures. The significant differences among the three types of melodies are determined by discriminant analysis. Regarding the second perspective, the influence of dynamical features of chaotic attractors, e.g., Lyapunov exponent, Hurst coefficient, and correlation dimension, on melodic features is determined by canonical correlation analysis. The last perspective is related to perception of originality, complexity, and degree of melodiousness (Euler's gradus suavitatis) of chaotic and classical melodies by nonparametric statistical tests.

Metabolomic Approach for Discrimination of Four- and Six-Year-Old Red Ginseng (Panax ginseng) Using UPLC-QToF-MS.

PubMed

Shin, Jung-Sub; Park, Hee-Won; In, Gyo; Seo, Hyun Kyu; Won, Tae Hyung; Jang, Kyoung Hwa; Cho, Byung-Goo; Han, Chang Kyun; Shin, Jongheon

2016-09-01

Panax ginseng C.A. MEYER is one of the most popular medicinal herbs in Asia and the chemical constituents are changed by processing methods such as steaming or sun drying. Metabolomic analysis was performed to distinguish age discrimination of four- and six-year-old red ginseng using ultra-performance liquid chromatography quadruple time of flight mass spectrometry (UPLC-QToF-MS) with multivariate statistical analysis. Principal component analysis (PCA) showed clear discrimination between extracts of red ginseng of different ages and suggest totally six discrimination markers (two for four-year-old and four for six-year-old red ginseng). Among these, one marker was isolated and the structure determined by NMR spectroscopic analysis was 13-cis-docosenamide (marker 6-1) from six-year-old red ginseng. This is the first report of a metabolomic study regarding the age differentiation of red ginseng using UPLC-QToF-MS and determination of the structure of the marker. These results will contribute to the quality control and standardization as well as provide a scientific basis for pharmacological research on red ginseng.

Multivariate analysis, mass balance techniques, and statistical tests as tools in igneous petrology: application to the Sierra de las Cruces volcanic range (Mexican Volcanic Belt).

PubMed

Velasco-Tapia, Fernando

2014-01-01

Magmatic processes have usually been identified and evaluated using qualitative or semiquantitative geochemical or isotopic tools based on a restricted number of variables. However, a more complete and quantitative view could be reached applying multivariate analysis, mass balance techniques, and statistical tests. As an example, in this work a statistical and quantitative scheme is applied to analyze the geochemical features for the Sierra de las Cruces (SC) volcanic range (Mexican Volcanic Belt). In this locality, the volcanic activity (3.7 to 0.5 Ma) was dominantly dacitic, but the presence of spheroidal andesitic enclaves and/or diverse disequilibrium features in majority of lavas confirms the operation of magma mixing/mingling. New discriminant-function-based multidimensional diagrams were used to discriminate tectonic setting. Statistical tests of discordancy and significance were applied to evaluate the influence of the subducting Cocos plate, which seems to be rather negligible for the SC magmas in relation to several major and trace elements. A cluster analysis following Ward's linkage rule was carried out to classify the SC volcanic rocks geochemical groups. Finally, two mass-balance schemes were applied for the quantitative evaluation of the proportion of the end-member components (dacitic and andesitic magmas) in the comingled lavas (binary mixtures).

Ripening-dependent metabolic changes in the volatiles of pineapple (Ananas comosus (L.) Merr.) fruit: II. Multivariate statistical profiling of pineapple aroma compounds based on comprehensive two-dimensional gas chromatography-mass spectrometry.

PubMed

Steingass, Christof Björn; Jutzi, Manfred; Müller, Jenny; Carle, Reinhold; Schmarr, Hans-Georg

2015-03-01

Ripening-dependent changes of pineapple volatiles were studied in a nontargeted profiling analysis. Volatiles were isolated via headspace solid phase microextraction and analyzed by comprehensive 2D gas chromatography and mass spectrometry (HS-SPME-GC×GC-qMS). Profile patterns presented in the contour plots were evaluated applying image processing techniques and subsequent multivariate statistical data analysis. Statistical methods comprised unsupervised hierarchical cluster analysis (HCA) and principal component analysis (PCA) to classify the samples. Supervised partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression were applied to discriminate different ripening stages and describe the development of volatiles during postharvest storage, respectively. Hereby, substantial chemical markers allowing for class separation were revealed. The workflow permitted the rapid distinction between premature green-ripe pineapples and postharvest-ripened sea-freighted fruits. Volatile profiles of fully ripe air-freighted pineapples were similar to those of green-ripe fruits postharvest ripened for 6 days after simulated sea freight export, after PCA with only two principal components. However, PCA considering also the third principal component allowed differentiation between air-freighted fruits and the four progressing postharvest maturity stages of sea-freighted pineapples.

The Great, Late Lesbian and Bisexual Women's Discrimination Survey.

PubMed

Rankine, J

2001-01-01

SUMMARY This 1992 New Zealand survey of discrimination against 261 lesbian and bisexual women found comparable rates of public abuse and workplace discrimination to those reported by surveys in other developed countries. The women reported higher rates of assault in public places than a random sample of New Zealand women. Indigenous Maori women reported higher rates of assault, threats, verbal abuse, and workplace discrimination than the non-Maori women surveyed. Aggression against the women was often in response to public expression of affection for another woman or to rejection of men's public sexual advances. The respondents reported hostile educational environments that coincided with peer harassment of students attracted to their own gender. Around two-thirds of the women had hidden their sexuality on some occasions at work to avoid discrimination. No significant differences between the discrimination experiences of lesbian and bisexual women emerged, although the bisexual sample was too small for statistical analysis.

Statistical classification approach to discrimination between weak earthquakes and quarry blasts recorded by the Israel Seismic Network

NASA Astrophysics Data System (ADS)

Kushnir, A. F.; Troitsky, E. V.; Haikin, L. M.; Dainty, A.

1999-06-01

A semi-automatic procedure has been developed to achieve statistically optimum discrimination between earthquakes and explosions at local or regional distances based on a learning set specific to a given region. The method is used for step-by-step testing of candidate discrimination features to find the optimum (combination) subset of features, with the decision taken on a rigorous statistical basis. Linear (LDF) and Quadratic (QDF) Discriminant Functions based on Gaussian distributions of the discrimination features are implemented and statistically grounded; the features may be transformed by the Box-Cox transformation z=(1/ α)( yα-1) to make them more Gaussian. Tests of the method were successfully conducted on seismograms from the Israel Seismic Network using features consisting of spectral ratios between and within phases. Results showed that the QDF was more effective than the LDF and required five features out of 18 candidates for the optimum set. It was found that discrimination improved with increasing distance within the local range, and that eliminating transformation of the features and failing to correct for noise led to degradation of discrimination.

A statistical mechanics approach to autopoietic immune networks

NASA Astrophysics Data System (ADS)

Barra, Adriano; Agliari, Elena

2010-07-01

In this work we aim to bridge theoretical immunology and disordered statistical mechanics. We introduce a model for the behavior of B-cells which naturally merges the clonal selection theory and the autopoietic network theory as a whole. From the analysis of its features we recover several basic phenomena such as low-dose tolerance, dynamical memory of antigens and self/non-self discrimination.

Classifiers utilized to enhance acoustic based sensors to identify round types of artillery/mortar

NASA Astrophysics Data System (ADS)

Grasing, David; Desai, Sachi; Morcos, Amir

2008-04-01

Feature extraction methods based on the statistical analysis of the change in event pressure levels over a period and the level of ambient pressure excitation facilitate the development of a robust classification algorithm. The features reliably discriminates mortar and artillery variants via acoustic signals produced during the launch events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants as type A, etcetera through analysis of the waveform. Distinct characteristics arise within the different mortar/artillery variants because varying HE mortar payloads and related charges emphasize varying size events at launch. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing and data mining techniques can employed to classify a given type. The skewness and other statistical processing techniques are used to extract the predominant components from the acoustic signatures at ranges exceeding 3000m. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feedforward neural network classifier trained on a feature space derived from the distribution of statistical coefficients, frequency spectrum, and higher frequency details found within different energy bands. The processes that are described herein extend current technologies, which emphasis acoustic sensor systems to provide such situational awareness.

Artillery/mortar type classification based on detected acoustic transients

NASA Astrophysics Data System (ADS)

Morcos, Amir; Grasing, David; Desai, Sachi

2008-04-01

Feature extraction methods based on the statistical analysis of the change in event pressure levels over a period and the level of ambient pressure excitation facilitate the development of a robust classification algorithm. The features reliably discriminates mortar and artillery variants via acoustic signals produced during the launch events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants as type A, etcetera through analysis of the waveform. Distinct characteristics arise within the different mortar/artillery variants because varying HE mortar payloads and related charges emphasize varying size events at launch. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing and data mining techniques can employed to classify a given type. The skewness and other statistical processing techniques are used to extract the predominant components from the acoustic signatures at ranges exceeding 3000m. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feed-forward neural network classifier trained on a feature space derived from the distribution of statistical coefficients, frequency spectrum, and higher frequency details found within different energy bands. The processes that are described herein extend current technologies, which emphasis acoustic sensor systems to provide such situational awareness.

Artillery/mortar round type classification to increase system situational awareness

NASA Astrophysics Data System (ADS)

Desai, Sachi; Grasing, David; Morcos, Amir; Hohil, Myron

2008-04-01

Feature extraction methods based on the statistical analysis of the change in event pressure levels over a period and the level of ambient pressure excitation facilitate the development of a robust classification algorithm. The features reliably discriminates mortar and artillery variants via acoustic signals produced during the launch events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants as type A, etcetera through analysis of the waveform. Distinct characteristics arise within the different mortar/artillery variants because varying HE mortar payloads and related charges emphasize varying size events at launch. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing and data mining techniques can employed to classify a given type. The skewness and other statistical processing techniques are used to extract the predominant components from the acoustic signatures at ranges exceeding 3000m. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feedforward neural network classifier trained on a feature space derived from the distribution of statistical coefficients, frequency spectrum, and higher frequency details found within different energy bands. The processes that are described herein extend current technologies, which emphasis acoustic sensor systems to provide such situational awareness.

Authentication of animal origin of heparin and low molecular weight heparin including ovine, porcine and bovine species using 1D NMR spectroscopy and chemometric tools.

PubMed

Monakhova, Yulia B; Diehl, Bernd W K; Fareed, Jawed

2018-02-05

High resolution (600MHz) nuclear magnetic resonance (NMR) spectroscopy is used to distinguish heparin and low-molecular weight heparins (LMWHs) produced from porcine, bovine and ovine mucosal tissues as well as their blends. For multivariate analysis several statistical methods such as principal component analysis (PCA), factor discriminant analysis (FDA), partial least squares - discriminant analysis (PLS-DA), linear discriminant analysis (LDA) were utilized for the modeling of NMR data of more than 100 authentic samples. Heparin and LMWH samples from the independent test set (n=15) were 100% correctly classified according to its animal origin. Moreover, by using 1 H NMR coupled with chemometrics and several batches of bovine heparins from two producers were differentiated. Thus, NMR spectroscopy combined with chemometrics is an efficient tool for simultaneous identification of animal origin and process based manufacturing difference in heparin products. Copyright © 2017 Elsevier B.V. All rights reserved.

FT-IR spectroscopy and multivariate analysis as an auxiliary tool for diagnosis of mental disorders: Bipolar and schizophrenia cases

NASA Astrophysics Data System (ADS)

Ogruc Ildiz, G.; Arslan, M.; Unsalan, O.; Araujo-Andrade, C.; Kurt, E.; Karatepe, H. T.; Yilmaz, A.; Yalcinkaya, O. B.; Herken, H.

2016-01-01

In this study, a methodology based on Fourier-transform infrared spectroscopy and principal component analysis and partial least square methods is proposed for the analysis of blood plasma samples in order to identify spectral changes correlated with some biomarkers associated with schizophrenia and bipolarity. Our main goal was to use the spectral information for the calibration of statistical models to discriminate and classify blood plasma samples belonging to bipolar and schizophrenic patients. IR spectra of 30 samples of blood plasma obtained from each, bipolar and schizophrenic patients and healthy control group were collected. The results obtained from principal component analysis (PCA) show a clear discrimination between the bipolar (BP), schizophrenic (SZ) and control group' (CG) blood samples that also give possibility to identify three main regions that show the major differences correlated with both mental disorders (biomarkers). Furthermore, a model for the classification of the blood samples was calibrated using partial least square discriminant analysis (PLS-DA), allowing the correct classification of BP, SZ and CG samples. The results obtained applying this methodology suggest that it can be used as a complimentary diagnostic tool for the detection and discrimination of these mental diseases.

NMR-based metabolomic analysis of spatial variation in soft corals.

PubMed

He, Qing; Sun, Ruiqi; Liu, Huijuan; Geng, Zhufeng; Chen, Dawei; Li, Yinping; Han, Jiao; Lin, Wenhan; Du, Shushan; Deng, Zhiwei

2014-03-28

Soft corals are common marine organisms that inhabit tropical and subtropical oceans. They are shown to be rich source of secondary metabolites with biological activities. In this work, soft corals from two geographical locations were investigated using ¹H-NMR spectroscopy coupled with multivariate statistical analysis at the metabolic level. A partial least-squares discriminant analysis showed clear separation among extracts of soft corals grown in Sanya Bay and Weizhou Island. The specific markers that contributed to discrimination between soft corals in two origins belonged to terpenes, sterols and N-containing compounds. The satisfied precision of classification obtained indicates this approach using combined ¹H-NMR and chemometrics is effective to discriminate soft corals collected in different geographical locations. The results revealed that metabolites of soft corals evidently depended on living environmental condition, which would provide valuable information for further relevant coastal marine environment evaluation.

A note on statistical analysis of shape through triangulation of landmarks

PubMed Central

Rao, C. Radhakrishna

2000-01-01

In an earlier paper, the author jointly with S. Suryawanshi proposed statistical analysis of shape through triangulation of landmarks on objects. It was observed that the angles of the triangles are invariant to scaling, location, and rotation of objects. No distinction was made between an object and its reflection. The present paper provides the methodology of shape discrimination when reflection is also taken into account and makes suggestions for modifications to be made when some of the landmarks are collinear. PMID:10737780

Examining the Effectiveness of Discriminant Function Analysis and Cluster Analysis in Species Identification of Male Field Crickets Based on Their Calling Songs

PubMed Central

Jaiswara, Ranjana; Nandi, Diptarup; Balakrishnan, Rohini

2013-01-01

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 classification and identification. PMID:24086666

Discriminative factor analysis of juvenile delinquency in South Korea.

PubMed

Kim, Hyun Sil; Kim, Hun Soo

2006-12-01

The present study was intended to compare difference in research variables between delinquent adolescents and student adolescents, and to analyze discriminative factors of delinquent behaviors among Korean adolescents. The research design of this study was a questionnaire survey. Questionnaires were administered to 2,167 adolescents (1,196 students and 971 delinquents), sampled from 8 middle and high school and 6 juvenile corrective institutions, using the proportional stratified random sampling method. Statistical methods employed were Chi-square, t-test, and logistic regression analysis. The discriminative factors of delinquent behaviors were smoking, alcohol use, other drug use, being sexually abused, viewing time of media violence and pornography. Among these discriminative factors, the factor most strongly associated with delinquency was smoking (odds ratio: 32.32). That is, smoking adolescent has a 32-fold higher possibility of becoming a delinquent adolescent than a non-smoking adolescent. Our findings, that smoking was the strongest discriminative factor of delinquent behavior, suggest that educational strategies to prevent adolescent smoking may reduce the rate of juvenile delinquency. Antismoking educational efforts are therefore urgently needed in South Korea.

Slice simulation from a model of the parenchymous vascularization to evaluate texture features: work in progress.

PubMed

Rolland, Y; Bézy-Wendling, J; Duvauferrier, R; Coatrieux, J L

1999-03-01

To demonstrate the usefulness of a model of the parenchymous vascularization to evaluate texture analysis methods. Slices with thickness varying from 1 to 4 mm were reformatted from a 3D vascular model corresponding to either normal tissue perfusion or local hypervascularization. Parameters of statistical methods were measured on 16128x128 regions of interest, and mean values and standard deviation were calculated. For each parameter, the performances (discrimination power and stability) were evaluated. Among 11 calculated statistical parameters, three (homogeneity, entropy, mean of gradients) were found to have a good discriminating power to differentiate normal perfusion from hypervascularization, but only the gradient mean was found to have a good stability with respect to the thickness. Five parameters (run percentage, run length distribution, long run emphasis, contrast, and gray level distribution) were found to have intermediate results. In the remaining three, curtosis and correlation was found to have little discrimination power, skewness none. This 3D vascular model, which allows the generation of various examples of vascular textures, is a powerful tool to assess the performance of texture analysis methods. This improves our knowledge of the methods and should contribute to their a priori choice when designing clinical studies.

Detection and discrimination of microorganisms on various substrates with quantum cascade laser spectroscopy

NASA Astrophysics Data System (ADS)

Padilla-Jiménez, Amira C.; Ortiz-Rivera, William; Rios-Velazquez, Carlos; Vazquez-Ayala, Iris; Hernández-Rivera, Samuel P.

2014-06-01

Investigations focusing on devising rapid and accurate methods for developing signatures for microorganisms that could be used as biological warfare agents' detection, identification, and discrimination have recently increased significantly. Quantum cascade laser (QCL)-based spectroscopic systems have revolutionized many areas of defense and security including this area of research. In this contribution, infrared spectroscopy detection based on QCL was used to obtain the mid-infrared (MIR) spectral signatures of Bacillus thuringiensis, Escherichia coli, and Staphylococcus epidermidis. These bacteria were used as microorganisms that simulate biothreats (biosimulants) very truthfully. The experiments were conducted in reflection mode with biosimulants deposited on various substrates including cardboard, glass, travel bags, wood, and stainless steel. Chemometrics multivariate statistical routines, such as principal component analysis regression and partial least squares coupled to discriminant analysis, were used to analyze the MIR spectra. Overall, the investigated infrared vibrational techniques were useful for detecting target microorganisms on the studied substrates, and the multivariate data analysis techniques proved to be very efficient for classifying the bacteria and discriminating them in the presence of highly IR-interfering media.

Intractable Ménière's disease. Modelling of the treatment by means of statistical analysis.

PubMed

Sanchez-Ferrandiz, Noelia; Fernandez-Gonzalez, Secundino; Guillen-Grima, Francisco; Perez-Fernandez, Nicolas

2010-08-01

To evaluate the value of different variables of the clinical history, auditory and vestibular tests and handicap measurements to define intractable or disabling Ménière's disease. This is a prospective study with 212 patients of which 155 were treated with intratympanic gentamicin and considered to be suffering a medically intractable Ménière's disease. Age and sex adjustments were performed with the 11 variables selected. Discriminant analysis was performed either using the aforementioned variables or following the stepwise method. Different variables needed to be sex and/or age adjusted and both data were included in the discriminant function. Two different mathematical formulas were obtained and four models were analyzed. With the model selected, diagnostic accuracy is 77.7%, sensitivity is 94.9% and specificity is 52.8%. After discriminant analysis we found that the most informative variables were the number of vertigo spells, the speech discrimination score, the time constant of the VOR and a measure of handicap, the "dizziness index". Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

MIDAS: Regionally linear multivariate discriminative statistical mapping.

PubMed

Varol, Erdem; Sotiras, Aristeidis; Davatzikos, Christos

2018-07-01

Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the statistical significance of the derived statistic by analytically approximating its null distribution without the need for computationally expensive permutation tests. The proposed framework was extensively validated using simulated atrophy in structural magnetic resonance imaging (MRI) and further tested using data from a task-based functional MRI study as well as a structural MRI study of cognitive performance. The performance of the proposed framework was evaluated against standard voxel-wise general linear models and other information mapping methods. The experimental results showed that MIDAS achieves relatively higher sensitivity and specificity in detecting group differences. Together, our results demonstrate the potential of the proposed approach to efficiently map effects of interest in both structural and functional data. Copyright © 2018. Published by Elsevier Inc.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

PubMed

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

NASA Astrophysics Data System (ADS)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Analysis of the quantitative dermatoglyphics of the digito-palmar complex in patients with multiple sclerosis.

PubMed

Supe, S; Milicić, J; Pavićević, R

1997-06-01

Recent studies on the etiopathogenesis of multiple sclerosis (MS) all point out that there is a polygenetical predisposition for this illness. The so called "MS Trait" determines the reactivity of the immunological system upon ecological factors. The development of the glyphological science and the study of the characteristics of the digito-palmar dermatoglyphic complex (for which it was established that they are polygenetically determined characteristics) all enable a better insight into the genetic development during early embriogenesis. The aim of this study was to estimate certain differences in the dermatoglyphics of digito-palmar complexes between the group with multiple sclerosis and the comparable, phenotypically healthy groups of both sexes. This study is based on the analysis of 18 quantitative characteristics of the digito-palmar complex in 125 patients with multiple sclerosis (41 males and 84 females) in comparison to a group of 400 phenotypically healthy patients (200 males and 200 females). The conducted analysis pointed towards a statistically significant decrease of the number of digital and palmar ridges, as well as with lower values of atd angles in a group of MS patients of both sexes. The main discriminators were the characteristic palmar dermatoglyphics with the possibility that the discriminate analysis classifies over 80% of the examinees which exceeds the statistical significance. The results of this study suggest a possible discrimination of patients with MS and the phenotypically health population through the analysis of the dermatoglyphic status, and therefore the possibility that multiple sclerosis is genetically predisposed disease.

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

PubMed

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. Copyright © 2016 John Wiley & Sons, Ltd.

The Raman spectrum character of skin tumor induced by UVB

NASA Astrophysics Data System (ADS)

Wu, Shulian; Hu, Liangjun; Wang, Yunxia; Li, Yongzeng

2016-03-01

In our study, the skin canceration processes induced by UVB were analyzed from the perspective of tissue spectrum. A home-made Raman spectral system with a millimeter order excitation laser spot size combined with a multivariate statistical analysis for monitoring the skin changed irradiated by UVB was studied and the discrimination were evaluated. Raman scattering signals of the SCC and normal skin were acquired. Spectral differences in Raman spectra were revealed. Linear discriminant analysis (LDA) based on principal component analysis (PCA) were employed to generate diagnostic algorithms for the classification of skin SCC and normal. The results indicated that Raman spectroscopy combined with PCA-LDA demonstrated good potential for improving the diagnosis of skin cancers.

A Review of Classical Methods of Item Analysis.

ERIC Educational Resources Information Center

French, Christine L.

Item analysis is a very important consideration in the test development process. It is a statistical procedure to analyze test items that combines methods used to evaluate the important characteristics of test items, such as difficulty, discrimination, and distractibility of the items in a test. This paper reviews some of the classical methods for…

Multivariate Analysis, Mass Balance Techniques, and Statistical Tests as Tools in Igneous Petrology: Application to the Sierra de las Cruces Volcanic Range (Mexican Volcanic Belt)

PubMed Central

Velasco-Tapia, Fernando

2014-01-01

Magmatic processes have usually been identified and evaluated using qualitative or semiquantitative geochemical or isotopic tools based on a restricted number of variables. However, a more complete and quantitative view could be reached applying multivariate analysis, mass balance techniques, and statistical tests. As an example, in this work a statistical and quantitative scheme is applied to analyze the geochemical features for the Sierra de las Cruces (SC) volcanic range (Mexican Volcanic Belt). In this locality, the volcanic activity (3.7 to 0.5 Ma) was dominantly dacitic, but the presence of spheroidal andesitic enclaves and/or diverse disequilibrium features in majority of lavas confirms the operation of magma mixing/mingling. New discriminant-function-based multidimensional diagrams were used to discriminate tectonic setting. Statistical tests of discordancy and significance were applied to evaluate the influence of the subducting Cocos plate, which seems to be rather negligible for the SC magmas in relation to several major and trace elements. A cluster analysis following Ward's linkage rule was carried out to classify the SC volcanic rocks geochemical groups. Finally, two mass-balance schemes were applied for the quantitative evaluation of the proportion of the end-member components (dacitic and andesitic magmas) in the comingled lavas (binary mixtures). PMID:24737994

Trial application of oxygen and carbon isotope analysis in tooth enamel for identification of past-war victims for discriminating between Japanese and US soldiers.

PubMed

Someda, Hidetoshi; Gakuhari, Takashi; Akai, Junko; Araki, Yoshiyuki; Kodera, Tsutomu; Tsumatori, Gentaro; Kobayashi, Yasushi; Matsunaga, Satoru; Abe, Shinichi; Hashimoto, Masatsugu; Saito, Megumi; Yoneda, Minoru; Ishida, Hajime

2016-04-01

Stable isotope analysis has undergone rapid development in recent years and yielded significant results in the field of forensic sciences. In particular, carbon and oxygen isotopic ratios in tooth enamel obtained from human remains can provide useful information for the crosschecking of morphological and DNA analyses and facilitate rapid on-site prescreening for the identification of remains. This study analyzes carbon and oxygen isotopic ratios in the tooth enamel of Japanese people born between 1878 and 1930, in order to obtain data for methodological differentiation of Japanese and American remains from the Second World War. The carbon and oxygen isotopic ratios in the tooth enamel of the examined Japanese individuals are compared to previously reported data for American individuals (born post WWII), and statistical analysis is conducted using a discrimination method based on a logistic regression analysis. The discrimination between the Japanese and US populations, including Alaska and Hawaii, is found to be highly accurate. Thus, the present method has potential as a discrimination technique for both populations for use in the examination of mixed remains comprising Japanese and American fallen soldiers. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Psychometric evaluation of the Persian version of the Templer's Death Anxiety Scale in cancer patients.

PubMed

Soleimani, Mohammad Ali; Yaghoobzadeh, Ameneh; Bahrami, Nasim; Sharif, Saeed Pahlevan; Sharif Nia, Hamid

2016-10-01

In this study, 398 Iranian cancer patients completed the 15-item Templer's Death Anxiety Scale (TDAS). Tests of internal consistency, principal components analysis, and confirmatory factor analysis were conducted to assess the internal consistency and factorial validity of the Persian TDAS. The construct reliability statistic and average variance extracted were also calculated to measure construct reliability, convergent validity, and discriminant validity. Principal components analysis indicated a 3-component solution, which was generally supported in the confirmatory analysis. However, acceptable cutoffs for construct reliability, convergent validity, and discriminant validity were not fulfilled for the three subscales that were derived from the principal component analysis. This study demonstrated both the advantages and potential limitations of using the TDAS with Persian-speaking cancer patients.

Fast neutron-gamma discrimination on neutron emission profile measurement on JT-60U.

PubMed

Ishii, K; Shinohara, K; Ishikawa, M; Baba, M; Isobe, M; Okamoto, A; Kitajima, S; Sasao, M

2010-10-01

A digital signal processing (DSP) system is applied to stilbene scintillation detectors of the multichannel neutron emission profile monitor in JT-60U. Automatic analysis of the neutron-γ pulse shape discrimination is a key issue to diminish the processing time in the DSP system, and it has been applied using the two-dimensional (2D) map. Linear discriminant function is used to determine the dividing line between neutron events and γ-ray events on a 2D map. In order to verify the validity of the dividing line determination, the pulse shape discrimination quality is evaluated. As a result, the γ-ray contamination in most of the beam heating phase was negligible compared with the statistical error with 10 ms time resolution.

Fast neutron-gamma discrimination on neutron emission profile measurement on JT-60U

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

Ishii, K.; Okamoto, A.; Kitajima, S.

2010-10-15

A digital signal processing (DSP) system is applied to stilbene scintillation detectors of the multichannel neutron emission profile monitor in JT-60U. Automatic analysis of the neutron-{gamma} pulse shape discrimination is a key issue to diminish the processing time in the DSP system, and it has been applied using the two-dimensional (2D) map. Linear discriminant function is used to determine the dividing line between neutron events and {gamma}-ray events on a 2D map. In order to verify the validity of the dividing line determination, the pulse shape discrimination quality is evaluated. As a result, the {gamma}-ray contamination in most of themore » beam heating phase was negligible compared with the statistical error with 10 ms time resolution.« less

Molecular activity prediction by means of supervised subspace projection based ensembles of classifiers.

PubMed

Cerruela García, G; García-Pedrajas, N; Luque Ruiz, I; Gómez-Nieto, M Á

2018-03-01

This paper proposes a method for molecular activity prediction in QSAR studies using ensembles of classifiers constructed by means of two supervised subspace projection methods, namely nonparametric discriminant analysis (NDA) and hybrid discriminant analysis (HDA). We studied the performance of the proposed ensembles compared to classical ensemble methods using four molecular datasets and eight different models for the representation of the molecular structure. Using several measures and statistical tests for classifier comparison, we observe that our proposal improves the classification results with respect to classical ensemble methods. Therefore, we show that ensembles constructed using supervised subspace projections offer an effective way of creating classifiers in cheminformatics.

Facial patterns in a tropical social wasp correlate with colony membership

NASA Astrophysics Data System (ADS)

Baracchi, David; Turillazzi, Stefano; Chittka, Lars

2016-10-01

Social insects excel in discriminating nestmates from intruders, typically relying on colony odours. Remarkably, some wasp species achieve such discrimination using visual information. However, while it is universally accepted that odours mediate a group level recognition, the ability to recognise colony members visually has been considered possible only via individual recognition by which wasps discriminate `friends' and `foes'. Using geometric morphometric analysis, which is a technique based on a rigorous statistical theory of shape allowing quantitative multivariate analyses on structure shapes, we first quantified facial marking variation of Liostenogaster flavolineata wasps. We then compared this facial variation with that of chemical profiles (generated by cuticular hydrocarbons) within and between colonies. Principal component analysis and discriminant analysis applied to sets of variables containing pure shape information showed that despite appreciable intra-colony variation, the faces of females belonging to the same colony resemble one another more than those of outsiders. This colony-specific variation in facial patterns was on a par with that observed for odours. While the occurrence of face discrimination at the colony level remains to be tested by behavioural experiments, overall our results suggest that, in this species, wasp faces display adequate information that might be potentially perceived and used by wasps for colony level recognition.

External validation of ADO, DOSE, COTE and CODEX at predicting death in primary care patients with COPD using standard and machine learning approaches.

PubMed

Morales, Daniel R; Flynn, Rob; Zhang, Jianguo; Trucco, Emmanuel; Quint, Jennifer K; Zutis, Kris

2018-05-01

Several models for predicting the risk of death in people with chronic obstructive pulmonary disease (COPD) exist but have not undergone large scale validation in primary care. The objective of this study was to externally validate these models using statistical and machine learning approaches. We used a primary care COPD cohort identified using data from the UK Clinical Practice Research Datalink. Age-standardised mortality rates were calculated for the population by gender and discrimination of ADO (age, dyspnoea, airflow obstruction), COTE (COPD-specific comorbidity test), DOSE (dyspnoea, airflow obstruction, smoking, exacerbations) and CODEX (comorbidity, dyspnoea, airflow obstruction, exacerbations) at predicting death over 1-3 years measured using logistic regression and a support vector machine learning (SVM) method of analysis. The age-standardised mortality rate was 32.8 (95%CI 32.5-33.1) and 25.2 (95%CI 25.4-25.7) per 1000 person years for men and women respectively. Complete data were available for 54879 patients to predict 1-year mortality. ADO performed the best (c-statistic of 0.730) compared with DOSE (c-statistic 0.645), COTE (c-statistic 0.655) and CODEX (c-statistic 0.649) at predicting 1-year mortality. Discrimination of ADO and DOSE improved at predicting 1-year mortality when combined with COTE comorbidities (c-statistic 0.780 ADO + COTE; c-statistic 0.727 DOSE + COTE). Discrimination did not change significantly over 1-3 years. Comparable results were observed using SVM. In primary care, ADO appears superior at predicting death in COPD. Performance of ADO and DOSE improved when combined with COTE comorbidities suggesting better models may be generated with additional data facilitated using novel approaches. Copyright © 2018. Published by Elsevier Ltd.

Wavelet analysis of polarization maps of polycrystalline biological fluids networks

NASA Astrophysics Data System (ADS)

Ushenko, Y. A.

2011-12-01

The optical model of human joints synovial fluid is proposed. The statistic (statistic moments), correlation (autocorrelation function) and self-similar (Log-Log dependencies of power spectrum) structure of polarization two-dimensional distributions (polarization maps) of synovial fluid has been analyzed. It has been shown that differentiation of polarization maps of joint synovial fluid with different physiological state samples is expected of scale-discriminative analysis. To mark out of small-scale domain structure of synovial fluid polarization maps, the wavelet analysis has been used. The set of parameters, which characterize statistic, correlation and self-similar structure of wavelet coefficients' distributions of different scales of polarization domains for diagnostics and differentiation of polycrystalline network transformation connected with the pathological processes, has been determined.

Grain size statistics and depositional pattern of the Ecca Group sandstones, Karoo Supergroup in the Eastern Cape Province, South Africa

NASA Astrophysics Data System (ADS)

Baiyegunhi, Christopher; Liu, Kuiwu; Gwavava, Oswald

2017-11-01

Grain size analysis is a vital sedimentological tool used to unravel the hydrodynamic conditions, mode of transportation and deposition of detrital sediments. In this study, detailed grain-size analysis was carried out on thirty-five sandstone samples from the Ecca Group in the Eastern Cape Province of South Africa. Grain-size statistical parameters, bivariate analysis, linear discriminate functions, Passega diagrams and log-probability curves were used to reveal the depositional processes, sedimentation mechanisms, hydrodynamic energy conditions and to discriminate different depositional environments. The grain-size parameters show that most of the sandstones are very fine to fine grained, moderately well sorted, mostly near-symmetrical and mesokurtic in nature. The abundance of very fine to fine grained sandstones indicate the dominance of low energy environment. The bivariate plots show that the samples are mostly grouped, except for the Prince Albert samples that show scattered trend, which is due to the either mixture of two modes in equal proportion in bimodal sediments or good sorting in unimodal sediments. The linear discriminant function analysis is dominantly indicative of turbidity current deposits under shallow marine environments for samples from the Prince Albert, Collingham and Ripon Formations, while those samples from the Fort Brown Formation are lacustrine or deltaic deposits. The C-M plots indicated that the sediments were deposited mainly by suspension and saltation, and graded suspension. Visher diagrams show that saltation is the major process of transportation, followed by suspension.

Electromagnetic Induction E-Sensor for Underwater UXO Detection

DTIC Science & Technology

2011-12-01

EMF Electromotive force FET Field Effect Transitor Hz Hertz ms millisecond nV nanoVolt QFS QUASAR Federal...processing. Statistical discrimination techniques based on model analysis, such as the Time-Domain Three Dipole (TD3D) model, can separate UXO-like objects

Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma

PubMed Central

Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K

2015-01-01

Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (p<0.001) and integrated discrimination improvement (p=0.04). The HALT-C model had a c-statistic of 0.60 (95%CI 0.50-0.70) in the validation cohort and was outperformed by the machine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273

Comparative study on fast classification of brick samples by combination of principal component analysis and linear discriminant analysis using stand-off and table-top laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Vítková, Gabriela; Prokeš, Lubomír; Novotný, Karel; Pořízka, Pavel; Novotný, Jan; Všianský, Dalibor; Čelko, Ladislav; Kaiser, Jozef

2014-11-01

Focusing on historical aspect, during archeological excavation or restoration works of buildings or different structures built from bricks it is important to determine, preferably in-situ and in real-time, the locality of bricks origin. Fast classification of bricks on the base of Laser-Induced Breakdown Spectroscopy (LIBS) spectra is possible using multivariate statistical methods. Combination of principal component analysis (PCA) and linear discriminant analysis (LDA) was applied in this case. LIBS was used to classify altogether the 29 brick samples from 7 different localities. Realizing comparative study using two different LIBS setups - stand-off and table-top it is shown that stand-off LIBS has a big potential for archeological in-field measurements.

Analysis of spontaneous MEG activity in mild cognitive impairment and Alzheimer's disease using spectral entropies and statistical complexity measures

NASA Astrophysics Data System (ADS)

Bruña, Ricardo; Poza, Jesús; Gómez, Carlos; García, María; Fernández, Alberto; Hornero, Roberto

2012-06-01

Alzheimer's disease (AD) is the most common cause of dementia. Over the last few years, a considerable effort has been devoted to exploring new biomarkers. Nevertheless, a better understanding of brain dynamics is still required to optimize therapeutic strategies. In this regard, the characterization of mild cognitive impairment (MCI) is crucial, due to the high conversion rate from MCI to AD. However, only a few studies have focused on the analysis of magnetoencephalographic (MEG) rhythms to characterize AD and MCI. In this study, we assess the ability of several parameters derived from information theory to describe spontaneous MEG activity from 36 AD patients, 18 MCI subjects and 26 controls. Three entropies (Shannon, Tsallis and Rényi entropies), one disequilibrium measure (based on Euclidean distance ED) and three statistical complexities (based on Lopez Ruiz-Mancini-Calbet complexity LMC) were used to estimate the irregularity and statistical complexity of MEG activity. Statistically significant differences between AD patients and controls were obtained with all parameters (p < 0.01). In addition, statistically significant differences between MCI subjects and controls were achieved by ED and LMC (p < 0.05). In order to assess the diagnostic ability of the parameters, a linear discriminant analysis with a leave-one-out cross-validation procedure was applied. The accuracies reached 83.9% and 65.9% to discriminate AD and MCI subjects from controls, respectively. Our findings suggest that MCI subjects exhibit an intermediate pattern of abnormalities between normal aging and AD. Furthermore, the proposed parameters provide a new description of brain dynamics in AD and MCI.

Determination of trace metal concentrations in ginseng (Panax Quinquefolius (American)) roots for forensic comparison using Inductively Coupled Plasma Mass-Spectrometry.

PubMed

Peake, Barrie M; Tong, Alfred Y C; Wells, William J; Harraway, John A; Niven, Brian E; Weege, Butch; LaFollette, Douglas J

2015-06-01

The trace metal content of roots of samples of the American ginseng natural herbal plant species (Panax quinquefolius) was investigated as a means of differentiating between this species grown on Wisconsin and New Zealand farms, and from Canadian and Chinese sources. ICP-MS measurements were undertaken by ashing samples of the roots and then digestion with conc. HNO3 and H2O2. There was considerable variation in the concentrations of 28 detectable elements along the length of a root, between different roots, between different farms/sources and between different countries. Statistical processing of the log-transformed concentration data was undertaken using principal component analysis (PCA) and discriminant function analysis (DFA). Although PCA showed some differentiation between samples, a much clearer discrimination of the Panax quinquefolius species of ginseng from the four countries was observed using DFA. 88% of the variation between countries could be accounted for by only using discriminant function 1 while 80% of the remaining 12% of the variation between countries is accounted for by discriminant function 2. The Fisher Classification Functions classify 98% of the 87 samples to the correct country of origin with 97% of the cross-validated cases correctly classified. The predictive ability of this DFA model was further tested by constructing 100 discriminant models each using a random selection of the data for two thirds of the 87 sampled ginseng root tops, and then using the resulting classification functions to determine correctly the country of origin of the remaining third of the cases. The mean success rate of the 100 classifications was 92%. These results suggest that measurement and statistical analysis of just the trace metal content of the roots of Panax quinquefolius promises to be an excellent predictor of the country of origin of this ginseng species. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Identification of the geographical origins of sea cucumber (Apostichopus japonicus) in northern China by using stable isotope ratios and fatty acid profiles.

PubMed

Zhang, Xufeng; Liu, Yu; Li, Ying; Zhao, Xinda

2017-03-01

Geographic traceability is an important issue for food quality and safety control of seafood. In this study,δ 13 C and δ 15 N values, as well as fatty acid (FA) content of 133 samples of A. japonicus from seven sampling points in northern China Sea were determined to evaluate their applicability in the origin traceability of A. japonicus. Principal component analysis (PCA) and discriminant analysis (DA) were applied to different data sets in order to evaluate their performance in terms of classification or predictive ability. δ 13 C and δ 15 N values could effectively discriminate between different origins of A. japonicus. Significant differences in the FA compositions showed the effectiveness of FA composition as a tool for distinguishing between different origins of A. japonicus. The two technologies, combined with multivariate statistical analysis, can be promising methods to discriminate A. japonicus from different geographical areas. Copyright © 2016. Published by Elsevier Ltd.

Singularity and Nonnormality in the Classification of Compositional Data

USGS Publications Warehouse

Bohling, Geoffrey C.; Davis, J.C.; Olea, R.A.; Harff, Jan

1998-01-01

Geologists may want to classify compositional data and express the classification as a map. Regionalized classification is a tool that can be used for this purpose, but it incorporates discriminant analysis, which requires the computation and inversion of a covariance matrix. Covariance matrices of compositional data always will be singular (noninvertible) because of the unit-sum constraint. Fortunately, discriminant analyses can be calculated using a pseudo-inverse of the singular covariance matrix; this is done automatically by some statistical packages such as SAS. Granulometric data from the Darss Sill region of the Baltic Sea is used to explore how the pseudo-inversion procedure influences discriminant analysis results, comparing the algorithm used by SAS to the more conventional Moore-Penrose algorithm. Logratio transforms have been recommended to overcome problems associated with analysis of compositional data, including singularity. A regionalized classification of the Darss Sill data after logratio transformation is different only slightly from one based on raw granulometric data, suggesting that closure problems do not influence severely regionalized classification of compositional data.

Traceability of 'Limone di Siracusa PGI' by a multidisciplinary analytical and chemometric approach.

PubMed

Amenta, M; Fabroni, S; Costa, C; Rapisarda, P

2016-11-15

Food traceability is increasingly relevant with respect to safety, quality and typicality issues. Lemon fruits grown in a typical lemon-growing area of southern Italy (Siracusa), have been awarded the PGI (Protected Geographical Indication) recognition as 'Limone di Siracusa'. Due to its peculiarity, consumers have an increasing interest about this product. The detection of potential fraud could be improved by using the tools linking the composition of this production to its typical features. This study used a wide range of analytical techniques, including conventional techniques and analytical approaches, such as spectral (NIR spectra), multi-elemental (Fe, Zn, Mn, Cu, Li, Sr) and isotopic ((13)C/(12)C, (18)O/(16)O) marker investigations, joined with multivariate statistical analysis, such as PLS-DA (Partial Least Squares Discriminant Analysis) and LDA (Linear Discriminant Analysis), to implement a traceability system to verify the authenticity of 'Limone di Siracusa' production. The results demonstrated a very good geographical discrimination rate. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

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

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

VARSEDIG: an algorithm for morphometric characters selection and statistical validation in morphological taxonomy.

PubMed

Guisande, Cástor; Vari, Richard P; Heine, Jürgen; García-Roselló, Emilio; González-Dacosta, Jacinto; Perez-Schofield, Baltasar J García; González-Vilas, Luis; Pelayo-Villamil, Patricia

2016-09-12

We present and discuss VARSEDIG, an algorithm which identifies the morphometric features that significantly discriminate two taxa and validates the morphological distinctness between them via a Monte-Carlo test. VARSEDIG is freely available as a function of the RWizard application PlotsR (http://www.ipez.es/RWizard) and as R package on CRAN. The variables selected by VARSEDIG with the overlap method were very similar to those selected by logistic regression and discriminant analysis, but overcomes some shortcomings of these methods. VARSEDIG is, therefore, a good alternative by comparison to current classical classification methods for identifying morphometric features that significantly discriminate a taxon and for validating its morphological distinctness from other taxa. As a demonstration of the potential of VARSEDIG for this purpose, we analyze morphological discrimination among some species of the Neotropical freshwater family Characidae.

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

NASA Astrophysics Data System (ADS)

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.

Trace element analysis of rough diamond by LA-ICP-MS: a case of source discrimination?

PubMed

Dalpé, Claude; Hudon, Pierre; Ballantyne, David J; Williams, Darrell; Marcotte, Denis

2010-11-01

Current profiling of rough diamond source is performed using different physical and/or morphological techniques that require strong knowledge and experience in the field. More recently, chemical impurities have been used to discriminate diamond source and with the advance of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) empirical profiling of rough diamonds is possible to some extent. In this study, we present a LA-ICP-MS methodology that we developed for analyzing ultra-trace element impurities in rough diamond for origin determination ("profiling"). Diamonds from two sources were analyzed by LA-ICP-MS and were statistically classified by accepted methods. For the two diamond populations analyzed in this study, binomial logistic regression produced a better overall correct classification than linear discriminant analysis. The results suggest that an anticipated matrix match reference material would improve the robustness of our methodology for forensic applications. © 2010 American Academy of Forensic Sciences.

Differentiating clinical groups using the serial color-word test (S-CWT).

PubMed

Hentschel, Uwe; Rubino, I Alex; Bijleveld, Catrien

2011-04-01

The present study attempted to differentiate 11 diagnostic groups by means of the Serial Color-Word Test (S-CWT), using multivariate discriminant analysis. Two alternative scoring systems of the S-CWT were outlined. Asample of 514 individuals who had clinical diagnoses of various types and 397 controls who had no diagnostic findings comprised the sample. The first discriminant analysis failed to differentiate the groups adequately. The groups were consequently reduced to four (schizophrenia, bipolar disorders, temporo-mandibular joint pain dysfunction syndrome, and eating disturbances), which gave better reclassification findings for a clinical application of the test. This classification gave over 55% correct assignments. The final four groups had a statistically significant discrimination on the test, which remained stable also in a bootstrap procedure. Implications for treatment indications and outcomes as well as strategies for further studies using the S-CWT are discussed.

Racialized customer service in restaurants: a quantitative assessment of the statistical discrimination explanatory framework.

PubMed

Brewster, Zachary W

2012-01-01

Despite popular claims that racism and discrimination are no longer salient issues in contemporary society, racial minorities continue to experience disparate treatment in everyday public interactions. The context of full-service restaurants is one such public setting wherein racial minority patrons, African Americans in particular, encounter racial prejudices and discriminate treatment. To further understand the causes of such discriminate treatment within the restaurant context, this article analyzes primary survey data derived from a community sample of servers (N = 200) to assess the explanatory power of one posited explanation—statistical discrimination. Taken as a whole, findings suggest that while a statistical discrimination framework toward understanding variability in servers’ discriminatory behaviors should not be disregarded, the framework’s explanatory utility is limited. Servers’ inferences about the potential profitability of waiting on customers across racial groups explain little of the overall variation in subjects’ self-reported discriminatory behaviors, thus suggesting that other factors not explored in this research are clearly operating and should be the focus of future inquires.

An Establishment-Level Test of the Statistical Discrimination Hypothesis.

ERIC Educational Resources Information Center

Tomaskovic-Devey, Donald; Skaggs, Sheryl

1999-01-01

Analysis of a sample of 306 workers shows that neither the gender nor racial composition of the workplace is associated with productivity. An alternative explanation for lower wages of women and minorities is social closure--the monopolizing of desirable positions by advantaged workers. (SK)

Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

PubMed

Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

2003-01-01

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

Multivariate fault isolation of batch processes via variable selection in partial least squares discriminant analysis.

PubMed

Yan, Zhengbing; Kuang, Te-Hui; Yao, Yuan

2017-09-01

In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Gap Shape Classification using Landscape Indices and Multivariate Statistics

PubMed Central

Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung

2016-01-01

This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks’ lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap. PMID:27901127

Gap Shape Classification using Landscape Indices and Multivariate Statistics.

PubMed

Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung

2016-11-30

This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks' lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap.

A Comparative Study of Land Cover Classification by Using Multispectral and Texture Data

PubMed Central

Qadri, Salman; Khan, Dost Muhammad; Ahmad, Farooq; Qadri, Syed Furqan; Babar, Masroor Ellahi; Shahid, Muhammad; Ul-Rehman, Muzammil; Razzaq, Abdul; Shah Muhammad, Syed; Fahad, Muhammad; Ahmad, Sarfraz; Pervez, Muhammad Tariq; Naveed, Nasir; Aslam, Naeem; Jamil, Mutiullah; Rehmani, Ejaz Ahmad; Ahmad, Nazir; Akhtar Khan, Naeem

2016-01-01

The main objective of this study is to find out the importance of machine vision approach for the classification of five types of land cover data such as bare land, desert rangeland, green pasture, fertile cultivated land, and Sutlej river land. A novel spectra-statistical framework is designed to classify the subjective land cover data types accurately. Multispectral data of these land covers were acquired by using a handheld device named multispectral radiometer in the form of five spectral bands (blue, green, red, near infrared, and shortwave infrared) while texture data were acquired with a digital camera by the transformation of acquired images into 229 texture features for each image. The most discriminant 30 features of each image were obtained by integrating the three statistical features selection techniques such as Fisher, Probability of Error plus Average Correlation, and Mutual Information (F + PA + MI). Selected texture data clustering was verified by nonlinear discriminant analysis while linear discriminant analysis approach was applied for multispectral data. For classification, the texture and multispectral data were deployed to artificial neural network (ANN: n-class). By implementing a cross validation method (80-20), we received an accuracy of 91.332% for texture data and 96.40% for multispectral data, respectively. PMID:27376088

[Men who have sex with men and human immunodeficiency virus testing in dental practice].

PubMed

Elizondo, Jesús Eduardo; Treviño, Ana Cecilia; Violant, Deborah; Rivas-Estilla, Ana María; Álvarez, Mario Moisés

To explore the attitudes of men who have sex with men (MSM) towards the implementation of rapid HIV-1/2 testing in the dental practice, and to evaluate MSM's perceptions of stigma and discrimination related to sexual orientation by dental care professionals. Cross-sectional study using a self-administered, anonymous, structured analytical questionnaire answered by 185 MSM in Mexico. The survey included sociodemographic variables, MSM's perceptions towards public and private dental providers, and dental services, as well as their perception towards rapid HIV-1/2 testing in the dental practice. In addition, the perception of stigma and discrimination associated with their sexual orientation was explored by designing a psychometric Likert-type scale. The statistical analysis included factor analysis and non-hierarchical cluster analysis. 86.5% of the respondents expressed their willingness to take a rapid HIV-1/2 screening test during their dental visit. Nevertheless, 91.9% of them considered it important that dental professionals must be well-trained before administering any rapid HIV-1/2 tests. Factor analysis revealed two factors: experiences of sexual orientation stigma and discrimination in dental settings, and feelings of concern about the attitude of the dentist and dental staff towards their sexual orientation. Based on these factors and cluster analysis, three user profiles were identified: users who have not experienced stigma and discrimination (90.3%); users who have not experienced stigma and discrimination, but feel a slight concern (8.1%), and users who have experienced some form of discrimination and feel concern (1.6%). The dental practice may represent a potential location for rapid HIV-1/2 testing contributing to early HIV infection diagnosis. Copyright © 2017 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Meta-analysis of studies assessing the efficacy of projective techniques in discriminating child sexual abuse.

PubMed

West, M M

1998-11-01

This meta-analysis of 12 studies assesses the efficacy of projective techniques to discriminate between sexually abused children and nonsexually abused children. A literature search was conducted to identify published studies that used projective instruments with sexually abused children. Those studies that reported statistics that allowed for an effect size to be calculated, were then included in the meta-analysis. There were 12 studies that fit the criteria. The projectives reviewed include The Rorschach, The Hand Test, The Thematic Apperception Test (TAT), the Kinetic Family Drawings, Human Figure Drawings, Draw Your Favorite Kind of Day, The Rosebush: A Visualization Strategy, and The House-Tree-Person. The results of this analysis gave an over-all effect size of d = .81, which is a large effect. Six studies included only a norm group of nondistressed, nonabused children with the sexual abuse group. The average effect size was d = .87, which is impressive. Six studies did include a clinical group of distressed nonsexually abused subjects and the effect size lowered to d = .76, which is a medium to large effect. This indicates that projective instruments can discriminate distressed children from nondistressed subjects, quite well. In the studies that included a clinical group of distressed children who were not sexually abused, the lower effect size indicates that the instruments were less able to discriminate the type of distress. This meta-analysis gives evidence that projective techniques have the ability to discriminate between children who have been sexually abused and those who were not abused sexually. However, further research that is designed to include clinical groups of distressed children is needed in order to determine how well the projectives can discriminate the type of distress.

Application of Near Infrared Reflectance Spectroscopy for Rapid and Non-Destructive Discrimination of Hulled Barley, Naked Barley, and Wheat Contaminated with Fusarium

PubMed Central

Lim, Jongguk; Kim, Giyoung; Mo, Changyeun; Oh, Kyoungmin; Kim, Geonseob; Ham, Hyeonheui; Kim, Seongmin; Kim, Moon S.

2018-01-01

Fusarium is a common fungal disease in grains that reduces the yield of barley and wheat. In this study, a near infrared reflectance spectroscopic technique was used with a statistical prediction model to rapidly and non-destructively discriminate grain samples contaminated with Fusarium. Reflectance spectra were acquired from hulled barley, naked barley, and wheat samples contaminated with Fusarium using near infrared reflectance (NIR) spectroscopy with a wavelength range of 1175–2170 nm. After measurement, the samples were cultured in a medium to discriminate contaminated samples. A partial least square discrimination analysis (PLS-DA) prediction model was developed using the acquired reflectance spectra and the culture results. The correct classification rate (CCR) of Fusarium for the hulled barley, naked barley, and wheat samples developed using raw spectra was 98% or higher. The accuracy of discrimination prediction improved when second and third-order derivative pretreatments were applied. The grains contaminated with Fusarium could be rapidly discriminated using spectroscopy technology and a PLS-DA discrimination model, and the potential of the non-destructive discrimination method could be verified. PMID:29301319

Statistical Characteristics of Single Sort of Grape Bulgarian Wines

NASA Astrophysics Data System (ADS)

Boyadzhiev, D.

2008-10-01

The aim of this paper is to evaluate the differences in the values of the 8 basic physicochemical indices of single sort of grape Bulgarian wines (white and red ones), obligatory for the standardization of ready production in the winery. Statistically significant differences in the values of various sorts and vintages are established and possibilities for identifying the sort and the vintage on the base of these indices by applying discriminant analysis are discussed.

Applying a statistical PTB detection procedure to complement the gold standard.

PubMed

Noor, Norliza Mohd; Yunus, Ashari; Bakar, S A R Abu; Hussin, Amran; Rijal, Omar Mohd

2011-04-01

This paper investigates a novel statistical discrimination procedure to detect PTB when the gold standard requirement is taken into consideration. Archived data were used to establish two groups of patients which are the control and test group. The control group was used to develop the statistical discrimination procedure using four vectors of wavelet coefficients as feature vectors for the detection of pulmonary tuberculosis (PTB), lung cancer (LC), and normal lung (NL). This discrimination procedure was investigated using the test group where the number of sputum positive and sputum negative cases that were correctly classified as PTB cases were noted. The proposed statistical discrimination method is able to detect PTB patients and LC with high true positive fraction. The method is also able to detect PTB patients that are sputum negative and therefore may be used as a complement to the gold standard. Copyright © 2010 Elsevier Ltd. All rights reserved.

Parametric Time-Frequency Analysis and Its Applications in Music Classification

NASA Astrophysics Data System (ADS)

Shen, Ying; Li, Xiaoli; Ma, Ngok-Wah; Krishnan, Sridhar

2010-12-01

Analysis of nonstationary signals, such as music signals, is a challenging task. The purpose of this study is to explore an efficient and powerful technique to analyze and classify music signals in higher frequency range (44.1 kHz). The pursuit methods are good tools for this purpose, but they aimed at representing the signals rather than classifying them as in Y. Paragakin et al., 2009. Among the pursuit methods, matching pursuit (MP), an adaptive true nonstationary time-frequency signal analysis tool, is applied for music classification. First, MP decomposes the sample signals into time-frequency functions or atoms. Atom parameters are then analyzed and manipulated, and discriminant features are extracted from atom parameters. Besides the parameters obtained using MP, an additional feature, central energy, is also derived. Linear discriminant analysis and the leave-one-out method are used to evaluate the classification accuracy rate for different feature sets. The study is one of the very few works that analyze atoms statistically and extract discriminant features directly from the parameters. From our experiments, it is evident that the MP algorithm with the Gabor dictionary decomposes nonstationary signals, such as music signals, into atoms in which the parameters contain strong discriminant information sufficient for accurate and efficient signal classifications.

Classification Techniques for Multivariate Data Analysis.

DTIC Science & Technology

1980-03-28

analysis among biologists, botanists, and ecologists, while some social scientists may refer "typology". Other frequently encountered terms are pattern...the determinantal equation: lB -XW 0 (42) 49 The solutions X. are the eigenvalues of the matrix W-1 B 1 as in discriminant analysis. There are t non...Statistical Package for Social Sciences (SPSS) (14) subprogram FACTOR was used for the principal components analysis. It is designed both for the factor

The NWRA Classification Infrastructure: description and extension to the Discriminant Analysis Flare Forecasting System (DAFFS)

NASA Astrophysics Data System (ADS)

Leka, K. D.; Barnes, Graham; Wagner, Eric

2018-04-01

A classification infrastructure built upon Discriminant Analysis (DA) has been developed at NorthWest Research Associates for examining the statistical differences between samples of two known populations. Originating to examine the physical differences between flare-quiet and flare-imminent solar active regions, we describe herein some details of the infrastructure including: parametrization of large datasets, schemes for handling "null" and "bad" data in multi-parameter analysis, application of non-parametric multi-dimensional DA, an extension through Bayes' theorem to probabilistic classification, and methods invoked for evaluating classifier success. The classifier infrastructure is applicable to a wide range of scientific questions in solar physics. We demonstrate its application to the question of distinguishing flare-imminent from flare-quiet solar active regions, updating results from the original publications that were based on different data and much smaller sample sizes. Finally, as a demonstration of "Research to Operations" efforts in the space-weather forecasting context, we present the Discriminant Analysis Flare Forecasting System (DAFFS), a near-real-time operationally-running solar flare forecasting tool that was developed from the research-directed infrastructure.

The discriminative capacity of CBCL/1½-5-DSM5 scales to identify disruptive and internalizing disorders in preschool children.

PubMed

de la Osa, Nuria; Granero, Roser; Trepat, Esther; Domenech, Josep Maria; Ezpeleta, Lourdes

2016-01-01

This paper studies the discriminative capacity of CBCL/1½-5 (Manual for the ASEBA Preschool-Age Forms & Profiles, University of Vermont, Research Center for Children, Youth, & Families, Burlington, 2000) DSM5 scales attention deficit and hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), anxiety and depressive problems for detecting the presence of DSM5 (DSM5 diagnostic and statistical manual of mental disorders, APA, Arlington, 2013) disorders, ADHD, ODD, Anxiety and Mood disorders, assessed through diagnostic interview, in children aged 3-5. Additionally, we compare the clinical utility of the CBCL/1½-5-DSM5 scales with respect to analogous CBCL/1½-5 syndrome scales. A large community sample of 616 preschool children was longitudinally assessed for the stated age group. Statistical analysis was based on ROC procedures and binary logistic regressions. ADHD and ODD CBCL/1½-5-DSM5 scales achieved good discriminative ability to identify ADHD and ODD interview's diagnoses, at any age. CBCL/1½-5-DSM5 Anxiety scale discriminative capacity was fair for unspecific anxiety disorders in all age groups. CBCL/1½-5-DSM5 depressive problems' scale showed the poorest discriminative capacity for mood disorders (including depressive episode with insufficient symptoms), oscillating into the poor-to-fair range. As a whole, DSM5-oriented scales generally did not provide evidence better for discriminative capacity than syndrome scales in identifying DSM5 diagnoses. CBCL/1½-5-DSM5 scales discriminate externalizing disorders better than internalizing disorders for ages 3-5. Scores on the ADHD and ODD CBCL/1½-5-DSM5 scales can be used to screen for DSM5 ADHD and ODD disorders in general populations of preschool children.

Accommodating the Spectrum of Individual Abilities. Clearinghouse Publication 81.

ERIC Educational Resources Information Center

Commission on Civil Rights, Washington, DC.

The monograph addresses legal issues involving discrimination against handicapped persons and the key legal requirement of reasonable accommodation. Four chapters in Part I examine background issues, including definitions and statistical overviews of handicaps; historical attitudes toward handicapped persons and an analysis of the extent of…

Title VII and the Male/Female Earnings Gap: An Economic Analysis.

ERIC Educational Resources Information Center

Beller, Andrea

1978-01-01

After controlling statistically for the effects of other factors that affect earnings, it was found that enforcement of sex discrimination charges under Title VII increased the relative demand for women and thus decreased the male/female earnings differential between 1967 and 1974. (Author)

Using color histograms and SPA-LDA to classify bacteria.

PubMed

de Almeida, Valber Elias; da Costa, Gean Bezerra; de Sousa Fernandes, David Douglas; Gonçalves Dias Diniz, Paulo Henrique; Brandão, Deysiane; de Medeiros, Ana Claudia Dantas; Véras, Germano

2014-09-01

In this work, a new approach is proposed to verify the differentiating characteristics of five bacteria (Escherichia coli, Enterococcus faecalis, Streptococcus salivarius, Streptococcus oralis, and Staphylococcus aureus) by using digital images obtained with a simple webcam and variable selection by the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA). In this sense, color histograms in the red-green-blue (RGB), hue-saturation-value (HSV), and grayscale channels and their combinations were used as input data, and statistically evaluated by using different multivariate classifiers (Soft Independent Modeling by Class Analogy (SIMCA), Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), Partial Least Squares Discriminant Analysis (PLS-DA) and Successive Projections Algorithm-Linear Discriminant Analysis (SPA-LDA)). The bacteria strains were cultivated in a nutritive blood agar base layer for 24 h by following the Brazilian Pharmacopoeia, maintaining the status of cell growth and the nature of nutrient solutions under the same conditions. The best result in classification was obtained by using RGB and SPA-LDA, which reached 94 and 100 % of classification accuracy in the training and test sets, respectively. This result is extremely positive from the viewpoint of routine clinical analyses, because it avoids bacterial identification based on phenotypic identification of the causative organism using Gram staining, culture, and biochemical proofs. Therefore, the proposed method presents inherent advantages, promoting a simpler, faster, and low-cost alternative for bacterial identification.

Toward improving fine needle aspiration cytology by applying Raman microspectroscopy

NASA Astrophysics Data System (ADS)

Becker-Putsche, Melanie; Bocklitz, Thomas; Clement, Joachim; Rösch, Petra; Popp, Jürgen

2013-04-01

Medical diagnosis of biopsies performed by fine needle aspiration has to be very reliable. Therefore, pathologists/cytologists need additional biochemical information on single cancer cells for an accurate diagnosis. Accordingly, we applied three different classification models for discriminating various features of six breast cancer cell lines by analyzing Raman microspectroscopic data. The statistical evaluations are implemented by linear discriminant analysis (LDA) and support vector machines (SVM). For the first model, a total of 61,580 Raman spectra from 110 single cells are discriminated at the cell-line level with an accuracy of 99.52% using an SVM. The LDA classification based on Raman data achieved an accuracy of 94.04% by discriminating cell lines by their origin (solid tumor versus pleural effusion). In the third model, Raman cell spectra are classified by their cancer subtypes. LDA results show an accuracy of 97.45% and specificities of 97.78%, 99.11%, and 98.97% for the subtypes basal-like, HER2+/ER-, and luminal, respectively. These subtypes are confirmed by gene expression patterns, which are important prognostic features in diagnosis. This work shows the applicability of Raman spectroscopy and statistical data handling in analyzing cancer-relevant biochemical information for advanced medical diagnosis on the single-cell level.

Environmental discrimination of wines using the content of lithium, potassium and rubidium.

PubMed

Del Signore, Antonella

2003-01-01

56 wine samples were analysed to determine their content of Li, K and Rb. These samples came from 28 species of vine grown on two plots of land, each of which had different pedo-climatic characteristics. The data collected were elaborated using Linear Discriminant Analysis (LDA); this statistical approach showed that it was possible to net differentiate both the soil where the different species of vines were grown and the colour of wines. The variable "species of vine nationality", instead, has not been discriminated by LDA. These results point out that it is possible to identify the place of origin of wines and that the "environment" variable prevails over the others, using the content of Li, K and Rb.

The Value of Routine Biochemical Tests in Discriminating Between Malignant and Benign Pancreatic Tumours

PubMed Central

Blind, P-J; Eriksson, S.

1991-01-01

The probability that routine hematological laboratory tests of liver and pancreatic function can discriminate between malignant and benign pancreatic tumours, incidentally detected during operation, was investigated. The records of 53 patients with a verified diagnosis of pancreatic carcinoma and 19 patients with chronic pancreatitis were reviewed with regard to preoperative total bilirubin, direct reacting bilirubin, alkaline phosphatase, glutamyltranspeptidase, aminotransferases, lactic dehydrogenase and amylase. Multivariate and discriminant analysis were performed to calculate the predictive value for cancer, using SYSTAT statistical package in a Macintosh II computer. Total and direct reacting bilirubin and glutamyltranspeptidase were significantly higher in patients with pancreatic carcinoma. However, only considerably increased levels of direct reating bilirubin were predictive of pancreatic carcinoma. PMID:1931781

Photospheric Magnetic Field Properties of Flaring versus Flare-quiet Active Regions. II. Discriminant Analysis

NASA Astrophysics Data System (ADS)

Leka, K. D.; Barnes, G.

2003-10-01

We apply statistical tests based on discriminant analysis to the wide range of photospheric magnetic parameters described in a companion paper by Leka & Barnes, with the goal of identifying those properties that are important for the production of energetic events such as solar flares. The photospheric vector magnetic field data from the University of Hawai'i Imaging Vector Magnetograph are well sampled both temporally and spatially, and we include here data covering 24 flare-event and flare-quiet epochs taken from seven active regions. The mean value and rate of change of each magnetic parameter are treated as separate variables, thus evaluating both the parameter's state and its evolution, to determine which properties are associated with flaring. Considering single variables first, Hotelling's T2-tests show small statistical differences between flare-producing and flare-quiet epochs. Even pairs of variables considered simultaneously, which do show a statistical difference for a number of properties, have high error rates, implying a large degree of overlap of the samples. To better distinguish between flare-producing and flare-quiet populations, larger numbers of variables are simultaneously considered; lower error rates result, but no unique combination of variables is clearly the best discriminator. The sample size is too small to directly compare the predictive power of large numbers of variables simultaneously. Instead, we rank all possible four-variable permutations based on Hotelling's T2-test and look for the most frequently appearing variables in the best permutations, with the interpretation that they are most likely to be associated with flaring. These variables include an increasing kurtosis of the twist parameter and a larger standard deviation of the twist parameter, but a smaller standard deviation of the distribution of the horizontal shear angle and a horizontal field that has a smaller standard deviation but a larger kurtosis. To support the ``sorting all permutations'' method of selecting the most frequently occurring variables, we show that the results of a single 10-variable discriminant analysis are consistent with the ranking. We demonstrate that individually, the variables considered here have little ability to differentiate between flaring and flare-quiet populations, but with multivariable combinations, the populations may be distinguished.

Assessing the reproducibility of discriminant function analyses

PubMed Central

Andrew, Rose L.; Albert, Arianne Y.K.; Renaut, Sebastien; Rennison, Diana J.; Bock, Dan G.

2015-01-01

Data are the foundation of empirical research, yet all too often the datasets underlying published papers are unavailable, incorrect, or poorly curated. This is a serious issue, because future researchers are then unable to validate published results or reuse data to explore new ideas and hypotheses. Even if data files are securely stored and accessible, they must also be accompanied by accurate labels and identifiers. To assess how often problems with metadata or data curation affect the reproducibility of published results, we attempted to reproduce Discriminant Function Analyses (DFAs) from the field of organismal biology. DFA is a commonly used statistical analysis that has changed little since its inception almost eight decades ago, and therefore provides an opportunity to test reproducibility among datasets of varying ages. Out of 100 papers we initially surveyed, fourteen were excluded because they did not present the common types of quantitative result from their DFA or gave insufficient details of their DFA. Of the remaining 86 datasets, there were 15 cases for which we were unable to confidently relate the dataset we received to the one used in the published analysis. The reasons ranged from incomprehensible or absent variable labels, the DFA being performed on an unspecified subset of the data, or the dataset we received being incomplete. We focused on reproducing three common summary statistics from DFAs: the percent variance explained, the percentage correctly assigned and the largest discriminant function coefficient. The reproducibility of the first two was fairly high (20 of 26, and 44 of 60 datasets, respectively), whereas our success rate with the discriminant function coefficients was lower (15 of 26 datasets). When considering all three summary statistics, we were able to completely reproduce 46 (65%) of 71 datasets. While our results show that a majority of studies are reproducible, they highlight the fact that many studies still are not the carefully curated research that the scientific community and public expects. PMID:26290793

Structural Equation Modeling to Assess Discrimination, Stress, Social Support, and Depression among the Elderly Women in South Korea.

PubMed

Lee, Hung Sa; Kim, Chunmi

2016-09-01

The purpose of this study was to find the relationship and conceptual model of discrimination, stress, support, and depression among the elderly in South Korea. This was a cross-sectional descriptive study involving 207 community-dwelling elders. Data were collected through questionnaires from May 5 to May 31, 2014 in community senior centers, and analyzed using descriptive statistics, t test, analysis of variance, Scheffé test, and structural equation modeling. There were significant effects of discrimination on stress, support on stress and stress on depression. Moreover, there were two significant indirect effects observed between discrimination and depression, and between support and depression. For each indirect effect, the mediating factor was stress. Additionally, there was no direct effect between discrimination and depression or support. This study found that social support and discrimination had indirect effects on depression through stress. More specifically, decreased stress led to a reduction of depression. Therefore, social support based on a thorough understanding of stress is very important for caring elderly who are depressive. Copyright © 2016. Published by Elsevier B.V.

Comparing geological and statistical approaches for element selection in sediment tracing research

NASA Astrophysics Data System (ADS)

Laceby, J. Patrick; McMahon, Joe; Evrard, Olivier; Olley, Jon

2015-04-01

Elevated suspended sediment loads reduce reservoir capacity and significantly increase the cost of operating water treatment infrastructure, making the management of sediment supply to reservoirs of increasingly importance. Sediment fingerprinting techniques can be used to determine the relative contributions of different sources of sediment accumulating in reservoirs. The objective of this research is to compare geological and statistical approaches to element selection for sediment fingerprinting modelling. Time-integrated samplers (n=45) were used to obtain source samples from four major subcatchments flowing into the Baroon Pocket Dam in South East Queensland, Australia. The geochemistry of potential sources were compared to the geochemistry of sediment cores (n=12) sampled in the reservoir. The geochemical approach selected elements for modelling that provided expected, observed and statistical discrimination between sediment sources. Two statistical approaches selected elements for modelling with the Kruskal-Wallis H-test and Discriminatory Function Analysis (DFA). In particular, two different significance levels (0.05 & 0.35) for the DFA were included to investigate the importance of element selection on modelling results. A distribution model determined the relative contributions of different sources to sediment sampled in the Baroon Pocket Dam. Elemental discrimination was expected between one subcatchment (Obi Obi Creek) and the remaining subcatchments (Lexys, Falls and Bridge Creek). Six major elements were expected to provide discrimination. Of these six, only Fe2O3 and SiO2 provided expected, observed and statistical discrimination. Modelling results with this geological approach indicated 36% (+/- 9%) of sediment sampled in the reservoir cores were from mafic-derived sources and 64% (+/- 9%) were from felsic-derived sources. The geological and the first statistical approach (DFA0.05) differed by only 1% (σ 5%) for 5 out of 6 model groupings with only the Lexys Creek modelling results differing significantly (35%). The statistical model with expanded elemental selection (DFA0.35) differed from the geological model by an average of 30% for all 6 models. Elemental selection for sediment fingerprinting therefore has the potential to impact modeling results. Accordingly is important to incorporate both robust geological and statistical approaches when selecting elements for sediment fingerprinting. For the Baroon Pocket Dam, management should focus on reducing the supply of sediments derived from felsic sources in each of the subcatchments.

Don't ask for fair treatment? A gender analysis of ethnic discrimination, response to discrimination, and self-rated health among marriage migrants in South Korea.

PubMed

Kim, Yugyun; Son, Inseo; Wie, Dainn; Muntaner, Carles; Kim, Hyunwoo; Kim, Seung-Sup

2016-07-19

Ethnic discrimination is increasingly common nowadays in South Korea with the influx of migrants. Despite the growing body of evidences suggests that ethnic discrimination negatively impacts health, only few researches have been conducted on the association between ethnic discrimination and health outcomes among marriage migrants in Korea. This study sought to examine how ethnic discrimination and response to the discrimination are related to self-rated health and whether the association differs by victim's gender. We conducted two-step analysis using cross-sectional dataset from the 'National Survey of Multicultural Families 2012'. First, we examined the association between perceived ethnic discrimination and self-rated health among 14,406 marriage migrants in Korea. Second, among the marriage migrants who experienced ethnic discrimination (n=5,880), we examined how response to discrimination (i.e., whether or not asking for fair treatment) is related to poor self-rated health. All analyses were conducted after being stratified by the migrant's gender. This research found the significant association between ethnic discrimination and poor self-rated health among female marriage migrants (OR: 1.53, 95 % CI: 1.32, 1.76), but not among male marriage migrants (OR: 1.16, 95 % CI: 0.81, 1.66). In the restricted analysis with marriage migrants who experienced ethnic discrimination, compared to the group who did not ask for fair treatment, female marriage migrants who asked for fair treatment were more likely to report poor self-rated health (OR: 1.21, 95 % CI: 0.98, 1.50); however, male marriage migrants who asked for fair treatment were less likely to report poor self-rated health (OR: 0.65, 95 % CI: 0.36, 1.04) although both were not statistically significant. This is the first study to investigate gender difference in the association between response to ethnic discrimination and self-rated health in South Korea. We discussed that gender may play an important role in the association between response to discrimination and self-rated health among marriage migrants in Korea. In order to prevent discrimination which could endanger the health of ethnic minorities including marriage migrants, relevant policies are needed.

Solution identification and quantitative analysis of fiber-capacitive drop analyzer based on multivariate statistical methods

NASA Astrophysics Data System (ADS)

Chen, Zhe; Qiu, Zurong; Huo, Xinming; Fan, Yuming; Li, Xinghua

2017-03-01

A fiber-capacitive drop analyzer is an instrument which monitors a growing droplet to produce a capacitive opto-tensiotrace (COT). Each COT is an integration of fiber light intensity signals and capacitance signals and can reflect the unique physicochemical property of a liquid. In this study, we propose a solution analytical and concentration quantitative method based on multivariate statistical methods. Eight characteristic values are extracted from each COT. A series of COT characteristic values of training solutions at different concentrations compose a data library of this kind of solution. A two-stage linear discriminant analysis is applied to analyze different solution libraries and establish discriminant functions. Test solutions can be discriminated by these functions. After determining the variety of test solutions, Spearman correlation test and principal components analysis are used to filter and reduce dimensions of eight characteristic values, producing a new representative parameter. A cubic spline interpolation function is built between the parameters and concentrations, based on which we can calculate the concentration of the test solution. Methanol, ethanol, n-propanol, and saline solutions are taken as experimental subjects in this paper. For each solution, nine or ten different concentrations are chosen to be the standard library, and the other two concentrations compose the test group. By using the methods mentioned above, all eight test solutions are correctly identified and the average relative error of quantitative analysis is 1.11%. The method proposed is feasible which enlarges the applicable scope of recognizing liquids based on the COT and improves the concentration quantitative precision, as well.

An evaluation of multiband photography for rock discrimination. [sedimentary rocks of Front Range, Colorado

NASA Technical Reports Server (NTRS)

Lee, K. (Principal Investigator); Raines, G. L.

1974-01-01

The author has identified the following significant results. With the advent of ERTS and Skylab satellites, multiband imagery and photography have become readily available to geologists. The ability of multiband photography to discriminate sedimentary rocks was examined. More than 8600 in situ measurements of band reflectance of the sedimentary rocks of the Front Range, Colorado, were acquired. Statistical analysis of these measurements showed that: (1) measurements from one site can be used at another site 100 miles away; (2) there is basically only one spectral reflectance curve for these rocks, with constant amplitude differences between the curves; and (3) the natural variation is so large that at least 150 measurements per formation are required to select best filters. These conclusions are supported by subjective tests with aerial multiband photography. The designed multiband photography concept for rock discrimination is not a practical method of improving sedimentary rock discrimination capabilities.

Diversity and association of phenotypic and metabolomic traits in the close model grasses Brachypodium distachyon, B. stacei and B. hybridum

PubMed Central

López-Álvarez, Diana; Zubair, Hassan; Beckmann, Manfred; Draper, John

2017-01-01

Abstract Background and Aims Morphological traits in combination with metabolite fingerprinting were used to investigate inter- and intraspecies diversity within the model annual grasses Brachypodium distachyon, Brachypodium stacei and Brachypodium hybridum. Methods Phenotypic variation of 15 morphological characters and 2219 nominal mass (m/z) signals generated using flow infusion electrospray ionization–mass spectrometry (FIE–MS) were evaluated in individuals from a total of 174 wild populations and six inbred lines, and 12 lines, of the three species, respectively. Basic statistics and multivariate principal component analysis and discriminant analysis were used to differentiate inter- and intraspecific variability of the two types of variable, and their association was assayed with the rcorr function. Key Results Basic statistics and analysis of variance detected eight phenotypic characters [(stomata) leaf guard cell length, pollen grain length, (plant) height, second leaf width, inflorescence length, number of spikelets per inflorescence, lemma length, awn length] and 434 tentatively annotated metabolite signals that significantly discriminated the three species. Three phenotypic traits (pollen grain length, spikelet length, number of flowers per inflorescence) might be genetically fixed. The three species showed different metabolomic profiles. Discriminant analysis significantly discriminated the three taxa with both morphometric and metabolome traits and the intraspecific phenotypic diversity within B. distachyon and B. stacei. The populations of B. hybridum were considerably less differentiated. Conclusions Highly explanatory metabolite signals together with morphological characters revealed concordant patterns of differentiation of the three taxa. Intraspecific phenotypic diversity was observed between northern and southern Iberian populations of B. distachyon and between eastern Mediterranean/south-western Asian and western Mediterranean populations of B. stacei. Significant association was found for pollen grain length and lemma length and ten and six metabolomic signals, respectively. These results would guide the selection of new germplasm lines of the three model grasses in ongoing genome-wide association studies. PMID:28040672

Collected Notes on the Workshop for Pattern Discovery in Large Databases

NASA Technical Reports Server (NTRS)

Buntine, Wray (Editor); Delalto, Martha (Editor)

1991-01-01

These collected notes are a record of material presented at the Workshop. The core data analysis is addressed that have traditionally required statistical or pattern recognition techniques. Some of the core tasks include classification, discrimination, clustering, supervised and unsupervised learning, discovery and diagnosis, i.e., general pattern discovery.

Isotope ratio mass spectrometry in combination with chemometrics for characterization of geographical origin and agronomic practices of table grape.

PubMed

Longobardi, Francesco; Casiello, Grazia; Centonze, Valentina; Catucci, Lucia; Agostiano, Angela

2017-08-01

Although table grape is one of the most cultivated and consumed fruits worldwide, no study has been reported on its geographical origin or agronomic practice based on stable isotope ratios. This study aimed to evaluate the usefulness of isotopic ratios (i.e. 2 H/ 1 H, 13 C/ 12 C, 15 N/ 14 N and 18 O/ 16 O) as possible markers to discriminate the agronomic practice (conventional versus organic farming) and provenance of table grape. In order to quantitatively evaluate which of the isotopic variables were more discriminating, a t test was carried out, in light of which only δ 13 C and δ 18 O provided statistically significant differences (P ≤ 0.05) for the discrimination of geographical origin and farming method. Principal component analysis (PCA) showed no good separation of samples differing in geographical area and agronomic practice; thus, for classification purposes, supervised approaches were carried out. In particular, general discriminant analysis (GDA) was used, resulting in prediction abilities of 75.0 and 92.2% for the discrimination of farming method and origin respectively. The present findings suggest that stable isotopes (i.e. δ 18 O, δ 2 H and δ 13 C) combined with chemometrics can be successfully applied to discriminate the provenance of table grape. However, the use of bulk nitrogen isotopes was not effective for farming method discrimination. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Characterization and discrimination of raw and vinegar-baked Bupleuri radix based on UHPLC-Q-TOF-MS coupled with multivariate statistical analysis.

PubMed

Lei, Tianli; Chen, Shifeng; Wang, Kai; Zhang, Dandan; Dong, Lin; Lv, Chongning; Wang, Jing; Lu, Jincai

2018-02-01

Bupleuri Radix is a commonly used herb in clinic, and raw and vinegar-baked Bupleuri Radix are both documented in the Pharmacopoeia of People's Republic of China. According to the theories of traditional Chinese medicine, Bupleuri Radix possesses different therapeutic effects before and after processing. However, the chemical mechanism of this processing is still unknown. In this study, ultra-high-performance liquid chromatography with quadruple time-of-flight mass spectrometry coupled with multivariate statistical analysis including principal component analysis and orthogonal partial least square-discriminant analysis was developed to holistically compare the difference between raw and vinegar-baked Bupleuri Radix for the first time. As a result, 50 peaks in raw and processed Bupleuri Radix were detected, respectively, and a total of 49 peak chemical compounds were identified. Saikosaponin a, saikosaponin d, saikosaponin b 3 , saikosaponin e, saikosaponin c, saikosaponin b 2 , saikosaponin b 1 , 4''-O-acetyl-saikosaponin d, hyperoside and 3',4'-dimethoxy quercetin were explored as potential markers of raw and vinegar-baked Bupleuri Radix. This study has been successfully applied for global analysis of raw and vinegar-processed samples. Furthermore, the underlying hepatoprotective mechanism of Bupleuri Radix was predicted, which was related to the changes of chemical profiling. Copyright © 2017 John Wiley & Sons, Ltd.

Development and Validation of a Racial Discrimination Measure for Cambodian American Adolescents

PubMed Central

Sangalang, Cindy C.; Chen, Angela C. C.; Kulis, Stephen S.; Yabiku, Scott T.

2015-01-01

To date, the majority of studies examining experiences of racial discrimination among youth use measures initially developed for African American and Latino adults or college students. Few studies have attended to the ways in which discrimination experiences may be unique for Asian American youth, particularly subgroups such as Southeast Asians. The purpose of this study was twofold: (a) to describe the development of a racial discrimination measure using community-based participatory research with Cambodian American adolescents and (b) to psychometrically test the measure with respect to validity and reliability. This research used mixed-methods and comprised 3 phases. Phase 1 consisted of qualitative focus group research to assess community-identified needs. Phase 2 included quantitative survey development with community members and resulted in an 18-item measure assessing the frequency of ethnicity-based discrimination. Phase 3 involved psychometric testing of the measure’s validity and reliability (n = 423). Exploratory factor analysis procedures yielded a 3-factor structure describing peer, school, and police discrimination from all items, capturing 96% of the combined variance. Using confirmatory factor analysis, the data demonstrated good fit with the 3-factor structure (CFI = .98; RMSEA = .054), with factor loadings ranging from .59 to .96 and all estimates statistically significant at the p < .05 level. Correlational analyses of racial discrimination subfactors and depression supported concurrent validity. In sum, this measure can be used to examine the degree and sources of racial discrimination reported by Cambodian American adolescents and potentially other adolescents of Southeast Asian descent living in diverse urban communities. PMID:26388972

Observational difference between gamma and X-ray properties of optically dark and bright GRBs

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

Balazs, L. G.; Horvath, I.; Bagoly, Zs.

2008-05-22

Using the discriminant analysis of the multivariate statistical analysis we compared the distribution of the physical quantities of the optically dark and bright GRBs, detected by the BAT and XRT on board of the Swift Satellite. We found that the GRBs having detected optical transients (OT) have systematically higher peak fluxes and lower HI column densities than those without OT.

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

NASA Astrophysics Data System (ADS)

Belianinov, Alex; Ganesh, Panchapakesan; Lin, Wenzhi; Sales, Brian C.; Sefat, Athena S.; Jesse, Stephen; Pan, Minghu; Kalinin, Sergei V.

2014-12-01

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe0.55Se0.45 (Tc = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe1-xSex structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.

A new strategy for statistical analysis-based fingerprint establishment: Application to quality assessment of Semen sojae praeparatum.

PubMed

Guo, Hui; Zhang, Zhen; Yao, Yuan; Liu, Jialin; Chang, Ruirui; Liu, Zhao; Hao, Hongyuan; Huang, Taohong; Wen, Jun; Zhou, Tingting

2018-08-30

Semen sojae praeparatum with homology of medicine and food is a famous traditional Chinese medicine. A simple and effective quality fingerprint analysis, coupled with chemometrics methods, was developed for quality assessment of Semen sojae praeparatum. First, similarity analysis (SA) and hierarchical clusting analysis (HCA) were applied to select the qualitative markers, which obviously influence the quality of Semen sojae praeparatum. 21 chemicals were selected and characterized by high resolution ion trap/time-of-flight mass spectrometry (LC-IT-TOF-MS). Subsequently, principal components analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted to select the quantitative markers of Semen sojae praeparatum samples from different origins. Moreover, 11 compounds with statistical significance were determined quantitatively, which provided an accurate and informative data for quality evaluation. This study proposes a new strategy for "statistic analysis-based fingerprint establishment", which would be a valuable reference for further study. Copyright © 2018 Elsevier Ltd. All rights reserved.

Application of cluster and discriminant analyses to diagnose lithological heterogeneity of the parent material according to its particle-size distribution

NASA Astrophysics Data System (ADS)

Giniyatullin, K. G.; Valeeva, A. A.; Smirnova, E. V.

2017-08-01

Particle-size distribution in soddy-podzolic and light gray forest soils of the Botanical Garden of Kazan Federal University has been studied. The cluster analysis of data on the samples from genetic soil horizons attests to the lithological heterogeneity of the profiles of all the studied soils. It is probable that they are developed from the two-layered sediments with the upper colluvial layer underlain by the alluvial layer. According to the discriminant analysis, the major contribution to the discrimination of colluvial and alluvial layers is that of the fraction >0.25 mm. The results of canonical analysis show that there is only one significant discriminant function that separates alluvial and colluvial sediments on the investigated territory. The discriminant function correlates with the contents of fractions 0.05-0.01, 0.25-0.05, and >0.25 mm. Classification functions making it possible to distinguish between alluvial and colluvial sediments have been calculated. Statistical assessment of particle-size distribution data obtained for the plow horizons on ten plowed fields within the garden indicates that this horizon is formed from colluvial sediments. We conclude that the contents of separate fractions and their ratios cannot be used as a universal criterion of the lithological heterogeneity. However, adequate combination of the cluster and discriminant analyses makes it possible to give a comprehensive assessment of the lithology of soil samples from data on the contents of sand and silt fractions, which considerably increases the information value and reliability of the results.

Evaluation of facial expression in acute pain in cats.

PubMed

Holden, E; Calvo, G; Collins, M; Bell, A; Reid, J; Scott, E M; Nolan, A M

2014-12-01

To describe the development of a facial expression tool differentiating pain-free cats from those in acute pain. Observers shown facial images from painful and pain-free cats were asked to identify if they were in pain or not. From facial images, anatomical landmarks were identified and distances between these were mapped. Selected distances underwent statistical analysis to identify features discriminating pain-free and painful cats. Additionally, thumbnail photographs were reviewed by two experts to identify discriminating facial features between the groups. Observers (n = 68) had difficulty in identifying pain-free from painful cats, with only 13% of observers being able to discriminate more than 80% of painful cats. Analysis of 78 facial landmarks and 80 distances identified six significant factors differentiating pain-free and painful faces including ear position and areas around the mouth/muzzle. Standardised mouth and ear distances when combined showed excellent discrimination properties, correctly differentiating pain-free and painful cats in 98% of cases. Expert review supported these findings and a cartoon-type picture scale was developed from thumbnail images. Initial investigation into facial features of painful and pain-free cats suggests potentially good discrimination properties of facial images. Further testing is required for development of a clinical tool. © 2014 British Small Animal Veterinary Association.

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

PubMed Central

2011-01-01

Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing. PMID:21849043

Origin discrimination of defatted pork via trace elements profiling, stable isotope ratios analysis, and multivariate statistical techniques.

PubMed

Park, Yu Min; Lee, Cheong Mi; Hong, Joon Ho; Jamila, Nargis; Khan, Naeem; Jung, Jong-Hyun; Jung, Young-Chul; Kim, Kyong Su

2018-09-01

This study verified the origin of 346 defatted Korean and non-Korean pork samples via trace elements profiling, and C and N stable isotope ratios analysis. The analyzed elements were 6 Li, 7 Li, 10 B, 11 B, 51 V , 50 Cr, 52 Cr, 53 Cr, 55 Mn, 58 Ni, 60 Ni, 59 Co, 63 Cu, 65 Cu, 64 Zn, 66 Zn, 69 Ga, 71 Ga, 75 As, 82 Se, 84 Sr, 86 Sr, 87 Sr, 88 Sr, 85 Rb, 94 Mo, 95 Mo, 97 Mo, 107 Ag, 109 Ag, 110 Cd, 111 Cd, 113 Cd, 112 Cd, 114 Cd, 116 Cd, 133 Cs, 206 Pb, 207 Pb, and 208 Pb. Content (mg/kg) of 51 V (0.012), 50 Cr (0.882), 75 As (0.017), 85 Rb (57.7), and 87 Sr (46.3) were high in Korean pork samples whereas 6 Li, 7 Li, 59 Co, 55 Mn, 58 Ni, 84 Sr, 86 Sr, 88 Sr, 111 Cd, and 133 Cs were found higher in non-Korean samples. The results of discriminant analysis showed that the trace elements content and stable isotope ratios were significant for the discrimination of geographical origins with a perfect discrimination rate of 100%. Copyright © 2018 Elsevier Ltd. All rights reserved.

[Nondestructive discrimination of strawberry varieties by NIR and BP-ANN].

PubMed

Niu, Xiao-ying; Shao, Li-min; Zhao, Zhi-lei; Zhang, Xiao-yu

2012-08-01

Strawberry variety is a main factor that can influence strawberry fruit quality. The use of near-infrared reflectance spectroscopy was explored discriminate among samples of strawberry of different varieties. And the significance of difference among different varieties was analyzed by comparison of the chemical composition of the different varieties samples. The performance of models established using back propagation-artificial neural networks (BP-ANN), least squares-support vector machine and discriminant analysis were evaluated on spectra range of 4545-9090 cm(-1). The optimal model was obtained by BP-ANN with a topology of 12-18-3, which correctly classified 96.68% of calibration set and 97.14% of prediction set. And the 94.95%, 97% and 98.29% classifications were given respectively for "Tianbao" (n=99), "Fengxiang" (n=100) and "Mingxing" (n=117). One-way analysis of variance was made for comparison of the mean values for soluble solids content (SSC), titratable acid (TA), pH value and SSC-TA ratio, and the statistically significant differences were found. Principal component analysis was performed on the four chemical compositions, and obvious clustering tendencies for different varieties were found. These results showed that NIR combined with BP-ANN can discriminate strawberry of different varieties effectively, and the difference in chemical compositions of different varieties strawberry might be a chemical validation for NIR results.

PROM and Labour Effects on Urinary Metabolome: A Pilot Study

PubMed Central

Meloni, Alessandra; Palmas, Francesco; Mereu, Rossella; Deiana, Sara Francesca; Fais, Maria Francesca; Mussap, Michele; Ragusa, Antonio; Pintus, Roberta; Fanos, Vassilios; Melis, Gian Benedetto

2018-01-01

Since pathologies and complications occurring during pregnancy and/or during labour may cause adverse outcomes for both newborns and mothers, there is a growing interest in metabolomic applications on pregnancy investigation. In fact, metabolomics has proved to be an efficient strategy for the description of several perinatal conditions. In particular, this study focuses on premature rupture of membranes (PROM) in pregnancy at term. For this project, urine samples were collected at three different clinical conditions: out of labour before PROM occurrence (Ph1), out of labour with PROM (Ph2), and during labour with PROM (Ph3). GC-MS analysis, followed by univariate and multivariate statistical analysis, was able to discriminate among the different classes, highlighting the metabolites most involved in the discrimination. PMID:29511388

Optical Fourier diffractometry applied to degraded bone structure recognition

NASA Astrophysics Data System (ADS)

Galas, Jacek; Godwod, Krzysztof; Szawdyn, Jacek; Sawicki, Andrzej

1993-09-01

Image processing and recognition methods are useful in many fields. This paper presents the hybrid optical and digital method applied to recognition of pathological changes in bones involved by metabolic bone diseases. The trabecular bone structure, registered by x ray on the photographic film, is analyzed in the new type of computer controlled diffractometer. The set of image parameters, extracted from diffractogram, is evaluated by statistical analysis. The synthetic image descriptors in discriminant space, constructed on the base of 3 training groups of images (control, osteoporosis, and osteomalacia groups) by discriminant analysis, allow us to recognize bone samples with degraded bone structure and to recognize the disease. About 89% of the images were classified correctly. This method after optimization process will be verified in medical investigations.

Invariant approach to the character classification

NASA Astrophysics Data System (ADS)

Šariri, Kristina; Demoli, Nazif

2008-04-01

Image moments analysis is a very useful tool which allows image description invariant to translation and rotation, scale change and some types of image distortions. The aim of this work was development of simple method for fast and reliable classification of characters by using Hu's and affine moment invariants. Measure of Eucleidean distance was used as a discrimination feature with statistical parameters estimated. The method was tested in classification of Times New Roman font letters as well as sets of the handwritten characters. It is shown that using all Hu's and three affine invariants as discrimination set improves recognition rate by 30%.

Optical diagnosis of cervical cancer by higher order spectra and boosting

NASA Astrophysics Data System (ADS)

Pratiher, Sawon; Mukhopadhyay, Sabyasachi; Barman, Ritwik; Pratiher, Souvik; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2017-03-01

In this contribution, we report the application of higher order statistical moments using decision tree and ensemble based learning methodology for the development of diagnostic algorithms for optical diagnosis of cancer. The classification results were compared to those obtained with an independent feature extractors like linear discriminant analysis (LDA). The performance and efficacy of these methodology using higher order statistics as a classifier using boosting has higher specificity and sensitivity while being much faster as compared to other time-frequency domain based methods.

Statistical Method Based on Confidence and Prediction Regions for Analysis of Volatile Organic Compounds in Human Breath Gas

NASA Astrophysics Data System (ADS)

Wimmer, G.

2008-01-01

In this paper we introduce two confidence and two prediction regions for statistical characterization of concentration measurements of product ions in order to discriminate various groups of persons for prospective better detection of primary lung cancer. Two MATLAB algorithms have been created for more adequate description of concentration measurements of volatile organic compounds in human breath gas for potential detection of primary lung cancer and for evaluation of the appropriate confidence and prediction regions.

Lung Cancer Risk Prediction Model Incorporating Lung Function: Development and Validation in the UK Biobank Prospective Cohort Study.

PubMed

Muller, David C; Johansson, Mattias; Brennan, Paul

2017-03-10

Purpose Several lung cancer risk prediction models have been developed, but none to date have assessed the predictive ability of lung function in a population-based cohort. We sought to develop and internally validate a model incorporating lung function using data from the UK Biobank prospective cohort study. Methods This analysis included 502,321 participants without a previous diagnosis of lung cancer, predominantly between 40 and 70 years of age. We used flexible parametric survival models to estimate the 2-year probability of lung cancer, accounting for the competing risk of death. Models included predictors previously shown to be associated with lung cancer risk, including sex, variables related to smoking history and nicotine addiction, medical history, family history of lung cancer, and lung function (forced expiratory volume in 1 second [FEV1]). Results During accumulated follow-up of 1,469,518 person-years, there were 738 lung cancer diagnoses. A model incorporating all predictors had excellent discrimination (concordance (c)-statistic [95% CI] = 0.85 [0.82 to 0.87]). Internal validation suggested that the model will discriminate well when applied to new data (optimism-corrected c-statistic = 0.84). The full model, including FEV1, also had modestly superior discriminatory power than one that was designed solely on the basis of questionnaire variables (c-statistic = 0.84 [0.82 to 0.86]; optimism-corrected c-statistic = 0.83; p FEV1 = 3.4 × 10 -13 ). The full model had better discrimination than standard lung cancer screening eligibility criteria (c-statistic = 0.66 [0.64 to 0.69]). Conclusion A risk prediction model that includes lung function has strong predictive ability, which could improve eligibility criteria for lung cancer screening programs.

Non-targeted 1H NMR fingerprinting and multivariate statistical analyses for the characterisation of the geographical origin of Italian sweet cherries.

PubMed

Longobardi, F; Ventrella, A; Bianco, A; Catucci, L; Cafagna, I; Gallo, V; Mastrorilli, P; Agostiano, A

2013-12-01

In this study, non-targeted (1)H NMR fingerprinting was used in combination with multivariate statistical techniques for the classification of Italian sweet cherries based on their different geographical origins (Emilia Romagna and Puglia). As classification techniques, Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Linear Discriminant Analysis (LDA) were carried out and the results were compared. For LDA, before performing a refined selection of the number/combination of variables, two different strategies for a preliminary reduction of the variable number were tested. The best average recognition and CV prediction abilities (both 100.0%) were obtained for all the LDA models, although PLS-DA also showed remarkable performances (94.6%). All the statistical models were validated by observing the prediction abilities with respect to an external set of cherry samples. The best result (94.9%) was obtained with LDA by performing a best subset selection procedure on a set of 30 principal components previously selected by a stepwise decorrelation. The metabolites that mostly contributed to the classification performances of such LDA model, were found to be malate, glucose, fructose, glutamine and succinate. Copyright © 2013 Elsevier Ltd. All rights reserved.

Study of Aided Diagnosis of Hepatic Carcinoma Based on Artificial Neural Network Combined with Tumor Marker Group

NASA Astrophysics Data System (ADS)

Tan, Shanjuan; Feng, Feifei; Wu, Yongjun; Wu, Yiming

To develop a computer-aided diagnostic scheme by using an artificial neural network (ANN) combined with tumor markers for diagnosis of hepatic carcinoma (HCC) as a clinical assistant method. 140 serum samples (50 malignant, 40 benign and 50 normal) were analyzed for α-fetoprotein (AFP), carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), sialic acid (SA) and calcium (Ca). The five tumor marker values were then used as ANN inputs data. The result of ANN was compared with that of discriminant analysis by receiver operating characteristic (ROC) curve (AUC) analysis. The diagnostic accuracy of ANN and discriminant analysis among all samples of the test group was 95.5% and 79.3%, respectively. Analysis of multiple tumor markers based on ANN may be a better choice than the traditional statistical methods for differentiating HCC from benign or normal.

Eye-gaze control of the computer interface: Discrimination of zoom intent

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

Goldberg, J.H.; Schryver, J.C.

1993-10-01

An analysis methodology and associated experiment were developed to assess whether definable and repeatable signatures of eye-gaze characteristics are evident, preceding a decision to zoom-in, zoom-out, or not to zoom at a computer interface. This user intent discrimination procedure can have broad application in disability aids and telerobotic control. Eye-gaze was collected from 10 subjects in a controlled experiment, requiring zoom decisions. The eye-gaze data were clustered, then fed into a multiple discriminant analysis (MDA) for optimal definition of heuristics separating the zoom-in, zoom-out, and no-zoom conditions. Confusion matrix analyses showed that a number of variable combinations classified at amore » statistically significant level, but practical significance was more difficult to establish. Composite contour plots demonstrated the regions in parameter space consistently assigned by the MDA to unique zoom conditions. Peak classification occurred at about 1200--1600 msec. Improvements in the methodology to achieve practical real-time zoom control are considered.« less

Control of oenological products: discrimination between different botanical sources of L-tartaric acid by isotope ratio mass spectrometry.

PubMed

Moreno Rojas, Jose Manuel; Cosofret, Sorin; Reniero, Fabiano; Guillou, Claude; Serra, Francesca

2007-01-01

Following previous studies on counterfeit of wines with synthetic ingredients, the possibility of frauds by natural external L-tartaric acid has also been investigated. The aim of this research was to map the stable isotope ratios of L-tartaric acid coming from botanical species containing large amounts of this compound: grape and tamarind. Samples of L-tartaric acid were extracted from the pulp of tamarind fruits originating from several countries and from grape must. delta(13)C and delta(18)O were measured for all samples. Additional delta(2)H measurements were performed as a complementary analysis to help discrimination of the botanical origin. Different isotopic patterns were observed for the different botanical origins. The multivariate statistical analysis of the data shows clear discrimination among the different botanical and synthetic sources. This approach could be a complementary tool for the control of L-tartaric acid used in oenology. Copyright (c) 2007 John Wiley & Sons, Ltd.

Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics

PubMed Central

Belo, David; Gamboa, Hugo

2017-01-01

The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components. PMID:28831239

Reliability of a Measure of Institutional Discrimination against Minorities

DTIC Science & Technology

1979-12-01

samples are presented. The first is based upon classical statistical theory and the second derives from a series of computer-generated Monte Carlo...Institutional racism and sexism . Englewood Cliffs, N. J.: Prentice-Hall, Inc., 1978. Hays, W. L. and Winkler, R. L. Statistics : probability, inference... statistical measure of the e of institutional discrimination are discussed. Two methods of dealing with the problem of reliability of the measure in small

Targeted versus statistical approaches to selecting parameters for modelling sediment provenance

NASA Astrophysics Data System (ADS)

Laceby, J. Patrick

2017-04-01

One effective field-based approach to modelling sediment provenance is the source fingerprinting technique. Arguably, one of the most important steps for this approach is selecting the appropriate suite of parameters or fingerprints used to model source contributions. Accordingly, approaches to selecting parameters for sediment source fingerprinting will be reviewed. Thereafter, opportunities and limitations of these approaches and some future research directions will be presented. For properties to be effective tracers of sediment, they must discriminate between sources whilst behaving conservatively. Conservative behavior is characterized by constancy in sediment properties, where the properties of sediment sources remain constant, or at the very least, any variation in these properties should occur in a predictable and measurable way. Therefore, properties selected for sediment source fingerprinting should remain constant through sediment detachment, transportation and deposition processes, or vary in a predictable and measurable way. One approach to select conservative properties for sediment source fingerprinting is to identify targeted tracers, such as caesium-137, that provide specific source information (e.g. surface versus subsurface origins). A second approach is to use statistical tests to select an optimal suite of conservative properties capable of modelling sediment provenance. In general, statistical approaches use a combination of a discrimination (e.g. Kruskal Wallis H-test, Mann-Whitney U-test) and parameter selection statistics (e.g. Discriminant Function Analysis or Principle Component Analysis). The challenge is that modelling sediment provenance is often not straightforward and there is increasing debate in the literature surrounding the most appropriate approach to selecting elements for modelling. Moving forward, it would be beneficial if researchers test their results with multiple modelling approaches, artificial mixtures, and multiple lines of evidence to provide secondary support to their initial modelling results. Indeed, element selection can greatly impact modelling results and having multiple lines of evidence will help provide confidence when modelling sediment provenance.

Fine-Tuning Dropout Prediction through Discriminant Analysis: The Ethnic Factor.

ERIC Educational Resources Information Center

Wilkinson, L. David; Frazer, Linda H.

In the 1988-89 school year, the Austin (Texas) Independent School District's Office of Research and Evaluation undertook a new dropout research project. Part of this initiative, termed Project GRAD, attempted to develop a statistical equation by which one could predict which students were likely to drop out. If reliable predictive information…

Spatial prediction of landslide hazard using discriminant analysis and GIS

Treesearch

Peter V. Gorsevski; Paul Gessler; Randy B. Foltz

2000-01-01

Environmental attributes relevant for spatial prediction of landslides triggered by rain and snowmelt events were derived from digital elevation model (DEM). Those data in conjunction with statistics and geographic information system (GIS) provided a detailed basis for spatial prediction of landslide hazard. The spatial prediction of landslide hazard in this paper is...

Economics: A Discriminant Analysis of Students' Perceptions of Web-Based Learning.

ERIC Educational Resources Information Center

Usip, Ebenge E.; Bee, Richard H.

1998-01-01

Users and nonusers of Web-based instruction (WBI) in an undergraduate statistics classes at Youngstown State University were surveyed. Users concluded that distance learning via the Web was a good method of obtaining general information and useful tool in improving their academic performance. Nonusers thought the university should provide…

Assessing the Effectiveness of Statistical Classification Techniques in Predicting Future Employment of Participants in the Temporary Assistance for Needy Families Program

ERIC Educational Resources Information Center

Montoya, Isaac D.

2008-01-01

Three classification techniques (Chi-square Automatic Interaction Detection [CHAID], Classification and Regression Tree [CART], and discriminant analysis) were tested to determine their accuracy in predicting Temporary Assistance for Needy Families program recipients' future employment. Technique evaluation was based on proportion of correctly…

A discrimlnant function approach to ecological site classification in northern New England

Treesearch

James M. Fincher; Marie-Louise Smith

1994-01-01

Describes one approach to ecologically based classification of upland forest community types of the White and Green Mountain physiographic regions. The classification approach is based on an intensive statistical analysis of the relationship between the communities and soil-site factors. Discriminant functions useful in distinguishing between types based on soil-site...

Vine Water Deficit Impacts Aging Bouquet in Fine Red Bordeaux Wine.

PubMed

Picard, Magali; van Leeuwen, Cornelis; Guyon, François; Gaillard, Laetitia; de Revel, Gilles; Marchand, Stéphanie

2017-01-01

The aim of this study was to investigate the influence of vine water status on bouquet typicality, revealed after aging, and the perception of three aromatic notes (mint, truffle, and undergrowth) in bottled fine red Bordeaux wines. To address the issue of the role of vine water deficit in the overall quality of fine aged wines, a large set of wines from four Bordeaux appellations were subjected to sensory analysis. As vine water status can be characterized by carbon isotope discrimination (δ 13 C), this ratio was quantified for each wine studied. Statistical analyses combining δ 13 C and sensory data highlighted that δ 13 C-values discriminated effectively between the most- and least-typical wines. In addition, Principal Component Analysis (PCA) revealed correlations between δ 13 C-values and truffle, undergrowth, and mint aromatic notes, three characteristics of the red Bordeaux wine aging bouquet. These correlations were confirmed to be significant using a Spearman statistical test. This study highlighted for the first time that vine water deficit positively relates to the perception of aging bouquet typicality, as well as the expression of its key aromatic nuances.

Vine water deficit impacts aging bouquet in fine red Bordeaux wine

NASA Astrophysics Data System (ADS)

Picard, Magali; van Leeuwen, Cornelis; Guyon, François; Gaillard, Laetitia; de Revel, Gilles; Marchand, Stéphanie

2017-08-01

The aim of this study was to investigate the influence of vine water status on bouquet typicality, revealed after aging, and the perception of three aromatic notes (mint, truffle, and undergrowth) in bottled fine red Bordeaux wines. To address the issue of the role of vine water deficit in the overall quality of fine aged wines, a large set of wines from four Bordeaux appellations were subjected to sensory analysis. As vine water status can be characterized by carbon isotope discrimination (δ13C), this ratio was quantified for each wine studied. Statistical analyses combining δ13C and sensory data highlighted that δ13C values discriminated effectively between the most- and least-typical wines. In addition, Principal Component Analysis revealed correlations between δ13C values and truffle, undergrowth, and mint aromatic notes, three characteristics of the red Bordeaux wine aging bouquet. These correlations were confirmed to be significant using a Spearman statistical test. This study highlighted for the first time that vine water deficit positively relates to the perception of aging bouquet typicality, as well as the expression of its key aromatic nuances.

Construction of inorganic elemental fingerprint and multivariate statistical analysis of marine traditional Chinese medicine Meretricis concha from Rushan Bay

NASA Astrophysics Data System (ADS)

Wu, Xia; Zheng, Kang; Zhao, Fengjia; Zheng, Yongjun; Li, Yantuan

2014-08-01

Meretricis concha is a kind of marine traditional Chinese medicine (TCM), and has been commonly used for the treatment of asthma and scald burns. In order to investigate the relationship between the inorganic elemental fingerprint and the geographical origin identification of Meretricis concha, the elemental contents of M. concha from five sampling points in Rushan Bay have been determined by means of inductively coupled plasma optical emission spectrometry (ICP-OES). Based on the contents of 14 inorganic elements (Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Se, and Zn), the inorganic elemental fingerprint which well reflects the elemental characteristics was constructed. All the data from the five sampling points were discriminated with accuracy through hierarchical cluster analysis (HCA) and principle component analysis (PCA), indicating that a four-factor model which could explain approximately 80% of the detection data was established, and the elements Al, As, Cd, Cu, Ni and Pb could be viewed as the characteristic elements. This investigation suggests that the inorganic elemental fingerprint combined with multivariate statistical analysis is a promising method for verifying the geographical origin of M. concha, and this strategy should be valuable for the authenticity discrimination of some marine TCM.

Reliability and validity of the workplace harassment questionnaire for Korean finance and service workers.

PubMed

Lee, Myeongjun; Kim, Hyunjung; Shin, Donghee; Lee, Sangyun

2016-01-01

Harassment means systemic and repeated unethical acts. Research on workplace harassment have been conducted widely and the NAQ-R has been widely used for the researches. But this tool, however the limitations in revealing differended in sub-factors depending on the culture and in reflecting that unique characteristics of the Koren society. So, The workplace harassment questionnaire for Korean finace and service workers has been developed to assess the level of personal harassment at work. This study aims to develop a tool to assess the level of personal harassment at work and to test its validity and reliability while examining specific characteristics of workplace harassment against finance and service workers in Korea. The framework of survey was established based on literature review, focused-group interview for the Korean finance and service workers. To verify its reliability, Cronbach's alpha coefficient was calculated; and to verify its validity, items and factors of the tool were analyzed. The correlation matrix analysis was examined to verify the tool's convergent validity and discriminant validity. Structural validity was verified by checking statistical significance in relation to the BDI-K. Cronbach's alpha coefficient of this survey was 0.93, which indicates a quite high level of reliability. To verify the appropriateness of this survey tool, its construct validity was examined through factor analysis. As a result of the factor analysis, 3 factors were extracted, explaining 56.5 % of the total variance. The loading values and communalities of the 20 items were 0.85 to 0.48 and 0.71 to 0.46. The convergent validity and discriminant validity were analyzed and rate of item discriminant validity was 100 %. Finally, for the concurrent validity, We examined the relationship between the WHI-KFSW and pschosocial stress by examining the correlation with the BDI-K. The results of chi-square test and multiple logistic analysis indicated that the correlation with the BDI-K was satatisctically significant. Workplace harassment in actual workplaces were investigated based on interviews, and the statistical analysis contributed to systematizing the types of actual workplace harassment. By statistical method, we developed the questionare, 20 items of 3 categories.

Discrimination of complex mixtures by a colorimetric sensor array: coffee aromas.

PubMed

Suslick, Benjamin A; Feng, Liang; Suslick, Kenneth S

2010-03-01

The analysis of complex mixtures presents a difficult challenge even for modern analytical techniques, and the ability to discriminate among closely similar such mixtures often remains problematic. Coffee provides a readily available archetype of such highly multicomponent systems. The use of a low-cost, sensitive colorimetric sensor array for the detection and identification of coffee aromas is reported. The color changes of the sensor array were used as a digital representation of the array response and analyzed with standard statistical methods, including principal component analysis (PCA) and hierarchical clustering analysis (HCA). PCA revealed that the sensor array has exceptionally high dimensionality with 18 dimensions required to define 90% of the total variance. In quintuplicate runs of 10 commercial coffees and controls, no confusions or errors in classification by HCA were observed in 55 trials. In addition, the effects of temperature and time in the roasting of green coffee beans were readily observed and distinguishable with a resolution better than 10 degrees C and 5 min, respectively. Colorimetric sensor arrays demonstrate excellent potential for complex systems analysis in real-world applications and provide a novel method for discrimination among closely similar complex mixtures.

Discrimination of Complex Mixtures by a Colorimetric Sensor Array: Coffee Aromas

PubMed Central

Suslick, Benjamin A.; Feng, Liang; Suslick, Kenneth S.

2010-01-01

The analysis of complex mixtures presents a difficult challenge even for modern analytical techniques, and the ability to discriminate among closely similar such mixtures often remains problematic. Coffee provides a readily available archetype of such highly multicomponent systems. The use of a low-cost, sensitive colorimetric sensor array for the detection and identification of coffee aromas is reported. The color changes of the sensor array were used as a digital representation of the array response and analyzed with standard statistical methods, including principal component analysis (PCA) and hierarchical clustering analysis (HCA). PCA revealed that the sensor array has exceptionally high dimensionality with 18 dimensions required to define 90% of the total variance. In quintuplicate runs of 10 commercial coffees and controls, no confusions or errors in classification by HCA were observed in 55 trials. In addition, the effects of temperature and time in the roasting of green coffee beans were readily observed and distinguishable with a resolution better than 10 °C and 5 min, respectively. Colorimetric sensor arrays demonstrate excellent potential for complex systems analysis in real-world applications and provide a novel method for discrimination among closely similar complex mixtures. PMID:20143838

Batch process fault detection and identification based on discriminant global preserving kernel slow feature analysis.

PubMed

Zhang, Hanyuan; Tian, Xuemin; Deng, Xiaogang; Cao, Yuping

2018-05-16

As an attractive nonlinear dynamic data analysis tool, global preserving kernel slow feature analysis (GKSFA) has achieved great success in extracting the high nonlinearity and inherently time-varying dynamics of batch process. However, GKSFA is an unsupervised feature extraction method and lacks the ability to utilize batch process class label information, which may not offer the most effective means for dealing with batch process monitoring. To overcome this problem, we propose a novel batch process monitoring method based on the modified GKSFA, referred to as discriminant global preserving kernel slow feature analysis (DGKSFA), by closely integrating discriminant analysis and GKSFA. The proposed DGKSFA method can extract discriminant feature of batch process as well as preserve global and local geometrical structure information of observed data. For the purpose of fault detection, a monitoring statistic is constructed based on the distance between the optimal kernel feature vectors of test data and normal data. To tackle the challenging issue of nonlinear fault variable identification, a new nonlinear contribution plot method is also developed to help identifying the fault variable after a fault is detected, which is derived from the idea of variable pseudo-sample trajectory projection in DGKSFA nonlinear biplot. Simulation results conducted on a numerical nonlinear dynamic system and the benchmark fed-batch penicillin fermentation process demonstrate that the proposed process monitoring and fault diagnosis approach can effectively detect fault and distinguish fault variables from normal variables. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Experimentally Derived δ13C and δ15N Discrimination Factors for Gray Wolves and the Impact of Prior Information in Bayesian Mixing Models

PubMed Central

Bucci, Melanie E.; Callahan, Peggy; Koprowski, John L.; Polfus, Jean L.; Krausman, Paul R.

2015-01-01

Stable isotope analysis of diet has become a common tool in conservation research. However, the multiple sources of uncertainty inherent in this analysis framework involve consequences that have not been thoroughly addressed. Uncertainty arises from the choice of trophic discrimination factors, and for Bayesian stable isotope mixing models (SIMMs), the specification of prior information; the combined effect of these aspects has not been explicitly tested. We used a captive feeding study of gray wolves (Canis lupus) to determine the first experimentally-derived trophic discrimination factors of C and N for this large carnivore of broad conservation interest. Using the estimated diet in our controlled system and data from a published study on wild wolves and their prey in Montana, USA, we then investigated the simultaneous effect of discrimination factors and prior information on diet reconstruction with Bayesian SIMMs. Discrimination factors for gray wolves and their prey were 1.97‰ for δ13C and 3.04‰ for δ15N. Specifying wolf discrimination factors, as opposed to the commonly used red fox (Vulpes vulpes) factors, made little practical difference to estimates of wolf diet, but prior information had a strong effect on bias, precision, and accuracy of posterior estimates. Without specifying prior information in our Bayesian SIMM, it was not possible to produce SIMM posteriors statistically similar to the estimated diet in our controlled study or the diet of wild wolves. Our study demonstrates the critical effect of prior information on estimates of animal diets using Bayesian SIMMs, and suggests species-specific trophic discrimination factors are of secondary importance. When using stable isotope analysis to inform conservation decisions researchers should understand the limits of their data. It may be difficult to obtain useful information from SIMMs if informative priors are omitted and species-specific discrimination factors are unavailable. PMID:25803664

Experimentally derived δ¹³C and δ¹⁵N discrimination factors for gray wolves and the impact of prior information in Bayesian mixing models.

PubMed

Derbridge, Jonathan J; Merkle, Jerod A; Bucci, Melanie E; Callahan, Peggy; Koprowski, John L; Polfus, Jean L; Krausman, Paul R

2015-01-01

Stable isotope analysis of diet has become a common tool in conservation research. However, the multiple sources of uncertainty inherent in this analysis framework involve consequences that have not been thoroughly addressed. Uncertainty arises from the choice of trophic discrimination factors, and for Bayesian stable isotope mixing models (SIMMs), the specification of prior information; the combined effect of these aspects has not been explicitly tested. We used a captive feeding study of gray wolves (Canis lupus) to determine the first experimentally-derived trophic discrimination factors of C and N for this large carnivore of broad conservation interest. Using the estimated diet in our controlled system and data from a published study on wild wolves and their prey in Montana, USA, we then investigated the simultaneous effect of discrimination factors and prior information on diet reconstruction with Bayesian SIMMs. Discrimination factors for gray wolves and their prey were 1.97‰ for δ13C and 3.04‰ for δ15N. Specifying wolf discrimination factors, as opposed to the commonly used red fox (Vulpes vulpes) factors, made little practical difference to estimates of wolf diet, but prior information had a strong effect on bias, precision, and accuracy of posterior estimates. Without specifying prior information in our Bayesian SIMM, it was not possible to produce SIMM posteriors statistically similar to the estimated diet in our controlled study or the diet of wild wolves. Our study demonstrates the critical effect of prior information on estimates of animal diets using Bayesian SIMMs, and suggests species-specific trophic discrimination factors are of secondary importance. When using stable isotope analysis to inform conservation decisions researchers should understand the limits of their data. It may be difficult to obtain useful information from SIMMs if informative priors are omitted and species-specific discrimination factors are unavailable.

A Label-Free Fluorescent Array Sensor Utilizing Liposome Encapsulating Calcein for Discriminating Target Proteins by Principal Component Analysis

PubMed Central

Imamura, Ryota; Murata, Naoki; Shimanouchi, Toshinori; Yamashita, Kaoru; Fukuzawa, Masayuki; Noda, Minoru

2017-01-01

A new fluorescent arrayed biosensor has been developed to discriminate species and concentrations of target proteins by using plural different phospholipid liposome species encapsulating fluorescent molecules, utilizing differences in permeation of the fluorescent molecules through the membrane to modulate liposome-target protein interactions. This approach proposes a basically new label-free fluorescent sensor, compared with the common technique of developed fluorescent array sensors with labeling. We have confirmed a high output intensity of fluorescence emission related to characteristics of the fluorescent molecules dependent on their concentrations when they leak from inside the liposomes through the perturbed lipid membrane. After taking an array image of the fluorescence emission from the sensor using a CMOS imager, the output intensities of the fluorescence were analyzed by a principal component analysis (PCA) statistical method. It is found from PCA plots that different protein species with several concentrations were successfully discriminated by using the different lipid membranes with high cumulative contribution ratio. We also confirmed that the accuracy of the discrimination by the array sensor with a single shot is higher than that of a single sensor with multiple shots. PMID:28714873

A Label-Free Fluorescent Array Sensor Utilizing Liposome Encapsulating Calcein for Discriminating Target Proteins by Principal Component Analysis.

PubMed

Imamura, Ryota; Murata, Naoki; Shimanouchi, Toshinori; Yamashita, Kaoru; Fukuzawa, Masayuki; Noda, Minoru

2017-07-15

A new fluorescent arrayed biosensor has been developed to discriminate species and concentrations of target proteins by using plural different phospholipid liposome species encapsulating fluorescent molecules, utilizing differences in permeation of the fluorescent molecules through the membrane to modulate liposome-target protein interactions. This approach proposes a basically new label-free fluorescent sensor, compared with the common technique of developed fluorescent array sensors with labeling. We have confirmed a high output intensity of fluorescence emission related to characteristics of the fluorescent molecules dependent on their concentrations when they leak from inside the liposomes through the perturbed lipid membrane. After taking an array image of the fluorescence emission from the sensor using a CMOS imager, the output intensities of the fluorescence were analyzed by a principal component analysis (PCA) statistical method. It is found from PCA plots that different protein species with several concentrations were successfully discriminated by using the different lipid membranes with high cumulative contribution ratio. We also confirmed that the accuracy of the discrimination by the array sensor with a single shot is higher than that of a single sensor with multiple shots.

Metabolic discrimination of sea buckthorn from different Hippophaë species by 1H NMR based metabolomics.

PubMed

Liu, Yue; Fan, Gang; Zhang, Jing; Zhang, Yi; Li, Jingjian; Xiong, Chao; Zhang, Qi; Li, Xiaodong; Lai, Xianrong

2017-05-08

Sea buckthorn (Hippophaë; Elaeagnaceae) berries are widely consumed in traditional folk medicines, nutraceuticals, and as a source of food. The growing demand of sea buckthorn berries and morphological similarity of Hippophaë species leads to confusions, which might cause misidentification of plants used in natural products. Detailed information and comparison of the complete set of metabolites of different Hippophaë species are critical for their objective identification and quality control. Herein, the variation among seven species and seven subspecies of Hippophaë was studied using proton nuclear magnetic resonance ( 1 H NMR) metabolomics combined with multivariate data analysis, and the important metabolites were quantified by quantitative 1 H NMR (qNMR) method. The results showed that different Hippophaë species can be clearly discriminated and the important interspecific discriminators, including organic acids, L-quebrachitol, and carbohydrates were identified. Statistical differences were found among most of the Hippophaë species and subspecies at the content levels of the aforementioned interspecific discriminators via qNMR and one-way analysis of variance (ANOVA) test. These findings demonstrated that 1 H NMR-based metabolomics is an applicable and effective approach for simultaneous metabolic profiling, species differentiation and quality assessment.

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

PubMed

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.

Quantitative chromatin pattern description in Feulgen-stained nuclei as a diagnostic tool to characterize the oligodendroglial and astroglial components in mixed oligo-astrocytomas.

PubMed

Decaestecker, C; Lopes, B S; Gordower, L; Camby, I; Cras, P; Martin, J J; Kiss, R; VandenBerg, S R; Salmon, I

1997-04-01

The oligoastrocytoma, as a mixed glioma, represents a nosologic dilemma with respect to precisely defining the oligodendroglial and astroglial phenotypes that constitute the neoplastic cell lineages of these tumors. In this study, cell image analysis with Feulgen-stained nuclei was used to distinguish between oligodendroglial and astrocytic phenotypes in oligodendrogliomas and astrocytomas and then applied to mixed oligoastrocytomas. Quantitative features with respect to chromatin pattern (30 variables) and DNA ploidy (8 variables) were evaluated on Feulgen-stained nuclei in a series of 71 gliomas using computer-assisted microscopy. These included 32 oligodendrogliomas (OLG group: 24 grade II and 8 grade III tumors according to the WHO classification), 32 astrocytomas (AST group: 13 grade II and 19 grade III tumors), and 7 oligoastrocytomas (OLGAST group). Initially, image analysis with multivariate statistical analyses (Discriminant Analysis) could identify each glial tumor group. Highly significant statistical differences were obtained distinguishing the morphonuclear features of oligodendrogliomas from those of astrocytomas, regardless of their histological grade. When compared with the 7 mixed oligoastrocytomas under study, 5 exhibited DNA ploidy and chromatin pattern characteristics similar to grade II oligodendrogliomas, I to grade III oligodendrogliomas, and I to grade II astrocytomas. Using multifactorial statistical analyses (Discriminant Analysis combined with Principal Component Analysis). It was possible to quantify the proportion of "typical" glial cell phenotypes that compose grade II and III oligodendrogliomas and grade II and III astrocytomas in each mixed glioma. Cytometric image analysis may be an important adjunct to routine histopathology for the reproducible identification of neoplasms containing a mixture of oligodendroglial and astrocytic phenotypes.

Discrimination of dementia with Lewy bodies from Alzheimer's disease using voxel-based morphometry of white matter by statistical parametric mapping 8 plus diffeomorphic anatomic registration through exponentiated Lie algebra.

PubMed

Nakatsuka, Tomoya; Imabayashi, Etsuko; Matsuda, Hiroshi; Sakakibara, Ryuji; Inaoka, Tsutomu; Terada, Hitoshi

2013-05-01

The purpose of this study was to identify brain atrophy specific for dementia with Lewy bodies (DLB) and to evaluate the discriminatory performance of this specific atrophy between DLB and Alzheimer's disease (AD). We retrospectively reviewed 60 DLB and 30 AD patients who had undergone 3D T1-weighted MRI. We randomly divided the DLB patients into two equal groups (A and B). First, we obtained a target volume of interest (VOI) for DLB-specific atrophy using correlation analysis of the percentage rate of significant whole white matter (WM) atrophy calculated using the Voxel-based Specific Regional Analysis System for Alzheimer's Disease (VSRAD) based on statistical parametric mapping 8 (SPM8) plus diffeomorphic anatomic registration through exponentiated Lie algebra, with segmented WM images in group A. We then evaluated the usefulness of this target VOI for discriminating the remaining 30 DLB patients in group B from the 30 AD patients. Z score values in this target VOI obtained from VSRAD were used as the determinant in receiver operating characteristic (ROC) analysis. Specific target VOIs for DLB were determined in the right-side dominant dorsal midbrain, right-side dominant dorsal pons, and bilateral cerebellum. ROC analysis revealed that the target VOI limited to the midbrain exhibited the highest area under the ROC curves of 0.75. DLB patients showed specific atrophy in the midbrain, pons, and cerebellum. Midbrain atrophy demonstrated the highest power for discriminating DLB and AD. This approach may be useful for determining the contributions of DLB and AD pathologies to the dementia syndrome.

Forensic Discrimination of Latent Fingerprints Using Laser-Induced Breakdown Spectroscopy (LIBS) and Chemometric Approaches.

PubMed

Yang, Jun-Ho; Yoh, Jack J

2018-01-01

A novel technique is reported for separating overlapping latent fingerprints using chemometric approaches that combine laser-induced breakdown spectroscopy (LIBS) and multivariate analysis. The LIBS technique provides the capability of real time analysis and high frequency scanning as well as the data regarding the chemical composition of overlapping latent fingerprints. These spectra offer valuable information for the classification and reconstruction of overlapping latent fingerprints by implementing appropriate statistical multivariate analysis. The current study employs principal component analysis and partial least square methods for the classification of latent fingerprints from the LIBS spectra. This technique was successfully demonstrated through a classification study of four distinct latent fingerprints using classification methods such as soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The novel method yielded an accuracy of more than 85% and was proven to be sufficiently robust. Furthermore, through laser scanning analysis at a spatial interval of 125 µm, the overlapping fingerprints were reconstructed as separate two-dimensional forms.

Spectral region optimization for Raman-based optical biopsy of inflammatory lesions.

PubMed

de Carvalho, Luis Felipe das Chagas E Silva; Bitar, Renata Andrade; Arisawa, Emília Angela Loschiavo; Brandão, Adriana Aigotti Haberbeck; Honório, Kathia Maria; Cabral, Luiz Antônio Guimarães; Martin, Airton Abrahão; Martinho, Herculano da Silva; Almeida, Janete Dias

2010-08-01

The biochemical alterations between inflammatory fibrous hyperplasia (IFH) and normal tissues of buccal mucosa were probed by using the FT-Raman spectroscopy technique. The aim was to find the minimal set of Raman bands that would furnish the best discrimination. Raman-based optical biopsy is a widely recognized potential technique for noninvasive real-time diagnosis. However, few studies had been devoted to the discrimination of very common subtle or early pathologic states as inflammatory processes that are always present on, for example, cancer lesion borders. Seventy spectra of IFH from 14 patients were compared with 30 spectra of normal tissues from six patients. The statistical analysis was performed with principal components analysis and soft independent modeling class analogy cross-validated, leave-one-out methods. Bands close to 574, 1,100, 1,250 to 1,350, and 1,500 cm(-1) (mainly amino acids and collagen bands) showed the main intragroup variations that are due to the acanthosis process in the IFH epithelium. The 1,200 (C-C aromatic/DNA), 1,350 (CH(2) bending/collagen 1), and 1,730 cm(-1) (collagen III) regions presented the main intergroup variations. This finding was interpreted as originating in an extracellular matrix-degeneration process occurring in the inflammatory tissues. The statistical analysis results indicated that the best discrimination capability (sensitivity of 95% and specificity of 100%) was found by using the 530-580 cm(-1) spectral region. The existence of this narrow spectral window enabling normal and inflammatory diagnosis also had useful implications for an in vivo dispersive Raman setup for clinical applications.

Analysis of biochemical genetic data on Jewish populations: II. Results and interpretations of heterogeneity indices and distance measures with respect to standards.

PubMed

Karlin, S; Kenett, R; Bonné-Tamir, B

1979-05-01

A nonparametric statistical methodology is used for the analysis of biochemical frequency data observed on a series of nine Jewish and six non-Jewish populations. Two categories of statistics are used: heterogeneity indices and various distance measures with respect to a standard. The latter are more discriminating in exploiting historical, geographical and culturally relevant information. A number of partial orderings and distance relationships among the populations are determined. Our concern in this study is to analyze similarities and differences among the Jewish populations, in terms of the gene frequency distributions for a number of genetic markers. Typical questions discussed are as follows: These Jewish populations differ in certain morphological and anthropometric traits. Are there corresponding differences in biochemical genetic constitution? How can we assess the extent of heterogeneity between and within groupings? Which class of markers (blood typings or protein loci) discriminates better among the separate populations? The results are quite surprising. For example, we found the Ashkenazi, Sephardi and Iraqi Jewish populations to be consistently close in genetic constitution and distant from all the other populations, namely the Yemenite and Cochin Jews, the Arabs, and the non-Jewish German and Russian populations. We found the Polish Jewish community the most heterogeneous among all Jewish populations. The blood loci discriminate better than the protein loci. A number of possible interpretations and hypotheses for these and other results are offered. The method devised for this analysis should prove useful in studying similarities and differences for other groups of populations for which substantial biochemical polymorphic data are available.

Machine Learning Biogeographic Processes from Biotic Patterns: A New Trait-Dependent Dispersal and Diversification Model with Model Choice By Simulation-Trained Discriminant Analysis.

PubMed

Sukumaran, Jeet; Economo, Evan P; Lacey Knowles, L

2016-05-01

Current statistical biogeographical analysis methods are limited in the ways ecology can be related to the processes of diversification and geographical range evolution, requiring conflation of geography and ecology, and/or assuming ecologies that are uniform across all lineages and invariant in time. This precludes the possibility of studying a broad class of macroevolutionary biogeographical theories that relate geographical and species histories through lineage-specific ecological and evolutionary dynamics, such as taxon cycle theory. Here we present a new model that generates phylogenies under a complex of superpositioned geographical range evolution, trait evolution, and diversification processes that can communicate with each other. We present a likelihood-free method of inference under our model using discriminant analysis of principal components of summary statistics calculated on phylogenies, with the discriminant functions trained on data generated by simulations under our model. This approach of model selection by classification of empirical data with respect to data generated under training models is shown to be efficient, robust, and performs well over a broad range of parameter space defined by the relative rates of dispersal, trait evolution, and diversification processes. We apply our method to a case study of the taxon cycle, that is testing for habitat and trophic level constraints in the dispersal regimes of the Wallacean avifaunal radiation. ©The Author(s) 2015. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Accurate discrimination of Alzheimer's disease from other dementia and/or normal subjects using SPECT specific volume analysis

NASA Astrophysics Data System (ADS)

Iyatomi, Hitoshi; Hashimoto, Jun; Yoshii, Fumuhito; Kazama, Toshiki; Kawada, Shuichi; Imai, Yutaka

2014-03-01

Discrimination between Alzheimer's disease and other dementia is clinically significant, however it is often difficult. In this study, we developed classification models among Alzheimer's disease (AD), other dementia (OD) and/or normal subjects (NC) using patient factors and indices obtained by brain perfusion SPECT. SPECT is commonly used to assess cerebral blood flow (CBF) and allows the evaluation of the severity of hypoperfusion by introducing statistical parametric mapping (SPM). We investigated a total of 150 cases (50 cases each for AD, OD, and NC) from Tokai University Hospital, Japan. In each case, we obtained a total of 127 candidate parameters from: (A) 2 patient factors (age and sex), (B) 12 CBF parameters and 113 SPM parameters including (C) 3 from specific volume analysis (SVA), and (D) 110 from voxel-based analysis stereotactic extraction estimation (vbSEE). We built linear classifiers with a statistical stepwise feature selection and evaluated the performance with the leave-one-out cross validation strategy. Our classifiers achieved very high classification performances with reasonable number of selected parameters. In the most significant discrimination in clinical, namely those of AD from OD, our classifier achieved both sensitivity (SE) and specificity (SP) of 96%. In a similar way, our classifiers achieved a SE of 90% and a SP of 98% in AD from NC, as well as a SE of 88% and a SP of 86% in AD from OD and NC cases. Introducing SPM indices such as SVA and vbSEE, classification performances improved around 7-15%. We confirmed that these SPM factors are quite important for diagnosing Alzheimer's disease.

Testing for Statistical Discrimination by Race/Ethnicity in Panel Data for Depression Treatment in Primary Care

PubMed Central

McGuire, Thomas G; Ayanian, John Z; Ford, Daniel E; Henke, Rachel E M; Rost, Kathryn M; Zaslavsky, Alan M

2008-01-01

Objective To test for discrimination by race/ethnicity arising from clinical uncertainty in treatment for depression, also known as “statistical discrimination.” Data Sources We used survey data from 1,321 African-American, Hispanic, and white adults identified with depression in primary care. Surveys were administered every six months for two years in the Quality Improvement for Depression (QID) studies. Study Design To examine whether and how change in depression severity affects change in treatment intensity by race/ethnicity, we used multivariate cross-sectional and change models that difference out unobserved time-invariant patient characteristics potentially correlated with race/ethnicity. Data Collection/Extraction Methods Treatment intensity was operationalized as expenditures on drugs, primary care, and specialty services, weighted by national prices from the Medical Expenditure Panel Survey. Patient race/ethnicity was collected at baseline by self-report. Principal Findings Change in depression severity is less associated with change in treatment intensity in minority patients than in whites, consistent with the hypothesis of statistical discrimination. The differential effect by racial/ethnic group was accounted for by use of mental health specialists. Conclusions Enhanced physician–patient communication and use of standardized depression instruments may reduce statistical discrimination arising from clinical uncertainty and be useful in reducing racial/ethnic inequities in depression treatment. PMID:18370966

Discrimination Method of the Volatiles from Fresh Mushrooms by an Electronic Nose Using a Trapping System and Statistical Standardization to Reduce Sensor Value Variation

PubMed Central

Fujioka, Kouki; Shimizu, Nobuo; Manome, Yoshinobu; Ikeda, Keiichi; Yamamoto, Kenji; Tomizawa, Yasuko

2013-01-01

Electronic noses have the benefit of obtaining smell information in a simple and objective manner, therefore, many applications have been developed for broad analysis areas such as food, drinks, cosmetics, medicine, and agriculture. However, measurement values from electronic noses have a tendency to vary under humidity or alcohol exposure conditions, since several types of sensors in the devices are affected by such variables. Consequently, we show three techniques for reducing the variation of sensor values: (1) using a trapping system to reduce the infering components; (2) performing statistical standardization (calculation of z-score); and (3) selecting suitable sensors. With these techniques, we discriminated the volatiles of four types of fresh mushrooms: golden needle (Flammulina velutipes), white mushroom (Agaricus bisporus), shiitake (Lentinus edodes), and eryngii (Pleurotus eryngii) among six fresh mushrooms (hen of the woods (Grifola frondosa), shimeji (Hypsizygus marmoreus) plus the above mushrooms). Additionally, we succeeded in discrimination of white mushroom, only comparing with artificial mushroom flavors, such as champignon flavor and truffle flavor. In conclusion, our techniques will expand the options to reduce variations in sensor values. PMID:24233028

Association Between Socioeconomic Position Discrimination and Psychological Distress: Findings From a Community-Based Sample of Gay and Bisexual Men in New York City

PubMed Central

Gamarel, Kristi E.; Reisner, Sari L.; Parsons, Jeffrey T.

2012-01-01

Objectives. We examined the association between discrimination and mental health distress, focusing specifically on the relative importance of discrimination because of particular demographic domains (i.e., race/ethnicity, socioeconomic position [SEP]). Methods. The research team surveyed a sample of gay and bisexual men (n = 294) at a community event in New York City. Participants completed a survey on demographics, discrimination experiences in the past 12 months, attributed domains of discrimination, and mental health distress. Results. In adjusted models, discrimination was associated with higher depressive (B = 0.31; P < .01) and anxious (B = 0.29; P < .01) symptoms. A statistically significant quadratic term (discrimination-squared; P < .01) fit both models, such that moderate levels of discrimination were most robustly associated with poorer mental health. Discrimination because of SEP was associated with higher discrimination scores and was predictive of higher depressive (B = 0.22; P < .01) and anxious (B = 0.50; P < .01) symptoms. No other statistically significant relationship was found between discrimination domains and distress. Conclusions. In this sample, SEP emerged as the most important domain of discrimination in its association with mental health distress. Future research should consider intersecting domains of discrimination to better understand social disparities in mental health. PMID:22994188

A Classroom Labor Market Game Illustrating the Existence, and Implications of, Statistical Discrimination

ERIC Educational Resources Information Center

Henrickson, Kevin E.

2014-01-01

Many undergraduate students report a lack of concern about facing labor market discrimination throughout their careers. However, there is ample evidence that discrimination based on race, gender, and age still persists within the labor market. The author outlines a classroom experiment demonstrating the existence of discrimination, even when the…

A new scoring system in Cystic Fibrosis: statistical tools for database analysis - a preliminary report.

PubMed

Hafen, G M; Hurst, C; Yearwood, J; Smith, J; Dzalilov, Z; Robinson, P J

2008-10-05

Cystic fibrosis is the most common fatal genetic disorder in the Caucasian population. Scoring systems for assessment of Cystic fibrosis disease severity have been used for almost 50 years, without being adapted to the milder phenotype of the disease in the 21st century. The aim of this current project is to develop a new scoring system using a database and employing various statistical tools. This study protocol reports the development of the statistical tools in order to create such a scoring system. The evaluation is based on the Cystic Fibrosis database from the cohort at the Royal Children's Hospital in Melbourne. Initially, unsupervised clustering of the all data records was performed using a range of clustering algorithms. In particular incremental clustering algorithms were used. The clusters obtained were characterised using rules from decision trees and the results examined by clinicians. In order to obtain a clearer definition of classes expert opinion of each individual's clinical severity was sought. After data preparation including expert-opinion of an individual's clinical severity on a 3 point-scale (mild, moderate and severe disease), two multivariate techniques were used throughout the analysis to establish a method that would have a better success in feature selection and model derivation: 'Canonical Analysis of Principal Coordinates' and 'Linear Discriminant Analysis'. A 3-step procedure was performed with (1) selection of features, (2) extracting 5 severity classes out of a 3 severity class as defined per expert-opinion and (3) establishment of calibration datasets. (1) Feature selection: CAP has a more effective "modelling" focus than DA.(2) Extraction of 5 severity classes: after variables were identified as important in discriminating contiguous CF severity groups on the 3-point scale as mild/moderate and moderate/severe, Discriminant Function (DF) was used to determine the new groups mild, intermediate moderate, moderate, intermediate severe and severe disease. (3) Generated confusion tables showed a misclassification rate of 19.1% for males and 16.5% for females, with a majority of misallocations into adjacent severity classes particularly for males. Our preliminary data show that using CAP for detection of selection features and Linear DA to derive the actual model in a CF database might be helpful in developing a scoring system. However, there are several limitations, particularly more data entry points are needed to finalize a score and the statistical tools have further to be refined and validated, with re-running the statistical methods in the larger dataset.

Enamel surface topography analysis for diet discrimination. A methodology to enhance and select discriminative parameters

NASA Astrophysics Data System (ADS)

Francisco, Arthur; Blondel, Cécile; Brunetière, Noël; Ramdarshan, Anusha; Merceron, Gildas

2018-03-01

Tooth wear and, more specifically, dental microwear texture is a dietary proxy that has been used for years in vertebrate paleoecology and ecology. DMTA, dental microwear texture analysis, relies on a few parameters related to the surface complexity, anisotropy and heterogeneity of the enamel facets at the micrometric scale. Working with few but physically meaningful parameters helps in comparing published results and in defining levels for classification purposes. Other dental microwear approaches are based on ISO parameters and coupled with statistical tests to find the more relevant ones. The present study roughly utilizes most of the aforementioned parameters in their more or less modified form. But more than parameters, we here propose a new approach: instead of a single parameter characterizing the whole surface, we sample the surface and thus generate 9 derived parameters in order to broaden the parameter set. The identification of the most discriminative parameters is performed with an automated procedure which is an extended and refined version of the workflows encountered in some studies. The procedure in its initial form includes the most common tools, like the ANOVA and the correlation analysis, along with the required mathematical tests. The discrimination results show that a simplified form of the procedure is able to more efficiently identify the desired number of discriminative parameters. Also highlighted are some trends like the relevance of working with both height and spatial parameters, as well as the potential benefits of dimensionless surfaces. On a set of 45 surfaces issued from 45 specimens of three modern ruminants with differences in feeding preferences (grazing, leaf-browsing and fruit-eating), it is clearly shown that the level of wear discrimination is improved with the new methodology compared to the other ones.

An electroglottographical analysis-based discriminant function model differentiating multiple sclerosis patients from healthy controls.

PubMed

Vavougios, George D; Doskas, Triantafyllos; Konstantopoulos, Kostas

2018-05-01

Dysarthrophonia is a predominant symptom in many neurological diseases, affecting the quality of life of the patients. In this study, we produced a discriminant function equation that can differentiate MS patients from healthy controls, using electroglottographic variables not analyzed in a previous study. We applied stepwise linear discriminant function analysis in order to produce a function and score derived from electroglottographic variables extracted from a previous study. The derived discriminant function's statistical significance was determined via Wilk's λ test (and the associated p value). Finally, a 2 × 2 confusion matrix was used to determine the function's predictive accuracy, whereas the cross-validated predictive accuracy is estimated via the "leave-one-out" classification process. Discriminant function analysis (DFA) was used to create a linear function of continuous predictors. DFA produced the following model (Wilk's λ = 0.043, χ2 = 388.588, p < 0.0001, Tables 3 and 4): D (MS vs controls) = 0.728*DQx1 mean monologue + 0.325*CQx monologue + 0.298*DFx1 90% range monologue + 0.443*DQx1 90% range reading - 1.490*DQx1 90% range monologue. The derived discriminant score (S1) was used subsequently in order to form the coordinates of a ROC curve. Thus, a cutoff score of - 0.788 for S1 corresponded to a perfect classification (100% sensitivity and 100% specificity, p = 1.67e -22 ). Consistent with previous findings, electroglottographic evaluation represents an easy to implement and potentially important assessment in MS patients, achieving adequate classification accuracy. Further evaluation is needed to determine its use as a biomarker.

An instrument to assess the statistical intensity of medical research papers.

PubMed

Nieminen, Pentti; Virtanen, Jorma I; Vähänikkilä, Hannu

2017-01-01

There is widespread evidence that statistical methods play an important role in original research articles, especially in medical research. The evaluation of statistical methods and reporting in journals suffers from a lack of standardized methods for assessing the use of statistics. The objective of this study was to develop and evaluate an instrument to assess the statistical intensity in research articles in a standardized way. A checklist-type measure scale was developed by selecting and refining items from previous reports about the statistical contents of medical journal articles and from published guidelines for statistical reporting. A total of 840 original medical research articles that were published between 2007-2015 in 16 journals were evaluated to test the scoring instrument. The total sum of all items was used to assess the intensity between sub-fields and journals. Inter-rater agreement was examined using a random sample of 40 articles. Four raters read and evaluated the selected articles using the developed instrument. The scale consisted of 66 items. The total summary score adequately discriminated between research articles according to their study design characteristics. The new instrument could also discriminate between journals according to their statistical intensity. The inter-observer agreement measured by the ICC was 0.88 between all four raters. Individual item analysis showed very high agreement between the rater pairs, the percentage agreement ranged from 91.7% to 95.2%. A reliable and applicable instrument for evaluating the statistical intensity in research papers was developed. It is a helpful tool for comparing the statistical intensity between sub-fields and journals. The novel instrument may be applied in manuscript peer review to identify papers in need of additional statistical review.

Morphological image analysis for classification of gastrointestinal tissues using optical coherence tomography

NASA Astrophysics Data System (ADS)

Garcia-Allende, P. Beatriz; Amygdalos, Iakovos; Dhanapala, Hiruni; Goldin, Robert D.; Hanna, George B.; Elson, Daniel S.

2012-01-01

Computer-aided diagnosis of ophthalmic diseases using optical coherence tomography (OCT) relies on the extraction of thickness and size measures from the OCT images, but such defined layers are usually not observed in emerging OCT applications aimed at "optical biopsy" such as pulmonology or gastroenterology. Mathematical methods such as Principal Component Analysis (PCA) or textural analyses including both spatial textural analysis derived from the two-dimensional discrete Fourier transform (DFT) and statistical texture analysis obtained independently from center-symmetric auto-correlation (CSAC) and spatial grey-level dependency matrices (SGLDM), as well as, quantitative measurements of the attenuation coefficient have been previously proposed to overcome this problem. We recently proposed an alternative approach consisting of a region segmentation according to the intensity variation along the vertical axis and a pure statistical technology for feature quantification. OCT images were first segmented in the axial direction in an automated manner according to intensity. Afterwards, a morphological analysis of the segmented OCT images was employed for quantifying the features that served for tissue classification. In this study, a PCA processing of the extracted features is accomplished to combine their discriminative power in a lower number of dimensions. Ready discrimination of gastrointestinal surgical specimens is attained demonstrating that the approach further surpasses the algorithms previously reported and is feasible for tissue classification in the clinical setting.

Ordinary chondrites - Multivariate statistical analysis of trace element contents

NASA Technical Reports Server (NTRS)

Lipschutz, Michael E.; Samuels, Stephen M.

1991-01-01

The contents of mobile trace elements (Co, Au, Sb, Ga, Se, Rb, Cs, Te, Bi, Ag, In, Tl, Zn, and Cd) in Antarctic and non-Antarctic populations of H4-6 and L4-6 chondrites, were compared using standard multivariate discriminant functions borrowed from linear discriminant analysis and logistic regression. A nonstandard randomization-simulation method was developed, making it possible to carry out probability assignments on a distribution-free basis. Compositional differences were found both between the Antarctic and non-Antarctic H4-6 chondrite populations and between two L4-6 chondrite populations. It is shown that, for various types of meteorites (in particular, for the H4-6 chondrites), the Antarctic/non-Antarctic compositional difference is due to preterrestrial differences in the genesis of their parent materials.

Teaching Research Methods and Statistics in eLearning Environments: Pedagogy, Practical Examples, and Possible Futures

PubMed Central

Rock, Adam J.; Coventry, William L.; Morgan, Methuen I.; Loi, Natasha M.

2016-01-01

Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology. PMID:27014147

Teaching Research Methods and Statistics in eLearning Environments: Pedagogy, Practical Examples, and Possible Futures.

PubMed

Rock, Adam J; Coventry, William L; Morgan, Methuen I; Loi, Natasha M

2016-01-01

Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology.

Predicting The Type Of Pregnancy Using Flexible Discriminate Analysis And Artificial Neural Networks: A Comparison Study

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

Hooman, A.; Mohammadzadeh, M

Some medical and epidemiological surveys have been designed to predict a nominal response variable with several levels. With regard to the type of pregnancy there are four possible states: wanted, unwanted by wife, unwanted by husband and unwanted by couple. In this paper, we have predicted the type of pregnancy, as well as the factors influencing it using three different models and comparing them. Regarding the type of pregnancy with several levels, we developed a multinomial logistic regression, a neural network and a flexible discrimination based on the data and compared their results using tow statistical indices: Surface under curvemore » (ROC) and kappa coefficient. Based on these tow indices, flexible discrimination proved to be a better fit for prediction on data in comparison to other methods. When the relations among variables are complex, one can use flexible discrimination instead of multinomial logistic regression and neural network to predict the nominal response variables with several levels in order to gain more accurate predictions.« less

Simultaneous use of geological, geophysical, and LANDSAT digital data in uranium exploration. [Libya

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

Missallati, A.; Prelat, A.E.; Lyon, R.J.P.

1979-08-01

The simultaneous use of geological, geophysical and Landsat data in uranium exploration in southern Libya is reported. The values of 43 geological, geophysical and digital data variables, including age and type of rock, geological contacts, aeroradio-metric and aeromagnetic values and brightness ratios, were used as input into a geomathematical model. Stepwise discriminant analysis was used to select grid cells most favorable for detailed mineral exploration and to evaluate the significance of each variable in discriminating between the anomalous (radioactive) and nonanomalous (nonradioactive) areas. It is found that the geological contact relationships, Landsat Bands 6 and Band 7/4 ratio values weremore » most useful in the discrimination. The procedure was found to be statistically and geologically reliable, and applicable to similar regions using only the most important geological and Landsat data.« less

School Finance Court Cases and Disparate Racial Impact: The Contribution of Statistical Analysis in New York

ERIC Educational Resources Information Center

Stiefel, Leanna; Schwartz, Amy Ellen; Berne, Robert; Chellman, Colin C.

2005-01-01

Although analyses of state school finance systems rarely focus on the distribution of funds to students of different races, the advent of racial discrimination as an issue in school finance court cases may change that situation. In this article, we describe the background, analyses, and results of plaintiffs' testimony regarding racial…

Differentiation of wines according to grape variety and geographical origin based on volatiles profiling using SPME-MS and SPME-GC/MS methods.

PubMed

Ziółkowska, Angelika; Wąsowicz, Erwin; Jeleń, Henryk H

2016-12-15

Among methods to detect wine adulteration, profiling volatiles is one with a great potential regarding robustness, analysis time and abundance of information for subsequent data treatment. Volatile fraction fingerprinting by solid-phase microextraction with direct analysis by mass spectrometry without compounds separation (SPME-MS) was used for differentiation of white as well as red wines. The aim was to differentiate between varieties used for wine production and to also differentiate wines by country of origin. The results obtained were compared to SPME-GC/MS analysis in which compounds were resolved by gas chromatography. For both approaches the same type of statistical procedure was used to compare samples: principal component analysis (PCA) followed by linear discriminant analysis (LDA). White wines (38) and red wines (41) representing different grape varieties and various regions of origin were analysed. SPME-MS proved to be advantageous in use due to better discrimination and higher sample throughput. Copyright © 2016 Elsevier Ltd. All rights reserved.

[Discrimination of types of polyacrylamide based on near infrared spectroscopy coupled with least square support vector machine].

PubMed

Zhang, Hong-Guang; Yang, Qin-Min; Lu, Jian-Gang

2014-04-01

In this paper, a novel discriminant methodology based on near infrared spectroscopic analysis technique and least square support vector machine was proposed for rapid and nondestructive discrimination of different types of Polyacrylamide. The diffuse reflectance spectra of samples of Non-ionic Polyacrylamide, Anionic Polyacrylamide and Cationic Polyacrylamide were measured. Then principal component analysis method was applied to reduce the dimension of the spectral data and extract of the principal compnents. The first three principal components were used for cluster analysis of the three different types of Polyacrylamide. Then those principal components were also used as inputs of least square support vector machine model. The optimization of the parameters and the number of principal components used as inputs of least square support vector machine model was performed through cross validation based on grid search. 60 samples of each type of Polyacrylamide were collected. Thus a total of 180 samples were obtained. 135 samples, 45 samples for each type of Polyacrylamide, were randomly split into a training set to build calibration model and the rest 45 samples were used as test set to evaluate the performance of the developed model. In addition, 5 Cationic Polyacrylamide samples and 5 Anionic Polyacrylamide samples adulterated with different proportion of Non-ionic Polyacrylamide were also prepared to show the feasibilty of the proposed method to discriminate the adulterated Polyacrylamide samples. The prediction error threshold for each type of Polyacrylamide was determined by F statistical significance test method based on the prediction error of the training set of corresponding type of Polyacrylamide in cross validation. The discrimination accuracy of the built model was 100% for prediction of the test set. The prediction of the model for the 10 mixing samples was also presented, and all mixing samples were accurately discriminated as adulterated samples. The overall results demonstrate that the discrimination method proposed in the present paper can rapidly and nondestructively discriminate the different types of Polyacrylamide and the adulterated Polyacrylamide samples, and offered a new approach to discriminate the types of Polyacrylamide.

Computer-assisted sperm morphometry fluorescence-based analysis has potential to determine progeny sex.

PubMed

Santolaria, Pilar; Pauciullo, Alfredo; Silvestre, Miguel A; Vicente-Fiel, Sandra; Villanova, Leyre; Pinton, Alain; Viruel, Juan; Sales, Ester; Yániz, Jesús L

2016-01-01

This study was designed to determine the ability of computer-assisted sperm morphometry analysis (CASA-Morph) with fluorescence to discriminate between spermatozoa carrying different sex chromosomes from the nuclear morphometrics generated and different statistical procedures in the bovine species. The study was divided into two experiments. The first was to study the morphometric differences between X- and Y-chromosome-bearing spermatozoa (SX and SY, respectively). Spermatozoa from eight bulls were processed to assess simultaneously the sex chromosome by FISH and sperm morphometry by fluorescence-based CASA-Morph. SX cells were larger than SY cells on average (P < 0.001) although with important differences between bulls. A simultaneous evaluation of all the measured features by discriminant analysis revealed that nuclear area and average fluorescence intensity were the variables selected by stepwise discriminant function analysis as the best discriminators between SX and SY. In the second experiment, the sperm nuclear morphometric results from CASA-Morph in nonsexed (mixed SX and SY) and sexed (SX) semen samples from four bulls were compared. FISH allowed a successful classification of spermatozoa according to their sex chromosome content. X-sexed spermatozoa displayed a larger size and fluorescence intensity than nonsexed spermatozoa (P < 0.05). We conclude that the CASA-Morph fluorescence-based method has the potential to find differences between X- and Y-chromosome-bearing spermatozoa in bovine species although more studies are needed to increase the precision of sex determination by this technique.

The Effects of Clinically Relevant Multiple-Choice Items on the Statistical Discrimination of Physician Clinical Competence.

ERIC Educational Resources Information Center

Downing, Steven M.; Maatsch, Jack L.

To test the effect of clinically relevant multiple-choice item content on the validity of statistical discriminations of physicians' clinical competence, data were collected from a field test of the Emergency Medicine Examination, test items for the certification of specialists in emergency medicine. Two 91-item multiple-choice subscales were…

[Step Fisher discriminant analysis on severe clinical features of hand foot and mouth disease between enterovirus (EV) 71 and other EV].

PubMed

Ruan, Feng; Tan, Ai-jun; Zhang, Xue-bao; Chen, Xue-qin; Xiao, Song-jian; Ye, Zhong-wen; Wang, Song

2011-07-01

To compare the clinical features of severe hand foot and mouth disease between enterovirus (EV) 71 and other EV to find specific diagnosis index of EV71 severe hand foot and mouth disease. Case definition were adopted from national guideline of hand foot and mouth disease diagnose (Version 2010). Clinical data of severe hand foot and mouth disease came from case history and contents of questionnaire would include the ones between the time of onset and diagnoses being made. EV and EV71, Cox A16 nucleic acid tested were by RT-PCR in stool samples. Clinical features of severe hand foot and mouth disease between EV71 and other EV were compare. There appeared statistical differences between neurologic symptoms such as tremor, myoclonic jerk, listlessness, convulsion and white blood cell counts in CSF (P < 0.05). Results from the step Fisher discriminant analysis showed only tremor and white blood cell had an increase in CSF, with statistically significant differences. The discriminant equation of EV71 was Y = 3.059X(1) + 3.83X(5) - 2.742 and the equation of other EV was Y = 1.634X(1) + 1.623X(5) - 1.693. The specificity of EV71 was 91% and the specificity of other EV was 40%. The increase of clinical features of tremor and white blood cell in CSF could be used as diagnosis index of severe EV71.

Comparative evaluation of spectroscopic models using different multivariate statistical tools in a multicancer scenario

NASA Astrophysics Data System (ADS)

Ghanate, A. D.; Kothiwale, S.; Singh, S. P.; Bertrand, Dominique; Krishna, C. Murali

2011-02-01

Cancer is now recognized as one of the major causes of morbidity and mortality. Histopathological diagnosis, the gold standard, is shown to be subjective, time consuming, prone to interobserver disagreement, and often fails to predict prognosis. Optical spectroscopic methods are being contemplated as adjuncts or alternatives to conventional cancer diagnostics. The most important aspect of these approaches is their objectivity, and multivariate statistical tools play a major role in realizing it. However, rigorous evaluation of the robustness of spectral models is a prerequisite. The utility of Raman spectroscopy in the diagnosis of cancers has been well established. Until now, the specificity and applicability of spectral models have been evaluated for specific cancer types. In this study, we have evaluated the utility of spectroscopic models representing normal and malignant tissues of the breast, cervix, colon, larynx, and oral cavity in a broader perspective, using different multivariate tests. The limit test, which was used in our earlier study, gave high sensitivity but suffered from poor specificity. The performance of other methods such as factorial discriminant analysis and partial least square discriminant analysis are at par with more complex nonlinear methods such as decision trees, but they provide very little information about the classification model. This comparative study thus demonstrates not just the efficacy of Raman spectroscopic models but also the applicability and limitations of different multivariate tools for discrimination under complex conditions such as the multicancer scenario.

Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?

PubMed

Karacavus, Seyhan; Yılmaz, Bülent; Tasdemir, Arzu; Kayaaltı, Ömer; Kaya, Eser; İçer, Semra; Ayyıldız, Oguzhan

2018-04-01

We investigated the association between the textural features obtained from 18 F-FDG images, metabolic parameters (SUVmax , SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws' approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws' method (r = 0.6, p = 0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k = 3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws' approach could be useful in the discrimination of tumor stage.

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

DOE PAGES

Belianinov, Alex; Panchapakesan, G.; Lin, Wenzhi; ...

2014-12-02

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe0.55Se0.45 (Tc = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe1 x Sex structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signaturemore » and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.« less

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

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

Belianinov, Alex, E-mail: belianinova@ornl.gov; Ganesh, Panchapakesan; Lin, Wenzhi

2014-12-01

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe{sub 0.55}Se{sub 0.45} (T{sub c} = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe{sub 1−x}Se{sub x} structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified bymore » their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.« less

Discrimination of irradiated MOX fuel from UOX fuel by multivariate statistical analysis of simulated activities of gamma-emitting isotopes

NASA Astrophysics Data System (ADS)

Åberg Lindell, M.; Andersson, P.; Grape, S.; Hellesen, C.; Håkansson, A.; Thulin, M.

2018-03-01

This paper investigates how concentrations of certain fission products and their related gamma-ray emissions can be used to discriminate between uranium oxide (UOX) and mixed oxide (MOX) type fuel. Discrimination of irradiated MOX fuel from irradiated UOX fuel is important in nuclear facilities and for transport of nuclear fuel, for purposes of both criticality safety and nuclear safeguards. Although facility operators keep records on the identity and properties of each fuel, tools for nuclear safeguards inspectors that enable independent verification of the fuel are critical in the recovery of continuity of knowledge, should it be lost. A discrimination methodology for classification of UOX and MOX fuel, based on passive gamma-ray spectroscopy data and multivariate analysis methods, is presented. Nuclear fuels and their gamma-ray emissions were simulated in the Monte Carlo code Serpent, and the resulting data was used as input to train seven different multivariate classification techniques. The trained classifiers were subsequently implemented and evaluated with respect to their capabilities to correctly predict the classes of unknown fuel items. The best results concerning successful discrimination of UOX and MOX-fuel were acquired when using non-linear classification techniques, such as the k nearest neighbors method and the Gaussian kernel support vector machine. For fuel with cooling times up to 20 years, when it is considered that gamma-rays from the isotope 134Cs can still be efficiently measured, success rates of 100% were obtained. A sensitivity analysis indicated that these methods were also robust.

Nontargeted metabolomics approach for the differentiation of cultivation ages of mountain cultivated ginseng leaves using UHPLC/QTOF-MS.

PubMed

Chang, Xiangwei; Zhang, Juanjuan; Li, Dekun; Zhou, Dazheng; Zhang, Yuling; Wang, Jincheng; Hu, Bing; Ju, Aichun; Ye, Zhengliang

2017-07-15

The adulteration or falsification of the cultivation age of mountain cultivated ginseng (MCG) has been a serious problem in the commercial MCG market. To develop an efficient discrimination tool for the cultivation age and to explore potential age-dependent markers, an optimized ultra high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC/QTOF-MS)-based metabolomics approach was applied in the global metabolite profiling of 156 MCG leaf (MGL) samples aged from 6 to 18 years. Multivariate statistical methods such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to compare the derived patterns between MGL samples of different cultivation ages. The present study demonstrated that 6-18-year-old MGL samples can be successfully discriminated using two simple successive steps, together with four PLS-DA discrimination models. Furthermore, 39 robust age-dependent markers enabling differentiation among the 6-18-year-old MGL samples were discovered. The results were validated by a permutation test and an external test set to verify the predictability and reliability of the established discrimination models. More importantly, without destroying the MCG roots, the proposed approach could also be applied to discriminate MCG root ages indirectly, using a minimum amount of homophyletic MGL samples combined with the established four PLS-DA models and identified markers. Additionally, to the best of our knowledge, this is the first study in which 6-18-year-old MCG root ages have been nondestructively differentiated by analyzing homophyletic MGL samples using UHPLC/QTOF-MS analysis and two simple successive steps together with four PLS-DA models. The method developed in this study can be used as a standard protocol for discriminating and predicting MGL ages directly and homophyletic MCG root ages indirectly. Copyright © 2017 Elsevier B.V. All rights reserved.

Lagged segmented Poincaré plot analysis for risk stratification in patients with dilated cardiomyopathy.

PubMed

Voss, Andreas; Fischer, Claudia; Schroeder, Rico; Figulla, Hans R; Goernig, Matthias

2012-07-01

The objectives of this study were to introduce a new type of heart-rate variability analysis improving risk stratification in patients with idiopathic dilated cardiomyopathy (DCM) and to provide additional information about impaired heart beat generation in these patients. Beat-to-beat intervals (BBI) of 30-min ECGs recorded from 91 DCM patients and 21 healthy subjects were analyzed applying the lagged segmented Poincaré plot analysis (LSPPA) method. LSPPA includes the Poincaré plot reconstruction with lags of 1-100, rotating the cloud of points, its normalized segmentation adapted to their standard deviations, and finally, a frequency-dependent clustering. The lags were combined into eight different clusters representing specific frequency bands within 0.012-1.153 Hz. Statistical differences between low- and high-risk DCM could be found within the clusters II-VIII (e.g., cluster IV: 0.033-0.038 Hz; p = 0.0002; sensitivity = 85.7 %; specificity = 71.4 %). The multivariate statistics led to a sensitivity of 92.9 %, specificity of 85.7 % and an area under the curve of 92.1 % discriminating these patient groups. We introduced the LSPPA method to investigate time correlations in BBI time series. We found that LSPPA contributes considerably to risk stratification in DCM and yields the highest discriminant power in the low and very low-frequency bands.

Palatal rugae pattern: An aid for sex identification

PubMed Central

Gadicherla, Prahlad; Saini, Divya; Bhaskar, Milana

2017-01-01

Background: Palatal rugoscopy, or palatoscopy, is the process by which human identification can be obtained by inspecting the transverse palatal rugae inside the mouth. Aim: The aim of the study is to investigate the potential of using palatal rugae as an aid for sex identification in Bengaluru population. Materials and Methods: One hundred plaster casts equally distributed between males and females belonging to age range of 4–16 years were examined for different rugae patterns. Thomas and Kotze classification was adopted for identification of these rugae patterns. Statistical Analysis: The data obtained were subjected to discriminant function analysis to determine the applicability of palatal rugae pattern as an aid for sex identification. Results: Difference in unification patterns among males and females was found to be statistically significant. No significant difference was found between males and females in terms of number of rugae. Overall, wavy and curvy were the most predominant type of rugae seen. Discriminant function analysis enabled sex identification with an accuracy of 80%. Conclusion: This preliminary study undertaken showed the existence of a distinct pattern of distribution of palatal rugae between males and females of Bengaluru population. This study opens scope for further research with a larger sample size to establish palatal rugae as a valuable tool for sex identification for forensic purposes. PMID:28584485

Predicting trauma patient mortality: ICD [or ICD-10-AM] versus AIS based approaches.

PubMed

Willis, Cameron D; Gabbe, Belinda J; Jolley, Damien; Harrison, James E; Cameron, Peter A

2010-11-01

The International Classification of Diseases Injury Severity Score (ICISS) has been proposed as an International Classification of Diseases (ICD)-10-based alternative to mortality prediction tools that use Abbreviated Injury Scale (AIS) data, including the Trauma and Injury Severity Score (TRISS). To date, studies have not examined the performance of ICISS using Australian trauma registry data. This study aimed to compare the performance of ICISS with other mortality prediction tools in an Australian trauma registry. This was a retrospective review of prospectively collected data from the Victorian State Trauma Registry. A training dataset was created for model development and a validation dataset for evaluation. The multiplicative ICISS model was compared with a worst injury ICISS approach, Victorian TRISS (V-TRISS, using local coefficients), maximum AIS severity and a multivariable model including ICD-10-AM codes as predictors. Models were investigated for discrimination (C-statistic) and calibration (Hosmer-Lemeshow statistic). The multivariable approach had the highest level of discrimination (C-statistic 0.90) and calibration (H-L 7.65, P= 0.468). Worst injury ICISS, V-TRISS and maximum AIS had similar performance. The multiplicative ICISS produced the lowest level of discrimination (C-statistic 0.80) and poorest calibration (H-L 50.23, P < 0.001). The performance of ICISS may be affected by the data used to develop estimates, the ICD version employed, the methods for deriving estimates and the inclusion of covariates. In this analysis, a multivariable approach using ICD-10-AM codes was the best-performing method. A multivariable ICISS approach may therefore be a useful alternative to AIS-based methods and may have comparable predictive performance to locally derived TRISS models. © 2010 The Authors. ANZ Journal of Surgery © 2010 Royal Australasian College of Surgeons.

[Comment on] Statistical discrimination

NASA Astrophysics Data System (ADS)

Chinn, Douglas

In the December 8, 1981, issue of Eos, a news item reported the conclusion of a National Research Council study that sexual discrimination against women with Ph.D.'s exists in the field of geophysics. Basically, the item reported that even when allowances are made for motherhood the percentage of female Ph.D.'s holding high university and corporate positions is significantly lower than the percentage of male Ph.D.'s holding the same types of positions. The sexual discrimination conclusion, based only on these statistics, assumes that there are no basic psychological differences between men and women that might cause different populations in the employment group studied. Therefore, the reasoning goes, after taking into account possible effects from differences related to anatomy, such as women stopping their careers in order to bear and raise children, the statistical distributions of positions held by male and female Ph.D.'s ought to be very similar to one another. Any significant differences between the distributions must be caused primarily by sexual discrimination.

Different faces of discrimination: perceived discrimination among homeless adults with mental illness in healthcare settings.

PubMed

Skosireva, Anna; O'Campo, Patricia; Zerger, Suzanne; Chambers, Catharine; Gapka, Susan; Stergiopoulos, Vicky

2014-09-07

Research on discrimination in healthcare settings has primarily focused on health implications of race-based discrimination among ethno-racial minority groups. Little is known about discrimination experiences of other marginalized populations, particularly groups facing multiple disadvantages who may be subjected to other/multiple forms of discrimination. (1) To examine the prevalence of perceived discrimination due to homelessness/poverty, mental illness/alcohol/drug related problems, and race/ethnicity/skin color while seeking healthcare in the past year among racially diverse homeless adults with mental illness; (2) To identify whether perceiving certain types of discrimination is associated with increased likelihood of perceiving other kinds of discrimination; and (3) To examine association of these perceived discrimination experiences with socio-demographic characteristics, self-reported measures of psychiatric symptomatology and substance use, and Emergency Department utilization. We used baseline data from the Toronto site of the At Home/Chez Soi randomized controlled trial of Housing First for homeless adults with mental illness (n = 550). Bivariate statistics and multivariable logistic regression models were used for the analysis. Perceived discrimination related to homelessness/poverty (30.4%) and mental illness/alcohol/substance use (32.5%) is prevalent among ethnically diverse homeless adults with mental illness in healthcare settings. Only 15% of the total participants reported discrimination due to race/ethnicity/skin color. After controlling for relevant confounders and presence of psychosis, all types of discrimination in healthcare settings were associated with more frequent ED use, a greater - 3 - severity of lifetime substance abuse, and mental health problems. Perceiving discrimination of one type was associated with increased likelihood of perceiving other kinds of discrimination. Understanding the experience of discrimination in healthcare settings and associated healthcare utilization is the first step towards designing policies and interventions to address health disparities among vulnerable populations. This study contributes to the knowledge base in this important area. This study has been registered with the International Standard Randomized Control Trial Number Register and assigned ISRCTN42520374.

Improved dynamical scaling analysis using the kernel method for nonequilibrium relaxation.

PubMed

Echinaka, Yuki; Ozeki, Yukiyasu

2016-10-01

The dynamical scaling analysis for the Kosterlitz-Thouless transition in the nonequilibrium relaxation method is improved by the use of Bayesian statistics and the kernel method. This allows data to be fitted to a scaling function without using any parametric model function, which makes the results more reliable and reproducible and enables automatic and faster parameter estimation. Applying this method, the bootstrap method is introduced and a numerical discrimination for the transition type is proposed.

Phenolic Analysis and Theoretic Design for Chinese Commercial Wines' Authentication.

PubMed

Li, Si-Yu; Zhu, Bao-Qing; Reeves, Malcolm J; Duan, Chang-Qing

2018-01-01

To develop a robust tool for Chinese commercial wines' varietal, regional, and vintage authentication, phenolic compounds in 121 Chinese commercial dry red wines were detected and quantified by using high-performance liquid chromatography triple-quadrupole mass spectrometry (HPLC-QqQ-MS/MS), and differentiation abilities of principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were compared. Better than PCA and PLS-DA, OPLS-DA models used to differentiate wines according to their varieties (Cabernet Sauvignon or other varieties), regions (east or west Cabernet Sauvignon wines), and vintages (young or old Cabernet Sauvignon wines) were ideally established. The S-plot provided in OPLS-DA models showed the key phenolic compounds which were both statistically and biochemically significant in sample differentiation. Besides, the potential of the OPLS-DA models in deeper sample differentiating of more detailed regional and vintage information of wines was proved optimistic. On the basis of our results, a promising theoretic design for wine authentication was further proposed for the first time, which might be helpful in practical authentication of more commercial wines. The phenolic data of 121 Chinese commercial dry red wines was processed with different statistical tools for varietal, regional, and vintage differentiation. A promising theoretical design was summarized, which might be helpful for wine authentication in practical situation. © 2017 Institute of Food Technologists®.

The application of artificial intelligence to microarray data: identification of a novel gene signature to identify bladder cancer progression.

PubMed

Catto, James W F; Abbod, Maysam F; Wild, Peter J; Linkens, Derek A; Pilarsky, Christian; Rehman, Ishtiaq; Rosario, Derek J; Denzinger, Stefan; Burger, Maximilian; Stoehr, Robert; Knuechel, Ruth; Hartmann, Arndt; Hamdy, Freddie C

2010-03-01

New methods for identifying bladder cancer (BCa) progression are required. Gene expression microarrays can reveal insights into disease biology and identify novel biomarkers. However, these experiments produce large datasets that are difficult to interpret. To develop a novel method of microarray analysis combining two forms of artificial intelligence (AI): neurofuzzy modelling (NFM) and artificial neural networks (ANN) and validate it in a BCa cohort. We used AI and statistical analyses to identify progression-related genes in a microarray dataset (n=66 tumours, n=2800 genes). The AI-selected genes were then investigated in a second cohort (n=262 tumours) using immunohistochemistry. We compared the accuracy of AI and statistical approaches to identify tumour progression. AI identified 11 progression-associated genes (odds ratio [OR]: 0.70; 95% confidence interval [CI], 0.56-0.87; p=0.0004), and these were more discriminate than genes chosen using statistical analyses (OR: 1.24; 95% CI, 0.96-1.60; p=0.09). The expression of six AI-selected genes (LIG3, FAS, KRT18, ICAM1, DSG2, and BRCA2) was determined using commercial antibodies and successfully identified tumour progression (concordance index: 0.66; log-rank test: p=0.01). AI-selected genes were more discriminate than pathologic criteria at determining progression (Cox multivariate analysis: p=0.01). Limitations include the use of statistical correlation to identify 200 genes for AI analysis and that we did not compare regression identified genes with immunohistochemistry. AI and statistical analyses use different techniques of inference to determine gene-phenotype associations and identify distinct prognostic gene signatures that are equally valid. We have identified a prognostic gene signature whose members reflect a variety of carcinogenic pathways that could identify progression in non-muscle-invasive BCa. 2009 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Customizing national models for a medical center's population to rapidly identify patients at high risk of 30-day all-cause hospital readmission following a heart failure hospitalization.

PubMed

Cox, Zachary L; Lai, Pikki; Lewis, Connie M; Lindenfeld, JoAnn; Collins, Sean P; Lenihan, Daniel J

2018-05-28

Nationally-derived models predicting 30-day readmissions following heart failure (HF) hospitalizations yield insufficient discrimination for institutional use. Develop a customized readmission risk model from Medicare-employed and institutionally-customized risk factors and compare the performance against national models in a medical center. Medicare patients age ≥ 65 years hospitalized for HF (n = 1,454) were studied in a derivation cohort and in a separate validation cohort (n = 243). All 30-day hospital readmissions were documented. The primary outcome was risk discrimination (c-statistic) compared to national models. A customized model demonstrated improved discrimination (c-statistic 0.72; 95% CI 0.69 - 0.74) compared to national models (c-statistics of 0.60 and 0.61) with a c-statistic of 0.63 in the validation cohort. Compared to national models, a customized model demonstrated superior readmission risk profiling by distinguishing a high-risk (38.3%) from a low-risk (9.4%) quartile. A customized model improved readmission risk discrimination from HF hospitalizations compared to national models. Copyright © 2018 Elsevier Inc. All rights reserved.

Analysis of biochemical genetic data on Jewish populations: II. Results and interpretations of heterogeneity indices and distance measures with respect to standards.

PubMed Central

Karlin, S; Kenett, R; Bonné-Tamir, B

1979-01-01

A nonparametric statistical methodology is used for the analysis of biochemical frequency data observed on a series of nine Jewish and six non-Jewish populations. Two categories of statistics are used: heterogeneity indices and various distance measures with respect to a standard. The latter are more discriminating in exploiting historical, geographical and culturally relevant information. A number of partial orderings and distance relationships among the populations are determined. Our concern in this study is to analyze similarities and differences among the Jewish populations, in terms of the gene frequency distributions for a number of genetic markers. Typical questions discussed are as follows: These Jewish populations differ in certain morphological and anthropometric traits. Are there corresponding differences in biochemical genetic constitution? How can we assess the extent of heterogeneity between and within groupings? Which class of markers (blood typings or protein loci) discriminates better among the separate populations? The results are quite surprising. For example, we found the Ashkenazi, Sephardi and Iraqi Jewish populations to be consistently close in genetic constitution and distant from all the other populations, namely the Yemenite and Cochin Jews, the Arabs, and the non-Jewish German and Russian populations. We found the Polish Jewish community the most heterogeneous among all Jewish populations. The blood loci discriminate better than the protein loci. A number of possible interpretations and hypotheses for these and other results are offered. The method devised for this analysis should prove useful in studying similarities and differences for other groups of populations for which substantial biochemical polymorphic data are available. PMID:380330

Validation of a proposal for evaluating hospital infection control programs.

PubMed

Silva, Cristiane Pavanello Rodrigues; Lacerda, Rúbia Aparecida

2011-02-01

To validate the construct and discriminant properties of a hospital infection prevention and control program. The program consisted of four indicators: technical-operational structure; operational prevention and control guidelines; epidemiological surveillance system; and prevention and control activities. These indicators, with previously validated content, were applied to 50 healthcare institutions in the city of São Paulo, Southeastern Brazil, in 2009. Descriptive statistics were used to characterize the hospitals and indicator scores, and Cronbach's α coefficient was used to evaluate the internal consistency. The discriminant validity was analyzed by comparing indicator scores between groups of hospitals: with versus without quality certification. The construct validity analysis was based on exploratory factor analysis with a tetrachoric correlation matrix. The indicators for the technical-operational structure and epidemiological surveillance presented almost 100% conformity in the whole sample. The indicators for the operational prevention and control guidelines and the prevention and control activities presented internal consistency ranging from 0.67 to 0.80. The discriminant validity of these indicators indicated higher and statistically significant mean conformity scores among the group of institutions with healthcare certification or accreditation processes. In the construct validation, two dimensions were identified for the operational prevention and control guidelines: recommendations for preventing hospital infection and recommendations for standardizing prophylaxis procedures, with good correlation between the analysis units that formed the guidelines. The same was found for the prevention and control activities: interfaces with treatment units and support units were identified. Validation of the measurement properties of the hospital infection prevention and control program indicators made it possible to develop a tool for evaluating these programs in an ethical and scientific manner in order to obtain a quality diagnosis in this field.

Discriminant analysis of cardiovascular and respiratory variables for classification of road cyclists by specialty.

PubMed

Nikolić, Biljana; Martinović, Jelena; Matić, Milan; Stefanović, Đorđe

2018-05-29

Different variables determine the performance of cyclists, which brings up the question how these parameters may help in their classification by specialty. The aim of the study was to determine differences in cardiorespiratory parameters of male cyclists according to their specialty, flat rider (N=21), hill rider (N=35) and sprinter (N=20) and obtain the multivariate model for further cyclists classification by specialties, based on selected variables. Seventeen variables were measured at submaximal and maximum load on the cycle ergometer Cosmed E 400HK (Cosmed, Rome, Italy) (initial 100W with 25W increase, 90-100 rpm). Multivariate discriminant analysis was used to determine which variables group cyclists within their specialty, and to predict which variables can direct cyclists to a particular specialty. Among nine variables that statistically contribute to the discriminant power of the model, achieved power on the anaerobic threshold and the produced CO2 had the biggest impact. The obtained discriminatory model correctly classified 91.43% of flat riders, 85.71% of hill riders, while sprinters were classified completely correct (100%), i.e. 92.10% of examinees were correctly classified, which point out the strength of the discriminatory model. Respiratory indicators mostly contribute to the discriminant power of the model, which may significantly contribute to training practice and laboratory tests in future.

Characterization and Discrimination of Oueslati Virgin Olive Oils from Adult and Young Trees in Different Ripening Stages Using Sterols, Pigments, and Alcohols in Tandem with Chemometrics.

PubMed

Chtourou, Fatma; Jabeur, Hazem; Lazzez, Ayda; Bouaziz, Mohamed

2017-05-03

Dynamics of squalene, sterol, aliphatic alcohol, pigment, and triterpenic diol accumulations in olive oils from adult and young trees of the Oueslati cultivar were studied for two consecutive years, 2013-2014 and 2014-2015. Data were compared statistically for differences by age of trees, maturation of olive, and year of harvesting. Results showed that the mean campesterol content in olive oil from adult trees at the green stage of maturation was significantly (p < 0.02) above the limit established by IOC legislation. However, the mean values of campesterol and Δ-7-stigmastenol were significantly (p < 0.01) above the limits in oils from young trees at the black stage of ripening. Principal component analysis was applied to alcohols, squalene, pigments, and sterols having noncompliance with the legislation. Then, data of 36 samples were subjected to a discriminant analysis with "maturation" as grouping variable and principal components as input variables. The model revealed clear discrimination of each tree age/maturation stage group.

Stability and bias of classification rates in biological applications of discriminant analysis

USGS Publications Warehouse

Williams, B.K.; Titus, K.; Hines, J.E.

1990-01-01

We assessed the sampling stability of classification rates in discriminant analysis by using a factorial design with factors for multivariate dimensionality, dispersion structure, configuration of group means, and sample size. A total of 32,400 discriminant analyses were conducted, based on data from simulated populations with appropriate underlying statistical distributions. Simulation results indicated strong bias in correct classification rates when group sample sizes were small and when overlap among groups was high. We also found that stability of the correct classification rates was influenced by these factors, indicating that the number of samples required for a given level of precision increases with the amount of overlap among groups. In a review of 60 published studies, we found that 57% of the articles presented results on classification rates, though few of them mentioned potential biases in their results. Wildlife researchers should choose the total number of samples per group to be at least 2 times the number of variables to be measured when overlap among groups is low. Substantially more samples are required as the overlap among groups increases

Discrimination of geographical origin of lentils (Lens culinaris Medik.) using isotope ratio mass spectrometry combined with chemometrics.

PubMed

Longobardi, F; Casiello, G; Cortese, M; Perini, M; Camin, F; Catucci, L; Agostiano, A

2015-12-01

The aim of this study was to predict the geographic origin of lentils by using isotope ratio mass spectrometry (IRMS) in combination with chemometrics. Lentil samples from two origins, i.e. Italy and Canada, were analysed obtaining the stable isotope ratios of δ(13)C, δ(15)N, δ(2)H, δ(18)O, and δ(34)S. A comparison between median values (U-test) highlighted statistically significant differences (p<0.05) for all isotopic parameters between the lentils produced in these two different geographic areas, except for δ(15)N. Applying principal component analysis, grouping of samples was observed on the basis of origin but with overlapping zones; consequently, two supervised discriminant techniques, i.e. partial least squares discriminant analysis and k-nearest neighbours algorithm were used. Both models showed good performances with external prediction abilities of about 93% demonstrating the suitability of the methods developed. Subsequently, isotopic determinations were also performed on the protein and starch fractions and the relevant results are reported. Copyright © 2015 Elsevier Ltd. All rights reserved.

Discrimination and characterization of strawberry juice based on electronic nose and tongue: comparison of different juice processing approaches by LDA, PLSR, RF, and SVM.

PubMed

Qiu, Shanshan; Wang, Jun; Gao, Liping

2014-07-09

An electronic nose (E-nose) and an electronic tongue (E-tongue) have been used to characterize five types of strawberry juices based on processing approaches (i.e., microwave pasteurization, steam blanching, high temperature short time pasteurization, frozen-thawed, and freshly squeezed). Juice quality parameters (vitamin C, pH, total soluble solid, total acid, and sugar/acid ratio) were detected by traditional measuring methods. Multivariate statistical methods (linear discriminant analysis (LDA) and partial least squares regression (PLSR)) and neural networks (Random Forest (RF) and Support Vector Machines) were employed to qualitative classification and quantitative regression. E-tongue system reached higher accuracy rates than E-nose did, and the simultaneous utilization did have an advantage in LDA classification and PLSR regression. According to cross-validation, RF has shown outstanding and indisputable performances in the qualitative and quantitative analysis. This work indicates that the simultaneous utilization of E-nose and E-tongue can discriminate processed fruit juices and predict quality parameters successfully for the beverage industry.

Supervised chemical pattern recognition in almond ( Prunus dulcis ) Portuguese PDO cultivars: PCA- and LDA-based triennial study.

PubMed

Barreira, João C M; Casal, Susana; Ferreira, Isabel C F R; Peres, António M; Pereira, José Alberto; Oliveira, M Beatriz P P

2012-09-26

Almonds harvested in three years in Trás-os-Montes (Portugal) were characterized to find differences among Protected Designation of Origin (PDO) Amêndoa Douro and commercial non-PDO cultivars. Nutritional parameters, fiber (neutral and acid detergent fibers, acid detergent lignin, and cellulose), fatty acids, triacylglycerols (TAG), and tocopherols were evaluated. Fat was the major component, followed by carbohydrates, protein, and moisture. Fatty acids were mostly detected as monounsaturated and polyunsaturated forms, with relevance of oleic and linoleic acids. Accordingly, 1,2,3-trioleoylglycerol and 1,2-dioleoyl-3-linoleoylglycerol were the major TAG. α-Tocopherol was the leading tocopherol. To verify statistical differences among PDO and non-PDO cultivars independent of the harvest year, data were analyzed through an analysis of variance, a principal component analysis, and a linear discriminant analysis (LDA). These differences identified classification parameters, providing an important tool for authenticity purposes. The best results were achieved with TAG analysis coupled with LDA, which proved its effectiveness to discriminate almond cultivars.

Prediction of the space adaptation syndrome

NASA Technical Reports Server (NTRS)

Reschke, M. F.; Homick, J. L.; Ryan, P.; Moseley, E. C.

1984-01-01

The univariate and multivariate relationships of provocative measures used to produce motion sickness symptoms were described. Normative subjects were used to develop and cross-validate sets of linear equations that optimally predict motion sickness in parabolic flights. The possibility of reducing the number of measurements required for prediction was assessed. After describing the variables verbally and statistically for 159 subjects, a factor analysis of 27 variables was completed to improve understanding of the relationships between variables and to reduce the number of measures for prediction purposes. The results of this analysis show that none of variables are significantly related to the responses to parabolic flights. A set of variables was selected to predict responses to KC-135 flights. A series of discriminant analyses were completed. Results indicate that low, moderate, or severe susceptibility could be correctly predicted 64 percent and 53 percent of the time on original and cross-validation samples, respectively. Both the factor analysis and the discriminant analysis provided no basis for reducing the number of tests.

Sugar and acid content of Citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis.

PubMed

Song, Seung Yeob; Lee, Young Koung; Kim, In-Jung

2016-01-01

A high-throughput screening system for Citrus lines were established with higher sugar and acid contents using Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. FT-IR spectra confirmed typical spectral differences between the frequency regions of 950-1100 cm(-1), 1300-1500 cm(-1), and 1500-1700 cm(-1). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate five Citrus lines into three separate clusters corresponding to their taxonomic relationships. The quantitative predictive modeling of sugar and acid contents from Citrus fruits was established using partial least square regression algorithms from FT-IR spectra. The regression coefficients (R(2)) between predicted values and estimated sugar and acid content values were 0.99. These results demonstrate that by using FT-IR spectra and applying quantitative prediction modeling to Citrus sugar and acid contents, excellent Citrus lines can be early detected with greater accuracy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Integration of Advanced Statistical Analysis Tools and Geophysical Modeling

DTIC Science & Technology

2012-08-01

Carin Duke University Douglas Oldenburg University of British Columbia Stephen Billings Leonard Pasion Laurens Beran Sky Research...data processing for UXO discrimination is the time (or frequency) dependent dipole model (Bell and Barrow (2001), Pasion and Oldenburg (2001), Zhang...described by a bimodal distribution (i.e. two Gaussians, see Pasion (2007)). Data features are nonetheless useful when data quality is not sufficient

Vine Water Deficit Impacts Aging Bouquet in Fine Red Bordeaux Wine

PubMed Central

Picard, Magali; van Leeuwen, Cornelis; Guyon, François; Gaillard, Laetitia; de Revel, Gilles; Marchand, Stéphanie

2017-01-01

The aim of this study was to investigate the influence of vine water status on bouquet typicality, revealed after aging, and the perception of three aromatic notes (mint, truffle, and undergrowth) in bottled fine red Bordeaux wines. To address the issue of the role of vine water deficit in the overall quality of fine aged wines, a large set of wines from four Bordeaux appellations were subjected to sensory analysis. As vine water status can be characterized by carbon isotope discrimination (δ13C), this ratio was quantified for each wine studied. Statistical analyses combining δ13C and sensory data highlighted that δ13C-values discriminated effectively between the most- and least-typical wines. In addition, Principal Component Analysis (PCA) revealed correlations between δ13C-values and truffle, undergrowth, and mint aromatic notes, three characteristics of the red Bordeaux wine aging bouquet. These correlations were confirmed to be significant using a Spearman statistical test. This study highlighted for the first time that vine water deficit positively relates to the perception of aging bouquet typicality, as well as the expression of its key aromatic nuances. PMID:28824904

Recurrence network measures for hypothesis testing using surrogate data: Application to black hole light curves

NASA Astrophysics Data System (ADS)

Jacob, Rinku; Harikrishnan, K. P.; Misra, R.; Ambika, G.

2018-01-01

Recurrence networks and the associated statistical measures have become important tools in the analysis of time series data. In this work, we test how effective the recurrence network measures are in analyzing real world data involving two main types of noise, white noise and colored noise. We use two prominent network measures as discriminating statistic for hypothesis testing using surrogate data for a specific null hypothesis that the data is derived from a linear stochastic process. We show that the characteristic path length is especially efficient as a discriminating measure with the conclusions reasonably accurate even with limited number of data points in the time series. We also highlight an additional advantage of the network approach in identifying the dimensionality of the system underlying the time series through a convergence measure derived from the probability distribution of the local clustering coefficients. As examples of real world data, we use the light curves from a prominent black hole system and show that a combined analysis using three primary network measures can provide vital information regarding the nature of temporal variability of light curves from different spectroscopic classes.

Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis.

PubMed

Ren, Y Y; Zhou, L C; Yang, L; Liu, P Y; Zhao, B W; Liu, H X

2016-09-01

The paper highlights the use of the logistic regression (LR) method in the construction of acceptable statistically significant, robust and predictive models for the classification of chemicals according to their aquatic toxic modes of action. Essentials accounting for a reliable model were all considered carefully. The model predictors were selected by stepwise forward discriminant analysis (LDA) from a combined pool of experimental data and chemical structure-based descriptors calculated by the CODESSA and DRAGON software packages. Model predictive ability was validated both internally and externally. The applicability domain was checked by the leverage approach to verify prediction reliability. The obtained models are simple and easy to interpret. In general, LR performs much better than LDA and seems to be more attractive for the prediction of the more toxic compounds, i.e. compounds that exhibit excess toxicity versus non-polar narcotic compounds and more reactive compounds versus less reactive compounds. In addition, model fit and regression diagnostics was done through the influence plot which reflects the hat-values, studentized residuals, and Cook's distance statistics of each sample. Overdispersion was also checked for the LR model. The relationships between the descriptors and the aquatic toxic behaviour of compounds are also discussed.

Equal Employment + Equal Pay = Multiple Problems for Colleges and Universities

ERIC Educational Resources Information Center

Steinbach, Sheldon Elliot; Reback, Joyce E.

1974-01-01

Issues involved in government regulation of university employment practices are discussed: confidentiality of records, pregnancy as a disability, alleged discrimination in benefits, tests and other employment criteria, seniority and layoff, reverse discrimination, use of statistics for determination of discrimination, and the Equal Pay Act. (JT)

The Effects of Haloperidol on Learning and Behavior in Autistic Children.

ERIC Educational Resources Information Center

Campbell, Magda; And Others

1982-01-01

Statistically, haloperidol was significantly superior to placebo in reducing behavioral symptoms. In a discrimination learning paradigm, autistic children receiving haloperidol learned the discrimination while those on placebo did not. Discrimination attained on haloperidol was retained when the children were switched to placebo. (Author)

Feature extraction and selection from volatile compounds for analytical classification of Chinese red wines from different varieties.

PubMed

Zhang, Jian; Li, Li; Gao, Nianfa; Wang, Depei; Gao, Qiang; Jiang, Shengping

2010-03-10

This work was undertaken to evaluate whether it is possible to determine the variety of a Chinese wine on the basis of its volatile compounds, and to investigate if discrimination models could be developed with the experimental wines that could be used for the commercial ones. A headspace solid-phase microextraction gas chromatographic (HS-SPME-GC) procedure was used to determine the volatile compounds and a blind analysis based on Ac/Ais (peak area of volatile compound/peak area of internal standard) was carried out for statistical purposes. One way analysis of variance (ANOVA), principal component analysis (PCA) and stepwise linear discriminant analysis (SLDA) were used to process data and to develop discriminant models. Only 11 peaks enabled to differentiate and classify the experimental wines. SLDA allowed 100% recognition ability for three grape varieties, 100% prediction ability for Cabernet Sauvignon and Cabernet Gernischt wines, but only 92.31% for Merlot wines. A more valid and robust way was to use the PCA scores to do the discriminant analysis. When we performed SLDA this way, 100% recognition ability and 100% prediction ability were obtained. At last, 11 peaks which selected by SLDA from raw analysis set had been identified. When we demonstrated the models using commercial wines, the models showed 100% recognition ability for the wines collected directly from winery and without ageing, but only 65% for the others. Therefore, the varietal factor was currently discredited as a differentiating parameter for commercial wines in China. Nevertheless, this method could be applied as a screening tool and as a complement to other methods for grape base liquors which do not need ageing and blending procedures. 2010 Elsevier B.V. All rights reserved.

[Application of Bayes Probability Model in Differentiation of Yin and Yang Jaundice Syndromes in Neonates].

PubMed

Mu, Chun-sun; Zhang, Ping; Kong, Chun-yan; Li, Yang-ning

2015-09-01

To study the application of Bayes probability model in differentiating yin and yang jaundice syndromes in neonates. Totally 107 jaundice neonates who admitted to hospital within 10 days after birth were assigned to two groups according to syndrome differentiation, 68 in the yang jaundice syndrome group and 39 in the yin jaundice syndrome group. Data collected for neonates were factors related to jaundice before, during and after birth. Blood routines, liver and renal functions, and myocardial enzymes were tested on the admission day or the next day. Logistic regression model and Bayes discriminating analysis were used to screen factors important for yin and yang jaundice syndrome differentiation. Finally, Bayes probability model for yin and yang jaundice syndromes was established and assessed. Factors important for yin and yang jaundice syndrome differentiation screened by Logistic regression model and Bayes discriminating analysis included mothers' age, mother with gestational diabetes mellitus (GDM), gestational age, asphyxia, or ABO hemolytic diseases, red blood cell distribution width (RDW-SD), platelet-large cell ratio (P-LCR), serum direct bilirubin (DBIL), alkaline phosphatase (ALP), cholinesterase (CHE). Bayes discriminating analysis was performed by SPSS to obtain Bayes discriminant function coefficient. Bayes discriminant function was established according to discriminant function coefficients. Yang jaundice syndrome: y1= -21. 701 +2. 589 x mother's age + 1. 037 x GDM-17. 175 x asphyxia + 13. 876 x gestational age + 6. 303 x ABO hemolytic disease + 2.116 x RDW-SD + 0. 831 x DBIL + 0. 012 x ALP + 1. 697 x LCR + 0. 001 x CHE; Yin jaundice syndrome: y2= -33. 511 + 2.991 x mother's age + 3.960 x GDM-12. 877 x asphyxia + 11. 848 x gestational age + 1. 820 x ABO hemolytic disease +2. 231 x RDW-SD +0. 999 x DBIL +0. 023 x ALP +1. 916 x LCR +0. 002 x CHE. Bayes discriminant function was hypothesis tested and got Wilks' λ =0. 393 (P =0. 000). So Bayes discriminant function was proved to be with statistical difference. To check Bayes probability model in discriminating yin and yang jaundice syndromes, coincidence rates for yin and yang jaundice syndromes were both 90% plus. Yin and yang jaundice syndromes in neonates could be accurately judged by Bayesian discriminating functions.

Spectral reflectance of surface soils - A statistical analysis

NASA Technical Reports Server (NTRS)

Crouse, K. R.; Henninger, D. L.; Thompson, D. R.

1983-01-01

The relationship of the physical and chemical properties of soils to their spectral reflectance as measured at six wavebands of Thematic Mapper (TM) aboard NASA's Landsat-4 satellite was examined. The results of performing regressions of over 20 soil properties on the six TM bands indicated that organic matter, water, clay, cation exchange capacity, and calcium were the properties most readily predicted from TM data. The middle infrared bands, bands 5 and 7, were the best bands for predicting soil properties, and the near infrared band, band 4, was nearly as good. Clustering 234 soil samples on the TM bands and characterizing the clusters on the basis of soil properties revealed several clear relationships between properties and reflectance. Discriminant analysis found organic matter, fine sand, base saturation, sand, extractable acidity, and water to be significant in discriminating among clusters.

So how far have we come? Pestilent and persistent gender gap in pay.

PubMed

Gibelman, Margaret

2003-01-01

This article explores the issue of women's salaries in the human services within a comparative framework of many service occupations. An analysis of year-end 1998 data from the Bureau of Labor Statistics clearly demonstrates that salary disparities continue to exist between men and women. The author argues that these differences are based on continued patterns of discrimination, despite a plethora of policy initiatives dating back to the 1960s civil rights era to address gender discrimination in the workplace. Relevant policies are reviewed and assessed in terms of how far we have come in achieving pay equity between men and women. Several strategic directions to combat inequities are discussed, including public and professional education; individual, group, and professional advocacy; and targeted policy practice. Parallels are drawn between the gender discrimination experienced by social workers and client groups served.

Feeling younger and identifying with older adults: Testing two routes to maintaining well-being in the face of age discrimination

PubMed Central

Stroebe, Katherine; Scheibe, Susanne; Postmes, Tom; Van Yperen, Nico W.

2017-01-01

Integrating the social identity and aging literatures, this work tested the hypothesis that there are two independent, but simultaneous, responses by which adults transitioning into old age can buffer themselves against age discrimination: an individual response, which entails adopting a younger subjective age when facing discrimination, and a collective response, which involves increasing identification with the group of older adults. In three experimental studies with a total number of 488 older adults (50 to 75 years of age), we manipulated age discrimination in a job application scenario and measured the effects of both responses on perceived health and self-esteem. Statistical analyses include individual study results as well as a meta-analysis on the combined results of the three studies. Findings show consistent evidence only for the individual response, which was in turn associated with well-being. Furthermore, challenging previous research, the two responses (adopting a younger subjective age and increasing group identification) were not only theoretically, but also empirically distinct. This research complements prior research by signaling the value of considering both responses to discrimination as complementary rather than mutually exclusive. PMID:29117257

How Can Dolphins Recognize Fish According to Their Echoes? A Statistical Analysis of Fish Echoes

PubMed Central

Yovel, Yossi; Au, Whitlow W. L.

2010-01-01

Echo-based object classification is a fundamental task of animals that use a biosonar system. Dolphins and porpoises should be able to rely on echoes to discriminate a predator from a prey or to select a desired prey from an undesired object. Many studies have shown that dolphins and porpoises can discriminate between objects according to their echoes. All of these studies however, used unnatural objects that can be easily characterized in human terminologies (e.g., metallic spheres, disks, cylinders). In this work, we collected real fish echoes from many angles of acquisition using a sonar system that mimics the emission properties of dolphins and porpoises. We then tested two alternative statistical approaches in classifying these echoes. Our results suggest that fish species can be classified according to echoes returning from porpoise- and dolphin-like signals. These results suggest how dolphins and porpoises can classify fish based on their echoes and provide some insight as to which features might enable the classification. PMID:21124908

How can dolphins recognize fish according to their echoes? A statistical analysis of fish echoes.

PubMed

Yovel, Yossi; Au, Whitlow W L

2010-11-19

Echo-based object classification is a fundamental task of animals that use a biosonar system. Dolphins and porpoises should be able to rely on echoes to discriminate a predator from a prey or to select a desired prey from an undesired object. Many studies have shown that dolphins and porpoises can discriminate between objects according to their echoes. All of these studies however, used unnatural objects that can be easily characterized in human terminologies (e.g., metallic spheres, disks, cylinders). In this work, we collected real fish echoes from many angles of acquisition using a sonar system that mimics the emission properties of dolphins and porpoises. We then tested two alternative statistical approaches in classifying these echoes. Our results suggest that fish species can be classified according to echoes returning from porpoise- and dolphin-like signals. These results suggest how dolphins and porpoises can classify fish based on their echoes and provide some insight as to which features might enable the classification.

Mapping vegetation communities using statistical data fusion in the Ozark National Scenic Riverways, Missouri, USA

USGS Publications Warehouse

Chastain, R.A.; Struckhoff, M.A.; He, H.S.; Larsen, D.R.

2008-01-01

A vegetation community map was produced for the Ozark National Scenic Riverways consistent with the association level of the National Vegetation Classification System. Vegetation communities were differentiated using a large array of variables derived from remote sensing and topographic data, which were fused into independent mathematical functions using a discriminant analysis classification approach. Remote sensing data provided variables that discriminated vegetation communities based on differences in color, spectral reflectance, greenness, brightness, and texture. Topographic data facilitated differentiation of vegetation communities based on indirect gradients (e.g., landform position, slope, aspect), which relate to variations in resource and disturbance gradients. Variables derived from these data sources represent both actual and potential vegetation community patterns on the landscape. A hybrid combination of statistical and photointerpretation methods was used to obtain an overall accuracy of 63 percent for a map with 49 vegetation community and land-cover classes, and 78 percent for a 33-class map of the study area.

Accuracy of metric sex analysis of skeletal remains using Fordisc based on a recent skull collection.

PubMed

Ramsthaler, F; Kreutz, K; Verhoff, M A

2007-11-01

It has been generally accepted in skeletal sex determination that the use of metric methods is limited due to the population dependence of the multivariate algorithms. The aim of the study was to verify the applicability of software-based sex estimations outside the reference population group for which discriminant equations have been developed. We examined 98 skulls from recent forensic cases of known age, sex, and Caucasian ancestry from cranium collections in Frankfurt and Mainz (Germany) to determine the accuracy of sex determination using the statistical software solution Fordisc which derives its database and functions from the US American Forensic Database. In a comparison between metric analysis using Fordisc and morphological determination of sex, average accuracy for both sexes was 86 vs 94%, respectively, and males were identified more accurately than females. The ratio of the true test result rate to the false test result rate was not statistically different for the two methodological approaches at a significance level of 0.05 but was statistically different at a level of 0.10 (p=0.06). Possible explanations for this difference comprise different ancestry, age distribution, and socio-economic status compared to the Fordisc reference sample. It is likely that a discriminant function analysis on the basis of more similar European reference samples will lead to more valid and reliable sexing results. The use of Fordisc as a single method for the estimation of sex of recent skeletal remains in Europe cannot be recommended without additional morphological assessment and without a built-in software update based on modern European reference samples.

Discrimination History, Backlash Fear, and Ethnic Identity among Arab Americans: Post-9/11 Snapshots

ERIC Educational Resources Information Center

Nassar-McMillan, Sylvia C.; Lambert, Richard G.; Hakim-Larson, Julie

2011-01-01

The authors examined discrimination history, backlash fear, and ethnic identity of Arab Americans nationally at 3 times, beginning shortly after September 11, 2001. Relations between variables were moderate, and discrimination history and backlash fear were statistically significant predictors of ethnic identity. Implications for acculturation and…

Space-time patterns in ignimbrite compositions revealed by GIS and R based statistical analysis

NASA Astrophysics Data System (ADS)

Brandmeier, Melanie; Wörner, Gerhard

2017-04-01

GIS-based multivariate statistical and geospatial analysis of a compilation of 890 geochemical and ca. 1,200 geochronological data for 194 mapped ignimbrites from Central Andes documents the compositional and temporal pattern of large volume ignimbrites (so-called "ignimbrite flare-ups") during Neogene times. Rapid advances in computational sciences during the past decade lead to a growing pool of algorithms for multivariate statistics on big datasets with many predictor variables. This study uses the potential of R and ArcGIS and applies cluster (CA) and linear discriminant analysis (LDA) on log-ratio transformed spatial data. CA on major and trace element data allows to group ignimbrites according to their geochemical characteristics into rhyolitic and a dacitic "end-members" and differentiates characteristic trace element signatures with respect to Eu anomaly, depletion of MREEs and variable enrichment in LREE. To highlight these distinct compositional signatures, we applied LDA to selected ignimbrites for which comprehensive data sets were available. The most important predictors for discriminating ignimbrites are La (LREE), Yb (HREE), Eu, Al2O3, K2O, P2O5, MgO, FeOt and TiO2. However, other REEs such as Gd, Pr, Tm, Sm and Er also contribute to the discrimination functions. Significant compositional differences were found between the older (>14 Ma) large-volume plateau-forming ignimbrites in northernmost Chile and southern Peru and the younger (< 10 Ma) Altiplano-Puna-Volcanic-Complex ignimbrites that are of similar volumes. Older ignimbrites are less depleted in HREEs and less radiogenic in Sr isotopes, indicating smaller crustal contributions during evolution in thinner and thermally less evolved crust. These compositional variations indicate a relation to crustal thickening with a "transition" from plagioclase to amphibole and garnet residual mineralogy between 13 to 9 Ma. We correlate compositional and volumetric variations to the N-S passage of the Juan-Fernandéz ridge and crustal shortening and thickening during the past 26 Ma. The value of GIS and multivariate statistics in comparison to traditional geochemical parameters are highlighted working with large datasets with many predictors in a spatial and temporal context. Algorithms implemented in R allow taking advantage of an n-dimensional space and, thus, of subtle compositional differences contained in the data, while space-time patterns can be analyzed easily in GIS.

Marginalized identities, discrimination burden, and mental health: Empirical exploration of an interpersonal-level approach to modeling intersectionality

PubMed Central

Seng, Julia S; Lopez, William D; Sperlich, Mickey; Hamama, Lydia; Meldrum, Caroline D Reed

2012-01-01

Intersectionality is a term used to describe the intersecting effects of race, class, gender, and other marginalizing characteristics that contribute to social identity and affect health. Adverse health effects are thought to occur via social processes including discrimination and structural inequalities (i.e., reduced opportunities for education and income). Although intersectionality has been well-described conceptually, approaches to modeling it in quantitative studies of health outcomes are still emerging. Strategies to date have focused on modeling demographic characteristics as proxies for structural inequality. Our objective was to extend these methodological efforts by modeling intersectionality across three levels: structural, contextual, and interpersonal, consistent with a social-ecological framework. We conducted a secondary analysis of a database that included two components of a widely used survey instrument, the Everyday Discrimination Scale. We operationalized a meso- or interpersonal-level of intersectionality using two variables, the frequency score of discrimination experiences and the sum of characteristics listed as reasons for these (i.e., the person’s race, ethnicity, gender, sexual orientation, nationality, religion, disability or pregnancy status, or physical appearance). We controlled for two structural inequality factors (low education, poverty) and three contextual factors (high crime neighborhood, racial minority status, and trauma exposures). The outcome variables we modeled were posttraumatic stress disorder symptoms and a quality of life index score. We used data from 619 women who completed the Everyday Discrimination Scale for a perinatal study in the U.S. state of Michigan. Statistical results indicated that the two interpersonal-level variables (i.e., number of marginalized identities, frequency of discrimination) explained 15% of variance in posttraumatic stress symptoms and 13% of variance in quality of life scores, improving the predictive value of the models over those using structural inequality and contextual factors alone. This study’s results point to instrument development ideas to improve the statistical modeling of intersectionality in health and social science research. PMID:23089613

Marginalized identities, discrimination burden, and mental health: empirical exploration of an interpersonal-level approach to modeling intersectionality.

PubMed

Seng, Julia S; Lopez, William D; Sperlich, Mickey; Hamama, Lydia; Reed Meldrum, Caroline D

2012-12-01

Intersectionality is a term used to describe the intersecting effects of race, class, gender, and other marginalizing characteristics that contribute to social identity and affect health. Adverse health effects are thought to occur via social processes including discrimination and structural inequalities (i.e., reduced opportunities for education and income). Although intersectionality has been well-described conceptually, approaches to modeling it in quantitative studies of health outcomes are still emerging. Strategies to date have focused on modeling demographic characteristics as proxies for structural inequality. Our objective was to extend these methodological efforts by modeling intersectionality across three levels: structural, contextual, and interpersonal, consistent with a social-ecological framework. We conducted a secondary analysis of a database that included two components of a widely used survey instrument, the Everyday Discrimination Scale. We operationalized a meso- or interpersonal-level of intersectionality using two variables, the frequency score of discrimination experiences and the sum of characteristics listed as reasons for these (i.e., the person's race, ethnicity, gender, sexual orientation, nationality, religion, disability or pregnancy status, or physical appearance). We controlled for two structural inequality factors (low education, poverty) and three contextual factors (high crime neighborhood, racial minority status, and trauma exposures). The outcome variables we modeled were posttraumatic stress disorder symptoms and a quality of life index score. We used data from 619 women who completed the Everyday Discrimination Scale for a perinatal study in the U.S. state of Michigan. Statistical results indicated that the two interpersonal-level variables (i.e., number of marginalized identities, frequency of discrimination) explained 15% of variance in posttraumatic stress symptoms and 13% of variance in quality of life scores, improving the predictive value of the models over those using structural inequality and contextual factors alone. This study's results point to instrument development ideas to improve the statistical modeling of intersectionality in health and social science research. Copyright © 2012 Elsevier Ltd. All rights reserved.

Discriminant function analysis as tool for subsurface geologist

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

Chesser, K.

1987-05-01

Sedimentary structures such as cross-bedding control porosity, permeability, and other petrophysical properties in sandstone reservoirs. Understanding the distribution of such structures in the subsurface not only aids in the prediction of reservoir properties but also provides information about depositional environments. Discriminant function analysis (DFA) is a simple yet powerful method incorporating petrophysical data from wireline logs, core analyses, or other sources into groups that have been previously defined through direct observation of sedimentary structures in cores. Once data have been classified into meaningful groups, the geologist can predict the distribution of specific sedimentary structures or important reservoir properties in areasmore » where cores are unavailable. DFA is efficient. Given several variables, DFA will choose the best combination to discriminate among groups. The initial classification function can be computed from relatively few observations, and additional data may be included as necessary. Furthermore, DFA provides quantitative goodness-of-fit estimates for each observation. Such estimates can be used as mapping parameters or to assess risk in petroleum ventures. Petrophysical data from the Skinner sandstone of Strauss field in southeastern Kansas tested the ability of DFA to discriminate between cross-bedded and ripple-bedded sandstones. Petroleum production in Strauss field is largely restricted to the more permeable cross-bedded sandstones. DFA based on permeability correctly placed 80% of samples into cross-bedded or ripple-bedded groups. Addition of formation factor to the discriminant function increased correct classifications to 83% - a small but statistically significant gain.« less

Markov model plus k-word distributions: a synergy that produces novel statistical measures for sequence comparison.

PubMed

Dai, Qi; Yang, Yanchun; Wang, Tianming

2008-10-15

Many proposed statistical measures can efficiently compare biological sequences to further infer their structures, functions and evolutionary information. They are related in spirit because all the ideas for sequence comparison try to use the information on the k-word distributions, Markov model or both. Motivated by adding k-word distributions to Markov model directly, we investigated two novel statistical measures for sequence comparison, called wre.k.r and S2.k.r. The proposed measures were tested by similarity search, evaluation on functionally related regulatory sequences and phylogenetic analysis. This offers the systematic and quantitative experimental assessment of our measures. Moreover, we compared our achievements with these based on alignment or alignment-free. We grouped our experiments into two sets. The first one, performed via ROC (receiver operating curve) analysis, aims at assessing the intrinsic ability of our statistical measures to search for similar sequences from a database and discriminate functionally related regulatory sequences from unrelated sequences. The second one aims at assessing how well our statistical measure is used for phylogenetic analysis. The experimental assessment demonstrates that our similarity measures intending to incorporate k-word distributions into Markov model are more efficient.

Testing for nonlinearity in time series: The method of surrogate data

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

Theiler, J.; Galdrikian, B.; Longtin, A.

1991-01-01

We describe a statistical approach for identifying nonlinearity in time series; in particular, we want to avoid claims of chaos when simpler models (such as linearly correlated noise) can explain the data. The method requires a careful statement of the null hypothesis which characterizes a candidate linear process, the generation of an ensemble of surrogate'' data sets which are similar to the original time series but consistent with the null hypothesis, and the computation of a discriminating statistic for the original and for each of the surrogate data sets. The idea is to test the original time series against themore » null hypothesis by checking whether the discriminating statistic computed for the original time series differs significantly from the statistics computed for each of the surrogate sets. We present algorithms for generating surrogate data under various null hypotheses, and we show the results of numerical experiments on artificial data using correlation dimension, Lyapunov exponent, and forecasting error as discriminating statistics. Finally, we consider a number of experimental time series -- including sunspots, electroencephalogram (EEG) signals, and fluid convection -- and evaluate the statistical significance of the evidence for nonlinear structure in each case. 56 refs., 8 figs.« less

Perceived Discrimination and Self-Rated Health in Europe: Evidence from the European Social Survey (2010)

PubMed Central

Alvarez-Galvez, Javier; Salvador-Carulla, Luis

2013-01-01

Introduction Studies have shown that perceived discrimination has an impact on our physical and mental health. A relevant part of literature has highlighted the influence of discrimination based on race or ethnicity on mental and physical health outcomes. However, the influence of other types of discrimination on health has been understudied. This study is aimed to explore how different types of discrimination are related to our subjective state of health, and so to compare the intensity of these relationships in the European context. Methods We have performed a multilevel ordered analysis on the fifth wave of the European Social Survey (ESS 2010). This dataset has 52,458 units at individual level that are grouped in 26 European countries. In this study, the dependent variable is self-rated health (SRH) that is analyzed in relationship to ten explanatory variables of perceived discrimination: color or race, nationality, religion, language, ethnic group, age, gender, sexuality, disability and others. Results The model identifies statistically significant differences in the effect that diverse types of perceived discrimination can generate on the self-rated health of Europeans. Specifically, this study identifies three well-defined types of perceived discrimination that can be related to poor health outcomes: (1) age discrimination; (2) disability discrimination; and (3) sexuality discrimination. In this sense, the effect on self-rated health of perceived discrimination related to aging and disabilities seems to be more relevant than other types of discrimination in the European context with a longer tradition in literature (e.g. ethnic and/or race-based). Conclusion The present study shows that the relationship between perceived discrimination and health inequities in Europe are not random, but systematically distributed depending on factors such as age, sexuality and disabilities. Therefore the future orientation of EU social policies should aim to reduce the impact of these social determinants on health equity. PMID:24040216

Perceived discrimination and self-rated health in Europe: evidence from the European Social Survey (2010).

PubMed

Alvarez-Galvez, Javier; Salvador-Carulla, Luis

2013-01-01

Studies have shown that perceived discrimination has an impact on our physical and mental health. A relevant part of literature has highlighted the influence of discrimination based on race or ethnicity on mental and physical health outcomes. However, the influence of other types of discrimination on health has been understudied. This study is aimed to explore how different types of discrimination are related to our subjective state of health, and so to compare the intensity of these relationships in the European context. We have performed a multilevel ordered analysis on the fifth wave of the European Social Survey (ESS 2010). This dataset has 52,458 units at individual level that are grouped in 26 European countries. In this study, the dependent variable is self-rated health (SRH) that is analyzed in relationship to ten explanatory variables of perceived discrimination: color or race, nationality, religion, language, ethnic group, age, gender, sexuality, disability and others. The model identifies statistically significant differences in the effect that diverse types of perceived discrimination can generate on the self-rated health of Europeans. Specifically, this study identifies three well-defined types of perceived discrimination that can be related to poor health outcomes: (1) age discrimination; (2) disability discrimination; and (3) sexuality discrimination. In this sense, the effect on self-rated health of perceived discrimination related to aging and disabilities seems to be more relevant than other types of discrimination in the European context with a longer tradition in literature (e.g. ethnic and/or race-based). The present study shows that the relationship between perceived discrimination and health inequities in Europe are not random, but systematically distributed depending on factors such as age, sexuality and disabilities. Therefore the future orientation of EU social policies should aim to reduce the impact of these social determinants on health equity.

Neutron/Gamma-ray discrimination through measures of fit

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

Amiri, Moslem; Prenosil, Vaclav; Cvachovec, Frantisek

2015-07-01

Statistical tests and their underlying measures of fit can be utilized to separate neutron/gamma-ray pulses in a mixed radiation field. In this article, first the application of a sample statistical test is explained. Fit measurement-based methods require true pulse shapes to be used as reference for discrimination. This requirement makes practical implementation of these methods difficult; typically another discrimination approach should be employed to capture samples of neutrons and gamma-rays before running the fit-based technique. In this article, we also propose a technique to eliminate this requirement. These approaches are applied to several sets of mixed neutron and gamma-ray pulsesmore » obtained through different digitizers using stilbene scintillator in order to analyze them and measure their discrimination quality. (authors)« less

Mass detection, localization and estimation for wind turbine blades based on statistical pattern recognition

NASA Astrophysics Data System (ADS)

Colone, L.; Hovgaard, M. K.; Glavind, L.; Brincker, R.

2018-07-01

A method for mass change detection on wind turbine blades using natural frequencies is presented. The approach is based on two statistical tests. The first test decides if there is a significant mass change and the second test is a statistical group classification based on Linear Discriminant Analysis. The frequencies are identified by means of Operational Modal Analysis using natural excitation. Based on the assumption of Gaussianity of the frequencies, a multi-class statistical model is developed by combining finite element model sensitivities in 10 classes of change location on the blade, the smallest area being 1/5 of the span. The method is experimentally validated for a full scale wind turbine blade in a test setup and loaded by natural wind. Mass change from natural causes was imitated with sand bags and the algorithm was observed to perform well with an experimental detection rate of 1, localization rate of 0.88 and mass estimation rate of 0.72.

Evaluation of Visual Field and Imaging Outcomes for Glaucoma Clinical Trials (An American Ophthalomological Society Thesis).

PubMed

Garway-Heath, David F; Quartilho, Ana; Prah, Philip; Crabb, David P; Cheng, Qian; Zhu, Haogang

2017-08-01

To evaluate the ability of various visual field (VF) analysis methods to discriminate treatment groups in glaucoma clinical trials and establish the value of time-domain optical coherence tomography (TD OCT) imaging as an additional outcome. VFs and retinal nerve fibre layer thickness (RNFLT) measurements (acquired by TD OCT) from 373 glaucoma patients in the UK Glaucoma Treatment Study (UKGTS) at up to 11 scheduled visits over a 2 year interval formed the cohort to assess the sensitivity of progression analysis methods. Specificity was assessed in 78 glaucoma patients with up to 11 repeated VF and OCT RNFLT measurements over a 3 month interval. Growth curve models assessed the difference in VF and RNFLT rate of change between treatment groups. Incident progression was identified by 3 VF-based methods: Guided Progression Analysis (GPA), 'ANSWERS' and 'PoPLR', and one based on VFs and RNFLT: 'sANSWERS'. Sensitivity, specificity and discrimination between treatment groups were evaluated. The rate of VF change was significantly faster in the placebo, compared to active treatment, group (-0.29 vs +0.03 dB/year, P <.001); the rate of RNFLT change was not different (-1.7 vs -1.1 dB/year, P =.14). After 18 months and at 95% specificity, the sensitivity of ANSWERS and PoPLR was similar (35%); sANSWERS achieved a sensitivity of 70%. GPA, ANSWERS and PoPLR discriminated treatment groups with similar statistical significance; sANSWERS did not discriminate treatment groups. Although the VF progression-detection method including VF and RNFLT measurements is more sensitive, it does not improve discrimination between treatment arms.

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

NASA Astrophysics Data System (ADS)

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

2003-03-01

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

Baseline estimation in flame's spectra by using neural networks and robust statistics

NASA Astrophysics Data System (ADS)

Garces, Hugo; Arias, Luis; Rojas, Alejandro

2014-09-01

This work presents a baseline estimation method in flame spectra based on artificial intelligence structure as a neural network, combining robust statistics with multivariate analysis to automatically discriminate measured wavelengths belonging to continuous feature for model adaptation, surpassing restriction of measuring target baseline for training. The main contributions of this paper are: to analyze a flame spectra database computing Jolliffe statistics from Principal Components Analysis detecting wavelengths not correlated with most of the measured data corresponding to baseline; to systematically determine the optimal number of neurons in hidden layers based on Akaike's Final Prediction Error; to estimate baseline in full wavelength range sampling measured spectra; and to train an artificial intelligence structure as a Neural Network which allows to generalize the relation between measured and baseline spectra. The main application of our research is to compute total radiation with baseline information, allowing to diagnose combustion process state for optimization in early stages.

Spatially resolved bimodal spectroscopy for classification/evaluation of mouse skin inflammatory and pre-cancerous stages

NASA Astrophysics Data System (ADS)

Díaz-Ayil, Gilberto; Amouroux, Marine; Clanché, Fabien; Granjon, Yves; Blondel, Walter C. P. M.

2009-07-01

Spatially-resolved bimodal spectroscopy (multiple AutoFluorescence AF excitation and Diffuse Reflectance DR), was used in vivo to discriminate various healthy and precancerous skin stages in a pre-clinical model (UV-irradiated mouse): Compensatory Hyperplasia CH, Atypical Hyperplasia AH and Dysplasia D. A specific data preprocessing scheme was applied to intensity spectra (filtering, spectral correction and intensity normalization), and several sets of spectral characteristics were automatically extracted and selected based on their discrimination power, statistically tested for every pair-wise comparison of histological classes. Data reduction with Principal Components Analysis (PCA) was performed and 3 classification methods were implemented (k-NN, LDA and SVM), in order to compare diagnostic performance of each method. Diagnostic performance was studied and assessed in terms of Sensibility (Se) and Specificity (Sp) as a function of the selected features, of the combinations of 3 different inter-fibres distances and of the numbers of principal components, such that: Se and Sp ~ 100% when discriminating CH vs. others; Sp ~ 100% and Se > 95% when discriminating Healthy vs. AH or D; Sp ~ 74% and Se ~ 63% for AH vs. D.

Statistical inference for classification of RRIM clone series using near IR reflectance properties

NASA Astrophysics Data System (ADS)

Ismail, Faridatul Aima; Madzhi, Nina Korlina; Hashim, Hadzli; Abdullah, Noor Ezan; Khairuzzaman, Noor Aishah; Azmi, Azrie Faris Mohd; Sampian, Ahmad Faiz Mohd; Harun, Muhammad Hafiz

2015-08-01

RRIM clone is a rubber breeding series produced by RRIM (Rubber Research Institute of Malaysia) through "rubber breeding program" to improve latex yield and producing clones attractive to farmers. The objective of this work is to analyse measurement of optical sensing device on latex of selected clone series. The device using transmitting NIR properties and its reflectance is converted in terms of voltage. The obtained reflectance index value via voltage was analyzed using statistical technique in order to find out the discrimination among the clones. From the statistical results using error plots and one-way ANOVA test, there is an overwhelming evidence showing discrimination of RRIM 2002, RRIM 2007 and RRIM 3001 clone series with p value = 0.000. RRIM 2008 cannot be discriminated with RRIM 2014; however both of these groups are distinct from the other clones.

Biochemical and molecular characterization of thyroid tissue by micro-Raman spectroscopy and gene expression analysis

NASA Astrophysics Data System (ADS)

Neto, Lázaro P. M.; Martin, Aírton A.; Soto, Claudio A. T.; Santos, André B. O.; Mello, Evandro S.; Pereira, Marina A.; Cernea, Cláudio R.; Brandão, Lenine G.; Canevari, Renata A.

2016-02-01

Thyroid carcinomas represent the main endocrine malignancy and their diagnosis may produce inconclusive results. Raman spectroscopy and gene expression analysis have shown excellent results on the differentiation of carcinomas. This study aimed to improve the discrimination between different thyroid pathologies combining of both analyses. A total of 35 thyroid tissues samples including normal tissue (n=10), goiter (n=10), papillary (n=10) and follicular carcinomas (n=5) were analyzed. Confocal Raman spectra was obtain by using a Rivers Diagnostic System, 785 nm laser excitation and CCD detector. The data was processed by the software Labspec5 and Origin 8.5 and analyzed by Minitab® program. The gene expression analysis was performed by qRT-PCR technique for TG, TPO, PDGFB, SERPINA1, LGALS3 and TFF3 genes and statistically analyzed by Mann-Whitney test. The confocal Raman spectroscopy allowed a maximum discrimination of 91.1% between normal and tumor tissues, 84.8% between benign and malignant pathologies and 84.6% among carcinomas analyzed. Significant differences was observed for TG, LGALS3, SERPINA1 and TFF3 genes between benign lesions and carcinomas, and SERPINA1 and TFF3 genes between papillary and follicular carcinomas. Principal component analysis was performed using PC1 and PC2 in the papillary carcinoma samples that showed over gene expression when compared with normal sample, where 90% of discrimination was observed at the Amide 1 (1655 cm-1), and at the tyrosine spectra region (856 cm-1). The discrimination of tissues thyroid carried out by confocal Raman spectroscopy and gene expression analysis indicate that these techniques are promising tools to be used in the diagnosis of thyroid lesions.

Use of the Oxford Handicap Scale at hospital discharge to predict Glasgow Outcome Scale at 6 months in patients with traumatic brain injury.

PubMed

Perel, Pablo; Edwards, Phil; Shakur, Haleema; Roberts, Ian

2008-11-06

Traumatic brain injury (TBI) is an important cause of acquired disability. In evaluating the effectiveness of clinical interventions for TBI it is important to measure disability accurately. The Glasgow Outcome Scale (GOS) is the most widely used outcome measure in randomised controlled trials (RCTs) in TBI patients. However GOS measurement is generally collected at 6 months after discharge when loss to follow up could have occurred. The objectives of this study were to evaluate the association and predictive validity between a simple disability scale at hospital discharge, the Oxford Handicap Scale (OHS), and the GOS at 6 months among TBI patients. The study was a secondary analysis of a randomised clinical trial among TBI patients (MRC CRASH Trial). A Spearman correlation was estimated to evaluate the association between the OHS and GOS. The validity of different dichotomies of the OHS for predicting GOS at 6 months was assessed by calculating sensitivity, specificity and the C statistic. Uni and multivariate logistic regression models were fitted including OHS as explanatory variable. For each model we analysed its discrimination and calibration. We found that the OHS is highly correlated with GOS at 6 months (spearman correlation 0.75) with evidence of a linear relationship between the two scales. The OHS dichotomy that separates patients with severe dependency or death showed the greatest discrimination (C statistic: 84.3). Among survivors at hospital discharge the OHS showed a very good discrimination (C statistic 0.78) and excellent calibration when used to predict GOS outcome at 6 months. We have shown that the OHS, a simple disability scale available at hospital discharge can predict disability accurately, according to the GOS, at 6 months. OHS could be used to improve the design and analysis of clinical trials in TBI patients and may also provide a valuable clinical tool for physicians to improve communication with patients and relatives when assessing a patient's prognosis at hospital discharge.

External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort: prospective multicentre cohort study.

PubMed

Lamain-de Ruiter, Marije; Kwee, Anneke; Naaktgeboren, Christiana A; de Groot, Inge; Evers, Inge M; Groenendaal, Floris; Hering, Yolanda R; Huisjes, Anjoke J M; Kirpestein, Cornel; Monincx, Wilma M; Siljee, Jacqueline E; Van 't Zelfde, Annewil; van Oirschot, Charlotte M; Vankan-Buitelaar, Simone A; Vonk, Mariska A A W; Wiegers, Therese A; Zwart, Joost J; Franx, Arie; Moons, Karel G M; Koster, Maria P H

2016-08-30

 To perform an external validation and direct comparison of published prognostic models for early prediction of the risk of gestational diabetes mellitus, including predictors applicable in the first trimester of pregnancy.  External validation of all published prognostic models in large scale, prospective, multicentre cohort study.  31 independent midwifery practices and six hospitals in the Netherlands.  Women recruited in their first trimester (<14 weeks) of pregnancy between December 2012 and January 2014, at their initial prenatal visit. Women with pre-existing diabetes mellitus of any type were excluded.  Discrimination of the prognostic models was assessed by the C statistic, and calibration assessed by calibration plots.  3723 women were included for analysis, of whom 181 (4.9%) developed gestational diabetes mellitus in pregnancy. 12 prognostic models for the disorder could be validated in the cohort. C statistics ranged from 0.67 to 0.78. Calibration plots showed that eight of the 12 models were well calibrated. The four models with the highest C statistics included almost all of the following predictors: maternal age, maternal body mass index, history of gestational diabetes mellitus, ethnicity, and family history of diabetes. Prognostic models had a similar performance in a subgroup of nulliparous women only. Decision curve analysis showed that the use of these four models always had a positive net benefit.  In this external validation study, most of the published prognostic models for gestational diabetes mellitus show acceptable discrimination and calibration. The four models with the highest discriminative abilities in this study cohort, which also perform well in a subgroup of nulliparous women, are easy models to apply in clinical practice and therefore deserve further evaluation regarding their clinical impact. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Quality of Life in Relation to Pain Response to Radiation Therapy for Painful Bone Metastases

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

Westhoff, Paulien G., E-mail: p.g.westhoff@umcutrecht.nl; Graeff, Alexander de; Monninkhof, Evelyn M.

Purpose: To study quality of life (QoL) in responders and nonresponders after radiation therapy for painful bone metastases; and to identify factors predictive for a pain response. Patients and Methods: The prospectively collected data of 956 patients with breast, prostate, and lung cancer within the Dutch Bone Metastasis Study were used. These patients, irradiated for painful bone metastases, rated pain, QoL, and overall health at baseline and weekly afterward for 12 weeks. Using generalized estimating equations analysis, the course of QoL was studied, adjusted for primary tumor. To identify predictive variables, proportional hazard analyses were performed, taking into account death asmore » a competing risk, and C-statistics were calculated for discriminative value. Results: In total, 722 patients (76%) responded to radiation therapy. During follow-up, responders had a better QoL in all domains compared with nonresponders. Patients with breast or prostate cancer had a better QoL than patients with lung cancer. In multivariate analysis, baseline predictors for a pain response were breast or prostate cancer as primary tumor, younger age, good performance status, absence of visceral metastases, and using opioids. The discriminative ability of the model was low (C-statistic: 0.56). Conclusions: Responding patients show a better QoL after radiation therapy for painful bone metastases than nonresponders. Our model did not have enough discriminative power to predict which patients are likely to respond to radiation therapy. Therefore, radiation therapy should be offered to all patients with painful bone metastases, aiming to decrease pain and improve QoL.« less

Defying Statistics or "Latch-key Children in the Late '80s: Family Composition, Working Mothers, and After School Supervision."

ERIC Educational Resources Information Center

Muller, Chandra; And Others

This study explored the effects of after-school supervision on 8th graders' academic performance. Data from the National Educational Longitudinal Study of 1988 relating to a total sample size of 20,491 students (after exclusions) in 802 public and 233 private schools were analyzed. The analysis indicated that parents do not discriminate between…

Multivariate analysis of heavy metal contamination using river sediment cores of Nankan River, northern Taiwan

NASA Astrophysics Data System (ADS)

Lee, An-Sheng; Lu, Wei-Li; Huang, Jyh-Jaan; Chang, Queenie; Wei, Kuo-Yen; Lin, Chin-Jung; Liou, Sofia Ya Hsuan

2016-04-01

Through the geology and climate characteristic in Taiwan, generally rivers carry a lot of suspended particles. After these particles settled, they become sediments which are good sorbent for heavy metals in river system. Consequently, sediments can be found recording contamination footprint at low flow energy region, such as estuary. Seven sediment cores were collected along Nankan River, northern Taiwan, which is seriously contaminated by factory, household and agriculture input. Physico-chemical properties of these cores were derived from Itrax-XRF Core Scanner and grain size analysis. In order to interpret these complex data matrices, the multivariate statistical techniques (cluster analysis, factor analysis and discriminant analysis) were introduced to this study. Through the statistical determination, the result indicates four types of sediment. One of them represents contamination event which shows high concentration of Cu, Zn, Pb, Ni and Fe, and low concentration of Si and Zr. Furthermore, three possible contamination sources of this type of sediment were revealed by Factor Analysis. The combination of sediment analysis and multivariate statistical techniques used provides new insights into the contamination depositional history of Nankan River and could be similarly applied to other river systems to determine the scale of anthropogenic contamination.

A Pulsed Thermographic Imaging System for Detection and Identification of Cotton Foreign Matter

PubMed Central

Kuzy, Jesse; Li, Changying

2017-01-01

Detection of foreign matter in cleaned cotton is instrumental to accurately grading cotton quality, which in turn impacts the marketability of the cotton. Current grading systems return estimates of the amount of foreign matter present, but provide no information about the identity of the contaminants. This paper explores the use of pulsed thermographic analysis to detect and identify cotton foreign matter. The design and implementation of a pulsed thermographic analysis system is described. A sample set of 240 foreign matter and cotton lint samples were collected. Hand-crafted waveform features and frequency-domain features were extracted and analyzed for statistical significance. Classification was performed on these features using linear discriminant analysis and support vector machines. Using waveform features and support vector machine classifiers, detection of cotton foreign matter was performed with 99.17% accuracy. Using frequency-domain features and linear discriminant analysis, identification was performed with 90.00% accuracy. These results demonstrate that pulsed thermographic imaging analysis produces data which is of significant utility for the detection and identification of cotton foreign matter. PMID:28273848

Parallel processing of genomics data

NASA Astrophysics Data System (ADS)

Agapito, Giuseppe; Guzzi, Pietro Hiram; Cannataro, Mario

2016-10-01

The availability of high-throughput experimental platforms for the analysis of biological samples, such as mass spectrometry, microarrays and Next Generation Sequencing, have made possible to analyze a whole genome in a single experiment. Such platforms produce an enormous volume of data per single experiment, thus the analysis of this enormous flow of data poses several challenges in term of data storage, preprocessing, and analysis. To face those issues, efficient, possibly parallel, bioinformatics software needs to be used to preprocess and analyze data, for instance to highlight genetic variation associated with complex diseases. In this paper we present a parallel algorithm for the parallel preprocessing and statistical analysis of genomics data, able to face high dimension of data and resulting in good response time. The proposed system is able to find statistically significant biological markers able to discriminate classes of patients that respond to drugs in different ways. Experiments performed on real and synthetic genomic datasets show good speed-up and scalability.

Multivariate statistical analysis of the polyphenolic constituents in kiwifruit juices to trace fruit varieties and geographical origins.

PubMed

Guo, Jing; Yuan, Yahong; Dou, Pei; Yue, Tianli

2017-10-01

Fifty-one kiwifruit juice samples of seven kiwifruit varieties from five regions in China were analyzed to determine their polyphenols contents and to trace fruit varieties and geographical origins by multivariate statistical analysis. Twenty-one polyphenols belonging to four compound classes were determined by ultra-high-performance liquid chromatography coupled with ultra-high-resolution TOF mass spectrometry. (-)-Epicatechin, (+)-catechin, procyanidin B1 and caffeic acid derivatives were the predominant phenolic compounds in the juices. Principal component analysis (PCA) allowed a clear separation of the juices according to kiwifruit varieties. Stepwise linear discriminant analysis (SLDA) yielded satisfactory categorization of samples, provided 100% success rate according to kiwifruit varieties and 92.2% success rate according to geographical origins. The result showed that polyphenolic profiles of kiwifruit juices contain enough information to trace fruit varieties and geographical origins. Copyright © 2017 Elsevier Ltd. All rights reserved.

K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads: Revisited

NASA Astrophysics Data System (ADS)

Wang, Dong

2016-03-01

Gears are the most commonly used components in mechanical transmission systems. Their failures may cause transmission system breakdown and result in economic loss. Identification of different gear crack levels is important to prevent any unexpected gear failure because gear cracks lead to gear tooth breakage. Signal processing based methods mainly require expertize to explain gear fault signatures which is usually not easy to be achieved by ordinary users. In order to automatically identify different gear crack levels, intelligent gear crack identification methods should be developed. The previous case studies experimentally proved that K-nearest neighbors based methods exhibit high prediction accuracies for identification of 3 different gear crack levels under different motor speeds and loads. In this short communication, to further enhance prediction accuracies of existing K-nearest neighbors based methods and extend identification of 3 different gear crack levels to identification of 5 different gear crack levels, redundant statistical features are constructed by using Daubechies 44 (db44) binary wavelet packet transform at different wavelet decomposition levels, prior to the use of a K-nearest neighbors method. The dimensionality of redundant statistical features is 620, which provides richer gear fault signatures. Since many of these statistical features are redundant and highly correlated with each other, dimensionality reduction of redundant statistical features is conducted to obtain new significant statistical features. At last, the K-nearest neighbors method is used to identify 5 different gear crack levels under different motor speeds and loads. A case study including 3 experiments is investigated to demonstrate that the developed method provides higher prediction accuracies than the existing K-nearest neighbors based methods for recognizing different gear crack levels under different motor speeds and loads. Based on the new significant statistical features, some other popular statistical models including linear discriminant analysis, quadratic discriminant analysis, classification and regression tree and naive Bayes classifier, are compared with the developed method. The results show that the developed method has the highest prediction accuracies among these statistical models. Additionally, selection of the number of new significant features and parameter selection of K-nearest neighbors are thoroughly investigated.

Discriminative and predictive validity of the scoliosis research society-22 questionnaire in management and curve-severity subgroups of adolescents with idiopathic scoliosis.

PubMed

Parent, Eric C; Hill, Doug; Mahood, Jim; Moreau, Marc; Raso, Jim; Lou, Edmond

2009-10-15

Prospective cross-sectional measurement study. To determine the ability of the Scoliosis Research Society (SRS)-22 questionnaire to discriminate among management and scoliosis severity subgroups and to correlate with internal and external measures of curve severity. In earlier studies of the SRS-22 discriminative ability, age was not a controlled factor. The ability of the SRS-22 to predict curve severity has not been thoroughly examined. The SRS-22 was completed by 227 females with adolescent idiopathic scoliosis. Using Analysis of covariance analyses controlling for age, the SRS-22 scores were compared among management subgroups (observation, brace, presurgery, and postsurgery) and curve-severity subgroups (in nonoperated subjects: Cobb angles of <30 degrees, 30 degrees -50 degrees, and >50 degrees). A stepwise discriminant analysis was used to identify the SRS-22 domains most discriminative for curve-severity categories. Correlation between SRS-22 scores and radiographic or surface topography measurements was used to determine the predictive ability of the questionnaire. Pain was better for subjects treated with braces than for those planning surgery. Self-image was better for subjects under observation or postsurgery than for those planning surgery. Satisfaction was better for the brace and postsurgery subgroups than for the observation or presurgery subgroups. Statistically significant mean differences between subgroups were all larger than 0.5, which is within the range of minimal clinically important differences recommended for each of the 5-point SRS-22 domain scoring scales. Pain and mental health were worse for those with Cobb angles of >50 degrees than with Cobb angles of 30 degrees to 50 degrees. Self-image and total scores were worse for those with Cobb angles of >50 degrees than both other subgroups. Using discriminant analysis, self-image was the only SRS-22 domain score selected to classify subjects within curve severity subgroups. The percentage of patients accurately classified was 54% when trying to classify within 3 curve severity subgroups. The percentage of patients accurately classified was 73% when classifying simply as those with curves larger or smaller than 50 degrees . Pain, self-image, and satisfaction scores could discriminate among management subgroups, but function, mental health and total scores could not. The total score and all domain scores except satisfaction discriminated among curve-severity subgroups. Using discriminant analysis, self-image was the only domain retained in a model predicting curve-severity categories.

Study of photon correlation techniques for processing of laser velocimeter signals

NASA Technical Reports Server (NTRS)

Mayo, W. T., Jr.

1977-01-01

The objective was to provide the theory and a system design for a new type of photon counting processor for low level dual scatter laser velocimeter (LV) signals which would be capable of both the first order measurements of mean flow and turbulence intensity and also the second order time statistics: cross correlation auto correlation, and related spectra. A general Poisson process model for low level LV signals and noise which is valid from the photon-resolved regime all the way to the limiting case of nonstationary Gaussian noise was used. Computer simulation algorithms and higher order statistical moment analysis of Poisson processes were derived and applied to the analysis of photon correlation techniques. A system design using a unique dual correlate and subtract frequency discriminator technique is postulated and analyzed. Expectation analysis indicates that the objective measurements are feasible.

Integration of statistical and physiological analyses of adaptation of near-isogenic barley lines.

PubMed

Romagosa, I; Fox, P N; García Del Moral, L F; Ramos, J M; García Del Moral, B; Roca de Togores, F; Molina-Cano, J L

1993-08-01

Seven near-isogenic barley lines, differing for three independent mutant genes, were grown in 15 environments in Spain. Genotype x environment interaction (G x E) for grain yield was examined with the Additive Main Effects and Multiplicative interaction (AMMI) model. The results of this statistical analysis of multilocation yield-data were compared with a morpho-physiological characterization of the lines at two sites (Molina-Cano et al. 1990). The first two principal component axes from the AMMI analysis were strongly associated with the morpho-physiological characters. The independent but parallel discrimination among genotypes reflects genetic differences and highlights the power of the AMMI analysis as a tool to investigate G x E. Characters which appear to be positively associated with yield in the germplasm under study could be identified for some environments.

Classification of passive auditory event-related potentials using discriminant analysis and self-organizing feature maps.

PubMed

Schönweiler, R; Wübbelt, P; Tolloczko, R; Rose, C; Ptok, M

2000-01-01

Discriminant analysis (DA) and self-organizing feature maps (SOFM) were used to classify passively evoked auditory event-related potentials (ERP) P(1), N(1), P(2) and N(2). Responses from 16 children with severe behavioral auditory perception deficits, 16 children with marked behavioral auditory perception deficits, and 14 controls were examined. Eighteen ERP amplitude parameters were selected for examination of statistical differences between the groups. Different DA methods and SOFM configurations were trained to the values. SOFM had better classification results than DA methods. Subsequently, measures on another 37 subjects that were unknown for the trained SOFM were used to test the reliability of the system. With 10-dimensional vectors, reliable classifications were obtained that matched behavioral auditory perception deficits in 96%, implying central auditory processing disorder (CAPD). The results also support the assumption that CAPD includes a 'non-peripheral' auditory processing deficit. Copyright 2000 S. Karger AG, Basel.

[Image Feature Extraction and Discriminant Analysis of Xinjiang Uygur Medicine Based on Color Histogram].

PubMed

Hamit, Murat; Yun, Weikang; Yan, Chuanbo; Kutluk, Abdugheni; Fang, Yang; Alip, Elzat

2015-06-01

Image feature extraction is an important part of image processing and it is an important field of research and application of image processing technology. Uygur medicine is one of Chinese traditional medicine and researchers pay more attention to it. But large amounts of Uygur medicine data have not been fully utilized. In this study, we extracted the image color histogram feature of herbal and zooid medicine of Xinjiang Uygur. First, we did preprocessing, including image color enhancement, size normalizition and color space transformation. Then we extracted color histogram feature and analyzed them with statistical method. And finally, we evaluated the classification ability of features by Bayes discriminant analysis. Experimental results showed that high accuracy for Uygur medicine image classification was obtained by using color histogram feature. This study would have a certain help for the content-based medical image retrieval for Xinjiang Uygur medicine.

Fingerprinting Breast Cancer vs. Normal Mammary Cells by Mass Spectrometric Analysis of Volatiles

NASA Astrophysics Data System (ADS)

He, Jingjing; Sinues, Pablo Martinez-Lozano; Hollmén, Maija; Li, Xue; Detmar, Michael; Zenobi, Renato

2014-06-01

There is increasing interest in the development of noninvasive diagnostic methods for early cancer detection, to improve the survival rate and quality of life of cancer patients. Identification of volatile metabolic compounds may provide an approach for noninvasive early diagnosis of malignant diseases. Here we analyzed the volatile metabolic signature of human breast cancer cell lines versus normal human mammary cells. Volatile compounds in the headspace of conditioned culture medium were directly fingerprinted by secondary electrospray ionization-mass spectrometry. The mass spectra were subsequently treated statistically to identify discriminating features between normal vs. cancerous cell types. We were able to classify different samples by using feature selection followed by principal component analysis (PCA). Additionally, high-resolution mass spectrometry allowed us to propose their chemical structures for some of the most discriminating molecules. We conclude that cancerous cells can release a characteristic odor whose constituents may be used as disease markers.

Improved detection of highly energetic materials traces on surfaces by standoff laser-induced thermal emission incorporating neural networks

NASA Astrophysics Data System (ADS)

Figueroa-Navedo, Amanda; Galán-Freyle, Nataly Y.; Pacheco-Londoño, Leonardo C.; Hernández-Rivera, Samuel P.

2013-05-01

Terrorists conceal highly energetic materials (HEM) as Improvised Explosive Devices (IED) in various types of materials such as PVC, wood, Teflon, aluminum, acrylic, carton and rubber to disguise them from detection equipment used by military and security agency personnel. Infrared emissions (IREs) of substrates, with and without HEM, were measured to generate models for detection and discrimination. Multivariable analysis techniques such as principal component analysis (PCA), soft independent modeling by class analogy (SIMCA), partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM) and neural networks (NN) were employed to generate models, in which the emission of IR light from heated samples was stimulated using a CO2 laser giving rise to laser induced thermal emission (LITE) of HEMs. Traces of a specific target threat chemical explosive: PETN in surface concentrations of 10 to 300 ug/cm2 were studied on the surfaces mentioned. Custom built experimental setup used a CO2 laser as a heating source positioned with a telescope, where a minimal loss in reflective optics was reported, for the Mid-IR at a distance of 4 m and 32 scans at 10 s. SVM-DA resulted in the best statistical technique for a discrimination performance of 97%. PLS-DA accurately predicted over 94% and NN 88%.

Discriminative Ocular Artifact Correction for Feature Learning in EEG Analysis.

PubMed

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

2017-08-01

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 independent component analysis 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 dataset 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.

Immunity to Salmonella typhi: considerations relevant to measurement of cellular immunity in typhoid-endemic regions.

PubMed Central

Murphy, J R; Wasserman, S S; Baqar, S; Schlesinger, L; Ferreccio, C; Lindberg, A A; Levine, M M

1989-01-01

Experiments were performed in Baltimore, Maryland and in Santiago, Chile, to determine the level of Salmonella typhi antigen-driven in vitro lymphocyte replication response which signifies specific acquired immunity to this bacterium and to determine the best method of data analysis and form of data presentation. Lymphocyte replication was measured as incorporation of 3H-thymidine into desoxyribonucleic acid. Data (ct/min/culture) were analyzed in raw form and following log transformation, by non-parametric and parametric statistical procedures. A preference was developed for log-transformed data and discriminant analysis. Discriminant analysis of log-transformed data revealed 3H-thymidine incorporation rates greater than 3,433 for particulate S. typhi, Ty2 antigen stimulated cultures signified acquired immunity at a sensitivity and specificity of 82.7; for soluble S. typhi O polysaccharide antigen-stimulated cultures, ct/min/culture values of greater than 1,237 signified immunity (sensitivity and specificity 70.5%). PMID:2702777

Discrimination between Bacillus and Alicyclobacillus isolates in apple juice by Fourier transform infrared spectroscopy and multivariate analysis.

PubMed

Al-Holy, Murad A; Lin, Mengshi; Alhaj, Omar A; Abu-Goush, Mahmoud H

2015-02-01

Alicyclobacillus is a causative agent of spoilage in pasteurized and heat-treated apple juice products. Differentiating between this genus and the closely related Bacillus is crucially important. In this study, Fourier transform infrared spectroscopy (FT-IR) was used to identify and discriminate between 4 Alicyclobacillus strains and 4 Bacillus isolates inoculated individually into apple juice. Loading plots over the range of 1350 and 1700 cm(-1) reflected the most distinctive biochemical features of Bacillus and Alicyclobacillus. Multivariate statistical methods (for example, principal component analysis and soft independent modeling of class analogy) were used to analyze the spectral data. Distinctive separation of spectral samples was observed. This study demonstrates that FT-IR spectroscopy in combination with multivariate analysis could serve as a rapid and effective tool for fruit juice industry to differentiate between Bacillus and Alicyclobacillus and to distinguish between species belonging to these 2 genera. © 2015 Institute of Food Technologists®

Evaluation of drinking quality of groundwater through multivariate techniques in urban area.

PubMed

Das, Madhumita; Kumar, A; Mohapatra, M; Muduli, S D

2010-07-01

Groundwater is a major source of drinking water in urban areas. Because of the growing threat of debasing water quality due to urbanization and development, monitoring water quality is a prerequisite to ensure its suitability for use in drinking. But analysis of a large number of properties and parameter to parameter basis evaluation of water quality is not feasible in a regular interval. Multivariate techniques could streamline the data without much loss of information to a reasonably manageable data set. In this study, using principal component analysis, 11 relevant properties of 58 water samples were grouped into three statistical factors. Discriminant analysis identified "pH influence" as the most distinguished factor and pH, Fe, and NO₃⁻ as the most discriminating variables and could be treated as water quality indicators. These were utilized to classify the sampling sites into homogeneous clusters that reflect location-wise importance of specific indicator/s for use to monitor drinking water quality in the whole study area.

Multisensor system for toxic gases detection generated on indoor environments

NASA Astrophysics Data System (ADS)

Durán, C. M.; Monsalve, P. A. G.; Mosquera, C. J.

2016-11-01

This work describes a wireless multisensory system for different toxic gases detection generated on indoor environments (i.e., Underground coal mines, etc.). The artificial multisensory system proposed in this study was developed through a set of six chemical gas sensors (MQ) of low cost with overlapping sensitivities to detect hazardous gases in the air. A statistical parameter was implemented to the data set and two pattern recognition methods such as Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA) were used for feature selection. The toxic gases categories were classified with a Probabilistic Neural Network (PNN) in order to validate the results previously obtained. The tests were carried out to verify feasibility of the application through a wireless communication model which allowed to monitor and store the information of the sensor signals for the appropriate analysis. The success rate in the measures discrimination was 100%, using an artificial neural network where leave-one-out was used as cross validation method.

Detection of Leukemia with Blood Samples Using Raman Spectroscopy and Multivariate Analysis

NASA Astrophysics Data System (ADS)

Martínez-Espinosa, J. C.; González-Solís, J. L.; Frausto-Reyes, C.; Miranda-Beltrán, M. L.; Soria-Fregoso, C.; Medina-Valtierra, J.

2009-06-01

The use of Raman spectroscopy to analyze blood biochemistry and hence distinguish between normal and abnormal blood was investigated. Blood samples were obtained from 6 patients who were clinically diagnosed with leukemia and 6 healthy volunteers. The imprint was put under the microscope and several points were chosen for Raman measurement. All the spectra were collected by a confocal Raman micro-spectroscopy (Renishaw) with a NIR 830 nm laser. It is shown that the serum samples from patients with leukemia and from the control group can be discriminated when the multivariate statistical methods of principal component analysis (PCA) and linear discriminated analysis (LDA) are applied to their Raman spectra. The ratios of some band intensities were analyzed and some band ratios were significant and corresponded to proteins, phospholipids, and polysaccharides. The preliminary results suggest that Raman Spectroscopy could be a new technique to study the degree of damage to the bone marrow using just blood samples instead of biopsies, treatment very painful for patients.

Forensic Comparison of Soil Samples Using Nondestructive Elemental Analysis.

PubMed

Uitdehaag, Stefan; Wiarda, Wim; Donders, Timme; Kuiper, Irene

2017-07-01

Soil can play an important role in forensic cases in linking suspects or objects to a crime scene by comparing samples from the crime scene with samples derived from items. This study uses an adapted ED-XRF analysis (sieving instead of grinding to prevent destruction of microfossils) to produce elemental composition data of 20 elements. Different data processing techniques and statistical distances were evaluated using data from 50 samples and the log-LR cost (C llr ). The best performing combination, Canberra distance, relative data, and square root values, is used to construct a discriminative model. Examples of the spatial resolution of the method in crime scenes are shown for three locations, and sampling strategy is discussed. Twelve test cases were analyzed, and results showed that the method is applicable. The study shows how the combination of an analysis technique, a database, and a discriminative model can be used to compare multiple soil samples quickly. © 2016 American Academy of Forensic Sciences.

A comparison between Bayes discriminant analysis and logistic regression for prediction of debris flow in southwest Sichuan, China

NASA Astrophysics Data System (ADS)

Xu, Wenbo; Jing, Shaocai; Yu, Wenjuan; Wang, Zhaoxian; Zhang, Guoping; Huang, Jianxi

2013-11-01

In this study, the high risk areas of Sichuan Province with debris flow, Panzhihua and Liangshan Yi Autonomous Prefecture, were taken as the studied areas. By using rainfall and environmental factors as the predictors and based on the different prior probability combinations of debris flows, the prediction of debris flows was compared in the areas with statistical methods: logistic regression (LR) and Bayes discriminant analysis (BDA). The results through the comprehensive analysis show that (a) with the mid-range scale prior probability, the overall predicting accuracy of BDA is higher than those of LR; (b) with equal and extreme prior probabilities, the overall predicting accuracy of LR is higher than those of BDA; (c) the regional predicting models of debris flows with rainfall factors only have worse performance than those introduced environmental factors, and the predicting accuracies of occurrence and nonoccurrence of debris flows have been changed in the opposite direction as the supplemented information.

Comparison of two headspace sampling techniques for the analysis of off-flavour volatiles from oat based products.

PubMed

Cognat, Claudine; Shepherd, Tom; Verrall, Susan R; Stewart, Derek

2012-10-01

Two different headspace sampling techniques were compared for analysis of aroma volatiles from freshly produced and aged plain oatcakes. Solid phase microextraction (SPME) using a Carboxen-Polydimethylsiloxane (PDMS) fibre and entrainment on Tenax TA within an adsorbent tube were used for collection of volatiles. The effects of variation in the sampling method were also considered using SPME. The data obtained using both techniques were processed by multivariate statistical analysis (PCA). Both techniques showed similar capacities to discriminate between the samples at different ages. Discrimination between fresh and rancid samples could be made on the basis of changes in the relative abundances of 14-15 of the constituents in the volatile profiles. A significant effect on the detection level of volatile compounds was observed when samples were crushed and analysed by SPME-GC-MS, in comparison to undisturbed product. The applicability and cost effectiveness of both methods were considered. Copyright © 2012 Elsevier Ltd. All rights reserved.

Short wavelength Raman spectroscopy applied to the discrimination and characterization of three cultivars of extra virgin olive oils in different maturation stages.

PubMed

Gouvinhas, Irene; Machado, Nelson; Carvalho, Teresa; de Almeida, José M M M; Barros, Ana I R N A

2015-01-01

Extra virgin olive oils produced from three cultivars on different maturation stages were characterized using Raman spectroscopy. Chemometric methods (principal component analysis, discriminant analysis, principal component regression and partial least squares regression) applied to Raman spectral data were utilized to evaluate and quantify the statistical differences between cultivars and their ripening process. The models for predicting the peroxide value and free acidity of olive oils showed good calibration and prediction values and presented high coefficients of determination (>0.933). Both the R(2), and the correlation equations between the measured chemical parameters, and the values predicted by each approach are presented; these comprehend both PCR and PLS, used to assess SNV normalized Raman data, as well as first and second derivative of the spectra. This study demonstrates that a combination of Raman spectroscopy with multivariate analysis methods can be useful to predict rapidly olive oil chemical characteristics during the maturation process. Copyright © 2014 Elsevier B.V. All rights reserved.

Comparison of the Trace Elements and Active Components of Lonicera japonica flos and Lonicera flos Using ICP-MS and HPLC-PDA.

PubMed

Zhao, Yueran; Dou, Deqiang; Guo, Yueqiu; Qi, Yue; Li, Jun; Jia, Dong

2018-06-01

Thirteen trace elements and active constituents of 40 batches of Lonicera japonica flos and Lonicera flos were comparatively studied using inductively coupled plasma mass-spectrometry (ICP-MS) and high-performance liquid chromatography-photodiode array (HPLC-PDA). The trace elements were 24 Mg, 52 Cr, 55 Mn, 57 Fe, 60 Ni, 63 Cu, 66 Zn, 75 As, 82 Se, 98 Mo, 114 Cd, 202 Hg, and 208 Pb, and the active compounds were chlorogenic acid, 3,5-O-dicaffeoylquinc acid, 4,5-O-dicaffeoylquinc acid, luteolin-7-O-glucoside, and 4-O-caffeoylquinic acid. The data of 18 variables were statistically processed using principal component analysis (PCA) and discriminate analysis (DA) to classify L. japonica flos and L. flos. The validated method was developed to divide the 40 samples into two groups based on the PCA in terms of 18 variables. Furthermore, the species of Lonicera was better discriminated by using DA with 12 variables. These results suggest that the method and statistical analysis of the contents of trace elements and chemical components can classify the L. japonica flos and L. flos using 12 variables, such as 3,5-O-dicaffeoylquincacid, luteolin-7-O-glucoside, Cd, Mn, Hg, Pb, Ni, 4-O-caffeoyl-quinic acid, 4,5-O-dicaffeoylquinc acid, Fe, Mg, and Cr.

Implication of the cause of differences in 3D structures of proteins with high sequence identity based on analyses of amino acid sequences and 3D structures.

PubMed

Matsuoka, Masanari; Sugita, Masatake; Kikuchi, Takeshi

2014-09-18

Proteins that share a high sequence homology while exhibiting drastically different 3D structures are investigated in this study. Recently, artificial proteins related to the sequences of the GA and IgG binding GB domains of human serum albumin have been designed. These artificial proteins, referred to as GA and GB, share 98% amino acid sequence identity but exhibit different 3D structures, namely, a 3α bundle versus a 4β + α structure. Discriminating between their 3D structures based on their amino acid sequences is a very difficult problem. In the present work, in addition to using bioinformatics techniques, an analysis based on inter-residue average distance statistics is used to address this problem. It was hard to distinguish which structure a given sequence would take only with the results of ordinary analyses like BLAST and conservation analyses. However, in addition to these analyses, with the analysis based on the inter-residue average distance statistics and our sequence tendency analysis, we could infer which part would play an important role in its structural formation. The results suggest possible determinants of the different 3D structures for sequences with high sequence identity. The possibility of discriminating between the 3D structures based on the given sequences is also discussed.

Improving accuracy and power with transfer learning using a meta-analytic database.

PubMed

Schwartz, Yannick; Varoquaux, Gaël; Pallier, Christophe; Pinel, Philippe; Poline, Jean-Baptiste; Thirion, Bertrand

2012-01-01

Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies the specification of regions of interest (ROIs) that are usually selected on the basis of the coordinates of previously detected effects. In this paper, we propose to use a database of images, rather than coordinates, and frame the problem as transfer learning: learning a discriminant model on a reference task to apply it to a different but related new task. To facilitate statistical analysis of small cohorts, we use a sparse discriminant model that selects predictive voxels on the reference task and thus provides a principled procedure to define ROIs. The benefits of our approach are twofold. First it uses the reference database for prediction, i.e., to provide potential biomarkers in a clinical setting. Second it increases statistical power on the new task. We demonstrate on a set of 18 pairs of functional MRI experimental conditions that our approach gives good prediction. In addition, on a specific transfer situation involving different scanners at different locations, we show that voxel selection based on transfer learning leads to higher detection power on small cohorts.

Assessment of statistical methods used in library-based approaches to microbial source tracking.

PubMed

Ritter, Kerry J; Carruthers, Ethan; Carson, C Andrew; Ellender, R D; Harwood, Valerie J; Kingsley, Kyle; Nakatsu, Cindy; Sadowsky, Michael; Shear, Brian; West, Brian; Whitlock, John E; Wiggins, Bruce A; Wilbur, Jayson D

2003-12-01

Several commonly used statistical methods for fingerprint identification in microbial source tracking (MST) were examined to assess the effectiveness of pattern-matching algorithms to correctly identify sources. Although numerous statistical methods have been employed for source identification, no widespread consensus exists as to which is most appropriate. A large-scale comparison of several MST methods, using identical fecal sources, presented a unique opportunity to assess the utility of several popular statistical methods. These included discriminant analysis, nearest neighbour analysis, maximum similarity and average similarity, along with several measures of distance or similarity. Threshold criteria for excluding uncertain or poorly matched isolates from final analysis were also examined for their ability to reduce false positives and increase prediction success. Six independent libraries used in the study were constructed from indicator bacteria isolated from fecal materials of humans, seagulls, cows and dogs. Three of these libraries were constructed using the rep-PCR technique and three relied on antibiotic resistance analysis (ARA). Five of the libraries were constructed using Escherichia coli and one using Enterococcus spp. (ARA). Overall, the outcome of this study suggests a high degree of variability across statistical methods. Despite large differences in correct classification rates among the statistical methods, no single statistical approach emerged as superior. Thresholds failed to consistently increase rates of correct classification and improvement was often associated with substantial effective sample size reduction. Recommendations are provided to aid in selecting appropriate analyses for these types of data.

Gait characterization in golden retriever muscular dystrophy dogs using linear discriminant analysis.

PubMed

Fraysse, Bodvaël; Barthélémy, Inès; Qannari, El Mostafa; Rouger, Karl; Thorin, Chantal; Blot, Stéphane; Le Guiner, Caroline; Chérel, Yan; Hogrel, Jean-Yves

2017-04-12

Accelerometric analysis of gait abnormalities in golden retriever muscular dystrophy (GRMD) dogs is of limited sensitivity, and produces highly complex data. The use of discriminant analysis may enable simpler and more sensitive evaluation of treatment benefits in this important preclinical model. Accelerometry was performed twice monthly between the ages of 2 and 12 months on 8 healthy and 20 GRMD dogs. Seven accelerometric parameters were analysed using linear discriminant analysis (LDA). Manipulation of the dependent and independent variables produced three distinct models. The ability of each model to detect gait alterations and their pattern change with age was tested using a leave-one-out cross-validation approach. Selecting genotype (healthy or GRMD) as the dependent variable resulted in a model (Model 1) allowing a good discrimination between the gait phenotype of GRMD and healthy dogs. However, this model was not sufficiently representative of the disease progression. In Model 2, age in months was added as a supplementary dependent variable (GRMD_2 to GRMD_12 and Healthy_2 to Healthy_9.5), resulting in a high overall misclassification rate (83.2%). To improve accuracy, a third model (Model 3) was created in which age was also included as an explanatory variable. This resulted in an overall misclassification rate lower than 12%. Model 3 was evaluated using blinded data pertaining to 81 healthy and GRMD dogs. In all but one case, the model correctly matched gait phenotype to the actual genotype. Finally, we used Model 3 to reanalyse data from a previous study regarding the effects of immunosuppressive treatments on muscular dystrophy in GRMD dogs. Our model identified significant effect of immunosuppressive treatments on gait quality, corroborating the original findings, with the added advantages of direct statistical analysis with greater sensitivity and more comprehensible data representation. Gait analysis using LDA allows for improved analysis of accelerometry data by applying a decision-making analysis approach to the evaluation of preclinical treatment benefits in GRMD dogs.

Linear regression models and k-means clustering for statistical analysis of fNIRS data.

PubMed

Bonomini, Viola; Zucchelli, Lucia; Re, Rebecca; Ieva, Francesca; Spinelli, Lorenzo; Contini, Davide; Paganoni, Anna; Torricelli, Alessandro

2015-02-01

We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets.

Linear regression models and k-means clustering for statistical analysis of fNIRS data

PubMed Central

Bonomini, Viola; Zucchelli, Lucia; Re, Rebecca; Ieva, Francesca; Spinelli, Lorenzo; Contini, Davide; Paganoni, Anna; Torricelli, Alessandro

2015-01-01

We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets. PMID:25780751

Volcano plots in analyzing differential expressions with mRNA microarrays.

PubMed

Li, Wentian

2012-12-01

A volcano plot displays unstandardized signal (e.g. log-fold-change) against noise-adjusted/standardized signal (e.g. t-statistic or -log(10)(p-value) from the t-test). We review the basic and interactive use of the volcano plot and its crucial role in understanding the regularized t-statistic. The joint filtering gene selection criterion based on regularized statistics has a curved discriminant line in the volcano plot, as compared to the two perpendicular lines for the "double filtering" criterion. This review attempts to provide a unifying framework for discussions on alternative measures of differential expression, improved methods for estimating variance, and visual display of a microarray analysis result. We also discuss the possibility of applying volcano plots to other fields beyond microarray.

Identifying the hydrochemical characteristics of rivers and groundwater by multivariate statistical analysis in the Sanjiang Plain, China

NASA Astrophysics Data System (ADS)

Cao, Yingjie; Tang, Changyuan; Song, Xianfang; Liu, Changming; Zhang, Yinghua

2016-06-01

Two multivariate statistical technologies, factor analysis (FA) and discriminant analysis (DA), are applied to study the river and groundwater hydrochemistry and its controlling processes in the Sanjiang Plain of the northeast China. Factor analysis identifies five factors which account for 79.65 % of the total variance in the dataset. Four factors bearing specific meanings as the river and groundwater hydrochemistry controlling processes are divided into two groups, the "natural hydrochemistry evolution" group and the "pollution" group. The "natural hydrochemistry evolution" group includes the salinity factor (factor 1) caused by rock weathering and the residence time factor (factor 2) reflecting the groundwater traveling time. The "pollution" group represents the groundwater quality deterioration due to geogenic pollution caused by elevated Fe and Mn (factor 3) and elevated nitrate (NO3 -) introduced by human activities such as agriculture exploitations (factor 5). The hydrochemical difference and hydraulic connection among rivers (surface water, SW), shallow groundwater (SG) and deep groundwater (DG) group are evaluated by the factor scores obtained from FA and DA (Fisher's method). It is showed that the river water is characterized as low salinity and slight pollution, and the shallow groundwater has the highest salinity and severe pollution. The SW is well separated from SG and DG by Fisher's discriminant function, but the SG and DG can not be well separated showing their hydrochemical similarities, and emphasize hydraulic connections between SG and DG.

An investigation of the use of discriminant analysis for the classification of blade edge type from cut marks made by metal and bamboo blades.

PubMed

Bonney, Heather

2014-08-01

Analysis of cut marks in bone is largely limited to two dimensional qualitative description. Development of morphological classification methods using measurements from cut mark cross sections could have multiple uses across palaeoanthropological and archaeological disciplines, where cutting edge types are used to investigate and reconstruct behavioral patterns. An experimental study was undertaken, using porcine bone, to determine the usefulness of discriminant function analysis in classifying cut marks by blade edge type, from a number of measurements taken from their cross-sectional profile. The discriminant analysis correctly classified 86.7% of the experimental cut marks into serrated, non-serrated and bamboo blade types. The technique was then used to investigate a series of cut marks of unknown origin from a collection of trophy skulls from the Torres Strait Islands, to investigate whether they were made by bamboo or metal blades. Nineteen out of twenty of the cut marks investigated were classified as bamboo which supports the non-contemporaneous ethnographic accounts of the knives used for trophy taking and defleshing remains. With further investigation across a variety of blade types, this technique could prove a valuable tool in the interpretation of cut mark evidence from a wide variety of contexts, particularly in forensic anthropology where the requirement for presentation of evidence in a statistical format is becoming increasingly important. © 2014 Wiley Periodicals, Inc.

Quantization of liver tissue in dual kVp computed tomography using linear discriminant analysis

NASA Astrophysics Data System (ADS)

Tkaczyk, J. Eric; Langan, David; Wu, Xiaoye; Xu, Daniel; Benson, Thomas; Pack, Jed D.; Schmitz, Andrea; Hara, Amy; Palicek, William; Licato, Paul; Leverentz, Jaynne

2009-02-01

Linear discriminate analysis (LDA) is applied to dual kVp CT and used for tissue characterization. The potential to quantitatively model both malignant and benign, hypo-intense liver lesions is evaluated by analysis of portal-phase, intravenous CT scan data obtained on human patients. Masses with an a priori classification are mapped to a distribution of points in basis material space. The degree of localization of tissue types in the material basis space is related to both quantum noise and real compositional differences. The density maps are analyzed with LDA and studied with system simulations to differentiate these factors. The discriminant analysis is formulated so as to incorporate the known statistical properties of the data. Effective kVp separation and mAs relates to precision of tissue localization. Bias in the material position is related to the degree of X-ray scatter and partial-volume effect. Experimental data and simulations demonstrate that for single energy (HU) imaging or image-based decomposition pixel values of water-like tissues depend on proximity to other iodine-filled bodies. Beam-hardening errors cause a shift in image value on the scale of that difference sought between in cancerous and cystic lessons. In contrast, projection-based decomposition or its equivalent when implemented on a carefully calibrated system can provide accurate data. On such a system, LDA may provide novel quantitative capabilities for tissue characterization in dual energy CT.

Application of recurrence quantification analysis to automatically estimate infant sleep states using a single channel of respiratory data.

PubMed

Terrill, Philip I; Wilson, Stephen J; Suresh, Sadasivam; Cooper, David M; Dakin, Carolyn

2012-08-01

Previous work has identified that non-linear variables calculated from respiratory data vary between sleep states, and that variables derived from the non-linear analytical tool recurrence quantification analysis (RQA) are accurate infant sleep state discriminators. This study aims to apply these discriminators to automatically classify 30 s epochs of infant sleep as REM, non-REM and wake. Polysomnograms were obtained from 25 healthy infants at 2 weeks, 3, 6 and 12 months of age, and manually sleep staged as wake, REM and non-REM. Inter-breath interval data were extracted from the respiratory inductive plethysmograph, and RQA applied to calculate radius, determinism and laminarity. Time-series statistic and spectral analysis variables were also calculated. A nested cross-validation method was used to identify the optimal feature subset, and to train and evaluate a linear discriminant analysis-based classifier. The RQA features radius and laminarity and were reliably selected. Mean agreement was 79.7, 84.9, 84.0 and 79.2 % at 2 weeks, 3, 6 and 12 months, and the classifier performed better than a comparison classifier not including RQA variables. The performance of this sleep-staging tool compares favourably with inter-human agreement rates, and improves upon previous systems using only respiratory data. Applications include diagnostic screening and population-based sleep research.

Reliability of blood color and blood gases in discriminating arterial from venous puncture during cardiopulmonary resuscitation.

PubMed

Park, Je Sung; Lee, Byung Kook; Jeung, Kyung Woon; Choi, Sung Soo; Park, Sang Wook; Song, Kyung Hwan; Lee, Sung Min; Heo, Tag; Min, Yong Il

2015-04-01

We investigated the use of blood color brightness and blood gas variables for discriminating arterial from venous puncture during cardiopulmonary resuscitation (CPR). The study's aims were to determine if discrimination using Po2 is superior to using blood color brightness, and if blood color brightness, Po2, and acid-base variables derived from blood gas analysis accurately discriminate arterial from venous blood during CPR. Fifteen pigs underwent ventricular fibrillation followed by CPR. During CPR, paired femoral arterial and venous blood samples were obtained, and 2 blinded observers were asked to identify the blood's origin. Blood color brightness was measured using a blood brightness scale (BBS). The discriminatory performances of the BBS and blood gas variables were evaluated by calculating the area under receiver operating characteristic curves (AUC). The observers accurately discriminated arterial from venous blood with a sensitivity of 97.0% (84.7%-99.5%) and specificity of 84.9% (69.1%-93.4%). The BBS (AUC = 0.983) and Po2 (AUC = 0.981) methods both showed comparable and excellent discriminatory performances. pH, Pco2, and HCO3(-) all discriminated arterial from venous blood (AUC = 0.831, 0.971, and 0.652, respectively). The AUC for Pco2 was comparable to that for Po2 but significantly larger than that for pH (P = .002) or HCO3(-) (P < .001). The BBS and Po2 methods showed comparable and excellent discrimination performances. Using pH, Pco2, and HCO3(-) levels also discriminated arterial from venous blood during CPR with statistical significance. Copyright © 2015 Elsevier Inc. All rights reserved.

Linear Discriminant Analysis Achieves High Classification Accuracy for the BOLD fMRI Response to Naturalistic Movie Stimuli

PubMed Central

Mandelkow, Hendrik; de Zwart, Jacco A.; Duyn, Jeff H.

2016-01-01

Naturalistic stimuli like movies evoke complex perceptual processes, which are of great interest in the study of human cognition by functional MRI (fMRI). However, conventional fMRI analysis based on statistical parametric mapping (SPM) and the general linear model (GLM) is hampered by a lack of accurate parametric models of the BOLD response to complex stimuli. In this situation, statistical machine-learning methods, a.k.a. multivariate pattern analysis (MVPA), have received growing attention for their ability to generate stimulus response models in a data-driven fashion. However, machine-learning methods typically require large amounts of training data as well as computational resources. In the past, this has largely limited their application to fMRI experiments involving small sets of stimulus categories and small regions of interest in the brain. By contrast, the present study compares several classification algorithms known as Nearest Neighbor (NN), Gaussian Naïve Bayes (GNB), and (regularized) Linear Discriminant Analysis (LDA) in terms of their classification accuracy in discriminating the global fMRI response patterns evoked by a large number of naturalistic visual stimuli presented as a movie. Results show that LDA regularized by principal component analysis (PCA) achieved high classification accuracies, above 90% on average for single fMRI volumes acquired 2 s apart during a 300 s movie (chance level 0.7% = 2 s/300 s). The largest source of classification errors were autocorrelations in the BOLD signal compounded by the similarity of consecutive stimuli. All classifiers performed best when given input features from a large region of interest comprising around 25% of the voxels that responded significantly to the visual stimulus. Consistent with this, the most informative principal components represented widespread distributions of co-activated brain regions that were similar between subjects and may represent functional networks. In light of these results, the combination of naturalistic movie stimuli and classification analysis in fMRI experiments may prove to be a sensitive tool for the assessment of changes in natural cognitive processes under experimental manipulation. PMID:27065832

Comparative forensic soil analysis of New Jersey state parks using a combination of simple techniques with multivariate statistics.

PubMed

Bonetti, Jennifer; Quarino, Lawrence

2014-05-01

This study has shown that the combination of simple techniques with the use of multivariate statistics offers the potential for the comparative analysis of soil samples. Five samples were obtained from each of twelve state parks across New Jersey in both the summer and fall seasons. Each sample was examined using particle-size distribution, pH analysis in both water and 1 M CaCl2 , and a loss on ignition technique. Data from each of the techniques were combined, and principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for multivariate data transformation. Samples from different locations could be visually differentiated from one another using these multivariate plots. Hold-one-out cross-validation analysis showed error rates as low as 3.33%. Ten blind study samples were analyzed resulting in no misclassifications using Mahalanobis distance calculations and visual examinations of multivariate plots. Seasonal variation was minimal between corresponding samples, suggesting potential success in forensic applications. © 2014 American Academy of Forensic Sciences.

Tissue multifractality and hidden Markov model based integrated framework for optimum precancer detection

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Sabyasachi; Das, Nandan K.; Kurmi, Indrajit; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2017-10-01

We report the application of a hidden Markov model (HMM) on multifractal tissue optical properties derived via the Born approximation-based inverse light scattering method for effective discrimination of precancerous human cervical tissue sites from the normal ones. Two global fractal parameters, generalized Hurst exponent and the corresponding singularity spectrum width, computed by multifractal detrended fluctuation analysis (MFDFA), are used here as potential biomarkers. We develop a methodology that makes use of these multifractal parameters by integrating with different statistical classifiers like the HMM and support vector machine (SVM). It is shown that the MFDFA-HMM integrated model achieves significantly better discrimination between normal and different grades of cancer as compared to the MFDFA-SVM integrated model.

The application of frontal sinus index and frontal sinus area in sex estimation based on lateral cephalograms among Han nationality adults in Xinjiang.

PubMed

Luo, Huifang; Wang, Jierui; Zhang, Shuang; Mi, Congbo

2018-05-01

The frontal sinus, due to its unique anatomical features, has become an important element in research for individual identification. Previous studies have demonstrated the use of frontal sinus as an indicator for sex discrimination; however, the sex discrimination rate using frontal sinus was lower compared to that using the traditional morphological methods. In order to improve the sex discrimination percentage, we developed a new method involving the measurement of the frontal sinus index and frontal sinus area from lateral cephalogram radiographs. In this study, 475 digital lateral cephalograms of adult Han citizens from Xinjiang were included. The maximum height, depth, and area of the frontal sinus were calculated using the NemoCeph NX software. The frontal sinus index (ratio of the maximum height to the depth of frontal sinus) was also computed. Statistical analysis results showed significant differences in the frontal sinus index and area between males and females. Discriminant function equation derived from this study differentiated between sexes with 76.6% accuracy. The results demonstrated that the use of frontal sinus index and area for sex discrimination was more accurate than using the frontal sinus index alone. Copyright © 2017. Published by Elsevier Ltd.

Random whole metagenomic sequencing for forensic discrimination of soils.

PubMed

Khodakova, Anastasia S; Smith, Renee J; Burgoyne, Leigh; Abarno, Damien; Linacre, Adrian

2014-01-01

Here we assess the ability of random whole metagenomic sequencing approaches to discriminate between similar soils from two geographically distinct urban sites for application in forensic science. Repeat samples from two parklands in residential areas separated by approximately 3 km were collected and the DNA was extracted. Shotgun, whole genome amplification (WGA) and single arbitrarily primed DNA amplification (AP-PCR) based sequencing techniques were then used to generate soil metagenomic profiles. Full and subsampled metagenomic datasets were then annotated against M5NR/M5RNA (taxonomic classification) and SEED Subsystems (metabolic classification) databases. Further comparative analyses were performed using a number of statistical tools including: hierarchical agglomerative clustering (CLUSTER); similarity profile analysis (SIMPROF); non-metric multidimensional scaling (NMDS); and canonical analysis of principal coordinates (CAP) at all major levels of taxonomic and metabolic classification. Our data showed that shotgun and WGA-based approaches generated highly similar metagenomic profiles for the soil samples such that the soil samples could not be distinguished accurately. An AP-PCR based approach was shown to be successful at obtaining reproducible site-specific metagenomic DNA profiles, which in turn were employed for successful discrimination of visually similar soil samples collected from two different locations.

Discrimination and chemical characterization of different Paeonia lactifloras (Radix Paeoniae Alba and Radix Paeoniae Rubra) by infrared macro-fingerprint analysis-through-separation

NASA Astrophysics Data System (ADS)

Wang, Yang; Wang, Ping; Xu, Changhua; Sun, Suqin; Zhou, Qun; Shi, Zhe; Li, Jin; Chen, Tao; Li, Zheng; Cui, Weili

2015-11-01

Paeonia lactiflora, a commonly used herbal medicine (HM) in Traditional Chinese Medicine (TCM), mainly has two species, Radix Paeoniae Alba (RPA) and Radix Paeoniae Rubra (RPR), for different clinical applications in TCM. For expounding the chemical profile of RPA and RPR and ensuring the clinical efficacy and safety, an infrared macro-fingerprint analysis-through-separation method integrated with statistical pattern recognition was developed to analyze and discriminate the two Paeonia lactifloras. In IR spectra, the major difference between the two was in the range of 1200-900 cm-1: the strongest peak of RPA was at 1024 cm-1, while that of RPR was 1049 cm-1. The difference was magnified in second derivative spectra. The findings were further verified by investigating the separation process of total glucosides, stepwisely monitored by both of IR and UPLC-MS/MS. Simultaneously, the aqueous extracts of RPA and RPR had been separated continuously to acquire the comprehensively hierarchical chemical characteristics for undoubtedly identification and subsequently discrimination of the two herbs. Moreover, 60 batches of the two HMs (30 for each) were objectively classified by principal component regression (PCR) model based on IR macro-fingerprints.

Evaluation of Oil-Palm Fungal Disease Infestation with Canopy Hyperspectral Reflectance Data

PubMed Central

Lelong, Camille C. D.; Roger, Jean-Michel; Brégand, Simon; Dubertret, Fabrice; Lanore, Mathieu; Sitorus, Nurul A.; Raharjo, Doni A.; Caliman, Jean-Pierre

2010-01-01

Fungal disease detection in perennial crops is a major issue in estate management and production. However, nowadays such diagnostics are long and difficult when only made from visual symptom observation, and very expensive and damaging when based on root or stem tissue chemical analysis. As an alternative, we propose in this study to evaluate the potential of hyperspectral reflectance data to help detecting the disease efficiently without destruction of tissues. This study focuses on the calibration of a statistical model of discrimination between several stages of Ganoderma attack on oil palm trees, based on field hyperspectral measurements at tree scale. Field protocol and measurements are first described. Then, combinations of pre-processing, partial least square regression and linear discriminant analysis are tested on about hundred samples to prove the efficiency of canopy reflectance in providing information about the plant sanitary status. A robust algorithm is thus derived, allowing classifying oil-palm in a 4-level typology, based on disease severity from healthy to critically sick stages, with a global performance close to 94%. Moreover, this model discriminates sick from healthy trees with a confidence level of almost 98%. Applications and further improvements of this experiment are finally discussed. PMID:22315565

Assessment of oximetry-based statistical classifiers as simplified screening tools in the management of childhood obstructive sleep apnea.

PubMed

Crespo, Andrea; Álvarez, Daniel; Kheirandish-Gozal, Leila; Gutiérrez-Tobal, Gonzalo C; Cerezo-Hernández, Ana; Gozal, David; Hornero, Roberto; Del Campo, Félix

2018-02-16

A variety of statistical models based on overnight oximetry has been proposed to simplify the detection of children with suspected obstructive sleep apnea syndrome (OSAS). Despite the usefulness reported, additional thorough comparative analyses are required. This study was aimed at assessing common binary classification models from oximetry for the detection of childhood OSAS. Overnight oximetry recordings from 176 children referred for clinical suspicion of OSAS were acquired during in-lab polysomnography. Several training and test datasets were randomly composed by means of bootstrapping for model optimization and independent validation. For every child, blood oxygen saturation (SpO 2 ) was parameterized by means of 17 features. Fast correlation-based filter (FCBF) was applied to search for the optimum features. The discriminatory power of three statistical pattern recognition algorithms was assessed: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and logistic regression (LR). The performance of each automated model was evaluated for the three common diagnostic polysomnographic cutoffs in pediatric OSAS: 1, 3, and 5 events/h. Best screening performances emerged using the 1 event/h cutoff for mild-to-severe childhood OSAS. LR achieved 84.3% accuracy (95% CI 76.8-91.5%) and 0.89 AUC (95% CI 0.83-0.94), while QDA reached 96.5% PPV (95% CI 90.3-100%) and 0.91 AUC (95% CI 0.85-0.96%). Moreover, LR and QDA reached diagnostic accuracies of 82.7% (95% CI 75.0-89.6%) and 82.1% (95% CI 73.8-89.5%) for a cutoff of 5 events/h, respectively. Automated analysis of overnight oximetry may be used to develop reliable as well as accurate screening tools for childhood OSAS.

Statistical Significance and Baseline Monitoring.

DTIC Science & Technology

1984-07-01

impacted at once........................... 24 6 Observed versus nominal a levels for multivariate tests of data sets (50 runs of 4 groups each...cumulative proportion of the observations found for each nominal level. The results of the comparisons of the observed versus nominal a levels for the...a values are always higher than nominal levels. Virtual- . .,ly all nominal a levels are below 0.20. In other words, the discriminant analysis models

Statistical prediction of space motion sickness

NASA Technical Reports Server (NTRS)

Reschke, Millard F.

1990-01-01

Studies designed to empirically examine the etiology of motion sickness to develop a foundation for enhancing its prediction are discussed. Topics addressed include early attempts to predict space motion sickness, multiple test data base that uses provocative and vestibular function tests, and data base subjects; reliability of provocative tests of motion sickness susceptibility; prediction of space motion sickness using linear discriminate analysis; and prediction of space motion sickness susceptibility using the logistic model.

Archaeological Investigations at Site 45-DO-326, Chief Joseph Dam Project, Washington.

DTIC Science & Technology

1984-01-01

Neal Crozier, Sarah K. Campbell and Julia E. Hammett wrote Chapter 2. Stephanie Livingston analyzed .- the faunal assemblage and wrote Chapter 4. Dorothy...mathematical equations derived from analysis of cases with known memberships. First, we assembled representative specimens for each -~~ ~ ,%, -. 79 Table 3-25...we used derived equations called discriminant functions to assign specimens In our collection lo the statistically defined projectile point types

Characterization of Microbiota in Children with Chronic Functional Constipation.

PubMed

de Meij, Tim G J; de Groot, Evelien F J; Eck, Anat; Budding, Andries E; Kneepkens, C M Frank; Benninga, Marc A; van Bodegraven, Adriaan A; Savelkoul, Paul H M

2016-01-01

Disruption of the intestinal microbiota is considered an etiological factor in pediatric functional constipation. Scientifically based selection of potential beneficial probiotic strains in functional constipation therapy is not feasible due to insufficient knowledge of microbiota composition in affected subjects. The aim of this study was to describe microbial composition and diversity in children with functional constipation, compared to healthy controls. Fecal samples from 76 children diagnosed with functional constipation according to the Rome III criteria (median age 8.0 years; range 4.2-17.8) were analyzed by IS-pro, a PCR-based microbiota profiling method. Outcome was compared with intestinal microbiota profiles of 61 healthy children (median 8.6 years; range 4.1-17.9). Microbiota dissimilarity was depicted by principal coordinate analysis (PCoA), diversity was calculated by Shannon diversity index. To determine the most discriminative species, cross validated logistic ridge regression was performed. Applying total microbiota profiles (all phyla together) or per phylum analysis, no disease-specific separation was observed by PCoA and by calculation of diversity indices. By ridge regression, however, functional constipation and controls could be discriminated with 82% accuracy. Most discriminative species were Bacteroides fragilis, Bacteroides ovatus, Bifidobacterium longum, Parabacteroides species (increased in functional constipation) and Alistipes finegoldii (decreased in functional constipation). None of the commonly used unsupervised statistical methods allowed for microbiota-based discrimination of children with functional constipation and controls. By ridge regression, however, both groups could be discriminated with 82% accuracy. Optimization of microbiota-based interventions in constipated children warrants further characterization of microbial signatures linked to clinical subgroups of functional constipation.

A Novel Method to Handle the Effect of Uneven Sampling Effort in Biodiversity Databases

PubMed Central

Pardo, Iker; Pata, María P.; Gómez, Daniel; García, María B.

2013-01-01

How reliable are results on spatial distribution of biodiversity based on databases? Many studies have evidenced the uncertainty related to this kind of analysis due to sampling effort bias and the need for its quantification. Despite that a number of methods are available for that, little is known about their statistical limitations and discrimination capability, which could seriously constrain their use. We assess for the first time the discrimination capacity of two widely used methods and a proposed new one (FIDEGAM), all based on species accumulation curves, under different scenarios of sampling exhaustiveness using Receiver Operating Characteristic (ROC) analyses. Additionally, we examine to what extent the output of each method represents the sampling completeness in a simulated scenario where the true species richness is known. Finally, we apply FIDEGAM to a real situation and explore the spatial patterns of plant diversity in a National Park. FIDEGAM showed an excellent discrimination capability to distinguish between well and poorly sampled areas regardless of sampling exhaustiveness, whereas the other methods failed. Accordingly, FIDEGAM values were strongly correlated with the true percentage of species detected in a simulated scenario, whereas sampling completeness estimated with other methods showed no relationship due to null discrimination capability. Quantifying sampling effort is necessary to account for the uncertainty in biodiversity analyses, however, not all proposed methods are equally reliable. Our comparative analysis demonstrated that FIDEGAM was the most accurate discriminator method in all scenarios of sampling exhaustiveness, and therefore, it can be efficiently applied to most databases in order to enhance the reliability of biodiversity analyses. PMID:23326357

A novel method to handle the effect of uneven sampling effort in biodiversity databases.

PubMed

Pardo, Iker; Pata, María P; Gómez, Daniel; García, María B

2013-01-01

How reliable are results on spatial distribution of biodiversity based on databases? Many studies have evidenced the uncertainty related to this kind of analysis due to sampling effort bias and the need for its quantification. Despite that a number of methods are available for that, little is known about their statistical limitations and discrimination capability, which could seriously constrain their use. We assess for the first time the discrimination capacity of two widely used methods and a proposed new one (FIDEGAM), all based on species accumulation curves, under different scenarios of sampling exhaustiveness using Receiver Operating Characteristic (ROC) analyses. Additionally, we examine to what extent the output of each method represents the sampling completeness in a simulated scenario where the true species richness is known. Finally, we apply FIDEGAM to a real situation and explore the spatial patterns of plant diversity in a National Park. FIDEGAM showed an excellent discrimination capability to distinguish between well and poorly sampled areas regardless of sampling exhaustiveness, whereas the other methods failed. Accordingly, FIDEGAM values were strongly correlated with the true percentage of species detected in a simulated scenario, whereas sampling completeness estimated with other methods showed no relationship due to null discrimination capability. Quantifying sampling effort is necessary to account for the uncertainty in biodiversity analyses, however, not all proposed methods are equally reliable. Our comparative analysis demonstrated that FIDEGAM was the most accurate discriminator method in all scenarios of sampling exhaustiveness, and therefore, it can be efficiently applied to most databases in order to enhance the reliability of biodiversity analyses.

A new unified framework for the early detection of the progression to diabetic retinopathy from fundus images.

PubMed

Leontidis, Georgios

2017-11-01

Human retina is a diverse and important tissue, vastly studied for various retinal and other diseases. Diabetic retinopathy (DR), a leading cause of blindness, is one of them. This work proposes a novel and complete framework for the accurate and robust extraction and analysis of a series of retinal vascular geometric features. It focuses on studying the registered bifurcations in successive years of progression from diabetes (no DR) to DR, in order to identify the vascular alterations. Retinal fundus images are utilised, and multiple experimental designs are employed. The framework includes various steps, such as image registration and segmentation, extraction of features, statistical analysis and classification models. Linear mixed models are utilised for making the statistical inferences, alongside the elastic-net logistic regression, boruta algorithm, and regularised random forests for the feature selection and classification phases, in order to evaluate the discriminative potential of the investigated features and also build classification models. A number of geometric features, such as the central retinal artery and vein equivalents, are found to differ significantly across the experiments and also have good discriminative potential. The classification systems yield promising results with the area under the curve values ranging from 0.821 to 0.968, across the four different investigated combinations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Single-Molecule Counting of Point Mutations by Transient DNA Binding

NASA Astrophysics Data System (ADS)

Su, Xin; Li, Lidan; Wang, Shanshan; Hao, Dandan; Wang, Lei; Yu, Changyuan

2017-03-01

High-confidence detection of point mutations is important for disease diagnosis and clinical practice. Hybridization probes are extensively used, but are hindered by their poor single-nucleotide selectivity. Shortening the length of DNA hybridization probes weakens the stability of the probe-target duplex, leading to transient binding between complementary sequences. The kinetics of probe-target binding events are highly dependent on the number of complementary base pairs. Here, we present a single-molecule assay for point mutation detection based on transient DNA binding and use of total internal reflection fluorescence microscopy. Statistical analysis of single-molecule kinetics enabled us to effectively discriminate between wild type DNA sequences and single-nucleotide variants at the single-molecule level. A higher single-nucleotide discrimination is achieved than in our previous work by optimizing the assay conditions, which is guided by statistical modeling of kinetics with a gamma distribution. The KRAS c.34 A mutation can be clearly differentiated from the wild type sequence (KRAS c.34 G) at a relative abundance as low as 0.01% mutant to WT. To demonstrate the feasibility of this method for analysis of clinically relevant biological samples, we used this technology to detect mutations in single-stranded DNA generated from asymmetric RT-PCR of mRNA from two cancer cell lines.

Introduction of High Throughput Magnetic Resonance T2-Weighted Image Texture Analysis for WHO Grade 2 and 3 Gliomas.

PubMed

Kinoshita, Manabu; Sakai, Mio; Arita, Hideyuki; Shofuda, Tomoko; Chiba, Yasuyoshi; Kagawa, Naoki; Watanabe, Yoshiyuki; Hashimoto, Naoya; Fujimoto, Yasunori; Yoshimine, Toshiki; Nakanishi, Katsuyuki; Kanemura, Yonehiro

2016-01-01

Reports have suggested that tumor textures presented on T2-weighted images correlate with the genetic status of glioma. Therefore, development of an image analyzing framework that is capable of objective and high throughput image texture analysis for large scale image data collection is needed. The current study aimed to address the development of such a framework by introducing two novel parameters for image textures on T2-weighted images, i.e., Shannon entropy and Prewitt filtering. Twenty-two WHO grade 2 and 28 grade 3 glioma patients were collected whose pre-surgical MRI and IDH1 mutation status were available. Heterogeneous lesions showed statistically higher Shannon entropy than homogenous lesions (p = 0.006) and ROC curve analysis proved that Shannon entropy on T2WI was a reliable indicator for discrimination of homogenous and heterogeneous lesions (p = 0.015, AUC = 0.73). Lesions with well-defined borders exhibited statistically higher Edge mean and Edge median values using Prewitt filtering than those with vague lesion borders (p = 0.0003 and p = 0.0005 respectively). ROC curve analysis also proved that both Edge mean and median values were promising indicators for discrimination of lesions with vague and well defined borders and both Edge mean and median values performed in a comparable manner (p = 0.0002, AUC = 0.81 and p < 0.0001, AUC = 0.83, respectively). Finally, IDH1 wild type gliomas showed statistically lower Shannon entropy on T2WI than IDH1 mutated gliomas (p = 0.007) but no difference was observed between IDH1 wild type and mutated gliomas in Edge median values using Prewitt filtering. The current study introduced two image metrics that reflect lesion texture described on T2WI. These two metrics were validated by readings of a neuro-radiologist who was blinded to the results. This observation will facilitate further use of this technique in future large scale image analysis of glioma.

Analysis of select Dalbergia and trade timber using direct analysis in real time and time-of-flight mass spectrometry for CITES enforcement.

PubMed

Lancaster, Cady; Espinoza, Edgard

2012-05-15

International trade of several Dalbergia wood species is regulated by The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). In order to supplement morphological identification of these species, a rapid chemical method of analysis was developed. Using Direct Analysis in Real Time (DART) ionization coupled with Time-of-Flight (TOF) Mass Spectrometry (MS), selected Dalbergia and common trade species were analyzed. Each of the 13 wood species was classified using principal component analysis and linear discriminant analysis (LDA). These statistical data clusters served as reliable anchors for species identification of unknowns. Analysis of 20 or more samples from the 13 species studied in this research indicates that the DART-TOFMS results are reproducible. Statistical analysis of the most abundant ions gave good classifications that were useful for identifying unknown wood samples. DART-TOFMS and LDA analysis of 13 species of selected timber samples and the statistical classification allowed for the correct assignment of unknown wood samples. This method is rapid and can be useful when anatomical identification is difficult but needed in order to support CITES enforcement. Published 2012. This article is a US Government work and is in the public domain in the USA.

A discriminant analysis prediction model of non-syndromic cleft lip with or without cleft palate based on risk factors.

PubMed

Li, Huixia; Luo, Miyang; Luo, Jiayou; Zheng, Jianfei; Zeng, Rong; Du, Qiyun; Fang, Junqun; Ouyang, Na

2016-11-23

A risk prediction model of non-syndromic cleft lip with or without cleft palate (NSCL/P) was established by a discriminant analysis to predict the individual risk of NSCL/P in pregnant women. A hospital-based case-control study was conducted with 113 cases of NSCL/P and 226 controls without NSCL/P. The cases and the controls were obtained from 52 birth defects' surveillance hospitals in Hunan Province, China. A questionnaire was administered in person to collect the variables relevant to NSCL/P by face to face interviews. Logistic regression models were used to analyze the influencing factors of NSCL/P, and a stepwise Fisher discriminant analysis was subsequently used to construct the prediction model. In the univariate analysis, 13 influencing factors were related to NSCL/P, of which the following 8 influencing factors as predictors determined the discriminant prediction model: family income, maternal occupational hazards exposure, premarital medical examination, housing renovation, milk/soymilk intake in the first trimester of pregnancy, paternal occupational hazards exposure, paternal strong tea drinking, and family history of NSCL/P. The model had statistical significance (lambda = 0.772, chi-square = 86.044, df = 8, P < 0.001). Self-verification showed that 83.8 % of the participants were correctly predicted to be NSCL/P cases or controls with a sensitivity of 74.3 % and a specificity of 88.5 %. The area under the receiver operating characteristic curve (AUC) was 0.846. The prediction model that was established using the risk factors of NSCL/P can be useful for predicting the risk of NSCL/P. Further research is needed to improve the model, and confirm the validity and reliability of the model.

Parasites as biological tags of fish stocks: a meta-analysis of their discriminatory power.

PubMed

Poulin, Robert; Kamiya, Tsukushi

2015-01-01

The use of parasites as biological tags to discriminate among marine fish stocks has become a widely accepted method in fisheries management. Here, we first link this approach to its unstated ecological foundation, the decay in the similarity of the species composition of assemblages as a function of increasing distance between them, a phenomenon almost universal in nature. We explain how distance decay of similarity can influence the use of parasites as biological tags. Then, we perform a meta-analysis of 61 uses of parasites as tags of marine fish populations in multivariate discriminant analyses, obtained from 29 articles. Our main finding is that across all studies, the observed overall probability of correct classification of fish based on parasite data was about 71%. This corresponds to a two-fold improvement over the rate of correct classification expected by chance alone, and the average effect size (Zr = 0·463) computed from the original values was also indicative of a medium-to-large effect. However, none of the moderator variables included in the meta-analysis had a significant effect on the proportion of correct classification; these moderators included the total number of fish sampled, the number of parasite species used in the discriminant analysis, the number of localities from which fish were sampled, the minimum and maximum distance between any pair of sampling localities, etc. Therefore, there are no clear-cut situations in which the use of parasites as tags is more useful than others. Finally, we provide recommendations for the future usage of parasites as tags for stock discrimination, to ensure that future applications of the method achieve statistical rigour and a high discriminatory power.

Revised Framingham Stroke Risk Score, Nontraditional Risk Markers, and Incident Stroke in a Multiethnic Cohort.

PubMed

Flueckiger, Peter; Longstreth, Will; Herrington, David; Yeboah, Joseph

2018-02-01

Limited data exist on the performance of the revised Framingham Stroke Risk Score (R-FSRS) and the R-FSRS in conjunction with nontraditional risk markers. We compared the R-FSRS, original FSRS, and the Pooled Cohort Equation for stroke prediction and assessed the improvement in discrimination by nontraditional risk markers. Six thousand seven hundred twelve of 6814 participants of the MESA (Multi-Ethnic Study of Atherosclerosis) were included. Cox proportional hazard, area under the curve, net reclassification improvement, and integrated discrimination increment analysis were used to assess and compare each stroke prediction risk score. Stroke was defined as fatal/nonfatal strokes (hemorrhagic or ischemic). After mean follow-up of 10.7 years, 231 of 6712 (3.4%) strokes were adjudicated (2.7% ischemic strokes). Mean stroke risks using the R-FSRS, original FSRS, and Pooled Cohort Equation were 4.7%, 5.9%, and 13.5%. The R-FSRS had the best calibration (Hosmer-Lemeshow goodness-of-fit, χ 2 =6.55; P =0.59). All risk scores were predictive of incident stroke. C statistics of R-FSRS (0.716) was similar to Pooled Cohort Equation (0.716), but significantly higher than the original FSRS (0.653; P =0.01 for comparison with R-FSRS). Adding nontraditional risk markers individually to the R-FSRS did not improve discrimination of the R-FSRS in the area under the curve analysis, but did improve category-less net reclassification improvement and integrated discrimination increment for incident stroke. The addition of coronary artery calcium to R-FSRS produced the highest category-less net reclassification improvement (0.36) and integrated discrimination increment (0.0027). Similar results were obtained when ischemic strokes were used as the outcome. The R-FSRS downgraded stroke risk but had better calibration and discriminative ability for incident stroke compared with the original FSRS. Nontraditional risk markers modestly improved the discriminative ability of the R-FSRS, with coronary artery calcium performing the best. © 2018 American Heart Association, Inc.

6C.04: INTEGRATED SNP ANALYSIS AND METABOLOMIC PROFILES OF METABOLIC SYNDROME.

PubMed

Marrachelli, V; Monleon, D; Morales, J M; Rentero, P; Martínez, F; Chaves, F J; Martin-Escudero, J C; Redon, J

2015-06-01

Metabolic syndrome (MS) has become a health and financial burden worldwide. Susceptibility of genetically determined metabotype of MS has not yet been investigated. We aimed to identify a distinctive metabolic profile of blood serum which might correlates to the early detection of the development of MS associated to genetic polymorphism. We applied high resolution NMR spectroscopy to profile blood serum from patients without MS (n = 945) or with (n = 291). Principal component analysis (PCA) and projection to latent structures for discriminant analysis (PLS-DA) were applied to NMR spectral datasets. Results were cross-validated using the Venetian Blinds approach. Additionally, five SNPs previously associated with MS were genotyped with SNPlex and tested for associations between the metabolic profiles and the genetic variants. Statistical analysis was performed using in-house MATLAB scripts and the PLS Toolbox statistical multivariate analysis library. Our analysis provided a PLS-DA Metabolic Syndrome discrimination model based on NMR metabolic profile (AUC = 0.86) with 84% of sensitivity and 72% specificity. The model identified 11 metabolites differentially regulated in patients with MS. Among others, fatty acids, glucose, alanine, hydroxyisovalerate, acetone, trimethylamine, 2-phenylpropionate, isobutyrate and valine, significantly contributed to the model. The combined analysis of metabolomics and SNP data revealed an association between the metabolic profile of MS and genes polymorphism involved in the adiposity regulation and fatty acids metabolism: rs2272903_TT (TFAP2B), rs3803_TT (GATA2), rs174589_CC (FADS2) and rs174577_AA (FADS2). In addition, individuals with the rs2272903-TT genotype seem to develop MS earlier than general population. Our study provides new insights on the metabolic alterations associated with a MS high-risk genotype. These results could help in future development of risk assessment and predictive models for subclinical cardiovascular disease.

Tower of London test: a comparison between conventional statistic approach and modelling based on artificial neural network in differentiating fronto-temporal dementia from Alzheimer's disease.

PubMed

Franceschi, Massimo; Caffarra, Paolo; Savarè, Rita; Cerutti, Renata; Grossi, Enzo

2011-01-01

The early differentiation of Alzheimer's disease (AD) from frontotemporal dementia (FTD) may be difficult. The Tower of London (ToL), thought to assess executive functions such as planning and visuo-spatial working memory, could help in this purpose. Twentytwo Dementia Centers consecutively recruited patients with early FTD or AD. ToL performances of these groups were analyzed using both the conventional statistical approaches and the Artificial Neural Networks (ANNs) modelling. Ninety-four non aphasic FTD and 160 AD patients were recruited. ToL Accuracy Score (AS) significantly (p < 0.05) differentiated FTD from AD patients. However, the discriminant validity of AS checked by ROC curve analysis, yielded no significant results in terms of sensitivity and specificity (AUC 0.63). The performances of the 12 Success Subscores (SS) together with age, gender and schooling years were entered into advanced ANNs developed by Semeion Institute. The best ANNs were selected and submitted to ROC curves. The non-linear model was able to discriminate FTD from AD with an average AUC for 7 independent trials of 0.82. The use of hidden information contained in the different items of ToL and the non linear processing of the data through ANNs allows a high discrimination between FTD and AD in individual patients.

Detecting global and local hippocampal shape changes in Alzheimer's disease using statistical shape models.

PubMed

Shen, Kai-kai; Fripp, Jurgen; Mériaudeau, Fabrice; Chételat, Gaël; Salvado, Olivier; Bourgeat, Pierrick

2012-02-01

The hippocampus is affected at an early stage in the development of Alzheimer's disease (AD). With the use of structural magnetic resonance (MR) imaging, we can investigate the effect of AD on the morphology of the hippocampus. The hippocampal shape variations among a population can be usually described using statistical shape models (SSMs). Conventional SSMs model the modes of variations among the population via principal component analysis (PCA). Although these modes are representative of variations within the training data, they are not necessarily discriminative on labeled data or relevant to the differences between the subpopulations. We use the shape descriptors from SSM as features to classify AD from normal control (NC) cases. In this study, a Hotelling's T2 test is performed to select a subset of landmarks which are used in PCA. The resulting variation modes are used as predictors of AD from NC. The discrimination ability of these predictors is evaluated in terms of their classification performances with bagged support vector machines (SVMs). Restricting the model to landmarks with better separation between AD and NC increases the discrimination power of SSM. The predictors extracted on the subregions also showed stronger correlation with the memory-related measurements such as Logical Memory, Auditory Verbal Learning Test (AVLT) and the memory subscores of Alzheimer Disease Assessment Scale (ADAS). Crown Copyright © 2011. Published by Elsevier Inc. All rights reserved.

Sources of Discrimination and Their Associations With Health in Sexual Minority Adults.

PubMed

Figueroa, Wilson S; Zoccola, Peggy M

2016-06-01

Health disparities exist between sexual minorities and heterosexuals. These health disparities may be due to stressful social situations and environments that are created by discrimination. The current study recruited 277 sexual minorities to complete an online survey to examine the effects of discrimination on health. Discrimination from family and friends, compared to non-family and friends, was found to be more strongly associated with poorer health. This effect was partially statistically mediated by perceived stress reactivity. Findings from this study highlight the importance of distinguishing between different sources of discrimination when examining the effect of discrimination on health in sexual minority adults.

Statistical learning and auditory processing in children with music training: An ERP study.

PubMed

Mandikal Vasuki, Pragati Rao; Sharma, Mridula; Ibrahim, Ronny; Arciuli, Joanne

2017-07-01

The question whether musical training is associated with enhanced auditory and cognitive abilities in children is of considerable interest. In the present study, we compared children with music training versus those without music training across a range of auditory and cognitive measures, including the ability to detect implicitly statistical regularities in input (statistical learning). Statistical learning of regularities embedded in auditory and visual stimuli was measured in musically trained and age-matched untrained children between the ages of 9-11years. In addition to collecting behavioural measures, we recorded electrophysiological measures to obtain an online measure of segmentation during the statistical learning tasks. Musically trained children showed better performance on melody discrimination, rhythm discrimination, frequency discrimination, and auditory statistical learning. Furthermore, grand-averaged ERPs showed that triplet onset (initial stimulus) elicited larger responses in the musically trained children during both auditory and visual statistical learning tasks. In addition, children's music skills were associated with performance on auditory and visual behavioural statistical learning tasks. Our data suggests that individual differences in musical skills are associated with children's ability to detect regularities. The ERP data suggest that musical training is associated with better encoding of both auditory and visual stimuli. Although causality must be explored in further research, these results may have implications for developing music-based remediation strategies for children with learning impairments. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Statistical Analysis of Mineral Concentration for the Geographic Identification of Garlic Samples from Sicily (Italy), Tunisia and Spain

PubMed Central

Vadalà, Rossella; Mottese, Antonio F.; Bua, Giuseppe D.; Salvo, Andrea; Mallamace, Domenico; Corsaro, Carmelo; Vasi, Sebastiano; Giofrè, Salvatore V.; Alfa, Maria; Cicero, Nicola; Dugo, Giacomo

2016-01-01

We performed a statistical analysis of the concentration of mineral elements, by means of inductively coupled plasma mass spectrometry (ICP-MS), in different varieties of garlic from Spain, Tunisia, and Italy. Nubia Red Garlic (Sicily) is one of the most known Italian varieties that belongs to traditional Italian food products (P.A.T.) of the Ministry of Agriculture, Food, and Forestry. The obtained results suggest that the concentrations of the considered elements may serve as geographical indicators for the discrimination of the origin of the different samples. In particular, we found a relatively high content of Selenium in the garlic variety known as Nubia red garlic, and, indeed, it could be used as an anticarcinogenic agent. PMID:28231115

Dentistry and HIV/AIDS related stigma.

PubMed

Elizondo, Jesus Eduardo; Treviño, Ana Cecilia; Violant, Deborah

2015-01-01

To analyze HIV/AIDS positive individual's perception and attitudes regarding dental services. One hundred and thirty-four subjects (30.0% of women and 70.0% of men) from Nuevo León, Mexico, took part in the study (2014). They filled out structured, analytical, self-administered, anonymous questionnaires. Besides the sociodemographic variables, the perception regarding public and private dental services and related professionals was evaluated, as well as the perceived stigma associated with HIV/AIDS, through a Likert-type scale. The statistical evaluation included a factorial and a non-hierarchical cluster analysis. Social inequalities were found regarding the search for public and private dental professionals and services. Most subjects reported omitting their HIV serodiagnosis and agreed that dentists must be trained and qualified to treat patients with HIV/AIDS. The factorial analysis revealed two elements: experiences of stigma and discrimination in dental appointments and feelings of concern regarding the attitudes of professionals or their teams concerning patients' HIV serodiagnosis. The cluster analysis identified three groups: users who have not experienced stigma or discrimination (85.0%); the ones who have not had those experiences, but feel somewhat concerned (12.7%); and the ones who underwent stigma and discrimination and feel concerned (2.3%). We observed a low percentage of stigma and discrimination in dental appointments; however, most HIV/AIDS patients do not reveal their serodiagnosis to dentists out of fear of being rejected. Such fact implies a workplace hazard to dental professionals, but especially to the very own health of HIV/AIDS patients, as dentists will not be able to provide them a proper clinical and pharmaceutical treatment.

High Dimensional Classification Using Features Annealed Independence Rules.

PubMed

Fan, Jianqing; Fan, Yingying

2008-01-01

Classification using high-dimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or other high-throughput data. The impact of dimensionality on classifications is largely poorly understood. In a seminal paper, Bickel and Levina (2004) show that the Fisher discriminant performs poorly due to diverging spectra and they propose to use the independence rule to overcome the problem. We first demonstrate that even for the independence classification rule, classification using all the features can be as bad as the random guessing due to noise accumulation in estimating population centroids in high-dimensional feature space. In fact, we demonstrate further that almost all linear discriminants can perform as bad as the random guessing. Thus, it is paramountly important to select a subset of important features for high-dimensional classification, resulting in Features Annealed Independence Rules (FAIR). The conditions under which all the important features can be selected by the two-sample t-statistic are established. The choice of the optimal number of features, or equivalently, the threshold value of the test statistics are proposed based on an upper bound of the classification error. Simulation studies and real data analysis support our theoretical results and demonstrate convincingly the advantage of our new classification procedure.

Efficient Coding and Statistically Optimal Weighting of Covariance among Acoustic Attributes in Novel Sounds

PubMed Central

Stilp, Christian E.; Kluender, Keith R.

2012-01-01

To the extent that sensorineural systems are efficient, redundancy should be extracted to optimize transmission of information, but perceptual evidence for this has been limited. Stilp and colleagues recently reported efficient coding of robust correlation (r = .97) among complex acoustic attributes (attack/decay, spectral shape) in novel sounds. Discrimination of sounds orthogonal to the correlation was initially inferior but later comparable to that of sounds obeying the correlation. These effects were attenuated for less-correlated stimuli (r = .54) for reasons that are unclear. Here, statistical properties of correlation among acoustic attributes essential for perceptual organization are investigated. Overall, simple strength of the principal correlation is inadequate to predict listener performance. Initial superiority of discrimination for statistically consistent sound pairs was relatively insensitive to decreased physical acoustic/psychoacoustic range of evidence supporting the correlation, and to more frequent presentations of the same orthogonal test pairs. However, increased range supporting an orthogonal dimension has substantial effects upon perceptual organization. Connectionist simulations and Eigenvalues from closed-form calculations of principal components analysis (PCA) reveal that perceptual organization is near-optimally weighted to shared versus unshared covariance in experienced sound distributions. Implications of reduced perceptual dimensionality for speech perception and plausible neural substrates are discussed. PMID:22292057

CONCENTRIC DECILE SEGMENTATION OF WHITE AND HYPOPIGMENTED AREAS IN DERMOSCOPY IMAGES OF SKIN LESIONS ALLOWS DISCRIMINATION OF MALIGNANT MELANOMA

PubMed Central

Dalal, Ankur; Moss, Randy H.; Stanley, R. Joe; Stoecker, William V.; Gupta, Kapil; Calcara, David A.; Xu, Jin; Shrestha, Bijaya; Drugge, Rhett; Malters, Joseph M.; Perry, Lindall A.

2011-01-01

Dermoscopy, also known as dermatoscopy or epiluminescence microscopy (ELM), permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. White areas, prominent in early malignant melanoma and melanoma in situ, contribute to early detection of these lesions. An adaptive detection method has been investigated to identify white and hypopigmented areas based on lesion histogram statistics. Using the Euclidean distance transform, the lesion is segmented in concentric deciles. Overlays of the white areas on the lesion deciles are determined. Calculated features of automatically detected white areas include lesion decile ratios, normalized number of white areas, absolute and relative size of largest white area, relative size of all white areas, and white area eccentricity, dispersion, and irregularity. Using a back-propagation neural network, the white area statistics yield over 95% diagnostic accuracy of melanomas from benign nevi. White and hypopigmented areas in melanomas tend to be central or paracentral. The four most powerful features on multivariate analysis are lesion decile ratios. Automatic detection of white and hypopigmented areas in melanoma can be accomplished using lesion statistics. A neural network can achieve good discrimination of melanomas from benign nevi using these areas. Lesion decile ratios are useful white area features. PMID:21074971

Eye-gaze determination of user intent at the computer interface

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

Goldberg, J.H.; Schryver, J.C.

1993-12-31

Determination of user intent at the computer interface through eye-gaze monitoring can significantly aid applications for the disabled, as well as telerobotics and process control interfaces. Whereas current eye-gaze control applications are limited to object selection and x/y gazepoint tracking, a methodology was developed here to discriminate a more abstract interface operation: zooming-in or out. This methodology first collects samples of eve-gaze location looking at controlled stimuli, at 30 Hz, just prior to a user`s decision to zoom. The sample is broken into data frames, or temporal snapshots. Within a data frame, all spatial samples are connected into a minimummore » spanning tree, then clustered, according to user defined parameters. Each cluster is mapped to one in the prior data frame, and statistics are computed from each cluster. These characteristics include cluster size, position, and pupil size. A multiple discriminant analysis uses these statistics both within and between data frames to formulate optimal rules for assigning the observations into zooming, zoom-out, or no zoom conditions. The statistical procedure effectively generates heuristics for future assignments, based upon these variables. Future work will enhance the accuracy and precision of the modeling technique, and will empirically test users in controlled experiments.« less

Differences in game-related statistics of basketball performance by game location for men's winning and losing teams.

PubMed

Gómez, Miguel A; Lorenzo, Alberto; Barakat, Rubén; Ortega, Enrique; Palao, José M

2008-02-01

The aim of the present study was to identify game-related statistics that differentiate winning and losing teams according to game location. The sample included 306 games of the 2004-2005 regular season of the Spanish professional men's league (ACB League). The independent variables were game location (home or away) and game result (win or loss). The game-related statistics registered were free throws (successful and unsuccessful), 2- and 3-point field goals (successful and unsuccessful), offensive and defensive rebounds, blocks, assists, fouls, steals, and turnovers. Descriptive and inferential analyses were done (one-way analysis of variance and discriminate analysis). The multivariate analysis showed that winning teams differ from losing teams in defensive rebounds (SC = .42) and in assists (SC = .38). Similarly, winning teams differ from losing teams when they play at home in defensive rebounds (SC = .40) and in assists (SC = .41). On the other hand, winning teams differ from losing teams when they play away in defensive rebounds (SC = .44), assists (SC = .30), successful 2-point field goals (SC = .31), and unsuccessful 3-point field goals (SC = -.35). Defensive rebounds and assists were the only game-related statistics common to all three analyses.

Preliminary analyses of SIB-B radar data for recent Hawaii lava flows

NASA Technical Reports Server (NTRS)

Kaupp, V. H.; Derryberry, B. A.; Macdonald, H. C.; Gaddis, L. R.; Mouginis-Mark, P. J.

1986-01-01

The Shuttle Imaging Radar (SIR-B) experiment acquired two L-band (23 cm wavelength) radar images (at about 28 and 48 deg incidence angles) over the Kilauea Volcano area of southeastern Hawaii. Geologic analysis of these data indicates that, although aa lava flows and pyroclastic deposits can be discriminated, pahoehoe lava flows are not readily distinguished from surrounding low return materials. Preliminary analysis of data extracted from isolated flows indicates that flow type (i.e., aa or pahoehoe) and relative age can be determined from their basic statistics and illumination angle.

Tree-space statistics and approximations for large-scale analysis of anatomical trees.

PubMed

Feragen, Aasa; Owen, Megan; Petersen, Jens; Wille, Mathilde M W; Thomsen, Laura H; Dirksen, Asger; de Bruijne, Marleen

2013-01-01

Statistical analysis of anatomical trees is hard to perform due to differences in the topological structure of the trees. In this paper we define statistical properties of leaf-labeled anatomical trees with geometric edge attributes by considering the anatomical trees as points in the geometric space of leaf-labeled trees. This tree-space is a geodesic metric space where any two trees are connected by a unique shortest path, which corresponds to a tree deformation. However, tree-space is not a manifold, and the usual strategy of performing statistical analysis in a tangent space and projecting onto tree-space is not available. Using tree-space and its shortest paths, a variety of statistical properties, such as mean, principal component, hypothesis testing and linear discriminant analysis can be defined. For some of these properties it is still an open problem how to compute them; others (like the mean) can be computed, but efficient alternatives are helpful in speeding up algorithms that use means iteratively, like hypothesis testing. In this paper, we take advantage of a very large dataset (N = 8016) to obtain computable approximations, under the assumption that the data trees parametrize the relevant parts of tree-space well. Using the developed approximate statistics, we illustrate how the structure and geometry of airway trees vary across a population and show that airway trees with Chronic Obstructive Pulmonary Disease come from a different distribution in tree-space than healthy ones. Software is available from http://image.diku.dk/aasa/software.php.

A discriminative test among the different theories proposed to explain the origin of the genetic code: the coevolution theory finds additional support.

PubMed

Giulio, Massimo Di

2018-05-19

A discriminative statistical test among the different theories proposed to explain the origin of the genetic code is presented. Gathering the amino acids into polarity and biosynthetic classes that are the first expression of the physicochemical theory of the origin of the genetic code and the second expression of the coevolution theory, these classes are utilized in the Fisher's exact test to establish their significance within the genetic code table. Linking to the rows and columns of the genetic code of probabilities that express the statistical significance of these classes, I have finally been in the condition to be able to calculate a χ value to link to both the physicochemical theory and to the coevolution theory that would express the corroboration level referred to these theories. The comparison between these two χ values showed that the coevolution theory is able to explain - in this strictly empirical analysis - the origin of the genetic code better than that of the physicochemical theory. Copyright © 2018 Elsevier B.V. All rights reserved.

Comparison of Ganglion Cell and Retinal Nerve Fiber Layer Thickness in Pigment Dispersion Syndrome, Pigmentary Glaucoma, and Healthy Subjects with Spectral-domain OCT.

PubMed

Arifoglu, Hasan Basri; Simavli, Huseyin; Midillioglu, Inci; Berk Ergun, Sule; Simsek, Saban

2017-01-01

To evaluate the ganglion cell complex (GCC) and retinal nerve fiber layer (RNFL) thickness in pigment dispersion syndrome (PDS) and pigmentary glaucoma (PG) with RTVue spectral domain optical coherence tomography (SD-OCT). A total of 102 subjects were enrolled: 29 with PDS, 18 with PG, and 55 normal subjects. Full ophthalmic examination including visual field analysis was performed. SD-OCT was used to analyze GCC superior, GCC inferior, and average RNFL thickness. To compare the discrimination capabilities, the areas under the receiver operating characteristic curves were assessed. Superior GCC, inferior GCC, and RNFL thickness values of patients with PG were statistically signicantly lower than those of patients with PDS (p < 0.001) and healthy individuals (p < 0.001 for all). No statistically significant difference was found between PDS and normal subjects in same parameters (p > 0.05). The SD-OCT-derived GCC and RNFL thickness parameters can be useful to discriminate PG from both PDS and normal subjects.

Patterns and correlates of self-reported racial discrimination among Australian Aboriginal and Torres Strait Islander adults, 2008–09: analysis of national survey data

PubMed Central

2013-01-01

Background 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. Methods 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. Results 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. Conclusions These data indicate that racial discrimination is commonly experienced across a wide variety of settings, with public, legal and work settings identified as particularly salient. The observed relationships, while not necessarily causal, help to build a detailed picture of self-reported racial discrimination experienced by Indigenous people in contemporary Australia, providing important evidence to inform anti-racism policy. PMID:23816052

Patterns and correlates of self-reported racial discrimination among Australian Aboriginal and Torres Strait Islander adults, 2008-09: analysis of national survey data.

PubMed

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 particularly salient. The observed relationships, while not necessarily causal, help to build a detailed picture of self-reported racial discrimination experienced by Indigenous people in contemporary Australia, providing important evidence to inform anti-racism policy.

Characterization of Italian honeys (Marche Region) on the basis of their mineral content and some typical quality parameters

PubMed Central

Conti, Marcelo Enrique; Stripeikis, Jorge; Campanella, Luigi; Cucina, Domenico; Tudino, Mabel Beatriz

2007-01-01

Background The characterization of three types of Marche (Italy) honeys (Acacia, Multifloral, Honeydew) was carried out on the basis of the their quality parameters (pH, sugar content, humidity) and mineral content (Na, K, Ca, Mg, Cu, Fe, and Mn). Pattern recognition methods such as principal components analysis (PCA) and linear discriminant analysis (LDA) were performed in order to classify honey samples whose botanical origins were different, and identify the most discriminant parameters. Lastly, using ANOVA and correlations for all parameters, significant differences between diverse types of honey were examined. Results Most of the samples' water content showed good maturity (98%) whilst pH values were in the range 3.50 – 4.21 confirming the good quality of the honeys analysed. Potassium was quantitatively the most relevant mineral (mean = 643 ppm), accounting for 79% of the total mineral content. The Ca, Na and Mg contents account for 14, 3 and 3% of the total mineral content respectively, while other minerals (Cu, Mn, Fe) were present at very low levels. PCA explained 75% or more of the variance with the first two PC variables. The variables with higher discrimination power according to the multivariate statistical procedure were Mg and pH. On the other hand, all samples of acacia and honeydew, and more than 90% of samples of multifloral type have been correctly classified using the LDA. ANOVA shows significant differences between diverse floral origins for all variables except sugar, moisture and Fe. Conclusion In general, the analytical results obtained for the Marche honeys indicate the products' high quality. The determination of physicochemical parameters and mineral content in combination with modern statistical techniques can be a useful tool for honey classification. PMID:17880749

Manchester Triage System: main flowcharts, discriminators and outcomes of a pediatric emergency care 1

PubMed Central

Amthauer, Camila; da Cunha, Maria Luzia Chollopetz

2016-01-01

ABSTRACT Objetive: to characterize the care services performed through risk rating by the Manchester Triage System, identifying demographics (age, gender), main flowcharts, discriminators and outcomes in pediatric emergency Method: cross-sectional quantitative study. Data on risk classification were obtained through a search of computerized registration data from medical records of patients treated in the pediatric emergency within one year. Descriptive statistics with absolute and relative frequencies was used for the analysis. Results: 10,921 visits were conducted in the pediatric emergency, mostly male (54.4%), aged between 29 days and two years (44.5%). There was a prevalence of the urgent risk category (43.6%). The main flowchart used in the care was worried parents (22.4%) and the most prevalent discriminator was recent event (15.3%). The hospitalization outcome occurred in 10.4% of care performed in the pediatric emergency, however 61.8% of care needed to stay under observation and / or being under the health team care in the pediatric emergency. Conclusion: worried parents was the main flowchart used and recent events the most prevalent discriminator, comprising the hospitalization outcomes and permanency in observation in the pediatric emergency before discharge from the hospital. PMID:27579934

Social Hazards as Manifested Workplace Discrimination and Health (Vietnamese and Ukrainian Female and Male Migrants in Czechia)

PubMed Central

Drbohlav, Dušan; Dzúrová, Dagmar

2017-01-01

Social hazards as one of the dimensions of workplace discrimination are a potential social determinant of health inequalities. The aim of this study was to investigate relations between self-reported health and social hazard characteristics (defined as—discrimination as such, violence or threat of violence, time pressure or work overload and risk of accident) among Vietnamese and Ukrainian migrants (males and females) in Czechia by age, education level and marital status. This study is based on data from a survey of 669 immigrants in Czechia in 2013. Logistic regression analysis indicates that the given independent variables (given social hazards and socio-demographic characteristics), as predictors of a quality of self-reported health are more important for immigrant females than for males, irrespective of citizenship, albeit only for some of them and to differing extents. We found out that being exposed to the selected social hazards in the workplace leads to worsening self-rated health, especially for females. On the other hand, there was no statistically significant relationship found between poor self-rated health and discrimination as such. Reality calls for more research and, consequently, better policies and practices in the field of health inequalities. PMID:28994700

Social Hazards as Manifested Workplace Discrimination and Health (Vietnamese and Ukrainian Female and Male Migrants in Czechia).

PubMed

Drbohlav, Dušan; Dzúrová, Dagmar

2017-10-10

Social hazards as one of the dimensions of workplace discrimination are a potential social determinant of health inequalities. The aim of this study was to investigate relations between self-reported health and social hazard characteristics (defined as-discrimination as such, violence or threat of violence, time pressure or work overload and risk of accident) among Vietnamese and Ukrainian migrants (males and females) in Czechia by age, education level and marital status. This study is based on data from a survey of 669 immigrants in Czechia in 2013. Logistic regression analysis indicates that the given independent variables (given social hazards and socio-demographic characteristics), as predictors of a quality of self-reported health are more important for immigrant females than for males, irrespective of citizenship, albeit only for some of them and to differing extents. We found out that being exposed to the selected social hazards in the workplace leads to worsening self-rated health, especially for females. On the other hand, there was no statistically significant relationship found between poor self-rated health and discrimination as such. Reality calls for more research and, consequently, better policies and practices in the field of health inequalities.

Accuracy of four commonly used color vision tests in the identification of cone disorders.

PubMed

Thiadens, Alberta A H J; Hoyng, Carel B; Polling, Jan Roelof; Bernaerts-Biskop, Riet; van den Born, L Ingeborgh; Klaver, Caroline C W

2013-04-01

To determine which color vision test is most appropriate for the identification of cone disorders. In a clinic-based study, four commonly used color vision tests were compared between patients with cone dystrophy (n = 37), controls with normal visual acuity (n = 35), and controls with low vision (n = 39) and legal blindness (n = 11). Mean outcome measures were specificity, sensitivity, positive predictive value and discriminative accuracy of the Ishihara test, Hardy-Rand-Rittler (HRR) test, and the Lanthony and Farnsworth Panel D-15 tests. In the comparison between cone dystrophy and all controls, sensitivity, specificity and predictive value were highest for the HRR and Ishihara tests. When patients were compared to controls with normal vision, discriminative accuracy was highest for the HRR test (c-statistic for PD-axes 1, for T-axis 0.851). When compared to controls with poor vision, discriminative accuracy was again highest for the HRR test (c-statistic for PD-axes 0.900, for T-axis 0.766), followed by the Lanthony Panel D-15 test (c-statistic for PD-axes 0.880, for T-axis 0.500) and Ishihara test (c-statistic 0.886). Discriminative accuracies of all tests did not further decrease when patients were compared to controls who were legally blind. The HRR, Lanthony Panel D-15 and Ishihara all have a high discriminative accuracy to identify cone disorders, but the highest scores were for the HRR test. Poor visual acuity slightly decreased the accuracy of all tests. Our advice is to use the HRR test since this test also allows for evaluation of all three color axes and quantification of color defects.

The discriminant (and convergent) validity of the Personality Inventory for DSM-5.

PubMed

Crego, Cristina; Gore, Whitney L; Rojas, Stephanie L; Widiger, Thomas A

2015-10-01

A considerable body of research has rapidly accumulated with respect to the validity of the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5) dimensional trait model as it is assessed by the Personality Inventory for Diagnostic and Statistical Manual of Mental Disorders (PID-5; Krueger et al., 2012). This research though has not focused specifically on discriminant validity, although allusions to potentially problematic discriminant validity have been raised. The current study addressed discriminant validity, reporting for the first time the correlations among the PID-5 domain scales. Also reported are the bivariate correlations of the 25 PID-5 maladaptive trait scales with the personality domain scales of the NEO Personality Inventory-Revised (Costa & McCrae, 1992), the International Personality Item Pool-NEO (Goldberg et al., 2006), the Inventory of Personal Characteristics (Almagor et al., 1995), the 5-Dimensional Personality Test (van Kampen, 2012), and the HEXACO Personality Inventory-Revised (Lee & Ashton, 2004). The results are discussed with respect to the implications of and alternative explanations for potentially problematic discriminant validity. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Mars: Noachian hydrology by its statistics and topology

NASA Technical Reports Server (NTRS)

Cabrol, N. A.; Grin, E. A.

1993-01-01

Discrimination between fluvial features generated by surface drainage and subsurface aquifer discharges will provide clues to the understanding of early Mars' climatic history. Our approach is to define the process of formation of the oldest fluvial valleys by statistical and topological analyses. Formation of fluvial valley systems reached its highest statistical concentration during the Noachian Period. Nevertheless, they are a scarce phenomenom in Martian history, localized on the craterized upland, and subject to latitudinal distribution. They occur sparsely on Noachian geological units with a weak distribution density, and appear in reduced isolated surface (around 5 x 10(exp 3)(sq km)), filled by short streams (100-300 km length). Topological analysis of the internal organization of 71 surveyed Noachian fluvial valley networks also provides information on the mechanisms of formation.

Classification image analysis: estimation and statistical inference for two-alternative forced-choice experiments

NASA Technical Reports Server (NTRS)

Abbey, Craig K.; Eckstein, Miguel P.

2002-01-01

We consider estimation and statistical hypothesis testing on classification images obtained from the two-alternative forced-choice experimental paradigm. We begin with a probabilistic model of task performance for simple forced-choice detection and discrimination tasks. Particular attention is paid to general linear filter models because these models lead to a direct interpretation of the classification image as an estimate of the filter weights. We then describe an estimation procedure for obtaining classification images from observer data. A number of statistical tests are presented for testing various hypotheses from classification images based on some more compact set of features derived from them. As an example of how the methods we describe can be used, we present a case study investigating detection of a Gaussian bump profile.

Perceived discrimination, socioeconomic disadvantage and refraining from seeking medical treatment in Sweden.

PubMed

Wamala, Sarah; Merlo, Juan; Boström, Gunnel; Hogstedt, Christer

2007-05-01

To analyse the association between perceived discrimination and refraining from seeking required medical treatment and the contribution of socioeconomic disadvantage. Data from the Swedish National Survey of Public Health 2004 were used for analysis. Respondents were asked whether they had refrained from seeking required medical treatment during the past 3 months. Perceived discrimination was based on whether respondents reported that they had been treated in a way that made them feel humiliated (due to ethnicity/race, religion, gender, sexual orientation, age or disability). The Socioeconomic Disadvantage Index (SDI) was developed to measure economic deprivation (social welfare beneficiary, being unemployed, financial crisis and lack of cash reserves). Swedish population-based survey of 14,736 men and 17,115 women. Both perceived discrimination and socioeconomic disadvantage were independently associated with refraining from seeking medical treatment. Experiences of frequent discrimination even without any socioeconomic disadvantage were associated with three to nine-fold increased odds for refraining from seeking medical treatment. A combination of both frequent discrimination and severe SDI was associated with a multiplicative effect on refraining from seeking medical treatment, but this effect was statistically more conclusive among women (OR = 11.6, 95% CI 8.1 to 16.6; Synergy Index (SI) = 2.0 (95% CI 1.2 to 3.2)) than among men (OR = 12, 95% CI 7.7 to 18.7; SI = 1.6 (95% CI 1.3 to 2.1)). The goal of equitable access to healthcare services cannot be achieved without public health strategies that confront and tackle discrimination in society and specifically in the healthcare setting.

Racial/Ethnic Disparities in Influenza Vaccination of Chronically Ill US Adults: The Mediating Role of Perceived Discrimination in Health Care.

PubMed

Bleser, William K; Miranda, Patricia Y; Jean-Jacques, Muriel

2016-06-01

Despite well-established programs, influenza vaccination rates in US adults are well below federal benchmarks and exhibit well-documented, persistent racial and ethnic disparities. The causes of these disparities are multifactorial and complex, though perceived racial/ethnic discrimination in health care is 1 hypothesized mechanism. To assess the role of perceived discrimination in health care in mediating influenza vaccination RACIAL/ETHNIC disparities in chronically ill US adults (at high risk for influenza-related complications). We utilized 2011-2012 data from the Aligning Forces for Quality Consumer Survey on health and health care (n=8127), nationally representative of chronically ill US adults. Logistic regression marginal effects examined the relationship between race/ethnicity and influenza vaccination, both unadjusted and in multivariate models adjusted for determinants of health service use. We then used binary mediation analysis to calculate and test the significance of the percentage of this relationship mediated by perceived discrimination in health care. Respondents reporting perceived discrimination in health care had half the uptake as those without discrimination (32% vs. 60%, P=0.009). The change in predicted probability of vaccination given perceived discrimination experiences (vs. none) was large but not significant in the fully adjusted model (-0.185; 95% CI, -0.385, 0.014). Perceived discrimination significantly mediated 16% of the unadjusted association between race/ethnicity and influenza vaccination, though this dropped to 6% and lost statistical significance in multivariate models. The causes of persistent racial/ethnic disparities are complex and a single explanation is unlikely to be sufficient. We suggest reevaluation in a larger cohort as well as potential directions for future research.

Unlawful Discrimination DEOCS 4.1 Construct Validity Summary

DTIC Science & Technology

2017-08-01

Included is a review of the 4.0 description and items, followed by the proposed modifications to the factor. The current DEOCS (4.0) contains multiple...Officer (E7 – E9) 586 10.8% Junior Officer (O1 – O3) 474 9% Senior Officer (O4 and above) 391 6.1% Descriptive Statistics and Reliability This section...displays descriptive statistics for the items on the Unlawful Discrimination scale. All items had a range from 1 to 7 (strongly disagree to strongly

Multivariate statistical analysis of the hydrogeochemical and isotopic composition of the groundwater resources in northeastern Peloponnesus (Greece).

PubMed

Matiatos, Ioannis; Alexopoulos, Apostolos; Godelitsas, Athanasios

2014-04-01

The present study involves an integration of the hydrogeological, hydrochemical and isotopic (both stable and radiogenic) data of the groundwater samples taken from aquifers occurring in the region of northeastern Peloponnesus. Special emphasis has been given to health-related ions and isotopes in relation to the WHO and USEPA guidelines, to highlight the concentrations of compounds (e.g., As and Ba) exceeding the drinking water thresholds. Multivariate statistical analyses, i.e. two principal component analyses (PCA) and one discriminant analysis (DA), combined with conventional hydrochemical methodologies, were applied, with the aim to interpret the spatial variations in the groundwater quality and to identify the main hydrogeochemical factors and human activities responsible for the high ion concentrations and isotopic content in the groundwater analysed. The first PCA resulted in a three component model, which explained approximately 82% of the total variance of the data sets and enabled the identification of the hydrogeological processes responsible for the isotopic content i.e., δ(18)Ο, tritium and (222)Rn. The second PCA, involving the trace element presence in the water samples, revealed a four component model, which explained approximately 89% of the total variance of the data sets, giving more insight into the geochemical and anthropogenic controls on the groundwater composition (e.g., water-rock interaction, hydrothermal activity and agricultural activities). Using discriminant analysis, a four parameter (δ(18)O, (Ca+Mg)/(HCO3+SO4), EC and Cl) discriminant function concerning the (222)Rn content was derived, which favoured a classification of the samples according to the concentration of (222)Rn as (222)Rn-safe (<11 Bq·L(-1)) and (222)Rn-contaminated (>11 Bq·L(-1)). The selection of radon builds on the fact that this radiogenic isotope has been generally related to increased health risk when consumed. Copyright © 2014 Elsevier B.V. All rights reserved.

Employment Discrimination against Visible Minorities and Employment Equity = Discrimination dans L'emploi a L'egard des Minorites Visibles et Equite en Matiere D'emploi.

ERIC Educational Resources Information Center

Jain, Harish C.

Canada has become a multiracial, multireligious, and multicultural society, but non-whites, the "visible minorities (VMs)," face pay and employment discrimination in both the public and the private sector. Lack of statistical data by race and by region about job segregation, income levels, and promotions make it difficult to determine…

Child–Pugh Versus MELD Score for the Assessment of Prognosis in Liver Cirrhosis

PubMed Central

Peng, Ying; Qi, Xingshun; Guo, Xiaozhong

2016-01-01

Abstract Child–Pugh and MELD scores have been widely used for the assessment of prognosis in liver cirrhosis. A systematic review and meta-analysis aimed to compare the discriminative ability of Child–Pugh versus MELD score to assess the prognosis of cirrhotic patients. PubMed and EMBASE databases were searched. The statistical results were summarized from every individual study. The summary areas under receiver operating characteristic curves, sensitivities, specificities, positive and negative likelihood ratios, and diagnostic odds ratios were also calculated. Of the 1095 papers initially identified, 119 were eligible for the systematic review. Study population was heterogeneous among studies. They included 269 comparisons, of which 44 favored MELD score, 16 favored Child–Pugh score, 99 did not find any significant difference between them, and 110 did not report the statistical significance. Forty-two papers were further included in the meta-analysis. In patients with acute-on-chronic liver failure, Child–Pugh score had a higher sensitivity and a lower specificity than MELD score. In patients admitted to ICU, MELD score had a smaller negative likelihood ratio and a higher sensitivity than Child–Pugh score. In patients undergoing surgery, Child–Pugh score had a higher specificity than MELD score. In other subgroup analyses, Child–Pugh and MELD scores had statistically similar discriminative abilities or could not be compared due to the presence of significant diagnostic threshold effects. Although Child–Pugh and MELD scores had similar prognostic values in most of cases, their benefits might be heterogeneous in some specific conditions. The indications for Child–Pugh and MELD scores should be further identified. PMID:26937922

Statistical Time Series Models of Pilot Control with Applications to Instrument Discrimination

NASA Technical Reports Server (NTRS)

Altschul, R. E.; Nagel, P. M.; Oliver, F.

1984-01-01

A general description of the methodology used in obtaining the transfer function models and verification of model fidelity, frequency domain plots of the modeled transfer functions, numerical results obtained from an analysis of poles and zeroes obtained from z plane to s-plane conversions of the transfer functions, and the results of a study on the sequential introduction of other variables, both exogenous and endogenous into the loop are contained.

Enhancing predictive accuracy and reproducibility in clinical evaluation research: Commentary on the special section of the Journal of Evaluation in Clinical Practice.

PubMed

Bryant, Fred B

2016-12-01

This paper introduces a special section of the current issue of the Journal of Evaluation in Clinical Practice that includes a set of 6 empirical articles showcasing a versatile, new machine-learning statistical method, known as optimal data (or discriminant) analysis (ODA), specifically designed to produce statistical models that maximize predictive accuracy. As this set of papers clearly illustrates, ODA offers numerous important advantages over traditional statistical methods-advantages that enhance the validity and reproducibility of statistical conclusions in empirical research. This issue of the journal also includes a review of a recently published book that provides a comprehensive introduction to the logic, theory, and application of ODA in empirical research. It is argued that researchers have much to gain by using ODA to analyze their data. © 2016 John Wiley & Sons, Ltd.

Metabolic dependence of green tea on plucking positions revisited: a metabolomic study.

PubMed

Lee, Jang-Eun; Lee, Bum-Jin; Hwang, Jeong-Ah; Ko, Kwang-Sup; Chung, Jin-Oh; Kim, Eun-Hee; Lee, Sang-Jun; Hong, Young-Shick

2011-10-12

The dependence of global green tea metabolome on plucking positions was investigated through (1)H nuclear magnetic resonance (NMR) analysis coupled with multivariate statistical data set. Pattern recognition methods, such as principal component analysis (PCA) and orthogonal projection on latent structure-discriminant analysis (OPLS-DA), were employed for a finding metabolic discrimination among fresh green tea leaves plucked at different positions from young to old leaves. In addition to clear metabolic discrimination among green tea leaves, elevations in theanine, caffeine, and gallic acid levels but reductions in catechins, such as epicatechin (EC), epigallocatechin (EGC), epicatechin-3-gallate (ECG), and epigallocatechin-3-gallate (EGCG), glucose, and sucrose levels were observed, as the green tea plant grows up. On the other hand, the younger the green tea leaf is, the more theanine, caffeine, and gallic acid but the lesser catechins accumlated in the green tea leaf, revealing a reverse assocation between theanine and catechins levels due to incorporaton of theanine into catechins with growing up green tea plant. Moreover, as compared to the tea leaf, the observation of marked high levels of theanine and low levels of catechins in green tea stems exhibited a distinct tea plant metabolism between the tea leaf and the stem. This metabolomic approach highlights taking insight to global metabolic dependence of green tea leaf on plucking position, thereby providing distinct information on green tea production with specific tea quality.

A systematic review of studies examining the relationship between reported racism and health and wellbeing for children and young people.

PubMed

Priest, Naomi; Paradies, Yin; Trenerry, Brigid; Truong, Mandy; Karlsen, Saffron; Kelly, Yvonne

2013-10-01

Racial discrimination is increasingly recognised as a determinant of racial and ethnic health inequalities, with growing evidence of strong associations between racial discrimination and adult health outcomes. There is a growing body of literature that considers the effects of racial discrimination on child and youth health. The aim of this paper is to provide a systematic review of studies that examine relationships between reported racial discrimination and child and youth health. We describe the characteristics of 121 studies identified by a comprehensive search strategy, including definitions and measurements of racial discrimination and the nature of reported associations. Most studies were published in the last seven years, used cross-sectional designs and were conducted in the United States with young people aged 12-18 years. African American, Latino/a, and Asian populations were most frequently included in these studies. Of the 461 associations examined in these studies, mental health outcomes (e.g. depression, anxiety) were most commonly reported, with statistically significant associations with racial discrimination found in 76% of outcomes examined. Statistically significant associations were also found for over 50% of associations between racial discrimination and positive mental health (e.g. self esteem, resilience), behaviour problems, wellbeing, and pregnancy/birth outcomes. The field is currently limited by a lack of longitudinal studies, limited psychometrically validated exposure instruments and poor conceptualisation and definition of racial discrimination. There is also a need to investigate the complex and varying pathways by which reported racial discrimination affect child and youth health. Ensuring study quality in this field will allow future research to reveal the complex role that racial discrimination plays as a determinant of child and youth health. Copyright © 2012 Elsevier Ltd. All rights reserved.

Instrumental and statistical methods for the comparison of class evidence

NASA Astrophysics Data System (ADS)

Liszewski, Elisa Anne

Trace evidence is a major field within forensic science. Association of trace evidence samples can be problematic due to sample heterogeneity and a lack of quantitative criteria for comparing spectra or chromatograms. The aim of this study is to evaluate different types of instrumentation for their ability to discriminate among samples of various types of trace evidence. Chemometric analysis, including techniques such as Agglomerative Hierarchical Clustering, Principal Components Analysis, and Discriminant Analysis, was employed to evaluate instrumental data. First, automotive clear coats were analyzed by using microspectrophotometry to collect UV absorption data. In total, 71 samples were analyzed with classification accuracy of 91.61%. An external validation was performed, resulting in a prediction accuracy of 81.11%. Next, fiber dyes were analyzed using UV-Visible microspectrophotometry. While several physical characteristics of cotton fiber can be identified and compared, fiber color is considered to be an excellent source of variation, and thus was examined in this study. Twelve dyes were employed, some being visually indistinguishable. Several different analyses and comparisons were done, including an inter-laboratory comparison and external validations. Lastly, common plastic samples and other polymers were analyzed using pyrolysis-gas chromatography/mass spectrometry, and their pyrolysis products were then analyzed using multivariate statistics. The classification accuracy varied dependent upon the number of classes chosen, but the plastics were grouped based on composition. The polymers were used as an external validation and misclassifications occurred with chlorinated samples all being placed into the category containing PVC.

Spatial and temporal air quality pattern recognition using environmetric techniques: a case study in Malaysia.

PubMed

Syed Abdul Mutalib, Sharifah Norsukhairin; Juahir, Hafizan; Azid, Azman; Mohd Sharif, Sharifah; Latif, Mohd Talib; Aris, Ahmad Zaharin; Zain, Sharifuddin M; Dominick, Doreena

2013-09-01

The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.

Progress in the detection of neoplastic progress and cancer by Raman spectroscopy

NASA Astrophysics Data System (ADS)

Bakker Schut, Tom C.; Stone, Nicholas; Kendall, Catherine A.; Barr, Hugh; Bruining, Hajo A.; Puppels, Gerwin J.

2000-05-01

Early detection of cancer is important because of the improved survival rates when the cancer is treated early. We study the application of NIR Raman spectroscopy for detection of dysplasia because this technique is sensitive to the small changes in molecular invasive in vivo detection using fiber-optic probes. The result of an in vitro study to detect neoplastic progress of esophageal Barrett's esophageal tissue will be presented. Using multivariate statistics, we developed three different linear discriminant analysis classification models to predict tissue type on the basis of the measured spectrum. Spectra of normal, metaplastic and dysplasia tissue could be discriminated with an accuracy of up to 88 percent. Therefore Raman spectroscopy seems to be a very suitable technique to detect dysplasia in Barrett's esophageal tissue.

A case of auditory agnosia with impairment of perception and expression of music: cognitive processing of tonality.

PubMed

Satoh, Masayuki; Takeda, Katsuhiko; Kuzuhara, Shigeki

2007-01-01

There is fairly general agreement that the melody and the rhythm are the independent components of the perception of music. In the theory of music, the melody and harmony determine to which tonality the music belongs. It remains an unsettled question whether the tonality is also an independent component of the perception of music, or a by-product of the melody and harmony. We describe a patient with auditory agnosia and expressive amusia that developed after a bilateral infarction of the temporal lobes. We carried out a detailed examination of musical ability in the patient and in control subjects. Comparing with a control population, we identified the following impairments in music perception: (a) discrimination of familiar melodies; (b) discrimination of unfamiliar phrases, and (c) discrimination of isolated chords. His performance in pitch discrimination and tonality were within normal limits. Although intrasubject statistical analysis revealed significant difference only between tonality task and unfamiliar phrase performance, comparison with control subjects suggested a dissociation between a preserved tonality analysis and impairment of perception of melody and chords. By comparing the results of our patient with those in the literature, we may say that there is a double dissociation between the tonality and the other components. Thus, it seems reasonable to suppose that tonality is an independent component of music perception. Based on our present and previous studies, we proposed the revised version of the cognitive model of musical processing in the brain. Copyright 2007 S. Karger AG, Basel.

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

PubMed

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.

Characterization of Microbiota in Children with Chronic Functional Constipation

PubMed Central

de Meij, Tim G. J.; de Groot, Evelien F. J.; Eck, Anat; Budding, Andries E.; Kneepkens, C. M. Frank; Benninga, Marc A.; van Bodegraven, Adriaan A.; Savelkoul, Paul H. M.

2016-01-01

Objectives Disruption of the intestinal microbiota is considered an etiological factor in pediatric functional constipation. Scientifically based selection of potential beneficial probiotic strains in functional constipation therapy is not feasible due to insufficient knowledge of microbiota composition in affected subjects. The aim of this study was to describe microbial composition and diversity in children with functional constipation, compared to healthy controls. Study Design Fecal samples from 76 children diagnosed with functional constipation according to the Rome III criteria (median age 8.0 years; range 4.2–17.8) were analyzed by IS-pro, a PCR-based microbiota profiling method. Outcome was compared with intestinal microbiota profiles of 61 healthy children (median 8.6 years; range 4.1–17.9). Microbiota dissimilarity was depicted by principal coordinate analysis (PCoA), diversity was calculated by Shannon diversity index. To determine the most discriminative species, cross validated logistic ridge regression was performed. Results Applying total microbiota profiles (all phyla together) or per phylum analysis, no disease-specific separation was observed by PCoA and by calculation of diversity indices. By ridge regression, however, functional constipation and controls could be discriminated with 82% accuracy. Most discriminative species were Bacteroides fragilis, Bacteroides ovatus, Bifidobacterium longum, Parabacteroides species (increased in functional constipation) and Alistipes finegoldii (decreased in functional constipation). Conclusions None of the commonly used unsupervised statistical methods allowed for microbiota-based discrimination of children with functional constipation and controls. By ridge regression, however, both groups could be discriminated with 82% accuracy. Optimization of microbiota-based interventions in constipated children warrants further characterization of microbial signatures linked to clinical subgroups of functional constipation. PMID:27760208

Development and preliminary validation of a leadership competency instrument for existing and emerging allied health professional leaders.

PubMed

Ang, Hui-Gek; Koh, Jeremy Meng-Yeow; Lee, Jeffrey; Pua, Yong-Hao

2016-02-19

No instruments, to our knowledge, exist to assess leadership competency in existing and emerging allied health professional (AHP) leaders. This paper describes the development and preliminary exploration of the psychometric properties of a leadership competency instrument for existing and emerging AHP leaders and examines (i) its factor structure, (ii) its convergent validity with the Leadership Practices Inventory (LPI), and (iii) its discriminative validity in AHPs with different grades. During development, we included 25 items in the AHEAD (Aspiring leaders in Healthcare-Empowering individuals, Achieving excellence, Developing talents) instrument. A cross-sectional study was then conducted in 106 high-potential AHPs from Singapore General Hospital (34 men and 72 women) of different professional grades (49 principal-grade AHPs, 41 senior-grade AHPs, and 16 junior-grade AHPs) who completed both AHEAD and LPI instruments. Exploratory factor analysis was used to test the theoretical structure of AHEAD. Spearman correlation analysis was performed to evaluate the convergent validity of AHEAD with LPI. Using proportional odds regression models, we evaluated the association of grades of AHPs with AHEAD and LPI. To assess discriminative validity, the c-statistics - a measure of discrimination - were derived from these ordinal models. As theorized, factor analysis suggested a two-factor solution, where "skills" and "values" formed separate factors. Internal consistency of AHEAD was excellent (α-values > 0.88). Total and component AHEAD and LPI scores correlated moderately (Spearman ρ-values, 0.37 to 0.58). The c-index for discriminating between AHP grades was higher for AHEAD than for the LPI (0.76 vs. 0.65). The factorial structure of AHEAD was generally supported in our study. AHEAD showed convergent validity with the LPI and outperformed the LPI in terms of discriminative validity. These results provide initial evidence for the use of AHEAD to assess leadership competency in AHPs.

Texture analysis of pulmonary parenchyma in normal and emphysematous lung

NASA Astrophysics Data System (ADS)

Uppaluri, Renuka; Mitsa, Theophano; Hoffman, Eric A.; McLennan, Geoffrey; Sonka, Milan

1996-04-01

Tissue characterization using texture analysis is gaining increasing importance in medical imaging. We present a completely automated method for discriminating between normal and emphysematous regions from CT images. This method involves extracting seventeen features which are based on statistical, hybrid and fractal texture models. The best subset of features is derived from the training set using the divergence technique. A minimum distance classifier is used to classify the samples into one of the two classes--normal and emphysema. Sensitivity and specificity and accuracy values achieved were 80% or greater in most cases proving that texture analysis holds great promise in identifying emphysema.

Statistical classification techniques for engineering and climatic data samples

NASA Technical Reports Server (NTRS)

Temple, E. C.; Shipman, J. R.

1981-01-01

Fisher's sample linear discriminant function is modified through an appropriate alteration of the common sample variance-covariance matrix. The alteration consists of adding nonnegative values to the eigenvalues of the sample variance covariance matrix. The desired results of this modification is to increase the number of correct classifications by the new linear discriminant function over Fisher's function. This study is limited to the two-group discriminant problem.

Tailoring a psychophysical discrimination experiment upon assessment of the psychometric function: Predictions and results

NASA Astrophysics Data System (ADS)

Vilardi, Andrea; Tabarelli, Davide; Ricci, Leonardo

2015-02-01

Decision making is a widespread research topic and plays a crucial role in neuroscience as well as in other research and application fields of, for example, biology, medicine and economics. The most basic implementation of decision making, namely binary discrimination, is successfully interpreted by means of signal detection theory (SDT), a statistical model that is deeply linked to physics. An additional, widespread tool to investigate discrimination ability is the psychometric function, which measures the probability of a given response as a function of the magnitude of a physical quantity underlying the stimulus. However, the link between psychometric functions and binary discrimination experiments is often neglected or misinterpreted. Aim of the present paper is to provide a detailed description of an experimental investigation on a prototypical discrimination task and to discuss the results in terms of SDT. To this purpose, we provide an outline of the theory and describe the implementation of two behavioural experiments in the visual modality: upon the assessment of the so-called psychometric function, we show how to tailor a binary discrimination experiment on performance and decisional bias, and to measure these quantities on a statistical base. Attention is devoted to the evaluation of uncertainties, an aspect which is also often overlooked in the scientific literature.

Identifying contextual influences of community reintegration among injured servicemembers.

PubMed

Hawkins, Brent L; McGuire, Francis A; Britt, Thomas W; Linder, Sandra M

2015-01-01

Research suggests that community reintegration (CR) after injury and rehabilitation is difficult for many injured servicemembers. However, little is known about the influence of the contextual factors, both personal and environmental, that influence CR. Framed within the International Classification of Functioning, Disability and Health and Social Cognitive Theory, the quantitative portion of a larger mixed-methods study of 51 injured, community-dwelling servicemembers compared the relative contribution of contextual factors between groups of servicemembers with different levels of CR. Cluster analysis indicated three groups of servicemembers showing low, moderate, and high levels of CR. Statistical analyses identified contextual factors (e.g., personal and environmental factors) that significantly discriminated between CR clusters. Multivariate analysis of variance and discriminant analysis indicated significant contributions of general self-efficacy, services and assistance barriers, physical and structural barriers, attitudes and support barriers, perceived level of disability and/or handicap, work and school barriers, and policy barriers on CR scores. Overall, analyses indicated that injured servicemembers with lower CR scores had lower general self-efficacy scores, reported more difficulty with environmental barriers, and reported their injuries as more disabling.

Electrochemical approach for acute myocardial infarction diagnosis based on direct antibodies-free analysis of human blood plasma.

PubMed

Suprun, Elena V; Saveliev, Anatoly A; Evtugyn, Gennady A; Lisitsa, Alexander V; Bulko, Tatiana V; Shumyantseva, Victoria V; Archakov, Alexander I

2012-03-15

A novel direct antibodies-free electrochemical approach for acute myocardial infarction (AMI) diagnosis has been developed. For this purpose, a combination of the electrochemical assay of plasma samples with chemometrics was proposed. Screen printed carbon electrodes modified with didodecyldimethylammonium bromide were used for plasma charactrerization by cyclic (CV) and square wave voltammetry and square wave (SWV) voltammetry. It was shown that the cathodic peak in voltammograms at about -250 mV vs. Ag/AgCl can be associated with AMI. In parallel tests, cardiac myoglobin and troponin I, the AMI biomarkers, were determined in each sample by RAMP immunoassay. The applicability of the electrochemical testing for AMI diagnostics was confirmed by statistical methods: generalized linear model (GLM), linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), artificial neural net (multi-layer perception, MLP), and support vector machine (SVM), all of which were created to obtain the "True-False" distribution prediction where "True" and "False" are, respectively, positive and negative decision about an illness event. Copyright © 2011 Elsevier B.V. All rights reserved.

Comparative study of wine tannin classification using Fourier transform mid-infrared spectrometry and sensory analysis.

PubMed

Fernández, Katherina; Labarca, Ximena; Bordeu, Edmundo; Guesalaga, Andrés; Agosin, Eduardo

2007-11-01

Wine tannins are fundamental to the determination of wine quality. However, the chemical and sensorial analysis of these compounds is not straightforward and a simple and rapid technique is necessary. We analyzed the mid-infrared spectra of white, red, and model wines spiked with known amounts of skin or seed tannins, collected using Fourier transform mid-infrared (FT-MIR) transmission spectroscopy (400-4000 cm(-1)). The spectral data were classified according to their tannin source, skin or seed, and tannin concentration by means of discriminant analysis (DA) and soft independent modeling of class analogy (SIMCA) to obtain a probabilistic classification. Wines were also classified sensorially by a trained panel and compared with FT-MIR. SIMCA models gave the most accurate classification (over 97%) and prediction (over 60%) among the wine samples. The prediction was increased (over 73%) using the leave-one-out cross-validation technique. Sensory classification of the wines was less accurate than that obtained with FT-MIR and SIMCA. Overall, these results show the potential of FT-MIR spectroscopy, in combination with adequate statistical tools, to discriminate wines with different tannin levels.

Hepatic MR imaging for in vivo differentiation of steatosis, iron deposition and combined storage disorder: single-ratio in/opposed phase analysis vs. dual-ratio Dixon discrimination.

PubMed

Bashir, Mustafa R; Merkle, Elmar M; Smith, Alastair D; Boll, Daniel T

2012-02-01

To assess whether in vivo dual-ratio Dixon discrimination can improve detection of diffuse liver disease, specifically steatosis, iron deposition and combined disease over traditional single-ratio in/opposed phase analysis. Seventy-one patients with biopsy-proven (17.7 ± 17.0 days) hepatic steatosis (n = 16), iron deposition (n = 11), combined deposition (n = 3) and neither disease (n = 41) underwent MR examinations. Dual-echo in/opposed-phase MR with Dixon water/fat reconstructions were acquired. Analysis consisted of: (a) single-ratio hepatic region-of-interest (ROI)-based assessment of in/opposed ratios; (b) dual-ratio hepatic ROI assessment of in/opposed and fat/water ratios; (c) computer-aided dual-ratio assessment evaluating all hepatic voxels. Disease-specific thresholds were determined; statistical analyses assessed disease-dependent voxel ratios, based on single-ratio (a) and dual-ratio (b and c) techniques. Single-ratio discrimination succeeded in identifying iron deposition (I/O(Ironthreshold)<0.88) and steatosis (I/O(Fatthreshold>1.15)) from normal parenchyma, sensitivity 70.0%; it failed to detect combined disease. Dual-ratio discrimination succeeded in identifying abnormal hepatic parenchyma (F/W(Normalthreshold)>0.05), sensitivity 96.7%; logarithmic functions for iron deposition (I/O(Irondiscriminator)e((F/W(Fat)-0.01)/0.48)) differentiated combined from isolated diseases, sensitivity 100.0%; computer-aided dual-ratio analysis was comparably sensitive but less specific, 90.2% vs. 97.6%. MR two-point-Dixon imaging using dual-ratio post-processing based on in/opposed and fat/water ratios improved in vivo detection of hepatic steatosis, iron deposition, and combined storage disease beyond traditional in/opposed analysis. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Heuristics to Facilitate Understanding of Discriminant Analysis.

ERIC Educational Resources Information Center

Van Epps, Pamela D.

This paper discusses the principles underlying discriminant analysis and constructs a simulated data set to illustrate its methods. Discriminant analysis is a multivariate technique for identifying the best combination of variables to maximally discriminate between groups. Discriminant functions are established on existing groups and used to…

A new metaphor for projection-based visual analysis and data exploration

NASA Astrophysics Data System (ADS)

Schreck, Tobias; Panse, Christian

2007-01-01

In many important application domains such as Business and Finance, Process Monitoring, and Security, huge and quickly increasing volumes of complex data are collected. Strong efforts are underway developing automatic and interactive analysis tools for mining useful information from these data repositories. Many data analysis algorithms require an appropriate definition of similarity (or distance) between data instances to allow meaningful clustering, classification, and retrieval, among other analysis tasks. Projection-based data visualization is highly interesting (a) for visual discrimination analysis of a data set within a given similarity definition, and (b) for comparative analysis of similarity characteristics of a given data set represented by different similarity definitions. We introduce an intuitive and effective novel approach for projection-based similarity visualization for interactive discrimination analysis, data exploration, and visual evaluation of metric space effectiveness. The approach is based on the convex hull metaphor for visually aggregating sets of points in projected space, and it can be used with a variety of different projection techniques. The effectiveness of the approach is demonstrated by application on two well-known data sets. Statistical evidence supporting the validity of the hull metaphor is presented. We advocate the hull-based approach over the standard symbol-based approach to projection visualization, as it allows a more effective perception of similarity relationships and class distribution characteristics.

Regional origin assignment of red wines from Valencia (Spain) by (2)H NMR and (13)C IRMS stable isotope analysis of fermentative ethanol.

PubMed

Giménez-Miralles, J E; Salazar, D M; Solana, I

1999-07-01

The use of the stable hydrogen and carbon isotope ratios of fermentative ethanol as suitable environmental fingerprints for the regional origin identification of red wines from Valencia (Spain) has been explored. Monovarietal Vitis vinifera L. cvs. Bobal, Tempranillo, and Monastrell wines have been investigated by (2)H NMR and (13)C IRMS for the natural ranges of site-specific (2)H/(1)H ratios and global delta(13)C values of ethanol over three vintage years. Statistically significant interregional and interannual (2)H and (13)C abundance differences have been noticed, which are interpreted in terms of environmental and ecophysiological factors of isotope content variation. Multivariate discriminant analysis is shown to provide a convenient means for integration of the classifying information, high discriminating abilities being demonstrated for the (2)H and (13)C fingerprints of ethanol. Reasonable differentiation results are achieved at a microregional scale in terms of geographic provenance and even grapevine genotypic features.

Discrimination of serum Raman spectroscopy between normal and colorectal cancer

NASA Astrophysics Data System (ADS)

Li, Xiaozhou; Yang, Tianyue; Yu, Ting; Li, Siqi

2011-07-01

Raman spectroscopy of tissues has been widely studied for the diagnosis of various cancers, but biofluids were seldom used as the analyte because of the low concentration. Herein, serum of 30 normal people, 46 colon cancer, and 44 rectum cancer patients were measured Raman spectra and analyzed. The information of Raman peaks (intensity and width) and that of the fluorescence background (baseline function coefficients) were selected as parameters for statistical analysis. Principal component regression (PCR) and partial least square regression (PLSR) were used on the selected parameters separately to see the performance of the parameters. PCR performed better than PLSR in our spectral data. Then linear discriminant analysis (LDA) was used on the principal components (PCs) of the two regression method on the selected parameters, and a diagnostic accuracy of 88% and 83% were obtained. The conclusion is that the selected features can maintain the information of original spectra well and Raman spectroscopy of serum has the potential for the diagnosis of colorectal cancer.

Age, Body Mass Index, and Daytime and Nocturnal Hypoxia as Predictors of Hypertension in Patients With Obstructive Sleep Apnea.

PubMed

Natsios, Georgios; Pastaka, Chaido; Vavougios, Georgios; Zarogiannis, Sotirios G; Tsolaki, Vasiliki; Dimoulis, Andreas; Seitanidis, Georgios; Gourgoulianis, Konstantinos I

2016-02-01

A growing body of evidence links obstructive sleep apnea (OSA) with hypertension. The authors performed a retrospective cohort study using the University Hospital of Larissa Sleep Apnea Database (1501 patients) to determine predictors of in-laboratory diagnosed OSA for development of hypertension. Differences in continuous variables were assessed via independent samples t test, whereas discrete variables were compared by Pearson's chi-square test. Multivariate analysis was performed via discriminant function analysis. There were several significant differences between hypertensive and normotensive patients. Age, body mass index, comorbidity, daytime oxygen saturation, and indices of hypoxia during sleep were deemed the most accurate predictors of hypertension, whereas apnea-hypopnea index and desaturation index were not. The single derived discriminant function was statistically significant (Wilk's lambda=0.771, χ(2) =289.070, P<.0001). Daytime and nocturnal hypoxia as consequences of chronic intermittent hypoxia play a central role in OSA-related hypertension and should be further evaluated as possible severity markers in OSA. ©2015 Wiley Periodicals, Inc.

Hypersensitivity to sound in tinnitus patients: an analysis of a construct based on questionnaire and audiological data.

PubMed

Bläsing, Lena; Goebel, Gerhard; Flötzinger, Uta; Berthold, Anke; Kröner-Herwig, Birgit

2010-07-01

The purpose of this study was to analyse the Questionnaire on Hypersensitivity to Sound (GUF; Nelting & Finlayson, 2004 ) and to improve its validity based on the analysis of intercorrelations (single item level) with other methods of assessing hyperacusis (uncomfortable loudness level, individual loudness function, self-rated severity of hyperacusis). Subjects consisted of 91 inpatients with tinnitus and hyperacusis. The GUF showed a good reliability (alpha = .92). The factorial structure of the questionnaire reported by Nelting et al (2002) was not completely supported by the evidence in this study. The total score and the single items showed small to moderate correlations with the other modes of measuring hyperacusis. Evidence for convergent and discriminant validity were found, but overall the results corroborate the conceptual heterogeneity of the construct hyperacusis and its dependency on the assessment method. Four items of the GUF with particularly low correlations were excluded from the questionnaire. The revised GUF total score showed slightly but not statistically significant higher convergent and discriminant validity.

High time for a change: psychometric analysis of multiple-choice questions in nursing.

PubMed

Redmond, Sandra P; Hartigan-Rogers, Jackie A; Cobbett, Shelley

2012-11-26

Nurse educators teach students to develop an informed nursing practice but can educators claim the same grounding in the available evidence when formulating multiple-choice assessment tools to evaluate student learning? Multiple-choice questions are a popular assessment format within nursing education. While widely accepted as a credible format to assess student knowledge across disciplines, debate exists among educators regarding the number of options necessary to adequately test cognitive reasoning and optimal discrimination between student abilities. The purpose of this quasi-experimental between groups study was to examine the psychometric properties of three option multiple-choice questions when compared to the more traditional four option questions. Data analysis revealed that there were no statistically significant differences in the item discrimination, difficulty or the mean examination scores when multiple-choice test questions were administered with three versus four option answer choices. This study provides additional guidance for nurse educators to assist in improving multiple-choice question writing and test design.

Multivariate methods to visualise colour-space and colour discrimination data.

PubMed

Hastings, Gareth D; Rubin, Alan

2015-01-01

Despite most modern colour spaces treating colour as three-dimensional (3-D), colour data is usually not visualised in 3-D (and two-dimensional (2-D) projection-plane segments and multiple 2-D perspective views are used instead). The objectives of this article are firstly, to introduce a truly 3-D percept of colour space using stereo-pairs, secondly to view colour discrimination data using that platform, and thirdly to apply formal statistics and multivariate methods to analyse the data in 3-D. This is the first demonstration of the software that generated stereo-pairs of RGB colour space, as well as of a new computerised procedure that investigated colour discrimination by measuring colour just noticeable differences (JND). An initial pilot study and thorough investigation of instrument repeatability were performed. Thereafter, to demonstrate the capabilities of the software, five colour-normal and one colour-deficient subject were examined using the JND procedure and multivariate methods of data analysis. Scatter plots of responses were meaningfully examined in 3-D and were useful in evaluating multivariate normality as well as identifying outliers. The extent and direction of the difference between each JND response and the stimulus colour point was calculated and appreciated in 3-D. Ellipsoidal surfaces of constant probability density (distribution ellipsoids) were fitted to response data; the volumes of these ellipsoids appeared useful in differentiating the colour-deficient subject from the colour-normals. Hypothesis tests of variances and covariances showed many statistically significant differences between the results of the colour-deficient subject and those of the colour-normals, while far fewer differences were found when comparing within colour-normals. The 3-D visualisation of colour data using stereo-pairs, as well as the statistics and multivariate methods of analysis employed, were found to be unique and useful tools in the representation and study of colour. Many additional studies using these methods along with the JND and other procedures have been identified and will be reported in future publications. © 2014 The Authors Ophthalmic & Physiological Optics © 2014 The College of Optometrists.

Dentistry and HIV/AIDS related stigma

PubMed Central

Elizondo, Jesus Eduardo; Treviño, Ana Cecilia; Violant, Deborah

2015-01-01

OBJECTIVE To analyze HIV/AIDS positive individual’s perception and attitudes regarding dental services. METHODS One hundred and thirty-four subjects (30.0% of women and 70.0% of men) from Nuevo León, Mexico, took part in the study (2014). They filled out structured, analytical, self-administered, anonymous questionnaires. Besides the sociodemographic variables, the perception regarding public and private dental services and related professionals was evaluated, as well as the perceived stigma associated with HIV/AIDS, through a Likert-type scale. The statistical evaluation included a factorial and a non-hierarchical cluster analysis. RESULTS Social inequalities were found regarding the search for public and private dental professionals and services. Most subjects reported omitting their HIV serodiagnosis and agreed that dentists must be trained and qualified to treat patients with HIV/AIDS. The factorial analysis revealed two elements: experiences of stigma and discrimination in dental appointments and feelings of concern regarding the attitudes of professionals or their teams concerning patients’ HIV serodiagnosis. The cluster analysis identified three groups: users who have not experienced stigma or discrimination (85.0%); the ones who have not had those experiences, but feel somewhat concerned (12.7%); and the ones who underwent stigma and discrimination and feel concerned (2.3%). CONCLUSIONS We observed a low percentage of stigma and discrimination in dental appointments; however, most HIV/AIDS patients do not reveal their serodiagnosis to dentists out of fear of being rejected. Such fact implies a workplace hazard to dental professionals, but especially to the very own health of HIV/AIDS patients, as dentists will not be able to provide them a proper clinical and pharmaceutical treatment. PMID:26538100

A comparison of statistical criteria for setting optimally discriminating MCAT and GPA thresholds in medical school admissions.

PubMed

Albanese, Mark A; Farrell, Philip; Dottl, Susan L

2005-01-01

Using Medical College Admission Test-grade point average (MCAT-GPA) scores as a threshold has the potential to address issues raised in recent Supreme Court cases, but it introduces complicated methodological issues for medical school admissions. To assess various statistical indexes to determine optimally discriminating thresholds for MCAT-GPA scores. Entering classes from 1992 through 1998 (N = 752) are used to develop guidelines for cut scores that optimize discrimination between students who pass and do not pass the United States Medical Licensing Examination (USMLE) Step 1 on the first attempt. Risk differences, odds ratios, sensitivity, and specificity discriminated best for setting thresholds. Compensatory versus noncompensatory procedures both accounted for 54% of Step 1 failures, but demanded different performance requirements (noncompensatory MCAT-biological sciences = 8, physical sciences = 7, verbal reasoning = 7--sum of scores = 22; compensatory MCAT total = 24). Rational and defensible intellectual achievement thresholds that are likely to comply with recent Supreme Court decisions can be set from MCAT scores and GPAs.

Forest tree species discrimination in western Himalaya using EO-1 Hyperion

NASA Astrophysics Data System (ADS)

George, Rajee; Padalia, Hitendra; Kushwaha, S. P. S.

2014-05-01

The information acquired in the narrow bands of hyperspectral remote sensing data has potential to capture plant species spectral variability, thereby improving forest tree species mapping. This study assessed the utility of spaceborne EO-1 Hyperion data in discrimination and classification of broadleaved evergreen and conifer forest tree species in western Himalaya. The pre-processing of 242 bands of Hyperion data resulted into 160 noise-free and vertical stripe corrected reflectance bands. Of these, 29 bands were selected through step-wise exclusion of bands (Wilk's Lambda). Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) algorithms were applied to the selected bands to assess their effectiveness in classification. SVM was also applied to broadband data (Landsat TM) to compare the variation in classification accuracy. All commonly occurring six gregarious tree species, viz., white oak, brown oak, chir pine, blue pine, cedar and fir in western Himalaya could be effectively discriminated. SVM produced a better species classification (overall accuracy 82.27%, kappa statistic 0.79) than SAM (overall accuracy 74.68%, kappa statistic 0.70). It was noticed that classification accuracy achieved with Hyperion bands was significantly higher than Landsat TM bands (overall accuracy 69.62%, kappa statistic 0.65). Study demonstrated the potential utility of narrow spectral bands of Hyperion data in discriminating tree species in a hilly terrain.

Sex determination from the talus in a contemporary Greek population using discriminant function analysis.

PubMed

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. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Can anthropometry measure gender discrimination? An analysis using WHO standards to assess the growth of Bangladeshi children.

PubMed

Moestue, Helen

2009-08-01

To examine the potential of anthropometry as a tool to measure gender discrimination, with particular attention to the WHO growth standards. Surveillance data collected from 1990 to 1999 were analysed. Height-for-age Z-scores were calculated using three norms: the WHO standards, the 1978 National Center for Health Statistics (NCHS) reference and the 1990 British growth reference (UK90). Bangladesh. Boys and girls aged 6-59 months (n 504 358). The three sets of growth curves provided conflicting pictures of the relative growth of girls and boys by age and over time. Conclusions on sex differences in growth depended also on the method used to analyse the curves, be it according to the shape or the relative position of the sex-specific curves. The shapes of the WHO-generated curves uniquely implied that Bangladeshi girls faltered faster or caught up slower than boys throughout their pre-school years, a finding consistent with the literature. In contrast, analysis of the relative position of the curves suggested that girls had higher WHO Z-scores than boys below 24 months of age. Further research is needed to help establish whether and how the WHO international standards can measure gender discrimination in practice, which continues to be a serious problem in many parts of the world.

Advanced signal processing analysis of laser-induced breakdown spectroscopy data for the discrimination of obsidian sources.

PubMed

Remus, Jeremiah J; Harmon, Russell S; Hark, Richard R; Haverstock, Gregory; Baron, Dirk; Potter, Ian K; Bristol, Samantha K; East, Lucille J

2012-03-01

Obsidian is a natural glass of volcanic origin and a primary resource used by indigenous peoples across North America for making tools. Geochemical studies of obsidian enhance understanding of artifact production and procurement and remain a priority activity within the archaeological community. Laser-induced breakdown spectroscopy (LIBS) is an analytical technique being examined as a means for identifying obsidian from different sources on the basis of its 'geochemical fingerprint'. This study tested whether two major California obsidian centers could be distinguished from other obsidian localities and the extent to which subsources could be recognized within each of these centers. LIBS data sets were collected in two different spectral bands (350±130 nm and 690±115 nm) using a Nd:YAG 1064 nm laser operated at ~23 mJ, a Czerny-Turner spectrograph with 0.2-0.3 nm spectral resolution and a high performance imaging charge couple device (ICCD) detector. Classification of the samples was performed using partial least-squares discriminant analysis (PLSDA), a common chemometric technique for performing statistical regression on high-dimensional data. Discrimination of samples from the Coso Volcanic Field, Bodie Hills, and other major obsidian areas in north-central California was possible with an accuracy of greater than 90% using either spectral band. © 2012 Optical Society of America

Evaluation of adding item-response theory analysis for evaluation of the European Board of Ophthalmology Diploma examination.

PubMed

Mathysen, Danny G P; Aclimandos, Wagih; Roelant, Ella; Wouters, Kristien; Creuzot-Garcher, Catherine; Ringens, Peter J; Hawlina, Marko; Tassignon, Marie-José

2013-11-01

To investigate whether introduction of item-response theory (IRT) analysis, in parallel to the 'traditional' statistical analysis methods available for performance evaluation of multiple T/F items as used in the European Board of Ophthalmology Diploma (EBOD) examination, has proved beneficial, and secondly, to study whether the overall assessment performance of the current written part of EBOD is sufficiently high (KR-20≥ 0.90) to be kept as examination format in future EBOD editions. 'Traditional' analysis methods for individual MCQ item performance comprise P-statistics, Rit-statistics and item discrimination, while overall reliability is evaluated through KR-20 for multiple T/F items. The additional set of statistical analysis methods for the evaluation of EBOD comprises mainly IRT analysis. These analysis techniques are used to monitor whether the introduction of negative marking for incorrect answers (since EBOD 2010) has a positive influence on the statistical performance of EBOD as a whole and its individual test items in particular. Item-response theory analysis demonstrated that item performance parameters should not be evaluated individually, but should be related to one another. Before the introduction of negative marking, the overall EBOD reliability (KR-20) was good though with room for improvement (EBOD 2008: 0.81; EBOD 2009: 0.78). After the introduction of negative marking, the overall reliability of EBOD improved significantly (EBOD 2010: 0.92; EBOD 2011:0.91; EBOD 2012: 0.91). Although many statistical performance parameters are available to evaluate individual items, our study demonstrates that the overall reliability assessment remains the only crucial parameter to be evaluated allowing comparison. While individual item performance analysis is worthwhile to undertake as secondary analysis, drawing final conclusions seems to be more difficult. Performance parameters need to be related, as shown by IRT analysis. Therefore, IRT analysis has proved beneficial for the statistical analysis of EBOD. Introduction of negative marking has led to a significant increase in the reliability (KR-20 > 0.90), indicating that the current examination format can be kept for future EBOD examinations. © 2013 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

[Study of beta-turns in globular proteins].

PubMed

Amirova, S R; Milchevskiĭ, Iu V; Filatov, I V; Esipova, N G; Tumanian, V G

2005-01-01

The formation of beta-turns in globular proteins has been studied by the method of molecular mechanics. Statistical method of discriminant analysis was applied to calculate energy components and sequences of oligopeptide segments, and after this prediction of I type beta-turns has been drawn. The accuracy of true positive prediction is 65%. Components of conformational energy considerably affecting beta-turn formation were delineated. There are torsional energy, energy of hydrogen bonds, and van der Waals energy.

Geometric, Statistical, and Topological Modeling of Intrinsic Data Manifolds: Application to 3D Shapes

DTIC Science & Technology

2009-01-01

representation to a simple curve in 3D by using the Whitney embedding theorem. In a very ludic way, we propose to combine phases one and two to...elimination principle which takes advantage of the designed parametrization. To further refine discrimination among objects, we introduce a post...packing numbers and design of principal curves. IEEE transactions on Pattern Analysis and Machine Intel- ligence, 22(3):281-297, 2000. [68] M. H. Yang, Face

Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study.

PubMed

Lee, Poh Foong; Kan, Donica Pei Xin; Croarkin, Paul; Phang, Cheng Kar; Doruk, Deniz

2018-01-01

There is an unmet need for practical and reliable biomarkers for mood disorders in young adults. Identifying the brain activity associated with the early signs of depressive disorders could have important diagnostic and therapeutic implications. In this study we sought to investigate the EEG characteristics in young adults with newly identified depressive symptoms. Based on the initial screening, a total of 100 participants (n = 50 euthymic, n = 50 depressive) underwent 32-channel EEG acquisition. Simple logistic regression and C-statistic were used to explore if EEG power could be used to discriminate between the groups. The strongest EEG predictors of mood using multivariate logistic regression models. Simple logistic regression analysis with subsequent C-statistics revealed that only high-alpha and beta power originating from the left central cortex (C3) have a reliable discriminative value (ROC curve >0.7 (70%)) for differentiating the depressive group from the euthymic group. Multivariate regression analysis showed that the single most significant predictor of group (depressive vs. euthymic) is the high-alpha power over C3 (p = 0.03). The present findings suggest that EEG is a useful tool in the identification of neurophysiological correlates of depressive symptoms in young adults with no previous psychiatric history. Our results could guide future studies investigating the early neurophysiological changes and surrogate outcomes in depression. Copyright © 2017 Elsevier Ltd. All rights reserved.

geneCommittee: a web-based tool for extensively testing the discriminatory power of biologically relevant gene sets in microarray data classification.

PubMed

Reboiro-Jato, Miguel; Arrais, Joel P; Oliveira, José Luis; Fdez-Riverola, Florentino

2014-01-30

The diagnosis and prognosis of several diseases can be shortened through the use of different large-scale genome experiments. In this context, microarrays can generate expression data for a huge set of genes. However, to obtain solid statistical evidence from the resulting data, it is necessary to train and to validate many classification techniques in order to find the best discriminative method. This is a time-consuming process that normally depends on intricate statistical tools. geneCommittee is a web-based interactive tool for routinely evaluating the discriminative classification power of custom hypothesis in the form of biologically relevant gene sets. While the user can work with different gene set collections and several microarray data files to configure specific classification experiments, the tool is able to run several tests in parallel. Provided with a straightforward and intuitive interface, geneCommittee is able to render valuable information for diagnostic analyses and clinical management decisions based on systematically evaluating custom hypothesis over different data sets using complementary classifiers, a key aspect in clinical research. geneCommittee allows the enrichment of microarrays raw data with gene functional annotations, producing integrated datasets that simplify the construction of better discriminative hypothesis, and allows the creation of a set of complementary classifiers. The trained committees can then be used for clinical research and diagnosis. Full documentation including common use cases and guided analysis workflows is freely available at http://sing.ei.uvigo.es/GC/.

Using Fourier transform infrared spectroscopy to evaluate biological effects induced by photodynamic therapy.

PubMed

Lima, Cassio A; Goulart, Viviane P; Correa, Luciana; Zezell, Denise M

2016-07-01

Vibrational spectroscopic methods associated with multivariate statistical techniques have been succeeded in discriminating skin lesions from normal tissues. However, there is no study exploring the potential of these techniques to assess the alterations promoted by photodynamic effect in tissue. The present study aims to demonstrate the ability of Fourier Transform Infrared (FTIR) spectroscopy on Attenuated total reflection (ATR) sampling mode associated with principal component-linear discriminant analysis (PC-LDA) to evaluate the biochemical changes caused by photodynamic therapy (PDT) in skin neoplastic tissue. Cutaneous neoplastic lesions, precursors of squamous cell carcinoma (SCC), were chemically induced in Swiss mice and submitted to a single session of 5-aminolevulinic acid (ALA)-mediated PDT. Tissue sections with 5 μm thickness were obtained from formalin-fixed paraffin-embedded (FFPE) and processed prior to the histopathological analysis and spectroscopic measurements. Spectra were collected in mid-infrared region using a FTIR spectrometer on ATR sampling mode. Principal Component-Linear Discriminant Analysis (PC-LDA) was applied on preprocessed second derivatives spectra. Biochemical changes were assessed using PCA-loadings and accuracy of classification was obtained from PC-LDA . Sub-bands of Amide I (1,624 and 1,650 cm(-1) ) and Amide II (1,517 cm(-1) ) indicated a protein overexpression in non-treated and post-PDT neoplastic tissue compared with healthy skin, as well as a decrease in collagen fibers (1,204, 1,236, 1,282, and 1,338 cm(-1) ) and glycogen (1,028, 1,082, and 1,151 cm(-1) ) content. Photosensitized neoplastic tissue revealed shifted peak position and decreased β-sheet secondary structure of proteins (1,624 cm(-1) ) amount in comparison to non-treated neoplastic lesions. PC-LDA score plots discriminated non-treated neoplastic skin spectra from post-PDT cutaneous lesions with accuracy of 92.8%, whereas non-treated neoplastic skin was discriminated from healthy tissue with 93.5% accuracy and post-PDT cutaneous lesions was discriminated from healthy tissue with 89.7% accuracy. PC-LDA was able to discriminate ATR-FTIR spectra of non-treated and post-PDT neoplastic lesions, as well as from healthy skin. Thus, the method can be used for early diagnosis of premalignant skin lesions, as well as to evaluate the response to photodynamic treatment. Lasers Surg. Med. 48:538-545, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Meta-analysis of prediction model performance across multiple studies: Which scale helps ensure between-study normality for the C-statistic and calibration measures?

PubMed

Snell, Kym Ie; Ensor, Joie; Debray, Thomas Pa; Moons, Karel Gm; Riley, Richard D

2017-01-01

If individual participant data are available from multiple studies or clusters, then a prediction model can be externally validated multiple times. This allows the model's discrimination and calibration performance to be examined across different settings. Random-effects meta-analysis can then be used to quantify overall (average) performance and heterogeneity in performance. This typically assumes a normal distribution of 'true' performance across studies. We conducted a simulation study to examine this normality assumption for various performance measures relating to a logistic regression prediction model. We simulated data across multiple studies with varying degrees of variability in baseline risk or predictor effects and then evaluated the shape of the between-study distribution in the C-statistic, calibration slope, calibration-in-the-large, and E/O statistic, and possible transformations thereof. We found that a normal between-study distribution was usually reasonable for the calibration slope and calibration-in-the-large; however, the distributions of the C-statistic and E/O were often skewed across studies, particularly in settings with large variability in the predictor effects. Normality was vastly improved when using the logit transformation for the C-statistic and the log transformation for E/O, and therefore we recommend these scales to be used for meta-analysis. An illustrated example is given using a random-effects meta-analysis of the performance of QRISK2 across 25 general practices.

Study of the questionnaire of the Polytechnic University of Valencia (UPV) teaching staff, using students opinion survey. Statistical treatment

NASA Astrophysics Data System (ADS)

Martinez Gomez, Monica

Quality improvement of university institutions represents the most important challenge in the next years, and the potential tool to achieve it is based on the institutional evaluation in general, and specially the evaluation of the teaching performance. The opinion questionnaire from the students is the most generalised tool used to evaluate the teaching performance at Spanish universities. The general objective of this thesis is to develop a statistical methodology suitable to extract, analyse and interpret the information contained in the Questionnaire of Teaching Evaluation from Student Opinion (CEDA) of the UPV, aimed at optimising its practical use. The study is centred in the application of different multivariate techniques and has been structured in three parts: (1) Evaluation of the reliability, validity and dimensionality of the tool. The multivariate method used for this purpose is the Factorial Analysis. (2) Determination of the capacity of the questionnaire to identify different profiles of lecturers based on the quality perceived by students. This target is conducted with different multivariate classification techniques: hierarchical cluster analysis, non-hierarchical and two-stage analysis. Moreover, those items that best discriminate among the teaching typologies obtained are identified in the questionnaire. (3) Identification of the teaching typologies according to different descriptive characteristics referent to the subject and lecturer, with the use of decision trees. Once identified these typologies, a new discriminant analysis is conducted aimed at identifying those items that best characterise each typology. Finally, a study is carried out with the classification method SIMCA (Soft Independent Modelling of Class Analogy) in order to determine the discriminant loading of every item among the identified teaching typologies, allowing the identification of those that best distinguish the different classes obtained. With the combined use of the proposed techniques, it is expected to optimise the use of CEDA as a measuring tool and an indicator of the teaching quality at the university, that would allow the introduction of actions for the continuous improvement in the teaching processes of the UPV.

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

ERIC Educational Resources Information Center

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…

Discriminant analysis of Raman spectra for body fluid identification for forensic purposes.

PubMed

Sikirzhytski, Vitali; Virkler, Kelly; Lednev, Igor K

2010-01-01

Detection and identification of blood, semen and saliva stains, the most common body fluids encountered at a crime scene, are very important aspects of forensic science today. This study targets the development of a nondestructive, confirmatory method for body fluid identification based on Raman spectroscopy coupled with advanced statistical analysis. Dry traces of blood, semen and saliva obtained from multiple donors were probed using a confocal Raman microscope with a 785-nm excitation wavelength under controlled laboratory conditions. Results demonstrated the capability of Raman spectroscopy to identify an unknown substance to be semen, blood or saliva with high confidence.

Correlation between the different therapeutic properties of Chinese medicinal herbs and delayed luminescence.

PubMed

Pang, Jingxiang; Fu, Jialei; Yang, Meina; Zhao, Xiaolei; van Wijk, Eduard; Wang, Mei; Fan, Hua; Han, Jinxiang

2016-03-01

In the practice and principle of Chinese medicine, herbal materials are classified according to their therapeutic properties. 'Cold' and 'heat' are the most important classes of Chinese medicinal herbs according to the theory of traditional Chinese medicine (TCM). In this work, delayed luminescence (DL) was measured for different samples of Chinese medicinal herbs using a sensitive photon multiplier detection system. A comparison of DL parameters, including mean intensity and statistic entropy, was undertaken to discriminate between the 'cold' and 'heat' properties of Chinese medicinal herbs. The results suggest that there are significant differences in mean intensity and statistic entropy and using this method combined with statistical analysis may provide novel parameters for the characterization of Chinese medicinal herbs in relation to their energetic properties. Copyright © 2015 John Wiley & Sons, Ltd.

A Complex Approach to UXO Discrimination: Combining Advanced EMI Forward Models and Statistical Signal Processing

DTIC Science & Technology

2012-01-01

discrimination at live-UXO sites. Namely, under this project first we developed and implemented advanced, physically complete forward EMI models such as, the...detection and discrimination at live-UXO sites. Namely, under this project first we developed and implemented advanced, physically complete forward EMI...Shubitidze of Sky Research and Dartmouth College, conceived, implemented , and tested most of the approaches presented in this report. He developed

Acoustic Mechanisms of a Species-Based Discrimination of the chick-a-dee Call in Sympatric Black-Capped (Poecile atricapillus) and Mountain Chickadees (P. gambeli).

PubMed

Guillette, Lauren M; Farrell, Tara M; Hoeschele, Marisa; Sturdy, Christopher B

2010-01-01

Previous perceptual research with black-capped and mountain chickadees has demonstrated that these species treat each other's namesake chick-a-dee calls as belonging to separate, open-ended categories. Further, the terminal dee portion of the call has been implicated as the most prominent species marker. However, statistical classification using acoustic summary features suggests that all note-types contained within the chick-a-dee call should be sufficient for species classification. The current study seeks to better understand the note-type based mechanisms underlying species-based classification of the chick-a-dee call by black-capped and mountain chickadees. In two, complementary, operant discrimination experiments, both species were trained to discriminate the species of the signaler using either entire chick-a-dee calls, or individual note-types from chick-a-dee calls. In agreement with previous perceptual work we find that the D note had significant stimulus control over species-based discrimination. However, in line with statistical classifications, we find that all note-types carry species information. We discuss reasons why the most easily discriminated note-types are likely candidates to carry species-based cues.

Analyzing Faculty Salaries When Statistics Fail.

ERIC Educational Resources Information Center

Simpson, William A.

The role played by nonstatistical procedures, in contrast to multivariant statistical approaches, in analyzing faculty salaries is discussed. Multivariant statistical methods are usually used to establish or defend against prima facia cases of gender and ethnic discrimination with respect to faculty salaries. These techniques are not applicable,…

Analysis of esterified and nonesterified fatty acids in serum from obese individuals after intake of breakfasts prepared with oils heated at frying temperature.

PubMed

Orozco-Solano, M I; Priego-Capote, F; Luque de Castro, M D

2013-07-01

In this study, levels of esterified and nonesterified fatty acids (EFAs and NEFAs, respectively) were compared in obese individuals (body mass index between 30 and 47 kg m(-2)) in basal state and after intake of four different breakfasts prepared with oils heated at frying temperature. The target oils were three sunflower oils--pure, enriched with dimethylsiloxane (400 μg mL(-1)) as lipophilic oxidation inhibitor, and enriched with phenolic compounds (400 μg mL(-1)) as hydrophilic oxidation inhibitors--and virgin olive oil with a natural content of phenolic compounds of 400 μg mL(-1). The intake of breakfasts was randomized to avoid trends associated to this variability source. EFAs and NEFAs were subjected to a sequential derivatization step for independent gas chromatography-mass spectrometry analysis of both fractions of metabolites in human serum. Derivatization was assisted by ultrasonic energy to accelerate the reaction kinetics, as required for high-throughput analysis. Statistical analysis supported on univariate (multifactor ANOVA) and multivariate approaches (principal component analysis and partial least squares-discriminant analysis) allowed identification of the main variability sources and also discriminating between individuals after intake of each breakfast. Individuals' samples after intake of breakfasts prepared with virgin olive oil were clearly separated from those who ingested the remaining breakfasts. The main compounds contributing to discrimination were omega-3 and omega-6 EFAs with special emphasis on arachidonic acid and eicosapentaenoic acid. These two polyunsaturated fatty acids are the precursors of eicosanoid metabolites, which are of vital importance as they play important roles in inflammation and in the pathogenesis of vascular and malignant diseases as cancer.

Discrimination of tornadic and non-tornadic severe weather outbreaks

NASA Astrophysics Data System (ADS)

Mercer, Andrew Edward

Outbreaks of severe weather affect the majority of the conterminous United States. An outbreak is characterized by multiple severe weather occurrences within a single synoptic system. Outbreaks can be categorized by whether or not they produce tornadoes. It is hypothesized that the antecedent synoptic signal contains important information about outbreak type. Accordingly, the scope of this research is to determine the extent that the synoptic signal can be utilized to classify outbreak type at various lead times. Outbreak types are classified using the NCEP/NCAR reanalysis data, which are arranged on a global 2.5° latitude-longitude grid, include 17 vertical pressure levels, and span from 1948 to the present (2008). Fifty major tornado outbreak (TO) cases and fifty major non-tornadic severe weather outbreak (NTO) cases are selected for this work. Two types of analyses are performed on these cases to assess discrimination ability. One analysis involves outbreak classification using the Weather Research and Forecasting (WRF) model initialized with the NCEP/NCAR reanalysis dataset. Meteorological covariates are computed from the WRF output and used in training and testing of statistical classification models. The covariate fields are depicted on a 21 X 21 gridpoint field with an 18 km grid spacing centered on the outbreak. Covariates with large discrimination potential are determined using permutation testing. A P-mode principal component analysis (PCA) is used on the subset of covariates determined by permutation testing to reduce data dimensionality, since numerous redundancies exist in the initial covariate set. Three statistical classification models are trained and tested with the resulting PC scores: a support vector machine (SVM), a logistic regression model (LogR), and a multiple linear regression model (LR). Promising results emerge from these methods, as a probability of detection (POD) of 0.89 and a false alarm ratio (FAR) of 0.13 are obtained from the best discriminating statistical technique (SVM) at 24-hours lead time. Results degrade only slightly by 72-hours lead time (maximum POD of 0.833 and minimum FAR of 0.276). Synoptic composites of the outbreak types are the second analysis considered. Composites are used to reveal synoptic features of outbreak types, which can be utilized to diagnose the differences between classes (in this case, TOs and NTOs). The composites are created using PCA. Five raw variables, height, temperature, relative humidity, and u and v wind components, are extracted from the NCEP/NCAR reanalysis data for North America. Converging longitude lines with increasing latitude on the reanalysis grid introduce bias into correlation calculations in higher latitudes; hence, the data are mapped onto both a latitudinal density grid and a Fibonacci grid. The resulting PCA produces two significant principal components (PCs), and a cluster analysis on these PCs for each outbreak type results in two types of TOs and NTOs. TO composites are characterized by a trough of low pressure over the central United States and major quasigeostrophic forcing features such as an upper level jet streak, cyclonic vorticity advection increasing with height, and warm air advection. These dynamics result in a strong surface cyclone in most tornado outbreaks. These features are considerably less pronounced in NTOs. The statistical analyses presented herein were successful in classifying outbreak types at various lead times, using synoptic scale data as input.

Perceived Empathy of Service Providers Mediates the Association between Perceived Discrimination and Behavioral Intention to Take Up HIV Antibody Testing Again among Men Who Have Sex with Men

PubMed Central

Gu, Jing; Lau, Joseph T. F.; Wang, Zixin; Wu, Anise M. S.; Tan, Xuhui

2015-01-01

HIV antibody testing is a key measure of HIV prevention for men who have sex with men (MSM). The World Health Organization recommends sexually active and at-risk MSM to take up HIV antibody testing regularly. This study aimed to investigate the prevalence of behavioral intention to take up HIV antibody testing in the next six months among Hong Kong MSM who were ever-testers. An anonymous cross-sectional survey recruited 326 MSM who had taken up HIV antibody testing from gay-friendly venues and internet in Hong Kong. Of the participants, 40.8% had had unprotected anal intercourse with regular or non-regular male sex partners in the last six months; they were at risk of HIV transmission despite experience in HIV antibody testing. Only 37.2% showed a strong intention to take up HIV antibody testing again in the next six months. Adjusted analysis showed that both perceived discrimination toward Hong Kong MSM (AOR = .60, 95% CI: .36–.98) and the CARE Measure assessing perceived empathy of service providers (AOR = 1.05, 95% CI: 1.02–1.08) were significantly associated with intention for retesting. Perceived discrimination, however, became statistically non-significant (AOR = .68, 95% CI: .41–1.14), when both CARE Measure and perceived discrimination entered into the adjusted model. It is warranted to increase HIV retesting rate by removing perceived discrimination and reducing the negative effect of perceived discrimination through enhancement of empathy of service providers. PMID:25693179

Perceived empathy of service providers mediates the association between perceived discrimination and behavioral intention to take up HIV antibody testing again among men who have sex with men.

PubMed

Gu, Jing; Lau, Joseph T F; Wang, Zixin; Wu, Anise M S; Tan, Xuhui

2015-01-01

HIV antibody testing is a key measure of HIV prevention for men who have sex with men (MSM). The World Health Organization recommends sexually active and at-risk MSM to take up HIV antibody testing regularly. This study aimed to investigate the prevalence of behavioral intention to take up HIV antibody testing in the next six months among Hong Kong MSM who were ever-testers. An anonymous cross-sectional survey recruited 326 MSM who had taken up HIV antibody testing from gay-friendly venues and internet in Hong Kong. Of the participants, 40.8% had had unprotected anal intercourse with regular or non-regular male sex partners in the last six months; they were at risk of HIV transmission despite experience in HIV antibody testing. Only 37.2% showed a strong intention to take up HIV antibody testing again in the next six months. Adjusted analysis showed that both perceived discrimination toward Hong Kong MSM (AOR = .60, 95% CI: .36-.98) and the CARE Measure assessing perceived empathy of service providers (AOR = 1.05, 95% CI: 1.02-1.08) were significantly associated with intention for retesting. Perceived discrimination, however, became statistically non-significant (AOR = .68, 95% CI: .41-1.14), when both CARE Measure and perceived discrimination entered into the adjusted model. It is warranted to increase HIV retesting rate by removing perceived discrimination and reducing the negative effect of perceived discrimination through enhancement of empathy of service providers.

Sex estimation standards for medieval and contemporary Croats

PubMed Central

Bašić, Željana; Kružić, Ivana; Jerković, Ivan; Anđelinović, Deny; Anđelinović, Šimun

2017-01-01

Aim To develop discriminant functions for sex estimation on medieval Croatian population and test their application on contemporary Croatian population. Methods From a total of 519 skeletons, we chose 84 adult excellently preserved skeletons free of antemortem and postmortem changes and took all standard measurements. Sex was estimated/determined using standard anthropological procedures and ancient DNA (amelogenin analysis) where pelvis was insufficiently preserved or where sex morphological indicators were not consistent. We explored which measurements showed sexual dimorphism and used them for developing univariate and multivariate discriminant functions for sex estimation. We included only those functions that reached accuracy rate ≥80%. We tested the applicability of developed functions on modern Croatian sample (n = 37). Results From 69 standard skeletal measurements used in this study, 56 of them showed statistically significant sexual dimorphism (74.7%). We developed five univariate discriminant functions with classification rate 80.6%-85.2% and seven multivariate discriminant functions with an accuracy rate of 81.8%-93.0%. When tested on the modern population functions showed classification rates 74.1%-100%, and ten of them reached aimed accuracy rate. Females showed higher classification rates in the medieval populations, whereas males were better classified in the modern populations. Conclusion Developed discriminant functions are sufficiently accurate for reliable sex estimation in both medieval Croatian population and modern Croatian samples and may be used in forensic settings. The methodological issues that emerged regarding the importance of considering external factors in development and application of discriminant functions for sex estimation should be further explored. PMID:28613039

Proceedings of the 11th Annual DARPA/AFGL Seismic Research symposium

NASA Astrophysics Data System (ADS)

Lewkowicz, James F.; McPhetres, Jeanne M.

1990-11-01

The following subjects are covered: near source observations of quarry explosions; small explosion discrimination and yield estimation; Rg as a depth discriminant for earthquakes and explosions: a case study in New England; a comparative study of high frequency seismic noise at selected sites in the USSR and USA; chemical explosions and the discrimination problem; application of simulated annealing to joint hypocenter determination; frequency dependence of Q(sub Lg) and Q in the continental crust; statistical approaches to testing for compliance with a threshold test ban treaty; broad-band studies of seismic sources at regional and teleseismic distances using advanced time series analysis methods; effects of depth of burial and tectonic release on regional and teleseismic explosion waveforms; finite difference simulations of seismic wave excitation at Soviet test sites with deterministic structures; stochastic geologic effects on near-field ground motions; the damage mechanics of porous rock; nonlinear attenuation mechanism in salt at moderate strain; compressional- and shear-wave polarizations at the Anza seismic array; and a generalized beamforming approach to real time network detection and phase association.

Raman spectroscopy of bio fluids: an exploratory study for oral cancer detection

NASA Astrophysics Data System (ADS)

Brindha, Elumalai; Rajasekaran, Ramu; Aruna, Prakasarao; Koteeswaran, Dornadula; Ganesan, Singaravelu

2016-03-01

ion for various disease diagnosis including cancers. Oral cancer is one of the most common cancers in India and it accounts for one third of the global oral cancer burden. Raman spectroscopy of tissues has gained much attention in the diagnostic oncology, as it provides unique spectral signature corresponding to metabolic alterations under different pathological conditions and micro-environment. Based on these, several studies have been reported on the use of Raman spectroscopy in the discrimination of diseased conditions from their normal counterpart at cellular and tissue level but only limited studies were available on bio-fluids. Recently, optical characterization of bio-fluids has also geared up for biomarker identification in the disease diagnosis. In this context, an attempt was made to study the metabolic variations in the blood, urine and saliva of oral cancer patients and normal subjects using Raman spectroscopy. Principal Component based Linear Discriminant Analysis (PC-LDA) followed by Leave-One-Out Cross-Validation (LOOCV) was employed to find the statistical significance of the present technique in discriminating the malignant conditions from normal subjects.

Optimal Experimental Design for Model Discrimination

ERIC Educational Resources Information Center

Myung, Jay I.; Pitt, Mark A.

2009-01-01

Models of a psychological process can be difficult to discriminate experimentally because it is not easy to determine the values of the critical design variables (e.g., presentation schedule, stimulus structure) that will be most informative in differentiating them. Recent developments in sampling-based search methods in statistics make it…

The Enduring Significance of Racism: Discrimination and Delinquency among Black American Youth

ERIC Educational Resources Information Center

Martin, Monica J.; McCarthy, Bill; Conger, Rand D.; Gibbons, Frederick X.; Simons, Ronald L.; Cutrona, Carolyn E.; Brody, Gene H.

2011-01-01

Prominent explanations of the overrepresentation of Black Americans in criminal justice statistics focus on the effects of neighborhood concentrated disadvantage, racial isolation, and social disorganization. We suggest that perceived personal discrimination is an important but frequently neglected complement to these factors. We test this…

Experience-Based Discrimination: Classroom Games

ERIC Educational Resources Information Center

Fryer, Roland G., Jr.; Goeree, Jacob K.; Holt, Charles A.

2005-01-01

The authors present a simple classroom game in which students are randomly designated as employers, purple workers, or green workers. This environment may generate "statistical" discrimination if workers of one color tend not to invest because they anticipate lower opportunities in the labor market, and these beliefs are self-confirming as…

Ohio Civil Rights Commission, 19th Annual Report, 1977-1978.

ERIC Educational Resources Information Center

Ohio State Civil Rights Commission, Columbus.

Presented in this document are the percentages, level, and basis of charges filed with the Commission regarding discrimination in the areas of housing, employment, credit, and public accommodations. Statistical information provided is based on such factors as racial, religious, and sex discrimination and handicap based charges. Other,…

A comparative study of hematological parameters of α and β thalassemias in a high prevalence zone: Saudi Arabia

PubMed Central

Mehdi, Syed Riaz; Al Dahmash, Badr Abdullah

2011-01-01

BACKGROUND AND AIMS: Saudi Arabia falls in the high prevalent zone of αα and β thalassemias. Early screening for the type of thalassemia is essential for further investigations and management. The study was carried out to differentiate the type of thalassemia based on red cell indices and other hematological parameters. MATERIALS AND METHODS: The study was carried out on 991 clinically suspected cases of thalassemias in Riyadh, Saudi Arabia. The hematological parameters were studied on Coulter STKS. Cellulose acetate hemoglobin electrophoresis and high-performance liquid chromatography (HPLC) were performed on all the blood samples. Gene deletion studies were carried out by restriction fragment length polymorphism (RFLP) technique using the restriction endonucleases Bam HI. STATISTICAL ANALYSIS: Statistical analysis was performed on SPSS 11.5 version. RESULTS: The hemoglobin electrophoresis and gene studies revealed that there were 406 (40.96%) and 59 (5.95 %) cases of β thalassemia trait and β thalassemia major respectively including adults and children. 426 cases of various deletion forms of α thalassemias were seen. Microcytosis was a common feature in β thalassemias trait and (-α/-α) and (--/αα) types of α thalassemias. MCH was a more significant distinguishing feature among thalassemias. β thalassemia major and α thalassemia (-α/αα) had almost normal hematological parameters. CONCLUSION: MCV and RBC counts are not statistically significant features for discriminating between α and β thalassemias. There is need for development of a discrimination index to differentiate between α and β thalassemias traits on the lines of discriminatory Indices available for distinguishing β thalassemias trait from iron deficiency anemia. PMID:22345994

Game Location and Team Quality Effects on Performance Profiles in Professional Soccer

PubMed Central

Lago-Peñas, Carlos; Lago-Ballesteros, Joaquin

2011-01-01

Home advantage in team sports has an important role in determining the outcome of a game. The aim of the present study was to identify the soccer game- related statistics that best discriminate home and visiting teams according to the team quality. The sample included all 380 games of the Spanish professional men’s league. The independent variables were game location (home or away) and the team quality. Teams were classified into four groups according to their final ranking at the end of the league. The game-related statistics registered were divided into three groups: (i) variables related to goals scored; (ii) variables related to offense and (iii) variables related to defense. A univariate (t-test and Mann-Whitney U) and multivariate (discriminant analysis) analysis of data was done. Results showed that home teams have significantly higher means for goal scored, total shots, shots on goal, attacking moves, box moves, crosses, offsides committed, assists, passes made, successful passes, dribbles made, successful dribbles, ball possession, and gains of possession, while visiting teams presented higher means for losses of possession and yellow cards. In addition, the findings of the current study confirm that game location and team quality are important in determining technical and tactical performances in matches. Teams described as superior and those described as inferior did not experience the same home advantage. Future research should consider the influence of other confounding variables such as weather conditions, game status and team form. Key points Home teams have significantly higher figures for attack indicators probably due to facilities familiarity and crowd effects. The teams’ game-related statistics profile varied according to game location and team quality. Teams described as superior and those described as inferior did not experience the same home advantage. PMID:24150619

Food cravings discriminate between anorexia and bulimia nervosa. Implications for "success" versus "failure" in dietary restriction.

PubMed

Moreno, Silvia; Warren, Cortney S; Rodríguez, Sonia; Fernández, M Carmen; Cepeda-Benito, Antonio

2009-06-01

Food cravings are subjective, motivational states thought to induce binge eating among eating disorder patients. This study compared food cravings across eating disorders. Women (N=135) diagnosed with anorexia nervosa, restrictive (ANR) or binge-purging (ANBP) types, or bulimia nervosa, non-purging (BNNP) or purging (BNP) types completed measures of food cravings. Discriminant analysis yielded two statistically significant functions. The first function differentiated between all the four group pairs except ANBP and BNNP, with levels of various food-craving dimensions successively increasing for ANR, ANBP, BNNP, and BNP participants. The second function differentiated between ANBP and BNNP participants. Overall, the functions improved classification accuracy above chance level (44% fewer errors). The findings suggest that cravings are more strongly associated with loss of control over eating than with dietary restraint tendencies.

Complexity-entropy causality plane: A useful approach for distinguishing songs

NASA Astrophysics Data System (ADS)

Ribeiro, Haroldo V.; Zunino, Luciano; Mendes, Renio S.; Lenzi, Ervin K.

2012-04-01

Nowadays we are often faced with huge databases resulting from the rapid growth of data storage technologies. This is particularly true when dealing with music databases. In this context, it is essential to have techniques and tools able to discriminate properties from these massive sets. In this work, we report on a statistical analysis of more than ten thousand songs aiming to obtain a complexity hierarchy. Our approach is based on the estimation of the permutation entropy combined with an intensive complexity measure, building up the complexity-entropy causality plane. The results obtained indicate that this representation space is very promising to discriminate songs as well as to allow a relative quantitative comparison among songs. Additionally, we believe that the here-reported method may be applied in practical situations since it is simple, robust and has a fast numerical implementation.

Comparison Of Eigenvector-Based Statistical Pattern Recognition Algorithms For Hybrid Processing

NASA Astrophysics Data System (ADS)

Tian, Q.; Fainman, Y.; Lee, Sing H.

1989-02-01

The pattern recognition algorithms based on eigenvector analysis (group 2) are theoretically and experimentally compared in this part of the paper. Group 2 consists of Foley-Sammon (F-S) transform, Hotelling trace criterion (HTC), Fukunaga-Koontz (F-K) transform, linear discriminant function (LDF) and generalized matched filter (GMF). It is shown that all eigenvector-based algorithms can be represented in a generalized eigenvector form. However, the calculations of the discriminant vectors are different for different algorithms. Summaries on how to calculate the discriminant functions for the F-S, HTC and F-K transforms are provided. Especially for the more practical, underdetermined case, where the number of training images is less than the number of pixels in each image, the calculations usually require the inversion of a large, singular, pixel correlation (or covariance) matrix. We suggest solving this problem by finding its pseudo-inverse, which requires inverting only the smaller, non-singular image correlation (or covariance) matrix plus multiplying several non-singular matrices. We also compare theoretically the effectiveness for classification with the discriminant functions from F-S, HTC and F-K with LDF and GMF, and between the linear-mapping-based algorithms and the eigenvector-based algorithms. Experimentally, we compare the eigenvector-based algorithms using a set of image data bases each image consisting of 64 x 64 pixels.

determination of sex in south african blacks by discriminant function analysis of mandibular linear dimensions : A preliminary investigation using the zulu local population.

PubMed

Franklin, Daniel; O'Higgins, Paul; Oxnard, Charles E; Dadour, Ian

2006-12-01

The determination of sex is a critical component in forensic anthropological investigation. The literature attests to numerous metrical standards, each utilizing diffetent skeletal elements, for sex determination in South A frican Blacks. Metrical standards are popular because they provide a high degree of expected accuracy and are less error-prone than subjective nonmetric visual techniques. We note, however, that there appears to be no established metric mandible discriminant function standards for sex determination in this population.We report here on a preliminary investigation designed to evaluate whether the mandible is a practical element for sex determination in South African Blacks. The sample analyzed comprises 40 nonpathological Zulu individuals drawn from the R.A. Dart Collection. Ten linear measurements, obtained from mathematically trans-formed three-dimensional landmark data, are analyzed using basic univariate statistics and discriminant function analyses. Seven of the 10 measurements examined are found to be sexually dimorphic; the dimensions of the ramus are most dimorphic. The sex classification accuracy of the discriminant functions ranged from 72.5 to 87.5% for the univariate method, 92.5% for the stepwise method, and 57.5 to 95% for the direct method. We conclude that the mandible is an extremely useful element for sex determination in this population.

Discriminating Simulated Vocal Tremor Source Using Amplitude Modulation Spectra

PubMed Central

Carbonell, Kathy M.; Lester, Rosemary A.; Story, Brad H.; Lotto, Andrew J.

2014-01-01

Objectives/Hypothesis Sources of vocal tremor are difficult to categorize perceptually and acoustically. This paper describes a preliminary attempt to discriminate vocal tremor sources through the use of spectral measures of the amplitude envelope. The hypothesis is that different vocal tremor sources are associated with distinct patterns of acoustic amplitude modulations. Study Design Statistical categorization methods (discriminant function analysis) were used to discriminate signals from simulated vocal tremor with different sources using only acoustic measures derived from the amplitude envelopes. Methods Simulations of vocal tremor were created by modulating parameters of a vocal fold model corresponding to oscillations of respiratory driving pressure (respiratory tremor), degree of vocal fold adduction (adductory tremor) and fundamental frequency of vocal fold vibration (F0 tremor). The acoustic measures were based on spectral analyses of the amplitude envelope computed across the entire signal and within select frequency bands. Results The signals could be categorized (with accuracy well above chance) in terms of the simulated tremor source using only measures of the amplitude envelope spectrum even when multiple sources of tremor were included. Conclusions These results supply initial support for an amplitude-envelope based approach to identify the source of vocal tremor and provide further evidence for the rich information about talker characteristics present in the temporal structure of the amplitude envelope. PMID:25532813

A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series.

PubMed

Demanuele, Charmaine; Bähner, Florian; Plichta, Michael M; Kirsch, Peter; Tost, Heike; Meyer-Lindenberg, Andreas; Durstewitz, Daniel

2015-01-01

Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity.

Prostate segmentation in MR images using discriminant boundary features.

PubMed

Yang, Meijuan; Li, Xuelong; Turkbey, Baris; Choyke, Peter L; Yan, Pingkun

2013-02-01

Segmentation of the prostate in magnetic resonance image has become more in need for its assistance to diagnosis and surgical planning of prostate carcinoma. Due to the natural variability of anatomical structures, statistical shape model has been widely applied in medical image segmentation. Robust and distinctive local features are critical for statistical shape model to achieve accurate segmentation results. The scale invariant feature transformation (SIFT) has been employed to capture the information of the local patch surrounding the boundary. However, when SIFT feature being used for segmentation, the scale and variance are not specified with the location of the point of interest. To deal with it, the discriminant analysis in machine learning is introduced to measure the distinctiveness of the learned SIFT features for each landmark directly and to make the scale and variance adaptive to the locations. As the gray values and gradients vary significantly over the boundary of the prostate, separate appearance descriptors are built for each landmark and then optimized. After that, a two stage coarse-to-fine segmentation approach is carried out by incorporating the local shape variations. Finally, the experiments on prostate segmentation from MR image are conducted to verify the efficiency of the proposed algorithms.

Refining Stimulus Parameters in Assessing Infant Speech Perception Using Visual Reinforcement Infant Speech Discrimination: Sensation Level.

PubMed

Uhler, Kristin M; Baca, Rosalinda; Dudas, Emily; Fredrickson, Tammy

2015-01-01

Speech perception measures have long been considered an integral piece of the audiological assessment battery. Currently, a prelinguistic, standardized measure of speech perception is missing in the clinical assessment battery for infants and young toddlers. Such a measure would allow systematic assessment of speech perception abilities of infants as well as the potential to investigate the impact early identification of hearing loss and early fitting of amplification have on the auditory pathways. To investigate the impact of sensation level (SL) on the ability of infants with normal hearing (NH) to discriminate /a-i/ and /ba-da/ and to determine if performance on the two contrasts are significantly different in predicting the discrimination criterion. The design was based on a survival analysis model for event occurrence and a repeated measures logistic model for binary outcomes. The outcome for survival analysis was the minimum SL for criterion and the outcome for the logistic regression model was the presence/absence of achieving the criterion. Criterion achievement was designated when an infant's proportion correct score was >0.75 on the discrimination performance task. Twenty-two infants with NH sensitivity participated in this study. There were 9 males and 13 females, aged 6-14 mo. Testing took place over two to three sessions. The first session consisted of a hearing test, threshold assessment of the two speech sounds (/a/ and /i/), and if time and attention allowed, visual reinforcement infant speech discrimination (VRISD). The second session consisted of VRISD assessment for the two test contrasts (/a-i/ and /ba-da/). The presentation level started at 50 dBA. If the infant was unable to successfully achieve criterion (>0.75) at 50 dBA, the presentation level was increased to 70 dBA followed by 60 dBA. Data examination included an event analysis, which provided the probability of criterion distribution across SL. The second stage of the analysis was a repeated measures logistic regression where SL and contrast were used to predict the likelihood of speech discrimination criterion. Infants were able to reach criterion for the /a-i/ contrast at statistically lower SLs when compared to /ba-da/. There were six infants who never reached criterion for /ba-da/ and one never reached criterion for /a-i/. The conditional probability of not reaching criterion by 70 dB SL was 0% for /a-i/ and 21% for /ba-da/. The predictive logistic regression model showed that children were more likely to discriminate the /a-i/ even when controlling for SL. Nearly all normal-hearing infants can demonstrate discrimination criterion of a vowel contrast at 60 dB SL, while a level of ≥70 dB SL may be needed to allow all infants to demonstrate discrimination criterion of a difficult consonant contrast. American Academy of Audiology.

Discrimination of dynamical system models for biological and chemical processes.

PubMed

Lorenz, Sönke; Diederichs, Elmar; Telgmann, Regina; Schütte, Christof

2007-06-01

In technical chemistry, systems biology and biotechnology, the construction of predictive models has become an essential step in process design and product optimization. Accurate modelling of the reactions requires detailed knowledge about the processes involved. However, when concerned with the development of new products and production techniques for example, this knowledge often is not available due to the lack of experimental data. Thus, when one has to work with a selection of proposed models, the main tasks of early development is to discriminate these models. In this article, a new statistical approach to model discrimination is described that ranks models wrt. the probability with which they reproduce the given data. The article introduces the new approach, discusses its statistical background, presents numerical techniques for its implementation and illustrates the application to examples from biokinetics.

Provenance establishment of coffee using solution ICP-MS and ICP-AES.

PubMed

Valentin, Jenna L; Watling, R John

2013-11-01

Statistical interpretation of the concentrations of 59 elements, determined using solution based inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma emission spectroscopy (ICP-AES), was used to establish the provenance of coffee samples from 15 countries across five continents. Data confirmed that the harvest year, degree of ripeness and whether the coffees were green or roasted had little effect on the elemental composition of the coffees. The application of linear discriminant analysis and principal component analysis of the elemental concentrations permitted up to 96.9% correct classification of the coffee samples according to their continent of origin. When samples from each continent were considered separately, up to 100% correct classification of coffee samples into their countries, and plantations of origin was achieved. This research demonstrates the potential of using elemental composition, in combination with statistical classification methods, for accurate provenance establishment of coffee. Copyright © 2013 Elsevier Ltd. All rights reserved.

Statistical mapping of zones of focused groundwater/surface-water exchange using fiber-optic distributed temperature sensing

USGS Publications Warehouse

Mwakanyamale, Kisa; Day-Lewis, Frederick D.; Slater, Lee D.

2013-01-01

Fiber-optic distributed temperature sensing (FO-DTS) increasingly is used to map zones of focused groundwater/surface-water exchange (GWSWE). Previous studies of GWSWE using FO-DTS involved identification of zones of focused GWSWE based on arbitrary cutoffs of FO-DTS time-series statistics (e.g., variance, cross-correlation between temperature and stage, or spectral power). New approaches are needed to extract more quantitative information from large, complex FO-DTS data sets while concurrently providing an assessment of uncertainty associated with mapping zones of focused GSWSE. Toward this end, we present a strategy combining discriminant analysis (DA) and spectral analysis (SA). We demonstrate the approach using field experimental data from a reach of the Columbia River adjacent to the Hanford 300 Area site. Results of the combined SA/DA approach are shown to be superior to previous results from qualitative interpretation of FO-DTS spectra alone.

The Discriminant Analysis Flare Forecasting System (DAFFS)

NASA Astrophysics Data System (ADS)

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.

E-Learning in Croatian Higher Education: An Analysis of Students' Perceptions

NASA Astrophysics Data System (ADS)

Dukić, Darko; Andrijanić, Goran

2010-06-01

Over the last years, e-learning has taken an important role in Croatian higher education as a result of strategies defined and measures undertaken. Nonetheless, in comparison to the developed countries, the achievements in e-learning implementation are still unsatisfactory. Therefore, the efforts to advance e-learning within Croatian higher education need to be intensified. It is further necessary to undertake ongoing activities in order to solve possible problems in e-learning system functioning, which requires the development of adequate evaluation instruments and methods. One of the key steps in this process would be examining and analyzing users' attitudes. This paper presents a study of Croatian students' perceptions with regard to certain aspects of e-learning usage. Given the character of this research, adequate statistical methods were required for the data processing. The results of the analysis indicate that, for the most part, Croatian students have positive perceptions of e-learning, particularly as support to time-honored forms of teaching. However, they are not prepared to completely give up the traditional classroom. Using factor analysis, we identified four underlying factors of a collection of variables related to students' perceptions of e-learning. Furthermore, a certain number of statistically significant differences in student attitudes have been confirmed, in terms of gender and year of study. In our study we used discriminant analysis to determine discriminant functions that distinguished defined groups of students. With this research we managed to a certain degree to alleviate the current data insufficiency in the area of e-learning evaluation among Croatian students. Since this type of learning is gaining in importance within higher education, such analyses have to be conducted continuously.

Evaluation of diagnostic criteria for panic attack using item response theory: findings from the National Comorbidity Survey in USA.

PubMed

Ietsugu, Tetsuji; Sukigara, Masune; Furukawa, Toshiaki A

2007-12-01

The dichotomous diagnostic systems such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD) lose much important information concerning what each symptom can offer. This study explored the characteristics and performances of DSM-IV and ICD-10 diagnostic criteria items for panic attack using modern item response theory (IRT). The National Comorbidity Survey used the Composite International Diagnostic Interview to assess 14 DSM-IV and ICD-10 panic attack diagnostic criteria items in the general population in the USA. The dimensionality and measurement properties of these items were evaluated using dichotomous factor analysis and the two-parameter IRT model. A total of 1213 respondents reported at least one subsyndromal or syndromal panic attack in their lifetime. Factor analysis indicated that all items constitute a unidimensional construct. The two-parameter IRT model produced meaningful and interpretable results. Among items with high discrimination parameters, the difficulty parameter for "palpitation" was relatively low, while those for "choking," "fear of dying" and "paresthesia" were relatively high. Several items including "dry mouth" and "fear of losing control" had low discrimination parameters. The item characteristics of diagnostic criteria among help-seeking clinical populations may be different from those that we observed in the general population and deserve further examination. "Paresthesia," "choking" and "fear of dying" can be thought to be good indicators of severe panic attacks, while "palpitation" can discriminate well between cases and non-cases at low level of panic attack severity. Items such as "dry mouth" would contribute less to the discrimination.

Population analysis of the cingulum bundle using the tubular surface model for schizophrenia detection

NASA Astrophysics Data System (ADS)

Mohan, Vandana; Sundaramoorthi, Ganesh; Kubicki, Marek; Terry, Douglas; Tannenbaum, Allen

2010-03-01

We propose a novel framework for population analysis of DW-MRI data using the Tubular Surface Model. We focus on the Cingulum Bundle (CB) - a major tract for the Limbic System and the main connection of the Cingulate Gyrus, which has been associated with several aspects of Schizophrenia symptomatology. The Tubular Surface Model represents a tubular surface as a center-line with an associated radius function. It provides a natural way to sample statistics along the length of the fiber bundle and reduces the registration of fiber bundle surfaces to that of 4D curves. We apply our framework to a population of 20 subjects (10 normal, 10 schizophrenic) and obtain excellent results with neural network based classification (90% sensitivity, 95% specificity) as well as unsupervised clustering (k-means). Further, we apply statistical analysis to the feature data and characterize the discrimination ability of local regions of the CB, as a step towards localizing CB regions most relevant to Schizophrenia.

Multielement analysis of Canadian wines by inductively coupled plasma mass spectrometry (ICP-MS) and multivariate statistics.

PubMed

Taylor, Vivien F; Longerich, Henry P; Greenough, John D

2003-02-12

Trace element fingerprints were deciphered for wines from Canada's two major wine-producing regions, the Okanagan Valley and the Niagara Peninsula, for the purpose of examining differences in wine element composition with region of origin and identifying elements important to determining provenance. Analysis by ICP-MS allowed simultaneous determination of 34 trace elements in wine (Li, Be, Mg, Al, P, Cl, Ca, Ti, V, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br, Rb, Sr, Mo, Ag, Cd, Sb, I, Cs, Ba, La, Ce, Tl, Pb, Bi, Th, and U) at low levels of detection, and patterns in trace element concentrations were deciphered by multivariate statistical analysis. The two regions were discriminated with 100% accuracy using 10 of these elements. Differences in soil chemistry between the Niagara and Okanagan vineyards were evident, without a good correlation between soil and wine composition. The element Sr was found to be a good indicator of provenance and has been reported in fingerprinting studies of other regions.

Relevant principal component analysis applied to the characterisation of Portuguese heather honey.

PubMed

Martins, Rui C; Lopes, Victor V; Valentão, Patrícia; Carvalho, João C M F; Isabel, Paulo; Amaral, Maria T; Batista, Maria T; Andrade, Paula B; Silva, Branca M

2008-01-01

The main purpose of this study was the characterisation of 'Serra da Lousã' heather honey by using novel statistical methodology, relevant principal component analysis, in order to assess the correlations between production year, locality and composition. Herein, we also report its chemical composition in terms of sugars, glycerol and ethanol, and physicochemical parameters. Sugars profiles from 'Serra da Lousã' heather and 'Terra Quente de Trás-os-Montes' lavender honeys were compared and allowed the discrimination: 'Serra da Lousã' honeys do not contain sucrose, generally exhibit lower contents of turanose, trehalose and maltose and higher contents of fructose and glucose. Different localities from 'Serra da Lousã' provided groups of samples with high and low glycerol contents. Glycerol and ethanol contents were revealed to be independent of the sugars profiles. These data and statistical models can be very useful in the comparison and detection of adulterations during the quality control analysis of 'Serra da Lousã' honey.

The Abdominal Aortic Aneurysm Statistically Corrected Operative Risk Evaluation (AAA SCORE) for predicting mortality after open and endovascular interventions.

PubMed

Ambler, Graeme K; Gohel, Manjit S; Mitchell, David C; Loftus, Ian M; Boyle, Jonathan R

2015-01-01

Accurate adjustment of surgical outcome data for risk is vital in an era of surgeon-level reporting. Current risk prediction models for abdominal aortic aneurysm (AAA) repair are suboptimal. We aimed to develop a reliable risk model for in-hospital mortality after intervention for AAA, using rigorous contemporary statistical techniques to handle missing data. Using data collected during a 15-month period in the United Kingdom National Vascular Database, we applied multiple imputation methodology together with stepwise model selection to generate preoperative and perioperative models of in-hospital mortality after AAA repair, using two thirds of the available data. Model performance was then assessed on the remaining third of the data by receiver operating characteristic curve analysis and compared with existing risk prediction models. Model calibration was assessed by Hosmer-Lemeshow analysis. A total of 8088 AAA repair operations were recorded in the National Vascular Database during the study period, of which 5870 (72.6%) were elective procedures. Both preoperative and perioperative models showed excellent discrimination, with areas under the receiver operating characteristic curve of .89 and .92, respectively. This was significantly better than any of the existing models (area under the receiver operating characteristic curve for best comparator model, .84 and .88; P < .001 and P = .001, respectively). Discrimination remained excellent when only elective procedures were considered. There was no evidence of miscalibration by Hosmer-Lemeshow analysis. We have developed accurate models to assess risk of in-hospital mortality after AAA repair. These models were carefully developed with rigorous statistical methodology and significantly outperform existing methods for both elective cases and overall AAA mortality. These models will be invaluable for both preoperative patient counseling and accurate risk adjustment of published outcome data. Copyright © 2015 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Control of Origin of Sesame Oil from Various Countries by Stable Isotope Analysis and DNA Based Markers—A Pilot Study

PubMed Central

Horacek, Micha; Hansel-Hohl, Karin; Burg, Kornel; Soja, Gerhard; Okello-Anyanga, Walter; Fluch, Silvia

2015-01-01

The indication of origin of sesame seeds and sesame oil is one of the important factors influencing its price, as it is produced in many regions worldwide and certain provenances are especially sought after. We joined stable carbon and hydrogen isotope analysis with DNA based molecular marker analysis to study their combined potential for the discrimination of different origins of sesame seeds. For the stable carbon and hydrogen isotope data a positive correlation between both isotope parameters was observed, indicating a dominant combined influence of climate and water availability. This enabled discrimination between sesame samples from tropical and subtropical/moderate climatic provenances. Carbon isotope values also showed differences between oil from black and white sesame seeds from identical locations, indicating higher water use efficiency of plants producing black seeds. DNA based markers gave independent evidence for geographic variation as well as provided information on the genetic relatedness of the investigated samples. Depending on the differences in ambient environmental conditions and in the genotypic fingerprint, a combination of both analytical methods is a very powerful tool to assess the declared geographic origin. To our knowledge this is the first paper on food authenticity combining the stable isotope analysis of bio-elements with DNA based markers and their combined statistical analysis. PMID:25831054

Control of origin of sesame oil from various countries by stable isotope analysis and DNA based markers--a pilot study.

PubMed

Horacek, Micha; Hansel-Hohl, Karin; Burg, Kornel; Soja, Gerhard; Okello-Anyanga, Walter; Fluch, Silvia

2015-01-01

The indication of origin of sesame seeds and sesame oil is one of the important factors influencing its price, as it is produced in many regions worldwide and certain provenances are especially sought after. We joined stable carbon and hydrogen isotope analysis with DNA based molecular marker analysis to study their combined potential for the discrimination of different origins of sesame seeds. For the stable carbon and hydrogen isotope data a positive correlation between both isotope parameters was observed, indicating a dominant combined influence of climate and water availability. This enabled discrimination between sesame samples from tropical and subtropical/moderate climatic provenances. Carbon isotope values also showed differences between oil from black and white sesame seeds from identical locations, indicating higher water use efficiency of plants producing black seeds. DNA based markers gave independent evidence for geographic variation as well as provided information on the genetic relatedness of the investigated samples. Depending on the differences in ambient environmental conditions and in the genotypic fingerprint, a combination of both analytical methods is a very powerful tool to assess the declared geographic origin. To our knowledge this is the first paper on food authenticity combining the stable isotope analysis of bio-elements with DNA based markers and their combined statistical analysis.

Two-photon optical imaging, spectral and fluorescence lifetime analysis to discriminate urothelial carcinoma grades.

PubMed

Pradère, B; Poulon, F; Compérat, E; Lucas, I; Bazin, D; Doizi, S; Cussenot, O; Traxer, O; Abi Haidar, D

2018-05-28

In the framework of urologic oncology, mini-invasive procedures have increased in the last few decades particularly for urothelial carcinoma. One of the essential elements in the management of this disease is still the diagnosis, which strongly influences the choice of treatment. The histopathologic evaluation of the tumor grade is a keystone of diagnosis, and tumor characterization is not possible with just a macroscopic evaluation. Even today intraoperative evaluation remains difficult despite the emergence of new technologies which use exogenous fluorophore. This study assessed an optical multimodal technique based on endogenous fluorescence, combining qualitative and quantitative analysis, for the diagnostic of urothelial carcinoma. It was found that the combination of two photon fluorescence, second harmonic generation microscopy, spectral analysis and fluorescence lifetime imaging were all able to discriminate tumor from healthy tissue, and to determine the grade of tumors. Spectral analysis of fluorescence intensity and the redox ratio used as quantitative evaluations showed statistical differences between low grade and high grade tumors. These results showed that multimodal optical analysis is a promising technology for the development of an optical fiber setup designed for an intraoperative diagnosis of urothelial carcinoma in the area of endourology. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Thermal infrared imaging of the variability of canopy-air temperature difference distribution for heavy metal stress levels discrimination in rice

NASA Astrophysics Data System (ADS)

Zhang, Biyao; Liu, Xiangnan; Liu, Meiling; Wang, Dongmin

2017-04-01

This paper addresses the assessment and interpretation of the canopy-air temperature difference (Tc-Ta) distribution as an indicator for discriminating between heavy metal stress levels. Tc-Ta distribution is simulated by coupling the energy balance equation with modified leaf angle distribution. Statistical indices including average value (AVG), standard deviation (SD), median, and span of Tc-Ta in the field of view of a digital thermal imager are calculated to describe Tc-Ta distribution quantitatively and, consequently, became the stress indicators. In the application, two grains of rice growing sites under "mild" and "severe" stress level were selected as study areas. A total of 96 thermal images obtained from the field measurements in the three growth stages were used for a separate application of a theoretical variation of Tc-Ta distribution. The results demonstrated that the statistical indices calculated from both simulated and measured data exhibited an upward trend as the stress level becomes serious because heavy metal stress would only raise a portion of the leaves in the canopy. Meteorological factors could barely affect the sensitivity of the statistical indices with the exception of the wind speed. Among the statistical indices, AVG and SD were demonstrated to be better indicators for stress levels discrimination.

Appraising the Corporate Sustainability Reports - Text Mining and Multi-Discriminatory Analysis

NASA Astrophysics Data System (ADS)

Modapothala, J. R.; Issac, B.; Jayamani, E.

The voluntary disclosure of the sustainability reports by the companies attracts wider stakeholder groups. Diversity in these reports poses challenge to the users of information and regulators. This study appraises the corporate sustainability reports as per GRI (Global Reporting Initiative) guidelines (the most widely accepted and used) across all industrial sectors. Text mining is adopted to carry out the initial analysis with a large sample size of 2650 reports. Statistical analyses were performed for further investigation. The results indicate that the disclosures made by the companies differ across the industrial sectors. Multivariate Discriminant Analysis (MDA) shows that the environmental variable is a greater significant contributing factor towards explanation of sustainability report.

Population connectivity of the plating coral Agaricia lamarcki from southwest Puerto Rico

NASA Astrophysics Data System (ADS)

Hammerman, Nicholas M.; Rivera-Vicens, Ramon E.; Galaska, Matthew P.; Weil, Ernesto; Appledoorn, Richard S.; Alfaro, Monica; Schizas, Nikolaos V.

2018-03-01

Identifying genetic connectivity and discrete population boundaries is an important objective for management of declining Caribbean reef-building corals. A double digest restriction-associated DNA sequencing protocol was utilized to generate 321 single nucleotide polymorphisms to estimate patterns of horizontal and vertical gene flow in the brooding Caribbean plate coral, Agaricia lamarcki. Individual colonies ( n = 59) were sampled from eight locations throughout southwestern Puerto Rico from six shallow ( 10-20 m) and two mesophotic habitats ( 30-40 m). Descriptive summary statistics (fixation index, F ST), analysis of molecular variance, and analysis through landscape and ecological associations and discriminant analysis of principal components estimated high population connectivity with subtle subpopulation structure among all sampling localities.

Texture and haptic cues in slant discrimination: reliability-based cue weighting without statistically optimal cue combination

NASA Astrophysics Data System (ADS)

Rosas, Pedro; Wagemans, Johan; Ernst, Marc O.; Wichmann, Felix A.

2005-05-01

A number of models of depth-cue combination suggest that the final depth percept results from a weighted average of independent depth estimates based on the different cues available. The weight of each cue in such an average is thought to depend on the reliability of each cue. In principle, such a depth estimation could be statistically optimal in the sense of producing the minimum-variance unbiased estimator that can be constructed from the available information. Here we test such models by using visual and haptic depth information. Different texture types produce differences in slant-discrimination performance, thus providing a means for testing a reliability-sensitive cue-combination model with texture as one of the cues to slant. Our results show that the weights for the cues were generally sensitive to their reliability but fell short of statistically optimal combination - we find reliability-based reweighting but not statistically optimal cue combination.

Source-Type Identification Analysis Using Regional Seismic Moment Tensors

NASA Astrophysics Data System (ADS)

Chiang, A.; Dreger, D. S.; Ford, S. R.; Walter, W. R.

2012-12-01

Waveform inversion to determine the seismic moment tensor is a standard approach in determining the source mechanism of natural and manmade seismicity, and may be used to identify, or discriminate different types of seismic sources. The successful applications of the regional moment tensor method at the Nevada Test Site (NTS) and the 2006 and 2009 North Korean nuclear tests (Ford et al., 2009a, 2009b, 2010) show that the method is robust and capable for source-type discrimination at regional distances. The well-separated populations of explosions, earthquakes and collapses on a Hudson et al., (1989) source-type diagram enables source-type discrimination; however the question remains whether or not the separation of events is universal in other regions, where we have limited station coverage and knowledge of Earth structure. Ford et al., (2012) have shown that combining regional waveform data and P-wave first motions removes the CLVD-isotropic tradeoff and uniquely discriminating the 2009 North Korean test as an explosion. Therefore, including additional constraints from regional and teleseismic P-wave first motions enables source-type discrimination at regions with limited station coverage. We present moment tensor analysis of earthquakes and explosions (M6) from Lop Nor and Semipalatinsk test sites for station paths crossing Kazakhstan and Western China. We also present analyses of smaller events from industrial sites. In these sparse coverage situations we combine regional long-period waveforms, and high-frequency P-wave polarity from the same stations, as well as from teleseismic arrays to constrain the source type. Discrimination capability with respect to velocity model and station coverage is examined, and additionally we investigate the velocity model dependence of vanishing free-surface traction effects on seismic moment tensor inversion of shallow sources and recovery of explosive scalar moment. Our synthetic data tests indicate that biases in scalar seismic moment and discrimination for shallow sources are small and can be understood in a systematic manner. We are presently investigating the frequency dependence of vanishing traction of a very shallow (10m depth) M2+ chemical explosion recorded at several kilometer distances, and preliminary results indicate at the typical frequency passband we employ the bias does not affect our ability to retrieve the correct source mechanism but may affect the retrieval of the correct scalar seismic moment. Finally, we assess discrimination capability in a composite P-value statistical framework.

Spectral Mining for Discriminating Blood Origins in the Presence of Substrate Interference via Attenuated Total Reflection Fourier Transform Infrared Spectroscopy: Postmortem or Antemortem Blood?

PubMed

Takamura, Ayari; Watanabe, Ken; Akutsu, Tomoko; Ikegaya, Hiroshi; Ozawa, Takeaki

2017-09-19

Often in criminal investigations, discrimination of types of body fluid evidence is crucially important to ascertain how a crime was committed. Compared to current methods using biochemical techniques, vibrational spectroscopic approaches can provide versatile applicability to identify various body fluid types without sample invasion. However, their applicability is limited to pure body fluid samples because important signals from body fluids incorporated in a substrate are affected strongly by interference from substrate signals. Herein, we describe a novel approach to recover body fluid signals that are embedded in strong substrate interferences using attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy and an innovative multivariate spectral processing. This technique supported detection of covert features of body fluid signals, and then identified origins of body fluid stains on substrates. We discriminated between ATR FT-IR spectra of postmortem blood (PB) and those of antemortem blood (AB) by creating a multivariate statistics model. From ATR FT-IR spectra of PB and AB stains on interfering substrates (polyester, cotton, and denim), blood-originated signals were extracted by a weighted linear regression approach we developed originally using principal components of both blood and substrate spectra. The blood-originated signals were finally classified by the discriminant model, demonstrating high discriminant accuracy. The present method can identify body fluid evidence independently of the substrate type, which is expected to promote the application of vibrational spectroscopic techniques in forensic body fluid analysis.

Pre-attentive auditory discrimination skill in Indian classical vocal musicians and non-musicians.

PubMed

Sanju, Himanshu Kumar; Kumar, Prawin

2016-09-01

To test for pre-attentive auditory discrimination skills in Indian classical vocal musicians and non-musicians. Mismatch negativity (MMN) was recorded to test for pre-attentive auditory discrimination skills with a pair of stimuli of /1000 Hz/ and /1100 Hz/, with /1000 Hz/ as the frequent stimulus and /1100 Hz/ as the infrequent stimulus. Onset, offset and peak latencies were the considered latency parameters, whereas peak amplitude and area under the curve were considered for amplitude analysis. Exactly 50 participants, out of which the experimental group had 25 adult Indian classical vocal musicians and 25 age-matched non-musicians served as the control group, were included in the study. Experimental group participants had a minimum professional music experience in Indian classic vocal music of 10 years. However, control group participants did not have any formal training in music. Descriptive statistics showed better waveform morphology in the experimental group as compared to the control. MANOVA showed significantly better onset latency, peak amplitude and area under the curve in the experimental group but no significant difference in the offset and peak latencies between the two groups. The present study probably points towards the enhancement of pre-attentive auditory discrimination skills in Indian classical vocal musicians compared to non-musicians. It indicates that Indian classical musical training enhances pre-attentive auditory discrimination skills in musicians, leading to higher peak amplitude and a greater area under the curve compared to non-musicians.

Automated palpation for breast tissue discrimination based on viscoelastic biomechanical properties.

PubMed

Tsukune, Mariko; Kobayashi, Yo; Miyashita, Tomoyuki; Fujie, G Masakatsu

2015-05-01

Accurate, noninvasive methods are sought for breast tumor detection and diagnosis. In particular, a need for noninvasive techniques that measure both the nonlinear elastic and viscoelastic properties of breast tissue has been identified. For diagnostic purposes, it is important to select a nonlinear viscoelastic model with a small number of parameters that highly correlate with histological structure. However, the combination of conventional viscoelastic models with nonlinear elastic models requires a large number of parameters. A nonlinear viscoelastic model of breast tissue based on a simple equation with few parameters was developed and tested. The nonlinear viscoelastic properties of soft tissues in porcine breast were measured experimentally using fresh ex vivo samples. Robotic palpation was used for measurements employed in a finite element model. These measurements were used to calculate nonlinear viscoelastic parameters for fat, fibroglandular breast parenchyma and muscle. The ability of these parameters to distinguish the tissue types was evaluated in a two-step statistical analysis that included Holm's pairwise [Formula: see text] test. The discrimination error rate of a set of parameters was evaluated by the Mahalanobis distance. Ex vivo testing in porcine breast revealed significant differences in the nonlinear viscoelastic parameters among combinations of three tissue types. The discrimination error rate was low among all tested combinations of three tissue types. Although tissue discrimination was not achieved using only a single nonlinear viscoelastic parameter, a set of four nonlinear viscoelastic parameters were able to reliably and accurately discriminate fat, breast fibroglandular tissue and muscle.

Features versus context: An approach for precise and detailed detection and delineation of faces and facial features.

PubMed

Ding, Liya; Martinez, Aleix M

2010-11-01

The appearance-based approach to face detection has seen great advances in the last several years. In this approach, we learn the image statistics describing the texture pattern (appearance) of the object class we want to detect, e.g., the face. However, this approach has had limited success in providing an accurate and detailed description of the internal facial features, i.e., eyes, brows, nose, and mouth. In general, this is due to the limited information carried by the learned statistical model. While the face template is relatively rich in texture, facial features (e.g., eyes, nose, and mouth) do not carry enough discriminative information to tell them apart from all possible background images. We resolve this problem by adding the context information of each facial feature in the design of the statistical model. In the proposed approach, the context information defines the image statistics most correlated with the surroundings of each facial component. This means that when we search for a face or facial feature, we look for those locations which most resemble the feature yet are most dissimilar to its context. This dissimilarity with the context features forces the detector to gravitate toward an accurate estimate of the position of the facial feature. Learning to discriminate between feature and context templates is difficult, however, because the context and the texture of the facial features vary widely under changing expression, pose, and illumination, and may even resemble one another. We address this problem with the use of subclass divisions. We derive two algorithms to automatically divide the training samples of each facial feature into a set of subclasses, each representing a distinct construction of the same facial component (e.g., closed versus open eyes) or its context (e.g., different hairstyles). The first algorithm is based on a discriminant analysis formulation. The second algorithm is an extension of the AdaBoost approach. We provide extensive experimental results using still images and video sequences for a total of 3,930 images. We show that the results are almost as good as those obtained with manual detection.

Applying the LANL Statistical Pattern Recognition Paradigm for Structural Health Monitoring to Data from a Surface-Effect Fast Patrol Boat

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

Hoon Sohn; Charles Farrar; Norman Hunter

2001-01-01

This report summarizes the analysis of fiber-optic strain gauge data obtained from a surface-effect fast patrol boat being studied by the staff at the Norwegian Defense Research Establishment (NDRE) in Norway and the Naval Research Laboratory (NRL) in Washington D.C. Data from two different structural conditions were provided to the staff at Los Alamos National Laboratory. The problem was then approached from a statistical pattern recognition paradigm. This paradigm can be described as a four-part process: (1) operational evaluation, (2) data acquisition & cleansing, (3) feature extraction and data reduction, and (4) statistical model development for feature discrimination. Given thatmore » the first two portions of this paradigm were mostly completed by the NDRE and NRL staff, this study focused on data normalization, feature extraction, and statistical modeling for feature discrimination. The feature extraction process began by looking at relatively simple statistics of the signals and progressed to using the residual errors from auto-regressive (AR) models fit to the measured data as the damage-sensitive features. Data normalization proved to be the most challenging portion of this investigation. A novel approach to data normalization, where the residual errors in the AR model are considered to be an unmeasured input and an auto-regressive model with exogenous inputs (ARX) is then fit to portions of the data exhibiting similar waveforms, was successfully applied to this problem. With this normalization procedure, a clear distinction between the two different structural conditions was obtained. A false-positive study was also run, and the procedure developed herein did not yield any false-positive indications of damage. Finally, the results must be qualified by the fact that this procedure has only been applied to very limited data samples. A more complete analysis of additional data taken under various operational and environmental conditions as well as other structural conditions is necessary before one can definitively state that the procedure is robust enough to be used in practice.« less

[Application of disparity discriminating accuracy test in evaluating the stereopsis of postoperative intermittent exotropia].

PubMed

Feng, L X; Yao, J Y; Chen, L; Tang, Y; Hou, F

2016-08-01

To discuss the application of disparity discriminating accuracy test in evaluating the stereopsis of postoperative intermittent exotropia. Patients with intermittent exotropia who underwent surgery during July 2011 to June 2013 were followed up. The stereoacuity was examined by Titmus Stereotest, Randot Stereotest and Frisby Stereotest. Twenty adult cases whose stereoacuity reached normal were chosen as experimental group. Twenty healthy adults were selected as normal control group. Both groups were examined with disparity discriminating accuracy test. Discriminating accuracy of the two groups were analyzed with Two-Way ANOVA method. Test-retest reliability was analyzed with Intraclass Correlation Coefficient analysis. The test-retest reliability of disparity discriminating accuracy test is excellent (ICC=0.99, P<0.01) . Discriminating accuracy under different disparities in experimental group were 0.56±0.09, 0.67±0.14, 0.77±0.15, 0.82±0.14, 0.85±0.11, 0.85±0.14, 0.87±0.10, 0.84±0.16, while those in control group were 0.77±0.09, 0.88±0.09, 0.93±0.08, 0.91±0.09, 0.95±0.08, 0.96±0.05, 0.97±0.06, 0.96±0.04. There were statistically significant differences between them (F=38.06, P<0.01) . The discriminating ability of group grating in both groups was affected by the size of disparity. Under situation of small disparity, a large difference was found between the experimental group (0.67±0.12)and control group(0.86±0.07) (F=4.84, P<0.05). Stereoscopic function can be evaluated comprehensively with disparity discriminating accuracy test. Use this test, a certain degree of dysfunction in stereopsis can still be found in postoperative intermittent exotropic patients who reached normal stereoacuity examined with traditional stereotests. (Chin J Ophthalmol, 2016, 52: 584-588).

Statistics and Title VII Proof: Prima Facie Case and Rebuttal.

ERIC Educational Resources Information Center

Whitten, David

1978-01-01

The method and means by which statistics can raise a prima facie case of Title VII violation are analyzed. A standard is identified that can be applied to determine whether a statistical disparity is sufficient to shift the burden to the employer to rebut a prima facie case of discrimination. (LBH)

Statistical differences between relative quantitative molecular fingerprints from microbial communities.

PubMed

Portillo, M C; Gonzalez, J M

2008-08-01

Molecular fingerprints of microbial communities are a common method for the analysis and comparison of environmental samples. The significance of differences between microbial community fingerprints was analyzed considering the presence of different phylotypes and their relative abundance. A method is proposed by simulating coverage of the analyzed communities as a function of sampling size applying a Cramér-von Mises statistic. Comparisons were performed by a Monte Carlo testing procedure. As an example, this procedure was used to compare several sediment samples from freshwater ponds using a relative quantitative PCR-DGGE profiling technique. The method was able to discriminate among different samples based on their molecular fingerprints, and confirmed the lack of differences between aliquots from a single sample.

Optimal Fisher Discriminant Ratio for an Arbitrary Spatial Light Modulator

NASA Technical Reports Server (NTRS)

Juday, Richard D.

1999-01-01

Optimizing the Fisher ratio is well established in statistical pattern recognition as a means of discriminating between classes. I show how to optimize that ratio for optical correlation intensity by choice of filter on an arbitrary spatial light modulator (SLM). I include the case of additive noise of known power spectral density.

Sex Discrimination in Employment. Research Report No. 171.

ERIC Educational Resources Information Center

Morris, J. David; Wood, Linda B.

This report examines the status of women and the laws that have been enacted to protect women from discrimination in employment. Written in lay language, it examines employment and occupational statistics for women in the United States and in Kentucky. Following an introduction in Chapter 1, the report presents four chapters surveying the problem,…

Machine learning to analyze images of shocked materials for precise and accurate measurements

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

Dresselhaus-Cooper, Leora; Howard, Marylesa; Hock, Margaret C.

A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast imagesmore » of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations.« less

Multivariate statistical analysis to investigate the subduction zone parameters favoring the occurrence of giant megathrust earthquakes

NASA Astrophysics Data System (ADS)

Brizzi, S.; Sandri, L.; Funiciello, F.; Corbi, F.; Piromallo, C.; Heuret, A.

2018-03-01

The observed maximum magnitude of subduction megathrust earthquakes is highly variable worldwide. One key question is which conditions, if any, favor the occurrence of giant earthquakes (Mw ≥ 8.5). Here we carry out a multivariate statistical study in order to investigate the factors affecting the maximum magnitude of subduction megathrust earthquakes. We find that the trench-parallel extent of subduction zones and the thickness of trench sediments provide the largest discriminating capability between subduction zones that have experienced giant earthquakes and those having significantly lower maximum magnitude. Monte Carlo simulations show that the observed spatial distribution of giant earthquakes cannot be explained by pure chance to a statistically significant level. We suggest that the combination of a long subduction zone with thick trench sediments likely promotes a great lateral rupture propagation, characteristic of almost all giant earthquakes.

No experience required: Violent crime and anticipated, vicarious, and experienced racial discrimination.

PubMed

Herda, Daniel; McCarthy, Bill

2018-02-01

There is a growing body of evidence linking racial discrimination and juvenile crime, and a number of theories explain this relationship. In this study, we draw on one popular approach, Agnew's general strain theory, and extend prior research by moving from a focus on experienced discrimination to consider two other forms, anticipated and vicarious discrimination. Using data on black, white, and Hispanic youth, from the Project on Human Development in Chicago Neighborhoods (PHDCN), we find that experienced, anticipated, and to a lesser extent, vicarious discrimination, significantly predict violent crime independent of a set of neighborhood, parental, and individual level controls, including prior violent offending. Additional analyses on the specific contexts of discrimination reveal that violence is associated with the anticipation of police discrimination. The effects tend to be larger for African American than Hispanic youth, but the differences are not statistically significant. These findings support the thesis that, like other strains, discrimination may not have to be experienced directly to influence offending. Copyright © 2017. Published by Elsevier Inc.

Self-reported experiences of discrimination and health: scientific advances, ongoing controversies, and emerging issues.

PubMed

Lewis, Tené T; Cogburn, Courtney D; Williams, David R

2015-01-01

Over the past two decades, research examining the impact of self-reported experiences of discrimination on mental and physical health has increased dramatically. Studies have found consistent associations between exposure to discrimination and a wide range of Diagnostic and Statistical Manual of Mental Disorders (DSM)-diagnosed mental disorders as well as objective physical health outcomes. Associations are seen in cross-sectional as well as longitudinal studies and persist even after adjustment for confounding variables, including personality characteristics and other threats to validity. However, controversies remain, particularly around the best approach to measuring experiences of discrimination, the significance of racial/ethnic discrimination versus overall mistreatment, the need to account for "intersectionalities," and the importance of comprehensive assessments. These issues are discussed in detail, along with emerging areas of emphasis including cyber discrimination, anticipatory stress or vigilance around discrimination, and interventions with potential to reduce the negative effects of discrimination on health. We also discuss priorities for future research and implications for interventions and policy.

Predicting Esophagitis After Chemoradiation Therapy for Non-Small Cell Lung Cancer: An Individual Patient Data Meta-Analysis

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

Palma, David A., E-mail: david.palma@uwo.ca; Senan, Suresh; Oberije, Cary

Purpose: Concurrent chemoradiation therapy (CCRT) improves survival compared with sequential treatment for locally advanced non-small cell lung cancer, but it increases toxicity, particularly radiation esophagitis (RE). Validated predictors of RE for clinical use are lacking. We performed an individual-patient-data meta-analysis to determine factors predictive of clinically significant RE. Methods and Materials: After a systematic review of the literature, data were obtained on 1082 patients who underwent CCRT, including patients from Europe, North America, Asia, and Australia. Patients were randomly divided into training and validation sets (2/3 vs 1/3 of patients). Factors predictive of RE (grade ≥2 and grade ≥3) weremore » assessed using logistic modeling, with the concordance statistic (c statistic) used to evaluate the performance of each model. Results: The median radiation therapy dose delivered was 65 Gy, and the median follow-up time was 2.1 years. Most patients (91%) received platinum-containing CCRT regimens. The development of RE was common, scored as grade 2 in 348 patients (32.2%), grade 3 in 185 (17.1%), and grade 4 in 10 (0.9%). There were no RE-related deaths. On univariable analysis using the training set, several baseline factors were statistically predictive of RE (P<.05), but only dosimetric factors had good discrimination scores (c > .60). On multivariable analysis, the esophageal volume receiving ≥60 Gy (V60) alone emerged as the best predictor of grade ≥2 and grade ≥3 RE, with good calibration and discrimination. Recursive partitioning identified 3 risk groups: low (V60 <0.07%), intermediate (V60 0.07% to 16.99%), and high (V60 ≥17%). With use of the validation set, the predictive model performed inferiorly for the grade ≥2 endpoint (c = .58) but performed well for the grade ≥3 endpoint (c = .66). Conclusions: Clinically significant RE is common, but life-threatening complications occur in <1% of patients. Although several factors are statistically predictive of RE, the V60 alone provides the best predictive ability. Efforts to reduce the V60 should be prioritized, with further research needed to identify and validate new predictive factors.« less

Discharge destination following lower limb fracture: development of a prediction model to assist with decision making.

PubMed

Kimmel, Lara A; Holland, Anne E; Edwards, Elton R; Cameron, Peter A; De Steiger, Richard; Page, Richard S; Gabbe, Belinda

2012-06-01

Accurate prediction of the likelihood of discharge to inpatient rehabilitation following lower limb fracture made on admission to hospital may assist patient discharge planning and decrease the burden on the hospital system caused by delays in decision making. To develop a prognostic model for discharge to inpatient rehabilitation. Isolated lower extremity fracture cases (excluding fractured neck of femur), captured by the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), were extracted for analysis. A training data set was created for model development and validation data set for evaluation. A multivariable logistic regression model was developed based on patient and injury characteristics. Models were assessed using measures of discrimination (C-statistic) and calibration (Hosmer-Lemeshow (H-L) statistic). A total of 1429 patients met the inclusion criteria and were randomly split into training and test data sets. Increasing age, more proximal fracture type, compensation or private fund source for the admission, metropolitan location of residence, not working prior to injury and having a self-reported pre-injury disability were included in the final prediction model. The C-statistic for the model was 0.92 (95% confidence interval (CI) 0.88, 0.95) with an H-L statistic of χ(2)=11.62, p=0.17. For the test data set, the C-statistic was 0.86 (95% CI 0.83, 0.90) with an H-L statistic of χ(2)=37.98, p<0.001. A model to predict discharge to inpatient rehabilitation following lower limb fracture was developed with excellent discrimination although the calibration was reduced in the test data set. This model requires prospective testing but could form an integral part of decision making in regards to discharge disposition to facilitate timely and accurate referral to rehabilitation and optimise resource allocation. Copyright © 2011 Elsevier Ltd. All rights reserved.

Compositional variations of ignimbrite magmas in the Central Andes over the past 26 Ma - A multivariate statistical perspective

NASA Astrophysics Data System (ADS)

Brandmeier, M.; Wörner, G.

2016-10-01

Multivariate statistical and geospatial analyses based on a compilation of 890 geochemical and 1200 geochronological data for 194 mapped ignimbrites from the Central Andes document the compositional and temporal patterns of large-volume ignimbrites (so-called "ignimbrite flare-ups") during Neogene times. Rapid advances in computational science during the past decade led to a growing pool of algorithms for multivariate statistics for large datasets with many predictor variables. This study applies cluster analysis (CA) and linear discriminant analysis (LDA) on log-ratio transformed data with the aim of (1) testing a tool for ignimbrite correlation and (2) distinguishing compositional groups that reflect different processes and sources of ignimbrite magmatism during the geodynamic evolution of the Central Andes. CA on major and trace elements allows grouping of ignimbrites according to their geochemical characteristics into rhyolitic and dacitic "end-members" and to differentiate characteristic trace element signatures with respect to Eu anomaly, depletions in middle and heavy rare earth elements (REE) and variable enrichments in light REE. To highlight these distinct compositional signatures, we applied LDA to selected ignimbrites for which comprehensive datasets were available. In comparison to traditional geochemical parameters we found that the advantage of multivariate statistics is their capability of dealing with large datasets and many variables (elements) and to take advantage of this n-dimensional space to detect subtle compositional differences contained in the data. The most important predictors for discriminating ignimbrites are La, Yb, Eu, Al2O3, K2O, P2O5, MgO, FeOt, and TiO2. However, other REE such as Gd, Pr, Tm, Sm, Dy and Er also contribute to the discrimination functions. Significant compositional differences were found between (1) the older (> 13 Ma) large-volume plateau-forming ignimbrites in northernmost Chile and southern Peru and (2) the younger (< 10 Ma) Altiplano-Puna-Volcanic-Complex (APVC) ignimbrites that are of similar volumes. Older ignimbrites are less depleted in HREE and less radiogenic in Sr isotopes, indicating smaller crustal contributions during evolution in a thinner and thermally less evolved crust. These compositional variations indicate a relation to crustal thickening with a "transition" from plagioclase to amphibole and garnet residual mineralogy between 13 and 9 Ma. Compositional and volumetric variations correlate to the N-S passage of the Juan-Fernandéz-Ridge, crustal shortening and thickening, and increased average crustal temperatures during the past 26 Ma. Table DR2 Mapped ignimbrite sheets.

A Comparison between Discrimination Indices and Item-Response Theory Using the Rasch Model in a Clinical Course Written Examination of a Medical School.

PubMed

Park, Jong Cook; Kim, Kwang Sig

2012-03-01

The reliability of test is determined by each items' characteristics. Item analysis is achieved by classical test theory and item response theory. The purpose of the study was to compare the discrimination indices with item response theory using the Rasch model. Thirty-one 4th-year medical school students participated in the clinical course written examination, which included 22 A-type items and 3 R-type items. Point biserial correlation coefficient (C(pbs)) was compared to method of extreme group (D), biserial correlation coefficient (C(bs)), item-total correlation coefficient (C(it)), and corrected item-total correlation coeffcient (C(cit)). Rasch model was applied to estimate item difficulty and examinee's ability and to calculate item fit statistics using joint maximum likelihood. Explanatory power (r2) of Cpbs is decreased in the following order: C(cit) (1.00), C(it) (0.99), C(bs) (0.94), and D (0.45). The ranges of difficulty logit and standard error and ability logit and standard error were -0.82 to 0.80 and 0.37 to 0.76, -3.69 to 3.19 and 0.45 to 1.03, respectively. Item 9 and 23 have outfit > or =1.3. Student 1, 5, 7, 18, 26, 30, and 32 have fit > or =1.3. C(pbs), C(cit), and C(it) are good discrimination parameters. Rasch model can estimate item difficulty parameter and examinee's ability parameter with standard error. The fit statistics can identify bad items and unpredictable examinee's responses.

On the use of attractor dimension as a feature in structural health monitoring

USGS Publications Warehouse

Nichols, J.M.; Virgin, L.N.; Todd, M.D.; Nichols, J.D.

2003-01-01

Recent works in the vibration-based structural health monitoring community have emphasised the use of correlation dimension as a discriminating statistic in seperating a damaged from undamaged response. This paper explores the utility of attractor dimension as a 'feature' and offers some comparisons between different metrics reflecting dimension. This focus is on evaluating the performance of two different measures of dimension as damage indicators in a structural health monitoring context. Results indicate that the correlation dimension is probably a poor choice of statistic for the purpose of signal discrimination. Other measures of dimension may be used for the same purposes with a higher degree of statistical reliability. The question of competing methodologies is placed in a hypothesis testing framework and answered with experimental data taken from a cantilivered beam.

Evaluation of direct and indirect ethanol biomarkers using a likelihood ratio approach to identify chronic alcohol abusers for forensic purposes.

PubMed

Alladio, Eugenio; Martyna, Agnieszka; Salomone, Alberto; Pirro, Valentina; Vincenti, Marco; Zadora, Grzegorz

2017-02-01

The detection of direct ethanol metabolites, such as ethyl glucuronide (EtG) and fatty acid ethyl esters (FAEEs), in scalp hair is considered the optimal strategy to effectively recognize chronic alcohol misuses by means of specific cut-offs suggested by the Society of Hair Testing. However, several factors (e.g. hair treatments) may alter the correlation between alcohol intake and biomarkers concentrations, possibly introducing bias in the interpretative process and conclusions. 125 subjects with various drinking habits were subjected to blood and hair sampling to determine indirect (e.g. CDT) and direct alcohol biomarkers. The overall data were investigated using several multivariate statistical methods. A likelihood ratio (LR) approach was used for the first time to provide predictive models for the diagnosis of alcohol abuse, based on different combinations of direct and indirect alcohol biomarkers. LR strategies provide a more robust outcome than the plain comparison with cut-off values, where tiny changes in the analytical results can lead to dramatic divergence in the way they are interpreted. An LR model combining EtG and FAEEs hair concentrations proved to discriminate non-chronic from chronic consumers with ideal correct classification rates, whereas the contribution of indirect biomarkers proved to be negligible. Optimal results were observed using a novel approach that associates LR methods with multivariate statistics. In particular, the combination of LR approach with either Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA) proved successful in discriminating chronic from non-chronic alcohol drinkers. These LR models were subsequently tested on an independent dataset of 43 individuals, which confirmed their high efficiency. These models proved to be less prone to bias than EtG and FAEEs independently considered. In conclusion, LR models may represent an efficient strategy to sustain the diagnosis of chronic alcohol consumption and provide a suitable gradation to support the judgment. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Discriminant Analysis of Raman Spectra for Body Fluid Identification for Forensic Purposes

PubMed Central

Sikirzhytski, Vitali; Virkler, Kelly; Lednev, Igor K.

2010-01-01

Detection and identification of blood, semen and saliva stains, the most common body fluids encountered at a crime scene, are very important aspects of forensic science today. This study targets the development of a nondestructive, confirmatory method for body fluid identification based on Raman spectroscopy coupled with advanced statistical analysis. Dry traces of blood, semen and saliva obtained from multiple donors were probed using a confocal Raman microscope with a 785-nm excitation wavelength under controlled laboratory conditions. Results demonstrated the capability of Raman spectroscopy to identify an unknown substance to be semen, blood or saliva with high confidence. PMID:22319277

3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading

PubMed Central

Cho, Nam-Hoon; Choi, Heung-Kook

2014-01-01

One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system. PMID:25371701

Statistical interpretation of chromatic indicators in correlation to phytochemical profile of a sulfur dioxide-free mulberry (Morus nigra) wine submitted to non-thermal maturation processes.

PubMed

Tchabo, William; Ma, Yongkun; Kwaw, Emmanuel; Zhang, Haining; Xiao, Lulu; Apaliya, Maurice T

2018-01-15

The four different methods of color measurement of wine proposed by Boulton, Giusti, Glories and Commission International de l'Eclairage (CIE) were applied to assess the statistical relationship between the phytochemical profile and chromatic characteristics of sulfur dioxide-free mulberry (Morus nigra) wine submitted to non-thermal maturation processes. The alteration in chromatic properties and phenolic composition of non-thermal aged mulberry wine were examined, aided by the used of Pearson correlation, cluster and principal component analysis. The results revealed a positive effect of non-thermal processes on phytochemical families of wines. From Pearson correlation analysis relationships between chromatic indexes and flavonols as well as anthocyanins were established. Cluster analysis highlighted similarities between Boulton and Giusti parameters, as well as Glories and CIE parameters in the assessment of chromatic properties of wines. Finally, principal component analysis was able to discriminate wines subjected to different maturation techniques on the basis of their chromatic and phenolics characteristics. Copyright © 2017. Published by Elsevier Ltd.

Characteristic fingerprinting based on macamides for discrimination of maca (Lepidium meyenii) by LC/MS/MS and multivariate statistical analysis.

PubMed

Pan, Yu; Zhang, Ji; Li, Hong; Wang, Yuan-Zhong; Li, Wan-Yi

2016-10-01

Macamides with a benzylalkylamide nucleus are characteristic and major bioactive compounds in the functional food maca (Lepidium meyenii Walp). The aim of this study was to explore variations in macamide content among maca from China and Peru. Twenty-seven batches of maca hypocotyls with different phenotypes, sampled from different geographical origins, were extracted and profiled by liquid chromatography with ultraviolet detection/tandem mass spectrometry (LC-UV/MS/MS). Twelve macamides were identified by MS operated in multiple scanning modes. Similarity analysis showed that maca samples differed significantly in their macamide fingerprinting. Partial least squares discriminant analysis (PLS-DA) was used to differentiate samples according to their geographical origin and to identify the most relevant variables in the classification model. The prediction accuracy for raw maca was 91% and five macamides were selected and considered as chemical markers for sample classification. When combined with a PLS-DA model, characteristic fingerprinting based on macamides could be recommended for labelling for the authentication of maca from different geographical origins. The results provided potential evidence for the relationships between environmental or other factors and distribution of macamides. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

The composition of M-type asteroids: Synthesis of spectroscopic and radar observations

NASA Astrophysics Data System (ADS)

Neeley, J. R.; Ockert-Bell, M. E.; Clark, B. E.; Shepard, M. K.; Cloutis, E. A.; Fornasier, S.; Bus, S. J.

2011-10-01

This work updates our and expands our long term radar-driven observational campaign of 27 main-belt asteroids (MBAs) focused on Bus-DeMeo Xc- and Xk-type objects (Tholen X and M class asteroids) using the Arecibo radar and NASA Infrared Telescope Facilities (IRTF). Seventeen of our targets were near-simultaneously observed with radar and those observations are described in companion paper (Shepard et al., 2010). We utilized visible wavelength for a more complete compositional analysis of our targets. Compositional evidence is derived from our target asteroid spectra using three different methods: 1) a χ2 search for spectral matches in the RELAB database, 2) parametric comparisons with meteorites and 3) linear discriminant analysis. This paper synthesizes the results of the RELAB search, parametric comparisons, and linear discriminant analysis with compositional suggestions based on radar observations. We find that for six of seventeen targets with radar data, our spectral results are consistent with their radar analog (16 Psyche, 21 Lutetia, 69 Hesperia, 135 Hertha, 216 Kleopatra, and 497 Iva). For twenty out of twenty-seven objects our statistical comparisons with RELAB meteorites result in consistent analog identification, providing a degree of confidence in our parametric methods.

Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems.

PubMed

Lê Cao, Kim-Anh; Boitard, Simon; Besse, Philippe

2011-06-22

Variable selection on high throughput biological data, such as gene expression or single nucleotide polymorphisms (SNPs), becomes inevitable to select relevant information and, therefore, to better characterize diseases or assess genetic structure. There are different ways to perform variable selection in large data sets. Statistical tests are commonly used to identify differentially expressed features for explanatory purposes, whereas Machine Learning wrapper approaches can be used for predictive purposes. In the case of multiple highly correlated variables, another option is to use multivariate exploratory approaches to give more insight into cell biology, biological pathways or complex traits. A simple extension of a sparse PLS exploratory approach is proposed to perform variable selection in a multiclass classification framework. sPLS-DA has a classification performance similar to other wrapper or sparse discriminant analysis approaches on public microarray and SNP data sets. More importantly, sPLS-DA is clearly competitive in terms of computational efficiency and superior in terms of interpretability of the results via valuable graphical outputs. sPLS-DA is available in the R package mixOmics, which is dedicated to the analysis of large biological data sets.

Two-dimensional statistical linear discriminant analysis for real-time robust vehicle-type recognition

NASA Astrophysics Data System (ADS)

Zafar, I.; Edirisinghe, E. A.; Acar, S.; Bez, H. E.

2007-02-01

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic License Plate Recognition (ALPR) systems. Several car MMR systems have been proposed in literature. However these approaches are based on feature detection algorithms that can perform sub-optimally under adverse lighting and/or occlusion conditions. In this paper we propose a real time, appearance based, car MMR approach using Two Dimensional Linear Discriminant Analysis that is capable of addressing this limitation. We provide experimental results to analyse the proposed algorithm's robustness under varying illumination and occlusions conditions. We have shown that the best performance with the proposed 2D-LDA based car MMR approach is obtained when the eigenvectors of lower significance are ignored. For the given database of 200 car images of 25 different make-model classifications, a best accuracy of 91% was obtained with the 2D-LDA approach. We use a direct Principle Component Analysis (PCA) based approach as a benchmark to compare and contrast the performance of the proposed 2D-LDA approach to car MMR. We conclude that in general the 2D-LDA based algorithm supersedes the performance of the PCA based approach.

Sex determination from the calcaneus in a 20th century Greek population using discriminant function analysis.

PubMed

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. Copyright © 2015 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved.