Documenting Differences between Early Stone Age Flake Production Systems: An Experimental Model and Archaeological Verification

PubMed Central

Presnyakova, Darya; Archer, Will; Braun, David R.; Flear, Wesley

2015-01-01

This study investigates morphological differences between flakes produced via “core and flake” technologies and those resulting from bifacial shaping strategies. We investigate systematic variation between two technological groups of flakes using experimentally produced assemblages, and then apply the experimental model to the Cutting 10 Mid -Pleistocene archaeological collection from Elandsfontein, South Africa. We argue that a specific set of independent variables—and their interactions—including external platform angle, platform depth, measures of thickness variance and flake curvature should distinguish between these two technological groups. The role of these variables in technological group separation was further investigated using the Generalized Linear Model as well as Linear Discriminant Analysis. The Discriminant model was used to classify archaeological flakes from the Cutting 10 locality in terms of their probability of association, within either experimentally developed technological group. The results indicate that the selected independent variables play a central role in separating core and flake from bifacial technologies. Thickness evenness and curvature had the greatest effect sizes in both the Generalized Linear and Discriminant models. Interestingly the interaction between thickness evenness and platform depth was significant and played an important role in influencing technological group membership. The identified interaction emphasizes the complexity in attempting to distinguish flake production strategies based on flake morphological attributes. The results of the discriminant function analysis demonstrate that the majority of flakes at the Cutting 10 locality were not associated with the production of the numerous Large Cutting Tools found at the site, which corresponds with previous suggestions regarding technological behaviors reflected in this assemblage. PMID:26111251

Stable orthogonal local discriminant embedding for linear dimensionality reduction.

PubMed

Gao, Quanxue; Ma, Jingjie; Zhang, Hailin; Gao, Xinbo; Liu, Yamin

2013-07-01

Manifold learning is widely used in machine learning and pattern recognition. However, manifold learning only considers the similarity of samples belonging to the same class and ignores the within-class variation of data, which will impair the generalization and stableness of the algorithms. For this purpose, we construct an adjacency graph to model the intraclass variation that characterizes the most important properties, such as diversity of patterns, and then incorporate the diversity into the discriminant objective function for linear dimensionality reduction. Finally, we introduce the orthogonal constraint for the basis vectors and propose an orthogonal algorithm called stable orthogonal local discriminate embedding. Experimental results on several standard image databases demonstrate the effectiveness of the proposed dimensionality reduction approach.

Perceptual asymmetry in texture perception.

PubMed

Williams, D; Julesz, B

1992-07-15

A fundamental property of human visual perception is our ability to distinguish between textures. A concerted effort has been made to account for texture segregation in terms of linear spatial filter models and their nonlinear extensions. However, for certain texture pairs the ease of discrimination changes when the role of figure and ground are reversed. This asymmetry poses a problem for both linear and nonlinear models. We have isolated a property of texture perception that can account for this asymmetry in discrimination: subjective closure. This property, which is also responsible for visual illusions, appears to be explainable by early visual processes alone. Our results force a reexamination of the process of human texture segregation and of some recent models that were introduced to explain it.

Rainfall induced landslide susceptibility mapping using weight-of-evidence, linear and quadratic discriminant and logistic model tree method

NASA Astrophysics Data System (ADS)

Hong, H.; Zhu, A. X.

2017-12-01

Climate change is a common phenomenon and it is very serious all over the world. The intensification of rainfall extremes with climate change is of key importance to society and then it may induce a large impact through landslides. This paper presents GIS-based new ensemble data mining techniques that weight-of-evidence, logistic model tree, linear and quadratic discriminant for landslide spatial modelling. This research was applied in Anfu County, which is a landslide-prone area in Jiangxi Province, China. According to a literature review and research the study area, we select the landslide influencing factor and their maps were digitized in a GIS environment. These landslide influencing factors are the altitude, plan curvature, profile curvature, slope degree, slope aspect, topographic wetness index (TWI), Stream Power Index (SPI), Topographic Wetness Index (SPI), distance to faults, distance to rivers, distance to roads, soil, lithology, normalized difference vegetation index and land use. According to historical information of individual landslide events, interpretation of the aerial photographs, and field surveys supported by the government of Jiangxi Meteorological Bureau of China, 367 landslides were identified in the study area. The landslide locations were divided into two subsets, namely, training and validating (70/30), based on a random selection scheme. In this research, Pearson's correlation was used for the evaluation of the relationship between the landslides and influencing factors. In the next step, three data mining techniques combined with the weight-of-evidence, logistic model tree, linear and quadratic discriminant, were used for the landslide spatial modelling and its zonation. Finally, the landslide susceptibility maps produced by the mentioned models were evaluated by the ROC curve. The results showed that the area under the curve (AUC) of all of the models was > 0.80. At the same time, the highest AUC value was for the linear and quadratic discriminant model (0.864), followed by logistic model tree (0.832), and weight-of-evidence (0.819). In general, the landslide maps can be applied for land use planning and management in the Anfu area.

Prediction of aquatic toxicity mode of action using linear discriminant and random forest models.

PubMed

Martin, Todd M; Grulke, Christopher M; Young, Douglas M; Russom, Christine L; Wang, Nina Y; Jackson, Crystal R; Barron, Mace G

2013-09-23

The ability to determine the mode of action (MOA) for a diverse group of chemicals is a critical part of ecological risk assessment and chemical regulation. However, existing MOA assignment approaches in ecotoxicology have been limited to a relatively few MOAs, have high uncertainty, or rely on professional judgment. In this study, machine based learning algorithms (linear discriminant analysis and random forest) were used to develop models for assigning aquatic toxicity MOA. These methods were selected since they have been shown to be able to correlate diverse data sets and provide an indication of the most important descriptors. A data set of MOA assignments for 924 chemicals was developed using a combination of high confidence assignments, international consensus classifications, ASTER (ASessment Tools for the Evaluation of Risk) predictions, and weight of evidence professional judgment based an assessment of structure and literature information. The overall data set was randomly divided into a training set (75%) and a validation set (25%) and then used to develop linear discriminant analysis (LDA) and random forest (RF) MOA assignment models. The LDA and RF models had high internal concordance and specificity and were able to produce overall prediction accuracies ranging from 84.5 to 87.7% for the validation set. These results demonstrate that computational chemistry approaches can be used to determine the acute toxicity MOAs across a large range of structures and mechanisms.

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.

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

PubMed

Bevilacqua, Marta; Marini, Federico

2014-08-01

The possibility of devising a simple, flexible and accurate non-linear classification method, by extending the locally weighted partial least squares (LW-PLS) approach to the cases where the algorithm is used in a discriminant way (partial least squares discriminant analysis, PLS-DA), is presented. In particular, to assess which category an unknown sample belongs to, the proposed algorithm operates by identifying which training objects are most similar to the one to be predicted and building a PLS-DA model using these calibration samples only. Moreover, the influence of the selected training samples on the local model can be further modulated by adopting a not uniform distance-based weighting scheme which allows the farthest calibration objects to have less impact than the closest ones. The performances of the proposed locally weighted-partial least squares-discriminant analysis (LW-PLS-DA) algorithm have been tested on three simulated data sets characterized by a varying degree of non-linearity: in all cases, a classification accuracy higher than 99% on external validation samples was achieved. Moreover, when also applied to a real data set (classification of rice varieties), characterized by a high extent of non-linearity, the proposed method provided an average correct classification rate of about 93% on the test set. By the preliminary results, showed in this paper, the performances of the proposed LW-PLS-DA approach have proved to be comparable and in some cases better than those obtained by other non-linear methods (k nearest neighbors, kernel-PLS-DA and, in the case of rice, counterpropagation neural networks). Copyright © 2014 Elsevier B.V. All rights reserved.

The Association Between Racial and Gender Discrimination and Body Mass Index Among Residents Living in Lower-income Housing

PubMed Central

Shelton, Rachel C.; Puleo, Elaine; Bennett, Gary G.; McNeill, Lorna H.; Sorensen, Glorian; Emmons, Karen M.

2010-01-01

Background Research on the association between self-reported racial or gender discrimination and body mass index (BMI) has been limited and inconclusive to date, particularly among lower-income populations. Objectives The aim of the current study was to examine the association between self-reported racial and gender discrimination and BMI among a sample of adult residents living in 12 urban lower-income housing sites in Boston, Masschusetts (USA). Methods Baseline survey data were collected among 1,307 (weighted N=1907) study participants. For analyses, linear regression models with a cluster design were conducted using SUDAAN and SAS statistical software. Results Our sample was predominately Black (weighted n=956) and Hispanic (weighted n=857), and female (weighted n=1420), with a mean age of 49.3 (SE: .40) and mean BMI of 30.2 kg m−2 (SE: .19). Nearly 47% of participants reported ever experiencing racial discrimination, and 24.8% reported ever experiencing gender discrimination. In bivariate and multivariable linear regression models, no main effect association was found between either racial or gender discrimination and BMI. Conclusions While our findings suggest that self-reported discrimination is not a key determinant of BMI among lower-income housing residents, these results should be considered in light of study limitations. Future researchers may want to investigate this association among other relevant samples, and other social contextual and cultural factors should be explored to understand how they contribute to disparities. PMID:19769005

The association between racial and gender discrimination and body mass index among residents living in lower-income housing.

PubMed

Shelton, Rachel C; Puleo, Elaine; Bennett, Gary G; McNeill, Lorna H; Sorensen, Glorian; Emmons, Karen M

2009-01-01

Research on the association between self-reported racial or gender discrimination and body mass index (BMI) has been limited and inconclusive to date, particularly among lower-income populations. The aim of the current study was to examine the association between self-reported racial and gender discrimination and BMI among a sample of adult residents living in 12 urban lower-income housing sites in Boston, Masschusetts (USA). Baseline survey data were collected among 1,307 (weighted N = 1907) study participants. For analyses, linear regression models with a cluster design were conducted using SUDAAN and SAS statistical software. Our sample was predominately Black (weighted n = 956) and Hispanic (weighted n = 857), and female (weighted n = 1420), with a mean age of 49.3 (SE: .40) and mean BMI of 30.2 kg m(-2) (SE: .19). Nearly 47% of participants reported ever experiencing racial discrimination, and 24.8% reported ever experiencing gender discrimination. In bivariate and multivariable linear regression models, no main effect association was found between either racial or gender discrimination and BMI. While our findings suggest that self-reported discrimination is not a key determinant of BMI among lower-income housing residents, these results should be considered in light of study limitations. Future researchers may want to investigate this association among other relevant samples, and other social contextual and cultural factors should be explored to understand how they contribute to disparities.

Pharmaceutical Raw Material Identification Using Miniature Near-Infrared (MicroNIR) Spectroscopy and Supervised Pattern Recognition Using Support Vector Machine

PubMed Central

Hsiung, Chang; Pederson, Christopher G.; Zou, Peng; Smith, Valton; von Gunten, Marc; O’Brien, Nada A.

2016-01-01

Near-infrared spectroscopy as a rapid and non-destructive analytical technique offers great advantages for pharmaceutical raw material identification (RMID) to fulfill the quality and safety requirements in pharmaceutical industry. In this study, we demonstrated the use of portable miniature near-infrared (MicroNIR) spectrometers for NIR-based pharmaceutical RMID and solved two challenges in this area, model transferability and large-scale classification, with the aid of support vector machine (SVM) modeling. We used a set of 19 pharmaceutical compounds including various active pharmaceutical ingredients (APIs) and excipients and six MicroNIR spectrometers to test model transferability. For the test of large-scale classification, we used another set of 253 pharmaceutical compounds comprised of both chemically and physically different APIs and excipients. We compared SVM with conventional chemometric modeling techniques, including soft independent modeling of class analogy, partial least squares discriminant analysis, linear discriminant analysis, and quadratic discriminant analysis. Support vector machine modeling using a linear kernel, especially when combined with a hierarchical scheme, exhibited excellent performance in both model transferability and large-scale classification. Hence, ultra-compact, portable and robust MicroNIR spectrometers coupled with SVM modeling can make on-site and in situ pharmaceutical RMID for large-volume applications highly achievable. PMID:27029624

ASTM clustering for improving coal analysis by near-infrared spectroscopy.

PubMed

Andrés, J M; Bona, M T

2006-11-15

Multivariate analysis techniques have been applied to near-infrared (NIR) spectra coals to investigate the relationship between nine coal properties (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal/kg), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) and the corresponding predictor variables. In this work, a whole set of coal samples was grouped into six more homogeneous clusters following the ASTM reference method for classification prior to the application of calibration methods to each coal set. The results obtained showed a considerable improvement of the error determination compared with the calibration for the whole sample set. For some groups, the established calibrations approached the quality required by the ASTM/ISO norms for laboratory analysis. To predict property values for a new coal sample it is necessary the assignation of that sample to its respective group. Thus, the discrimination and classification ability of coal samples by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) in the NIR range was also studied by applying Soft Independent Modelling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) techniques. Modelling of the groups by SIMCA led to overlapping models that cannot discriminate for unique classification. On the other hand, the application of Linear Discriminant Analysis improved the classification of the samples but not enough to be satisfactory for every group considered.

Kullback-Leibler information function and the sequential selection of experiments to discriminate among several linear models. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1972-01-01

A sequential adaptive experimental design procedure for a related problem is studied. It is assumed that a finite set of potential linear models relating certain controlled variables to an observed variable is postulated, and that exactly one of these models is correct. The problem is to sequentially design most informative experiments so that the correct model equation can be determined with as little experimentation as possible. Discussion includes: structure of the linear models; prerequisite distribution theory; entropy functions and the Kullback-Leibler information function; the sequential decision procedure; and computer simulation results. An example of application is given.

Multivariate calibration on NIR data: development of a model for the rapid evaluation of ethanol content in bakery products.

PubMed

Bello, Alessandra; Bianchi, Federica; Careri, Maria; Giannetto, Marco; Mori, Giovanni; Musci, Marilena

2007-11-05

A new NIR method based on multivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new "leave one out" method, so that the number of original variables resulted further reduced.

Object recognition with hierarchical discriminant saliency networks.

PubMed

Han, Sunhyoung; Vasconcelos, Nuno

2014-01-01

The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and computer vision literatures. This demonstrates benefits for all the functional enhancements of the HDSN, the class tuning inherent to discriminant saliency, and saliency layers based on templates of increasing target selectivity and invariance. Altogether, these experiments suggest that there are non-trivial benefits in integrating attention and recognition.

Discrimination and mental health problems among homeless minority young people.

PubMed

Milburn, Norweeta G; Batterham, Philip; Ayala, George; Rice, Eric; Solorio, Rosa; Desmond, Kate; Lord, Lynwood; Iribarren, Javier; Rotheram-Borus, Mary Jane

2010-01-01

We examined the associations among perceived discrimination, racial/ethnic identification, and emotional distress in newly homeless adolescents. We assessed a sample of newly homeless adolescents (n=254) in Los Angeles, California, with measures of perceived discrimination and racial/ethnic identification. We assessed emotional distress using the Brief Symptom Inventory and used multivariate linear regression modeling to gauge the impact of discrimination and racial identity on emotional distress. Controlling for race and immigration status, gender, and age, young people with a greater sense of ethnic identification experienced less emotional distress. Young people with a history of racial/ethnic discrimination experienced more emotional distress. Intervention programs that contextualize discrimination and enhance racial/ethnic identification and pride among homeless young people are needed.

A GPS Phase-Locked Loop Performance Metric Based on the Phase Discriminator Output

PubMed Central

Stevanovic, Stefan; Pervan, Boris

2018-01-01

We propose a novel GPS phase-lock loop (PLL) performance metric based on the standard deviation of tracking error (defined as the discriminator’s estimate of the true phase error), and explain its advantages over the popular phase jitter metric using theory, numerical simulation, and experimental results. We derive an augmented GPS phase-lock loop (PLL) linear model, which includes the effect of coherent averaging, to be used in conjunction with this proposed metric. The augmented linear model allows more accurate calculation of tracking error standard deviation in the presence of additive white Gaussian noise (AWGN) as compared to traditional linear models. The standard deviation of tracking error, with a threshold corresponding to half of the arctangent discriminator pull-in region, is shown to be a more reliable/robust measure of PLL performance under interference conditions than the phase jitter metric. In addition, the augmented linear model is shown to be valid up until this threshold, which facilitates efficient performance prediction, so that time-consuming direct simulations and costly experimental testing can be reserved for PLL designs that are much more likely to be successful. The effect of varying receiver reference oscillator quality on the tracking error metric is also considered. PMID:29351250

Evaluation of linear discriminant analysis for automated Raman histological mapping of esophageal high-grade dysplasia

NASA Astrophysics Data System (ADS)

Hutchings, Joanne; Kendall, Catherine; Shepherd, Neil; Barr, Hugh; Stone, Nicholas

2010-11-01

Rapid Raman mapping has the potential to be used for automated histopathology diagnosis, providing an adjunct technique to histology diagnosis. The aim of this work is to evaluate the feasibility of automated and objective pathology classification of Raman maps using linear discriminant analysis. Raman maps of esophageal tissue sections are acquired. Principal component (PC)-fed linear discriminant analysis (LDA) is carried out using subsets of the Raman map data (6483 spectra). An overall (validated) training classification model performance of 97.7% (sensitivity 95.0 to 100% and specificity 98.6 to 100%) is obtained. The remainder of the map spectra (131,672 spectra) are projected onto the classification model resulting in Raman images, demonstrating good correlation with contiguous hematoxylin and eosin (HE) sections. Initial results suggest that LDA has the potential to automate pathology diagnosis of esophageal Raman images, but since the classification of test spectra is forced into existing training groups, further work is required to optimize the training model. A small pixel size is advantageous for developing the training datasets using mapping data, despite lengthy mapping times, due to additional morphological information gained, and could facilitate differentiation of further tissue groups, such as the basal cells/lamina propria, in the future, but larger pixels sizes (and faster mapping) may be more feasible for clinical application.

Is Perceived Discrimination in Pregnancy Prospectively Linked to Postpartum Depression? Exploring the Role of Education.

PubMed

Stepanikova, Irena; Kukla, Lubomir

2017-08-01

Objectives The role of perceived discrimination in postpartum depression is largely unknown. We investigate whether perceived discrimination reported in pregnancy contributes to postpartum depression, and whether its impact varies by education level. Methods Prospective data are a part of European Longitudinal Study of Pregnancy and Childhood, the Czech Republic. Surveys were collected in mid-pregnancy and at 6 months after delivery. Depression was measured using Edinburgh Postnatal Depression Scale. Generalized linear models were estimated to test the effects of perceived discrimination on postpartum depression. Results Multivariate models revealed that among women with low education, discrimination in pregnancy was prospectively associated with 2.43 times higher odds of postpartum depression (p < .01), after adjusting for antenatal depression, history of earlier depression, and socio-demographic background. In contrast, perceived discrimination was not linked to postpartum depression among women with high education. Conclusions Perceived discrimination is a risk factor for postpartum depression among women with low education. Screening for discrimination and socio-economic disadvantage during pregnancy could benefit women who are at risk for mental health problems.

Predictive models reduce talent development costs in female gymnastics.

PubMed

Pion, Johan; Hohmann, Andreas; Liu, Tianbiao; Lenoir, Matthieu; Segers, Veerle

2017-04-01

This retrospective study focuses on the comparison of different predictive models based on the results of a talent identification test battery for female gymnasts. We studied to what extent these models have the potential to optimise selection procedures, and at the same time reduce talent development costs in female artistic gymnastics. The dropout rate of 243 female elite gymnasts was investigated, 5 years past talent selection, using linear (discriminant analysis) and non-linear predictive models (Kohonen feature maps and multilayer perceptron). The coaches classified 51.9% of the participants correct. Discriminant analysis improved the correct classification to 71.6% while the non-linear technique of Kohonen feature maps reached 73.7% correctness. Application of the multilayer perceptron even classified 79.8% of the gymnasts correctly. The combination of different predictive models for talent selection can avoid deselection of high-potential female gymnasts. The selection procedure based upon the different statistical analyses results in decrease of 33.3% of cost because the pool of selected athletes can be reduced to 92 instead of 138 gymnasts (as selected by the coaches). Reduction of the costs allows the limited resources to be fully invested in the high-potential athletes.

Does perceived racial discrimination predict changes in psychological distress and substance use over time? An examination among Black emerging adults.

PubMed

Hurd, Noelle M; Varner, Fatima A; Caldwell, Cleopatra H; Zimmerman, Marc A

2014-07-01

We assessed whether perceived discrimination predicted changes in psychological distress and substance use over time and whether psychological distress and substance use predicted change in perceived discrimination over time. We also assessed whether associations between these constructs varied by gender. Our sample included 607 Black emerging adults (53% female) followed for 4 years. Participants reported the frequency with which they had experienced racial hassles during the past year, symptoms of anxiety and depression during the past week, and cigarette and alcohol use during the past 30 days. We estimated a series of latent growth models to test our study hypotheses. We found that the intercept of perceived discrimination predicted the linear slopes of anxiety symptoms, depressive symptoms, and alcohol use. We did not find any associations between the intercept factors of our mental health or substance use variables and the perceived discrimination linear slope factor. We found limited differences across paths by gender. Our findings suggest a temporal ordering in the associations among perceived racial discrimination, psychological distress, and alcohol use over time among emerging adults. Further, our findings suggest that perceived racial discrimination may be similarly harmful among men and women. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Application of discriminant analysis-based model for prediction of risk of low back disorders due to workplace design in industrial jobs.

PubMed

Ganga, G M D; Esposto, K F; Braatz, D

2012-01-01

The occupational exposure limits of different risk factors for development of low back disorders (LBDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors for LBDs interact in causing injury, since the nature and mechanism of these disorders are relatively unknown phenomena. Industrial ergonomists' role becomes further complicated because the potential risk factors that may contribute towards the onset of LBDs interact in a complex manner, which makes it difficult to discriminate in detail among the jobs that place workers at high or low risk of LBDs. The purpose of this paper was to develop a comparative study between predictions based on the neural network-based model proposed by Zurada, Karwowski & Marras (1997) and a linear discriminant analysis model, for making predictions about industrial jobs according to their potential risk of low back disorders due to workplace design. The results obtained through applying the discriminant analysis-based model proved that it is as effective as the neural network-based model. Moreover, the discriminant analysis-based model proved to be more advantageous regarding cost and time savings for future data gathering.

Spectral-Spatial Shared Linear Regression for Hyperspectral Image Classification.

PubMed

Haoliang Yuan; Yuan Yan Tang

2017-04-01

Classification of the pixels in hyperspectral image (HSI) is an important task and has been popularly applied in many practical applications. Its major challenge is the high-dimensional small-sized problem. To deal with this problem, lots of subspace learning (SL) methods are developed to reduce the dimension of the pixels while preserving the important discriminant information. Motivated by ridge linear regression (RLR) framework for SL, we propose a spectral-spatial shared linear regression method (SSSLR) for extracting the feature representation. Comparing with RLR, our proposed SSSLR has the following two advantages. First, we utilize a convex set to explore the spatial structure for computing the linear projection matrix. Second, we utilize a shared structure learning model, which is formed by original data space and a hidden feature space, to learn a more discriminant linear projection matrix for classification. To optimize our proposed method, an efficient iterative algorithm is proposed. Experimental results on two popular HSI data sets, i.e., Indian Pines and Salinas demonstrate that our proposed methods outperform many SL methods.

Discrimination of lymphoma using laser-induced breakdown spectroscopy conducted on whole blood samples

PubMed Central

Chen, Xue; Li, Xiaohui; Yang, Sibo; Yu, Xin; Liu, Aichun

2018-01-01

Lymphoma is a significant cancer that affects the human lymphatic and hematopoietic systems. In this work, discrimination of lymphoma using laser-induced breakdown spectroscopy (LIBS) conducted on whole blood samples is presented. The whole blood samples collected from lymphoma patients and healthy controls are deposited onto standard quantitative filter papers and ablated with a 1064 nm Q-switched Nd:YAG laser. 16 atomic and ionic emission lines of calcium (Ca), iron (Fe), magnesium (Mg), potassium (K) and sodium (Na) are selected to discriminate the cancer disease. Chemometric methods, including principal component analysis (PCA), linear discriminant analysis (LDA) classification, and k nearest neighbor (kNN) classification are used to build the discrimination models. Both LDA and kNN models have achieved very good discrimination performances for lymphoma, with an accuracy of over 99.7%, a sensitivity of over 0.996, and a specificity of over 0.997. These results demonstrate that the whole-blood-based LIBS technique in combination with chemometric methods can serve as a fast, less invasive, and accurate method for detection and discrimination of human malignancies. PMID:29541503

Artificial nose, NIR and UV-visible spectroscopy for the characterisation of the PDO Chianti Classico olive oil.

PubMed

Forina, M; Oliveri, P; Bagnasco, L; Simonetti, R; Casolino, M C; Nizzi Grifi, F; Casale, M

2015-11-01

An authentication study of the Italian PDO (Protected Designation of Origin) olive oil Chianti Classico, based on artificial nose, near-infrared and UV-visible spectroscopy, with a set of samples representative of the whole Chianti Classico production area and a considerable number of samples from other Italian PDO regions was performed. The signals provided by the three analytical techniques were used both individually and jointly, after fusion of the respective variables, in order to build a model for the Chianti Classico PDO olive oil. Different signal pre-treatments were performed in order to investigate their importance and their effects in enhancing and extracting information from experimental data, correcting backgrounds or removing baseline variations. Stepwise-Linear Discriminant Analysis (STEP-LDA) was used as a feature selection technique and, afterward, Linear Discriminant Analysis (LDA) and the class-modelling technique Quadratic Discriminant Analysis-UNEQual dispersed classes (QDA-UNEQ) were applied to sub-sets of selected variables, in order to obtain efficient models capable of characterising the extra virgin olive oils produced in the Chianti Classico PDO area. Copyright © 2015 Elsevier B.V. All rights reserved.

Color model and method for video fire flame and smoke detection using Fisher linear discriminant

NASA Astrophysics Data System (ADS)

Wei, Yuan; Jie, Li; Jun, Fang; Yongming, Zhang

2013-02-01

Video fire detection is playing an increasingly important role in our life. But recent research is often based on a traditional RGB color model used to analyze the flame, which may be not the optimal color space for fire recognition. It is worse when we research smoke simply using gray images instead of color ones. We clarify the importance of color information for fire detection. We present a fire discriminant color (FDC) model for flame or smoke recognition based on color images. The FDC models aim to unify fire color image representation and fire recognition task into one framework. With the definition of between-class scatter matrices and within-class scatter matrices of Fisher linear discriminant, the proposed models seek to obtain one color-space-transform matrix and a discriminate projection basis vector by maximizing the ratio of these two scatter matrices. First, an iterative basic algorithm is designed to get one-component color space transformed from RGB. Then, a general algorithm is extended to generate three-component color space for further improvement. Moreover, we propose a method for video fire detection based on the models using the kNN classifier. To evaluate the recognition performance, we create a database including flame, smoke, and nonfire images for training and testing. The test experiments show that the proposed model achieves a flame verification rate receiver operating characteristic (ROC I) of 97.5% at a false alarm rate (FAR) of 1.06% and a smoke verification rate (ROC II) of 91.5% at a FAR of 1.2%, and lots of fire video experiments demonstrate that our method reaches a high accuracy for fire recognition.

Community-based comprehensive intervention for people with schizophrenia in Guangzhou, China: Effects on clinical symptoms, social functioning, internalized stigma and discrimination.

PubMed

Li, Jie; Huang, Yuan-Guang; Ran, Mao-Sheng; Fan, Yu; Chen, Wen; Evans-Lacko, Sara; Thornicroft, Graham

2018-04-01

Comprehensive interventions including components of stigma and discrimination reduction in schizophrenia in low- and middle-income countries (LMICs) are lacking. We developed a community-based comprehensive intervention to evaluate its effects on clinical symptoms, social functioning, internalized stigma and discrimination among patients with schizophrenia. A randomized controlled trial including an intervention group (n = 169) and a control group (n = 158) was performed. The intervention group received comprehensive intervention (strategies against stigma and discrimination, psycho-education, social skills training and cognitive behavioral therapy) and the control group received face to face interview. Both lasted for nine months. Participants were measured at baseline, 6 months and 9 months using the Internalized Stigma of Mental Illness scale (ISMI), Discrimination and Stigma Scale (DISC-12), Global Assessment of Functioning (GAF), Schizophrenia Quality of Life Scale (SQLS), Self-Esteem Scale (SES), Brief Psychiatric Rating Scale (BPRS) and PANSS negative scale (PANSS-N). Insight and medication compliance were evaluated by senior psychiatrists. Data were analyzed by descriptive statistics, t-test, chi-square test or Fisher's exact test. Linear Mixed Models were used to show intervention effectiveness on scales. General Linear Mixed Models with multinomial logistic link function were used to assess the effectiveness on medication compliance and insight. We found a significant reduction on anticipated discrimination, BPRS and PANSS-N total scores, and an elevation on overcoming stigma and GAF in the intervention group after 9 months. These suggested the intervention may be effective in reducing anticipated discrimination, increasing skills overcoming stigma as well as improving clinical symptoms and social functioning in Chinese patients with schizophrenia. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

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

NASA Astrophysics Data System (ADS)

Zhu, Ying; Tan, Tuck Lee

2016-04-01

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

Orthogonal sparse linear discriminant analysis

NASA Astrophysics Data System (ADS)

Liu, Zhonghua; Liu, Gang; Pu, Jiexin; Wang, Xiaohong; Wang, Haijun

2018-03-01

Linear discriminant analysis (LDA) is a linear feature extraction approach, and it has received much attention. On the basis of LDA, researchers have done a lot of research work on it, and many variant versions of LDA were proposed. However, the inherent problem of LDA cannot be solved very well by the variant methods. The major disadvantages of the classical LDA are as follows. First, it is sensitive to outliers and noises. Second, only the global discriminant structure is preserved, while the local discriminant information is ignored. In this paper, we present a new orthogonal sparse linear discriminant analysis (OSLDA) algorithm. The k nearest neighbour graph is first constructed to preserve the locality discriminant information of sample points. Then, L2,1-norm constraint on the projection matrix is used to act as loss function, which can make the proposed method robust to outliers in data points. Extensive experiments have been performed on several standard public image databases, and the experiment results demonstrate the performance of the proposed OSLDA algorithm.

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.

Pattern classification of fMRI data: applications for analysis of spatially distributed cortical networks.

PubMed

Yourganov, Grigori; Schmah, Tanya; Churchill, Nathan W; Berman, Marc G; Grady, Cheryl L; Strother, Stephen C

2014-08-01

The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naïve Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

Catheter tracking via online learning for dynamic motion compensation in transcatheter aortic valve implantation.

PubMed

Wang, Peng; Zheng, Yefeng; John, Matthias; Comaniciu, Dorin

2012-01-01

Dynamic overlay of 3D models onto 2D X-ray images has important applications in image guided interventions. In this paper, we present a novel catheter tracking for motion compensation in the Transcatheter Aortic Valve Implantation (TAVI). To address such challenges as catheter shape and appearance changes, occlusions, and distractions from cluttered backgrounds, we present an adaptive linear discriminant learning method to build a measurement model online to distinguish catheters from background. An analytic solution is developed to effectively and efficiently update the discriminant model and to minimize the classification errors between the tracking object and backgrounds. The online learned discriminant model is further combined with an offline learned detector and robust template matching in a Bayesian tracking framework. Quantitative evaluations demonstrate the advantages of this method over current state-of-the-art tracking methods in tracking catheters for clinical applications.

Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification.

PubMed

Spinnato, J; Roubaud, M-C; Burle, B; Torrésani, B

2015-06-01

The main goal of this work is to develop a model for multisensor signals, such as magnetoencephalography or electroencephalography (EEG) signals that account for inter-trial variability, suitable for corresponding binary classification problems. An important constraint is that the model be simple enough to handle small size and unbalanced datasets, as often encountered in BCI-type experiments. The method involves the linear mixed effects statistical model, wavelet transform, and spatial filtering, and aims at the characterization of localized discriminant features in multisensor signals. After discrete wavelet transform and spatial filtering, a projection onto the relevant wavelet and spatial channels subspaces is used for dimension reduction. The projected signals are then decomposed as the sum of a signal of interest (i.e., discriminant) and background noise, using a very simple Gaussian linear mixed model. Thanks to the simplicity of the model, the corresponding parameter estimation problem is simplified. Robust estimates of class-covariance matrices are obtained from small sample sizes and an effective Bayes plug-in classifier is derived. The approach is applied to the detection of error potentials in multichannel EEG data in a very unbalanced situation (detection of rare events). Classification results prove the relevance of the proposed approach in such a context. The combination of the linear mixed model, wavelet transform and spatial filtering for EEG classification is, to the best of our knowledge, an original approach, which is proven to be effective. This paper improves upon earlier results on similar problems, and the three main ingredients all play an important role.

Modeling of an 8-12 GHz receiver front-end based on an in-line MEMS frequency discriminator

NASA Astrophysics Data System (ADS)

Chu, Chenlei; Liao, Xiaoping

2018-06-01

This paper focuses on the modeling of an 8-12 GHz RF (radio frequency) receiver front-end based on an in-line MEMS (microelectromechanical systems) frequency discriminator. Actually, the frequency detection is realized by measuring the output dc thermal voltage generated by the MEMS thermoelectric power sensor. Based on this thermal voltage, it has a great potential to tune the resonant frequency of the VCO (voltage controlled oscillator) in the RF receiver front-end application. The equivalent circuit model of the in-line frequency discriminator is established and the measurement verification is also implemented. Measurement and simulation results show that the output dc thermal voltage has a nearly linear relation with frequency. A new construction of RF receiver front-end is then obtained by connecting the in-line frequency discriminator with the voltage controlling port of VCO. Lastly, a systemic simulation is processed by computer-aided software and the real-time simulation waveform at each key point is observed clearly.

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

PubMed

Zhu, Ying; Tan, Tuck Lee

2016-04-15

An effective and simple analytical method using Fourier transform infrared (FTIR) spectroscopy to distinguish wild-grown high-quality Ganoderma lucidum (G. lucidum) from cultivated one is of essential importance for its quality assurance and medicinal value estimation. Commonly used chemical and analytical methods using full spectrum are not so effective for the detection and interpretation due to the complex system of the herbal medicine. In this study, two penalized discriminant analysis models, penalized linear discriminant analysis (PLDA) and elastic net (Elnet),using FTIR spectroscopy have been explored for the purpose of discrimination and interpretation. The classification performances of the two penalized models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The Elnet model involving a combination of L1 and L2 norm penalties enabled an automatic selection of a small number of informative spectral absorption bands and gave an excellent classification accuracy of 99% for discrimination between spectra of wild-grown and cultivated G. lucidum. Its classification performance was superior to that of the PLDA model in a pure L1 setting and outperformed the PCDA and PLSDA models using full wavelength. The well-performed selection of informative spectral features leads to substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of G. lucidum regarding its anti-cancer effects. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

Robust linear discriminant models to solve financial crisis in banking sectors

NASA Astrophysics Data System (ADS)

Lim, Yai-Fung; Yahaya, Sharipah Soaad Syed; Idris, Faoziah; Ali, Hazlina; Omar, Zurni

2014-12-01

Linear discriminant analysis (LDA) is a widely-used technique in patterns classification via an equation which will minimize the probability of misclassifying cases into their respective categories. However, the performance of classical estimators in LDA highly depends on the assumptions of normality and homoscedasticity. Several robust estimators in LDA such as Minimum Covariance Determinant (MCD), S-estimators and Minimum Volume Ellipsoid (MVE) are addressed by many authors to alleviate the problem of non-robustness of the classical estimates. In this paper, we investigate on the financial crisis of the Malaysian banking institutions using robust LDA and classical LDA methods. Our objective is to distinguish the "distress" and "non-distress" banks in Malaysia by using the LDA models. Hit ratio is used to validate the accuracy predictive of LDA models. The performance of LDA is evaluated by estimating the misclassification rate via apparent error rate. The results and comparisons show that the robust estimators provide a better performance than the classical estimators for LDA.

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.

Discrimination, acculturation and other predictors of depression among pregnant Hispanic women.

PubMed

Walker, Janiece L; Ruiz, R Jeanne; Chinn, Juanita J; Marti, Nathan; Ricks, Tiffany N

2012-01-01

The purpose of our study was to examine the effects of socioeconomic status, acculturative stress, discrimination, and marginalization as predictors of depression in pregnant Hispanic women. A prospective observational design was used. Central and Gulf coast areas of Texas in obstetrical offices. A convenience sample of 515 pregnant, low income, low medical risk, and self-identified Hispanic women who were between 22-24 weeks gestation was used to collect data. The predictor variables were socioeconomic status, discrimination, acculturative stress, and marginalization. The outcome variable was depression. Education, frequency of discrimination, age, and Anglo marginality were significant predictors of depressive symptoms in a linear regression model, F (6, 458) = 8.36, P<.0001. Greater frequency of discrimination was the strongest positive predictor of increased depressive symptoms. It is important that health care providers further understand the impact that age and experiences of discrimination throughout the life course have on depressive symptoms during pregnancy.

Relationship between acculturation, discrimination, and suicidal ideation and attempts among US Hispanics in the National Epidemiologic Survey of Alcohol and Related Conditions.

PubMed

Perez-Rodriguez, M Mercedes; Baca-Garcia, Enrique; Oquendo, Maria A; Wang, Shuai; Wall, Melanie M; Liu, Shang-Min; Blanco, Carlos

2014-04-01

Acculturation is the process by which immigrants acquire the culture of the dominant society. Little is known about the relationship between acculturation and suicidal ideation and attempts among US Hispanics. Our aim was to examine the impact of 5 acculturation measures (age at migration, time in the United States, social network composition, language, race/ethnic orientation) on suicidal ideation and attempts in the largest available nationally representative sample of US Hispanics. Study participants were US Hispanics (N = 6,359) from Wave 2 of the 2004-2005 National Epidemiologic Survey of Alcohol and Related Conditions (N = 34,653). We used linear χ(2) tests and logistic regression models to analyze the association between acculturation and risk of suicidal ideation and attempts. Factors associated with a linear increase in lifetime risk for suicidal ideation and attempts were (1) younger age at migration (linear χ(2)(1) = 57.15; P < .0001), (2) longer time in the United States (linear χ(2)(1)= 36.09; P < .0001), (3) higher degree of English-language orientation (linear χ(2)(1) = 74.08; P <.0001), (4) lower Hispanic composition of social network (linear χ(2)(1) = 36.34; P < .0001), and (5) lower Hispanic racial/ethnic identification (linear χ(2)(1) = 47.77; P <.0001). Higher levels of perceived discrimination were associated with higher lifetime risk for suicidal ideation (β = 0.051; P < .001) and attempts (β = 0.020; P = .003). There was a linear association between multiple dimensions of acculturation and lifetime suicidal ideation and attempts. Discrimination was also associated with lifetime risk for suicidal ideation and attempts. Our results highlight protective aspects of the traditional Hispanic culture, such as high social support, coping strategies, and moral objections to suicide, which are modifiable factors and potential targets for public health interventions aimed at decreasing suicide risk. Culturally sensitive mental health resources need to be made more available to decrease discrimination and stigma. © Copyright 2014 Physicians Postgraduate Press, Inc.

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.

Analysis of Optimal Sequential State Discrimination for Linearly Independent Pure Quantum States.

PubMed

Namkung, Min; Kwon, Younghun

2018-04-25

Recently, J. A. Bergou et al. proposed sequential state discrimination as a new quantum state discrimination scheme. In the scheme, by the successful sequential discrimination of a qubit state, receivers Bob and Charlie can share the information of the qubit prepared by a sender Alice. A merit of the scheme is that a quantum channel is established between Bob and Charlie, but a classical communication is not allowed. In this report, we present a method for extending the original sequential state discrimination of two qubit states to a scheme of N linearly independent pure quantum states. Specifically, we obtain the conditions for the sequential state discrimination of N = 3 pure quantum states. We can analytically provide conditions when there is a special symmetry among N = 3 linearly independent pure quantum states. Additionally, we show that the scenario proposed in this study can be applied to quantum key distribution. Furthermore, we show that the sequential state discrimination of three qutrit states performs better than the strategy of probabilistic quantum cloning.

MR PROSTATE SEGMENTATION VIA DISTRIBUTED DISCRIMINATIVE DICTIONARY (DDD) LEARNING.

PubMed

Guo, Yanrong; Zhan, Yiqiang; Gao, Yaozong; Jiang, Jianguo; Shen, Dinggang

2013-01-01

Segmenting prostate from MR images is important yet challenging. Due to non-Gaussian distribution of prostate appearances in MR images, the popular active appearance model (AAM) has its limited performance. Although the newly developed sparse dictionary learning method[1, 2] can model the image appearance in a non-parametric fashion, the learned dictionaries still lack the discriminative power between prostate and non-prostate tissues, which is critical for accurate prostate segmentation. In this paper, we propose to integrate deformable model with a novel learning scheme, namely the Distributed Discriminative Dictionary ( DDD ) learning, which can capture image appearance in a non-parametric and discriminative fashion. In particular, three strategies are designed to boost the tissue discriminative power of DDD. First , minimum Redundancy Maximum Relevance (mRMR) feature selection is performed to constrain the dictionary learning in a discriminative feature space. Second , linear discriminant analysis (LDA) is employed to assemble residuals from different dictionaries for optimal separation between prostate and non-prostate tissues. Third , instead of learning the global dictionaries, we learn a set of local dictionaries for the local regions (each with small appearance variations) along prostate boundary, thus achieving better tissue differentiation locally. In the application stage, DDDs will provide the appearance cues to robustly drive the deformable model onto the prostate boundary. Experiments on 50 MR prostate images show that our method can yield a Dice Ratio of 88% compared to the manual segmentations, and have 7% improvement over the conventional AAM.

The association between income, education, and experiences of discrimination in older African American and European American patients.

PubMed

Halanych, Jewell H; Safford, Monika M; Shikany, James M; Cuffee, Yendelela; Person, Sharina D; Scarinci, Isabel C; Kiefe, Catarina I; Allison, Jeroan J

2011-01-01

Racial/ethnic discrimination has adverse effects on health outcomes, as does low income and education, but the relationship between discrimination, income, and education is not well characterized. In this study, we describe the associations of discrimination with income and education in elderly African Americans (AA) and European Americans (EA). Cross-sectional observational study involving computer-assisted telephone survey. Southeastern United States. AA and EA Medicare managed care enrollees. Discrimination was measured with the Experience of Discrimination (EOD) scale (range 0-35). We used zero-inflated negative binomial models to determine the association between self-reported income and education and 1) presence of any discrimination and 2) intensity of discrimination. Among 1,800 participants (45% AA, 56% female, and mean age 73 years), EA reported less discrimination than AA (4% vs. 47%; P < .001). AA men reported more discrimination and more intense discrimination than AA women (EOD scores 4.35 vs. 2.50; P < .001). Both income and education were directly and linearly associated with both presence of discrimination and intensity of discrimination in AA, so that people with higher incomes and education experienced more discrimination. In adjusted models, predicted EOD scores among AA decreased with increasing age categories (3.42, 3.21, 2.99, 2.53; P < .01) and increased with increasing income (2.36, 3.44, 4.17; P < .001) and education categories (2.31, 3.09, 5.12; P < .001). This study suggests future research should focus less on differences between racial/ethnic groups and more on factors within minority populations that may contribute to healthcare disparities.

Multispectral imaging burn wound tissue classification system: a comparison of test accuracies between several common machine learning algorithms

NASA Astrophysics Data System (ADS)

Squiers, John J.; Li, Weizhi; King, Darlene R.; Mo, Weirong; Zhang, Xu; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.

2016-03-01

The clinical judgment of expert burn surgeons is currently the standard on which diagnostic and therapeutic decisionmaking regarding burn injuries is based. Multispectral imaging (MSI) has the potential to increase the accuracy of burn depth assessment and the intraoperative identification of viable wound bed during surgical debridement of burn injuries. A highly accurate classification model must be developed using machine-learning techniques in order to translate MSI data into clinically-relevant information. An animal burn model was developed to build an MSI training database and to study the burn tissue classification ability of several models trained via common machine-learning algorithms. The algorithms tested, from least to most complex, were: K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), weighted linear discriminant analysis (W-LDA), quadratic discriminant analysis (QDA), ensemble linear discriminant analysis (EN-LDA), ensemble K-nearest neighbors (EN-KNN), and ensemble decision tree (EN-DT). After the ground-truth database of six tissue types (healthy skin, wound bed, blood, hyperemia, partial injury, full injury) was generated by histopathological analysis, we used 10-fold cross validation to compare the algorithms' performances based on their accuracies in classifying data against the ground truth, and each algorithm was tested 100 times. The mean test accuracy of the algorithms were KNN 68.3%, DT 61.5%, LDA 70.5%, W-LDA 68.1%, QDA 68.9%, EN-LDA 56.8%, EN-KNN 49.7%, and EN-DT 36.5%. LDA had the highest test accuracy, reflecting the bias-variance tradeoff over the range of complexities inherent to the algorithms tested. Several algorithms were able to match the current standard in burn tissue classification, the clinical judgment of expert burn surgeons. These results will guide further development of an MSI burn tissue classification system. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.

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

PubMed

Wen, Zaidao; Hou, Biao; Jiao, Licheng

2017-05-03

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

A face and palmprint recognition approach based on discriminant DCT feature extraction.

PubMed

Jing, Xiao-Yuan; Zhang, David

2004-12-01

In the field of image processing and recognition, discrete cosine transform (DCT) and linear discrimination are two widely used techniques. Based on them, we present a new face and palmprint recognition approach in this paper. It first uses a two-dimensional separability judgment to select the DCT frequency bands with favorable linear separability. Then from the selected bands, it extracts the linear discriminative features by an improved Fisherface method and performs the classification by the nearest neighbor classifier. We detailedly analyze theoretical advantages of our approach in feature extraction. The experiments on face databases and palmprint database demonstrate that compared to the state-of-the-art linear discrimination methods, our approach obtains better classification performance. It can significantly improve the recognition rates for face and palmprint data and effectively reduce the dimension of feature space.

SENSITIVITY ANALYSIS AND PRELIMINARY EVALUATION OF RELMAP (REGIONAL LAGRANGIAN MODEL OF AIR POLLUTION) INVOLVING FINE AND COURSE PARTICULATE MATTER

EPA Science Inventory

In response to the new, size-discriminate federal standards for Inhalable Particulate Matter, the Regional Lagrangian Model of Air Pollution (RELMAP) has been modified to include simple, linear parameterizations. As an initial step in the possible refinement, RELMAP has been subj...

Medical mistrust as a key mediator in the association between perceived discrimination and adherence to antiretroviral therapy among HIV-positive Latino men.

PubMed

Galvan, Frank H; Bogart, Laura M; Klein, David J; Wagner, Glenn J; Chen, Ying-Tung

2017-10-01

Discrimination has been found to have deleterious effects on physical health. The goal of the present study was to examine the association between perceived discrimination and adherence to antiretroviral therapy (ART) among HIV-positive Latino men and the extent to which medical mistrust serves as a mediator of that association. A series of linear and logistic regression models was used to test for mediation for three types of perceived discrimination (related to being Latino, being perceived as gay and being HIV-positive). Medical mistrust was found to be significantly associated with perceived discrimination based on Latino ethnicity and HIV serostatus. Medical mistrust was found to mediate the associations between two types of perceived discrimination (related to being Latino and being HIV-positive) and ART adherence. Given these findings, interventions should be developed that increase the skills of HIV-positive Latino men to address both perceived discrimination and medical mistrust.

The association of cumulative discrimination on quality of care, patient-centered care, and dissatisfaction with care in adults with type 2 diabetes.

PubMed

Cykert, David M; Williams, Joni S; Walker, Rebekah J; Davis, Kimberly S; Egede, Leonard E

2017-01-01

Discrimination is linked to negative health outcomes, but little research has investigated how the cumulative effect of discrimination impacts perceptions of care. This study investigated the influence of cumulative perceived discrimination on quality of care, patient-centeredness, and dissatisfaction with care in adults with type 2 diabetes. Six hundred two patients from two primary care clinics in Charleston, SC. Linear regression models assessed associations between perceived discrimination and quality of care, patient-centered care, and dissatisfaction with care. The models control for race, site, age, gender, marital status, duration of diabetes, education, hours worked weekly, income, and health status. The mean age was 61.5years, with 66.3% non-Hispanic blacks, and 41.9% earning less than $20,000 annually. In final adjusted analyses, lower patient-centered care was associated with a higher discrimination score (β=-0.28; p=0.006), reporting at least 1 category of discrimination (β=-1.47; p=0.002), and reporting at least 2 categories of discrimination (β=-1.34; p=0.004). Dissatisfaction with care was associated with at least 2 categories of discrimination (β=0.45; p=0.002). No significant associations were seen with quality of care indicators. Increased cumulative discrimination was associated with decreased feeling of patient-centeredness and increased dissatisfaction with care. However, these perceptions of discrimination were not significantly associated with quality indicators. Copyright © 2017 Elsevier Inc. All rights reserved.

How pigeons discriminate the relative frequency of events.

PubMed

Keen, R; Machado, A

1999-09-01

This study examined how pigeons discriminate the relative frequencies of events when the events occur serially. In a discrete-trials procedure, 6 pigeons were shown one light nf times and then another nl times. Next, they received food for choosing the light that had occurred the least number of times during the sample. At issue were (a) how the discrimination was related to two variables, the difference between the frequencies of the two lights, D = nf - nl, and the total number of lights in the sample, T = nf + nl; and (b) whether a simple mathematical model of the discrimination process could account for the data. In contrast with models that assume that pigeons count the stimulus lights, engage in mental arithmetic on numerons, or remember the number of stimuli, the present model assumed only that the influence of a sample stimulus on choice increases linearly when the stimulus is presented, but decays exponentially when the stimulus is absent. The results showed that, overall, the pigeons discriminated the relative frequencies well. Their accuracy always increased with the absolute value of the difference D and, for D > 0, it decreased with T. Performance also showed clear recency, primacy, and contextual effects. The model accounted well for the major trends in the data.

Linear Discriminant Analysis for the in Silico Discovery of Mechanism-Based Reversible Covalent Inhibitors of a Serine Protease: Application of Hydration Thermodynamics Analysis and Semi-empirical Molecular Orbital Calculation.

PubMed

Masuda, Yosuke; Yoshida, Tomoki; Yamaotsu, Noriyuki; Hirono, Shuichi

2018-01-01

We recently reported that the Gibbs free energy of hydrolytic water molecules (ΔG wat ) in acyl-trypsin intermediates calculated by hydration thermodynamics analysis could be a useful metric for estimating the catalytic rate constants (k cat ) of mechanism-based reversible covalent inhibitors. For thorough evaluation, the proposed method was tested with an increased number of covalent ligands that have no corresponding crystal structures. After modeling acyl-trypsin intermediate structures using flexible molecular superposition, ΔG wat values were calculated according to the proposed method. The orbital energies of antibonding π* molecular orbitals (MOs) of carbonyl C=O in covalently modified catalytic serine (E orb ) were also calculated by semi-empirical MO calculations. Then, linear discriminant analysis (LDA) was performed to build a model that can discriminate covalent inhibitor candidates from substrate-like ligands using ΔG wat and E orb . The model was built using a training set (10 compounds) and then validated by a test set (4 compounds). As a result, the training set and test set ligands were perfectly discriminated by the model. Hydrolysis was slower when (1) the hydrolytic water molecule has lower ΔG wat ; (2) the covalent ligand presents higher E orb (higher reaction barrier). Results also showed that the entropic term of hydrolytic water molecule (-TΔS wat ) could be used for estimating k cat and for covalent inhibitor optimization; when the rotational freedom of the hydrolytic water molecule is limited, the chance for favorable interaction with the electrophilic acyl group would also be limited. The method proposed in this study would be useful for screening and optimizing the mechanism-based reversible covalent inhibitors.

Angular velocity discrimination

NASA Technical Reports Server (NTRS)

Kaiser, Mary K.

1990-01-01

Three experiments designed to investigate the ability of naive observers to discriminate rotational velocities of two simultaneously viewed objects are described. Rotations are constrained to occur about the x and y axes, resulting in linear two-dimensional image trajectories. The results indicate that observers can discriminate angular velocities with a competence near that for linear velocities. However, perceived angular rate is influenced by structural aspects of the stimuli.

Protein linear indices of the 'macromolecular pseudograph alpha-carbon atom adjacency matrix' in bioinformatics. Part 1: prediction of protein stability effects of a complete set of alanine substitutions in Arc repressor.

PubMed

Marrero-Ponce, Yovani; Medina-Marrero, Ricardo; Castillo-Garit, Juan A; Romero-Zaldivar, Vicente; Torrens, Francisco; Castro, Eduardo A

2005-04-15

A novel approach to bio-macromolecular design from a linear algebra point of view is introduced. A protein's total (whole protein) and local (one or more amino acid) linear indices are a new set of bio-macromolecular descriptors of relevance to protein QSAR/QSPR studies. These amino-acid level biochemical descriptors are based on the calculation of linear maps on Rn[f k(xmi):Rn-->Rn] in canonical basis. These bio-macromolecular indices are calculated from the kth power of the macromolecular pseudograph alpha-carbon atom adjacency matrix. Total linear indices are linear functional on Rn. That is, the kth total linear indices are linear maps from Rn to the scalar R[f k(xm):Rn-->R]. Thus, the kth total linear indices are calculated by summing the amino-acid linear indices of all amino acids in the protein molecule. A study of the protein stability effects for a complete set of alanine substitutions in the Arc repressor illustrates this approach. A quantitative model that discriminates near wild-type stability alanine mutants from the reduced-stability ones in a training series was obtained. This model permitted the correct classification of 97.56% (40/41) and 91.67% (11/12) of proteins in the training and test set, respectively. It shows a high Matthews correlation coefficient (MCC=0.952) for the training set and an MCC=0.837 for the external prediction set. Additionally, canonical regression analysis corroborated the statistical quality of the classification model (Rcanc=0.824). This analysis was also used to compute biological stability canonical scores for each Arc alanine mutant. On the other hand, the linear piecewise regression model compared favorably with respect to the linear regression one on predicting the melting temperature (tm) of the Arc alanine mutants. The linear model explains almost 81% of the variance of the experimental tm (R=0.90 and s=4.29) and the LOO press statistics evidenced its predictive ability (q2=0.72 and scv=4.79). Moreover, the TOMOCOMD-CAMPS method produced a linear piecewise regression (R=0.97) between protein backbone descriptors and tm values for alanine mutants of the Arc repressor. A break-point value of 51.87 degrees C characterized two mutant clusters and coincided perfectly with the experimental scale. For this reason, we can use the linear discriminant analysis and piecewise models in combination to classify and predict the stability of the mutant Arc homodimers. These models also permitted the interpretation of the driving forces of such folding process, indicating that topologic/topographic protein backbone interactions control the stability profile of wild-type Arc and its alanine mutants.

Diagnosis of human malignancies using laser-induced breakdown spectroscopy in combination with chemometric methods

NASA Astrophysics Data System (ADS)

Chen, Xue; Li, Xiaohui; Yu, Xin; Chen, Deying; Liu, Aichun

2018-01-01

Diagnosis of malignancies is a challenging clinical issue. In this work, we present quick and robust diagnosis and discrimination of lymphoma and multiple myeloma (MM) using laser-induced breakdown spectroscopy (LIBS) conducted on human serum samples, in combination with chemometric methods. The serum samples collected from lymphoma and MM cancer patients and healthy controls were deposited on filter papers and ablated with a pulsed 1064 nm Nd:YAG laser. 24 atomic lines of Ca, Na, K, H, O, and N were selected for malignancy diagnosis. Principal component analysis (PCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k nearest neighbors (kNN) classification were applied to build the malignancy diagnosis and discrimination models. The performances of the models were evaluated using 10-fold cross validation. The discrimination accuracy, confusion matrix and receiver operating characteristic (ROC) curves were obtained. The values of area under the ROC curve (AUC), sensitivity and specificity at the cut-points were determined. The kNN model exhibits the best performances with overall discrimination accuracy of 96.0%. Distinct discrimination between malignancies and healthy controls has been achieved with AUC, sensitivity and specificity for healthy controls all approaching 1. For lymphoma, the best discrimination performance values are AUC = 0.990, sensitivity = 0.970 and specificity = 0.956. For MM, the corresponding values are AUC = 0.986, sensitivity = 0.892 and specificity = 0.994. The results show that the serum-LIBS technique can serve as a quick, less invasive and robust method for diagnosis and discrimination of human malignancies.

Chronic discrimination and bodily pain in a multiethnic cohort of midlife women in the Study of Women's Health Across the Nation.

PubMed

Dugan, Sheila A; Lewis, Tené T; Everson-Rose, Susan A; Jacobs, Elizabeth A; Harlow, Siobán D; Janssen, Imke

2017-09-01

A growing literature links discrimination to key markers of biobehavioral health. While racial or ethnic differences in pain are seen in experimental and clinical studies, the authors were interested in how chronic discrimination contributes to pain within multiple racial or ethnic groups over time. Participants were 3056 African American, Caucasian, Chinese, Hispanic, and Japanese women from the Study of Women's Health Across the Nation. The Everyday Discrimination Scale was assessed from baseline through 13 follow-up examinations. The bodily pain subscale of the MOS 36-Item Short-Form Health Survey (SF-36) was assessed annually. There were large racial or ethnic differences in reports of discrimination and pain. Discrimination attributions also varied by race or ethnicity. In linear mixed model analyses, initially adjusted for age, education, and pain medications, chronic everyday discrimination was associated with more bodily pain in all ethnic groups (beta = -5.84; P < 0.002 for Japanese; beta = -6.17; P < 0.001 for African American; beta = -8.74; P < 0.001 for Chinese; beta = -10.54; P < 0.001 for Caucasians; beta = -12.82; P < 0.001 for Hispanic). Associations remained significant in all ethnic groups after adjusting for additional covariates in subsequent models until adding depressive symptoms as covariate; in the final fully-adjusted models, discrimination remained a significant predictor of pain for African American (beta = -4.50; P < 0.001), Chinese (beta = -6.62; P < 0.001), and Caucasian (beta = -7.86; P < 0.001) women. In this longitudinal study, experiences of everyday discrimination were strongly linked to reports of bodily pain for the majority of women. Further research is needed to determine if addressing psychosocial stressors, such as discrimination, with patients can enhance clinical management of pain symptoms.

Non-linear models for the detection of impaired cerebral blood flow autoregulation.

PubMed

Chacón, Max; Jara, José Luis; Miranda, Rodrigo; Katsogridakis, Emmanuel; Panerai, Ronney B

2018-01-01

The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for learning and validation. Dynamic SVM implementations used either moving average or autoregressive structures. The efficiency of dynamic CA was estimated from the model's derived CBFV response to a step change in BP as an autoregulation index for both linear and non-linear models. Non-linear models with recurrences (autoregressive) showed the best results, with CA indexes of 5.9 ± 1.5 in normocapnia, and 2.5 ± 1.2 for hypercapnia with an area under the receiver-operator curve of 0.955. The high performance achieved by non-linear SVM models to detect deterioration of dynamic CA should encourage further assessment of its applicability to clinical conditions where CA might be impaired.

Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update.

PubMed

Gao, Changxin; Shi, Huizhang; Yu, Jin-Gang; Sang, Nong

2016-04-15

Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the "good" models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm.

Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update

PubMed Central

Gao, Changxin; Shi, Huizhang; Yu, Jin-Gang; Sang, Nong

2016-01-01

Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm. PMID:27092505

Sparse network-based models for patient classification using fMRI

PubMed Central

Rosa, Maria J.; Portugal, Liana; Hahn, Tim; Fallgatter, Andreas J.; Garrido, Marta I.; Shawe-Taylor, John; Mourao-Miranda, Janaina

2015-01-01

Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces. PMID:25463459

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.

Discriminant forest classification method and system

DOEpatents

Chen, Barry Y.; Hanley, William G.; Lemmond, Tracy D.; Hiller, Lawrence J.; Knapp, David A.; Mugge, Marshall J.

2012-11-06

A hybrid machine learning methodology and system for classification that combines classical random forest (RF) methodology with discriminant analysis (DA) techniques to provide enhanced classification capability. A DA technique which uses feature measurements of an object to predict its class membership, such as linear discriminant analysis (LDA) or Andersen-Bahadur linear discriminant technique (AB), is used to split the data at each node in each of its classification trees to train and grow the trees and the forest. When training is finished, a set of n DA-based decision trees of a discriminant forest is produced for use in predicting the classification of new samples of unknown class.

Perceived Interpersonal Discrimination and Older Women’s Mental Health: Accumulation Across Domains, Attributions, and Time

PubMed Central

Bécares, Laia; Zhang, Nan

2018-01-01

Abstract Experiencing discrimination is associated with poor mental health, but how cumulative experiences of perceived interpersonal discrimination across attributes, domains, and time are associated with mental disorders is still unknown. Using data from the Study of Women’s Health Across the Nation (1996–2008), we applied latent class analysis and generalized linear models to estimate the association between cumulative exposure to perceived interpersonal discrimination and older women’s mental health. We found 4 classes of perceived interpersonal discrimination, ranging from cumulative exposure to discrimination over attributes, domains, and time to none or minimal reports of discrimination. Women who experienced cumulative perceived interpersonal discrimination over time and across attributes and domains had the highest risk of depression (Center for Epidemiologic Studies Depression Scale score ≥16) compared with women in all other classes. This was true for all women regardless of race/ethnicity, although the type and severity of perceived discrimination differed across racial/ethnic groups. Cumulative exposure to perceived interpersonal discrimination across attributes, domains, and time has an incremental negative long-term association with mental health. Studies that examine exposure to perceived discrimination due to a single attribute in 1 domain or at 1 point in time underestimate the magnitude and complexity of discrimination and its association with health. PMID:29036550

Visual recovery in cortical blindness is limited by high internal noise

PubMed Central

Cavanaugh, Matthew R.; Zhang, Ruyuan; Melnick, Michael D.; Das, Anasuya; Roberts, Mariel; Tadin, Duje; Carrasco, Marisa; Huxlin, Krystel R.

2015-01-01

Damage to the primary visual cortex typically causes cortical blindness (CB) in the hemifield contralateral to the damaged hemisphere. Recent evidence indicates that visual training can partially reverse CB at trained locations. Whereas training induces near-complete recovery of coarse direction and orientation discriminations, deficits in fine motion processing remain. Here, we systematically disentangle components of the perceptual inefficiencies present in CB fields before and after coarse direction discrimination training. In seven human CB subjects, we measured threshold versus noise functions before and after coarse direction discrimination training in the blind field and at corresponding intact field locations. Threshold versus noise functions were analyzed within the framework of the linear amplifier model and the perceptual template model. Linear amplifier model analysis identified internal noise as a key factor differentiating motion processing across the tested areas, with visual training reducing internal noise in the blind field. Differences in internal noise also explained residual perceptual deficits at retrained locations. These findings were confirmed with perceptual template model analysis, which further revealed that the major residual deficits between retrained and intact field locations could be explained by differences in internal additive noise. There were no significant differences in multiplicative noise or the ability to process external noise. Together, these results highlight the critical role of altered internal noise processing in mediating training-induced visual recovery in CB fields, and may explain residual perceptual deficits relative to intact regions of the visual field. PMID:26389544

Discrimination of raw and processed Dipsacus asperoides by near infrared spectroscopy combined with least squares-support vector machine and random forests

NASA Astrophysics Data System (ADS)

Xin, Ni; Gu, Xiao-Feng; Wu, Hao; Hu, Yu-Zhu; Yang, Zhong-Lin

2012-04-01

Most herbal medicines could be processed to fulfill the different requirements of therapy. The purpose of this study was to discriminate between raw and processed Dipsacus asperoides, a common traditional Chinese medicine, based on their near infrared (NIR) spectra. Least squares-support vector machine (LS-SVM) and random forests (RF) were employed for full-spectrum classification. Three types of kernels, including linear kernel, polynomial kernel and radial basis function kernel (RBF), were checked for optimization of LS-SVM model. For comparison, a linear discriminant analysis (LDA) model was performed for classification, and the successive projections algorithm (SPA) was executed prior to building an LDA model to choose an appropriate subset of wavelengths. The three methods were applied to a dataset containing 40 raw herbs and 40 corresponding processed herbs. We ran 50 runs of 10-fold cross validation to evaluate the model's efficiency. The performance of the LS-SVM with RBF kernel (RBF LS-SVM) was better than the other two kernels. The RF, RBF LS-SVM and SPA-LDA successfully classified all test samples. The mean error rates for the 50 runs of 10-fold cross validation were 1.35% for RBF LS-SVM, 2.87% for RF, and 2.50% for SPA-LDA. The best classification results were obtained by using LS-SVM with RBF kernel, while RF was fast in the training and making predictions.

Linear discriminant analysis of dermoscopic parameters for the differentiation of early melanomas from Clark naevi.

PubMed

Oka, Hiroshi; Tanaka, Masaru; Kobayashi, Seiichiro; Argenziano, Giuseppe; Soyer, H Peter; Nishikawa, Takeji

2004-04-01

As a first step to develop a screening system for pigmented skin lesions, we performed digital discriminant analyses between early melanomas and Clark naevi. A total of 59 cases of melanoma, including 23 melanoma in situ and 36 thin invasive melanomas (Breslow thickness < or =0.75 mm), and 188 clinically equivocal, histopathologically diagnosed Clark naevi were used in our study. After calculating 62 mathematical variables related to the colour, texture, asymmetry and circularity based on the dermoscopic findings of the pigmented skin lesions, we performed multivariate stepwise discriminant analysis using these variables to differentiate melanomas from naevi. The sensitivities and specificities of our model were 94.4 and 98.4%, respectively, for discriminating between melanomas (Breslow thickness < or =0.75 mm) and Clark naevi, and 73.9 and 85.6%, respectively, for discriminating between melanoma in situ and Clark naevi. Our algorithm accurately discriminated invasive melanomas from Clark naevi, but not melanomas in situ from Clark naevi.

Discrimination, Acculturation and Other Predictors of Depression among Pregnant Hispanic Women

PubMed Central

Walker, Janiece L.; Ruiz, R. Jeanne; Chinn, Juanita J.; Marti, Nathan; Ricks, Tiffany N.

2012-01-01

Objective The purpose of our study was to examine the effects of socioeconomic status, acculturative stress, discrimination, and marginalization as predictors of depression in pregnant Hispanic women. Design A prospective observational design was used. Setting Central and Gulf coast areas of Texas in obstetrical offices. Participants A convenience sample of 515 pregnant, low income, low medical risk, and self-identified Hispanic women who were between 22–24 weeks gestation was used to collect data. Measures The predictor variables were socioeconomic status, discrimination, acculturative stress, and marginalization. The outcome variable was depression. Results Education, frequency of discrimination, age, and Anglo marginality were significant predictors of depressive symptoms in a linear regression model, F (6, 458) = 8.36, P<.0001. Greater frequency of discrimination was the strongest positive predictor of increased depressive symptoms. Conclusions It is important that health care providers further understand the impact that age and experiences of discrimination throughout the life course have on depressive symptoms during pregnancy. PMID:23140083

Exploring intentions to discriminate against patients living with HIV/AIDS among future healthcare providers in Malaysia

PubMed Central

Earnshaw, Valerie A.; Jin, Harry; Wickersham, Jeffrey; Kamarulzaman, Adeeba; John, Jacob; Altice, Frederick L.

2015-01-01

OBJECTIVES Stigma towards people living with HIV/AIDS (PLWHA) is strong in Malaysia. Although stigma has been understudied, it may be a barrier to treating the approximately 81 000 Malaysian PLWHA. The current study explores correlates of intentions to discriminate against PLWHA among medical and dental students, the future healthcare providers of Malaysia. METHODS An online, cross-sectional survey of 1296 medical and dental students was conducted in 2012 at seven Malaysian universities; 1165 (89.9%) completed the survey and were analysed. Sociodemographic characteristics, stigma-related constructs and intentions to discriminate against PLWHA were measured. Linear mixed models were conducted, controlling for clustering by university. RESULTS The final multivariate model demonstrated that students who intended to discriminate more against PLWHA were female, less advanced in their training, and studying dentistry. They further endorsed more negative attitudes towards PLWHA, internalised greater HIV-related shame, reported more HIV-related fear and disagreed more strongly that PLWHA deserve good care. The final model accounted for 38% of the variance in discrimination intent, with 10% accounted for by sociodemographic characteristics and 28% accounted for by stigma-related constructs. CONCLUSIONS It is critical to reduce stigma among medical and dental students to eliminate intentions to discriminate and achieve equitable care for Malaysian PLWHA. Stigma-reduction interventions should be multipronged, addressing attitudes, internalised shame, fear and perceptions of deservingness of care. PMID:24666546

Exploring intentions to discriminate against patients living with HIV/AIDS among future healthcare providers in Malaysia.

PubMed

Earnshaw, Valerie A; Jin, Harry; Wickersham, Jeffrey; Kamarulzaman, Adeeba; John, Jacob; Altice, Frederick L

2014-06-01

Stigma towards people living with HIV/AIDS (PLWHA) is strong in Malaysia. Although stigma has been understudied, it may be a barrier to treating the approximately 81 000 Malaysian PLWHA. The current study explores correlates of intentions to discriminate against PLWHA among medical and dental students, the future healthcare providers of Malaysia. An online, cross-sectional survey of 1296 medical and dental students was conducted in 2012 at seven Malaysian universities; 1165 (89.9%) completed the survey and were analysed. Socio-demographic characteristics, stigma-related constructs and intentions to discriminate against PLWHA were measured. Linear mixed models were conducted, controlling for clustering by university. The final multivariate model demonstrated that students who intended to discriminate more against PLWHA were female, less advanced in their training, and studying dentistry. They further endorsed more negative attitudes towards PLWHA, internalised greater HIV-related shame, reported more HIV-related fear and disagreed more strongly that PLWHA deserve good care. The final model accounted for 38% of the variance in discrimination intent, with 10% accounted for by socio-demographic characteristics and 28% accounted for by stigma-related constructs. It is critical to reduce stigma among medical and dental students to eliminate intentions to discriminate and achieve equitable care for Malaysian PLWHA. Stigma-reduction interventions should be multipronged, addressing attitudes, internalised shame, fear and perceptions of deservingness of care. © 2014 John Wiley & Sons Ltd.

Differential effect of race, education, gender, and language discrimination on glycemic control in adults with type 2 diabetes.

PubMed

Reynolds, D Brice; Walker, Rebekah J; Campbell, Jennifer A; Egede, Leonard E

2015-04-01

Discrimination has been linked to negative health outcomes, but little research has investigated different types of discrimination to determine if some have a greater impact on outcomes. We examined the differential effect of discrimination based on race, level of education, gender, and language on glycemic control in adults with type 2 diabetes. Six hundred two patients with type 2 diabetes from two adult primary care clinics in the southeastern United States completed validated questionnaires. Questions included perceived discrimination because of race/ethnicity, level of education, sex/gender, or language. A multiple linear regression model assessed the differential effect of each type of perceived discrimination on glycemic control while adjusting for relevant covariates, including race, site, gender, marital status, duration of diabetes, number of years in school, number of hours worked per week, income, and health status. The mean age was 61.5 years, and the mean duration of diabetes was 12.3 years. Of the sample, 61.6% were men, and 64.9% were non-Hispanic black. In adjusted models, education discrimination remained significantly associated with glycemic control (β=0.47; 95% confidence interval, 0.03, 0.92). Race, gender and language discrimination were not significantly associated with poor glycemic control in either unadjusted or adjusted analyses. Discrimination based on education was found to be significantly associated with poor glycemic control. The findings suggest that education discrimination may be an important social determinant to consider when providing care to patients with type 2 diabetes and should be assessed separate from other types of discrimination, such as that based on race.

Boronlectin/Polyelectrolyte Ensembles as Artificial Tongue: Design, Construction, and Application for Discriminative Sensing of Complex Glycoconjugates from Panax ginseng.

PubMed

Zhang, Xiao-Tai; Wang, Shu; Xing, Guo-Wen

2017-02-01

Ginsenoside is a large family of triterpenoid saponins from Panax ginseng, which possesses various important biological functions. Due to the very similar structures of these complex glycoconjugates, it is crucial to develop a powerful analytic method to identify ginsenosides qualitatively or quantitatively. We herein report an eight-channel fluorescent sensor array as artificial tongue to achieve the discriminative sensing of ginsenosides. The fluorescent cross-responsive array was constructed by four boronlectins bearing flexible boronic acid moieties (FBAs) with multiple reactive sites and two linear poly(phenylene-ethynylene) (PPEs). An "on-off-on" response pattern was afforded on the basis of superquenching of fluorescent indicator PPEs and an analyte-induced allosteric indicator displacement (AID) process. Most importantly, it was found that the canonical distribution of ginsenoside data points analyzed by linear discriminant analysis (LDA) was highly correlated with the inherent molecular structures of the analytes, and the absence of overlaps among the five point groups reflected the effectiveness of the sensor array in the discrimination process. Almost all of the unknown ginsenoside samples at different concentrations were correctly identified on the basis of the established mathematical model. Our current work provided a general and constructive method to improve the quality assessment and control of ginseng and its extracts, which are useful and helpful for further discriminating other complex glycoconjugate families.

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.

Global spectral irradiance variability and material discrimination at Boulder, Colorado.

PubMed

Pan, Zhihong; Healey, Glenn; Slater, David

2003-03-01

We analyze 7,258 global spectral irradiance functions over 0.4-2.2 microm that were acquired over a wide range of conditions at Boulder, Colorado, during the summer of 1997. We show that low-dimensional linear models can be used to capture the variability in these spectra over both the visible and the 0.4-2.2 microm spectral ranges. Using a linear model, we compare the Boulder data with the previous study of Judd et al. [J. Opt. Soc. Am. 54, 1031 (1964)] over the visible wavelengths. We also examine the agreement of the Boulder data with a spectral database generated by using the MODTRAN 4.0 radiative transfer code. We use a database of 223 minerals to consider the effect of the spectral variability in the global spectral irradiance functions on hyperspectral material identification. We show that the 223 minerals can be discriminated accurately over the variability in the Boulder data with subspace projection techniques.

Non-linear models for the detection of impaired cerebral blood flow autoregulation

PubMed Central

Miranda, Rodrigo; Katsogridakis, Emmanuel

2018-01-01

The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for learning and validation. Dynamic SVM implementations used either moving average or autoregressive structures. The efficiency of dynamic CA was estimated from the model’s derived CBFV response to a step change in BP as an autoregulation index for both linear and non-linear models. Non-linear models with recurrences (autoregressive) showed the best results, with CA indexes of 5.9 ± 1.5 in normocapnia, and 2.5 ± 1.2 for hypercapnia with an area under the receiver-operator curve of 0.955. The high performance achieved by non-linear SVM models to detect deterioration of dynamic CA should encourage further assessment of its applicability to clinical conditions where CA might be impaired. PMID:29381724

Changes in experiences with discrimination across pregnancy and postpartum: age differences and consequences for mental health.

PubMed

Rosenthal, Lisa; Earnshaw, Valerie A; Lewis, Tené T; Reid, Allecia E; Lewis, Jessica B; Stasko, Emily C; Tobin, Jonathan N; Ickovics, Jeannette R

2015-04-01

We aimed to contribute to growing research and theory suggesting the importance of examining patterns of change over time and critical life periods to fully understand the effects of discrimination on health, with a focus on the period of pregnancy and postpartum and mental health outcomes. We used hierarchical linear modeling to examine changes across pregnancy and postpartum in everyday discrimination and the resulting consequences for mental health among predominantly Black and Latina, socioeconomically disadvantaged young women who were receiving prenatal care in New York City. Patterns of change in experiences with discrimination varied according to age. Among the youngest participants, discrimination increased from the second to third trimesters and then decreased to lower than the baseline level by 1 year postpartum; among the oldest participants, discrimination decreased from the second trimester to 6 months postpartum and then returned to the baseline level by 1 year postpartum. Within-subjects changes in discrimination over time predicted changes in depressive and anxiety symptoms at subsequent points. Discrimination more strongly predicted anxiety symptoms among participants reporting food insecurity. Our results support a life course approach to understanding the impact of experiences with discrimination on health and when to intervene.

Comparison of Modeling Methods to Determine Liver-to-blood Inocula and Parasite Multiplication Rates During Controlled Human Malaria Infection

PubMed Central

Douglas, Alexander D.; Edwards, Nick J.; Duncan, Christopher J. A.; Thompson, Fiona M.; Sheehy, Susanne H.; O'Hara, Geraldine A.; Anagnostou, Nicholas; Walther, Michael; Webster, Daniel P.; Dunachie, Susanna J.; Porter, David W.; Andrews, Laura; Gilbert, Sarah C.; Draper, Simon J.; Hill, Adrian V. S.; Bejon, Philip

2013-01-01

Controlled human malaria infection is used to measure efficacy of candidate malaria vaccines before field studies are undertaken. Mathematical modeling using data from quantitative polymerase chain reaction (qPCR) parasitemia monitoring can discriminate between vaccine effects on the parasite's liver and blood stages. Uncertainty regarding the most appropriate modeling method hinders interpretation of such trials. We used qPCR data from 267 Plasmodium falciparum infections to compare linear, sine-wave, and normal-cumulative-density-function models. We find that the parameters estimated by these models are closely correlated, and their predictive accuracy for omitted data points was similar. We propose that future studies include the linear model. PMID:23570846

Improved discrimination between monocotyledonous and dicotyledonous plants for weed control based on the blue-green region of ultraviolet-induced fluorescence spectra.

PubMed

Panneton, Bernard; Guillaume, Serge; Roger, Jean-Michel; Samson, Guy

2010-01-01

Precision weeding by spot spraying in real time requires sensors to discriminate between weeds and crop without contact. Among the optical based solutions, the ultraviolet (UV) induced fluorescence of the plants appears as a promising alternative. In a first paper, the feasibility of discriminating between corn hybrids, monocotyledonous, and dicotyledonous weeds was demonstrated on the basis of the complete spectra. Some considerations about the different sources of fluorescence oriented the focus to the blue-green fluorescence (BGF) part, ignoring the chlorophyll fluorescence that is inherently more variable in time. This paper investigates the potential of performing weed/crop discrimination on the basis of several large spectral bands in the BGF area. A partial least squares discriminant analysis (PLS-DA) was performed on a set of 1908 spectra of corn and weed plants over 3 years and various growing conditions. The discrimination between monocotyledonous and dicotyledonous plants based on the blue-green fluorescence yielded robust models (classification error between 1.3 and 4.6% for between-year validation). On the basis of the analysis of the PLS-DA model, two large bands were chosen in the blue-green fluorescence zone (400-425 nm and 425-490 nm). A linear discriminant analysis based on the signal from these two bands also provided very robust inter-year results (classification error from 1.5% to 5.2%). The same selection process was applied to discriminate between monocotyledonous weeds and maize but yielded no robust models (up to 50% inter-year error). Further work will be required to solve this problem and provide a complete UV fluorescence based sensor for weed-maize discrimination.

Scintillation decay time and pulse shape discrimination in oxygenated and deoxygenated solutions of linear alkylbenzene for the SNO+ experiment

NASA Astrophysics Data System (ADS)

O'Keeffe, H. M.; O'Sullivan, E.; Chen, M. C.

2011-06-01

The SNO+ liquid scintillator experiment is under construction in the SNOLAB facility in Canada. The success of this experiment relies upon accurate characterization of the liquid scintillator, linear alkylbenzene (LAB). In this paper, scintillation decay times for alpha and electron excitations in LAB with 2 g/L PPO are presented for both oxygenated and deoxygenated solutions. While deoxygenation is expected to improve pulse shape discrimination in liquid scintillators, it is not commonly demonstrated in the literature. This paper shows that for linear alkylbenzene, deoxygenation improves discrimination between electron and alpha excitations in the scintillator.

Optical system for tablet variety discrimination using visible/near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Shao, Yongni; He, Yong; Hu, Xingyue

2007-12-01

An optical system based on visible/near-infrared spectroscopy (Vis/NIRS) for variety discrimination of ginkgo (Ginkgo biloba L.) tablets was developed. This system consisted of a light source, beam splitter system, sample chamber, optical detector (diffuse reflection detector), and data collection. The tablet varieties used in the research include Da na kang, Xin bang, Tian bao ning, Yi kang, Hua na xing, Dou le, Lv yuan, Hai wang, and Ji yao. All samples (n=270) were scanned in the Vis/NIR region between 325 and 1075 nm using a spectrograph. The chemometrics method of principal component artificial neural network (PC-ANN) was used to establish discrimination models of them. In PC-ANN models, the scores of the principal components were chosen as the input nodes for the input layer of ANN, and the best discrimination rate of 91.1% was reached. Principal component analysis was also executed to select several optimal wavelengths based on loading values. Wavelengths at 481, 458, 466, 570, 1000, 662, and 400 nm were then used as the input data of stepwise multiple linear regression, the regression equation of ginkgo tablets was obtained, and the discrimination rate was researched 84.4%. The results indicated that this optical system could be applied to discriminating ginkgo (Ginkgo biloba L.) tablets, and it supplied a new method for fast ginkgo tablet variety discrimination.

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.

Using Artificial Neural Networks in Educational Research: Some Comparisons with Linear Statistical Models.

ERIC Educational Resources Information Center

Everson, Howard T.; And Others

This paper explores the feasibility of neural computing methods such as artificial neural networks (ANNs) and abductory induction mechanisms (AIM) for use in educational measurement. ANNs and AIMS methods are contrasted with more traditional statistical techniques, such as multiple regression and discriminant function analyses, for making…

Prediction of aquatic toxicity mode of action using linear discriminant and random forest models

EPA Science Inventory

The ability to determine the mode of action (MOA) for a diverse group of chemicals is a critical part of ecological risk assessment and chemical regulation. However, existing MOA assignment approaches in ecotoxicology have been limited to a relatively few MOAs, have high uncertai...

Racial discrimination, post-traumatic stress and prescription drug problems among Aboriginal Canadians.

PubMed

Currie, Cheryl; Wild, T Cameron; Schopflocher, Donald; Laing, Lory

2015-06-24

1) To examine associations between racial discrimination and drug problems among urban-based Aboriginal adults; and 2) to determine whether these associations are best explained by symptoms of psychological stress, distress or post-traumatic stress disorder (PTSD). Data were collected through in-person surveys with a community-based sample of Aboriginal adults (N = 372) living in a mid-sized city in western Canada in 2010. Associations were examined using bootstrapped linear regression models adjusted for confounders, with continuous prescription and illicit drug problem scores as outcomes. Mediation was examined using the cross-products of coefficients method. More than 80% of Aboriginal adults had experienced racial discrimination in the past year, with the majority reporting high levels in that period. Past-year discrimination was a risk factor for PTSD symptoms and prescription drug problems in models adjusted for confounders and other forms of psychological trauma. In mediation models, PTSD symptoms explained the association between discrimination and prescription drug problems; psychological stress and distress did not. PTSD symptoms also explained this association when the covariance between mediators was controlled. The results also indicate that participation in Aboriginal cultural traditions was associated with increased discrimination. Most efforts to address Aboriginal health inequities in Canada have focused on the role Aboriginal people play in these disparities. The current findings combine with others to call for an expanded focus. Non-Aboriginal Canadians may also play a role in the health inequities observed. The findings of this study suggest efforts to reduce discrimination experienced by Aboriginal adults in cities may reduce PTSD symptomology and prescription drug problems in these populations.

The mean-square error optimal linear discriminant function and its application to incomplete data vectors

NASA Technical Reports Server (NTRS)

Walker, H. F.

1979-01-01

In many pattern recognition problems, data vectors are classified although one or more of the data vector elements are missing. This problem occurs in remote sensing when the ground is obscured by clouds. Optimal linear discrimination procedures for classifying imcomplete data vectors are discussed.

A Predictive Model to Identify Patients With Fecal Incontinence Based on High-Definition Anorectal Manometry.

PubMed

Zifan, Ali; Ledgerwood-Lee, Melissa; Mittal, Ravinder K

2016-12-01

Three-dimensional high-definition anorectal manometry (3D-HDAM) is used to assess anal sphincter function; it determines profiles of regional pressure distribution along the length and circumference of the anal canal. There is no consensus, however, on the best way to analyze data from 3D-HDAM to distinguish healthy individuals from persons with sphincter dysfunction. We developed a computer analysis system to analyze 3D-HDAM data and to aid in the diagnosis and assessment of patients with fecal incontinence (FI). In a prospective study, we performed 3D-HDAM analysis of 24 asymptomatic healthy subjects (control subjects; all women; mean age, 39 ± 10 years) and 24 patients with symptoms of FI (all women; mean age, 58 ± 13 years). Patients completed a standardized questionnaire (FI severity index) to score the severity of FI symptoms. We developed and evaluated a robust prediction model to distinguish patients with FI from control subjects using linear discriminant, quadratic discriminant, and logistic regression analyses. In addition to collecting pressure information from the HDAM data, we assessed regional features based on shape characteristics and the anal sphincter pressure symmetry index. The combination of pressure values, anal sphincter area, and reflective symmetry values was identified in patients with FI versus control subjects with an area under the curve value of 1.0. In logistic regression analyses using different predictors, the model identified patients with FI with an area under the curve value of 0.96 (interquartile range, 0.22). In discriminant analysis, results were classified with a minimum error of 0.02, calculated using 10-fold cross-validation; different combinations of predictors produced median classification errors of 0.16 in linear discriminant analysis (interquartile range, 0.25) and 0.08 in quadratic discriminant analysis (interquartile range, 0.25). We developed and validated a novel prediction model to analyze 3D-HDAM data. This system can accurately distinguish patients with FI from control subjects. Copyright © 2016 AGA Institute. Published by Elsevier Inc. All rights reserved.

Evaluation of a Decision Support System for Obstructive Sleep Apnea with Nonlinear Analysis of Respiratory Signals.

PubMed

Kaimakamis, Evangelos; Tsara, Venetia; Bratsas, Charalambos; Sichletidis, Lazaros; Karvounis, Charalambos; Maglaveras, Nikolaos

2016-01-01

Obstructive Sleep Apnea (OSA) is a common sleep disorder requiring the time/money consuming polysomnography for diagnosis. Alternative methods for initial evaluation are sought. Our aim was the prediction of Apnea-Hypopnea Index (AHI) in patients potentially suffering from OSA based on nonlinear analysis of respiratory biosignals during sleep, a method that is related to the pathophysiology of the disorder. Patients referred to a Sleep Unit (135) underwent full polysomnography. Three nonlinear indices (Largest Lyapunov Exponent, Detrended Fluctuation Analysis and Approximate Entropy) extracted from two biosignals (airflow from a nasal cannula, thoracic movement) and one linear derived from Oxygen saturation provided input to a data mining application with contemporary classification algorithms for the creation of predictive models for AHI. A linear regression model presented a correlation coefficient of 0.77 in predicting AHI. With a cutoff value of AHI = 8, the sensitivity and specificity were 93% and 71.4% in discrimination between patients and normal subjects. The decision tree for the discrimination between patients and normal had sensitivity and specificity of 91% and 60%, respectively. Certain obtained nonlinear values correlated significantly with commonly accepted physiological parameters of people suffering from OSA. We developed a predictive model for the presence/severity of OSA using a simple linear equation and additional decision trees with nonlinear features extracted from 3 respiratory recordings. The accuracy of the methodology is high and the findings provide insight to the underlying pathophysiology of the syndrome. Reliable predictions of OSA are possible using linear and nonlinear indices from only 3 respiratory signals during sleep. The proposed models could lead to a better study of the pathophysiology of OSA and facilitate initial evaluation/follow up of suspected patients OSA utilizing a practical low cost methodology. ClinicalTrials.gov NCT01161381.

Racial/ethnic and gender differences in the association between self-reported experiences of racial/ethnic discrimination and inflammation in the CARDIA cohort of 4 US communities.

PubMed

Cunningham, Timothy J; Seeman, Teresa E; Kawachi, Ichiro; Gortmaker, Steven L; Jacobs, David R; Kiefe, Catarina I; Berkman, Lisa F

2012-09-01

Inflammation is etiologically implicated in cardiometabolic diseases for which there are known racial/ethnic disparities. Prior studies suggest there may be an association between self-reported experiences of racial/ethnic discrimination and inflammation, particularly C-reactive protein (CRP). It is not known whether that association is influenced by race/ethnicity and gender. In separate hierarchical linear models with time-varying covariates, we examined that association among 901 Black women, 614 Black men, 958 White women, and 863 White men in the Coronary Artery Risk Development in Young Adults (CARDIA) study in four US communities. Self-reported experiences of racial/ethnic discrimination were ascertained in 1992-93 and 2000-01. Inflammation was measured as log-transformed CRP in those years and 2005-06. All analyses were adjusted for blood pressure, plasma total cholesterol, triglycerides, homeostatic model assessment for insulin resistance (HOMA-IR), age, education, and community. Our findings extend prior research by suggesting that, broadly speaking, self-reported experiences of racial/ethnic discrimination are associated with inflammation; however, this association is complex and varies for Black and White women and men. Black women reporting 1 or 2 experiences of discrimination had higher levels of CRP compared to Black women reporting no experiences of discrimination (β = 0.141, SE = 0.062, P < 0.05). This association was not statistically significant among Black women reporting 3 or more experiences of discrimination and not independent of modifiable risks (smoking and obesity) in the final model. White women reporting 3 or more experiences of discrimination had significantly higher levels of CRP compared to White women reporting no experiences of discrimination independent of modifiable risks in the final model (β = 0.300, SE = 0.113, P < 0.01). The association between self-reported experiences of racial/ethnic discrimination and CRP was not statistically significant among Black and White men reporting 1 or 2 experiences of discrimination. Further research in other populations is needed. Published by Elsevier Ltd.

Identification of important image features for pork and turkey ham classification using colour and wavelet texture features and genetic selection.

PubMed

Jackman, Patrick; Sun, Da-Wen; Allen, Paul; Valous, Nektarios A; Mendoza, Fernando; Ward, Paddy

2010-04-01

A method to discriminate between various grades of pork and turkey ham was developed using colour and wavelet texture features. Image analysis methods originally developed for predicting the palatability of beef were applied to rapidly identify the ham grade. With high quality digital images of 50-94 slices per ham it was possible to identify the greyscale that best expressed the differences between the various ham grades. The best 10 discriminating image features were then found with a genetic algorithm. Using the best 10 image features, simple linear discriminant analysis models produced 100% correct classifications for both pork and turkey on both calibration and validation sets. 2009 Elsevier Ltd. All rights reserved.

Contrast effects on speed perception for linear and radial motion.

PubMed

Champion, Rebecca A; Warren, Paul A

2017-11-01

Speed perception is vital for safe activity in the environment. However, considerable evidence suggests that perceived speed changes as a function of stimulus contrast, with some investigators suggesting that this might have meaningful real-world consequences (e.g. driving in fog). In the present study we investigate whether the neural effects of contrast on speed perception occur at the level of local or global motion processing. To do this we examine both speed discrimination thresholds and contrast-dependent speed perception for two global motion configurations that have matched local spatio-temporal structure. Specifically we compare linear and radial configurations, the latter of which arises very commonly due to self-movement. In experiment 1 the stimuli comprised circular grating patches. In experiment 2, to match stimuli even more closely, motion was presented in multiple local Gabor patches equidistant from central fixation. Each patch contained identical linear motion but the global configuration was either consistent with linear or radial motion. In both experiments 1 and 2, discrimination thresholds and contrast-induced speed biases were similar in linear and radial conditions. These results suggest that contrast-based speed effects occur only at the level of local motion processing, irrespective of global structure. This result is interpreted in the context of previous models of speed perception and evidence suggesting differences in perceived speed of locally matched linear and radial stimuli. Copyright © 2017 Elsevier Ltd. All rights reserved.

Socioeconomic status discrimination and C-reactive protein in African-American and White adults.

PubMed

Van Dyke, Miriam E; Vaccarino, Viola; Dunbar, Sandra B; Pemu, Priscilla; Gibbons, Gary H; Quyyumi, Arshed A; Lewis, Tené T

2017-08-01

We examined the association between socioeconomic status (SES) discrimination and C-reactive protein (CRP) in a biracial cohort of middle-aged adults using an intersectionality framework. Participants were 401 African-American and White adults from a population-based cohort in the Southeastern United States. SES discrimination was self-reported with a modified Experiences of Discrimination Scale, and CRP levels were assayed from blood samples. Linear regression analyses were used to examine the associations among SES discrimination, race, education, and CRP after controlling for age, gender, racial and gender discrimination, financial and general stress, body mass index, smoking, sleep quality, and depressive symptoms. Intersectional effects were tested using race×SES discrimination, education×SES discrimination and race×education×SES discrimination interactions. Adjusting for sociodemographics, racial discrimination, gender discrimination, and all relevant two-way interaction terms, we observed a significant race×education×SES discrimination interaction (p=0.019). In adjusted models stratified by race and education, SES discrimination was associated with elevated CRP among higher educated African-Americans (β=0.29, p=0.018), but not lower educated African-Americans (β=-0.13, p=0.32); or lower educated (β=-0.02, p=0.92) or higher educated (β=-0.01, p=0.90) Whites. Findings support the relevance of SES discrimination as an important discriminatory stressor for CRP specifically among higher educated African-Americans. Copyright © 2017 Elsevier Ltd. All rights reserved.

Perceived discrimination and depressive symptoms among US Latinos: the modifying role of educational attainment.

PubMed

Ward, Julia B; Feinstein, Lydia; Vines, Anissa I; Robinson, Whitney R; Haan, Mary N; Aiello, Allison E

2017-04-12

Despite growing evidence that discrimination may contribute to poor mental health, few studies have assessed this association among US Latinos. Furthermore, the interaction between discrimination and educational attainment in shaping Latino mental health is virtually unexplored. This study aims to examine the association between perceived discrimination and depressive symptoms and the modifying role of education among a population of Mexican-origin adults. We utilized population-based data from 629 Mexican-origin adults (mean age = 52.8 years) participating the Niños Lifestyle and Diabetes Study (2013-2014). Perceived discrimination was defined as responding 'sometimes' or 'often' to at least one item on the 9-item Everyday Discrimination Scale. High depressive symptoms were defined as scoring ≥10 on the CESD-10. We used log-binomial and linear-binomial models to estimate prevalence ratios (PR) and prevalence differences (PD), respectively, of high depressive symptoms for levels of perceived discrimination. Final models were adjusted for age, sex, education, cultural orientation, and nativity. General estimating equations were employed to account for within-family clustering. Prevalence of perceived discrimination and high depressive symptoms were 49.5% and 29.2%, respectively. Participants experiencing discrimination had higher depressive symptom prevalence than those never or rarely experiencing discrimination [PR = 1.94, 95% confidence interval (CI): 1.46-2.58; PD = 0.19, 95% CI: 0.12-0.27]. The strength of this association varied by education level. The association between discrimination and depressive symptoms was stronger among those with >12 years of education (PR = 2.69; PD = 0.24) compared to those with ≤12 years of education (PR = 1.36; PD = 0.09). US Latinos suffer a high burden of depressive symptoms, and discrimination may be an important driver of this burden. Our results suggest that effortful coping strategies, such as achieving high education despite high perceived discrimination, may magnify discrimination's adverse effect on Latino mental health.

Changes in Experiences With Discrimination Across Pregnancy and Postpartum: Age Differences and Consequences for Mental Health

PubMed Central

Earnshaw, Valerie A.; Lewis, Tené T.; Reid, Allecia E.; Lewis, Jessica B.; Stasko, Emily C.; Tobin, Jonathan N.; Ickovics, Jeannette R.

2015-01-01

Objectives. We aimed to contribute to growing research and theory suggesting the importance of examining patterns of change over time and critical life periods to fully understand the effects of discrimination on health, with a focus on the period of pregnancy and postpartum and mental health outcomes. Methods. We used hierarchical linear modeling to examine changes across pregnancy and postpartum in everyday discrimination and the resulting consequences for mental health among predominantly Black and Latina, socioeconomically disadvantaged young women who were receiving prenatal care in New York City. Results. Patterns of change in experiences with discrimination varied according to age. Among the youngest participants, discrimination increased from the second to third trimesters and then decreased to lower than the baseline level by 1 year postpartum; among the oldest participants, discrimination decreased from the second trimester to 6 months postpartum and then returned to the baseline level by 1 year postpartum. Within-subjects changes in discrimination over time predicted changes in depressive and anxiety symptoms at subsequent points. Discrimination more strongly predicted anxiety symptoms among participants reporting food insecurity. Conclusions. Our results support a life course approach to understanding the impact of experiences with discrimination on health and when to intervene. PMID:24922166

Discriminatory experiences associated with posttraumatic stress disorder symptoms among transgender adults

PubMed Central

Reisner, Sari L.; White Hughto, Jaclyn M.; Gamarel, Kristi E.; Keuroghlian, Alex S.; Mizock, Lauren; Pachankis, John

2016-01-01

Discrimination has been shown to disproportionately burden transgender people; however, there has been a lack of clinical attention to the mental health sequelae of discrimination, including posttraumatic stress disorder (PTSD) symptoms. Additionally, few studies contextualize discrimination alongside other traumatic stressors in predicting PTSD symptomatology. The current study sought to fill these gaps. A community-based sample of 412 transgender adults (mean age 33, SD=13; 63% female-to-male spectrum; 19% people of color; 88% sampled online) completed a cross-sectional self-report survey of everyday discrimination experiences and PTSD symptoms. Multivariable linear regression models examined the association between self-reported everyday discrimination experiences, number of attributed domains of discrimination, and PTSD symptoms, adjusting for prior trauma, sociodemographics, and psychosocial co-morbidity. The mean number of discrimination attributions endorsed was 4.8 (SD=2.4) and the five most frequently reported reasons for discrimination were: gender identity and/or expression (83%), masculine and feminine appearance (79%), sexual orientation (68%), sex (57%), and age (44%). Higher everyday discrimination scores (β=0.25; 95% CL=0.21–0.30) and greater number of attributed reasons for discrimination experiences (β=0.05; 95% CL=0.01–0.10) were independently associated with PTSD symptoms, even after adjusting for prior trauma experiences. Everyday discrimination experiences from multiple sources necessitate clinical consideration in treatment for PTSD symptoms in transgender people. PMID:26866637

Discriminatory experiences associated with posttraumatic stress disorder symptoms among transgender adults.

PubMed

Reisner, Sari L; White Hughto, Jaclyn M; Gamarel, Kristi E; Keuroghlian, Alex S; Mizock, Lauren; Pachankis, John E

2016-10-01

Discrimination has been shown to disproportionately burden transgender people; however, there has been a lack of clinical attention to the mental health sequelae of discrimination, including posttraumatic stress disorder (PTSD) symptoms. Additionally, few studies contextualize discrimination alongside other traumatic stressors in predicting PTSD symptomatology. The current study sought to fill these gaps. A community-based sample of 412 transgender adults (mean age 33, SD = 13; 63% female-to-male spectrum; 19% people of color; 88% sampled online) completed a cross-sectional self-report survey of everyday discrimination experiences and PTSD symptoms. Multivariable linear regression models examined the association between self-reported everyday discrimination experiences, number of attributed domains of discrimination, and PTSD symptoms, adjusting for prior trauma, sociodemographics, and psychosocial comorbidity. The mean number of discrimination attributions endorsed was 4.8 (SD = 2.4) and the 5 most frequently reported reasons for discrimination were: gender identity and/or expression (83%), masculine and feminine appearance (79%), sexual orientation (68%), sex (57%), and age (44%). Higher everyday discrimination scores (β = 0.25; 95% CL [0.21, 0.30]) and greater number of attributed reasons for discrimination experiences (β = 0.05; 95% CL [0.01, 0.10]) were independently associated with PTSD symptoms, even after adjusting for prior trauma experiences. Everyday discrimination experiences from multiple sources necessitate clinical consideration in treatment for PTSD symptoms in transgender people. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition.

PubMed

Wang, Zhengjue; Wang, Yinghua; Liu, Hongwei; Zhang, Hao

2017-06-21

In this paper, we propose a new discriminative non-linear dictionary learning approach, called correlation constrained structured kernel KSVD, for object recognition. The objective function for dictionary learning contains a reconstructive term and a discriminative term. In the reconstructive term, signals are implicitly non-linearly mapped into a space, where a structured kernel dictionary, each sub-dictionary of which lies in the span of the mapped signals from the corresponding class, is established. In the discriminative term, by analyzing the classification mechanism, the correlation constraint is proposed in kernel form, constraining the correlations between different discriminative codes, and restricting the coefficient vectors to be transformed into a feature space, where the features are highly correlated inner-class and nearly independent between-classes. The objective function is optimized by the proposed structured kernel KSVD. During the classification stage, the specific form of the discriminative feature is needless to be known, while the inner product of the discriminative feature with kernel matrix embedded is available, and is suitable for a linear SVM classifier. Experimental results demonstrate that the proposed approach outperforms many state-of-the-art dictionary learning approaches for face, scene and synthetic aperture radar (SAR) vehicle target recognition.

Novel nonlinear knowledge-based mean force potentials based on machine learning.

PubMed

Dong, Qiwen; Zhou, Shuigeng

2011-01-01

The prediction of 3D structures of proteins from amino acid sequences is one of the most challenging problems in molecular biology. An essential task for solving this problem with coarse-grained models is to deduce effective interaction potentials. The development and evaluation of new energy functions is critical to accurately modeling the properties of biological macromolecules. Knowledge-based mean force potentials are derived from statistical analysis of proteins of known structures. Current knowledge-based potentials are almost in the form of weighted linear sum of interaction pairs. In this study, a class of novel nonlinear knowledge-based mean force potentials is presented. The potential parameters are obtained by nonlinear classifiers, instead of relative frequencies of interaction pairs against a reference state or linear classifiers. The support vector machine is used to derive the potential parameters on data sets that contain both native structures and decoy structures. Five knowledge-based mean force Boltzmann-based or linear potentials are introduced and their corresponding nonlinear potentials are implemented. They are the DIH potential (single-body residue-level Boltzmann-based potential), the DFIRE-SCM potential (two-body residue-level Boltzmann-based potential), the FS potential (two-body atom-level Boltzmann-based potential), the HR potential (two-body residue-level linear potential), and the T32S3 potential (two-body atom-level linear potential). Experiments are performed on well-established decoy sets, including the LKF data set, the CASP7 data set, and the Decoys “R”Us data set. The evaluation metrics include the energy Z score and the ability of each potential to discriminate native structures from a set of decoy structures. Experimental results show that all nonlinear potentials significantly outperform the corresponding Boltzmann-based or linear potentials, and the proposed discriminative framework is effective in developing knowledge-based mean force potentials. The nonlinear potentials can be widely used for ab initio protein structure prediction, model quality assessment, protein docking, and other challenging problems in computational biology.

Disproportionation of rosin on an industrial Pd/C catalyst: reaction pathway and kinetic model discrimination.

PubMed

Souto, Juan Carlos; Yustos, Pedro; Ladero, Miguel; Garcia-Ochoa, Felix

2011-02-01

In this work, a phenomenological study of the isomerisation and disproportionation of rosin acids using an industrial 5% Pd on charcoal catalyst from 200 to 240°C is carried out. Medium composition is determined by elemental microanalysis, GC-MS and GC-FID. Dehydrogenated and hydrogenated acid species molar amounts in the final product show that dehydrogenation is the main reaction. Moreover, both hydrogen and non-hydrogen concentration considering kinetic models are fitted to experimental data using a multivariable non-linear technique. Statistical discrimination among the proposed kinetic models lead to the conclusion hydrogen considering models fit much better to experimental results. The final kinetic model involves first-order isomerisation reactions of neoabietic and palustric acids to abietic acid, first-order dehydrogenation and hydrogenation of this latter acid, and hydrogenation of pimaric acids. Hydrogenation reactions are partial first-order regarding the acid and hydrogen. Copyright © 2010 Elsevier Ltd. All rights reserved.

Robust linear discriminant analysis with distance based estimators

NASA Astrophysics Data System (ADS)

Lim, Yai-Fung; Yahaya, Sharipah Soaad Syed; Ali, Hazlina

2017-11-01

Linear discriminant analysis (LDA) is one of the supervised classification techniques concerning relationship between a categorical variable and a set of continuous variables. The main objective of LDA is to create a function to distinguish between populations and allocating future observations to previously defined populations. Under the assumptions of normality and homoscedasticity, the LDA yields optimal linear discriminant rule (LDR) between two or more groups. However, the optimality of LDA highly relies on the sample mean and pooled sample covariance matrix which are known to be sensitive to outliers. To alleviate these conflicts, a new robust LDA using distance based estimators known as minimum variance vector (MVV) has been proposed in this study. The MVV estimators were used to substitute the classical sample mean and classical sample covariance to form a robust linear discriminant rule (RLDR). Simulation and real data study were conducted to examine on the performance of the proposed RLDR measured in terms of misclassification error rates. The computational result showed that the proposed RLDR is better than the classical LDR and was comparable with the existing robust LDR.

Workplace discrimination and alcohol consumption: findings from the San Francisco Muni Health and Safety Study.

PubMed

Yen, I H; Ragland, D R; Greiner, B A; Fisher, J M

1999-01-01

There is evidence of an association between occupational stress and alcohol consumption. This study investigates the association between workplace racial discrimination and alcohol consumption in a sample of urban transit operators. During 1993-1995, after undergoing a medical exam, 1,542 transit operators completed an interview. Depending on the outcome, we used logistic or linear regression models to examine the cross-sectional relationship between discrimination experience and alcohol consumption. Operators who reported discrimination in at least one situation, out of a possible four, were more likely to have had negative life consequences as a result of drinking (adjusted OR = 1.97; 95% CI, 1.20-3.83) and were more likely to be classified as having an alcohol disorder (OR = 1.56 [0.96-2.54]), compared to those who reported no instances of workplace discrimination. Results adjusted simultaneously for age, sex, race/ethnicity, education, income, marital status, and seniority. There was no association between workplace discrimination and heavy drinking or drinks per month. Cross-sectional data from a sample of urban transit operators indicates an association between workplace racial discrimination and some measures of alcohol consumption.

Improved pulse shape discriminator for fast neutron-gamma ray detection system

NASA Technical Reports Server (NTRS)

Lockwood, J. A.; St. Onge, R.

1969-01-01

Discriminator in nuclear particle detection system distinguishes nuclear particle type and energy among many different nuclear particles. Discriminator incorporates passive, linear circuit elements so that it will operate over a wide dynamic range.

Using Discrete Loss Functions and Weighted Kappa for Classification: An Illustration Based on Bayesian Network Analysis

ERIC Educational Resources Information Center

Zwick, Rebecca; Lenaburg, Lubella

2009-01-01

In certain data analyses (e.g., multiple discriminant analysis and multinomial log-linear modeling), classification decisions are made based on the estimated posterior probabilities that individuals belong to each of several distinct categories. In the Bayesian network literature, this type of classification is often accomplished by assigning…

Analysis of longitudinal diffusion-weighted images in healthy and pathological aging: An ADNI study.

PubMed

Kruggel, Frithjof; Masaki, Fumitaro; Solodkin, Ana

2017-02-15

The widely used framework of voxel-based morphometry for analyzing neuroimages is extended here to model longitudinal imaging data by exchanging the linear model with a linear mixed-effects model. The new approach is employed for analyzing a large longitudinal sample of 756 diffusion-weighted images acquired in 177 subjects of the Alzheimer's Disease Neuroimaging initiative (ADNI). While sample- and group-level results from both approaches are equivalent, the mixed-effect model yields information at the single subject level. Interestingly, the neurobiological relevance of the relevant parameter at the individual level describes specific differences associated with aging. In addition, our approach highlights white matter areas that reliably discriminate between patients with Alzheimer's disease and healthy controls with a predictive power of 0.99 and include the hippocampal alveus, the para-hippocampal white matter, the white matter of the posterior cingulate, and optic tracts. In this context, notably the classifier includes a sub-population of patients with minimal cognitive impairment into the pathological domain. Our classifier offers promising features for an accessible biomarker that predicts the risk of conversion to Alzheimer's disease. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how to apply/ADNI Acknowledgement List.pdf. Significance statement This study assesses neuro-degenerative processes in the brain's white matter as revealed by diffusion-weighted imaging, in order to discriminate healthy from pathological aging in a large sample of elderly subjects. The analysis of time-series examinations in a linear mixed effects model allowed the discrimination of population-based aging processes from individual determinants. We demonstrate that a simple classifier based on white matter imaging data is able to predict the conversion to Alzheimer's disease with a high predictive power. Copyright © 2017 Elsevier B.V. All rights reserved.

Sparse Modeling of Human Actions from Motion Imagery

DTIC Science & Technology

2011-09-02

is here developed. Spatio-temporal features that char- acterize local changes in the image are rst extracted. This is followed by the learning of a...video comes from the optimal sparse linear com- bination of the learned basis vectors (action primitives) representing the actions. A low...computational cost deep-layer model learning the inter- class correlations of the data is added for increasing discriminative power. In spite of its simplicity

Feature selection and recognition from nonspecific volatile profiles for discrimination of apple juices according to variety and geographical origin.

PubMed

Guo, Jing; Yue, Tianli; Yuan, Yahong

2012-10-01

Apple juice is a complex mixture of volatile and nonvolatile components. To develop discrimination models on the basis of the volatile composition for an efficient classification of apple juices according to apple variety and geographical origin, chromatography volatile profiles of 50 apple juice samples belonging to 6 varieties and from 5 counties of Shaanxi (China) were obtained by headspace solid-phase microextraction coupled with gas chromatography. The volatile profiles were processed as continuous and nonspecific signals through multivariate analysis techniques. Different preprocessing methods were applied to raw chromatographic data. The blind chemometric analysis of the preprocessed chromatographic profiles was carried out. Stepwise linear discriminant analysis (SLDA) revealed satisfactory discriminations of apple juices according to variety and geographical origin, provided respectively 100% and 89.8% success rate in terms of prediction ability. Finally, the discriminant volatile compounds selected by SLDA were identified by gas chromatography-mass spectrometry. The proposed strategy was able to verify the variety and geographical origin of apple juices involving only a reduced number of discriminate retention times selected by the stepwise procedure. This result encourages the similar procedures to be considered in quality control of apple juices. This work presented a method for an efficient discrimination of apple juices according to apple variety and geographical origin using HS-SPME-GC-MS together with chemometric tools. Discrimination models developed could help to achieve greater control over the quality of the juice and to detect possible adulteration of the product. © 2012 Institute of Food Technologists®

Discrimination of nuclear explosions and earthquakes from teleseismic distances with a local network of short period seismic stations using artificial neural networks

NASA Astrophysics Data System (ADS)

Tiira, Timo

1996-10-01

Seismic discrimination capability of artificial neural networks (ANNs) was studied using earthquakes and nuclear explosions from teleseismic distances. The events were selected from two areas, which were analyzed separately. First, 23 nuclear explosions from Semipalatinsk and Lop Nor test sites were compared with 46 earthquakes from adjacent areas. Second, 39 explosions from Nevada test site were compared with 27 earthquakes from close-by areas. The basic discriminants were complexity, spectral ratio and third moment of frequency. The spectral discriminants were computed in five different ways to obtain all the information embedded in the signals, some of which were relatively weak. The discriminants were computed using data from six short period stations in Central and southern Finland. The spectral contents of the signals of both classes varied considerably between the stations. The 66 discriminants were formed into 65 optimum subsets of different sizes by using stepwise linear regression. A type of ANN called multilayer perceptron (MLP) was applied to each of the subsets. As a comparison the classification was repeated using linear discrimination analysis (LDA). Since the number of events was small the testing was made with the leave-one-out method. The ANN gave significantly better results than LDA. As a final tool for discrimination a combination of the ten neural nets with the best performance were used. All events from Central Asia were clearly discriminated and over 90% of the events from Nevada region were confidently discriminated. The better performance of ANNs was attributed to its ability to form complex decision regions between the groups and to its highly non-linear nature.

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery - part II: an illustrative example.

PubMed

Cevenini, Gabriele; Barbini, Emanuela; Scolletta, Sabino; Biagioli, Bonizella; Giomarelli, Pierpaolo; Barbini, Paolo

2007-11-22

Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluated in an illustrative example. Eight models were developed: Bayes linear and quadratic models, k-nearest neighbour model, logistic regression model, Higgins and direct scoring systems and two feed-forward artificial neural networks with one and two layers. Cardiovascular, respiratory, neurological, renal, infectious and hemorrhagic complications were defined as morbidity. Training and testing sets each of 545 cases were used. The optimal set of predictors was chosen among a collection of 78 preoperative, intraoperative and postoperative variables by a stepwise procedure. Discrimination and calibration were evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test, respectively. Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and k-nearest neighbour models were much more parsimonious. In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance. All models showed satisfactory generalization ability: again the Bayes quadratic model exhibited the best generalization, while artificial neural networks and scoring systems gave the worst results. Finally, poor calibration was obtained when using scoring systems, k-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results. Although all the predictive models showed acceptable discrimination performance in the example considered, the Bayes and logistic regression models seemed better than the others, because they also had good generalization and calibration. The Bayes quadratic model seemed to be a convincing alternative to the much more usual Bayes linear and logistic regression models. It showed its capacity to identify a minimum core of predictors generally recognized as essential to pragmatically evaluate the risk of developing morbidity after heart surgery.

Neighbourhood poverty, perceived discrimination, and central adiposity: Independent associations in a repeated measures analysis

PubMed Central

Kwarteng, Jamila L.; Schulz, Amy J.; Mentz, Graciela B.; Israel, Barbara A.; Shanks, Trina R.; White Perkins, Denise

2018-01-01

SUMMARY This study examines the independent effects of neighbourhood context (i.e. neighbourhood poverty) and exposure to perceived discrimination in shaping risk of obesity over time. Weighted 3-level hierarchical linear regression models for a continuous outcome were used to assess independent effects of neighbourhood poverty and perceived discrimination on obesity over time in a sample of 157 Non-Hispanic Black, Non-Hispanic White, and Hispanic adults 2002/2003 and 2007/2008. Independent associations were found between neighbourhood poverty and perceived discrimination with central adiposity over time. Residents of neighbourhoods with high concentrations of poverty were more likely to show increases in central adiposity compared to those in neighbourhoods with lower concentrations of poverty. In models adjusted for BMI, neighbourhood poverty at baseline was associated with greater change in central adiposity among participants who lived in neighbourhoods in the second (B=3.79, P=0.025) and third (B=3.73, P=0.024) quartiles, compared with those in the lowest poverty neighbourhoods. Results from models that included both neighbourhood poverty and perceived discrimination showed that both neighbourhood poverty and discrimination were associated with increased risk of increased central adiposity over time. Residents of neighbourhoods in the second (B=9.58, P<0.001), third (B=8.25, P=0.004), and fourth (B=7.66, P=0.030) quartiles of poverty remained more likely to show greater increases in central adiposity over time, compared with those in the lowest poverty quartile, mean discrimination at baseline independently and positively associated with increases in central adiposity over time (B= 2.36, P=0.020). These results suggest that neighbourhood poverty and perceived discrimination are independently associated with heightened risk of increases in central adiposity over time. Efforts to address persistent disparities in the central adiposity in the USA should include strategies to reduce high concentrations of neighbourhood poverty as well as discrimination. PMID:27238086

Psychometric functions for pure-tone frequency discrimination.

PubMed

Dai, Huanping; Micheyl, Christophe

2011-07-01

The form of the psychometric function (PF) for auditory frequency discrimination is of theoretical interest and practical importance. In this study, PFs for pure-tone frequency discrimination were measured for several standard frequencies (200-8000 Hz) and levels [35-85 dB sound pressure level (SPL)] in normal-hearing listeners. The proportion-correct data were fitted using a cumulative-Gaussian function of the sensitivity index, d', computed as a power transformation of the frequency difference, Δf. The exponent of the power function corresponded to the slope of the PF on log(d')-log(Δf) coordinates. The influence of attentional lapses on PF-slope estimates was investigated. When attentional lapses were not taken into account, the estimated PF slopes on log(d')-log(Δf) coordinates were found to be significantly lower than 1, suggesting a nonlinear relationship between d' and Δf. However, when lapse rate was included as a free parameter in the fits, PF slopes were found not to differ significantly from 1, consistent with a linear relationship between d' and Δf. This was the case across the wide ranges of frequencies and levels tested in this study. Therefore, spectral and temporal models of frequency discrimination must account for a linear relationship between d' and Δf across a wide range of frequencies and levels. © 2011 Acoustical Society of America

Kernel PLS-SVC for Linear and Nonlinear Discrimination

NASA Technical Reports Server (NTRS)

Rosipal, Roman; Trejo, Leonard J.; Matthews, Bryan

2003-01-01

A new methodology for discrimination is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by support vector machines for classification. Close connection of orthonormalized PLS and Fisher's approach to linear discrimination or equivalently with canonical correlation analysis is described. This gives preference to use orthonormalized PLS over principal component analysis. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of the classification finger movement periods versus non-movement periods based on electroencephalogram.

TACD: a transportable ant colony discrimination model for corporate bankruptcy prediction

NASA Astrophysics Data System (ADS)

Lalbakhsh, Pooia; Chen, Yi-Ping Phoebe

2017-05-01

This paper presents a transportable ant colony discrimination strategy (TACD) to predict corporate bankruptcy, a topic of vital importance that is attracting increasing interest in the field of economics. The proposed algorithm uses financial ratios to build a binary prediction model for companies with the two statuses of bankrupt and non-bankrupt. The algorithm takes advantage of an improved version of continuous ant colony optimisation (CACO) at the core, which is used to create an accurate, simple and understandable linear model for discrimination. This also enables the algorithm to work with continuous values, leading to more efficient learning and adaption by avoiding data discretisation. We conduct a comprehensive performance evaluation on three real-world data sets under a stratified cross-validation strategy. In three different scenarios, TACD is compared with 11 other bankruptcy prediction strategies. We also discuss the efficiency of the attribute selection methods used in the experiments. In addition to its simplicity and understandability, statistical significance tests prove the efficiency of TACD against the other prediction algorithms in both measures of AUC and accuracy.

Identification of pesticide varieties by testing microalgae using Visible/Near Infrared Hyperspectral Imaging technology

NASA Astrophysics Data System (ADS)

Shao, Yongni; Jiang, Linjun; Zhou, Hong; Pan, Jian; He, Yong

2016-04-01

In our study, the feasibility of using visible/near infrared hyperspectral imaging technology to detect the changes of the internal components of Chlorella pyrenoidosa so as to determine the varieties of pesticides (such as butachlor, atrazine and glyphosate) at three concentrations (0.6 mg/L, 3 mg/L, 15 mg/L) was investigated. Three models (partial least squares discriminant analysis combined with full wavelengths, FW-PLSDA; partial least squares discriminant analysis combined with competitive adaptive reweighted sampling algorithm, CARS-PLSDA; linear discrimination analysis combined with regression coefficients, RC-LDA) were built by the hyperspectral data of Chlorella pyrenoidosa to find which model can produce the most optimal result. The RC-LDA model, which achieved an average correct classification rate of 97.0% was more superior than FW-PLSDA (72.2%) and CARS-PLSDA (84.0%), and it proved that visible/near infrared hyperspectral imaging could be a rapid and reliable technique to identify pesticide varieties. It also proved that microalgae can be a very promising medium to indicate characteristics of pesticides.

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

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.

A comparison of different chemometrics approaches for the robust classification of electronic nose data.

PubMed

Gromski, Piotr S; Correa, Elon; Vaughan, Andrew A; Wedge, David C; Turner, Michael L; Goodacre, Royston

2014-11-01

Accurate detection of certain chemical vapours is important, as these may be diagnostic for the presence of weapons, drugs of misuse or disease. In order to achieve this, chemical sensors could be deployed remotely. However, the readout from such sensors is a multivariate pattern, and this needs to be interpreted robustly using powerful supervised learning methods. Therefore, in this study, we compared the classification accuracy of four pattern recognition algorithms which include linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), random forests (RF) and support vector machines (SVM) which employed four different kernels. For this purpose, we have used electronic nose (e-nose) sensor data (Wedge et al., Sensors Actuators B Chem 143:365-372, 2009). In order to allow direct comparison between our four different algorithms, we employed two model validation procedures based on either 10-fold cross-validation or bootstrapping. The results show that LDA (91.56% accuracy) and SVM with a polynomial kernel (91.66% accuracy) were very effective at analysing these e-nose data. These two models gave superior prediction accuracy, sensitivity and specificity in comparison to the other techniques employed. With respect to the e-nose sensor data studied here, our findings recommend that SVM with a polynomial kernel should be favoured as a classification method over the other statistical models that we assessed. SVM with non-linear kernels have the advantage that they can be used for classifying non-linear as well as linear mapping from analytical data space to multi-group classifications and would thus be a suitable algorithm for the analysis of most e-nose sensor data.

Applying linear discriminant analysis to predict groundwater redox conditions conducive to denitrification

NASA Astrophysics Data System (ADS)

Wilson, S. R.; Close, M. E.; Abraham, P.

2018-01-01

Diffuse nitrate losses from agricultural land pollute groundwater resources worldwide, but can be attenuated under reducing subsurface conditions. In New Zealand, the ability to predict where groundwater denitrification occurs is important for understanding the linkage between land use and discharges of nitrate-bearing groundwater to streams. This study assesses the application of linear discriminant analysis (LDA) for predicting groundwater redox status for Southland, a major dairy farming region in New Zealand. Data cases were developed by assigning a redox status to samples derived from a regional groundwater quality database. Pre-existing regional-scale geospatial databases were used as training variables for the discriminant functions. The predictive accuracy of the discriminant functions was slightly improved by optimising the thresholds between sample depth classes. The models predict 23% of the region as being reducing at shallow depths (<15 m), and 37% at medium depths (15-75 m). Predictions were made at a sub-regional level to determine whether improvements could be made with discriminant functions trained by local data. The results indicated that any gains in predictive success were offset by loss of confidence in the predictions due to the reduction in the number of samples used. The regional scale model predictions indicate that subsurface reducing conditions predominate at low elevations on the coastal plains where poorly drained soils are widespread. Additional indicators for subsurface denitrification are a high carbon content of the soil, a shallow water table, and low-permeability clastic sediments. The coastal plains are an area of widespread groundwater discharge, and the soil and hydrology characteristics require the land to be artificially drained to render the land suitable for farming. For the improvement of water quality in coastal areas, it is therefore important that land and water management efforts focus on understanding hydrological bypassing that may occur via artificial drainage systems.

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

PubMed

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

2016-08-01

Reducing conditions are necessary for denitrification, thus the groundwater redox status can be used to identify subsurface zones where potentially significant nitrate reduction can occur. Groundwater chemistry in two contrasting regions of New Zealand was classified with respect to redox status and related to mappable factors, such as geology, topography and soil characteristics using discriminant analysis. Redox assignment was carried out for water sampled from 568 and 2223 wells in the Waikato and Canterbury regions, respectively. For the Waikato region 64% of wells sampled indicated oxic conditions in the water; 18% indicated reduced conditions and 18% had attributes indicating both reducing and oxic conditions termed "mixed". In Canterbury 84% of wells indicated oxic conditions; 10% were mixed; and only 5% indicated reduced conditions. The analysis was performed over three different well depths, <25m, 25 to 100 and >100m. For both regions, the percentage of oxidised groundwater decreased with increasing well depth. Linear discriminant analysis was used to develop models to differentiate between the three redox states. Models were derived for each depth and region using 67% of the data, and then subsequently validated on the remaining 33%. The average agreement between predicted and measured redox status was 63% and 70% for the Waikato and Canterbury regions, respectively. The models were incorporated into GIS and the prediction of redox status was extended over the whole region, excluding mountainous land. This knowledge improves spatial prediction of reduced groundwater zones, and therefore, when combined with groundwater flow paths, improves estimates of denitrification. Copyright © 2016 Elsevier B.V. All rights reserved.

The use of the logistic model in space motion sickness prediction

NASA Technical Reports Server (NTRS)

Lin, Karl K.; Reschke, Millard F.

1987-01-01

The one-equation and the two-equation logistic models were used to predict subjects' susceptibility to motion sickness in KC-135 parabolic flights using data from other ground-based motion sickness tests. The results show that the logistic models correctly predicted substantially more cases (an average of 13 percent) in the data subset used for model building. Overall, the logistic models ranged from 53 to 65 percent predictions of the three endpoint parameters, whereas the Bayes linear discriminant procedure ranged from 48 to 65 percent correct for the cross validation sample.

A geobotanical investigation based on linear discriminant and profile analyses of airborne Thematic Mapper Simulator data

NASA Technical Reports Server (NTRS)

Schwaller, Mathew R.

1987-01-01

This paper discusses the application of linear discriminant and profile analyses to detailed investigation of an airborne Thematic Mapper Simulator (TMS) image collected over a geobotanical test site. The test site was located on the Keweenaw Peninsula of Michigan's Upper Peninsula, and remote sensing data collection coincided with the onset of leaf senescence in the regional deciduous flora. Linear discriminant analysis revealed that sites overlying soil geochemical anomalies were distinguishable from background sites by the reflectance and thermal emittance of the tree canopy imaged in the airborne TMS data. The correlation of individual bands with the linear discriminant function suggested that the TMS thermal Channel 7 (10.32-12.33 microns) contributed most, while TMS Bands 2 (0.53-0.60 microns), 3 (0.63-0.69 microns), and 5 (1.53-1.73 microns) contributed somewhat more modestly to the separation of anomalous and background sites imaged by the TMS. The observed changes in canopy reflectance and thermal emittance of the deciduous flora overlying geochemically anomalous areas are consistent with the biophysical changes which are known or presumed to occur as a result of injury induced in metal-stressed vegetation.

Linear models to perform treaty verification tasks for enhanced information security

DOE PAGES

MacGahan, Christopher J.; Kupinski, Matthew A.; Brubaker, Erik M.; ...

2016-11-12

Linear mathematical models were applied to binary-discrimination tasks relevant to arms control verification measurements in which a host party wishes to convince a monitoring party that an item is or is not treaty accountable. These models process data in list-mode format and can compensate for the presence of variability in the source, such as uncertain object orientation and location. The Hotelling observer applies an optimal set of weights to binned detector data, yielding a test statistic that is thresholded to make a decision. The channelized Hotelling observer applies a channelizing matrix to the vectorized data, resulting in a lower dimensionalmore » vector available to the monitor to make decisions. We demonstrate how incorporating additional terms in this channelizing-matrix optimization offers benefits for treaty verification. We present two methods to increase shared information and trust between the host and monitor. The first method penalizes individual channel performance in order to maximize the information available to the monitor while maintaining optimal performance. Second, we present a method that penalizes predefined sensitive information while maintaining the capability to discriminate between binary choices. Data used in this study was generated using Monte Carlo simulations for fission neutrons, accomplished with the GEANT4 toolkit. Custom models for plutonium inspection objects were measured in simulation by a radiation imaging system. Model performance was evaluated and presented using the area under the receiver operating characteristic curve.« less

Linear models to perform treaty verification tasks for enhanced information security

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

MacGahan, Christopher J.; Kupinski, Matthew A.; Brubaker, Erik M.

Linear mathematical models were applied to binary-discrimination tasks relevant to arms control verification measurements in which a host party wishes to convince a monitoring party that an item is or is not treaty accountable. These models process data in list-mode format and can compensate for the presence of variability in the source, such as uncertain object orientation and location. The Hotelling observer applies an optimal set of weights to binned detector data, yielding a test statistic that is thresholded to make a decision. The channelized Hotelling observer applies a channelizing matrix to the vectorized data, resulting in a lower dimensionalmore » vector available to the monitor to make decisions. We demonstrate how incorporating additional terms in this channelizing-matrix optimization offers benefits for treaty verification. We present two methods to increase shared information and trust between the host and monitor. The first method penalizes individual channel performance in order to maximize the information available to the monitor while maintaining optimal performance. Second, we present a method that penalizes predefined sensitive information while maintaining the capability to discriminate between binary choices. Data used in this study was generated using Monte Carlo simulations for fission neutrons, accomplished with the GEANT4 toolkit. Custom models for plutonium inspection objects were measured in simulation by a radiation imaging system. Model performance was evaluated and presented using the area under the receiver operating characteristic curve.« less

Linear models to perform treaty verification tasks for enhanced information security

NASA Astrophysics Data System (ADS)

MacGahan, Christopher J.; Kupinski, Matthew A.; Brubaker, Erik M.; Hilton, Nathan R.; Marleau, Peter A.

2017-02-01

Linear mathematical models were applied to binary-discrimination tasks relevant to arms control verification measurements in which a host party wishes to convince a monitoring party that an item is or is not treaty accountable. These models process data in list-mode format and can compensate for the presence of variability in the source, such as uncertain object orientation and location. The Hotelling observer applies an optimal set of weights to binned detector data, yielding a test statistic that is thresholded to make a decision. The channelized Hotelling observer applies a channelizing matrix to the vectorized data, resulting in a lower dimensional vector available to the monitor to make decisions. We demonstrate how incorporating additional terms in this channelizing-matrix optimization offers benefits for treaty verification. We present two methods to increase shared information and trust between the host and monitor. The first method penalizes individual channel performance in order to maximize the information available to the monitor while maintaining optimal performance. Second, we present a method that penalizes predefined sensitive information while maintaining the capability to discriminate between binary choices. Data used in this study was generated using Monte Carlo simulations for fission neutrons, accomplished with the GEANT4 toolkit. Custom models for plutonium inspection objects were measured in simulation by a radiation imaging system. Model performance was evaluated and presented using the area under the receiver operating characteristic curve.

Quantifying and visualizing variations in sets of images using continuous linear optimal transport

NASA Astrophysics Data System (ADS)

Kolouri, Soheil; Rohde, Gustavo K.

2014-03-01

Modern advancements in imaging devices have enabled us to explore the subcellular structure of living organisms and extract vast amounts of information. However, interpreting the biological information mined in the captured images is not a trivial task. Utilizing predetermined numerical features is usually the only hope for quantifying this information. Nonetheless, direct visual or biological interpretation of results obtained from these selected features is non-intuitive and difficult. In this paper, we describe an automatic method for modeling visual variations in a set of images, which allows for direct visual interpretation of the most significant differences, without the need for predefined features. The method is based on a linearized version of the continuous optimal transport (OT) metric, which provides a natural linear embedding for the image data set, in which linear combination of images leads to a visually meaningful image. This enables us to apply linear geometric data analysis techniques such as principal component analysis and linear discriminant analysis in the linearly embedded space and visualize the most prominent modes, as well as the most discriminant modes of variations, in the dataset. Using the continuous OT framework, we are able to analyze variations in shape and texture in a set of images utilizing each image at full resolution, that otherwise cannot be done by existing methods. The proposed method is applied to a set of nuclei images segmented from Feulgen stained liver tissues in order to investigate the major visual differences in chromatin distribution of Fetal-Type Hepatoblastoma (FHB) cells compared to the normal cells.

Sex discrimination from the acetabulum in a twentieth-century skeletal sample from France using digital photogrammetry.

PubMed

Macaluso, P J

2011-02-01

Digital photogrammetric methods were used to collect diameter, area, and perimeter data of the acetabulum for a twentieth-century skeletal sample from France (Georges Olivier Collection, Musée de l'Homme, Paris) consisting of 46 males and 36 females. The measurements were then subjected to both discriminant function and logistic regression analyses in order to develop osteometric standards for sex assessment. Univariate discriminant functions and logistic regression equations yielded overall correct classification accuracy rates for both the left and the right acetabula ranging from 84.1% to 89.6%. The multivariate models developed in this study did not provide increased accuracy over those using only a single variable. Classification sex bias ratios ranged between 1.1% and 7.3% for the majority of models. The results of this study, therefore, demonstrate that metric analysis of acetabular size provides a highly accurate, and easily replicable, method of discriminating sex in this documented skeletal collection. The results further suggest that the addition of area and perimeter data derived from digital images may provide a more effective method of sex assessment than that offered by traditional linear measurements alone. Copyright © 2010 Elsevier GmbH. All rights reserved.

Joint Entropy for Space and Spatial Frequency Domains Estimated from Psychometric Functions of Achromatic Discrimination

PubMed Central

Silveira, Vladímir de Aquino; Souza, Givago da Silva; Gomes, Bruno Duarte; Rodrigues, Anderson Raiol; Silveira, Luiz Carlos de Lima

2014-01-01

We used psychometric functions to estimate the joint entropy for space discrimination and spatial frequency discrimination. Space discrimination was taken as discrimination of spatial extent. Seven subjects were tested. Gábor functions comprising unidimensionalsinusoidal gratings (0.4, 2, and 10 cpd) and bidimensionalGaussian envelopes (1°) were used as reference stimuli. The experiment comprised the comparison between reference and test stimulithat differed in grating's spatial frequency or envelope's standard deviation. We tested 21 different envelope's standard deviations around the reference standard deviation to study spatial extent discrimination and 19 different grating's spatial frequencies around the reference spatial frequency to study spatial frequency discrimination. Two series of psychometric functions were obtained for 2%, 5%, 10%, and 100% stimulus contrast. The psychometric function data points for spatial extent discrimination or spatial frequency discrimination were fitted with Gaussian functions using the least square method, and the spatial extent and spatial frequency entropies were estimated from the standard deviation of these Gaussian functions. Then, joint entropy was obtained by multiplying the square root of space extent entropy times the spatial frequency entropy. We compared our results to the theoretical minimum for unidimensional Gábor functions, 1/4π or 0.0796. At low and intermediate spatial frequencies and high contrasts, joint entropy reached levels below the theoretical minimum, suggesting non-linear interactions between two or more visual mechanisms. We concluded that non-linear interactions of visual pathways, such as the M and P pathways, could explain joint entropy values below the theoretical minimum at low and intermediate spatial frequencies and high contrasts. These non-linear interactions might be at work at intermediate and high contrasts at all spatial frequencies once there was a substantial decrease in joint entropy for these stimulus conditions when contrast was raised. PMID:24466158

Joint entropy for space and spatial frequency domains estimated from psychometric functions of achromatic discrimination.

PubMed

Silveira, Vladímir de Aquino; Souza, Givago da Silva; Gomes, Bruno Duarte; Rodrigues, Anderson Raiol; Silveira, Luiz Carlos de Lima

2014-01-01

We used psychometric functions to estimate the joint entropy for space discrimination and spatial frequency discrimination. Space discrimination was taken as discrimination of spatial extent. Seven subjects were tested. Gábor functions comprising unidimensionalsinusoidal gratings (0.4, 2, and 10 cpd) and bidimensionalGaussian envelopes (1°) were used as reference stimuli. The experiment comprised the comparison between reference and test stimulithat differed in grating's spatial frequency or envelope's standard deviation. We tested 21 different envelope's standard deviations around the reference standard deviation to study spatial extent discrimination and 19 different grating's spatial frequencies around the reference spatial frequency to study spatial frequency discrimination. Two series of psychometric functions were obtained for 2%, 5%, 10%, and 100% stimulus contrast. The psychometric function data points for spatial extent discrimination or spatial frequency discrimination were fitted with Gaussian functions using the least square method, and the spatial extent and spatial frequency entropies were estimated from the standard deviation of these Gaussian functions. Then, joint entropy was obtained by multiplying the square root of space extent entropy times the spatial frequency entropy. We compared our results to the theoretical minimum for unidimensional Gábor functions, 1/4π or 0.0796. At low and intermediate spatial frequencies and high contrasts, joint entropy reached levels below the theoretical minimum, suggesting non-linear interactions between two or more visual mechanisms. We concluded that non-linear interactions of visual pathways, such as the M and P pathways, could explain joint entropy values below the theoretical minimum at low and intermediate spatial frequencies and high contrasts. These non-linear interactions might be at work at intermediate and high contrasts at all spatial frequencies once there was a substantial decrease in joint entropy for these stimulus conditions when contrast was raised.

Robust L1-norm two-dimensional linear discriminant analysis.

PubMed

Li, Chun-Na; Shao, Yuan-Hai; Deng, Nai-Yang

2015-05-01

In this paper, we propose an L1-norm two-dimensional linear discriminant analysis (L1-2DLDA) with robust performance. Different from the conventional two-dimensional linear discriminant analysis with L2-norm (L2-2DLDA), where the optimization problem is transferred to a generalized eigenvalue problem, the optimization problem in our L1-2DLDA is solved by a simple justifiable iterative technique, and its convergence is guaranteed. Compared with L2-2DLDA, our L1-2DLDA is more robust to outliers and noises since the L1-norm is used. This is supported by our preliminary experiments on toy example and face datasets, which show the improvement of our L1-2DLDA over L2-2DLDA. Copyright © 2015 Elsevier Ltd. All rights reserved.

Screening analysis of biodiesel feedstock using UV-vis, NIR and synchronous fluorescence spectrometries and the successive projections algorithm.

PubMed

Insausti, Matías; Gomes, Adriano A; Cruz, Fernanda V; Pistonesi, Marcelo F; Araujo, Mario C U; Galvão, Roberto K H; Pereira, Claudete F; Band, Beatriz S F

2012-08-15

This paper investigates the use of UV-vis, near infrared (NIR) and synchronous fluorescence (SF) spectrometries coupled with multivariate classification methods to discriminate biodiesel samples with respect to the base oil employed in their production. More specifically, the present work extends previous studies by investigating the discrimination of corn-based biodiesel from two other biodiesel types (sunflower and soybean). Two classification methods are compared, namely full-spectrum SIMCA (soft independent modelling of class analogies) and SPA-LDA (linear discriminant analysis with variables selected by the successive projections algorithm). Regardless of the spectrometric technique employed, full-spectrum SIMCA did not provide an appropriate discrimination of the three biodiesel types. In contrast, all samples were correctly classified on the basis of a reduced number of wavelengths selected by SPA-LDA. It can be concluded that UV-vis, NIR and SF spectrometries can be successfully employed to discriminate corn-based biodiesel from the two other biodiesel types, but wavelength selection by SPA-LDA is key to the proper separation of the classes. Copyright © 2012 Elsevier B.V. All rights reserved.

A chemiluminescence sensor array for discriminating natural sugars and artificial sweeteners.

PubMed

Niu, Weifen; Kong, Hao; Wang, He; Zhang, Yantu; Zhang, Sichun; Zhang, Xinrong

2012-01-01

In this paper, we report a chemiluminescence (CL) sensor array based on catalytic nanomaterials for the discrimination of ten sweeteners, including five natural sugars and five artificial sweeteners. The CL response patterns ("fingerprints") can be obtained for a given compound on the nanomaterial array and then identified through linear discriminant analysis (LDA). Moreover, each pure sweetener was quantified based on the emission intensities of selected sensor elements. The linear ranges for these sweeteners lie within 0.05-100 mM, but vary with the type of sweetener. The applicability of this array to real-life samples was demonstrated by applying it to various beverages, and the results showed that the sensor array possesses excellent discrimination power and reversibility.

Relative sensitivity of depth discrimination for ankle inversion and plantar flexion movements.

PubMed

Black, Georgia; Waddington, Gordon; Adams, Roger

2014-02-01

25 participants (20 women, 5 men) were tested for sensitivity in discrimination between sets of six movements centered on 8 degrees, 11 degrees, and 14 degrees, and separated by 0.3 degrees. Both inversion and plantar flexion movements were tested. Discrimination of the extent of inversion movement was observed to decline linearly with increasing depth; however, for plantar flexion, the discrimination function for movement extent was found to be non-linear. The relatively better discrimination of plantar flexion movements than inversion movements at around 11 degrees from horizontal is interpreted as an effect arising from differential amounts of practice through use, because this position is associated with the plantar flexion movement made in normal walking. The fact that plantar flexion movements are discriminated better than inversion at one region but not others argues against accounts of superior proprioceptive sensitivity for plantar flexion compared to inversion that are based on general properties of plantar flexion such as the number of muscle fibres on stretch.

Linear scaling relationships and volcano plots in homogeneous catalysis - revisiting the Suzuki reaction.

PubMed

Busch, Michael; Wodrich, Matthew D; Corminboeuf, Clémence

2015-12-01

Linear free energy scaling relationships and volcano plots are common tools used to identify potential heterogeneous catalysts for myriad applications. Despite the striking simplicity and predictive power of volcano plots, they remain unknown in homogeneous catalysis. Here, we construct volcano plots to analyze a prototypical reaction from homogeneous catalysis, the Suzuki cross-coupling of olefins. Volcano plots succeed both in discriminating amongst different catalysts and reproducing experimentally known trends, which serves as validation of the model for this proof-of-principle example. These findings indicate that the combination of linear scaling relationships and volcano plots could serve as a valuable methodology for identifying homogeneous catalysts possessing a desired activity through a priori computational screening.

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

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

Deep and Structured Robust Information Theoretic Learning for Image Analysis.

PubMed

Deng, Yue; Bao, Feng; Deng, Xuesong; Wang, Ruiping; Kong, Youyong; Dai, Qionghai

2016-07-07

This paper presents a robust information theoretic (RIT) model to reduce the uncertainties, i.e. missing and noisy labels, in general discriminative data representation tasks. The fundamental pursuit of our model is to simultaneously learn a transformation function and a discriminative classifier that maximize the mutual information of data and their labels in the latent space. In this general paradigm, we respectively discuss three types of the RIT implementations with linear subspace embedding, deep transformation and structured sparse learning. In practice, the RIT and deep RIT are exploited to solve the image categorization task whose performances will be verified on various benchmark datasets. The structured sparse RIT is further applied to a medical image analysis task for brain MRI segmentation that allows group-level feature selections on the brain tissues.

NEIGHBOURHOOD POVERTY, PERCEIVED DISCRIMINATION AND CENTRAL ADIPOSITY IN THE USA: INDEPENDENT ASSOCIATIONS IN A REPEATED MEASURES ANALYSIS.

PubMed

Kwarteng, Jamila L; Schulz, Amy J; Mentz, Graciela B; Israel, Barbara A; Shanks, Trina R; Perkins, Denise White

2016-11-01

This study examines the independent effects of neighbourhood context (i.e. neighbourhood poverty) and exposure to perceived discrimination in shaping risk of obesity over time. Weighted three-level hierarchical linear regression models for a continuous outcome were used to assess the independent effects of neighbourhood poverty and perceived discrimination on obesity over time in a sample of 157 non-Hispanic Black, non-Hispanic White and Hispanic adults in Detroit, USA, in 2002/2003 and 2007/2008. Independent associations were found between neighbourhood poverty and perceived discrimination with central adiposity over time. Residents of neighbourhoods with high concentrations of poverty were more likely to show increases in central adiposity compared with those in neighbourhoods with lower concentrations of poverty. In models adjusted for BMI, neighbourhood poverty at baseline was associated with a greater change in central adiposity among participants who lived in neighbourhoods in the second (B=3.79, p=0.025) and third (B=3.73, p=0.024) poverty quartiles, compared with those in the lowest poverty neighbourhoods. The results from models that included both neighbourhood poverty and perceived discrimination showed that both were associated with increased risk of increased central adiposity over time. Residents of neighbourhoods in the second (B=9.58, p<0.001), third (B=8.25, p=0.004) and fourth (B=7.66, p=0.030) quartiles of poverty were more likely to show greater increases in central adiposity over time, compared with those in the lowest poverty quartile, with mean discrimination at baseline independently and positively associated with increases in central adiposity over time (B=2.36, p=0.020). The results suggest that neighbourhood poverty and perceived discrimination are independently associated with a heightened risk of increase in central adiposity over time. Efforts to address persistent disparities in central adiposity in the USA should include strategies to reduce high concentrations of neighbourhood poverty as well as discrimination.

Discriminative components of data.

PubMed

Peltonen, Jaakko; Kaski, Samuel

2005-01-01

A simple probabilistic model is introduced to generalize classical linear discriminant analysis (LDA) in finding components that are informative of or relevant for data classes. The components maximize the predictability of the class distribution which is asymptotically equivalent to 1) maximizing mutual information with the classes, and 2) finding principal components in the so-called learning or Fisher metrics. The Fisher metric measures only distances that are relevant to the classes, that is, distances that cause changes in the class distribution. The components have applications in data exploration, visualization, and dimensionality reduction. In empirical experiments, the method outperformed, in addition to more classical methods, a Renyi entropy-based alternative while having essentially equivalent computational cost.

Discriminative analysis of non-linear brain connectivity for leukoaraiosis with resting-state fMRI

NASA Astrophysics Data System (ADS)

Lai, Youzhi; Xu, Lele; Yao, Li; Wu, Xia

2015-03-01

Leukoaraiosis (LA) describes diffuse white matter abnormalities on CT or MR brain scans, often seen in the normal elderly and in association with vascular risk factors such as hypertension, or in the context of cognitive impairment. The mechanism of cognitive dysfunction is still unclear. The recent clinical studies have revealed that the severity of LA was not corresponding to the cognitive level, and functional connectivity analysis is an appropriate method to detect the relation between LA and cognitive decline. However, existing functional connectivity analyses of LA have been mostly limited to linear associations. In this investigation, a novel measure utilizing the extended maximal information coefficient (eMIC) was applied to construct non-linear functional connectivity in 44 LA subjects (9 dementia, 25 mild cognitive impairment (MCI) and 10 cognitively normal (CN)). The strength of non-linear functional connections for the first 1% of discriminative power increased in MCI compared with CN and dementia, which was opposed to its linear counterpart. Further functional network analysis revealed that the changes of the non-linear and linear connectivity have similar but not completely the same spatial distribution in human brain. In the multivariate pattern analysis with multiple classifiers, the non-linear functional connectivity mostly identified dementia, MCI and CN from LA with a relatively higher accuracy rate than the linear measure. Our findings revealed the non-linear functional connectivity provided useful discriminative power in classification of LA, and the spatial distributed changes between the non-linear and linear measure may indicate the underlying mechanism of cognitive dysfunction in LA.

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

Effect of perceived racial discrimination on self-care behaviors, glycemic control, and quality of life in adults with type 2 diabetes.

PubMed

Dawson, Aprill Z; Walker, Rebekah J; Campbell, Jennifer A; Egede, Leonard E

2015-06-01

This study used a large sample size of black and white patients with type 2 diabetes to investigate the influence of perceived racial discrimination on biologic measures (glycemic control, blood pressure, and LDL cholesterol), the mental component of quality of life (MCS), and health behaviors known to improve diabetes outcomes. 602 patients were recruited from two adult primary care clinics in the southeastern United States. Linear regression models were used to assess the associations between perceived racial discrimination, self-care, clinical outcomes, MCS, adjusting for relevant covariates. Race-stratified models were conducted to examine differential associations by race. The mean age was 61 years, with 64.9 % non-Hispanic black, and 41.6 % earning less than $20,000 annually. Perceived discrimination was significantly negatively associated with MCS (β = -0.56, 95 % CI -0.90, 0.23), general diet (β = -0.37, CI -0.65, -0.08), and specific diet (β = -0.25, CI -0.47, -0.03). In African Americans, perceived discrimination was significantly associated with higher systolic blood pressure (β = 10.17, CI 1.13, -19.22). In Whites, perceived discrimination was significantly associated with lower MCS (β = -0.51, CI -0.89, -0.14), general diet (β = -0.40, CI -0.69, -0.99), specific diet (β = -0.25, CI -0.47, -0.03), and blood glucose testing (β = -0.43, CI -0.80, -0.06). While no association was found with biologic measures, perceived discrimination was associated with health behaviors and the MCS. In addition, results showed a difference in influence of perceived discrimination by race.

Incremental Structured Dictionary Learning for Video Sensor-Based Object Tracking

PubMed Central

Xue, Ming; Yang, Hua; Zheng, Shibao; Zhou, Yi; Yu, Zhenghua

2014-01-01

To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT) is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU) strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV) function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks. PMID:24549252

Associative Learning Through Acquired Salience

PubMed Central

Treviño, Mario

2016-01-01

Most associative learning studies describe the salience of stimuli as a fixed learning-rate parameter. Presumptive saliency signals, however, have also been linked to motivational and attentional processes. An interesting possibility, therefore, is that discriminative stimuli could also acquire salience as they become powerful predictors of outcomes. To explore this idea, we first characterized and extracted the learning curves from mice trained with discriminative images offering varying degrees of structural similarity. Next, we fitted a linear model of associative learning coupled to a series of mathematical representations for stimulus salience. We found that the best prediction, from the set of tested models, was one in which the visual salience depended on stimulus similarity and a non-linear function of the associative strength. Therefore, these analytic results support the idea that the net salience of a stimulus depends both on the items' effective salience and the motivational state of the subject that learns about it. Moreover, this dual salience model can explain why learning about a stimulus not only depends on the effective salience during acquisition but also on the specific learning trajectory that was used to reach this state. Our mathematical description could be instrumental for understanding aberrant salience acquisition under stressful situations and in neuropsychiatric disorders like schizophrenia, obsessive-compulsive disorder, and addiction. PMID:26793078

Associative Learning Through Acquired Salience.

PubMed

Treviño, Mario

2015-01-01

Most associative learning studies describe the salience of stimuli as a fixed learning-rate parameter. Presumptive saliency signals, however, have also been linked to motivational and attentional processes. An interesting possibility, therefore, is that discriminative stimuli could also acquire salience as they become powerful predictors of outcomes. To explore this idea, we first characterized and extracted the learning curves from mice trained with discriminative images offering varying degrees of structural similarity. Next, we fitted a linear model of associative learning coupled to a series of mathematical representations for stimulus salience. We found that the best prediction, from the set of tested models, was one in which the visual salience depended on stimulus similarity and a non-linear function of the associative strength. Therefore, these analytic results support the idea that the net salience of a stimulus depends both on the items' effective salience and the motivational state of the subject that learns about it. Moreover, this dual salience model can explain why learning about a stimulus not only depends on the effective salience during acquisition but also on the specific learning trajectory that was used to reach this state. Our mathematical description could be instrumental for understanding aberrant salience acquisition under stressful situations and in neuropsychiatric disorders like schizophrenia, obsessive-compulsive disorder, and addiction.

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2017-01-01

Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Deep Hashing for Scalable Image Search.

PubMed

Lu, Jiwen; Liong, Venice Erin; Zhou, Jie

2017-05-01

In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for scalable image search. Unlike most existing binary codes learning methods, which usually seek a single linear projection to map each sample into a binary feature vector, we develop a deep neural network to seek multiple hierarchical non-linear transformations to learn these binary codes, so that the non-linear relationship of samples can be well exploited. Our model is learned under three constraints at the top layer of the developed deep network: 1) the loss between the compact real-valued code and the learned binary vector is minimized, 2) the binary codes distribute evenly on each bit, and 3) different bits are as independent as possible. To further improve the discriminative power of the learned binary codes, we extend DH into supervised DH (SDH) and multi-label SDH by including a discriminative term into the objective function of DH, which simultaneously maximizes the inter-class variations and minimizes the intra-class variations of the learned binary codes with the single-label and multi-label settings, respectively. Extensive experimental results on eight widely used image search data sets show that our proposed methods achieve very competitive results with the state-of-the-arts.

Effects of measurement errors on psychometric measurements in ergonomics studies: Implications for correlations, ANOVA, linear regression, factor analysis, and linear discriminant analysis.

PubMed

Liu, Yan; Salvendy, Gavriel

2009-05-01

This paper aims to demonstrate the effects of measurement errors on psychometric measurements in ergonomics studies. A variety of sources can cause random measurement errors in ergonomics studies and these errors can distort virtually every statistic computed and lead investigators to erroneous conclusions. The effects of measurement errors on five most widely used statistical analysis tools have been discussed and illustrated: correlation; ANOVA; linear regression; factor analysis; linear discriminant analysis. It has been shown that measurement errors can greatly attenuate correlations between variables, reduce statistical power of ANOVA, distort (overestimate, underestimate or even change the sign of) regression coefficients, underrate the explanation contributions of the most important factors in factor analysis and depreciate the significance of discriminant function and discrimination abilities of individual variables in discrimination analysis. The discussions will be restricted to subjective scales and survey methods and their reliability estimates. Other methods applied in ergonomics research, such as physical and electrophysiological measurements and chemical and biomedical analysis methods, also have issues of measurement errors, but they are beyond the scope of this paper. As there has been increasing interest in the development and testing of theories in ergonomics research, it has become very important for ergonomics researchers to understand the effects of measurement errors on their experiment results, which the authors believe is very critical to research progress in theory development and cumulative knowledge in the ergonomics field.

Placement Model for First-Time Freshmen in Calculus I (Math 131): University of Northern Colorado

ERIC Educational Resources Information Center

Heiny, Robert L.; Heiny, Erik L.; Raymond, Karen

2017-01-01

Two approaches, Linear Discriminant Analysis, and Logistic Regression are used and compared to predict success or failure for first-time freshmen in the first calculus course at a medium-sized public, 4-year institution prior to Fall registration. The predictor variables are high school GPA, the number, and GPA's of college prep mathematics…

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.

Darker Skin Tone Increases Perceived Discrimination among Male but Not Female Caribbean Black Youth.

PubMed

Assari, Shervin; Caldwell, Cleopatra Howard

2017-12-12

Among most minority groups, males seem to report higher levels of exposure and vulnerability to racial discrimination. Although darker skin tone may increase exposure to racial discrimination, it is yet unknown whether skin tone similarly influences perceived discrimination among male and female Caribbean Black youth. The current cross-sectional study tests the role of gender on the effects of skin tone on perceived discrimination among Caribbean Black youth. Data came from the National Survey of American Life-Adolescent Supplement (NSAL-A), 2003-2004, which included 360 Caribbean Black youth (ages 13 to 17). Demographic factors (age and gender), socioeconomic status (SES; family income, income to needs ratio, and subjective SES), skin tone, and perceived everyday discrimination were measured. Linear regressions were used for data analysis. In the pooled sample, darker skin tone was associated with higher levels of perceived discrimination among Caribbean Black youth ( b = 0.48; 95% Confidence Interval (CI) = 0.07-0.89). A significant interaction was found between gender and skin tone ( b = 1.17; 95% CI = 0.49-1.86), suggesting a larger effect of skin tone on perceived discrimination for males than females. In stratified models, darker skin tone was associated with more perceived discrimination for males ( b = 1.20; 95% CI = 0.69-0.72) but not females ( b = 0.06; 95% CI = -0.42-0.55). Similar to the literature documenting male gender as a vulnerability factor to the effects of racial discrimination, we found that male but not female Caribbean Black youth with darker skin tones perceive more discrimination.

The association between discrimination and PTSD in African Americans: exploring the role of gender.

PubMed

Brooks Holliday, Stephanie; Dubowitz, Tamara; Haas, Ann; Ghosh-Dastidar, Bonnie; DeSantis, Amy; Troxel, Wendy M

2018-02-28

Research has demonstrated the adverse impact that discrimination has on physical and mental health. However, few studies have examined the association between discrimination and symptoms of posttraumatic stress disorder (PTSD). There is evidence that African Americans experience higher rates of PTSD and are more likely to develop PTSD following trauma exposure than Whites, and discrimination may be one reason for this disparity. To examine the association between discrimination and PTSD among a cross-sectional sample largely comprising African American women, controlling for other psychosocial stressors (psychological distress, neighborhood safety, crime). A sample of 806 participants was recruited from two low-income predominantly African American neighborhoods. Participants completed self-report measures of PTSD symptoms, perceived discrimination, perceived safety, and psychological distress. Information on neighborhood crime was obtained through data requested from the city. Multivariate linear regression models were estimated to assess adjusted relationships between PTSD symptoms and discrimination. Discrimination was significantly associated with PTSD symptoms with a small effect size, controlling for relevant sociodemographic variables. This association remained consistent after controlling for psychological distress, perceived safety, and total neighborhood crime. There was no evidence of a gender by discrimination interaction. Participants who experienced any discrimination were significantly more likely to screen positive for PTSD. Discrimination may contribute to the disparate rates of PTSD experienced by African Americans. PTSD is associated with a range of negative consequences, including poorer physical health, mental health, and quality of life. These results suggest the importance of finding ways to promote resilience in this at-risk population.

Assessment of computer techniques for processing digital LANDSAT MSS data for lithological discrimination of Serra do Ramalho, State of Bahia

NASA Technical Reports Server (NTRS)

Paradella, W. R. (Principal Investigator); Vitorello, I.; Monteiro, M. D.

1984-01-01

Enhancement techniques and thematic classifications were applied to the metasediments of Bambui Super Group (Upper Proterozoic) in the Region of Serra do Ramalho, SW of the state of Bahia. Linear contrast stretch, band-ratios with contrast stretch, and color-composites allow lithological discriminations. The effects of human activities and of vegetation cover mask and limit, in several ways, the lithological discrimination with digital MSS data. Principal component images and color composite of linear contrast stretch of these products, show lithological discrimination through tonal gradations. This set of products allows the delineations of several metasedimentary sequences to a level superior to reconnaissance mapping. Supervised (maximum likelihood classifier) and nonsupervised (K-Means classifier) classification of the limestone sequence, host to fluorite mineralization show satisfactory results.

Socioeconomic Status Discrimination is Associated with Poor Sleep in African-Americans, but not Whites

PubMed Central

Van Dyke, Miriam E.; Vaccarino, Viola; Quyyumi, Arshed A.; Lewis, Tené T.

2016-01-01

Rationale Research on self-reported experiences of discrimination and health has grown in recent decades, but has largely focused on racial discrimination or overall mistreatment. Less is known about reports of discrimination on the basis of socioeconomic status (SES), despite the fact that SES is one of the most powerful social determinants of health. Objective We sought to examine the cross-sectional association between self-reported SES discrimination and subjective sleep quality, an emerging risk factor for disease. We further examined whether associations differed by race or SES. Methods We used logistic and linear regression to analyze data from a population-based cohort of 425 African-American and White middle-aged adults (67.5% female) in the Southeastern United States. SES discrimination was assessed with a modified Experiences of Discrimination Scale and poor subjective sleep quality was assessed with the Pittsburgh Sleep Quality Index. Results In logistic regression models adjusted for age, gender, and education, reports of SES discrimination were associated with poor sleep quality among African-Americans (OR=2.39, 95% CI =1.35, 4.24), but not Whites (OR=1.03, 95% CI= 0.57, 1.87), and the race × SES discrimination interaction was significant at p=0.04. After additional adjustments for reports of racial and gender discrimination, other psychosocial stressors, body mass index and depressive symptoms, SES discrimination remained a significant predictor of poor sleep among African-Americans, but not Whites. In contrast to findings by race, SES discrimination and sleep associations did not significantly differ by SES. Conclusion Findings suggest that reports of SES discrimination may be an important risk factor for subjective sleep quality among African-Americans and support the need to consider the health impact of SES-related stressors in the context of race. PMID:26896878

Socioeconomic status discrimination is associated with poor sleep in African-Americans, but not Whites.

PubMed

Van Dyke, Miriam E; Vaccarino, Viola; Quyyumi, Arshed A; Lewis, Tené T

2016-03-01

Research on self-reported experiences of discrimination and health has grown in recent decades, but has largely focused on racial discrimination or overall mistreatment. Less is known about reports of discrimination on the basis of socioeconomic status (SES), despite the fact that SES is one of the most powerful social determinants of health. We sought to examine the cross-sectional association between self-reported SES discrimination and subjective sleep quality, an emerging risk factor for disease. We further examined whether associations differed by race or SES. We used logistic and linear regression to analyze data from a population-based cohort of 425 African-American and White middle-aged adults (67.5% female) in the Southeastern United States. SES discrimination was assessed with a modified Experiences of Discrimination Scale and poor subjective sleep quality was assessed with the Pittsburgh Sleep Quality Index. In logistic regression models adjusted for age, gender, and education, reports of SES discrimination were associated with poor sleep quality among African-Americans (OR = 2.39 95%, CI = 1.35, 4.24), but not Whites (OR = 1.03, 95% CI = 0.57, 1.87), and the race × SES discrimination interaction was significant at p = 0.04. After additional adjustments for reports of racial and gender discrimination, other psychosocial stressors, body mass index and depressive symptoms, SES discrimination remained a significant predictor of poor sleep among African-Americans, but not Whites. In contrast to findings by race, SES discrimination and sleep associations did not significantly differ by SES. Findings suggest that reports of SES discrimination may be an important risk factor for subjective sleep quality among African-Americans and support the need to consider the health impact of SES-related stressors in the context of race. Copyright © 2016 Elsevier Ltd. All rights reserved.

Application of linear discriminant analysis and Attenuated Total Reflectance Fourier Transform Infrared microspectroscopy for diagnosis of colon cancer.

PubMed

Khanmohammadi, Mohammadreza; Bagheri Garmarudi, Amir; Samani, Simin; Ghasemi, Keyvan; Ashuri, Ahmad

2011-06-01

Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) microspectroscopy was applied for detection of colon cancer according to the spectral features of colon tissues. Supervised classification models can be trained to identify the tissue type based on the spectroscopic fingerprint. A total of 78 colon tissues were used in spectroscopy studies. Major spectral differences were observed in 1,740-900 cm(-1) spectral region. Several chemometric methods such as analysis of variance (ANOVA), cluster analysis (CA) and linear discriminate analysis (LDA) were applied for classification of IR spectra. Utilizing the chemometric techniques, clear and reproducible differences were observed between the spectra of normal and cancer cases, suggesting that infrared microspectroscopy in conjunction with spectral data processing would be useful for diagnostic classification. Using LDA technique, the spectra were classified into cancer and normal tissue classes with an accuracy of 95.8%. The sensitivity and specificity was 100 and 93.1%, respectively.

Experimental optimal maximum-confidence discrimination and optimal unambiguous discrimination of two mixed single-photon states

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

Steudle, Gesine A.; Knauer, Sebastian; Herzog, Ulrike

2011-05-15

We present an experimental implementation of optimum measurements for quantum state discrimination. Optimum maximum-confidence discrimination and optimum unambiguous discrimination of two mixed single-photon polarization states were performed. For the latter the states of rank 2 in a four-dimensional Hilbert space are prepared using both path and polarization encoding. Linear optics and single photons from a true single-photon source based on a semiconductor quantum dot are utilized.

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis

PubMed Central

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Background: Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. Methods: In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. Results: The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Conclusion: Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended. PMID:26793655

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

PubMed

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

Detection of non-milk fat in milk fat by gas chromatography and linear discriminant analysis.

PubMed

Gutiérrez, R; Vega, S; Díaz, G; Sánchez, J; Coronado, M; Ramírez, A; Pérez, J; González, M; Schettino, B

2009-05-01

Gas chromatography was utilized to determine triacylglycerol profiles in milk and non-milk fat. The values of triacylglycerol were subjected to linear discriminant analysis to detect and quantify non-milk fat in milk fat. Two groups of milk fat were analyzed: A) raw milk fat from the central region of Mexico (n = 216) and B) ultrapasteurized milk fat from 3 industries (n = 36), as well as pork lard (n = 2), bovine tallow (n = 2), fish oil (n = 2), peanut (n = 2), corn (n = 2), olive (n = 2), and soy (n = 2). The samples of raw milk fat were adulterated with non-milk fats in proportions of 0, 5, 10, 15, and 20% to form 5 groups. The first function obtained from the linear discriminant analysis allowed the correct classification of 94.4% of the samples with levels <10% of adulteration. The triacylglycerol values of the ultrapasteurized milk fats were evaluated with the discriminant function, demonstrating that one industry added non-milk fat to its product in 80% of the samples analyzed.

Validation of SplitVectors Encoding for Quantitative Visualization of Large-Magnitude-Range Vector Fields

PubMed Central

Zhao, Henan; Bryant, Garnett W.; Griffin, Wesley; Terrill, Judith E.; Chen, Jian

2017-01-01

We designed and evaluated SplitVectors, a new vector field display approach to help scientists perform new discrimination tasks on large-magnitude-range scientific data shown in three-dimensional (3D) visualization environments. SplitVectors uses scientific notation to display vector magnitude, thus improving legibility. We present an empirical study comparing the SplitVectors approach with three other approaches - direct linear representation, logarithmic, and text display commonly used in scientific visualizations. Twenty participants performed three domain analysis tasks: reading numerical values (a discrimination task), finding the ratio between values (a discrimination task), and finding the larger of two vectors (a pattern detection task). Participants used both mono and stereo conditions. Our results suggest the following: (1) SplitVectors improve accuracy by about 10 times compared to linear mapping and by four times to logarithmic in discrimination tasks; (2) SplitVectors have no significant differences from the textual display approach, but reduce cluttering in the scene; (3) SplitVectors and textual display are less sensitive to data scale than linear and logarithmic approaches; (4) using logarithmic can be problematic as participants' confidence was as high as directly reading from the textual display, but their accuracy was poor; and (5) Stereoscopy improved performance, especially in more challenging discrimination tasks. PMID:28113469

Validation of SplitVectors Encoding for Quantitative Visualization of Large-Magnitude-Range Vector Fields.

PubMed

Henan Zhao; Bryant, Garnett W; Griffin, Wesley; Terrill, Judith E; Jian Chen

2017-06-01

We designed and evaluated SplitVectors, a new vector field display approach to help scientists perform new discrimination tasks on large-magnitude-range scientific data shown in three-dimensional (3D) visualization environments. SplitVectors uses scientific notation to display vector magnitude, thus improving legibility. We present an empirical study comparing the SplitVectors approach with three other approaches - direct linear representation, logarithmic, and text display commonly used in scientific visualizations. Twenty participants performed three domain analysis tasks: reading numerical values (a discrimination task), finding the ratio between values (a discrimination task), and finding the larger of two vectors (a pattern detection task). Participants used both mono and stereo conditions. Our results suggest the following: (1) SplitVectors improve accuracy by about 10 times compared to linear mapping and by four times to logarithmic in discrimination tasks; (2) SplitVectors have no significant differences from the textual display approach, but reduce cluttering in the scene; (3) SplitVectors and textual display are less sensitive to data scale than linear and logarithmic approaches; (4) using logarithmic can be problematic as participants' confidence was as high as directly reading from the textual display, but their accuracy was poor; and (5) Stereoscopy improved performance, especially in more challenging discrimination tasks.

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

A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS

NASA Astrophysics Data System (ADS)

Naghibi, Seyed Amir; Pourghasemi, Hamid Reza; Abbaspour, Karim

2018-02-01

Considering the unstable condition of water resources in Iran and many other countries in arid and semi-arid regions, groundwater studies are very important. Therefore, the aim of this study is to model groundwater potential by qanat locations as indicators and ten advanced and soft computing models applied to the Beheshtabad Watershed, Iran. Qanat is a man-made underground construction which gathers groundwater from higher altitudes and transmits it to low land areas where it can be used for different purposes. For this purpose, at first, the location of the qanats was detected using extensive field surveys. These qanats were classified into two datasets including training (70%) and validation (30%). Then, 14 influence factors depicting the region's physical, morphological, lithological, and hydrological features were identified to model groundwater potential. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), flexible discriminant analysis (FDA), penalized discriminant analysis (PDA), boosted regression tree (BRT), random forest (RF), artificial neural network (ANN), K-nearest neighbor (KNN), multivariate adaptive regression splines (MARS), and support vector machine (SVM) models were applied in R scripts to produce groundwater potential maps. For evaluation of the performance accuracies of the developed models, ROC curve and kappa index were implemented. According to the results, RF had the best performance, followed by SVM and BRT models. Our results showed that qanat locations could be used as a good indicator for groundwater potential. Furthermore, altitude, slope, plan curvature, and profile curvature were found to be the most important influence factors. On the other hand, lithology, land use, and slope aspect were the least significant factors. The methodology in the current study could be used by land use and terrestrial planners and water resource managers to reduce the costs of groundwater resource discovery.

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.

Demographic and clinical features related to perceived discrimination in schizophrenia.

PubMed

Fresán, Ana; Robles-García, Rebeca; Madrigal, Eduardo; Tovilla-Zarate, Carlos-Alfonso; Martínez-López, Nicolás; Arango de Montis, Iván

2018-04-01

Perceived discrimination contributes to the development of internalized stigma among those with schizophrenia. Evidence on demographic and clinical factors related to the perception of discrimination among this population is both contradictory and scarce in low- and middle-income countries. Accordingly, the main purpose of this study is to determine the demographic and clinical factors predicting the perception of discrimination among Mexican patients with schizophrenia. Two hundred and seventeen adults with paranoid schizophrenia completed an interview on their demographic status and clinical characteristics. Symptom severity was assessed using the Positive and Negative Syndrome Scale; and perceived discrimination using 13 items from the King's Internalized Stigma Scale. Bivariate linear associations were determined to identify the variables of interest to be included in a linear regression analysis. Years of education, age of illness onset and length of hospitalization were associated with discrimination. However, only age of illness onset and length of hospitalization emerged as predictors of perceived discrimination in the final regression analysis, with longer length of hospitalization being the independent variable with the greatest contribution. Fortunately, this is a modifiable factor regarding the perception of discrimination and self-stigma. Strategies for achieving this as part of community-based mental health care are also discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

Ultrahigh-Dimensional Multiclass Linear Discriminant Analysis by Pairwise Sure Independence Screening

PubMed Central

Pan, Rui; Wang, Hansheng; Li, Runze

2016-01-01

This paper is concerned with the problem of feature screening for multi-class linear discriminant analysis under ultrahigh dimensional setting. We allow the number of classes to be relatively large. As a result, the total number of relevant features is larger than usual. This makes the related classification problem much more challenging than the conventional one, where the number of classes is small (very often two). To solve the problem, we propose a novel pairwise sure independence screening method for linear discriminant analysis with an ultrahigh dimensional predictor. The proposed procedure is directly applicable to the situation with many classes. We further prove that the proposed method is screening consistent. Simulation studies are conducted to assess the finite sample performance of the new procedure. We also demonstrate the proposed methodology via an empirical analysis of a real life example on handwritten Chinese character recognition. PMID:28127109

Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis

PubMed Central

Acquah, Gifty E.; Via, Brian K.; Billor, Nedret; Fasina, Oladiran O.; Eckhardt, Lori G.

2016-01-01

As new markets, technologies and economies evolve in the low carbon bioeconomy, forest logging residue, a largely untapped renewable resource will play a vital role. The feedstock can however be variable depending on plant species and plant part component. This heterogeneity can influence the physical, chemical and thermochemical properties of the material, and thus the final yield and quality of products. Although it is challenging to control compositional variability of a batch of feedstock, it is feasible to monitor this heterogeneity and make the necessary changes in process parameters. Such a system will be a first step towards optimization, quality assurance and cost-effectiveness of processes in the emerging biofuel/chemical industry. The objective of this study was therefore to qualitatively classify forest logging residue made up of different plant parts using both near infrared spectroscopy (NIRS) and Fourier transform infrared spectroscopy (FTIRS) together with linear discriminant analysis (LDA). Forest logging residue harvested from several Pinus taeda (loblolly pine) plantations in Alabama, USA, were classified into three plant part components: clean wood, wood and bark and slash (i.e., limbs and foliage). Five-fold cross-validated linear discriminant functions had classification accuracies of over 96% for both NIRS and FTIRS based models. An extra factor/principal component (PC) was however needed to achieve this in FTIRS modeling. Analysis of factor loadings of both NIR and FTIR spectra showed that, the statistically different amount of cellulose in the three plant part components of logging residue contributed to their initial separation. This study demonstrated that NIR or FTIR spectroscopy coupled with PCA and LDA has the potential to be used as a high throughput tool in classifying the plant part makeup of a batch of forest logging residue feedstock. Thus, NIR/FTIR could be employed as a tool to rapidly probe/monitor the variability of forest biomass so that the appropriate online adjustments to parameters can be made in time to ensure process optimization and product quality. PMID:27618901

Performance and strategy comparisons of human listeners and logistic regression in discriminating underwater targets.

PubMed

Yang, Lixue; Chen, Kean

2015-11-01

To improve the design of underwater target recognition systems based on auditory perception, this study compared human listeners with automatic classifiers. Performances measures and strategies in three discrimination experiments, including discriminations between man-made and natural targets, between ships and submarines, and among three types of ships, were used. In the experiments, the subjects were asked to assign a score to each sound based on how confident they were about the category to which it belonged, and logistic regression, which represents linear discriminative models, also completed three similar tasks by utilizing many auditory features. The results indicated that the performances of logistic regression improved as the ratio between inter- and intra-class differences became larger, whereas the performances of the human subjects were limited by their unfamiliarity with the targets. Logistic regression performed better than the human subjects in all tasks but the discrimination between man-made and natural targets, and the strategies employed by excellent human subjects were similar to that of logistic regression. Logistic regression and several human subjects demonstrated similar performances when discriminating man-made and natural targets, but in this case, their strategies were not similar. An appropriate fusion of their strategies led to further improvement in recognition accuracy.

Development and validation of risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team.

PubMed

Harrison, David A; Patel, Krishna; Nixon, Edel; Soar, Jasmeet; Smith, Gary B; Gwinnutt, Carl; Nolan, Jerry P; Rowan, Kathryn M

2014-08-01

The National Cardiac Arrest Audit (NCAA) is the UK national clinical audit for in-hospital cardiac arrest. To make fair comparisons among health care providers, clinical indicators require case mix adjustment using a validated risk model. The aim of this study was to develop and validate risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team in UK hospitals. Risk models for two outcomes-return of spontaneous circulation (ROSC) for greater than 20min and survival to hospital discharge-were developed and validated using data for in-hospital cardiac arrests between April 2011 and March 2013. For each outcome, a full model was fitted and then simplified by testing for non-linearity, combining categories and stepwise reduction. Finally, interactions between predictors were considered. Models were assessed for discrimination, calibration and accuracy. 22,479 in-hospital cardiac arrests in 143 hospitals were included (14,688 development, 7791 validation). The final risk model for ROSC>20min included: age (non-linear), sex, prior length of stay in hospital, reason for attendance, location of arrest, presenting rhythm, and interactions between presenting rhythm and location of arrest. The model for hospital survival included the same predictors, excluding sex. Both models had acceptable performance across the range of measures, although discrimination for hospital mortality exceeded that for ROSC>20min (c index 0.81 versus 0.72). Validated risk models for ROSC>20min and hospital survival following in-hospital cardiac arrest have been developed. These models will strengthen comparative reporting in NCAA and support local quality improvement. Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Development and validation of risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team☆

PubMed Central

Harrison, David A.; Patel, Krishna; Nixon, Edel; Soar, Jasmeet; Smith, Gary B.; Gwinnutt, Carl; Nolan, Jerry P.; Rowan, Kathryn M.

2014-01-01

Aim The National Cardiac Arrest Audit (NCAA) is the UK national clinical audit for in-hospital cardiac arrest. To make fair comparisons among health care providers, clinical indicators require case mix adjustment using a validated risk model. The aim of this study was to develop and validate risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team in UK hospitals. Methods Risk models for two outcomes—return of spontaneous circulation (ROSC) for greater than 20 min and survival to hospital discharge—were developed and validated using data for in-hospital cardiac arrests between April 2011 and March 2013. For each outcome, a full model was fitted and then simplified by testing for non-linearity, combining categories and stepwise reduction. Finally, interactions between predictors were considered. Models were assessed for discrimination, calibration and accuracy. Results 22,479 in-hospital cardiac arrests in 143 hospitals were included (14,688 development, 7791 validation). The final risk model for ROSC > 20 min included: age (non-linear), sex, prior length of stay in hospital, reason for attendance, location of arrest, presenting rhythm, and interactions between presenting rhythm and location of arrest. The model for hospital survival included the same predictors, excluding sex. Both models had acceptable performance across the range of measures, although discrimination for hospital mortality exceeded that for ROSC > 20 min (c index 0.81 versus 0.72). Conclusions Validated risk models for ROSC > 20 min and hospital survival following in-hospital cardiac arrest have been developed. These models will strengthen comparative reporting in NCAA and support local quality improvement. PMID:24830872

Tribal vs. Public Schools: Perceived Discrimination and School Adjustment among Indigenous Children from Early to Mid-Adolescence.

PubMed

Crawford, Devan M; Cheadle, Jacob E; Whitbeck, Les B

2010-04-01

The purpose of this study is to assess the differential effects of perceived discrimination by type of school on positive school adjustment among Indigenous children during late elementary and early middle school years. The analysis utilizes a sample of 654 Indigenous children from four reservations in the Northern Midwest and four Canadian First Nation reserves. Multiple group linear growth modeling within a structural equation framework is employed to investigate the moderating effects of school type on the relationship between discrimination and positive school adjustment. Results show that students in all school types score relatively high on positive school adjustment at time one (ages 10-12). However, in contrast to students in tribal schools for whom positive school adjustment remains stable, those attending public schools and those moving between school types show a decline in school adjustment over time. Furthermore, the negative effects of discrimination on positive school adjustment are greater for those attending public schools and those moving between schools. Possible reasons for this finding and potential explanations for why tribal schools may provide protection from the negative effects of discrimination are discussed.

Quantifying the Number of Discriminable Coincident Dendritic Input Patterns through Dendritic Tree Morphology

PubMed Central

Zippo, Antonio G.; Biella, Gabriele E. M.

2015-01-01

Current developments in neuronal physiology are unveiling novel roles for dendrites. Experiments have shown mechanisms of non-linear synaptic NMDA dependent activations, able to discriminate input patterns through the waveforms of the excitatory postsynaptic potentials. Contextually, the synaptic clustering of inputs is the principal cellular strategy to separate groups of common correlated inputs. Dendritic branches appear to work as independent discriminating units of inputs potentially reflecting an extraordinary repertoire of pattern memories. However, it is unclear how these observations could impact our comprehension of the structural correlates of memory at the cellular level. This work investigates the discrimination capabilities of neurons through computational biophysical models to extract a predicting law for the dendritic input discrimination capability (M). By this rule we compared neurons from a neuron reconstruction repository (neuromorpho.org). Comparisons showed that primate neurons were not supported by an equivalent M preeminence and that M is not uniformly distributed among neuron types. Remarkably, neocortical neurons had substantially less memory capacity in comparison to those from non-cortical regions. In conclusion, the proposed rule predicts the inherent neuronal spatial memory gathering potentially relevant anatomical and evolutionary considerations about the brain cytoarchitecture. PMID:26100354

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

Random forests, a novel approach for discrimination of fish populations using parasites as biological tags.

PubMed

Perdiguero-Alonso, Diana; Montero, Francisco E; Kostadinova, Aneta; Raga, Juan Antonio; Barrett, John

2008-10-01

Due to the complexity of host-parasite relationships, discrimination between fish populations using parasites as biological tags is difficult. This study introduces, to our knowledge for the first time, random forests (RF) as a new modelling technique in the application of parasite community data as biological markers for population assignment of fish. This novel approach is applied to a dataset with a complex structure comprising 763 parasite infracommunities in population samples of Atlantic cod, Gadus morhua, from the spawning/feeding areas in five regions in the North East Atlantic (Baltic, Celtic, Irish and North seas and Icelandic waters). The learning behaviour of RF is evaluated in comparison with two other algorithms applied to class assignment problems, the linear discriminant function analysis (LDA) and artificial neural networks (ANN). The three algorithms are used to develop predictive models applying three cross-validation procedures in a series of experiments (252 models in total). The comparative approach to RF, LDA and ANN algorithms applied to the same datasets demonstrates the competitive potential of RF for developing predictive models since RF exhibited better accuracy of prediction and outperformed LDA and ANN in the assignment of fish to their regions of sampling using parasite community data. The comparative analyses and the validation experiment with a 'blind' sample confirmed that RF models performed more effectively with a large and diverse training set and a large number of variables. The discrimination results obtained for a migratory fish species with largely overlapping parasite communities reflects the high potential of RF for developing predictive models using data that are both complex and noisy, and indicates that it is a promising tool for parasite tag studies. Our results suggest that parasite community data can be used successfully to discriminate individual cod from the five different regions of the North East Atlantic studied using RF.

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

A model of plant canopy polarization

NASA Technical Reports Server (NTRS)

Vanderbilt, V. C.

1980-01-01

A model for the amount of linearly polarized light reflected by the shiny leaves of grain crops is based on the morphological and phenological characteristics of the plant canopy and upon the Fresnel equations which describe the light reflection process at the smooth boundary separating two dielectrics. The theory used demonstrates that, potentially, measurements of the linearly polarized light from a crop canopy may be used as an additional feature to discriminate between crops such as wheat and barley, two crops which are so spectrally similar that they are misclassified with unacceptable frequency. Examination of the model suggests that, potentially, satellite polarization measurements may be used to monitor crop development stage, leaf water content, leaf area index, hail damage, and certain plant diseases. The information content of these measurements is needed to evaluate the proposed polarization sensor for the satellite-borne multispectral resource sampler.

Discriminative Learning of Receptive Fields from Responses to Non-Gaussian Stimulus Ensembles

PubMed Central

Meyer, Arne F.; Diepenbrock, Jan-Philipp; Happel, Max F. K.; Ohl, Frank W.; Anemüller, Jörn

2014-01-01

Analysis of sensory neurons' processing characteristics requires simultaneous measurement of presented stimuli and concurrent spike responses. The functional transformation from high-dimensional stimulus space to the binary space of spike and non-spike responses is commonly described with linear-nonlinear models, whose linear filter component describes the neuron's receptive field. From a machine learning perspective, this corresponds to the binary classification problem of discriminating spike-eliciting from non-spike-eliciting stimulus examples. The classification-based receptive field (CbRF) estimation method proposed here adapts a linear large-margin classifier to optimally predict experimental stimulus-response data and subsequently interprets learned classifier weights as the neuron's receptive field filter. Computational learning theory provides a theoretical framework for learning from data and guarantees optimality in the sense that the risk of erroneously assigning a spike-eliciting stimulus example to the non-spike class (and vice versa) is minimized. Efficacy of the CbRF method is validated with simulations and for auditory spectro-temporal receptive field (STRF) estimation from experimental recordings in the auditory midbrain of Mongolian gerbils. Acoustic stimulation is performed with frequency-modulated tone complexes that mimic properties of natural stimuli, specifically non-Gaussian amplitude distribution and higher-order correlations. Results demonstrate that the proposed approach successfully identifies correct underlying STRFs, even in cases where second-order methods based on the spike-triggered average (STA) do not. Applied to small data samples, the method is shown to converge on smaller amounts of experimental recordings and with lower estimation variance than the generalized linear model and recent information theoretic methods. Thus, CbRF estimation may prove useful for investigation of neuronal processes in response to natural stimuli and in settings where rapid adaptation is induced by experimental design. PMID:24699631

Discriminative learning of receptive fields from responses to non-Gaussian stimulus ensembles.

PubMed

Meyer, Arne F; Diepenbrock, Jan-Philipp; Happel, Max F K; Ohl, Frank W; Anemüller, Jörn

2014-01-01

Analysis of sensory neurons' processing characteristics requires simultaneous measurement of presented stimuli and concurrent spike responses. The functional transformation from high-dimensional stimulus space to the binary space of spike and non-spike responses is commonly described with linear-nonlinear models, whose linear filter component describes the neuron's receptive field. From a machine learning perspective, this corresponds to the binary classification problem of discriminating spike-eliciting from non-spike-eliciting stimulus examples. The classification-based receptive field (CbRF) estimation method proposed here adapts a linear large-margin classifier to optimally predict experimental stimulus-response data and subsequently interprets learned classifier weights as the neuron's receptive field filter. Computational learning theory provides a theoretical framework for learning from data and guarantees optimality in the sense that the risk of erroneously assigning a spike-eliciting stimulus example to the non-spike class (and vice versa) is minimized. Efficacy of the CbRF method is validated with simulations and for auditory spectro-temporal receptive field (STRF) estimation from experimental recordings in the auditory midbrain of Mongolian gerbils. Acoustic stimulation is performed with frequency-modulated tone complexes that mimic properties of natural stimuli, specifically non-Gaussian amplitude distribution and higher-order correlations. Results demonstrate that the proposed approach successfully identifies correct underlying STRFs, even in cases where second-order methods based on the spike-triggered average (STA) do not. Applied to small data samples, the method is shown to converge on smaller amounts of experimental recordings and with lower estimation variance than the generalized linear model and recent information theoretic methods. Thus, CbRF estimation may prove useful for investigation of neuronal processes in response to natural stimuli and in settings where rapid adaptation is induced by experimental design.

Regularization strategies for hyperplane classifiers: application to cancer classification with gene expression data.

PubMed

Andries, Erik; Hagstrom, Thomas; Atlas, Susan R; Willman, Cheryl

2007-02-01

Linear discrimination, from the point of view of numerical linear algebra, can be treated as solving an ill-posed system of linear equations. In order to generate a solution that is robust in the presence of noise, these problems require regularization. Here, we examine the ill-posedness involved in the linear discrimination of cancer gene expression data with respect to outcome and tumor subclasses. We show that a filter factor representation, based upon Singular Value Decomposition, yields insight into the numerical ill-posedness of the hyperplane-based separation when applied to gene expression data. We also show that this representation yields useful diagnostic tools for guiding the selection of classifier parameters, thus leading to improved performance.

Evaluation of airborne lidar data to predict vegetation Presence/Absence

USGS Publications Warehouse

Palaseanu-Lovejoy, M.; Nayegandhi, A.; Brock, J.; Woodman, R.; Wright, C.W.

2009-01-01

This study evaluates the capabilities of the Experimental Advanced Airborne Research Lidar (EAARL) in delineating vegetation assemblages in Jean Lafitte National Park, Louisiana. Five-meter-resolution grids of bare earth, canopy height, canopy-reflection ratio, and height of median energy were derived from EAARL data acquired in September 2006. Ground-truth data were collected along transects to assess species composition, canopy cover, and ground cover. To decide which model is more accurate, comparisons of general linear models and generalized additive models were conducted using conventional evaluation methods (i.e., sensitivity, specificity, Kappa statistics, and area under the curve) and two new indexes, net reclassification improvement and integrated discrimination improvement. Generalized additive models were superior to general linear models in modeling presence/absence in training vegetation categories, but no statistically significant differences between the two models were achieved in determining the classification accuracy at validation locations using conventional evaluation methods, although statistically significant improvements in net reclassifications were observed. ?? 2009 Coastal Education and Research Foundation.

Darker Skin Tone Increases Perceived Discrimination among Male but Not Female Caribbean Black Youth

PubMed Central

Caldwell, Cleopatra Howard

2017-01-01

Background: Among most minority groups, males seem to report higher levels of exposure and vulnerability to racial discrimination. Although darker skin tone may increase exposure to racial discrimination, it is yet unknown whether skin tone similarly influences perceived discrimination among male and female Caribbean Black youth. Objective: The current cross-sectional study tests the role of gender on the effects of skin tone on perceived discrimination among Caribbean Black youth. Methods: Data came from the National Survey of American Life-Adolescent Supplement (NSAL-A), 2003–2004, which included 360 Caribbean Black youth (ages 13 to 17). Demographic factors (age and gender), socioeconomic status (SES; family income, income to needs ratio, and subjective SES), skin tone, and perceived everyday discrimination were measured. Linear regressions were used for data analysis. Results: In the pooled sample, darker skin tone was associated with higher levels of perceived discrimination among Caribbean Black youth (b = 0.48; 95% Confidence Interval (CI) = 0.07–0.89). A significant interaction was found between gender and skin tone (b = 1.17; 95% CI = 0.49–1.86), suggesting a larger effect of skin tone on perceived discrimination for males than females. In stratified models, darker skin tone was associated with more perceived discrimination for males (b = 1.20; 95% CI = 0.69–0.72) but not females (b = 0.06; 95% CI = −0.42–0.55). Conclusion: Similar to the literature documenting male gender as a vulnerability factor to the effects of racial discrimination, we found that male but not female Caribbean Black youth with darker skin tones perceive more discrimination. PMID:29231903

Meta-analytic review of the development of face discrimination in infancy: Face race, face gender, infant age, and methodology moderate face discrimination.

PubMed

Sugden, Nicole A; Marquis, Alexandra R

2017-11-01

Infants show facility for discriminating between individual faces within hours of birth. Over the first year of life, infants' face discrimination shows continued improvement with familiar face types, such as own-race faces, but not with unfamiliar face types, like other-race faces. The goal of this meta-analytic review is to provide an effect size for infants' face discrimination ability overall, with own-race faces, and with other-race faces within the first year of life, how this differs with age, and how it is influenced by task methodology. Inclusion criteria were (a) infant participants aged 0 to 12 months, (b) completing a human own- or other-race face discrimination task, (c) with discrimination being determined by infant looking. Our analysis included 30 works (165 samples, 1,926 participants participated in 2,623 tasks). The effect size for infants' face discrimination was small, 6.53% greater than chance (i.e., equal looking to the novel and familiar). There was a significant difference in discrimination by race, overall (own-race, 8.18%; other-race, 3.18%) and between ages (own-race: 0- to 4.5-month-olds, 7.32%; 5- to 7.5-month-olds, 9.17%; and 8- to 12-month-olds, 7.68%; other-race: 0- to 4.5-month-olds, 6.12%; 5- to 7.5-month-olds, 3.70%; and 8- to 12-month-olds, 2.79%). Multilevel linear (mixed-effects) models were used to predict face discrimination; infants' capacity to discriminate faces is sensitive to face characteristics including race, gender, and emotion as well as the methods used, including task timing, coding method, and visual angle. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Frequency-domain full-waveform inversion with non-linear descent directions

NASA Astrophysics Data System (ADS)

Geng, Yu; Pan, Wenyong; Innanen, Kristopher A.

2018-05-01

Full-waveform inversion (FWI) is a highly non-linear inverse problem, normally solved iteratively, with each iteration involving an update constructed through linear operations on the residuals. Incorporating a flexible degree of non-linearity within each update may have important consequences for convergence rates, determination of low model wavenumbers and discrimination of parameters. We examine one approach for doing so, wherein higher order scattering terms are included within the sensitivity kernel during the construction of the descent direction, adjusting it away from that of the standard Gauss-Newton approach. These scattering terms are naturally admitted when we construct the sensitivity kernel by varying not the current but the to-be-updated model at each iteration. Linear and/or non-linear inverse scattering methodologies allow these additional sensitivity contributions to be computed from the current data residuals within any given update. We show that in the presence of pre-critical reflection data, the error in a second-order non-linear update to a background of s0 is, in our scheme, proportional to at most (Δs/s0)3 in the actual parameter jump Δs causing the reflection. In contrast, the error in a standard Gauss-Newton FWI update is proportional to (Δs/s0)2. For numerical implementation of more complex cases, we introduce a non-linear frequency-domain scheme, with an inner and an outer loop. A perturbation is determined from the data residuals within the inner loop, and a descent direction based on the resulting non-linear sensitivity kernel is computed in the outer loop. We examine the response of this non-linear FWI using acoustic single-parameter synthetics derived from the Marmousi model. The inverted results vary depending on data frequency ranges and initial models, but we conclude that the non-linear FWI has the capability to generate high-resolution model estimates in both shallow and deep regions, and to converge rapidly, relative to a benchmark FWI approach involving the standard gradient.

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.

Validity and reliability of the Internalized Stigma of Smoking Inventory: An exploration of shame, isolation, and discrimination in smokers with mental health diagnoses.

PubMed

Brown-Johnson, Cati G; Cataldo, Janine K; Orozco, Nicholas; Lisha, Nadra E; Hickman, Norval J; Prochaska, Judith J

2015-08-01

De-normalization of smoking as a public health strategy may create shame and isolation in vulnerable groups unable to quit. To examine the nature and impact of smoking stigma, we developed the Internalized Stigma of Smoking Inventory (ISSI), tested its validity and reliability, and explored factors that may contribute to smoking stigma. We evaluated the ISSI in a sample of smokers with mental health diagnoses (N = 956), using exploratory and confirmatory factor analysis, and assessed construct validity. Results reduced the ISSI to eight items with three subscales: smoking self-stigma related to shame, felt stigma related to social isolation, and discrimination experiences. Discrimination was the most commonly endorsed of the three subscales. A multivariate generalized linear model predicted 21-30% of the variance in the smoking stigma subscales. Self-stigma was greatest among those intending to quit; felt stigma was highest among those experiencing stigma in other domains, namely ethnicity and mental illness-based; and smoking-related discrimination was highest among women, Caucasians, and those with more education. Smoking stigma may compound stigma experiences in other areas. Aspects of smoking stigma in the domains of shame, isolation, and discrimination were related to modeled stigma responses, particularly readiness to quit and cigarette addiction, and were found to be more salient for groups where tobacco use is least prevalent. The ISSI measure is useful for quantifying smoking-related stigma in multiple domains. © American Academy of Addiction Psychiatry.

Differentiation of Chinese rice wines from different wineries based on mineral elemental fingerprinting.

PubMed

Shen, Fei; Wu, Jian; Ying, Yibin; Li, Bobin; Jiang, Tao

2013-12-15

Discrimination of Chinese rice wines from three well-known wineries ("Guyuelongshan", "Kuaijishan", and "Pagoda") in China has been carried out according to mineral element contents in this study. Nineteen macro and trace mineral elements (Na, Mg, Al, K, Ca, Mn, Fe, Cu, Zn, V, Cr, Co, Ni, As, Se, Mo, Cd, Ba and Pb) were determined by inductively coupled plasma mass spectrometry (ICP-MS) in 117 samples. Then the experimental data were subjected to analysis of variance (ANOVA) and principal component analysis (PCA) to reveal significant differences and potential patterns between samples. Stepwise linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA) were applied to develop classification models and achieved correct classified rates of 100% and 97.4% for the prediction sample set, respectively. The discrimination could be attributed to different raw materials (mainly water) and elaboration processes employed. The results indicate that the element compositions combined with multivariate analysis can be used as fingerprinting techniques to protect prestigious wineries and enable the authenticity of Chinese rice wine. Copyright © 2013 Elsevier Ltd. All rights reserved.

Discrimination of nonlinear frequency glides.

PubMed

Thyer, Nick; Mahar, Doug

2006-05-01

Discrimination thresholds for short duration nonlinear tone glides that differed in glide rate were measured in order to determine whether cues related to rate of frequency change alone were sufficient for discrimination. Thresholds for rising and falling nonlinear glides of 50-ms and 400-ms duration, spanning three frequency excursions (0.5, 1, and 2 ERBs) at three center frequencies (0.5, 2.0, and 6.0 kHz) were measured. Results showed that glide discrimination was possible when duration and initial and final frequencies were identical. Thresholds were of a different order to those found in previous studies using linear frequency glides where endpoint frequency or duration information is available as added cues. The pattern of results was suggestive of a mechanism sensitive to spectral changes in time. Thresholds increased as the rate of transition span increased, particularly above spans of 1 ERB. The Weber fraction associated with these changes was 0.6-0.7. Overall, the results were consistent with an excitation pattern model of nonlinear glide detection that has difficulty in tracking signals with rapid frequency changes that exceed the width of an auditory filter and are of short duration.

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.

Discriminative Bayesian Dictionary Learning for Classification.

PubMed

Akhtar, Naveed; Shafait, Faisal; Mian, Ajmal

2016-12-01

We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.

Comparison of two weighted integration models for the cueing task: linear and likelihood

NASA Technical Reports Server (NTRS)

Shimozaki, Steven S.; Eckstein, Miguel P.; Abbey, Craig K.

2003-01-01

In a task in which the observer must detect a signal at two locations, presenting a precue that predicts the location of a signal leads to improved performance with a valid cue (signal location matches the cue), compared to an invalid cue (signal location does not match the cue). The cue validity effect has often been explained with a limited capacity attentional mechanism improving the perceptual quality at the cued location. Alternatively, the cueing effect can also be explained by unlimited capacity models that assume a weighted combination of noisy responses across the two locations. We compare two weighted integration models, a linear model and a sum of weighted likelihoods model based on a Bayesian observer. While qualitatively these models are similar, quantitatively they predict different cue validity effects as the signal-to-noise ratios (SNR) increase. To test these models, 3 observers performed in a cued discrimination task of Gaussian targets with an 80% valid precue across a broad range of SNR's. Analysis of a limited capacity attentional switching model was also included and rejected. The sum of weighted likelihoods model best described the psychophysical results, suggesting that human observers approximate a weighted combination of likelihoods, and not a weighted linear combination.

Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning.

PubMed

Oh, Jooyoung; Cho, Dongrae; Park, Jaesub; Na, Se Hee; Kim, Jongin; Heo, Jaeseok; Shin, Cheung Soo; Kim, Jae-Jin; Park, Jin Young; Lee, Boreom

2018-03-27

Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distinguished from non-delirious patients by using heart rate variability (HRV) and machine learning. Electrocardiography data of 140 patients was acquired during daily ICU care, and HRV data were analyzed. Delirium, including its type, severity, and etiologies, was evaluated daily by trained psychiatrists. HRV data and various machine learning algorithms including linear support vector machine (SVM), SVM with radial basis function (RBF) kernels, linear extreme learning machine (ELM), ELM with RBF kernels, linear discriminant analysis, and quadratic discriminant analysis were utilized to distinguish delirium patients from non-delirium patients. HRV data of 4797 ECGs were included, and 39 patients had delirium at least once during their ICU stay. The maximum classification accuracy was acquired using SVM with RBF kernels. Our prediction method based on HRV with machine learning was comparable to previous delirium prediction models using massive amounts of clinical information. Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.

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.

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

PubMed

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

2007-01-01

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

Hierarchical Feature Extraction With Local Neural Response for Image Recognition.

PubMed

Li, Hong; Wei, Yantao; Li, Luoqing; Chen, C L P

2013-04-01

In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.

Kullback-Leibler information function and the sequential selection of experiments to discriminate among several linear models

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1972-01-01

The error variance of the process prior multivariate normal distributions of the parameters of the models are assumed to be specified, prior probabilities of the models being correct. A rule for termination of sampling is proposed. Upon termination, the model with the largest posterior probability is chosen as correct. If sampling is not terminated, posterior probabilities of the models and posterior distributions of the parameters are computed. An experiment was chosen to maximize the expected Kullback-Leibler information function. Monte Carlo simulation experiments were performed to investigate large and small sample behavior of the sequential adaptive procedure.

[Study on the early detection of Sclerotinia of Brassica napus based on combinational-stimulated bands].

PubMed

Liu, Fei; Feng, Lei; Lou, Bing-gan; Sun, Guang-ming; Wang, Lian-ping; He, Yong

2010-07-01

The combinational-stimulated bands were used to develop linear and nonlinear calibrations for the early detection of sclerotinia of oilseed rape (Brassica napus L.). Eighty healthy and 100 Sclerotinia leaf samples were scanned, and different preprocessing methods combined with successive projections algorithm (SPA) were applied to develop partial least squares (PLS) discriminant models, multiple linear regression (MLR) and least squares-support vector machine (LS-SVM) models. The results indicated that the optimal full-spectrum PLS model was achieved by direct orthogonal signal correction (DOSC), then De-trending and Raw spectra with correct recognition ratio of 100%, 95.7% and 95.7%, respectively. When using combinational-stimulated bands, the optimal linear models were SPA-MLR (DOSC) and SPA-PLS (DOSC) with correct recognition ratio of 100%. All SPA-LSSVM models using DOSC, De-trending and Raw spectra achieved perfect results with recognition of 100%. The overall results demonstrated that it was feasible to use combinational-stimulated bands for the early detection of Sclerotinia of oilseed rape, and DOSC-SPA was a powerful way for informative wavelength selection. This method supplied a new approach to the early detection and portable monitoring instrument of sclerotinia.

Discrimination in the workplace, reported by people with major depressive disorder: a cross-sectional study in 35 countries

PubMed Central

Brouwers, E P M; Mathijssen, J; Van Bortel, T; Knifton, L; Wahlbeck, K; Van Audenhove, C; Kadri, N; Chang, Ch; Goud, B R; Ballester, D; Tófoli, LF; Bello, R; Jorge-Monteiro, M F; Zäske, H; Milaćić, I; Uçok, A; Bonetto, C; Lasalvia, A; Thornicroft, G; Van Weeghel, J

2016-01-01

Objective Whereas employment has been shown to be beneficial for people with Major Depressive Disorder (MDD) across different cultures, employers’ attitudes have been shown to be negative towards workers with MDD. This may form an important barrier to work participation. Today, little is known about how stigma and discrimination affect work participation of workers with MDD, especially from their own perspective. We aimed to assess, in a working age population including respondents with MDD from 35 countries: (1) if people with MDD anticipate and experience discrimination when trying to find or keep paid employment; (2) if participants in high, middle and lower developed countries differ in these respects; and (3) if discrimination experiences are related to actual employment status (ie, having a paid job or not). Method Participants in this cross-sectional study (N=834) had a diagnosis of MDD in the previous 12 months. They were interviewed using the Discrimination and Stigma Scale (DISC-12). Analysis of variance and generalised linear mixed models were used to analyse the data. Results Overall, 62.5% had anticipated and/or experienced discrimination in the work setting. In very high developed countries, almost 60% of respondents had stopped themselves from applying for work, education or training because of anticipated discrimination. Having experienced workplace discrimination was independently related to unemployment. Conclusions Across different countries and cultures, people with MDD very frequently reported discrimination in the work setting. Effective interventions are needed to enhance work participation in people with MDD, focusing simultaneously on decreasing stigma in the work environment and on decreasing self-discrimination by empowering workers with MDD. PMID:26908523

Discrimination in the workplace, reported by people with major depressive disorder: a cross-sectional study in 35 countries.

PubMed

Brouwers, E P M; Mathijssen, J; Van Bortel, T; Knifton, L; Wahlbeck, K; Van Audenhove, C; Kadri, N; Chang, Ch; Goud, B R; Ballester, D; Tófoli, L F; Bello, R; Jorge-Monteiro, M F; Zäske, H; Milaćić, I; Uçok, A; Bonetto, C; Lasalvia, A; Thornicroft, G; Van Weeghel, J

2016-02-23

Whereas employment has been shown to be beneficial for people with Major Depressive Disorder (MDD) across different cultures, employers' attitudes have been shown to be negative towards workers with MDD. This may form an important barrier to work participation. Today, little is known about how stigma and discrimination affect work participation of workers with MDD, especially from their own perspective. We aimed to assess, in a working age population including respondents with MDD from 35 countries: (1) if people with MDD anticipate and experience discrimination when trying to find or keep paid employment; (2) if participants in high, middle and lower developed countries differ in these respects; and (3) if discrimination experiences are related to actual employment status (ie, having a paid job or not). Participants in this cross-sectional study (N=834) had a diagnosis of MDD in the previous 12 months. They were interviewed using the Discrimination and Stigma Scale (DISC-12). Analysis of variance and generalised linear mixed models were used to analyse the data. Overall, 62.5% had anticipated and/or experienced discrimination in the work setting. In very high developed countries, almost 60% of respondents had stopped themselves from applying for work, education or training because of anticipated discrimination. Having experienced workplace discrimination was independently related to unemployment. Across different countries and cultures, people with MDD very frequently reported discrimination in the work setting. Effective interventions are needed to enhance work participation in people with MDD, focusing simultaneously on decreasing stigma in the work environment and on decreasing self-discrimination by empowering workers with MDD. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Research of Face Recognition with Fisher Linear Discriminant

NASA Astrophysics Data System (ADS)

Rahim, R.; Afriliansyah, T.; Winata, H.; Nofriansyah, D.; Ratnadewi; Aryza, S.

2018-01-01

Face identification systems are developing rapidly, and these developments drive the advancement of biometric-based identification systems that have high accuracy. However, to develop a good face recognition system and to have high accuracy is something that’s hard to find. Human faces have diverse expressions and attribute changes such as eyeglasses, mustache, beard and others. Fisher Linear Discriminant (FLD) is a class-specific method that distinguishes facial image images into classes and also creates distance between classes and intra classes so as to produce better classification.

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.

Generalized t-statistic for two-group classification.

PubMed

Komori, Osamu; Eguchi, Shinto; Copas, John B

2015-06-01

In the classic discriminant model of two multivariate normal distributions with equal variance matrices, the linear discriminant function is optimal both in terms of the log likelihood ratio and in terms of maximizing the standardized difference (the t-statistic) between the means of the two distributions. In a typical case-control study, normality may be sensible for the control sample but heterogeneity and uncertainty in diagnosis may suggest that a more flexible model is needed for the cases. We generalize the t-statistic approach by finding the linear function which maximizes a standardized difference but with data from one of the groups (the cases) filtered by a possibly nonlinear function U. We study conditions for consistency of the method and find the function U which is optimal in the sense of asymptotic efficiency. Optimality may also extend to other measures of discriminatory efficiency such as the area under the receiver operating characteristic curve. The optimal function U depends on a scalar probability density function which can be estimated non-parametrically using a standard numerical algorithm. A lasso-like version for variable selection is implemented by adding L1-regularization to the generalized t-statistic. Two microarray data sets in the study of asthma and various cancers are used as motivating examples. © 2014, The International Biometric Society.

L1-norm kernel discriminant analysis via Bayes error bound optimization for robust feature extraction.

PubMed

Zheng, Wenming; Lin, Zhouchen; Wang, Haixian

2014-04-01

A novel discriminant analysis criterion is derived in this paper under the theoretical framework of Bayes optimality. In contrast to the conventional Fisher's discriminant criterion, the major novelty of the proposed one is the use of L1 norm rather than L2 norm, which makes it less sensitive to the outliers. With the L1-norm discriminant criterion, we propose a new linear discriminant analysis (L1-LDA) method for linear feature extraction problem. To solve the L1-LDA optimization problem, we propose an efficient iterative algorithm, in which a novel surrogate convex function is introduced such that the optimization problem in each iteration is to simply solve a convex programming problem and a close-form solution is guaranteed to this problem. Moreover, we also generalize the L1-LDA method to deal with the nonlinear robust feature extraction problems via the use of kernel trick, and hereafter proposed the L1-norm kernel discriminant analysis (L1-KDA) method. Extensive experiments on simulated and real data sets are conducted to evaluate the effectiveness of the proposed method in comparing with the state-of-the-art methods.

Progress toward the determination of correct classification rates in fire debris analysis.

PubMed

Waddell, Erin E; Song, Emma T; Rinke, Caitlin N; Williams, Mary R; Sigman, Michael E

2013-07-01

Principal components analysis (PCA), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) were used to develop a multistep classification procedure for determining the presence of ignitable liquid residue in fire debris and assigning any ignitable liquid residue present into the classes defined under the American Society for Testing and Materials (ASTM) E 1618-10 standard method. A multistep classification procedure was tested by cross-validation based on model data sets comprised of the time-averaged mass spectra (also referred to as total ion spectra) of commercial ignitable liquids and pyrolysis products from common building materials and household furnishings (referred to simply as substrates). Fire debris samples from laboratory-scale and field test burns were also used to test the model. The optimal model's true-positive rate was 81.3% for cross-validation samples and 70.9% for fire debris samples. The false-positive rate was 9.9% for cross-validation samples and 8.9% for fire debris samples. © 2013 American Academy of Forensic Sciences.

An algal model for predicting attainment of tiered biological criteria of Maine's streams and rivers

USGS Publications Warehouse

Danielson, Thomas J.; Loftin, Cyndy; Tsomides, Leonidas; DiFranco, Jeanne L.; Connors, Beth; Courtemanch, David L.; Drummond, Francis; Davies, Susan

2012-01-01

State water-quality professionals developing new biological assessment methods often have difficulty relating assessment results to narrative criteria in water-quality standards. An alternative to selecting index thresholds arbitrarily is to include the Biological Condition Gradient (BCG) in the development of the assessment method. The BCG describes tiers of biological community condition to help identify and communicate the position of a water body along a gradient of water quality ranging from natural to degraded. Although originally developed for fish and macroinvertebrate communities of streams and rivers, the BCG is easily adapted to other habitats and taxonomic groups. We developed a discriminant analysis model with stream algal data to predict attainment of tiered aquatic-life uses in Maine's water-quality standards. We modified the BCG framework for Maine stream algae, related the BCG tiers to Maine's tiered aquatic-life uses, and identified appropriate algal metrics for describing BCG tiers. Using a modified Delphi method, 5 aquatic biologists independently evaluated algal community metrics for 230 samples from streams and rivers across the state and assigned a BCG tier (1–6) and Maine water quality class (AA/A, B, C, nonattainment of any class) to each sample. We used minimally disturbed reference sites to approximate natural conditions (Tier 1). Biologist class assignments were unanimous for 53% of samples, and 42% of samples differed by 1 class. The biologists debated and developed consensus class assignments. A linear discriminant model built to replicate a priori class assignments correctly classified 95% of 150 samples in the model training set and 91% of 80 samples in the model validation set. Locally derived metrics based on BCG taxon tolerance groupings (e.g., sensitive, intermediate, tolerant) were more effective than were metrics developed in other regions. Adding the algal discriminant model to Maine's existing macroinvertebrate discriminant model will broaden detection of biological impairment and further diagnose sources of impairment. The algal discriminant model is specific to Maine, but our approach of explicitly tying an assessment tool to tiered aquatic-life goals is widely transferrable to other regions, taxonomic groups, and waterbody types.

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.

Diagnostic power of optic disc morphology, peripapillary retinal nerve fiber layer thickness, and macular inner retinal layer thickness in glaucoma diagnosis with fourier-domain optical coherence tomography.

PubMed

Huang, Jehn-Yu; Pekmezci, Melike; Mesiwala, Nisreen; Kao, Andrew; Lin, Shan

2011-02-01

To evaluate the capability of the optic disc, peripapillary retinal nerve fiber layer (P-RNFL), macular inner retinal layer (M-IRL) parameters, and their combination obtained by Fourier-domain optical coherent tomography (OCT) in differentiating a glaucoma suspect from perimetric glaucoma. Two hundred and twenty eyes from 220 patients were enrolled in this study. The optic disc morphology, P-RNFL, and M-IRL were assessed by the Fourier-domain OCT (RTVue OCT, Model RT100, Optovue, Fremont, CA). A linear discriminant function was generated by stepwise linear discriminant analysis on the basis of OCT parameters and demographic factors. The diagnostic power of these parameters was evaluated with receiver operating characteristic (ROC) curve analysis. The diagnostic power in the clinically relevant range (specificity ≥ 80%) was presented as the partial area under the ROC curve (partial AROC). The individual OCT parameter with the largest AROC and partial AROC in the high specificity (≥ 80%) range were cup/disc vertical ratio (AROC = 0.854 and partial AROC = 0.142) for the optic disc parameters, average thickness (AROC = 0.919 and partial AROC = 0.147) for P-RNFL parameters, inferior hemisphere thickness (AROC = 0.871 and partial AROC = 0.138) for M-IRL parameters, respectively. The linear discriminant function further enhanced the ability in detecting perimetric glaucoma (AROC = 0.970 and partial AROC = 0.172). Average P-RNFL thickness is the optimal individual OCT parameter to detect perimetric glaucoma. Simultaneous evaluation on disc morphology, P-RNFL, and M-IRL thickness can improve the diagnostic accuracy in diagnosing glaucoma.

Analysis of lithology: Vegetation mixes in multispectral images

NASA Technical Reports Server (NTRS)

Adams, J. B.; Smith, M.; Adams, J. D.

1982-01-01

Discrimination and identification of lithologies from multispectral images is discussed. Rock/soil identification can be facilitated by removing the component of the signal in the images that is contributed by the vegetation. Mixing models were developed to predict the spectra of combinations of pure end members, and those models were refined using laboratory measurements of real mixtures. Models in use include a simple linear (checkerboard) mix, granular mixing, semi-transparent coatings, and combinations of the above. The use of interactive computer techniques that allow quick comparison of the spectrum of a pixel stack (in a multiband set) with laboratory spectra is discussed.

Enzyme class identification in cleaning products by hydrolysis followed by derivatization with o-phthaldialdehyde, HPLC and linear discriminant analysis.

PubMed

Beneito-Cambra, M; Bernabé-Zafón, V; Herrero-Martínez, J M; Simó-Alfonso, E F; Ramis-Ramos, G

2009-07-15

The enzymes present in raw materials of the cleaning industry (enzyme industrial concentrates) and in household cleaners were isolated by precipitation with acetone and hydrolyzed with HCl. The resulting amino acids were derivatized with o-phthaldialdehyde, and the derivatives were separated by HPLC. The peaks of 14 amino acids were observed using a C18 column and a multi-segmented gradient of acetonitrile-water in the presence of a 5 mM citric/citrate buffer of pH 6.5. Using either normalized peak areas (divided by the sum of the peak areas of the chromatogram) or ratios of pairs of peak areas as predictor variables, linear discriminant analysis models, capable of predicting the enzyme class, including proteases, lipases, amylases and cellulases, were constructed. For this purpose, both enzyme industrial concentrates and detergent bases spiked with them were included in the training set. In all cases, the enzymes of the evaluation set, including industrial concentrates, spiked detergent bases and commercial cleaners were correctly classified with assignment probabilities higher than 99%.

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.

Multivariate analysis of volatile compounds detected by headspace solid-phase microextraction/gas chromatography: A tool for sensory classification of cork stoppers.

PubMed

Prat, Chantal; Besalú, Emili; Bañeras, Lluís; Anticó, Enriqueta

2011-06-15

The volatile fraction of aqueous cork macerates of tainted and non-tainted agglomerate cork stoppers was analysed by headspace solid-phase microextraction (HS-SPME)/gas chromatography. Twenty compounds containing terpenoids, aliphatic alcohols, lignin-related compounds and others were selected and analysed in individual corks. Cork stoppers were previously classified in six different classes according to sensory descriptions including, 2,4,6-trichloroanisole taint and other frequent, non-characteristic odours found in cork. A multivariate analysis of the chromatographic data of 20 selected chemical compounds using linear discriminant analysis models helped in the differentiation of the a priori made groups. The discriminant model selected five compounds as the best combination. Selected compounds appear in the model in the following order; 2,4,6 TCA, fenchyl alcohol, 1-octen-3-ol, benzyl alcohol and benzothiazole. Unfortunately, not all six a priori differentiated sensory classes were clearly discriminated in the model, probably indicating that no measurable differences exist in the chromatographic data for some categories. The predictive analyses of a refined model in which two sensory classes were fused together resulted in a good classification. Prediction rates of control (non-tainted), TCA, musty-earthy-vegetative, vegetative and chemical descriptions were 100%, 100%, 85%, 67.3% and 100%, respectively, when the modified model was used. The multivariate analysis of chromatographic data will help in the classification of stoppers and provide a perfect complement to sensory analyses. Copyright © 2010 Elsevier Ltd. All rights reserved.

A hybrid credibility-based fuzzy multiple objective optimisation to differential pricing and inventory policies with arbitrage consideration

NASA Astrophysics Data System (ADS)

Ghasemy Yaghin, R.; Fatemi Ghomi, S. M. T.; Torabi, S. A.

2015-10-01

In most markets, price differentiation mechanisms enable manufacturers to offer different prices for their products or services in different customer segments; however, the perfect price discrimination is usually impossible for manufacturers. The importance of accounting for uncertainty in such environments spurs an interest to develop appropriate decision-making tools to deal with uncertain and ill-defined parameters in joint pricing and lot-sizing problems. This paper proposes a hybrid bi-objective credibility-based fuzzy optimisation model including both quantitative and qualitative objectives to cope with these issues. Taking marketing and lot-sizing decisions into account simultaneously, the model aims to maximise the total profit of manufacturer and to improve service aspects of retailing simultaneously to set different prices with arbitrage consideration. After applying appropriate strategies to defuzzify the original model, the resulting non-linear multi-objective crisp model is then solved by a fuzzy goal programming method. An efficient stochastic search procedure using particle swarm optimisation is also proposed to solve the non-linear crisp model.

Independent components analysis to increase efficiency of discriminant analysis methods (FDA and LDA): Application to NMR fingerprinting of wine.

PubMed

Monakhova, Yulia B; Godelmann, Rolf; Kuballa, Thomas; Mushtakova, Svetlana P; Rutledge, Douglas N

2015-08-15

Discriminant analysis (DA) methods, such as linear discriminant analysis (LDA) or factorial discriminant analysis (FDA), are well-known chemometric approaches for solving classification problems in chemistry. In most applications, principle components analysis (PCA) is used as the first step to generate orthogonal eigenvectors and the corresponding sample scores are utilized to generate discriminant features for the discrimination. Independent components analysis (ICA) based on the minimization of mutual information can be used as an alternative to PCA as a preprocessing tool for LDA and FDA classification. To illustrate the performance of this ICA/DA methodology, four representative nuclear magnetic resonance (NMR) data sets of wine samples were used. The classification was performed regarding grape variety, year of vintage and geographical origin. The average increase for ICA/DA in comparison with PCA/DA in the percentage of correct classification varied between 6±1% and 8±2%. The maximum increase in classification efficiency of 11±2% was observed for discrimination of the year of vintage (ICA/FDA) and geographical origin (ICA/LDA). The procedure to determine the number of extracted features (PCs, ICs) for the optimum DA models was discussed. The use of independent components (ICs) instead of principle components (PCs) resulted in improved classification performance of DA methods. The ICA/LDA method is preferable to ICA/FDA for recognition tasks based on NMR spectroscopic measurements. Copyright © 2015 Elsevier B.V. All rights reserved.

Minority Stress and Inflammatory Mediators: Covering Moderates Associations between Perceived Discrimination and Salivary Interleukin-6 in Gay Men

PubMed Central

Doyle, David Matthew; Molix, Lisa

2016-01-01

Physical health disparities by sexual orientation are widespread yet under-investigated. Drawing upon theories of biological embedding of social adversity, we tested whether minority stress (in the form of perceived discrimination) is associated with salivary interleukin-6 (IL-6), an inflammatory mediator. Furthermore, we examined whether covering, a strategy involving downplaying a stigmatized social identity, modified this association. A community sample (N = 99) of gay men (n = 78) and lesbian women (n = 21) completed self-report measures of minority stress and identity management and provided saliva samples which were assayed for IL-6. Among gay men, results from generalized linear models supported a hypothesized interaction between perceived discrimination and covering, such that perceived discrimination was predictive of higher levels of IL-6 for those who engaged in less covering but not for those who engaged in more covering. This interaction was robust to a number of potential covariates (alcohol, medication, body mass index, race and age). Results for lesbian women suggested a different pattern: the only statistically significant association detected was between greater perceived discrimination and lower levels of IL-6. Findings from the current study point to an important role for inflammatory processes in understanding and remediating health disparities based upon sexual orientation that stem from exposure to prejudice and discrimination. PMID:27534538

Minority stress and inflammatory mediators: covering moderates associations between perceived discrimination and salivary interleukin-6 in gay men.

PubMed

Doyle, David Matthew; Molix, Lisa

2016-10-01

Physical health disparities by sexual orientation are widespread yet under-investigated. Drawing upon theories of biological embedding of social adversity, we tested whether minority stress (in the form of perceived discrimination) is associated with salivary interleukin-6 (IL-6), an inflammatory mediator. Furthermore, we examined whether covering, a strategy involving downplaying a stigmatized social identity, modified this association. A community sample (N = 99) of gay men (n = 78) and lesbian women (n = 21) completed self-report measures of minority stress and identity management and provided saliva samples which were assayed for IL-6. Among gay men, results from generalized linear models supported a hypothesized interaction between perceived discrimination and covering, such that perceived discrimination was predictive of higher levels of IL-6 for those who engaged in less covering but not for those who engaged in more covering. This interaction was robust to a number of potential covariates (alcohol, medication, body mass index, race and age). Results for lesbian women suggested a different pattern: the only statistically significant association detected was between greater perceived discrimination and lower levels of IL-6. Findings from the current study point to an important role for inflammatory processes in understanding and remediating health disparities based upon sexual orientation that stem from exposure to prejudice and discrimination.

Synthesis and analysis of discriminators under influence of broadband non-Gaussian noise

NASA Astrophysics Data System (ADS)

Artyushenko, V. M.; Volovach, V. I.

2018-01-01

We considered the problems of the synthesis and analysis of discriminators, when the useful signal is exposed to non-Gaussian additive broadband noise. It is shown that in this case, the discriminator of the tracking meter should contain the nonlinear transformation unit, the characteristics of which are determined by the Fisher information relative to the probability density function of the mixture of non-Gaussian broadband noise and mismatch errors. The parameters of the discriminatory and phase characteristics of the discriminators working under the above conditions are obtained. It is shown that the efficiency of non-linear processing depends on the ratio of power of FM noise to the power of Gaussian noise. The analysis of the information loss of signal transformation caused by the linear section of discriminatory characteristics of the unit of nonlinear transformations of the discriminator is carried out. It is shown that the average slope of the nonlinear transformation characteristic is determined by the Fisher information relative to the probability density function of the mixture of non-Gaussian noise and mismatch errors.

Chemometric brand differentiation of commercial spices using direct analysis in real time mass spectrometry.

PubMed

Pavlovich, Matthew J; Dunn, Emily E; Hall, Adam B

2016-05-15

Commercial spices represent an emerging class of fuels for improvised explosives. Being able to classify such spices not only by type but also by brand would represent an important step in developing methods to analytically investigate these explosive compositions. Therefore, a combined ambient mass spectrometric/chemometric approach was developed to quickly and accurately classify commercial spices by brand. Direct analysis in real time mass spectrometry (DART-MS) was used to generate mass spectra for samples of black pepper, cayenne pepper, and turmeric, along with four different brands of cinnamon, all dissolved in methanol. Unsupervised learning techniques showed that the cinnamon samples clustered according to brand. Then, we used supervised machine learning algorithms to build chemometric models with a known training set and classified the brands of an unknown testing set of cinnamon samples. Ten independent runs of five-fold cross-validation showed that the training set error for the best-performing models (i.e., the linear discriminant and neural network models) was lower than 2%. The false-positive percentages for these models were 3% or lower, and the false-negative percentages were lower than 10%. In particular, the linear discriminant model perfectly classified the testing set with 0% error. Repeated iterations of training and testing gave similar results, demonstrating the reproducibility of these models. Chemometric models were able to classify the DART mass spectra of commercial cinnamon samples according to brand, with high specificity and low classification error. This method could easily be generalized to other classes of spices, and it could be applied to authenticating questioned commercial samples of spices or to examining evidence from improvised explosives. Copyright © 2016 John Wiley & Sons, Ltd.

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.

Partial Least Squares for Discrimination in fMRI Data

PubMed Central

Andersen, Anders H.; Rayens, William S.; Liu, Yushu; Smith, Charles D.

2011-01-01

Multivariate methods for discrimination were used in the comparison of brain activation patterns between groups of cognitively normal women who are at either high or low Alzheimer's disease risk based on family history and apolipoprotein-E4 status. Linear discriminant analysis (LDA) was preceded by dimension reduction using either principal component analysis (PCA), partial least squares (PLS), or a new oriented partial least squares (OrPLS) method. The aim was to identify a spatial pattern of functionally connected brain regions that was differentially expressed by the risk groups and yielded optimal classification accuracy. Multivariate dimension reduction is required prior to LDA when the data contains more feature variables than there are observations on individual subjects. Whereas PCA has been commonly used to identify covariance patterns in neuroimaging data, this approach only identifies gross variability and is not capable of distinguishing among-groups from within-groups variability. PLS and OrPLS provide a more focused dimension reduction by incorporating information on class structure and therefore lead to more parsimonious models for discrimination. Performance was evaluated in terms of the cross-validated misclassification rates. The results support the potential of using fMRI as an imaging biomarker or diagnostic tool to discriminate individuals with disease or high risk. PMID:22227352

A novel Bayesian framework for discriminative feature extraction in Brain-Computer Interfaces.

PubMed

Suk, Heung-Il; Lee, Seong-Whan

2013-02-01

As there has been a paradigm shift in the learning load from a human subject to a computer, machine learning has been considered as a useful tool for Brain-Computer Interfaces (BCIs). In this paper, we propose a novel Bayesian framework for discriminative feature extraction for motor imagery classification in an EEG-based BCI in which the class-discriminative frequency bands and the corresponding spatial filters are optimized by means of the probabilistic and information-theoretic approaches. In our framework, the problem of simultaneous spatiospectral filter optimization is formulated as the estimation of an unknown posterior probability density function (pdf) that represents the probability that a single-trial EEG of predefined mental tasks can be discriminated in a state. In order to estimate the posterior pdf, we propose a particle-based approximation method by extending a factored-sampling technique with a diffusion process. An information-theoretic observation model is also devised to measure discriminative power of features between classes. From the viewpoint of classifier design, the proposed method naturally allows us to construct a spectrally weighted label decision rule by linearly combining the outputs from multiple classifiers. We demonstrate the feasibility and effectiveness of the proposed method by analyzing the results and its success on three public databases.

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

Multivariate modelling of prostate cancer combining magnetic resonance derived T2, diffusion, dynamic contrast-enhanced and spectroscopic parameters.

PubMed

Riches, S F; Payne, G S; Morgan, V A; Dearnaley, D; Morgan, S; Partridge, M; Livni, N; Ogden, C; deSouza, N M

2015-05-01

The objectives are determine the optimal combination of MR parameters for discriminating tumour within the prostate using linear discriminant analysis (LDA) and to compare model accuracy with that of an experienced radiologist. Multiparameter MRIs in 24 patients before prostatectomy were acquired. Tumour outlines from whole-mount histology, T2-defined peripheral zone (PZ), and central gland (CG) were superimposed onto slice-matched parametric maps. T2, Apparent Diffusion Coefficient, initial area under the gadolinium curve, vascular parameters (K(trans),Kep,Ve), and (choline+polyamines+creatine)/citrate were compared between tumour and non-tumour tissues. Receiver operating characteristic (ROC) curves determined sensitivity and specificity at spectroscopic voxel resolution and per lesion, and LDA determined the optimal multiparametric model for identifying tumours. Accuracy was compared with an expert observer. Tumours were significantly different from PZ and CG for all parameters (all p < 0.001). Area under the ROC curve for discriminating tumour from non-tumour was significantly greater (p < 0.001) for the multiparametric model than for individual parameters; at 90 % specificity, sensitivity was 41 % (MRSI voxel resolution) and 59 % per lesion. At this specificity, an expert observer achieved 28 % and 49 % sensitivity, respectively. The model was more accurate when parameters from all techniques were included and performed better than an expert observer evaluating these data. • The combined model increases diagnostic accuracy in prostate cancer compared with individual parameters • The optimal combined model includes parameters from diffusion, spectroscopy, perfusion, and anatominal MRI • The computed model improves tumour detection compared to an expert viewing parametric maps.

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

PubMed Central

Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

2014-01-01

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images. PMID:24989402

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning.

PubMed

Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

2014-07-01

Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.

Automatic detection and classification of artifacts in single-channel EEG.

PubMed

Olund, Thomas; Duun-Henriksen, Jonas; Kjaer, Troels W; Sorensen, Helge B D

2014-01-01

Ambulatory EEG monitoring can provide medical doctors important diagnostic information, without hospitalizing the patient. These recordings are however more exposed to noise and artifacts compared to clinically recorded EEG. An automatic artifact detection and classification algorithm for single-channel EEG is proposed to help identifying these artifacts. Features are extracted from the EEG signal and wavelet subbands. Subsequently a selection algorithm is applied in order to identify the best discriminating features. A non-linear support vector machine is used to discriminate among different artifact classes using the selected features. Single-channel (Fp1-F7) EEG recordings are obtained from experiments with 12 healthy subjects performing artifact inducing movements. The dataset was used to construct and validate the model. Both subject-specific and generic implementation, are investigated. The detection algorithm yield an average sensitivity and specificity above 95% for both the subject-specific and generic models. The classification algorithm show a mean accuracy of 78 and 64% for the subject-specific and generic model, respectively. The classification model was additionally validated on a reference dataset with similar results.

Supervised linear dimensionality reduction with robust margins for object recognition

NASA Astrophysics Data System (ADS)

Dornaika, F.; Assoum, A.

2013-01-01

Linear Dimensionality Reduction (LDR) techniques have been increasingly important in computer vision and pattern recognition since they permit a relatively simple mapping of data onto a lower dimensional subspace, leading to simple and computationally efficient classification strategies. Recently, many linear discriminant methods have been developed in order to reduce the dimensionality of visual data and to enhance the discrimination between different groups or classes. Many existing linear embedding techniques relied on the use of local margins in order to get a good discrimination performance. However, dealing with outliers and within-class diversity has not been addressed by margin-based embedding method. In this paper, we explored the use of different margin-based linear embedding methods. More precisely, we propose to use the concepts of Median miss and Median hit for building robust margin-based criteria. Based on such margins, we seek the projection directions (linear embedding) such that the sum of local margins is maximized. Our proposed approach has been applied to the problem of appearance-based face recognition. Experiments performed on four public face databases show that the proposed approach can give better generalization performance than the classic Average Neighborhood Margin Maximization (ANMM). Moreover, thanks to the use of robust margins, the proposed method down-grades gracefully when label outliers contaminate the training data set. In particular, we show that the concept of Median hit was crucial in order to get robust performance in the presence of outliers.

Relating Stellar Cycle Periods to Dynamo Calculations

NASA Technical Reports Server (NTRS)

Tobias, S. M.

1998-01-01

Stellar magnetic activity in slowly rotating stars is often cyclic, with the period of the magnetic cycle depending critically on the rotation rate and the convective turnover time of the star. Here we show that the interpretation of this law from dynamo models is not a simple task. It is demonstrated that the period is (unsurprisingly) sensitive to the precise type of non-linearity employed. Moreover the calculation of the wave-speed of plane-wave solutions does not (as was previously supposed) give an indication of the magnetic period in a more realistic dynamo model, as the changes in length-scale of solutions are not easily captured by this approach. Progress can be made, however, by considering a realistic two-dimensional model, in which the radial length-scale of waves is included. We show that it is possible in this case to derive a more robust relation between cycle period and dynamo number. For all the non-linearities considered in the most realistic model, the magnetic cycle period is a decreasing function of IDI (the amplitude of the dynamo number). However, discriminating between different non-linearities is difficult in this case and care must therefore be taken before advancing explanations for the magnetic periods of stars.

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.

Chemometric classification of apple juices according to variety and geographical origin based on polyphenolic profiles.

PubMed

Guo, Jing; Yue, Tianli; Yuan, Yahong; Wang, Yutang

2013-07-17

To characterize and classify apple juices according to apple variety and geographical origin on the basis of their polyphenol composition, the polyphenolic profiles of 58 apple juice samples belonging to 5 apple varieties and from 6 regions in Shaanxi province of China were assessed. Fifty-one of the samples were from protected designation of origin (PDO) districts. Polyphenols were determined by high-performance liquid chromatography coupled to photodiode array detection (HPLC-PDA) and to a Q Exactive quadrupole-Orbitrap mass spectrometer. Chemometric techniques including principal component analysis (PCA) and stepwise linear discriminant analysis (SLDA) were carried out on polyphenolic profiles of the samples to develop discrimination models. SLDA achieved satisfactory discriminations of apple juices according to variety and geographical origin, providing respectively 98.3 and 91.2% success rate in terms of prediction ability. This result demonstrated that polyphenols could served as characteristic indices to verify the variety and geographical origin of apple juices.

Single-cultivar extra virgin olive oil classification using a potentiometric electronic tongue.

PubMed

Dias, Luís G; Fernandes, Andreia; Veloso, Ana C A; Machado, Adélio A S C; Pereira, José A; Peres, António M

2014-10-01

Label authentication of monovarietal extra virgin olive oils is of great importance. A novel approach based on a potentiometric electronic tongue is proposed to classify oils obtained from single olive cultivars (Portuguese cvs. Cobrançosa, Madural, Verdeal Transmontana; Spanish cvs. Arbequina, Hojiblanca, Picual). A meta-heuristic simulated annealing algorithm was applied to select the most informative sets of sensors to establish predictive linear discriminant models. Olive oils were correctly classified according to olive cultivar (sensitivities greater than 97%) and each Spanish olive oil was satisfactorily discriminated from the Portuguese ones with the exception of cv. Arbequina (sensitivities from 61% to 98%). Also, the discriminant ability was related to the polar compounds contents of olive oils and so, indirectly, with organoleptic properties like bitterness, astringency or pungency. Therefore the proposed E-tongue can be foreseen as a useful auxiliary tool for trained sensory panels for the classification of monovarietal extra virgin olive oils. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

PubMed

Yudthavorasit, Soparat; Wongravee, Kanet; Leepipatpiboon, Natchanun

2014-09-01

Chromatographic fingerprints of gingers from five different ginger-producing countries (China, India, Malaysia, Thailand and Vietnam) were newly established to discriminate the origin of ginger. The pungent bioactive principles of ginger, gingerols and six other gingerol-related compounds were determined and identified. Their variations in HPLC profiles create the characteristic pattern of each origin by employing similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) and linear discriminant analysis (LDA). As results, the ginger profiles tended to be grouped and separated on the basis of the geographical closeness of the countries of origin. An effective mathematical model with high predictive ability was obtained and chemical markers for each origin were also identified as the characteristic active compounds to differentiate the ginger origin. The proposed method is useful for quality control of ginger in case of origin labelling and to assess food authenticity issues. Copyright © 2014 Elsevier Ltd. All rights reserved.

[Discriminant analysis to predict the clinical diagnosis of primary immunodeficiencies: a preliminary report].

PubMed

Murata, Chiharu; Ramírez, Ana Belén; Ramírez, Guadalupe; Cruz, Alonso; Morales, José Luis; Lugo-Reyes, Saul Oswaldo

2015-01-01

The features in a clinical history from a patient with suspected primary immunodeficiency (PID) direct the differential diagnosis through pattern recognition. PIDs are a heterogeneous group of more than 250 congenital diseases with increased susceptibility to infection, inflammation, autoimmunity, allergy and malignancy. Linear discriminant analysis (LDA) is a multivariate supervised classification method to sort objects of study into groups by finding linear combinations of a number of variables. To identify the features that best explain membership of pediatric PID patients to a group of defect or disease. An analytic cross-sectional study was done with a pre-existing database with clinical and laboratory records from 168 patients with PID, followed at the National Institute of Pediatrics during 1991-2012, it was used to build linear discriminant models that would explain membership of each patient to the different group defects and to the most prevalent PIDs in our registry. After a preliminary run only 30 features were included (4 demographic, 10 clinical, 10 laboratory, 6 germs), with which the training models were developed through a stepwise regression algorithm. We compared the automatic feature selection with a selection made by a human expert, and then assessed the diagnostic usefulness of the resulting models (sensitivity, specificity, prediction accuracy and kappa coefficient), with 95% confidence intervals. The models incorporated 6 to 14 features to explain membership of PID patients to the five most abundant defect groups (combined, antibody, well-defined, dysregulation and phagocytosis), and to the four most prevalent PID diseases (X-linked agammaglobulinemia, chronic granulomatous disease, common variable immunodeficiency and ataxiatelangiectasia). In practically all cases of feature selection the machine outperformed the human expert. Diagnosis prediction using the equations created had a global accuracy of 83 to 94%, with sensitivity of 60 to 100%, specificity of 83 to 95% and kappa coefficient of 0.37 to 0.76. In general, the selection of features has clinical plausibility, and the practical advantage of utilizing only clinical attributes, infecting germs and routine lab results (blood cell counts and serum immunoglobulins). The performance of the model as a diagnostic tool was acceptable. The study's main limitations are a limited sample size and a lack of cross validation. This is only the first step in the construction of a machine learning system, with a wider approach that includes a larger database and different methodologies, to assist the clinical diagnosis of primary immunodeficiencies.

Validity and Reliability of the Internalized Stigma of Smoking Inventory: An Exploration of Shame, Isolation, and Discrimination in Smokers with Mental Health Diagnoses

PubMed Central

Brown-Johnson, Cati G.; Cataldo PhD, Janine K.; Orozco, Nicholas; Lisha, Nadra E.; Hickman, Norval; Prochaska, Judith J.

2015-01-01

Background and Objectives De-normalization of smoking as a public health strategy may create shame and isolation in vulnerable groups unable to quit. To examine the nature and impact of smoking stigma, we developed the Internalized Stigma of Smoking Inventory (ISSI), tested its validity and reliability, and explored factors that may contribute to smoking stigma. Methods We evaluated the ISSI in a sample of smokers with mental health diagnoses (N=956), using exploratory and confirmatory factor analysis, and assessed construct validity. Results Results reduced the ISSI to 8 items with three subscales: smoking self-stigma related to shame, felt stigma related to social isolation, and discrimination experiences. Discrimination was the most commonly endorsed of the three subscales. A multivariate generalized linear model predicted 21-30% of the variance in the smoking stigma subscales. Self-stigma was greatest among those intending to quit; felt stigma was highest among those experiencing stigma in other domains, namely ethnicity and mental illness-based; and smoking-related discrimination was highest among women, Caucasians, and those with more education. Discussion and Conclusion Smoking stigma may compound stigma experiences in other areas. Aspects of smoking stigma in the domains of shame, isolation, and discrimination related to modeled stigma responses, particularly readiness to quit and cigarette addiction and was found to be more salient for groups where tobacco use is least prevalent. Scientific Significance The ISSI measure is useful for quantifying smoking-related stigma in multiple domains. PMID:25930661

Novel methods of time-resolved fluorescence data analysis for in-vivo tissue characterization: application to atherosclerosis.

PubMed

Jo, J A; Fang, Q; Papaioannou, T; Qiao, J H; Fishbein, M C; Dorafshar, A; Reil, T; Baker, D; Freischlag, J; Marcu, L

2004-01-01

This study investigates the ability of new analytical methods of time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) data to characterize tissue in-vivo, such as the composition of atherosclerotic vulnerable plaques. A total of 73 TR-LIFS measurements were taken in-vivo from the aorta of 8 rabbits, and subsequently analyzed using the Laguerre deconvolution technique. The investigated spots were classified as normal aorta, thin or thick lesions, and lesions rich in either collagen or macrophages/foam-cells. Different linear and nonlinear classification algorithms (linear discriminant analysis, stepwise linear discriminant analysis, principal component analysis, and feedforward neural networks) were developed using spectral and TR features (ratios of intensity values and Laguerre expansion coefficients, respectively). Normal intima and thin lesions were discriminated from thick lesions (sensitivity >90%, specificity 100%) using only spectral features. However, both spectral and time-resolved features were necessary to discriminate thick lesions rich in collagen from thick lesions rich in foam cells (sensitivity >85%, specificity >93%), and thin lesions rich in foam cells from normal aorta and thin lesions rich in collagen (sensitivity >85%, specificity >94%). Based on these findings, we believe that TR-LIFS information derived from the Laguerre expansion coefficients can provide a valuable additional dimension for in-vivo tissue characterization.

Nutrient profiles discriminate between foods according to their contribution to nutritionally adequate diets: a validation study using linear programming and the SAIN,LIM system.

PubMed

Darmon, Nicole; Vieux, Florent; Maillot, Matthieu; Volatier, Jean-Luc; Martin, Ambroise

2009-04-01

The nutrient profile concept implies that it is possible to discriminate between foods according to their contribution to a healthy diet on the basis of their nutrient contents only. The objective was to test the compatibility between nutrient profiling and nutrient-based recommendations by using diet modeling with linear programming. Food consumption data from the French "Individuelle et Nationale sur les Consommations Alimentaires" dietary survey and its associated food-composition database were used as input data. Each food was allocated to 1 of 4 classes, according to the SAIN,LIM system -- a nutrient profiling system based on 2 independent scores, including a total of 8 basic plus 4 optional nutrients. The possibility to model diets fulfilling a set of 40 nutrient recommendations (healthy models) was tested by using foods from a given nutrient profile class only or from a combination of classes. The possibility to fulfill a set of nutrient constraints in contradiction with the recommendations (unhealthy models) was also tested. For each model, the feasible energy range was assessed by minimizing and maximizing total energy content. With foods from the most favorable nutrient profile class, healthy diets could be modeled, but it was impossible to design unhealthy diets within a realistic range of energy intake with these foods. With foods from the least favorable class, unhealthy, but not healthy, diets could be designed. Both healthy and unhealthy diets could be designed with foods from intermediate classes. On the basis of a few key nutrients, it is possible to predict the ability of a given food to facilitate -- or to impair -- the fulfillment of a large number of nutrient recommendations.

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.

Plausibility assessment of a 2-state self-paced mental task-based BCI using the no-control performance analysis.

PubMed

Faradji, Farhad; Ward, Rabab K; Birch, Gary E

2009-06-15

The feasibility of having a self-paced brain-computer interface (BCI) based on mental tasks is investigated. The EEG signals of four subjects performing five mental tasks each are used in the design of a 2-state self-paced BCI. The output of the BCI should only be activated when the subject performs a specific mental task and should remain inactive otherwise. For each subject and each task, the feature coefficient and the classifier that yield the best performance are selected, using the autoregressive coefficients as the features. The classifier with a zero false positive rate and the highest true positive rate is selected as the best classifier. The classifiers tested include: linear discriminant analysis, quadratic discriminant analysis, Mahalanobis discriminant analysis, support vector machine, and radial basis function neural network. The results show that: (1) some classifiers obtained the desired zero false positive rate; (2) the linear discriminant analysis classifier does not yield acceptable performance; (3) the quadratic discriminant analysis classifier outperforms the Mahalanobis discriminant analysis classifier and performs almost as well as the radial basis function neural network; and (4) the support vector machine classifier has the highest true positive rates but unfortunately has nonzero false positive rates in most cases.

Application of Sparse Linear Discriminant Analysis and Elastic Net for Diagnosis of IgA Nephropathy: Statistical and Biological Viewpoints

PubMed

Mohammadi Majd, Tahereh; Kalantari, Shiva; Raeisi Shahraki, Hadi; Nafar, Mohsen; Almasi, Afshin; Samavat, Shiva; Parvin, Mahmoud; Hashemian, Amirhossein

2018-03-10

IgA nephropathy (IgAN) is the most common primary glomerulonephritis diagnosed based on renal biopsy. Mesangial IgA deposits along with the proliferation of mesangial cells are the histologic hallmark of IgAN. Non-invasive diagnostic tools may help to prompt diagnosis and therapy. The discovery of potential and reliable urinary biomarkers for diagnosis of IgAN depends on applying robust and suitable models. Applying two multivariate modeling methods on a urine proteomic dataset obtained from IgAN patients, and comparison of the results of these methods were the purpose of this study. Two models were constructed for urinary protein profiles of 13 patients and 8 healthy individuals, based on sparse linear discriminant analysis (SLDA) and elastic net regression methods. A panel of selected biomarkers with the best coefficients were proposed and further analyzed for biological relevance using functional annotation and pathway analysis. Transferrin, α1-antitrypsin, and albumin fragments were the most important up-regulated biomarkers, while fibulin-5, YIP1 family member 3, prasoposin, and osteopontin were the most important down-regulated biomarkers. Pathway analysis revealed that complement and coagulation cascades and extracellular matrix-receptor interaction pathways impaired in the pathogenesis of IgAN. SLDA and elastic net had an equal importance for diagnosis of IgAN and were useful methods for exploring and processing proteomic data. In addition, the suggested biomarkers are reliable candidates for further validation to non-invasive diagnose of IgAN based on urine examination.

Discrimination and Acculturative Stress among First-Generation Dominicans

ERIC Educational Resources Information Center

Dawson, Beverly Araujo; Panchanadeswaran, Subadra

2010-01-01

The present study examined the relationship between discriminatory experiences and acculturative stress levels among a sample of 283 Dominican immigrants. Findings from a linear regression analysis revealed that experiences of daily racial discrimination and major racist events were significant predictors of acculturative stress after controlling…

An automatic flow system for NIR screening analysis of liquefied petroleum gas with respect to propane content.

PubMed

Dantas, Hebertty V; Barbosa, Mayara F; Nascimento, Elaine C L; Moreira, Pablo N T; Galvão, Roberto K H; Araújo, Mário C U

2013-03-15

This paper proposes a NIR spectrometric method for screening analysis of liquefied petroleum gas (LPG) samples. The proposed method is aimed at discriminating samples with low and high propane content, which can be useful for the adjustment of burn settings in industrial applications. A gas flow system was developed to introduce the LPG sample into a NIR flow cell at constant pressure. In addition, a gas chromatographer was employed to determine the propane content of the sample for reference purposes. The results of a principal component analysis, as well as a classification study using SIMCA (soft independent modeling of class analogies), revealed that the samples can be successfully discriminated with respect to propane content by using the NIR spectrum in the range 8100-8800 cm(-1). In addition, by using SPA-LDA (linear discriminant analysis with variables selected by the successive projections algorithm), it was found that perfect discrimination can also be achieved by using only two wavenumbers (8215 and 8324 cm(-1)). This finding may be of value for the design of a dedicated, low-cost instrument for routine analyses. Copyright © 2012 Elsevier B.V. All rights reserved.

Associations Between Discrimination and Cardiovascular Health Among Asian Indians in the United States

PubMed Central

Dulin-Keita, A.; Salas, C.; Kanaya, A. M.; Kandula, Namratha R.

2016-01-01

Asian Indians (AI) have a high risk of atherosclerotic cardiovascular disease. The study investigated associations between discrimination and (1) cardiovascular risk and (2) self-rated health among AI. Higher discrimination scores were hypothesized to relate to a higher cardiovascular risk score (CRS) and poorer self-rated health. Asian Indians (n = 757) recruited between 2010 and 2013 answered discrimination and self-reported health questions. The CRS (0–8 points) included body-mass index, systolic blood pressure, total cholesterol, and fasting blood glucose levels of AI. Multiple linear regression analyses were conducted to evaluate relationships between discrimination and the CRS and discrimination and self-rated health, adjusting for psychosocial and clinical factors. There were no significant relationships between discrimination and the CRS (p ≥ .05). Discrimination was related to poorer self-reported health, B = −.41 (SE = .17), p = .02. Findings suggest perhaps there are important levels at which discrimination may harm health. PMID:27039100

[Determination of somatotype of man in cranio-facial personality identification].

PubMed

2004-01-01

On the basis of their independent research and through the analysis of published data the authors suggested quantitative criteria for the diagnosis of a somatotype of man by the dimensional features of the face and skull. M. A. Negasheva method, based on the discriminative analysis of 7 measurement features, was used in the individual diagnosis of a somatotype by V. V. Bunaka scheme (somatotypes-pectoral, muscular, abdominal and indefinite). The authors suggest 2 diagnostic models based on the linear and discriminative analysis of 11 and 7 measurement features for the skull. The diagnostic accuracy in case of main male som-atotypes makes 87 and 64.4%, respectively, with the canonic correlations of 0.574 and 0.292. The designed methods can be used in forensic medicine for the cranio-facial and portrait expertise.

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.

Discrimination, perceived social inequity, and mental health among rural-to-urban migrants in China.

PubMed

Lin, Danhua; Li, Xiaoming; Wang, Bo; Hong, Yan; Fang, Xiaoyi; Qin, Xiong; Stanton, Bonita

2011-04-01

Status-based discrimination and inequity have been associated with the process of migration, especially with economics-driven internal migration. However, their association with mental health among economy-driven internal migrants in developing countries is rarely assessed. This study examines discriminatory experiences and perceived social inequity in relation to mental health status among rural-to-urban migrants in China. Cross-sectional data were collected from 1,006 rural-to-urban migrants in 2004-2005 in Beijing, China. Participants reported their perceptions and experiences of being discriminated in daily life in urban destination and perceived social inequity. Mental health was measured using the symptom checklist-90 (SCL-90). Multivariate analyses using general linear model were performed to test the effect of discriminatory experience and perceived social inequity on mental health. Experience of discrimination was positively associated with male gender, being married at least once, poorer health status, shorter duration of migration, and middle range of personal income. Likewise, perceived social inequity was associated with poorer health status, higher education attainment, and lower personal income. Multivariate analyses indicate that both experience of discrimination and perceived social inequity were strongly associated with mental health problems of rural-to-urban migrants. Experience of discrimination in daily life and perceived social inequity have a significant influence on mental health among rural-to-urban migrants. The findings underscore the needs to reduce public or societal discrimination against rural-to-urban migrants, to eliminate structural barriers (i.e., dual household registrations) for migrants to fully benefit from the urban economic development, and to create a positive atmosphere to improve migrant's psychological well-being.

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.

Complexity-reduced implementations of complete and null-space-based linear discriminant analysis.

PubMed

Lu, Gui-Fu; Zheng, Wenming

2013-10-01

Dimensionality reduction has become an important data preprocessing step in a lot of applications. Linear discriminant analysis (LDA) is one of the most well-known dimensionality reduction methods. However, the classical LDA cannot be used directly in the small sample size (SSS) problem where the within-class scatter matrix is singular. In the past, many generalized LDA methods has been reported to address the SSS problem. Among these methods, complete linear discriminant analysis (CLDA) and null-space-based LDA (NLDA) provide good performances. The existing implementations of CLDA are computationally expensive. In this paper, we propose a new and fast implementation of CLDA. Our proposed implementation of CLDA, which is the most efficient one, is equivalent to the existing implementations of CLDA in theory. Since CLDA is an extension of null-space-based LDA (NLDA), our implementation of CLDA also provides a fast implementation of NLDA. Experiments on some real-world data sets demonstrate the effectiveness of our proposed new CLDA and NLDA algorithms. Copyright © 2013 Elsevier Ltd. All rights reserved.

Evaluation of an electronic nose for odorant and process monitoring of alkaline-stabilized biosolids production.

PubMed

Romero-Flores, Adrian; McConnell, Laura L; Hapeman, Cathleen J; Ramirez, Mark; Torrents, Alba

2017-11-01

Electronic noses have been widely used in the food industry to monitor process performance and quality control, but use in wastewater and biosolids treatment has not been fully explored. Therefore, we examined the feasibility of an electronic nose to discriminate between treatment conditions of alkaline stabilized biosolids and compared its performance with quantitative analysis of key odorants. Seven lime treatments (0-30% w/w) were prepared and the resultant off-gas was monitored by GC-MS and by an electronic nose equipped with ten metal oxide sensors. A pattern recognition model was created using linear discriminant analysis (LDA) and principal component analysis (PCA) of the electronic nose data. In general, LDA performed better than PCA. LDA showed clear discrimination when single tests were evaluated, but when the full data set was included, discrimination between treatments was reduced. Frequency of accurate recognition was tested by three algorithms with Euclidan and Mahalanobis performing at 81% accuracy and discriminant function analysis at 70%. Concentrations of target compounds by GC-MS were in agreement with those reported in literature and helped to elucidate the behavior of the pattern recognition via comparison of individual sensor responses to different biosolids treatment conditions. Results indicated that the electronic nose can discriminate between lime percentages, thus providing the opportunity to create classes of under-dosed and over-dosed relative to regulatory requirements. Full scale application will require careful evaluation to maintain accuracy under variable process and environmental conditions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Discrimination of Isomers of Released N- and O-Glycans Using Diagnostic Product Ions in Negative Ion PGC-LC-ESI-MS/MS

NASA Astrophysics Data System (ADS)

Ashwood, Christopher; Lin, Chi-Hung; Thaysen-Andersen, Morten; Packer, Nicolle H.

2018-03-01

Profiling cellular protein glycosylation is challenging due to the presence of highly similar glycan structures that play diverse roles in cellular physiology. As the anomericity and the exact linkage type of a single glycosidic bond can influence glycan function, there is a demand for improved and automated methods to confirm detailed structural features and to discriminate between structurally similar isomers, overcoming a significant bottleneck in the analysis of data generated by glycomics experiments. We used porous graphitized carbon-LC-ESI-MS/MS to separate and detect released N- and O-glycan isomers from mammalian model glycoproteins using negative mode resonance activation CID-MS/MS. By interrogating similar fragment spectra from closely related glycan isomers that differ only in arm position and sialyl linkage, product fragment ions for discrimination between these features were discovered. Using the Skyline software, at least two diagnostic fragment ions of high specificity were validated for automated discrimination of sialylation and arm position in N-glycan structures, and sialylation in O-glycan structures, complementing existing structural diagnostic ions. These diagnostic ions were shown to be useful for isomer discrimination using both linear and 3D ion trap mass spectrometers when analyzing complex glycan mixtures from cell lysates. Skyline was found to serve as a useful tool for automated assessment of glycan isomer discrimination. This platform-independent workflow can potentially be extended to automate the characterization and quantitation of other challenging glycan isomers. [Figure not available: see fulltext.

Multiple degree of freedom object recognition using optical relational graph decision nets

NASA Technical Reports Server (NTRS)

Casasent, David P.; Lee, Andrew J.

1988-01-01

Multiple-degree-of-freedom object recognition concerns objects with no stable rest position with all scale, rotation, and aspect distortions possible. It is assumed that the objects are in a fairly benign background, so that feature extractors are usable. In-plane distortion invariance is provided by use of a polar-log coordinate transform feature space, and out-of-plane distortion invariance is provided by linear discriminant function design. Relational graph decision nets are considered for multiple-degree-of-freedom pattern recognition. The design of Fisher (1936) linear discriminant functions and synthetic discriminant function for use at the nodes of binary and multidecision nets is discussed. Case studies are detailed for two-class and multiclass problems. Simulation results demonstrate the robustness of the processors to quantization of the filter coefficients and to noise.

Linear scaling relationships and volcano plots in homogeneous catalysis – revisiting the Suzuki reaction† †Electronic supplementary information (ESI) available: Detailed derivation of the linear scaling relationships and construction of the volcano plots as well as comparisons of computed values using PBE0-dDsC and M06 functionals is included. See DOI: 10.1039/c5sc02910d Click here for additional data file.

PubMed Central

Busch, Michael; Wodrich, Matthew D.

2015-01-01

Linear free energy scaling relationships and volcano plots are common tools used to identify potential heterogeneous catalysts for myriad applications. Despite the striking simplicity and predictive power of volcano plots, they remain unknown in homogeneous catalysis. Here, we construct volcano plots to analyze a prototypical reaction from homogeneous catalysis, the Suzuki cross-coupling of olefins. Volcano plots succeed both in discriminating amongst different catalysts and reproducing experimentally known trends, which serves as validation of the model for this proof-of-principle example. These findings indicate that the combination of linear scaling relationships and volcano plots could serve as a valuable methodology for identifying homogeneous catalysts possessing a desired activity through a priori computational screening. PMID:28757966

Standoff detection of chemical and biological threats using laser-induced breakdown spectroscopy.

PubMed

Gottfried, Jennifer L; De Lucia, Frank C; Munson, Chase A; Miziolek, Andrzej W

2008-04-01

Laser-induced breakdown spectroscopy (LIBS) is a promising technique for real-time chemical and biological warfare agent detection in the field. We have demonstrated the detection and discrimination of the biological warfare agent surrogates Bacillus subtilis (BG) (2% false negatives, 0% false positives) and ovalbumin (0% false negatives, 1% false positives) at 20 meters using standoff laser-induced breakdown spectroscopy (ST-LIBS) and linear correlation. Unknown interferent samples (not included in the model), samples on different substrates, and mixtures of BG and Arizona road dust have been classified with reasonable success using partial least squares discriminant analysis (PLS-DA). A few of the samples tested such as the soot (not included in the model) and the 25% BG:75% dust mixture resulted in a significant number of false positives or false negatives, respectively. Our preliminary results indicate that while LIBS is able to discriminate biomaterials with similar elemental compositions at standoff distances based on differences in key intensity ratios, further work is needed to reduce the number of false positives/negatives by refining the PLS-DA model to include a sufficient range of material classes and carefully selecting a detection threshold. In addition, we have demonstrated that LIBS can distinguish five different organophosphate nerve agent simulants at 20 meters, despite their similar stoichiometric formulas. Finally, a combined PLS-DA model for chemical, biological, and explosives detection using a single ST-LIBS sensor has been developed in order to demonstrate the potential of standoff LIBS for universal hazardous materials detection.

Large-scale structure in superfluid Chaplygin gas cosmology

NASA Astrophysics Data System (ADS)

Yang, Rongjia

2014-03-01

We investigate the growth of the large-scale structure in the superfluid Chaplygin gas (SCG) model. Both linear and nonlinear growth, such as σ8 and the skewness S3, are discussed. We find the growth factor of SCG reduces to the Einstein-de Sitter case at early times while it differs from the cosmological constant model (ΛCDM) case in the large a limit. We also find there will be more stricture growth on large scales in the SCG scenario than in ΛCDM and the variations of σ8 and S3 between SCG and ΛCDM cannot be discriminated.

Rapid discrimination and determination of antibiotics drugs in plastic syringes using near infrared spectroscopy with chemometric analysis: Application to amoxicillin and penicillin.

PubMed

Lê, Laetitia Minh Mai; Eveleigh, Luc; Hasnaoui, Ikram; Prognon, Patrice; Baillet-Guffroy, Arlette; Caudron, Eric

2017-05-10

The aim of this study was to investigate near infrared spectroscopy (NIRS) combined to chemometric analysis to discriminate and quantify three antibiotics by direct measurement in plastic syringes.Solutions of benzylpenicillin (PENI), amoxicillin (AMOX) and amoxicillin/clavulanic acid (AMOX/CLAV) were analyzed at therapeutic concentrations in glass vials and plastic syringes with NIR spectrometer by direct measurement. Chemometric analysis using partial least squares regression and discriminative analysis was conducted to develop qualitative and quantitative calibration models. Discrimination of the three antibiotics was optimal for concentrated solutions with 100% of accuracy. For quantitative analysis, the three antibiotics furnished a linear response (R²>0.9994) for concentrations ranging from 0.05 to 0.2 g/mL for AMOX, 0.1 to 1.0 MUI/mL for PENI and 0.005 to 0.05 g/mL for AMOX/CLAV with excellent repeatability (maximum 1.3%) and intermediate precision (maximum of 3.2%). Based on proposed models, 94.4% of analyzed AMOX syringes, 80.0% of AMOX/CLAV syringes and 85.7% of PENI syringes were compliant with a relative error including the limit of ± 15%.NIRS as rapid, non-invasive and non-destructive analytical method represents a potentially powerful tool to further develop for securing the drug administration circuit of healthcare institutions to ensure that patients receive the correct product at the right dose. Copyright © 2017 Elsevier B.V. All rights reserved.

Rapid and automatic chemical identification of the medicinal flower buds of Lonicera plants by the benchtop and hand-held Fourier transform infrared spectroscopy

NASA Astrophysics Data System (ADS)

Chen, Jianbo; Guo, Baolin; Yan, Rui; Sun, Suqin; Zhou, Qun

2017-07-01

With the utilization of the hand-held equipment, Fourier transform infrared (FT-IR) spectroscopy is a promising analytical technique to minimize the time cost for the chemical identification of herbal materials. This research examines the feasibility of the hand-held FT-IR spectrometer for the on-site testing of herbal materials, using Lonicerae Japonicae Flos (LJF) and Lonicerae Flos (LF) as examples. Correlation-based linear discriminant models for LJF and LF are established based on the benchtop and hand-held FT-IR instruments. The benchtop FT-IR models can exactly recognize all articles of LJF and LF. Although a few LF articles are misjudged at the sub-class level, the hand-held FT-IR models are able to exactly discriminate LJF and LF. As a direct and label-free analytical technique, FT-IR spectroscopy has great potential in the rapid and automatic chemical identification of herbal materials either in laboratories or in fields. This is helpful to prevent the spread and use of adulterated herbal materials in time.

Discrimination thresholds of normal and anomalous trichromats: Model of senescent changes in ocular media density on the Cambridge Colour Test

PubMed Central

Shinomori, Keizo; Panorgias, Athanasios; Werner, John S.

2017-01-01

Age-related changes in chromatic discrimination along dichromatic confusion lines were measured with the Cambridge Colour Test (CCT). One hundred and sixty-two individuals (16 to 88 years old) with normal Rayleigh matches were the major focus of this paper. An additional 32 anomalous trichromats classified by their Rayleigh matches were also tested. All subjects were screened to rule out abnormalities of the anterior and posterior segments. Thresholds on all three chromatic vectors measured with the CCT showed age-related increases. Protan and deutan vector thresholds increased linearly with age while the tritan vector threshold was described with a bilinear model. Analysis and modeling demonstrated that the nominal vectors of the CCT are shifted by senescent changes in ocular media density, and a method for correcting the CCT vectors is demonstrated. A correction for these shifts indicates that classification among individuals of different ages is unaffected. New vector thresholds for elderly observers and for all age groups are suggested based on calculated tolerance limits. PMID:26974943

The combined effects of genetic risk and perceived discrimination on blood pressure among African Americans in the Jackson Heart Study

PubMed Central

Taylor, Jacquelyn Y.; Sun, Yan V.; Barcelona de Mendoza, Veronica; Ifatunji, Mosi; Rafferty, Jane; Fox, Ervin R.; Musani, Solomon K.; Sims, Mario; Jackson, James S.

2017-01-01

Abstract Both genomics and environmental stressors play a significant role in increases in blood pressure (BP). In an attempt to further explain the hypertension (HTN) disparity among African Americans (AA), both genetic underpinnings (selected candidate genes) and stress due to perceived racial discrimination (as reported in the literature) have independently been linked to increased BP among AAs. Although Gene x Environment interactions on BP have been examined, the environmental component of these investigations has focused more on lifestyle behaviors such as smoking, diet, and physical activity, and less on psychosocial stressors such as perceived discrimination. The present study uses candidate gene analyses to identify the relationship between Everyday Discrimination (ED) and Major Life Discrimination (MLD) with increases in systolic BP (SBP) and diastolic BP (DBP) among AA in the Jackson Heart Study. Multiple linear regression models reveal no association between discrimination and BP after adjusting for age, sex, body mass index (BMI), antihypertensive medication use, and current smoking status. Subsequent candidate gene analysis identified 5 SNPs (rs7602215, rs3771724, rs1006502, rs1791926, and rs2258119) that interacted with perceived discrimination and SBP, and 3 SNPs (rs2034454, rs7602215, and rs3771724) that interacted with perceived discrimination and DBP. Most notably, there was a significant SNP × discrimination interaction for 2 SNPs on the SLC4A5 gene: rs3771724 (MLD: SBP P = .034, DBP P = .031; ED: DBP: P = .016) and rs1006502 (MLD: SBP P = .034, DBP P = .030; ED: DBP P = .015). This study supports the idea that SNP × discrimination interactions combine to influence clinically relevant traits such as BP. Replication with similar epidemiological samples is required to ascertain the role of genes and psychosocial stressors in the development and expression of high BP in this understudied population. PMID:29069027

The combined effects of genetic risk and perceived discrimination on blood pressure among African Americans in the Jackson Heart Study.

PubMed

Taylor, Jacquelyn Y; Sun, Yan V; Barcelona de Mendoza, Veronica; Ifatunji, Mosi; Rafferty, Jane; Fox, Ervin R; Musani, Solomon K; Sims, Mario; Jackson, James S

2017-10-01

Both genomics and environmental stressors play a significant role in increases in blood pressure (BP). In an attempt to further explain the hypertension (HTN) disparity among African Americans (AA), both genetic underpinnings (selected candidate genes) and stress due to perceived racial discrimination (as reported in the literature) have independently been linked to increased BP among AAs. Although Gene x Environment interactions on BP have been examined, the environmental component of these investigations has focused more on lifestyle behaviors such as smoking, diet, and physical activity, and less on psychosocial stressors such as perceived discrimination.The present study uses candidate gene analyses to identify the relationship between Everyday Discrimination (ED) and Major Life Discrimination (MLD) with increases in systolic BP (SBP) and diastolic BP (DBP) among AA in the Jackson Heart Study. Multiple linear regression models reveal no association between discrimination and BP after adjusting for age, sex, body mass index (BMI), antihypertensive medication use, and current smoking status.Subsequent candidate gene analysis identified 5 SNPs (rs7602215, rs3771724, rs1006502, rs1791926, and rs2258119) that interacted with perceived discrimination and SBP, and 3 SNPs (rs2034454, rs7602215, and rs3771724) that interacted with perceived discrimination and DBP. Most notably, there was a significant SNP × discrimination interaction for 2 SNPs on the SLC4A5 gene: rs3771724 (MLD: SBP P = .034, DBP P = .031; ED: DBP: P = .016) and rs1006502 (MLD: SBP P = .034, DBP P = .030; ED: DBP P = .015).This study supports the idea that SNP × discrimination interactions combine to influence clinically relevant traits such as BP. Replication with similar epidemiological samples is required to ascertain the role of genes and psychosocial stressors in the development and expression of high BP in this understudied population.

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.

Understanding Deep Representations Learned in Modeling Users Likes.

PubMed

Guntuku, Sharath Chandra; Zhou, Joey Tianyi; Roy, Sujoy; Lin, Weisi; Tsang, Ivor W

2016-08-01

Automatically understanding and discriminating different users' liking for an image is a challenging problem. This is because the relationship between image features (even semantic ones extracted by existing tools, viz., faces, objects, and so on) and users' likes is non-linear, influenced by several subtle factors. This paper presents a deep bi-modal knowledge representation of images based on their visual content and associated tags (text). A mapping step between the different levels of visual and textual representations allows for the transfer of semantic knowledge between the two modalities. Feature selection is applied before learning deep representation to identify the important features for a user to like an image. The proposed representation is shown to be effective in discriminating users based on images they like and also in recommending images that a given user likes, outperforming the state-of-the-art feature representations by  ∼ 15 %-20%. Beyond this test-set performance, an attempt is made to qualitatively understand the representations learned by the deep architecture used to model user likes.

Binocular combination of stimulus orientation.

PubMed

Yehezkel, O; Ding, J; Sterkin, A; Polat, U; Levi, D M

2016-11-01

When two sine waves that differ slightly in orientation are presented to the two eyes separately, a single cyclopean sine wave is perceived. However, it is unclear how the brain calculates its orientation. Here, we used a signal detection rating method to estimate the perceived orientation when the two eyes were presented with Gabor patches that differed in both orientation and contrast. We found a nearly linear combination of orientation when both targets had the same contrast. However, the binocular percept shifted away from the linear prediction towards the orientation with the higher contrast, depending on both the base contrast and the contrast ratio. We found that stimuli that differ slightly in orientation are combined into a single percept, similarly for monocular and binocular presentation, with a bias that depends on the interocular contrast ratio. Our results are well fitted by gain-control models, and are consistent with a previous study that favoured the DSKL model that successfully predicts binocular phase and contrast combination and binocular contrast discrimination. In this model, the departures from linearity may be explained on the basis of mutual suppression and mutual enhancement, both of which are stronger under dichoptic than monocular conditions.

Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

NASA Astrophysics Data System (ADS)

Phinyomark, A.; Hu, H.; Phukpattaranont, P.; Limsakul, C.

2012-01-01

The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier.

Rapid classification of enzymes in cleaning products by hydrolysis, mass spectrometry and linear discriminant analysis.

PubMed

Beneito-Cambra, Miriam; Herrero-Martínez, José Manuel; Simó-Alfonso, Ernesto F; Ramis-Ramos, Guillermo

2008-11-01

A method for the rapid classification of proteases, lipases, amylases and cellulases used as enhancers in cleaning products, based on precipitation with acetone, hydrolysis with HCl, dilution of the hydrolysates with ethanol, and direct infusion into the electrospray ion source of an ion-trap mass spectrometer, has been developed. The abundances of the ([M+H]+ ions of the amino acids, from the hydrolysates of both the enzyme industrial concentrates and the detergent bases spiked with them, were used to construct linear discriminant analysis models, capable of distinguishing between the enzyme classes. For this purpose, the variables were normalized as follows: (A) the ion abundance of each amino acid was divided by the sum of the ion abundances of all the amino acids in the corresponding mass spectrum; (B) the ratios of pairs of ion abundances were obtained by dividing the ion abundance of each amino acid by each one of the ion abundances of the other 17 amino acids in the corresponding mass spectrum. Using normalization procedure B, excellent class-resolution between proteases, lipases, amylases and cellulases was achieved. In all cases, enzymes in industrial concentrates and manufactured cleaning products were correctly classified with >98% assignment probability.

Foods contributing to vitamin B6, folate, and vitamin B12 intakes and biomarkers status in European adolescents: The HELENA study.

PubMed

Iglesia, Iris; Mouratidou, Theodora; González-Gross, Marcela; Huybrechts, Inge; Breidenassel, Christina; Santabárbara, Javier; Díaz, Ligia-Esperanza; Hällström, Lena; De Henauw, Stefaan; Gottrand, Frédéric; Kafatos, Anthony; Widhalm, Kurt; Manios, Yannis; Molnar, Denes; Stehle, Peter; Moreno, Luis A

2017-06-01

To examine the association between food groups consumption and vitamin B 6 , folate and B 12 intakes and biomarkers in adolescents. In total 2189 individuals participating in the cross-sectional Healthy Lifestyle in Europe by Nutrition in Adolescence study met the eligibility criteria for analysis of dietary intakes (46 % males) and 632 for biomarker analysis (47 % males). Food intakes were assessed by two non-consecutive 24-h recalls. Biomarkers were measured by chromatography and immunoassay. Food groups which best discriminated participants in the extreme tertiles of the distribution of vitamins were identified by discriminant analyses. Food groups with standardised canonical coefficients higher or equal to 0.3 were selected as valid discriminators of vitamins intake and biomarkers extreme tertiles. Linear mixed model elucidated the association between food groups and vitamins intakes and biomarkers. Vitamin B 6 intakes and biomarkers were best discriminated by meat (males and females), margarine and mixed origin lipids only in males and breakfast cereals (females). Breakfast cereals (males), and fruits, margarine and mixed origin lipids, vegetables excluding potatoes, breakfast cereals, and soups/bouillon (females) determined the most folate intakes and biomarkers. Considering vitamin B 12 intakes and biomarkers, meat, and white and butter milk (males and females), snacks (males), and dairy products (females) best discriminated individual in the extremes of the distribution. Fewer associations were obtained with mixed model for biomarkers than for vitamins intakes with food groups. Whereas B-vitamin intakes were associated with their food sources, biomarkers did with overall food consumption. Low-nutrient-density foods may compromise adolescents' vitamin status.

An ensemble of dissimilarity based classifiers for Mackerel gender determination

NASA Astrophysics Data System (ADS)

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

2014-03-01

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

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

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

Guo, Yanrong; Shao, Yeqin; Gao, Yaozong

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integratemore » the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.« less

General subspace learning with corrupted training data via graph embedding.

PubMed

Bao, Bing-Kun; Liu, Guangcan; Hong, Richang; Yan, Shuicheng; Xu, Changsheng

2013-11-01

We address the following subspace learning problem: supposing we are given a set of labeled, corrupted training data points, how to learn the underlying subspace, which contains three components: an intrinsic subspace that captures certain desired properties of a data set, a penalty subspace that fits the undesired properties of the data, and an error container that models the gross corruptions possibly existing in the data. Given a set of data points, these three components can be learned by solving a nuclear norm regularized optimization problem, which is convex and can be efficiently solved in polynomial time. Using the method as a tool, we propose a new discriminant analysis (i.e., supervised subspace learning) algorithm called Corruptions Tolerant Discriminant Analysis (CTDA), in which the intrinsic subspace is used to capture the features with high within-class similarity, the penalty subspace takes the role of modeling the undesired features with high between-class similarity, and the error container takes charge of fitting the possible corruptions in the data. We show that CTDA can well handle the gross corruptions possibly existing in the training data, whereas previous linear discriminant analysis algorithms arguably fail in such a setting. Extensive experiments conducted on two benchmark human face data sets and one object recognition data set show that CTDA outperforms the related algorithms.

Dual linear structured support vector machine tracking method via scale correlation filter

NASA Astrophysics Data System (ADS)

Li, Weisheng; Chen, Yanquan; Xiao, Bin; Feng, Chen

2018-01-01

Adaptive tracking-by-detection methods based on structured support vector machine (SVM) performed well on recent visual tracking benchmarks. However, these methods did not adopt an effective strategy of object scale estimation, which limits the overall tracking performance. We present a tracking method based on a dual linear structured support vector machine (DLSSVM) with a discriminative scale correlation filter. The collaborative tracker comprised of a DLSSVM model and a scale correlation filter obtains good results in tracking target position and scale estimation. The fast Fourier transform is applied for detection. Extensive experiments show that our tracking approach outperforms many popular top-ranking trackers. On a benchmark including 100 challenging video sequences, the average precision of the proposed method is 82.8%.

Automated fine structure image analysis method for discrimination of diabetic retinopathy stage using conjunctival microvasculature images

PubMed Central

Khansari, Maziyar M; O’Neill, William; Penn, Richard; Chau, Felix; Blair, Norman P; Shahidi, Mahnaz

2016-01-01

The conjunctiva is a densely vascularized mucus membrane covering the sclera of the eye with a unique advantage of accessibility for direct visualization and non-invasive imaging. The purpose of this study is to apply an automated quantitative method for discrimination of different stages of diabetic retinopathy (DR) using conjunctival microvasculature images. Fine structural analysis of conjunctival microvasculature images was performed by ordinary least square regression and Fisher linear discriminant analysis. Conjunctival images between groups of non-diabetic and diabetic subjects at different stages of DR were discriminated. The automated method’s discriminate rates were higher than those determined by human observers. The method allowed sensitive and rapid discrimination by assessment of conjunctival microvasculature images and can be potentially useful for DR screening and monitoring. PMID:27446692

Signal Detection Methods and Discriminant Analysis Applied to Categorization of Newspaper and Government Documents: A Preliminary Study.

ERIC Educational Resources Information Center

Ng, Kwong Bor; Rieh, Soo Young; Kantor, Paul

2000-01-01

Discussion of natural language processing focuses on experiments using linear discriminant analysis to distinguish "Wall Street Journal" texts from "Federal Register" tests using information about the frequency of occurrence of word boundaries, sentence boundaries, and punctuation marks. Displays and interprets results in terms…

Energy-discrimination x-ray computed tomography system utilizing a scanning cadmium-telluride detector

NASA Astrophysics Data System (ADS)

Sato, Eiichi; Abduraxit, Ablajan; Enomoto, Toshiyuki; Watanabe, Manabu; Hitomi, Keitaro; Takahashi, Kiyomi; Sato, Shigehiro; Ogawa, Akira; Onagawa, Jun

2010-04-01

An energy-discrimination K-edge x-ray computed tomography (CT) system is useful for controlling the image contrast of a target region by selecting both the photon energy and the energy width. The CT system has an oscillation-type linear cadmium telluride (CdTe) detectror. CT is performed by repeated linear scans and rotations of an object. Penetrating x-ray photons from the object are detected by a CdTe detector, and event signals of x-ray photons are produced using charge-sensitive and shaping amplifiers. Both photon energy and energy width are selected out using a multichannel analyzer, and the number of photons is counted by a counter card. In energy-discrimination CT, the tube voltage and tube current were 80 kV and 20 μA, respectively, and the x-ray intensity was 1.92 μGy/s at a distance of 1.0 m from the source and a tube voltage of 80 kV. The energy-discrimination CT was carried out by selecting x-ray photon energies.

Vibrational Markovian modelling of footprints after the interaction of antibiotics with the packaging region of HIV type 1.

PubMed

Díaz, Humberto González; de Armas, Ronal Ramos; Molina, Reinaldo

2003-11-01

The design of novel anti-HIV compounds has now become a crucial area for scientists working in numerous interrelated fields of science such as molecular biology, medicinal chemistry, mathematical biology, molecular modelling and bioinformatics. In this context, the development of simple but physically meaningful mathematical models to represent the interaction between anti-HIV drugs and their biological targets is of major interest. One such area currently under investigation involves the targets in the HIV-RNA-packaging region. In the work described here, we applied Markov chain theory in an attempt to describe the interaction between the antibiotic paromomycin and the packaging region of the RNA in Type-1 HIV. In this model, a nucleic acid squeezed graph is used. The vertices of the graph represent the nucleotides while the edges are the phosphodiester bonds. A stochastic (Markovian) matrix was subsequently defined on this graph, an operation that codifies the probabilities of interaction between specific nucleotides of HIV-RNA and the antibiotic. The strength of these local interactions can be calculated through an inelastic vibrational model. The successive power of this matrix codifies the probabilities with which the vibrations after drug-RNA interactions vanish along the polynucleotide main chain. The sums of self-return probabilities in the k-vicinity of each nucleotide represent physically meaningful descriptors. A linear discriminant function was developed and gave rise to excellent discrimination in 80.8% of interacting and footprinted nucleotides. The Jackknife method was employed to assess the stability and predictability of the model. On the other hand, a linear regression model predicted the local binding affinity constants between a specific nucleotide and the antibiotic (R(2)=0.91, Q(2)=0.86). These kinds of models could play an important role either in the discovery of new anti-HIV compounds or the study of their mode of action.

Associations Between Discrimination and Cardiovascular Health Among Asian Indians in the United States.

PubMed

Nadimpalli, S B; Dulin-Keita, A; Salas, C; Kanaya, A M; Kandula, Namratha R

2016-12-01

Asian Indians (AI) have a high risk of atherosclerotic cardiovascular disease. The study investigated associations between discrimination and (1) cardiovascular risk and (2) self-rated health among AI. Higher discrimination scores were hypothesized to relate to a higher cardiovascular risk score (CRS) and poorer self-rated health. Asian Indians (n = 757) recruited between 2010 and 2013 answered discrimination and self-reported health questions. The CRS (0-8 points) included body-mass index, systolic blood pressure, total cholesterol, and fasting blood glucose levels of AI. Multiple linear regression analyses were conducted to evaluate relationships between discrimination and the CRS and discrimination and self-rated health, adjusting for psychosocial and clinical factors. There were no significant relationships between discrimination and the CRS (p ≥ .05). Discrimination was related to poorer self-reported health, B = -.41 (SE = .17), p = .02. Findings suggest perhaps there are important levels at which discrimination may harm health.

Micro-Raman spectroscopy of natural and synthetic indigo samples.

PubMed

Vandenabeele, Peter; Moens, Luc

2003-02-01

In this work indigo samples from three different sources are studied by using Raman spectroscopy: the synthetic pigment and pigments from the woad (Isatis tinctoria) and the indigo plant (Indigofera tinctoria). 21 samples were obtained from 8 suppliers; for each sample 5 Raman spectra were recorded and used for further chemometrical analysis. Principal components analysis (PCA) was performed as data reduction method before applying hierarchical cluster analysis. Linear discriminant analysis (LDA) was implemented as a non-hierarchical supervised pattern recognition method to build a classification model. In order to avoid broad-shaped interferences from the fluorescence background, the influence of 1st and 2nd derivatives on the classification was studied by using cross-validation. Although chemically identical, it is shown that Raman spectroscopy in combination with suitable chemometric methods has the potential to discriminate between synthetic and natural indigo samples.

Statistical sex determination from craniometrics: Comparison of linear discriminant analysis, logistic regression, and support vector machines.

PubMed

Santos, Frédéric; Guyomarc'h, Pierre; Bruzek, Jaroslav

2014-12-01

Accuracy of identification tools in forensic anthropology primarily rely upon the variations inherent in the data upon which they are built. Sex determination methods based on craniometrics are widely used and known to be specific to several factors (e.g. sample distribution, population, age, secular trends, measurement technique, etc.). The goal of this study is to discuss the potential variations linked to the statistical treatment of the data. Traditional craniometrics of four samples extracted from documented osteological collections (from Portugal, France, the U.S.A., and Thailand) were used to test three different classification methods: linear discriminant analysis (LDA), logistic regression (LR), and support vector machines (SVM). The Portuguese sample was set as a training model on which the other samples were applied in order to assess the validity and reliability of the different models. The tests were performed using different parameters: some included the selection of the best predictors; some included a strict decision threshold (sex assessed only if the related posterior probability was high, including the notion of indeterminate result); and some used an unbalanced sex-ratio. Results indicated that LR tends to perform slightly better than the other techniques and offers a better selection of predictors. Also, the use of a decision threshold (i.e. p>0.95) is essential to ensure an acceptable reliability of sex determination methods based on craniometrics. Although the Portuguese, French, and American samples share a similar sexual dimorphism, application of Western models on the Thai sample (that displayed a lower degree of dimorphism) was unsuccessful. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Tracing the Geographical Origin of Onions by Strontium Isotope Ratio and Strontium Content.

PubMed

Hiraoka, Hisaaki; Morita, Sakie; Izawa, Atsunobu; Aoyama, Keisuke; Shin, Ki-Cheol; Nakano, Takanori

2016-01-01

The strontium (Sr) isotope ratio ((87)Sr/(86)Sr) and Sr content were used to trace the geographical origin of onions from Japan and other countries, including China, the United States of America, New Zealand, Australia, and Thailand. The mean (87)Sr/(86)Sr ratio and Sr content (dry weight basis) for onions from Japan were 0.70751 and 4.6 mg kg(-1), respectively, and the values for onions from the other countries were 0.71199 and 12.4 mg kg(-1), respectively. Linear discriminant analysis was performed to classify onions produced in Japan from those produced in the other countries based on the Sr data. The discriminant equation derived from linear discriminant analysis was evaluated by 10-fold cross validation. As a result, the origins of 92% of onions were correctly classified between Japan and the other countries.

Discrimination of Active and Weakly Active Human BACE1 Inhibitors Using Self-Organizing Map and Support Vector Machine.

PubMed

Li, Hang; Wang, Maolin; Gong, Ya-Nan; Yan, Aixia

2016-01-01

β-secretase (BACE1) is an aspartyl protease, which is considered as a novel vital target in Alzheimer`s disease therapy. We collected a data set of 294 BACE1 inhibitors, and built six classification models to discriminate active and weakly active inhibitors using Kohonen's Self-Organizing Map (SOM) method and Support Vector Machine (SVM) method. Each molecular descriptor was calculated using the program ADRIANA.Code. We adopted two different methods: random method and Self-Organizing Map method, for training/test set split. The descriptors were selected by F-score and stepwise linear regression analysis. The best SVM model Model2C has a good prediction performance on test set with prediction accuracy, sensitivity (SE), specificity (SP) and Matthews correlation coefficient (MCC) of 89.02%, 90%, 88%, 0.78, respectively. Model 1A is the best SOM model, whose accuracy and MCC of the test set were 94.57% and 0.98, respectively. The lone pair electronegativity and polarizability related descriptors importantly contributed to bioactivity of BACE1 inhibitor. The Extended-Connectivity Finger-Prints_4 (ECFP_4) analysis found some vitally key substructural features, which could be helpful for further drug design research. The SOM and SVM models built in this study can be obtained from the authors by email or other contacts.

Discrimination, Perceived Social Inequity, and Mental Health Among Rural-to-Urban Migrants in China

PubMed Central

Lin, Danhua; Wang, Bo; Hong, Yan; Qin, Xiong; Stanton, Bonita

2010-01-01

Status-based discrimination and inequity have been associated with the process of migration, especially with economics-driven internal migration. However, their association with mental health among economy-driven internal migrants in developing countries is rarely assessed. This study examines discriminatory experiences and perceived social inequity in relation to mental health status among rural-to-urban migrants in China. Cross-sectional data were collected from 1,006 rural-to-urban migrants in 2004–2005 in Beijing, China. Participants reported their perceptions and experiences of being discriminated in daily life in urban destination and perceived social inequity. Mental health was measured using the symptom checklist-90 (SCL-90). Multivariate analyses using general linear model were performed to test the effect of discriminatory experience and perceived social inequity on mental health. Experience of discrimination was positively associated with male gender, being married at least once, poorer health status, shorter duration of migration, and middle range of personal income. Likewise, perceived social inequity was associated with poorer health status, higher education attainment, and lower personal income. Multivariate analyses indicate that both experience of discrimination and perceived social inequity were strongly associated with mental health problems of rural-to-urban migrants. Experience of discrimination in daily life and perceived social inequity have a significant influence on mental health among rural-to-urban migrants. The findings underscore the needs to reduce public or societal discrimination against rural-to-urban migrants, to eliminate structural barriers (i.e., dual household registrations) for migrants to fully benefit from the urban economic development, and to create a positive atmosphere to improve migrant's psychological well-being. PMID:20033772

Taking it like a man: masculine role norms as moderators of the racial discrimination-depressive symptoms association among African American men.

PubMed

Hammond, Wizdom Powell

2012-05-01

I examined the association between everyday racial discrimination and depressive symptoms and explored the moderating role of 2 dimensions of masculine role norms, restrictive emotionality and self-reliance. Cross-sectional survey data from 674 African American men aged 18 years and older recruited primarily from barbershops in 4 US regions (2003-2010) were used. Direct and moderated associations were assessed with multivariate linear regression analyses for the overall sample and different age groups. Models were adjusted for recruitment site, sociodemographics, masculine role norms salience, and general social stress. Everyday racial discrimination was associated with more depressive symptoms across all age groups. Higher restrictive emotionality was associated with more depressive symptoms among men aged 18 to 29 and 30 to 39 years. Self-reliance was associated with fewer depressive symptoms among men aged 18 to 29 years and 40 years and older. The positive association between everyday racial discrimination and depressive symptoms was stronger among men with high restrictive emotionality, but this moderated effect was limited to men older than 30 years. Interventions designed to reduce African American men's depression instigated by racism should be life-course specific and address masculine role norms that encourage emotion restriction.

Detection of indoor biological hazards using the man-portable laser induced breakdown spectrometer

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

Munson, Chase A.; Gottfried, Jennifer L.; Snyder, Emily Gibb

2008-11-01

The performance of a man-portable laser induced breakdown spectrometer was evaluated for the detection of biological powders on indoor office surfaces and wipe materials. Identification of pure unknown powders was performed by comparing against a library of spectra containing biological agent surrogates and confusant materials, such as dusts, diesel soot, natural and artificial sweeteners, and drink powders, using linear correlation analysis. Simple models constructed using a second technique, partial least squares discriminant analysis, successfully identified Bacillus subtilis (BG) spores on wipe materials and office surfaces. Furthermore, these models were able to identify BG on materials not used in the trainingmore » of the model.« less

Classification of electroencephalograph signals using time-frequency decomposition and linear discriminant analysis

NASA Astrophysics Data System (ADS)

Szuflitowska, B.; Orlowski, P.

2017-08-01

Automated detection system consists of two key steps: extraction of features from EEG signals and classification for detection of pathology activity. The EEG sequences were analyzed using Short-Time Fourier Transform and the classification was performed using Linear Discriminant Analysis. The accuracy of the technique was tested on three sets of EEG signals: epilepsy, healthy and Alzheimer's Disease. The classification error below 10% has been considered a success. The higher accuracy are obtained for new data of unknown classes than testing data. The methodology can be helpful in differentiation epilepsy seizure and disturbances in the EEG signal in Alzheimer's Disease.

Fast Depiction Invariant Visual Similarity for Content Based Image Retrieval Based on Data-driven Visual Similarity using Linear Discriminant Analysis

NASA Astrophysics Data System (ADS)

Wihardi, Y.; Setiawan, W.; Nugraha, E.

2018-01-01

On this research we try to build CBIRS based on Learning Distance/Similarity Function using Linear Discriminant Analysis (LDA) and Histogram of Oriented Gradient (HoG) feature. Our method is invariant to depiction of image, such as similarity of image to image, sketch to image, and painting to image. LDA can decrease execution time compared to state of the art method, but it still needs an improvement in term of accuracy. Inaccuracy in our experiment happen because we did not perform sliding windows search and because of low number of negative samples as natural-world images.

Evaluation of several two-step scoring functions based on linear interaction energy, effective ligand size, and empirical pair potentials for prediction of protein-ligand binding geometry and free energy.

PubMed

Rahaman, Obaidur; Estrada, Trilce P; Doren, Douglas J; Taufer, Michela; Brooks, Charles L; Armen, Roger S

2011-09-26

The performances of several two-step scoring approaches for molecular docking were assessed for their ability to predict binding geometries and free energies. Two new scoring functions designed for "step 2 discrimination" were proposed and compared to our CHARMM implementation of the linear interaction energy (LIE) approach using the Generalized-Born with Molecular Volume (GBMV) implicit solvation model. A scoring function S1 was proposed by considering only "interacting" ligand atoms as the "effective size" of the ligand and extended to an empirical regression-based pair potential S2. The S1 and S2 scoring schemes were trained and 5-fold cross-validated on a diverse set of 259 protein-ligand complexes from the Ligand Protein Database (LPDB). The regression-based parameters for S1 and S2 also demonstrated reasonable transferability in the CSARdock 2010 benchmark using a new data set (NRC HiQ) of diverse protein-ligand complexes. The ability of the scoring functions to accurately predict ligand geometry was evaluated by calculating the discriminative power (DP) of the scoring functions to identify native poses. The parameters for the LIE scoring function with the optimal discriminative power (DP) for geometry (step 1 discrimination) were found to be very similar to the best-fit parameters for binding free energy over a large number of protein-ligand complexes (step 2 discrimination). Reasonable performance of the scoring functions in enrichment of active compounds in four different protein target classes established that the parameters for S1 and S2 provided reasonable accuracy and transferability. Additional analysis was performed to definitively separate scoring function performance from molecular weight effects. This analysis included the prediction of ligand binding efficiencies for a subset of the CSARdock NRC HiQ data set where the number of ligand heavy atoms ranged from 17 to 35. This range of ligand heavy atoms is where improved accuracy of predicted ligand efficiencies is most relevant to real-world drug design efforts.

Comparing Linear Discriminant Function with Logistic Regression for the Two-Group Classification Problem.

ERIC Educational Resources Information Center

Fan, Xitao; Wang, Lin

The Monte Carlo study compared the performance of predictive discriminant analysis (PDA) and that of logistic regression (LR) for the two-group classification problem. Prior probabilities were used for classification, but the cost of misclassification was assumed to be equal. The study used a fully crossed three-factor experimental design (with…

Raman micro-spectroscopy analysis of human lens epithelial cells exposed to a low-dose-range of ionizing radiation.

PubMed

Allen, Christian Harry; Kumar, Achint; Qutob, Sami; Nyiri, Balazs; Chauhan, Vinita; Murugkar, Sangeeta

2018-01-09

Recent findings in populations exposed to ionizing radiation (IR) indicate dose-related lens opacification occurs at much lower doses (<2 Gy) than indicated in radiation protection guidelines. As a result, research efforts are now being directed towards identifying early predictors of lens degeneration resulting in cataractogenesis. In this study, Raman micro-spectroscopy was used to investigate the effects of varying doses of radiation, ranging from 0.01 Gy to 5 Gy, on human lens epithelial (HLE) cells which were chemically fixed 24 h post-irradiation. Raman spectra were acquired from the nucleus and cytoplasm of the HLE cells. Spectra were collected from points in a 3  ×  3 grid pattern and then averaged. The raw spectra were preprocessed and principal component analysis followed by linear discriminant analysis was used to discriminate between dose and control for 0.25, 0.5, 2, and 5 Gy. Using leave-one-out cross-validation accuracies of greater than 74% were attained for each dose/control combination. The ultra-low doses 0.01 and 0.05 Gy were included in an analysis of band intensities for Raman bands found to be significant in the linear discrimination, and an induced repair model survival curve was fit to a band-difference-ratio plot of this data, suggesting HLE cells undergo a nonlinear response to low-doses of IR. A survival curve was also fit to clonogenic assay data done on the irradiated HLE cells, showing a similar nonlinear response.

Raman micro-spectroscopy analysis of human lens epithelial cells exposed to a low-dose-range of ionizing radiation

NASA Astrophysics Data System (ADS)

Allen, Christian Harry; Kumar, Achint; Qutob, Sami; Nyiri, Balazs; Chauhan, Vinita; Murugkar, Sangeeta

2018-01-01

Recent findings in populations exposed to ionizing radiation (IR) indicate dose-related lens opacification occurs at much lower doses (<2 Gy) than indicated in radiation protection guidelines. As a result, research efforts are now being directed towards identifying early predictors of lens degeneration resulting in cataractogenesis. In this study, Raman micro-spectroscopy was used to investigate the effects of varying doses of radiation, ranging from 0.01 Gy to 5 Gy, on human lens epithelial (HLE) cells which were chemically fixed 24 h post-irradiation. Raman spectra were acquired from the nucleus and cytoplasm of the HLE cells. Spectra were collected from points in a 3  ×  3 grid pattern and then averaged. The raw spectra were preprocessed and principal component analysis followed by linear discriminant analysis was used to discriminate between dose and control for 0.25, 0.5, 2, and 5 Gy. Using leave-one-out cross-validation accuracies of greater than 74% were attained for each dose/control combination. The ultra-low doses 0.01 and 0.05 Gy were included in an analysis of band intensities for Raman bands found to be significant in the linear discrimination, and an induced repair model survival curve was fit to a band-difference-ratio plot of this data, suggesting HLE cells undergo a nonlinear response to low-doses of IR. A survival curve was also fit to clonogenic assay data done on the irradiated HLE cells, showing a similar nonlinear response.

Hormonally active agents in the environment and children's behavior: assessing effects on children's gender-dimorphic outcomes.

PubMed

Sandberg, David E; Vena, John E; Weiner, John; Beehler, Gregory P; Swanson, Mya; Meyer-Bahlburg, Heino F L

2003-03-01

Early sex hormone exposure contributes to gender-dimorphic behavioral development in mammals, including humans. Environmental toxicants concentrated in contaminated sport fish can interfere with the actions of sex steroids. This study developed an outcome variable by combining gender-dimorphic behaviors that differentiates boys and girls. Offspring of participants in the New York State Angler Cohort Study (NYSACS) were targeted in a parent-report postal survey. Instruments were selected based on findings of gender differences in the general population. A linear discriminant function model incorporating three gender behavior scales correctly classified the sex of 97.7% of children (252 boys and 234 girls) from a random NYSACS sample. The discriminant function was cross-validated by correctly classifying the sex of 98.4% of children (457 boys and 425 girls) from the remaining NYSACS cases and 97.6% of children (154 boys and 142 girls) from an independent school sample. Within-sex stepwise multiple regression analyses revealed that masculine behavior increased among boys with age and with the number of years of maternal sport fish consumption. In girls, older age and previous live-born siblings were associated with more masculine behavior, whereas feminine behavior increased with the duration of breast feeding. These associations were replicated in an independent sample. A linear discriminant function effectively transformed the binary classification of sex (male-female) to a bipolar continuum of gender (masculinity-femininity). Findings from this study are consistent with the hypothesis that environmental contaminants contribute to shifts in gender-role behavior. Future investigations will need to account for competing explanations of this effect.

Joint recognition and discrimination in nonlinear feature space

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1997-09-01

A new general method for linear and nonlinear feature extraction is presented. It is novel since it provides both representation and discrimination while most other methods are concerned with only one of these issues. We call this approach the maximum representation and discrimination feature (MRDF) method and show that the Bayes classifier and the Karhunen- Loeve transform are special cases of it. We refer to our nonlinear feature extraction technique as nonlinear eigen- feature extraction. It is new since it has a closed-form solution and produces nonlinear decision surfaces with higher rank than do iterative methods. Results on synthetic databases are shown and compared with results from standard Fukunaga- Koontz transform and Fisher discriminant function methods. The method is also applied to an automated product inspection problem (discrimination) and to the classification and pose estimation of two similar objects (representation and discrimination).

Everyday Discrimination, Diabetes-Related Distress, and Depressive Symptoms Among African Americans and Latinos with Diabetes

PubMed Central

Valerio, Melissa A.; Kieffer, Edith; Sinco, Brandy; Rosland, Ann-Marie; Hawkins, Jaclynn; Espitia, Nicolaus; Palmisano, Gloria; Spencer, Michael

2013-01-01

It is not known how discrimination might affect diabetes-related distress (DRD), an important correlate of diabetes outcomes. We examined correlates of discrimination and the influence of discrimination on DRD and depressive symptoms (DS) for African Americans and Latinos with type 2 diabetes. We analyzed survey data (n = 157) collected at enrollment into a diabetes management intervention. Using multiple linear regression, we examined correlates of discrimination and the association between discrimination and DRD and DS. Discrimination was significantly associated with higher DRD for Latinos (b 1.58, 95 % CI 1.08, 2.31, p < 0.05), but not significant for African Americans (b 0.96, 95 % CI 0.59, 1.57). Discrimination was marginally significantly associated with more DS for Latinos (b 1.43, 95 % CI 0.97, 2.12, p < 0.10), but not significant for African Americans (b 1.21, 95 % CI 0.87, 1.70). These findings suggest the need to address stressors unique to racial/ethnic minorities to improve diabetes-related outcomes. PMID:23689972

Pattern recognition and genetic algorithms for discrimination of orange juices and reduction of significant components from headspace solid-phase microextraction.

PubMed

Rinaldi, Maurizio; Gindro, Roberto; Barbeni, Massimo; Allegrone, Gianna

2009-01-01

Orange (Citrus sinensis L.) juice comprises a complex mixture of volatile components that are difficult to identify and quantify. Classification and discrimination of the varieties on the basis of the volatile composition could help to guarantee the quality of a juice and to detect possible adulteration of the product. To provide information on the amounts of volatile constituents in fresh-squeezed juices from four orange cultivars and to establish suitable discrimination rules to differentiate orange juices using new chemometric approaches. Fresh juices of four orange cultivars were analysed by headspace solid-phase microextraction (HS-SPME) coupled with GC-MS. Principal component analysis, linear discriminant analysis and heuristic methods, such as neural networks, allowed clustering of the data from HS-SPME analysis while genetic algorithms addressed the problem of data reduction. To check the quality of the results the chemometric techniques were also evaluated on a sample. Thirty volatile compounds were identified by HS-SPME and GC-MS analyses and their relative amounts calculated. Differences in composition of orange juice volatile components were observed. The chosen orange cultivars could be discriminated using neural networks, genetic relocation algorithms and linear discriminant analysis. Genetic algorithms applied to the data were also able to detect the most significant compounds. SPME is a useful technique to investigate orange juice volatile composition and a flexible chemometric approach is able to correctly separate the juices.

Tackling non-linearities with the effective field theory of dark energy and modified gravity

NASA Astrophysics Data System (ADS)

Frusciante, Noemi; Papadomanolakis, Georgios

2017-12-01

We present the extension of the effective field theory framework to the mildly non-linear scales. The effective field theory approach has been successfully applied to the late time cosmic acceleration phenomenon and it has been shown to be a powerful method to obtain predictions about cosmological observables on linear scales. However, mildly non-linear scales need to be consistently considered when testing gravity theories because a large part of the data comes from those scales. Thus, non-linear corrections to predictions on observables coming from the linear analysis can help in discriminating among different gravity theories. We proceed firstly by identifying the necessary operators which need to be included in the effective field theory Lagrangian in order to go beyond the linear order in perturbations and then we construct the corresponding non-linear action. Moreover, we present the complete recipe to map any single field dark energy and modified gravity models into the non-linear effective field theory framework by considering a general action in the Arnowitt-Deser-Misner formalism. In order to illustrate this recipe we proceed to map the beyond-Horndeski theory and low-energy Hořava gravity into the effective field theory formalism. As a final step we derived the 4th order action in term of the curvature perturbation. This allowed us to identify the non-linear contributions coming from the linear order perturbations which at the next order act like source terms. Moreover, we confirm that the stability requirements, ensuring the positivity of the kinetic term and the speed of propagation for scalar mode, are automatically satisfied once the viability of the theory is demanded at linear level. The approach we present here will allow to construct, in a model independent way, all the relevant predictions on observables at mildly non-linear scales.

Classification of commercial wines from the Canary Islands (Spain) by chemometric techniques using metallic contents.

PubMed

Frías, Sergio; Conde, José E; Rodríguez-Bencomo, Juan J; García-Montelongo, Francisco; Pérez-Trujillo, Juan P

2003-02-06

Eleven elements, K, Na, Ca, Mg, Fe, Cu, Zn, Mn, Sr, Li and Rb, were determined in dry and sweet wines bearing the denominations of origin of El Hierro, La Palma and Lanzarote islands (Canary Islands, Spain). Analyses were performed by flame atomic absorption spectrophotometry, with the exceptions of lithium and rubidium for which flame atomic emission spectrophotometry was used. Sweet wines from La Palma were elaborated as naturally sweet with over-ripe grapes and significant differences were found in all the analysed elements with the exceptions of sodium, iron and rubidium with regard to dry wines from the same island. Contrarily, sweet wines from Lanzarote elaborated with grapes in a similar ripening state to dry wines did not present significant differences between them with the exception of strontium, the content of which was greater in dry wines. Among the three islands, significant differences in mean content were found with the exceptions of iron and copper. Cluster analysis and principal component analysis show differences in wines according to the island of origin and the ripening state of the grapes. Linear discriminant analysis using rubidium, sodium, manganese and strontium, the four most discriminant elements, gave 100% recognition ability and 95.6% prediction ability. The sensitivity and specificity obtained using soft independent modelling of class analogy (SIMCA) as a modelling multivariate technique were both 100% for El Hierro and Lanzarote, and 100 and 95%, respectively, for La Palma. The modelling and discriminant capacities of the different metals were also studied.

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.

Digitized Spiral Drawing: A Possible Biomarker for Early Parkinson's Disease.

PubMed

San Luciano, Marta; Wang, Cuiling; Ortega, Roberto A; Yu, Qiping; Boschung, Sarah; Soto-Valencia, Jeannie; Bressman, Susan B; Lipton, Richard B; Pullman, Seth; Saunders-Pullman, Rachel

2016-01-01

Pre-clinical markers of Parkinson's Disease (PD) are needed, and to be relevant in pre-clinical disease, they should be quantifiably abnormal in early disease as well. Handwriting is impaired early in PD and can be evaluated using computerized analysis of drawn spirals, capturing kinematic, dynamic, and spatial abnormalities and calculating indices that quantify motor performance and disability. Digitized spiral drawing correlates with motor scores and may be more sensitive in detecting early changes than subjective ratings. However, whether changes in spiral drawing are abnormal compared with controls and whether changes are detected in early PD are unknown. 138 PD subjects (50 with early PD) and 150 controls drew spirals on a digitizing tablet, generating x, y, z (pressure) data-coordinates and time. Derived indices corresponded to overall spiral execution (severity), shape and kinematic irregularity (second order smoothness, first order zero-crossing), tightness, mean speed and variability of spiral width. Linear mixed effect adjusted models comparing these indices and cross-validation were performed. Receiver operating characteristic analysis was applied to examine discriminative validity of combined indices. All indices were significantly different between PD cases and controls, except for zero-crossing. A model using all indices had high discriminative validity (sensitivity = 0.86, specificity = 0.81). Discriminative validity was maintained in patients with early PD. Spiral analysis accurately discriminates subjects with PD and early PD from controls supporting a role as a promising quantitative biomarker. Further assessment is needed to determine whether spiral changes are PD specific compared with other disorders and if present in pre-clinical PD.

Digitized Spiral Drawing: A Possible Biomarker for Early Parkinson’s Disease

PubMed Central

San Luciano, Marta; Wang, Cuiling; Ortega, Roberto A.; Yu, Qiping; Boschung, Sarah; Soto-Valencia, Jeannie; Bressman, Susan B.; Lipton, Richard B.; Pullman, Seth; Saunders-Pullman, Rachel

2016-01-01

Introduction Pre-clinical markers of Parkinson’s Disease (PD) are needed, and to be relevant in pre-clinical disease, they should be quantifiably abnormal in early disease as well. Handwriting is impaired early in PD and can be evaluated using computerized analysis of drawn spirals, capturing kinematic, dynamic, and spatial abnormalities and calculating indices that quantify motor performance and disability. Digitized spiral drawing correlates with motor scores and may be more sensitive in detecting early changes than subjective ratings. However, whether changes in spiral drawing are abnormal compared with controls and whether changes are detected in early PD are unknown. Methods 138 PD subjects (50 with early PD) and 150 controls drew spirals on a digitizing tablet, generating x, y, z (pressure) data-coordinates and time. Derived indices corresponded to overall spiral execution (severity), shape and kinematic irregularity (second order smoothness, first order zero-crossing), tightness, mean speed and variability of spiral width. Linear mixed effect adjusted models comparing these indices and cross-validation were performed. Receiver operating characteristic analysis was applied to examine discriminative validity of combined indices. Results All indices were significantly different between PD cases and controls, except for zero-crossing. A model using all indices had high discriminative validity (sensitivity = 0.86, specificity = 0.81). Discriminative validity was maintained in patients with early PD. Conclusion Spiral analysis accurately discriminates subjects with PD and early PD from controls supporting a role as a promising quantitative biomarker. Further assessment is needed to determine whether spiral changes are PD specific compared with other disorders and if present in pre-clinical PD. PMID:27732597

Differential solute gas response in ionic-liquid-based QCM arrays: elucidating design factors responsible for discriminative explosive gas sensing.

PubMed

Rehman, Abdul; Hamilton, Andrew; Chung, Alfred; Baker, Gary A; Wang, Zhe; Zeng, Xiangqun

2011-10-15

An eight-sensor array coupling a chemoselective room-temperature ionic liquid (RTIL) with quartz crystal microbalance (QCM) transduction is presented in this work in order to demonstrate the power of this approach in differentiating closely related analytes in sensory devices. The underlying mechanism behind the specific sensory response was explored by (i) studying mass loading and viscoelasticity effects of the sensing layers, predominantly through variation in damping impedance, the combination of which determines the sensitivity; (ii) creation of a solvation model based on Abraham's solvation descriptors which reveals the fact that polarizability and lipophilicity are the main factors influencing the dissolution of gas analytes into the RTILs; and (iii) determination of enthalpy and entropy values for the studied interactions and comparison via a simulation model, which is also effective for pattern discrimination, in order to establish a foundation for the analytical scientist as well as inspiration for synthetic pathways and innovative research into next-generation sensory approaches. The reported sensors displayed an excellent sensitivity with detection limit of <0.2%, fast response and recovery, and a workable temperature range of 27-55 °C and even higher. Linear discriminant analysis (LDA) showed a discrimination accuracy of 86-92% for nitromethane and 1-ethyl-2-nitrobenzene, 71% for different mixtures of nitromethane, and 100% for these analytes when thermodynamic parameters were used as input data. We envisage applications to detecting other nitroaromatics and security-related gas targets, and high-temperature or real-time situations where manual access is restricted, opening up new horizons in chemical sensing. © 2011 American Chemical Society

Determining quality of caviar from Caspian Sea based on Raman spectroscopy and using artificial neural networks.

PubMed

Mohamadi Monavar, H; Afseth, N K; Lozano, J; Alimardani, R; Omid, M; Wold, J P

2013-07-15

The purpose of this study was to evaluate the feasibility of Raman spectroscopy for predicting purity of caviars. The 93 wild caviar samples of three different types, namely; Beluga, Asetra and Sevruga were analysed by Raman spectroscopy in the range 1995 cm(-1) to 545 cm(-1). Also, 60 samples from combinations of every two types were examined. The chemical origin of the samples was identified by reference measurements on pure samples. Linear chemometric methods like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used for data visualisation and classification which permitted clear distinction between different caviars. Non-linear methods like Artificial Neural Networks (ANN) were used to classify caviar samples. Two different networks were tested in the classification: Probabilistic Neural Network with Radial-Basis Function (PNN) and Multilayer Feed Forward Networks with Back Propagation (BP-NN). In both cases, scores of principal components (PCs) were chosen as input nodes for the input layer in PC-ANN models in order to reduce the redundancy of data and time of training. Leave One Out (LOO) cross validation was applied in order to check the performance of the networks. Results of PCA indicated that, features like type and purity can be used to discriminate different caviar samples. These findings were also supported by LDA with efficiency between 83.77% and 100%. These results were confirmed with the results obtained by developed PC-ANN models, able to classify pure caviar samples with 93.55% and 71.00% accuracy in BP network and PNN, respectively. In comparison, LDA, PNN and BP-NN models for predicting caviar types have 90.3%, 73.1% and 91.4% accuracy. Partial least squares regression (PLSR) models were built under cross validation and tested with different independent data sets, yielding determination coefficients (R(2)) of 0.86, 0.83, 0.92 and 0.91 with root mean square error (RMSE) of validation of 0.32, 0.11, 0.03 and 0.09 for fatty acids of 16.0, 20.5, 22.6 and fat, respectively. Crown Copyright © 2013. Published by Elsevier B.V. All rights reserved.

Variation in fluxes estimated from nitrogen isotope discrimination corresponds with independent measures of nitrogen flux in Populus balsamifera L.

PubMed

Kalcsits, Lee A; Guy, Robert D

2016-02-01

Acquisition of mineral nitrogen by roots from the surrounding environment is often not completely efficient, in which a variable amount of leakage (efflux) relative to gross uptake (influx) occurs. The efflux/influx ratio (E/I) is, therefore, inversely related to the efficiency of nutrient uptake at the root level. Time-integrated estimates of E/I and other nitrogen-use traits may be obtainable from variation in stable isotope ratios or through compartmental analysis of tracer efflux (CATE) using radioactive or stable isotopes. To compare these two methods, Populus balsamifera L. genotypes were selected, a priori, for high or low nitrogen isotope discrimination. Vegetative cuttings were grown hydroponically, and E/I was calculated using an isotope mass balance model (IMB) and compared to E/I calculated using (15) N CATE. Both methods indicated that plants grown with ammonium had greater E/I than nitrate-grown plants. Genotypes with high or low E/I using CATE also had similarly high or low estimates of E/I using IMB, respectively. Genotype-specific means were linearly correlated (r = 0.77; P = 0.0065). Discrepancies in E/I between methods may reflect uncertainties in discrimination factors for the assimilatory enzymes, or temporal differences in uptake patterns. By utilizing genotypes with known variation in nitrogen isotope discrimination, a relationship between nitrogen isotope discrimination and bidirectional nitrogen fluxes at the root level was observed. © 2015 John Wiley & Sons Ltd.

Prediction models discriminating between nonlocomotive and locomotive activities in children using a triaxial accelerometer with a gravity-removal physical activity classification algorithm.

PubMed

Hikihara, Yuki; Tanaka, Chiaki; Oshima, Yoshitake; Ohkawara, Kazunori; Ishikawa-Takata, Kazuko; Tanaka, Shigeho

2014-01-01

The aims of our study were to examine whether a gravity-removal physical activity classification algorithm (GRPACA) is applicable for discrimination between nonlocomotive and locomotive activities for various physical activities (PAs) of children and to prove that this approach improves the estimation accuracy of a prediction model for children using an accelerometer. Japanese children (42 boys and 26 girls) attending primary school were invited to participate in this study. We used a triaxial accelerometer with a sampling interval of 32 Hz and within a measurement range of ±6 G. Participants were asked to perform 6 nonlocomotive and 5 locomotive activities. We measured raw synthetic acceleration with the triaxial accelerometer and monitored oxygen consumption and carbon dioxide production during each activity with the Douglas bag method. In addition, the resting metabolic rate (RMR) was measured with the subject sitting on a chair to calculate metabolic equivalents (METs). When the ratio of unfiltered synthetic acceleration (USA) and filtered synthetic acceleration (FSA) was 1.12, the rate of correct discrimination between nonlocomotive and locomotive activities was excellent, at 99.1% on average. As a result, a strong linear relationship was found for both nonlocomotive (METs = 0.013×synthetic acceleration +1.220, R2 = 0.772) and locomotive (METs = 0.005×synthetic acceleration +0.944, R2 = 0.880) activities, except for climbing down and up. The mean differences between the values predicted by our model and measured METs were -0.50 to 0.23 for moderate to vigorous intensity (>3.5 METs) PAs like running, ball throwing and washing the floor, which were regarded as unpredictable PAs. In addition, the difference was within 0.25 METs for sedentary to mild moderate PAs (<3.5 METs). Our specific calibration model that discriminates between nonlocomotive and locomotive activities for children can be useful to evaluate the sedentary to vigorous PAs intensity of both nonlocomotive and locomotive activities.

Markovian negentropies in bioinformatics. 1. A picture of footprints after the interaction of the HIV-1 Psi-RNA packaging region with drugs.

PubMed

Díaz, Humberto González; de Armas, Ronal Ramos; Molina, Reinaldo

2003-11-01

Many experts worldwide have highlighted the potential of RNA molecules as drug targets for the chemotherapeutic treatment of a range of diseases. In particular, the molecular pockets of RNA in the HIV-1 packaging region have been postulated as promising sites for antiviral action. The discovery of simpler methods to accurately represent drug-RNA interactions could therefore become an interesting and rapid way to generate models that are complementary to docking-based systems. The entropies of a vibrational Markov chain have been introduced here as physically meaningful descriptors for the local drug-nucleic acid complexes. A study of the interaction of the antibiotic Paromomycin with the packaging region of the RNA present in type-1 HIV has been carried out as an illustrative example of this approach. A linear discriminant function gave rise to excellent discrimination among 80.13% of interacting/non-interacting sites. More specifically, the model classified 36/45 nucleotides (80.0%) that interacted with paromomycin and, in addition, 85/106 (80.2%) footprinted (non-interacting) sites from the RNA viral sequence were recognized. The model showed a high Matthews' regression coefficient (C = 0.64). The Jackknife method was also used to assess the stability and predictability of the model by leaving out adenines, C, G, or U. Matthews' coefficients and overall accuracies for these approaches were between 0.55 and 0.68 and 75.8 and 82.7, respectively. On the other hand, a linear regression model predicted the local binding affinity constants between a specific nucleotide and the aforementioned antibiotic (R2 = 0.83,Q2 = 0.825). These kinds of models may play an important role either in the discovery of new anti-HIV compounds or in the elucidation of their mode of action. On request from the corresponding author (humbertogd@cbq.uclv.edu.cu or humbertogd@navegalia.com).

Discrimination and Depressive Symptoms Among Latina/o Adolescents of Immigrant Parents.

PubMed

Lopez, William D; LeBrón, Alana M W; Graham, Louis F; Grogan-Kaylor, Andrew

2016-01-01

Discrimination is associated with negative mental health outcomes for Latina/o adolescents. While Latino/a adolescents experience discrimination from a number of sources and across contexts, little research considers how the source of discrimination and the context in which it occurs affect mental health outcomes among Latina/o children of immigrants. We examined the association between source-specific discrimination, racial or ethnic background of the source, and school ethnic context with depressive symptoms for Latina/o adolescents of immigrant parents. Using multilevel linear regression with time-varying covariates, we regressed depressive symptoms on source-specific discrimination, racial or ethnic background of the source of discrimination, and school percent Latina/o. Discrimination from teachers (β = 0.06, p < .05), students (β = 0.05, p < .05), Cubans (β = 0.19, p < .001), and Latinas/os (β = 0.19, p < .001) were positively associated with depressive symptoms. These associations were not moderated by school percent Latina/o. The findings indicate a need to reduce discrimination to improve Latina/o adolescents' mental health. © The Author(s) 2016.

Getting mad but ending up sad: the mental health consequences for African Americans using anger to cope with racism.

PubMed

Pittman, Chavella T

2011-01-01

Anger is a common reaction to stressful life events. However, little is known about anger’s use and efficacy as a coping strategy for racism. Is anger a coping strategy for racism that improves mental health? Or does anger operate in an opposing way, deteriorating mental health? The analyses for this research focused on a probability sample of African Americans who reported experiences of acute (n = 246) or chronic (n = 120) racial discrimination in a survey interview. General linear model results revealed that using anger to cope with racial discrimination negatively affected the general well-being and psychological distress of African Americans. These findings raise concerns about the effectiveness (or lack therefore of) of anger as a common coping mechanism for racism, given the deleterious effects it may have on African Americans’ mental health.

Robotic wheelchair commanded by SSVEP, motor imagery and word generation.

PubMed

Bastos, Teodiano F; Muller, Sandra M T; Benevides, Alessandro B; Sarcinelli-Filho, Mario

2011-01-01

This work presents a robotic wheelchair that can be commanded by a Brain Computer Interface (BCI) through Steady-State Visual Evoked Potential (SSVEP), Motor Imagery and Word Generation. When using SSVEP, a statistical test is used to extract the evoked response and a decision tree is used to discriminate the stimulus frequency, allowing volunteers to online operate the BCI, with hit rates varying from 60% to 100%, and guide a robotic wheelchair through an indoor environment. When using motor imagery and word generation, three mental task are used: imagination of left or right hand, and imagination of generation of words starting with the same random letter. Linear Discriminant Analysis is used to recognize the mental tasks, and the feature extraction uses Power Spectral Density. The choice of EEG channel and frequency uses the Kullback-Leibler symmetric divergence and a reclassification model is proposed to stabilize the classifier.

Estimating erosion risk on forest lands using improved methods of discriminant analysis

Treesearch

J. Lewis; R. M. Rice

1990-01-01

A population of 638 timber harvest areas in northwestern California was sampled for data related to the occurrence of critical amounts of erosion (>153 m3 within 0.81 ha). Separate analyses were done for forest roads and logged areas. Linear discriminant functions were computed in each analysis to contrast site conditions at critical plots with randomly selected...

Evaluation of volatile aldehydes as discriminating parameters in quality vinegars with protected European geographical indication.

PubMed

Durán-Guerrero, Enrique; Chinnici, Fabio; Natali, Nadia; Riponi, Claudio

2015-09-01

Thirty-six high-quality vinegars with geographical indication belonging to Sherry and Modena areas (vinegars of Jerez, balsamic vinegars of Modena and traditional balsamic vinegars of Modena) with all possible aging periods were analyzed to determine the content of volatile aldehydes. A solid-phase extraction method with in-cartridge derivatization using O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine followed by gas chromatography-mass spectrometry was employed. Twenty-two volatile aldehydes were identified and determined in the samples. Analysis of variance provided significant differences among the samples as a function of the type of vinegar, aging time and raw material. Principal component analysis and linear discriminant analysis demonstrated the possibility of discriminating the samples in terms of aging time and raw material. Linear aldehydes and compounds such as furfural, methional, nonenal, hexenal, 2-methylbutanal and i-butyraldehyde were the most significant variables able to discriminate the samples. Aldehyde content of premium quality vinegars is a function of both ageing time and raw material. Their evaluation could be a useful tool with a view to ascertaining vinegar origin and genuineness. © 2014 Society of Chemical Industry.

Development of Raman microspectroscopy for automated detection and imaging of basal cell carcinoma

NASA Astrophysics Data System (ADS)

Larraona-Puy, Marta; Ghita, Adrian; Zoladek, Alina; Perkins, William; Varma, Sandeep; Leach, Iain H.; Koloydenko, Alexey A.; Williams, Hywel; Notingher, Ioan

2009-09-01

We investigate the potential of Raman microspectroscopy (RMS) for automated evaluation of excised skin tissue during Mohs micrographic surgery (MMS). The main aim is to develop an automated method for imaging and diagnosis of basal cell carcinoma (BCC) regions. Selected Raman bands responsible for the largest spectral differences between BCC and normal skin regions and linear discriminant analysis (LDA) are used to build a multivariate supervised classification model. The model is based on 329 Raman spectra measured on skin tissue obtained from 20 patients. BCC is discriminated from healthy tissue with 90+/-9% sensitivity and 85+/-9% specificity in a 70% to 30% split cross-validation algorithm. This multivariate model is then applied on tissue sections from new patients to image tumor regions. The RMS images show excellent correlation with the gold standard of histopathology sections, BCC being detected in all positive sections. We demonstrate the potential of RMS as an automated objective method for tumor evaluation during MMS. The replacement of current histopathology during MMS by a ``generalization'' of the proposed technique may improve the feasibility and efficacy of MMS, leading to a wider use according to clinical need.

[Discrimination of Red Tide algae by fluorescence spectra and principle component analysis].

PubMed

Su, Rong-guo; Hu, Xu-peng; Zhang, Chuan-song; Wang, Xiu-lin

2007-07-01

Fluorescence discrimination technology for 11 species of the Red Tide algae at genus level was constructed by principle component analysis and non-negative least squares. Rayleigh and Raman scattering peaks of 3D fluorescence spectra were eliminated by Delaunay triangulation method. According to the results of Fisher linear discrimination, the first principle component score and the second component score of 3D fluorescence spectra were chosen as discriminant feature and the feature base was established. The 11 algae species were tested, and more than 85% samples were accurately determinated, especially for Prorocentrum donghaiense, Skeletonema costatum, Gymnodinium sp., which have frequently brought Red tide in the East China Sea. More than 95% samples were right discriminated. The results showed that the genus discriminant feature of 3D fluorescence spectra of Red Tide algae given by principle component analysis could work well.

Characterizing entanglement with global and marginal entropic measures

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

Adesso, Gerardo; Illuminati, Fabrizio; De Siena, Silvio

2003-12-01

We qualify the entanglement of arbitrary mixed states of bipartite quantum systems by comparing global and marginal mixednesses quantified by different entropic measures. For systems of two qubits we discriminate the class of maximally entangled states with fixed marginal mixednesses, and determine an analytical upper bound relating the entanglement of formation to the marginal linear entropies. This result partially generalizes to mixed states the quantification of entanglement with marginal mixednesses holding for pure states. We identify a class of entangled states that, for fixed marginals, are globally more mixed than product states when measured by the linear entropy. Such statesmore » cannot be discriminated by the majorization criterion.« less

Life satisfaction and trauma in clinical and non-clinical children living in a war-torn environment: A discriminant analysis.

PubMed

Veronese, Guido; Pepe, Alessandro

2017-07-01

The aim of this work was to discriminate between healthy children and children at risk of developing mental impairments by evaluating the impact on contextual and individual factors of a context characterized by war. We tested the hypothesis that a linear discriminant function composed of trauma, life satisfaction, and affect balance has the power to classify the children as community or clinical referred. Membership of the clinical-referred group was associated with poorer life satisfaction and higher levels of trauma. Community-referred profiles were associated with lesser trauma. Perceived life satisfaction regarding family and school was the main contributor to the discriminant function.

LDA merging and splitting with applications to multiagent cooperative learning and system alteration.

PubMed

Pang, Shaoning; Ban, Tao; Kadobayashi, Youki; Kasabov, Nikola K

2012-04-01

To adapt linear discriminant analysis (LDA) to real-world applications, there is a pressing need to equip it with an incremental learning ability to integrate knowledge presented by one-pass data streams, a functionality to join multiple LDA models to make the knowledge sharing between independent learning agents more efficient, and a forgetting functionality to avoid reconstruction of the overall discriminant eigenspace caused by some irregular changes. To this end, we introduce two adaptive LDA learning methods: LDA merging and LDA splitting. These provide the benefits of ability of online learning with one-pass data streams, retained class separability identical to the batch learning method, high efficiency for knowledge sharing due to condensed knowledge representation by the eigenspace model, and more preferable time and storage costs than traditional approaches under common application conditions. These properties are validated by experiments on a benchmark face image data set. By a case study on the application of the proposed method to multiagent cooperative learning and system alternation of a face recognition system, we further clarified the adaptability of the proposed methods to complex dynamic learning tasks.

Authentication of fattening diet of Iberian pigs according to their volatile compounds profile from raw subcutaneous fat.

PubMed

Narváez-Rivas, M; Pablos, F; Jurado, J M; León-Camacho, M

2011-02-01

The composition of volatile components of subcutaneous fat from Iberian pig has been studied. Purge and trap gas chromatography-mass spectrometry has been used. The composition of the volatile fraction of subcutaneous fat has been used for authentication purposes of different types of Iberian pig fat. Three types of this product have been considered, montanera, extensive cebo and intensive cebo. With classification purposes, several pattern recognition techniques have been applied. In order to find out possible tendencies in the sample distribution as well as the discriminant power of the variables, principal component analysis was applied as visualisation technique. Linear discriminant analysis (LDA) and soft independent modelling by class analogy (SIMCA) were used to obtain suitable classification models. LDA and SIMCA allowed the differentiation of three fattening diets by using the contents in 2,2,4,6,6-pentamethyl-heptane, m-xylene, 2,4-dimethyl-heptane, 6-methyl-tridecane, 1-methoxy-2-propanol, isopropyl alcohol, o-xylene, 3-ethyl-2,2-dimethyl-oxirane, 2,6-dimethyl-undecane, 3-methyl-3-pentanol and limonene.

Healthy Aging Delays Scalp EEG Sensitivity to Noise in a Face Discrimination Task

PubMed Central

Rousselet, Guillaume A.; Gaspar, Carl M.; Pernet, Cyril R.; Husk, Jesse S.; Bennett, Patrick J.; Sekuler, Allison B.

2010-01-01

We used a single-trial ERP approach to quantify age-related changes in the time-course of noise sensitivity. A total of 62 healthy adults, aged between 19 and 98, performed a non-speeded discrimination task between two faces. Stimulus information was controlled by parametrically manipulating the phase spectrum of these faces. Behavioral 75% correct thresholds increased with age. This result may be explained by lower signal-to-noise ratios in older brains. ERP from each subject were entered into a single-trial general linear regression model to identify variations in neural activity statistically associated with changes in image structure. The fit of the model, indexed by R2, was computed at multiple post-stimulus time points. The time-course of the R2 function showed significantly delayed noise sensitivity in older observers. This age effect is reliable, as demonstrated by test–retest in 24 subjects, and started about 120 ms after stimulus onset. Our analyses suggest also a qualitative change from a young to an older pattern of brain activity at around 47 ± 4 years old. PMID:21833194

Classification of breast tissue in mammograms using efficient coding.

PubMed

Costa, Daniel D; Campos, Lúcio F; Barros, Allan K

2011-06-24

Female breast cancer is the major cause of death by cancer in western countries. Efforts in Computer Vision have been made in order to improve the diagnostic accuracy by radiologists. Some methods of lesion diagnosis in mammogram images were developed based in the technique of principal component analysis which has been used in efficient coding of signals and 2D Gabor wavelets used for computer vision applications and modeling biological vision. In this work, we present a methodology that uses efficient coding along with linear discriminant analysis to distinguish between mass and non-mass from 5090 region of interest from mammograms. The results show that the best rates of success reached with Gabor wavelets and principal component analysis were 85.28% and 87.28%, respectively. In comparison, the model of efficient coding presented here reached up to 90.07%. Altogether, the results presented demonstrate that independent component analysis performed successfully the efficient coding in order to discriminate mass from non-mass tissues. In addition, we have observed that LDA with ICA bases showed high predictive performance for some datasets and thus provide significant support for a more detailed clinical investigation.

Joint modeling of longitudinal data and discrete-time survival outcome.

PubMed

Qiu, Feiyou; Stein, Catherine M; Elston, Robert C

2016-08-01

A predictive joint shared parameter model is proposed for discrete time-to-event and longitudinal data. A discrete survival model with frailty and a generalized linear mixed model for the longitudinal data are joined to predict the probability of events. This joint model focuses on predicting discrete time-to-event outcome, taking advantage of repeated measurements. We show that the probability of an event in a time window can be more precisely predicted by incorporating the longitudinal measurements. The model was investigated by comparison with a two-step model and a discrete-time survival model. Results from both a study on the occurrence of tuberculosis and simulated data show that the joint model is superior to the other models in discrimination ability, especially as the latent variables related to both survival times and the longitudinal measurements depart from 0. © The Author(s) 2013.

An automated image processing method for classification of diabetic retinopathy stages from conjunctival microvasculature images

NASA Astrophysics Data System (ADS)

Khansari, Maziyar M.; O'Neill, William; Penn, Richard; Blair, Norman P.; Chau, Felix; Shahidi, Mahnaz

2017-03-01

The conjunctiva is a densely vascularized tissue of the eye that provides an opportunity for imaging of human microcirculation. In the current study, automated fine structure analysis of conjunctival microvasculature images was performed to discriminate stages of diabetic retinopathy (DR). The study population consisted of one group of nondiabetic control subjects (NC) and 3 groups of diabetic subjects, with no clinical DR (NDR), non-proliferative DR (NPDR), or proliferative DR (PDR). Ordinary least square regression and Fisher linear discriminant analyses were performed to automatically discriminate images between group pairs of subjects. Human observers who were masked to the grouping of subjects performed image discrimination between group pairs. Over 80% and 70% of images of subjects with clinical and non-clinical DR were correctly discriminated by the automated method, respectively. The discrimination rates of the automated method were higher than human observers. The fine structure analysis of conjunctival microvasculature images provided discrimination of DR stages and can be potentially useful for DR screening and monitoring.

Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study

PubMed Central

Burgansky-Eliash, Zvia; Wollstein, Gadi; Chu, Tianjiao; Ramsey, Joseph D.; Glymour, Clark; Noecker, Robert J.; Ishikawa, Hiroshi; Schuman, Joel S.

2007-01-01

Purpose Machine-learning classifiers are trained computerized systems with the ability to detect the relationship between multiple input parameters and a diagnosis. The present study investigated whether the use of machine-learning classifiers improves optical coherence tomography (OCT) glaucoma detection. Methods Forty-seven patients with glaucoma (47 eyes) and 42 healthy subjects (42 eyes) were included in this cross-sectional study. Of the glaucoma patients, 27 had early disease (visual field mean deviation [MD] ≥ −6 dB) and 20 had advanced glaucoma (MD < −6 dB). Machine-learning classifiers were trained to discriminate between glaucomatous and healthy eyes using parameters derived from OCT output. The classifiers were trained with all 38 parameters as well as with only 8 parameters that correlated best with the visual field MD. Five classifiers were tested: linear discriminant analysis, support vector machine, recursive partitioning and regression tree, generalized linear model, and generalized additive model. For the last two classifiers, a backward feature selection was used to find the minimal number of parameters that resulted in the best and most simple prediction. The cross-validated receiver operating characteristic (ROC) curve and accuracies were calculated. Results The largest area under the ROC curve (AROC) for glaucoma detection was achieved with the support vector machine using eight parameters (0.981). The sensitivity at 80% and 95% specificity was 97.9% and 92.5%, respectively. This classifier also performed best when judged by cross-validated accuracy (0.966). The best classification between early glaucoma and advanced glaucoma was obtained with the generalized additive model using only three parameters (AROC = 0.854). Conclusions Automated machine classifiers of OCT data might be useful for enhancing the utility of this technology for detecting glaucomatous abnormality. PMID:16249492

Decision-Related Activity in Macaque V2 for Fine Disparity Discrimination Is Not Compatible with Optimal Linear Readout

PubMed Central

Clery, Stephane; Cumming, Bruce G.

2017-01-01

Fine judgments of stereoscopic depth rely mainly on relative judgments of depth (relative binocular disparity) between objects, rather than judgments of the distance to where the eyes are fixating (absolute disparity). In macaques, visual area V2 is the earliest site in the visual processing hierarchy for which neurons selective for relative disparity have been observed (Thomas et al., 2002). Here, we found that, in macaques trained to perform a fine disparity discrimination task, disparity-selective neurons in V2 were highly selective for the task, and their activity correlated with the animals' perceptual decisions (unexplained by the stimulus). This may partially explain similar correlations reported in downstream areas. Although compatible with a perceptual role of these neurons for the task, the interpretation of such decision-related activity is complicated by the effects of interneuronal “noise” correlations between sensory neurons. Recent work has developed simple predictions to differentiate decoding schemes (Pitkow et al., 2015) without needing measures of noise correlations, and found that data from early sensory areas were compatible with optimal linear readout of populations with information-limiting correlations. In contrast, our data here deviated significantly from these predictions. We additionally tested this prediction for previously reported results of decision-related activity in V2 for a related task, coarse disparity discrimination (Nienborg and Cumming, 2006), thought to rely on absolute disparity. Although these data followed the predicted pattern, they violated the prediction quantitatively. This suggests that optimal linear decoding of sensory signals is not generally a good predictor of behavior in simple perceptual tasks. SIGNIFICANCE STATEMENT Activity in sensory neurons that correlates with an animal's decision is widely believed to provide insights into how the brain uses information from sensory neurons. Recent theoretical work developed simple predictions to differentiate decoding schemes, and found support for optimal linear readout of early sensory populations with information-limiting correlations. Here, we observed decision-related activity for neurons in visual area V2 of macaques performing fine disparity discrimination, as yet the earliest site for this task. These findings, and previously reported results from V2 in a different task, deviated from the predictions for optimal linear readout of a population with information-limiting correlations. Our results suggest that optimal linear decoding of early sensory information is not a general decoding strategy used by the brain. PMID:28100751

Automatic analysis and classification of surface electromyography.

PubMed

Abou-Chadi, F E; Nashar, A; Saad, M

2001-01-01

In this paper, parametric modeling of surface electromyography (EMG) algorithms that facilitates automatic SEMG feature extraction and artificial neural networks (ANN) are combined for providing an integrated system for the automatic analysis and diagnosis of myopathic disorders. Three paradigms of ANN were investigated: the multilayer backpropagation algorithm, the self-organizing feature map algorithm and a probabilistic neural network model. The performance of the three classifiers was compared with that of the old Fisher linear discriminant (FLD) classifiers. The results have shown that the three ANN models give higher performance. The percentage of correct classification reaches 90%. Poorer diagnostic performance was obtained from the FLD classifier. The system presented here indicates that surface EMG, when properly processed, can be used to provide the physician with a diagnostic assist device.

Linear operating region in the ozone dial photon counting system

NASA Technical Reports Server (NTRS)

Andrawis, Madeleine

1995-01-01

Ozone is a relatively unstable molecule found in Earth's atmosphere. An ozone molecule is made up of three atoms of oxygen. Depending on where ozone resides, it can protect or harm life on Earth. High in the atmosphere, about 15 miles up, ozone acts as a shield to protect Earth's surface from the sun's harmful ultraviolet radiation. Without this shield, we would be more susceptible to skin cancer, cataracts, and impaired immune systems. Closer to Earth, in the air we breathe, ozone is a harmful pollutant that causes damage to lung tissue and plants. Since the early 1980's, airborne lidar systems have been used for making measurements of ozone. The differential absorption lidar (DIAL) technique is used in the remote measurement of O3. This system allows the O3 to be measured as function of the range in the atmosphere. Two frequency-doubled Nd:YAG lasers are used to pump tunable dye lasers. The lasers are operating at 289 nm for the DIAL on-line wavelength of O3, and the other one is operated at 300 nm for the off-line wavelength. The DIAL wavelengths are produced in sequential laser pulses with a time separation of 300 micro s. The backscattered laser energy is collected by telescopes and measured using photon counting systems. The photon counting system measures the light signal by making use of the photon nature of light. The output pulse from the Photo-Multiplier Tube (PE), caused by a photon striking the PMT photo-cathode, is amplified and passed to a pulse height discriminator. The peak value of the pulse is compared to a reference voltage (discrimination level). If the pulse amplitude exceeds the discrimination level, the discriminator generates a standard pulse which is counted by the digital counter. Non-linearity in the system is caused by the overlapping of pulses and the finite response time of the electronics. At low count rates one expects the system to register one event for each output pulse from the PMT corresponding to a photon incident upon the photocathode, however, at higher rates the limitations of the discrimination/counting system will cause the observed count rate to be non-linear with respect to the true count rate. Depending on the pulse height distribution and the discriminator level, the overlapping of pulses (pulse pile-up) can cause count loss or even an additional apparent count gain as the signal levels increase. Characterization of the system, including the pulse height distribution, the signal to noise ratio, and the effect of the discriminator threshold level, is critical in maximizing the linear operating region of the system, thus greatly increasing the useful dynamic range of the system.

Using recorded sound spectra profile as input data for real-time short-term urban road-traffic-flow estimation.

PubMed

Torija, Antonio J; Ruiz, Diego P

2012-10-01

Road traffic has a heavy impact on the urban sound environment, constituting the main source of noise and widely dominating its spectral composition. In this context, our research investigates the use of recorded sound spectra as input data for the development of real-time short-term road traffic flow estimation models. For this, a series of models based on the use of Multilayer Perceptron Neural Networks, multiple linear regression, and the Fisher linear discriminant were implemented to estimate road traffic flow as well as to classify it according to the composition of heavy vehicles and motorcycles/mopeds. In view of the results, the use of the 50-400 Hz and 1-2.5 kHz frequency ranges as input variables in multilayer perceptron-based models successfully estimated urban road traffic flow with an average percentage of explained variance equal to 86%, while the classification of the urban road traffic flow gave an average success rate of 96.1%. Copyright © 2012 Elsevier B.V. All rights reserved.

Discriminating model for diagnosis of basal cell carcinoma and melanoma in vitro based on the Raman spectra of selected biochemicals

NASA Astrophysics Data System (ADS)

Silveira, Landulfo; Silveira, Fabrício Luiz; Bodanese, Benito; Zângaro, Renato Amaro; Pacheco, Marcos Tadeu T.

2012-07-01

Raman spectroscopy has been employed to identify differences in the biochemical constitution of malignant [basal cell carcinoma (BCC) and melanoma (MEL)] cells compared to normal skin tissues, with the goal of skin cancer diagnosis. We collected Raman spectra from compounds such as proteins, lipids, and nucleic acids, which are expected to be represented in human skin spectra, and developed a linear least-squares fitting model to estimate the contributions of these compounds to the tissue spectra. We used a set of 145 spectra from biopsy fragments of normal (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues, collected using a near-infrared Raman spectrometer (830 nm, 50 to 200 mW, and 20 s exposure time) coupled to a Raman probe. We applied the best-fitting model to the spectra of biochemicals and tissues, hypothesizing that the relative spectral contribution of each compound to the tissue Raman spectrum changes according to the disease. We verified that actin, collagen, elastin, and triolein were the most important biochemicals representing the spectral features of skin tissues. A classification model applied to the relative contribution of collagen III, elastin, and melanin using Euclidean distance as a discriminator could differentiate normal from BCC and MEL.

A Novel Hyperspectral Microscopic Imaging System for Evaluating Fresh Degree of Pork.

PubMed

Xu, Yi; Chen, Quansheng; Liu, Yan; Sun, Xin; Huang, Qiping; Ouyang, Qin; Zhao, Jiewen

2018-04-01

This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control.

A Novel Hyperspectral Microscopic Imaging System for Evaluating Fresh Degree of Pork

PubMed Central

Xu, Yi; Chen, Quansheng; Liu, Yan; Sun, Xin; Huang, Qiping; Ouyang, Qin; Zhao, Jiewen

2018-01-01

Abstract This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control. PMID:29805285

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.

Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening.

PubMed

Castillo-Garit, Juan Alberto; del Toro-Cortés, Oremia; Vega, Maria C; Rolón, Miriam; Rojas de Arias, Antonieta; Casañola-Martin, Gerardo M; Escario, José A; Gómez-Barrio, Alicia; Marrero-Ponce, Yovani; Torrens, Francisco; Abad, Concepción

2015-01-01

Two-dimensional bond-based bilinear indices and linear discriminant analysis are used in this report to perform a quantitative structure-activity relationship study to identify new trypanosomicidal compounds. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop the theoretical models. Two discriminant models, computed using bond-based bilinear indices, are developed and both show accuracies higher than 86% for training and test sets. The stochastic model correctly indentifies nine out of eleven compounds of a set of organic chemicals obtained from our synthetic collaborators. The in vitro antitrypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi is assayed. Both models show a good agreement between theoretical predictions and experimental results. Three compounds showed IC50 values for epimastigote elimination (AE) lower than 50 μM, while for the benznidazole the IC50 = 54.7 μM which was used as reference compound. The value of IC50 for cytotoxicity of these compounds is at least 5 times greater than their value of IC50 for AE. Finally, we can say that, the present algorithm constitutes a step forward in the search for efficient ways of discovering new antitrypanosomal compounds. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Inter- and intraspecific diversity in Cistus L. (Cistaceae) seeds, analysed with computer vision techniques.

PubMed

Lo Bianco, M; Grillo, O; Cañadas, E; Venora, G; Bacchetta, G

2017-03-01

This work aims to discriminate among different species of the genus Cistus, using seed parameters and following the scientific plant names included as accepted in The Plant List. Also, the intraspecific phenotypic differentiation of C. creticus, through comparison with three subspecies (C. creticus subsp. creticus, C. c. subsp. eriocephalus and C. c. subsp. corsicus), as well as the interpopulation variability among five C. creticus subsp. eriocephalus populations was evaluated. Seed mean weight and 137 morphocolorimetric quantitative variables, describing shape, size, colour and textural seed traits, were measured using image analysis techniques. Measured data were analysed applying step-wise linear discriminant analysis. An overall cross-validated classification performance of 80.6% was recorded at species level. With regard to C. creticus, as case study, percentages of correct discrimination of 96.7% and 99.6% were achieved at intraspecific and interpopulation levels, respectively. In this classification model, the relevance of the colorimetric and textural descriptive features was highlighted, as well as the seed mean weight, which was the most discriminant feature at specific and intraspecific level. These achievements proved the ability of the image analysis system as highly diagnostic for systematic purposes and confirm that seeds in the genus Cistus have important diagnostic value. © 2016 German Botanical Society and The Royal Botanical Society of the Netherlands.

Characterization of Chinese rice wine taste attributes using liquid chromatographic analysis, sensory evaluation, and an electronic tongue.

PubMed

Yu, HaiYan; Zhao, Jie; Li, Fenghua; Tian, Huaixiang; Ma, Xia

2015-08-01

To evaluate the taste characteristics of Chinese rice wine, wine samples sourced from different vintage years were analyzed using liquid chromatographic analysis, sensory evaluation, and an electronic tongue. Six organic acids and seventeen amino acids were measured using high performance liquid chromatography (HPLC). Five monosaccharides were measured using anion-exchange chromatography. The global taste attributes were analyzed using an electronic tongue (E-tongue). The correlations between the 28 taste-active compounds and the sensory attributes, and the correlations between the E-tongue response and the sensory attributes were established via partial least square discriminant analysis (PLSDA). E-tongue response data combined with linear discriminant analysis (LDA) were used to discriminate the Chinese rice wine samples sourced from different vintage years. Sensory evaluation indicated significant differences in the Chinese rice wine samples sourced from 2003, 2005, 2008, and 2010 vintage years in the sensory attributes of harmony and mellow. The PLSDA model for the taste-active compounds and the sensory attributes showed that proline, fucose, arabinose, lactic acid, glutamic acid, arginine, isoleucine, valine, threonine, and lysine had an influence on the taste characteristic of Chinese rice wine. The Chinese rice wine samples were all correctly classified using the E-tongue and LDA. The electronic tongue was an effective tool for rapid discrimination of Chinese rice wine. Copyright © 2015 Elsevier B.V. All rights reserved.

Visuomotor sensitivity to visual information about surface orientation.

PubMed

Knill, David C; Kersten, Daniel

2004-03-01

We measured human visuomotor sensitivity to visual information about three-dimensional surface orientation by analyzing movements made to place an object on a slanted surface. We applied linear discriminant analysis to the kinematics of subjects' movements to surfaces with differing slants (angle away form the fronto-parallel) to derive visuomotor d's for discriminating surfaces differing in slant by 5 degrees. Subjects' visuomotor sensitivity to information about surface orientation was very high, with discrimination "thresholds" ranging from 2 to 3 degrees. In a first experiment, we found that subjects performed only slightly better using binocular cues alone than monocular texture cues and that they showed only weak evidence for combining the cues when both were available, suggesting that monocular cues can be just as effective in guiding motor behavior in depth as binocular cues. In a second experiment, we measured subjects' perceptual discrimination and visuomotor thresholds in equivalent stimulus conditions to decompose visuomotor sensitivity into perceptual and motor components. Subjects' visuomotor thresholds were found to be slightly greater than their perceptual thresholds for a range of memory delays, from 1 to 3 s. The data were consistent with a model in which perceptual noise increases with increasing delay between stimulus presentation and movement initiation, but motor noise remains constant. This result suggests that visuomotor and perceptual systems rely on the same visual estimates of surface slant for memory delays ranging from 1 to 3 s.

A risk analysis approach for using discriminant functions to manage logging-related landslides on granitic terrain

Treesearch

Raymond M. Rice; Norman H. Pillsbury; Kurt W. Schmidt

1985-01-01

Abstract - A linear discriminant function, developed to predict debris avalanches after clearcut logging on a granitic batholith in northwestern California, was tested on data from two batholiths. The equation was inaccurate in predicting slope stability on one of them. A new equation based on slope, crown cover, and distance from a stream (retained from the original...

Identification of Variables Associated with Group Separation in Descriptive Discriminant Analysis: Comparison of Methods for Interpreting Structure Coefficients

ERIC Educational Resources Information Center

Finch, Holmes

2010-01-01

Discriminant Analysis (DA) is a tool commonly used for differentiating among 2 or more groups based on 2 or more predictor variables. DA works by finding 1 or more linear combinations of the predictors that yield maximal difference among the groups. One common goal of researchers using DA is to characterize the nature of group difference by…

Development of a universal water signature for the LANDSAT-3 Multispectral Scanner, part 1

NASA Technical Reports Server (NTRS)

Schlosser, E. H.

1980-01-01

A generalized four channel hyperplane to discriminate water from nonwater was developed using LANDSAT-3 multispectral scaner (MSS) scenes and matching same/next day color infrared aerial photography. The MSS scenes varied in sun elevation angle from 40 to 58 deg. The 28 matching air photo frames contained over 1400 water bodies larger than one surface acre. A preliminary water discriminant, was used to screen the data and eliminate from further consideration all pixels distant from water in MSS spectral space. A linear discriminant was iteratively fitted to the labelled pixels. This discriminant correctly classified 98.7% of the water pixels and 98.6% of the nonwater pixels. The discriminant detected 91.3% of the 414 water bodies over 10 acres in surface area, and misclassified as water 36 groups of contiguous nonwater pixels.

Partially supervised speaker clustering.

PubMed

Tang, Hao; Chu, Stephen Mingyu; Hasegawa-Johnson, Mark; Huang, Thomas S

2012-05-01

Content-based multimedia indexing, retrieval, and processing as well as multimedia databases demand the structuring of the media content (image, audio, video, text, etc.), one significant goal being to associate the identity of the content to the individual segments of the signals. In this paper, we specifically address the problem of speaker clustering, the task of assigning every speech utterance in an audio stream to its speaker. We offer a complete treatment to the idea of partially supervised speaker clustering, which refers to the use of our prior knowledge of speakers in general to assist the unsupervised speaker clustering process. By means of an independent training data set, we encode the prior knowledge at the various stages of the speaker clustering pipeline via 1) learning a speaker-discriminative acoustic feature transformation, 2) learning a universal speaker prior model, and 3) learning a discriminative speaker subspace, or equivalently, a speaker-discriminative distance metric. We study the directional scattering property of the Gaussian mixture model (GMM) mean supervector representation of utterances in the high-dimensional space, and advocate exploiting this property by using the cosine distance metric instead of the euclidean distance metric for speaker clustering in the GMM mean supervector space. We propose to perform discriminant analysis based on the cosine distance metric, which leads to a novel distance metric learning algorithm—linear spherical discriminant analysis (LSDA). We show that the proposed LSDA formulation can be systematically solved within the elegant graph embedding general dimensionality reduction framework. Our speaker clustering experiments on the GALE database clearly indicate that 1) our speaker clustering methods based on the GMM mean supervector representation and vector-based distance metrics outperform traditional speaker clustering methods based on the “bag of acoustic features” representation and statistical model-based distance metrics, 2) our advocated use of the cosine distance metric yields consistent increases in the speaker clustering performance as compared to the commonly used euclidean distance metric, 3) our partially supervised speaker clustering concept and strategies significantly improve the speaker clustering performance over the baselines, and 4) our proposed LSDA algorithm further leads to state-of-the-art speaker clustering performance.

Optimal number of features as a function of sample size for various classification rules.

PubMed

Hua, Jianping; Xiong, Zixiang; Lowey, James; Suh, Edward; Dougherty, Edward R

2005-04-15

Given the joint feature-label distribution, increasing the number of features always results in decreased classification error; however, this is not the case when a classifier is designed via a classification rule from sample data. Typically (but not always), for fixed sample size, the error of a designed classifier decreases and then increases as the number of features grows. The potential downside of using too many features is most critical for small samples, which are commonplace for gene-expression-based classifiers for phenotype discrimination. For fixed sample size and feature-label distribution, the issue is to find an optimal number of features. Since only in rare cases is there a known distribution of the error as a function of the number of features and sample size, this study employs simulation for various feature-label distributions and classification rules, and across a wide range of sample and feature-set sizes. To achieve the desired end, finding the optimal number of features as a function of sample size, it employs massively parallel computation. Seven classifiers are treated: 3-nearest-neighbor, Gaussian kernel, linear support vector machine, polynomial support vector machine, perceptron, regular histogram and linear discriminant analysis. Three Gaussian-based models are considered: linear, nonlinear and bimodal. In addition, real patient data from a large breast-cancer study is considered. To mitigate the combinatorial search for finding optimal feature sets, and to model the situation in which subsets of genes are co-regulated and correlation is internal to these subsets, we assume that the covariance matrix of the features is blocked, with each block corresponding to a group of correlated features. Altogether there are a large number of error surfaces for the many cases. These are provided in full on a companion website, which is meant to serve as resource for those working with small-sample classification. For the companion website, please visit http://public.tgen.org/tamu/ofs/ e-dougherty@ee.tamu.edu.

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.

Reliable Entity Subtyping in Non-small Cell Lung Cancer by Matrix-assisted Laser Desorption/Ionization Imaging Mass Spectrometry on Formalin-fixed Paraffin-embedded Tissue Specimens*

PubMed Central

Kriegsmann, Mark; Casadonte, Rita; Kriegsmann, Jörg; Dienemann, Hendrik; Schirmacher, Peter; Hendrik Kobarg, Jan; Schwamborn, Kristina; Stenzinger, Albrecht; Warth, Arne; Weichert, Wilko

2016-01-01

Histopathological subtyping of non-small cell lung cancer (NSCLC) into adenocarcinoma (ADC), and squamous cell carcinoma (SqCC) is of utmost relevance for treatment stratification. However, current immunohistochemistry (IHC) based typing approaches on biopsies are imperfect, therefore novel analytical methods for reliable subtyping are needed. We analyzed formalin-fixed paraffin-embedded tissue cores of NSCLC by Matrix-assisted laser desorption/ionization (MALDI) imaging on tissue microarrays to identify and validate discriminating MALDI imaging profiles for NSCLC subtyping. 110 ADC and 98 SqCC were used to train a Linear Discriminant Analysis (LDA) model. Results were validated on a separate set of 58 ADC and 60 SqCC. Selected differentially expressed proteins were identified by tandem mass spectrometry and validated by IHC. The LDA classification model incorporated 339 m/z values. In the validation cohort, in 117 cases (99.1%) MALDI classification on tissue cores was in accordance with the pathological diagnosis made on resection specimen. Overall, three cases in the combined cohorts were discordant, after reevaluation two were initially misclassified by pathology whereas one was classified incorrectly by MALDI. Identification of differentially expressed peptides detected well-known IHC discriminators (CK5, CK7), but also less well known differentially expressed proteins (CK15, HSP27). In conclusion, MALDI imaging on NSCLC tissue cores as small biopsy equivalents is capable to discriminate lung ADC and SqCC with a very high accuracy. In addition, replacing multislide IHC by an one-slide MALDI approach may also save tissue for subsequent predictive molecular testing. We therefore advocate to pursue routine diagnostic implementation strategies for MALDI imaging in solid tumor typing. PMID:27473201

Reliable Entity Subtyping in Non-small Cell Lung Cancer by Matrix-assisted Laser Desorption/Ionization Imaging Mass Spectrometry on Formalin-fixed Paraffin-embedded Tissue Specimens.

PubMed

Kriegsmann, Mark; Casadonte, Rita; Kriegsmann, Jörg; Dienemann, Hendrik; Schirmacher, Peter; Hendrik Kobarg, Jan; Schwamborn, Kristina; Stenzinger, Albrecht; Warth, Arne; Weichert, Wilko

2016-10-01

Histopathological subtyping of non-small cell lung cancer (NSCLC) into adenocarcinoma (ADC), and squamous cell carcinoma (SqCC) is of utmost relevance for treatment stratification. However, current immunohistochemistry (IHC) based typing approaches on biopsies are imperfect, therefore novel analytical methods for reliable subtyping are needed. We analyzed formalin-fixed paraffin-embedded tissue cores of NSCLC by Matrix-assisted laser desorption/ionization (MALDI) imaging on tissue microarrays to identify and validate discriminating MALDI imaging profiles for NSCLC subtyping. 110 ADC and 98 SqCC were used to train a Linear Discriminant Analysis (LDA) model. Results were validated on a separate set of 58 ADC and 60 SqCC. Selected differentially expressed proteins were identified by tandem mass spectrometry and validated by IHC. The LDA classification model incorporated 339 m/z values. In the validation cohort, in 117 cases (99.1%) MALDI classification on tissue cores was in accordance with the pathological diagnosis made on resection specimen. Overall, three cases in the combined cohorts were discordant, after reevaluation two were initially misclassified by pathology whereas one was classified incorrectly by MALDI. Identification of differentially expressed peptides detected well-known IHC discriminators (CK5, CK7), but also less well known differentially expressed proteins (CK15, HSP27). In conclusion, MALDI imaging on NSCLC tissue cores as small biopsy equivalents is capable to discriminate lung ADC and SqCC with a very high accuracy. In addition, replacing multislide IHC by an one-slide MALDI approach may also save tissue for subsequent predictive molecular testing. We therefore advocate to pursue routine diagnostic implementation strategies for MALDI imaging in solid tumor typing. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

Factors Associated with Medical Doctors' Intentions to Discriminate Against Transgender Patients in Kuala Lumpur, Malaysia.

PubMed

Vijay, Aishwarya; Earnshaw, Valerie A; Tee, Ying Chew; Pillai, Veena; White Hughto, Jaclyn M; Clark, Kirsty; Kamarulzaman, Adeeba; Altice, Frederick L; Wickersham, Jeffrey A

2018-01-01

Transgender people are frequent targets of discrimination. Discrimination against transgender people in the context of healthcare can lead to poor health outcomes and facilitate the growth of health disparities. This study explores factors associated with medical doctors' intentions to discriminate against transgender people in Malaysia. A total of 436 physicians at two major university medical centers in Kuala Lumpur, Malaysia, completed an online survey. Sociodemographic characteristics, stigma-related constructs, and intentions to discriminate against transgender people were measured. Bivariate and multivariate linear regression were used to evaluate independent covariates of discrimination intent. Medical doctors who felt more fearful of transgender people and more personal shame associated with transgender people expressed greater intention to discriminate against transgender people, whereas doctors who endorsed the belief that transgender people deserve good care reported lower discrimination intent. Stigma-related constructs accounted for 42% of the variance and 8% was accounted for by sociodemographic characteristics. Constructs associated with transgender stigma play an important role in medical doctors' intentions to discriminate against transgender patients. Development of interventions to improve medical doctors' knowledge about and attitudes toward transgender people are necessary to reduce discriminatory intent in healthcare settings.

Factors Associated with Medical Doctors' Intentions to Discriminate Against Transgender Patients in Kuala Lumpur, Malaysia

PubMed Central

Vijay, Aishwarya; Earnshaw, Valerie A.; Tee, Ying Chew; Pillai, Veena; White Hughto, Jaclyn M.; Clark, Kirsty; Kamarulzaman, Adeeba; Altice, Frederick L.

2018-01-01

Abstract Purpose: Transgender people are frequent targets of discrimination. Discrimination against transgender people in the context of healthcare can lead to poor health outcomes and facilitate the growth of health disparities. This study explores factors associated with medical doctors' intentions to discriminate against transgender people in Malaysia. Methods: A total of 436 physicians at two major university medical centers in Kuala Lumpur, Malaysia, completed an online survey. Sociodemographic characteristics, stigma-related constructs, and intentions to discriminate against transgender people were measured. Bivariate and multivariate linear regression were used to evaluate independent covariates of discrimination intent. Results: Medical doctors who felt more fearful of transgender people and more personal shame associated with transgender people expressed greater intention to discriminate against transgender people, whereas doctors who endorsed the belief that transgender people deserve good care reported lower discrimination intent. Stigma-related constructs accounted for 42% of the variance and 8% was accounted for by sociodemographic characteristics. Conclusions: Constructs associated with transgender stigma play an important role in medical doctors' intentions to discriminate against transgender patients. Development of interventions to improve medical doctors' knowledge about and attitudes toward transgender people are necessary to reduce discriminatory intent in healthcare settings. PMID:29227183

Assessment of contrast gain signature in inferred magnocellular and parvocellular pathways in patients with glaucoma

PubMed Central

Sun, Hao; Swanson, William H.; Arvidson, Brian; Dul, Mitchell W.

2010-01-01

PURPOSE Contrast gain signatures of inferred magnocellular and parvocellular postreceptoral pathways were assessed for patients with glaucoma using a contrast discrimination paradigm developed by Pokorny and Smith. The potential causes for changes in contrast gain signature were investigated using model simulations of ganglion cell contrast responses. METHODS Foveal contrast discrimination thresholds were measured with a pedestal-Δ-pedestal paradigm developed by Pokorny and Smith (1997). Stimuli were 27 msec luminance increments superimposed on 227 msec pulsed Δ-pedestals. Contrast thresholds and contrast gain signatures mediated by the inferred magnocellular (MC) and parvocellular (PC) pathways were assessed using linear fits to contrast discrimination thresholds at either lower or higher Δ-pedestal contrasts, respectively. Twenty-seven patients with glaucoma were tested, as well as 16 age-similar control subjects free of eye disease. RESULTS Contrast sensitivity and contrast gain signature mediated by the inferred MC pathway were lower for the glaucoma group, and reduced contrast gain signature was correlated with reduced contrast sensitivity (r2=45%, p<0.0005). These two parameters mediated by the inferred PC pathway were little affected for the glaucoma group. Model simulations suggest that the reduced contrast sensitivity and contrast gain signature were consistent with the hypothesis that reduced MC ganglion cell dendritic complexity can lead to reduced effective retinal illuminance, and hence increased semi-saturation contrast of the ganglion cell contrast response functions. CONCLUSIONS The contrast sensitivity and contrast gain signature of the inferred MC pathway were reduced in patients with glaucoma. The results were consistent with a model of ganglion cell dysfunction due to reduced synaptic density. PMID:18501947

Detection of Genetically Modified Sugarcane by Using Terahertz Spectroscopy and Chemometrics

NASA Astrophysics Data System (ADS)

Liu, J.; Xie, H.; Zha, B.; Ding, W.; Luo, J.; Hu, C.

2018-03-01

A methodology is proposed to identify genetically modified sugarcane from non-genetically modified sugarcane by using terahertz spectroscopy and chemometrics techniques, including linear discriminant analysis (LDA), support vector machine-discriminant analysis (SVM-DA), and partial least squares-discriminant analysis (PLS-DA). The classification rate of the above mentioned methods is compared, and different types of preprocessing are considered. According to the experimental results, the best option is PLS-DA, with an identification rate of 98%. The results indicated that THz spectroscopy and chemometrics techniques are a powerful tool to identify genetically modified and non-genetically modified sugarcane.

Discriminating the reaction types of plant type III polyketide synthases

PubMed Central

Shimizu, Yugo; Ogata, Hiroyuki; Goto, Susumu

2017-01-01

Abstract Motivation: Functional prediction of paralogs is challenging in bioinformatics because of rapid functional diversification after gene duplication events combined with parallel acquisitions of similar functions by different paralogs. Plant type III polyketide synthases (PKSs), producing various secondary metabolites, represent a paralogous family that has undergone gene duplication and functional alteration. Currently, there is no computational method available for the functional prediction of type III PKSs. Results: We developed a plant type III PKS reaction predictor, pPAP, based on the recently proposed classification of type III PKSs. pPAP combines two kinds of similarity measures: one calculated by profile hidden Markov models (pHMMs) built from functionally and structurally important partial sequence regions, and the other based on mutual information between residue positions. pPAP targets PKSs acting on ring-type starter substrates, and classifies their functions into four reaction types. The pHMM approach discriminated two reaction types with high accuracy (97.5%, 39/40), but its accuracy decreased when discriminating three reaction types (87.8%, 43/49). When combined with a correlation-based approach, all 49 PKSs were correctly discriminated, and pPAP was still highly accurate (91.4%, 64/70) even after adding other reaction types. These results suggest pPAP, which is based on linear discriminant analyses of similarity measures, is effective for plant type III PKS function prediction. Availability and Implementation: pPAP is freely available at ftp://ftp.genome.jp/pub/tools/ppap/ Contact: goto@kuicr.kyoto-u.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28334262

Feasibility of use of fatty acid and triacylglycerol profiles for the authentication of commercial labelling in Iberian dry-cured sausages.

PubMed

Horcada, Alberto; Fernández-Cabanás, Víctor M; Polvillo, Oliva; Botella, Baltasar; Cubiles, M Dolores; Pino, Rafael; Narváez-Rivas, Mónica; León-Camacho, Manuel; Acuña, Rafael Rodríguez

2013-12-15

In the present study, fatty acid and triacylglycerol profiles were used to evaluate the possibility of authenticating Iberian dry-cured sausages according to their label specifications. 42 Commercial brand 'chorizo' and 39 commercial brand 'salchichón' sausages from Iberian pigs were purchased. 36 Samples were labelled Bellota and 45 bore the generic Ibérico label. In the market, Bellota is considered to be a better class than the generic Ibérico since products with the Bellota label are manufactured with high quality fat obtained from extensively reared pigs fed on acorns and pasture. Analyses of fatty acids and triacylglycerols were carried out by gas chromatography and a flame ion detector. A CP-SIL 88 column (highly substituted cyanopropyl phase; 50 m × 0.25 mm i.d., 0.2 µm film thickness) (Varian, Palo Alto, USA) was used for fatty acid analysis and a fused silica capillary DB-17HT column (50% phenyl-50% methylpolysiloxane; 30 m × 0.25 mm i.d., 0.15 µm film thickness) was used for triacylglycerols. Twelve fatty acids and 16 triacylglycerols were identified. Various discriminant models (linear quadratic discriminant analyses, logistic regression and support vector machines) were trained to predict the sample class (Bellota or Ibérico). These models included fatty acids and triacylglycerols separately and combined fatty acid and triacylglycerol profiles. The number of correctly classified samples according to discriminant analyses can be considered low (lower than 65%). The greatest discriminant rate was obtained when triacylglycerol profiles were included in the model, whilst using a combination of fatty acid and triacylglycerol profiles did not improve the rate of correct assignation. The values that represent the reliability of prediction of the samples according to the label specification were higher for the Ibérico class than for the Bellota class. In fact, quadratic and Support Vector Machine discriminate analyses were not able to assign the Bellota class (0%) when combined fatty acids and triacylglycerols were included in the model. The use of fatty acid and triacylglycerol profiles to discriminate Iberian dry-cured sausages in the market according to their labelling information is unclear. In order to ensure the genuineness of Iberian dry-cured sausages in the market, identification of fatty acid and triacylglycerol profiles should be combined with the application of quality standard traceability techniques. © 2013 Published by Elsevier B.V.

An increase in visceral fat is associated with a decrease in the taste and olfactory capacity

PubMed Central

Fernandez-Garcia, Jose Carlos; Alcaide, Juan; Santiago-Fernandez, Concepcion; Roca-Rodriguez, MM.; Aguera, Zaida; Baños, Rosa; Botella, Cristina; de la Torre, Rafael; Fernandez-Real, Jose M.; Fruhbeck, Gema; Gomez-Ambrosi, Javier; Jimenez-Murcia, Susana; Menchon, Jose M.; Casanueva, Felipe F.; Fernandez-Aranda, Fernando; Tinahones, Francisco J.; Garrido-Sanchez, Lourdes

2017-01-01

Introduction Sensory factors may play an important role in the determination of appetite and food choices. Also, some adipokines may alter or predict the perception and pleasantness of specific odors. We aimed to analyze differences in smell–taste capacity between females with different weights and relate them with fat and fat-free mass, visceral fat, and several adipokines. Materials and methods 179 females with different weights (from low weight to morbid obesity) were studied. We analyzed the relation between fat, fat-free mass, visceral fat (indirectly estimated by bioelectrical impedance analysis with visceral fat rating (VFR)), leptin, adiponectin and visfatin. The smell and taste assessments were performed through the "Sniffin’ Sticks" and "Taste Strips" respectively. Results We found a lower score in the measurement of smell (TDI-score (Threshold, Discrimination and Identification)) in obese subjects. All the olfactory functions measured, such as threshold, discrimination, identification and the TDI-score, correlated negatively with age, body mass index (BMI), leptin, fat mass, fat-free mass and VFR. In a multiple linear regression model, VFR mainly predicted the TDI-score. With regard to the taste function measurements, the normal weight subjects showed a higher score of taste functions. However a tendency to decrease was observed in the groups with greater or lesser BMI. In a multiple linear regression model VFR and age mainly predicted the total taste scores. Discussion We show for the first time that a reverse relationship exists between visceral fat and sensory signals, such as smell and taste, across a population with different body weight conditions. PMID:28158237

The use of kernel local Fisher discriminant analysis for the channelization of the Hotelling model observer

NASA Astrophysics Data System (ADS)

Wen, Gezheng; Markey, Mia K.

2015-03-01

It is resource-intensive to conduct human studies for task-based assessment of medical image quality and system optimization. Thus, numerical model observers have been developed as a surrogate for human observers. The Hotelling observer (HO) is the optimal linear observer for signal-detection tasks, but the high dimensionality of imaging data results in a heavy computational burden. Channelization is often used to approximate the HO through a dimensionality reduction step, but how to produce channelized images without losing significant image information remains a key challenge. Kernel local Fisher discriminant analysis (KLFDA) uses kernel techniques to perform supervised dimensionality reduction, which finds an embedding transformation that maximizes betweenclass separability and preserves within-class local structure in the low-dimensional manifold. It is powerful for classification tasks, especially when the distribution of a class is multimodal. Such multimodality could be observed in many practical clinical tasks. For example, primary and metastatic lesions may both appear in medical imaging studies, but the distributions of their typical characteristics (e.g., size) may be very different. In this study, we propose to use KLFDA as a novel channelization method. The dimension of the embedded manifold (i.e., the result of KLFDA) is a counterpart to the number of channels in the state-of-art linear channelization. We present a simulation study to demonstrate the potential usefulness of KLFDA for building the channelized HOs (CHOs) and generating reliable decision statistics for clinical tasks. We show that the performance of the CHO with KLFDA channels is comparable to that of the benchmark CHOs.

Personality and affect characteristics of outpatients with depression.

PubMed

Petrocelli, J V; Glaser, B A; Calhoun, G B; Campbell, L F

2001-08-01

This investigation was designed to examine the relationship between depression severity and personality disorders measured by the Millon Clinical Multiaxial Inventory-II (Millon, 1987) and affectivity measured by the Positive Affectivity/Negative Affectivity Schedule (Watson, Clark, & Tellegen, 1988). Discriminant analyses were employed to identify the personality and affective dimensions that maximally discriminate between 4 different levels of depressive severity. Differences between the 4 levels of depressive severity are suggestive of unique patterns of personality characteristics. Discriminant analysis showed that 74.8% of the cases were correctly classified by a single linear discriminant function, and that 61% of the variance in depression severity was accounted for by selected personality and affect variables. Results extend current conceptualizations of comorbidity and are discussed with respect to depression severity.

Forest discrimination with multipolarization imaging radar

NASA Technical Reports Server (NTRS)

Ford, J. P.; Wickland, D. E.

1985-01-01

The use of radar polarization diversity for discriminating forest canopy variables on airborne synthetic-aperture radar (SAR) images is evaluated. SAR images were acquired at L-Band (24.6 cm) simultaneously in four linear polarization states (HH, HV, VH, and VV) in South Carolina on March 1, 1984. In order to relate the polarization signatures to biophysical properties, false-color composite images were compared to maps of forest stands in the timber compartment. In decreasing order, the most useful correlative forest data are stand basal area, forest age, site condition index, and forest management type. It is found that multipolarization images discriminate variation in tree density and difference in the amount of understory, but do not discriminate between evergreen and deciduous forest types.

Quantum teleportation via quantum channels with non-maximal Schmidt rank

NASA Astrophysics Data System (ADS)

Solís-Prosser, M. A.; Jiménez, O.; Neves, L.; Delgado, A.

2013-03-01

We study the problem of teleporting unknown pure states of a single qudit via a pure quantum channel with non-maximal Schmidt rank. We relate this process to the discrimination of linearly dependent symmetric states with the help of the maximum-confidence discrimination strategy. We show that with a certain probability, it is possible to teleport with a fidelity larger than the fidelity optimal deterministic teleportation.

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.

Smart smoke alarm

DOEpatents

Warmack, Robert J. Bruce; Wolf, Dennis A; Frank, Steven Shane

2015-04-28

Methods and apparatus for smoke detection are disclosed. In one embodiment, a smoke detector uses linear discriminant analysis (LDA) to determine whether observed conditions indicate that an alarm is warranted.

Self-Reported Experiences of Discrimination and Depression in Native Hawaiians.

PubMed

Antonio, Mapuana Ck; Ahn, Hyeong Jun; Ing, Claire Townsend; Dillard, Adrienne; Cassel, Kevin; Kekauoha, B Puni; Kaholokula, Joseph Keawe'aimoku

2016-09-01

Discrimination is an acute and chronic stressor that negatively impacts the health of many ethnic groups in the United States. Individuals who perceive increased levels of discrimination are at risk of experiencing psychological distress and symptoms of depression. No study to date has examined the relationship between perceived discrimination and mental health in Native Hawaiians. The purpose of this study is to explore the relationship between perceived discrimination and depression based on the Homestead Health Survey mailed to Native Hawaiian residents of Hawaiian Home Lands. This study also explores the role of cultural identity and how it may impact experiences of discrimination and symptoms of depression. Based on cross-sectional data obtained from 104 Native Hawaiian residents, a significant positive correlation was found between perceived discrimination and symptoms of depression (r= 0.32, P<.001). Cultural identity did not significantly correlate with discrimination or depression. Multiple linear regression analyses indicate that the relationship between depression and discrimination remained statistically significant (coefficient estimate of 0.18; P<.01), after accounting for differences in socio-demographics and degree of identification with the Native Hawaiian and American cultures. These findings are consistent with other studies that have focused on the effects of discrimination on psychological wellbeing for other ethnic minority populations.

Self-Reported Experiences of Discrimination and Depression in Native Hawaiians

PubMed Central

Ahn, Hyeong Jun; Ing, Claire Townsend; Dillard, Adrienne; Cassel, Kevin; Kekauoha, B Puni; Kaholokula, Joseph Keawe‘aimoku

2016-01-01

Discrimination is an acute and chronic stressor that negatively impacts the health of many ethnic groups in the United States. Individuals who perceive increased levels of discrimination are at risk of experiencing psychological distress and symptoms of depression. No study to date has examined the relationship between perceived discrimination and mental health in Native Hawaiians. The purpose of this study is to explore the relationship between perceived discrimination and depression based on the Homestead Health Survey mailed to Native Hawaiian residents of Hawaiian Home Lands. This study also explores the role of cultural identity and how it may impact experiences of discrimination and symptoms of depression. Based on cross-sectional data obtained from 104 Native Hawaiian residents, a significant positive correlation was found between perceived discrimination and symptoms of depression (r= 0.32, P<.001). Cultural identity did not significantly correlate with discrimination or depression. Multiple linear regression analyses indicate that the relationship between depression and discrimination remained statistically significant (coefficient estimate of 0.18; P<.01), after accounting for differences in socio-demographics and degree of identification with the Native Hawaiian and American cultures. These findings are consistent with other studies that have focused on the effects of discrimination on psychological wellbeing for other ethnic minority populations. PMID:27688952

Racial Discrimination and Alcohol Use: The Moderating Role of Religious Orientation.

PubMed

Parenteau, Stacy C; Waters, Kristen; Cox, Brittany; Patterson, Tarsha; Carr, Richard

2017-01-02

An outgrowth of research has established a relationship between racial discrimination and alcohol use, as well as factors that moderate this association. The main objective of this study was to determine if religious orientation moderates the relationship between perceived racial discrimination and alcohol use. This study utilized a cross-sectional data collection strategy to examine the relationship among discrimination, religious orientation, and alcohol use among undergraduate students (N = 349) at a midsize southeastern university. Data was collected in 2014. Participants completed a demographic questionnaire, the General Ethnic Discrimination Scale, the Extrinsic/Intrinsic Religious Orientation Scale-Revised and the Drinking and Drug Habits Questionnaire. Analyses using hierarchical linear regression indicate a significant interaction effect (lifetime discrimination × extrinsic religious orientation) on problem drinking. Additional moderation analyses reveal a significant interaction effect between lifetime discrimination and the extrinsic-personal religious orientation on problem drinking. Results suggest that an extrinsic religious orientation, and particularly, an extrinsic-personal religious orientation, moderates the relationship between lifetime discrimination and problem drinking, suggesting that turning to religion for comfort and protection, rather than for the superficial purpose of seeing/making friends at church, may buffer against the deleterious effects of discrimination-specifically, engaging in problem drinking to cope with the stress of discrimination. Limitations, directions for future research, and clinical implications are discussed.

Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping.

PubMed

Shafizadeh-Moghadam, Hossein; Valavi, Roozbeh; Shahabi, Himan; Chapi, Kamran; Shirzadi, Ataollah

2018-07-01

In this research, eight individual machine learning and statistical models are implemented and compared, and based on their results, seven ensemble models for flood susceptibility assessment are introduced. The individual models included artificial neural networks, classification and regression trees, flexible discriminant analysis, generalized linear model, generalized additive model, boosted regression trees, multivariate adaptive regression splines, and maximum entropy, and the ensemble models were Ensemble Model committee averaging (EMca), Ensemble Model confidence interval Inferior (EMciInf), Ensemble Model confidence interval Superior (EMciSup), Ensemble Model to estimate the coefficient of variation (EMcv), Ensemble Model to estimate the mean (EMmean), Ensemble Model to estimate the median (EMmedian), and Ensemble Model based on weighted mean (EMwmean). The data set covered 201 flood events in the Haraz watershed (Mazandaran province in Iran) and 10,000 randomly selected non-occurrence points. Among the individual models, the Area Under the Receiver Operating Characteristic (AUROC), which showed the highest value, belonged to boosted regression trees (0.975) and the lowest value was recorded for generalized linear model (0.642). On the other hand, the proposed EMmedian resulted in the highest accuracy (0.976) among all models. In spite of the outstanding performance of some models, nevertheless, variability among the prediction of individual models was considerable. Therefore, to reduce uncertainty, creating more generalizable, more stable, and less sensitive models, ensemble forecasting approaches and in particular the EMmedian is recommended for flood susceptibility assessment. Copyright © 2018 Elsevier Ltd. All rights reserved.

Multivariate Classification of Major Depressive Disorder Using the Effective Connectivity and Functional Connectivity

PubMed Central

Geng, Xiangfei; Xu, Junhai; Liu, Baolin; Shi, Yonggang

2018-01-01

Major depressive disorder (MDD) is a mental disorder characterized by at least 2 weeks of low mood, which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. To our best knowledge, no studies aim at classification with effective connectivity and functional connectivity measures between MDD patients and healthy controls. In this study, we performed a data-driving classification analysis using the whole brain connectivity measures which included the functional connectivity from two brain templates and effective connectivity measures created by the default mode network (DMN), dorsal attention network (DAN), frontal-parietal network (FPN), and silence network (SN). Effective connectivity measures were extracted using spectral Dynamic Causal Modeling (spDCM) and transformed into a vectorial feature space. Linear Support Vector Machine (linear SVM), non-linear SVM, k-Nearest Neighbor (KNN), and Logistic Regression (LR) were used as the classifiers to identify the differences between MDD patients and healthy controls. Our results showed that the highest accuracy achieved 91.67% (p < 0.0001) when using 19 effective connections and 89.36% when using 6,650 functional connections. The functional connections with high discriminative power were mainly located within or across the whole brain resting-state networks while the discriminative effective connections located in several specific regions, such as posterior cingulate cortex (PCC), ventromedial prefrontal cortex (vmPFC), dorsal cingulate cortex (dACC), and inferior parietal lobes (IPL). To further compare the discriminative power of functional connections and effective connections, a classification analysis only using the functional connections from those four networks was conducted and the highest accuracy achieved 78.33% (p < 0.0001). Our study demonstrated that the effective connectivity measures might play a more important role than functional connectivity in exploring the alterations between patients and health controls and afford a better mechanistic interpretability. Moreover, our results showed a diagnostic potential of the effective connectivity for the diagnosis of MDD patients with high accuracies allowing for earlier prevention or intervention. PMID:29515348

Temperature Gradient Effect on Gas Discrimination Power of a Metal-Oxide Thin-Film Sensor Microarray

PubMed Central

Sysoev, Victor V.; Kiselev, Ilya; Frietsch, Markus; Goschnick, Joachim

2004-01-01

The paper presents results concerning the effect of spatial inhomogeneous operating temperature on the gas discrimination power of a gas-sensor microarray, with the latter based on a thin SnO2 film employed in the KAMINA electronic nose. Three different temperature distributions over the substrate are discussed: a nearly homogeneous one and two temperature gradients, equal to approx. 3.3 °C/mm and 6.7 °C/mm, applied across the sensor elements (segments) of the array. The gas discrimination power of the microarray is judged by using the Mahalanobis distance in the LDA (Linear Discrimination Analysis) coordinate system between the data clusters obtained by the response of the microarray to four target vapors: ethanol, acetone, propanol and ammonia. It is shown that the application of a temperature gradient increases the gas discrimination power of the microarray by up to 35 %.

[Development of the soft independent modelling of class analogies model to discrimination Vibrio parahemolyticus by Smartongue].

PubMed

Huang, Jianfeng; Zhao, Guangying; Dou, Wenchao

2011-04-01

To explore a new rapid detection method for detecting of Food pathogens. We used the Smartongue, to determine the composition informations of the liquid culture samples and combined with soft independent modelling of class analogies (SIMCA) to analyze their respective species, then set up a Smartongue -SIMCA model to discriminate the V. parahaemolyticus. The Smartongue has 6 working electrodes and three frequency segments, we can built 18 discrimination models in one detection. After comparing all the 18 discrimination models, the optimal working electrodes and frequency segments were selected out, they were: palladium electrode in 1 Hz frequency segment, tungsten electrode in 100 Hz and silver electrode in 100 Hz. Then 10 species of pathogenic Vibrio were discriminated by the 3 models. The V. damsela, V. metschnikovii, V. alginalyticus, V. cincinnatiensis, V. metschnikovii and V. cholerae O serogroup samples could be discriminated by the SIMCA model of V. parahaemolyticus with palladium electrode 1 Hz frequency segment; V. mimicus and V. vulnincus samples could be discriminated by the SIMCA model of V. parahaemolyticus with tungsten electrode 100 Hz frequency segment; V. carcariae and V. cholerae non-O serogroup samples could be discriminated with the SIMCA model of V. parahaemolyticus in silver electrode 100 Hz frequency segment. The accurate discrimination of ten species of Vibrio samples is 100%. The Smartongue combined with SIMCA can discriminate V. parahaemolyticus with other pathogenic Vibrio effectively. It has a promising future as a new rapid detection method for V. parahaemolyticus.

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.

Direct discriminant locality preserving projection with Hammerstein polynomial expansion.

PubMed

Chen, Xi; Zhang, Jiashu; Li, Defang

2012-12-01

Discriminant locality preserving projection (DLPP) is a linear approach that encodes discriminant information into the objective of locality preserving projection and improves its classification ability. To enhance the nonlinear description ability of DLPP, we can optimize the objective function of DLPP in reproducing kernel Hilbert space to form a kernel-based discriminant locality preserving projection (KDLPP). However, KDLPP suffers the following problems: 1) larger computational burden; 2) no explicit mapping functions in KDLPP, which results in more computational burden when projecting a new sample into the low-dimensional subspace; and 3) KDLPP cannot obtain optimal discriminant vectors, which exceedingly optimize the objective of DLPP. To overcome the weaknesses of KDLPP, in this paper, a direct discriminant locality preserving projection with Hammerstein polynomial expansion (HPDDLPP) is proposed. The proposed HPDDLPP directly implements the objective of DLPP in high-dimensional second-order Hammerstein polynomial space without matrix inverse, which extracts the optimal discriminant vectors for DLPP without larger computational burden. Compared with some other related classical methods, experimental results for face and palmprint recognition problems indicate the effectiveness of the proposed HPDDLPP.

Demonstration of iodine K-edge imaging by use of an energy-discrimination X-ray computed tomography system with a cadmium telluride detector.

PubMed

Abudurexiti, Abulajiang; Kameda, Masashi; Sato, Eiichi; Abderyim, Purkhet; Enomoto, Toshiyuki; Watanabe, Manabu; Hitomi, Keitaro; Tanaka, Etsuro; Mori, Hidezo; Kawai, Toshiaki; Takahashi, Kiyomi; Sato, Shigehiro; Ogawa, Akira; Onagawa, Jun

2010-07-01

An energy-discrimination K-edge X-ray computed tomography (CT) system is useful for increasing the contrast resolution of a target region by utilizing contrast media. The CT system has a cadmium telluride (CdTe) detector, and a projection curve is obtained by linear scanning with use of the CdTe detector in conjunction with an X-stage. An object is rotated by a rotation step angle with use of a turntable between the linear scans. Thus, CT is carried out by repetition of the linear scanning and the rotation of an object. Penetrating X-ray photons from the object are detected by the CdTe detector, and event signals of X-ray photons are produced with use of charge-sensitive and shaping amplifiers. Both the photon energy and the energy width are selected by use of a multi-channel analyzer, and the number of photons is counted by a counter card. For performing energy discrimination, a low-dose-rate X-ray generator for photon counting was developed; the maximum tube voltage and the minimum tube current were 110 kV and 1.0 microA, respectively. In energy-discrimination CT, the tube voltage and the current were 60 kV and 20.0 microA, respectively, and the X-ray intensity was 0.735 microGy/s at 1.0 m from the source and with a tube voltage of 60 kV. Demonstration of enhanced iodine K-edge X-ray CT was carried out by selection of photons with energies just beyond the iodine K-edge energy of 33.2 keV.

Gender classification of running subjects using full-body kinematics

NASA Astrophysics Data System (ADS)

Williams, Christina M.; Flora, Jeffrey B.; Iftekharuddin, Khan M.

2016-05-01

This paper proposes novel automated gender classification of subjects while engaged in running activity. The machine learning techniques include preprocessing steps using principal component analysis followed by classification with linear discriminant analysis, and nonlinear support vector machines, and decision-stump with AdaBoost. The dataset consists of 49 subjects (25 males, 24 females, 2 trials each) all equipped with approximately 80 retroreflective markers. The trials are reflective of the subject's entire body moving unrestrained through a capture volume at a self-selected running speed, thus producing highly realistic data. The classification accuracy using leave-one-out cross validation for the 49 subjects is improved from 66.33% using linear discriminant analysis to 86.74% using the nonlinear support vector machine. Results are further improved to 87.76% by means of implementing a nonlinear decision stump with AdaBoost classifier. The experimental findings suggest that the linear classification approaches are inadequate in classifying gender for a large dataset with subjects running in a moderately uninhibited environment.

Q-mode versus R-mode principal component analysis for linear discriminant analysis (LDA)

NASA Astrophysics Data System (ADS)

Lee, Loong Chuen; Liong, Choong-Yeun; Jemain, Abdul Aziz

2017-05-01

Many literature apply Principal Component Analysis (PCA) as either preliminary visualization or variable con-struction methods or both. Focus of PCA can be on the samples (R-mode PCA) or variables (Q-mode PCA). Traditionally, R-mode PCA has been the usual approach to reduce high-dimensionality data before the application of Linear Discriminant Analysis (LDA), to solve classification problems. Output from PCA composed of two new matrices known as loadings and scores matrices. Each matrix can then be used to produce a plot, i.e. loadings plot aids identification of important variables whereas scores plot presents spatial distribution of samples on new axes that are also known as Principal Components (PCs). Fundamentally, the scores matrix always be the input variables for building classification model. A recent paper uses Q-mode PCA but the focus of analysis was not on the variables but instead on the samples. As a result, the authors have exchanged the use of both loadings and scores plots in which clustering of samples was studied using loadings plot whereas scores plot has been used to identify important manifest variables. Therefore, the aim of this study is to statistically validate the proposed practice. Evaluation is based on performance of external error obtained from LDA models according to number of PCs. On top of that, bootstrapping was also conducted to evaluate the external error of each of the LDA models. Results show that LDA models produced by PCs from R-mode PCA give logical performance and the matched external error are also unbiased whereas the ones produced with Q-mode PCA show the opposites. With that, we concluded that PCs produced from Q-mode is not statistically stable and thus should not be applied to problems of classifying samples, but variables. We hope this paper will provide some insights on the disputable issues.

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.

Linear Quantitative Profiling Method Fast Monitors Alkaloids of Sophora Flavescens That Was Verified by Tri-Marker Analyses.

PubMed

Hou, Zhifei; Sun, Guoxiang; Guo, Yong

2016-01-01

The present study demonstrated the use of the Linear Quantitative Profiling Method (LQPM) to evaluate the quality of Alkaloids of Sophora flavescens (ASF) based on chromatographic fingerprints in an accurate, economical and fast way. Both linear qualitative and quantitative similarities were calculated in order to monitor the consistency of the samples. The results indicate that the linear qualitative similarity (LQLS) is not sufficiently discriminating due to the predominant presence of three alkaloid compounds (matrine, sophoridine and oxymatrine) in the test samples; however, the linear quantitative similarity (LQTS) was shown to be able to obviously identify the samples based on the difference in the quantitative content of all the chemical components. In addition, the fingerprint analysis was also supported by the quantitative analysis of three marker compounds. The LQTS was found to be highly correlated to the contents of the marker compounds, indicating that quantitative analysis of the marker compounds may be substituted with the LQPM based on the chromatographic fingerprints for the purpose of quantifying all chemicals of a complex sample system. Furthermore, once reference fingerprint (RFP) developed from a standard preparation in an immediate detection way and the composition similarities calculated out, LQPM could employ the classical mathematical model to effectively quantify the multiple components of ASF samples without any chemical standard.

Defining Aggressive Prostate Cancer Using a 12-Gene Model1

PubMed Central

Riva, Alberto; Kim, Robert; Varambally, Sooryanarayana; He, Le; Kutok, Jeff; Aster, Jonathan C; Tang, Jeffery; Kuefer, Rainer; Hofer, Matthias D; Febbo, Phillip G; Chinnaiyan, Arul M; Rubin, Mark A

2006-01-01

Abstract The critical clinical question in prostate cancer research is: How do we develop means of distinguishing aggressive disease from indolent disease? Using a combination of proteomic and expression array data, we identified a set of 36 genes with concordant dysregulation of protein products that could be evaluated in situ by quantitative immunohistochemistry. Another five prostate cancer biomarkers were included using linear discriminant analysis, we determined that the optimal model used to predict prostate cancer progression consisted of 12 proteins. Using a separate patient population, transcriptional levels of the 12 genes encoding for these proteins predicted prostate-specific antigen failure in 79 men following surgery for clinically localized prostate cancer (P = .0015). This study demonstrates that cross-platform models can lead to predictive models with the possible advantage of being more robust through this selection process. PMID:16533427

Genetic network inference as a series of discrimination tasks.

PubMed

Kimura, Shuhei; Nakayama, Satoshi; Hatakeyama, Mariko

2009-04-01

Genetic network inference methods based on sets of differential equations generally require a great deal of time, as the equations must be solved many times. To reduce the computational cost, researchers have proposed other methods for inferring genetic networks by solving sets of differential equations only a few times, or even without solving them at all. When we try to obtain reasonable network models using these methods, however, we must estimate the time derivatives of the gene expression levels with great precision. In this study, we propose a new method to overcome the drawbacks of inference methods based on sets of differential equations. Our method infers genetic networks by obtaining classifiers capable of predicting the signs of the derivatives of the gene expression levels. For this purpose, we defined a genetic network inference problem as a series of discrimination tasks, then solved the defined series of discrimination tasks with a linear programming machine. Our experimental results demonstrated that the proposed method is capable of correctly inferring genetic networks, and doing so more than 500 times faster than the other inference methods based on sets of differential equations. Next, we applied our method to actual expression data of the bacterial SOS DNA repair system. And finally, we demonstrated that our approach relates to the inference method based on the S-system model. Though our method provides no estimation of the kinetic parameters, it should be useful for researchers interested only in the network structure of a target system. Supplementary data are available at Bioinformatics online.

Near-infrared Raman spectroscopy for estimating biochemical changes associated with different pathological conditions of cervix

NASA Astrophysics Data System (ADS)

Daniel, Amuthachelvi; Prakasarao, Aruna; Ganesan, Singaravelu

2018-02-01

The molecular level changes associated with oncogenesis precede the morphological changes in cells and tissues. Hence molecular level diagnosis would promote early diagnosis of the disease. Raman spectroscopy is capable of providing specific spectral signature of various biomolecules present in the cells and tissues under various pathological conditions. The aim of this work is to develop a non-linear multi-class statistical methodology for discrimination of normal, neoplastic and malignant cells/tissues. The tissues were classified as normal, pre-malignant and malignant by employing Principal Component Analysis followed by Artificial Neural Network (PC-ANN). The overall accuracy achieved was 99%. Further, to get an insight into the quantitative biochemical composition of the normal, neoplastic and malignant tissues, a linear combination of the major biochemicals by non-negative least squares technique was fit to the measured Raman spectra of the tissues. This technique confirms the changes in the major biomolecules such as lipids, nucleic acids, actin, glycogen and collagen associated with the different pathological conditions. To study the efficacy of this technique in comparison with histopathology, we have utilized Principal Component followed by Linear Discriminant Analysis (PC-LDA) to discriminate the well differentiated, moderately differentiated and poorly differentiated squamous cell carcinoma with an accuracy of 94.0%. And the results demonstrated that Raman spectroscopy has the potential to complement the good old technique of histopathology.

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.

Encoding of natural and artificial stimuli in the auditory midbrain

NASA Astrophysics Data System (ADS)

Lyzwa, Dominika

How complex acoustic stimuli are encoded in the main center of convergence in the auditory midbrain is not clear. Here, the representation of neural spiking responses to natural and artificial sounds across this subcortical structure is investigated based on neurophysiological recordings from the mammalian midbrain. Neural and stimulus correlations of neuronal pairs are analyzed with respect to the neurons' distance, and responses to different natural communication sounds are discriminated. A model which includes linear and nonlinear neural response properties of this nucleus is presented and employed to predict temporal spiking responses to new sounds. Supported by BMBF Grant 01GQ0811.

Discriminant Analysis of Defective and Non-Defective Field Pea (Pisum sativum L.) into Broad Market Grades Based on Digital Image Features.

PubMed

McDonald, Linda S; Panozzo, Joseph F; Salisbury, Phillip A; Ford, Rebecca

2016-01-01

Field peas (Pisum sativum L.) are generally traded based on seed appearance, which subjectively defines broad market-grades. In this study, we developed an objective Linear Discriminant Analysis (LDA) model to classify market grades of field peas based on seed colour, shape and size traits extracted from digital images. Seeds were imaged in a high-throughput system consisting of a camera and laser positioned over a conveyor belt. Six colour intensity digital images were captured (under 405, 470, 530, 590, 660 and 850nm light) for each seed, and surface height was measured at each pixel by laser. Colour, shape and size traits were compiled across all seed in each sample to determine the median trait values. Defective and non-defective seed samples were used to calibrate and validate the model. Colour components were sufficient to correctly classify all non-defective seed samples into correct market grades. Defective samples required a combination of colour, shape and size traits to achieve 87% and 77% accuracy in market grade classification of calibration and validation sample-sets respectively. Following these results, we used the same colour, shape and size traits to develop an LDA model which correctly classified over 97% of all validation samples as defective or non-defective.

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.

Discriminant Analysis of Defective and Non-Defective Field Pea (Pisum sativum L.) into Broad Market Grades Based on Digital Image Features

PubMed Central

McDonald, Linda S.; Panozzo, Joseph F.; Salisbury, Phillip A.; Ford, Rebecca

2016-01-01

Field peas (Pisum sativum L.) are generally traded based on seed appearance, which subjectively defines broad market-grades. In this study, we developed an objective Linear Discriminant Analysis (LDA) model to classify market grades of field peas based on seed colour, shape and size traits extracted from digital images. Seeds were imaged in a high-throughput system consisting of a camera and laser positioned over a conveyor belt. Six colour intensity digital images were captured (under 405, 470, 530, 590, 660 and 850nm light) for each seed, and surface height was measured at each pixel by laser. Colour, shape and size traits were compiled across all seed in each sample to determine the median trait values. Defective and non-defective seed samples were used to calibrate and validate the model. Colour components were sufficient to correctly classify all non-defective seed samples into correct market grades. Defective samples required a combination of colour, shape and size traits to achieve 87% and 77% accuracy in market grade classification of calibration and validation sample-sets respectively. Following these results, we used the same colour, shape and size traits to develop an LDA model which correctly classified over 97% of all validation samples as defective or non-defective. PMID:27176469

Relationship between fear of falling and outcomes of an inpatient geriatric rehabilitation population--fear of the fear of falling.

PubMed

Denkinger, Michael D; Igl, Wilmar; Lukas, Albert; Bader, Anne; Bailer, Stefanie; Franke, Sebastian; Denkinger, Claudia M; Nikolaus, Thorsten; Jamour, Michael

2010-04-01

To examine the effects of various risk factors on three functional outcomes during rehabilitation. Geriatric inpatient rehabilitation unit. Observational longitudinal study. One hundred sixty-one geriatric rehabilitation inpatients (men, women), mean age 82, who were capable of walking at baseline. Functional status was assessed weekly between admission and discharge and at a follow-up 4 months later at home using the function component of the Short Form-Late Life Function and Disability Instrument, the Barthel Index, and Habitual Gait Speed. Various risk factors, such as falls-related self-efficacy (Falls Efficacy Scale-International), were measured. Associations between predictors and functional status at discharge and follow-up were analyzed using linear regression models and bivariate plots. Fear of falling predicted functioning across all outcomes except for habitual gait speed at discharge and follow-up. Visual comparison of functional trajectories between subgroups confirmed these findings, with different levels of fear of falling across time in linear plots. Thus, superior ability of this measure to discriminate between functional status at baseline across all outcomes and to discriminate between functional change especially with regard to the performance-based outcome was demonstrated. Falls-related self-efficacy is the only parameter that significantly predicts rehabilitation outcome at discharge and follow-up across all outcomes. Therefore, it should be routinely assessed in future studies in (geriatric) rehabilitation and considered to be an important treatment goal.

Discrimination of curvature from motion during smooth pursuit eye movements and fixation.

PubMed

Ross, Nicholas M; Goettker, Alexander; Schütz, Alexander C; Braun, Doris I; Gegenfurtner, Karl R

2017-09-01

Smooth pursuit and motion perception have mainly been investigated with stimuli moving along linear trajectories. Here we studied the quality of pursuit movements to curved motion trajectories in human observers and examined whether the pursuit responses would be sensitive enough to discriminate various degrees of curvature. In a two-interval forced-choice task subjects pursued a Gaussian blob moving along a curved trajectory and then indicated in which interval the curve was flatter. We also measured discrimination thresholds for the same curvatures during fixation. Motion curvature had some specific effects on smooth pursuit properties: trajectories with larger amounts of curvature elicited lower open-loop acceleration, lower pursuit gain, and larger catch-up saccades compared with less curved trajectories. Initially, target motion curvatures were underestimated; however, ∼300 ms after pursuit onset pursuit responses closely matched the actual curved trajectory. We calculated perceptual thresholds for curvature discrimination, which were on the order of 1.5 degrees of visual angle (°) for a 7.9° curvature standard. Oculometric sensitivity to curvature discrimination based on the whole pursuit trajectory was quite similar to perceptual performance. Oculometric thresholds based on smaller time windows were higher. Thus smooth pursuit can quite accurately follow moving targets with curved trajectories, but temporal integration over longer periods is necessary to reach perceptual thresholds for curvature discrimination. NEW & NOTEWORTHY Even though motion trajectories in the real world are frequently curved, most studies of smooth pursuit and motion perception have investigated linear motion. We show that pursuit initially underestimates the curvature of target motion and is able to reproduce the target curvature ∼300 ms after pursuit onset. Temporal integration of target motion over longer periods is necessary for pursuit to reach the level of precision found in perceptual discrimination of curvature. Copyright © 2017 the American Physiological Society.

Improved neutron-gamma discrimination for a 3He neutron detector using subspace learning methods

DOE PAGES

Wang, C. L.; Funk, L. L.; Riedel, R. A.; ...

2017-02-10

3He gas based neutron linear-position-sensitive detectors (LPSDs) have been applied for many neutron scattering instruments. Traditional Pulse-Height Analysis (PHA) for Neutron-Gamma Discrimination (NGD) resulted in the neutron-gamma efficiency ratio on the orders of 10 5-10 6. The NGD ratios of 3He detectors need to be improved for even better scientific results from neutron scattering. Digital Signal Processing (DSP) analyses of waveforms were proposed for obtaining better NGD ratios, based on features extracted from rise-time, pulse amplitude, charge integration, a simplified Wiener filter, and the cross-correlation between individual and template waveforms of neutron and gamma events. Fisher linear discriminant analysis (FLDA)more » and three multivariate analyses (MVAs) of the features were performed. The NGD ratios are improved by about 10 2-10 3 times compared with the traditional PHA method. Finally, our results indicate the NGD capabilities of 3He tube detectors can be significantly improved with subspace-learning based methods, which may result in a reduced data-collection time and better data quality for further data reduction.« less

Combustion monitoring of a water tube boiler using a discriminant radial basis network.

PubMed

Sujatha, K; Pappa, N

2011-01-01

This research work includes a combination of Fisher's linear discriminant (FLD) analysis and a radial basis network (RBN) for monitoring the combustion conditions for a coal fired boiler so as to allow control of the air/fuel ratio. For this, two-dimensional flame images are required, which were captured with a CCD camera; the features of the images-average intensity, area, brightness and orientation etc of the flame-are extracted after preprocessing the images. The FLD is applied to reduce the n-dimensional feature size to a two-dimensional feature size for faster learning of the RBN. Also, three classes of images corresponding to different burning conditions of the flames have been extracted from continuous video processing. In this, the corresponding temperatures, and the carbon monoxide (CO) emissions and those of other flue gases have been obtained through measurement. Further, the training and testing of Fisher's linear discriminant radial basis network (FLDRBN), with the data collected, have been carried out and the performance of the algorithms is presented. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Sex assessment using measurements of the first lumbar vertebra.

PubMed

Zheng, Wen Xu; Cheng, Fu Bo; Cheng, Kai Liang; Tian, Yong; Lai, Ying; Zhang, Wen Song; Zheng, Ya Juan; Li, You Qiong

2012-06-10

Sex determination is a vital part of the medico-legal system but can be difficult in cases where the integrity of the body has been compromised. The purpose of this study was to develop a technique for sex assessment from measurements of the first lumber vertebrate. Twenty-nine linear measurements and five ratios were collected from 113 Chinese adult males and 97 Chinese adult females using digital three-dimensional anthropometry methods. By using discriminant analysis, we found that 23 linear measurements and two ratios identified sexual dimorphism (P<0.01), with predictive accuracy ranging from 57.1% to 86.6%. Using a stepwise method of discriminant function analysis, we found three dimensions predicted sex with 88.6% accuracy: (a) upper end-plate width (EPWu), (b) left pedicle height (PHl), and (c) middle end-plate depth (EPDm). This study shows that a single first lumber vertebra can be used for this purpose, and that the discriminant equation will help forensic determination of sex in the Chinese population. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

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.

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.

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

Decision-Related Activity in Macaque V2 for Fine Disparity Discrimination Is Not Compatible with Optimal Linear Readout.

PubMed

Clery, Stephane; Cumming, Bruce G; Nienborg, Hendrikje

2017-01-18

Fine judgments of stereoscopic depth rely mainly on relative judgments of depth (relative binocular disparity) between objects, rather than judgments of the distance to where the eyes are fixating (absolute disparity). In macaques, visual area V2 is the earliest site in the visual processing hierarchy for which neurons selective for relative disparity have been observed (Thomas et al., 2002). Here, we found that, in macaques trained to perform a fine disparity discrimination task, disparity-selective neurons in V2 were highly selective for the task, and their activity correlated with the animals' perceptual decisions (unexplained by the stimulus). This may partially explain similar correlations reported in downstream areas. Although compatible with a perceptual role of these neurons for the task, the interpretation of such decision-related activity is complicated by the effects of interneuronal "noise" correlations between sensory neurons. Recent work has developed simple predictions to differentiate decoding schemes (Pitkow et al., 2015) without needing measures of noise correlations, and found that data from early sensory areas were compatible with optimal linear readout of populations with information-limiting correlations. In contrast, our data here deviated significantly from these predictions. We additionally tested this prediction for previously reported results of decision-related activity in V2 for a related task, coarse disparity discrimination (Nienborg and Cumming, 2006), thought to rely on absolute disparity. Although these data followed the predicted pattern, they violated the prediction quantitatively. This suggests that optimal linear decoding of sensory signals is not generally a good predictor of behavior in simple perceptual tasks. Activity in sensory neurons that correlates with an animal's decision is widely believed to provide insights into how the brain uses information from sensory neurons. Recent theoretical work developed simple predictions to differentiate decoding schemes, and found support for optimal linear readout of early sensory populations with information-limiting correlations. Here, we observed decision-related activity for neurons in visual area V2 of macaques performing fine disparity discrimination, as yet the earliest site for this task. These findings, and previously reported results from V2 in a different task, deviated from the predictions for optimal linear readout of a population with information-limiting correlations. Our results suggest that optimal linear decoding of early sensory information is not a general decoding strategy used by the brain. Copyright © 2017 the authors 0270-6474/17/370715-11$15.00/0.

A photonic chip based frequency discriminator for a high performance microwave photonic link.

PubMed

Marpaung, David; Roeloffzen, Chris; Leinse, Arne; Hoekman, Marcel

2010-12-20

We report a high performance phase modulation direct detection microwave photonic link employing a photonic chip as a frequency discriminator. The photonic chip consists of five optical ring resonators (ORRs) which are fully programmable using thermo-optical tuning. In this discriminator a drop-port response of an ORR is cascaded with a through response of another ORR to yield a linear phase modulation (PM) to intensity modulation (IM) conversion. The balanced photonic link employing the PM to IM conversion exhibits high second-order and third-order input intercept points of + 46 dBm and + 36 dBm, respectively, which are simultaneously achieved at one bias point.

General methodology for simultaneous representation and discrimination of multiple object classes

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-03-01

We address a new general method for linear and nonlinear feature extraction for simultaneous representation and classification. We call this approach the maximum representation and discrimination feature (MRDF) method. We develop a novel nonlinear eigenfeature extraction technique to represent data with closed-form solutions and use it to derive a nonlinear MRDF algorithm. Results of the MRDF method on synthetic databases are shown and compared with results from standard Fukunaga-Koontz transform and Fisher discriminant function methods. The method is also applied to an automated product inspection problem and for classification and pose estimation of two similar objects under 3D aspect angle variations.

Linear photonic frequency discriminator on As₂S₃-ring-on-Ti:LiNbO₃ hybrid platform.

PubMed

Kim, Jaehyun; Sung, Won Ju; Eknoyan, Ohannes; Madsen, Christi K

2013-10-21

We report a photonic frequency discriminator built on the vertically integrated As₂S₃-ring-on-Ti:LiNbO₃ hybrid platform. The discriminator consists of a Mach Zehnder interferometer (MZI) formed by the optical path length difference (OPD) between polarization modes of Ti-diffused waveguide on LiNbO₃ substrate and a vertically integrated As₂S₃ race-track ring resonator on top of the substrate. The figures of merit of the device, enhancement of the signal-to-3rd order intermodulation distortion (IMD3) power ratio and corresponding 3rd order intercept point (IP3) over a traditional MZI, are demonstrated through device characterization.

Graphical methods for the sensitivity analysis in discriminant analysis

DOE PAGES

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

2015-09-30

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

Application of an oscillation-type linear cadmium telluride detector to enhanced gadolinium K-edge computed tomography

NASA Astrophysics Data System (ADS)

Matsukiyo, Hiroshi; Sato, Eiichi; Hagiwara, Osahiko; Abudurexiti, Abulajiang; Osawa, Akihiro; Enomoto, Toshiyuki; Watanabe, Manabu; Nagao, Jiro; Sato, Shigehiro; Ogawa, Akira; Onagawa, Jun

2011-03-01

A linear cadmium telluride (CdTe) detector is useful for carrying out energy-discrimination X-ray imaging, including computed tomography (CT). To perform enhanced gadolinium K-edge CT, we used an oscillation-type linear CdTe detector with an energy resolution of 1.2 keV. CT is performed by repeating the linear scan and the rotation of an object. Penetrating X-ray photons from the object are detected by the CdTe detector, and event signals of X-ray photons are produced using charge-sensitive and shaping amplifiers. Both the photon energy and the energy width are selected using a multichannel analyzer, and the number of photons is counted by a counter card. In energy-discrimination CT, tube voltage and current were 80 kV and 20 μA, respectively, and X-ray intensity was 1.55 μGy/s at 1.0 m from the source at a tube voltage of 80 kV. Demonstration of enhanced gadolinium K-edge X-ray CT was carried out by selecting photons with energies just beyond gadolinium K-edge energy of 50.3 keV.

Prediction Models Discriminating between Nonlocomotive and Locomotive Activities in Children Using a Triaxial Accelerometer with a Gravity-removal Physical Activity Classification Algorithm

PubMed Central

Hikihara, Yuki; Tanaka, Chiaki; Oshima, Yoshitake; Ohkawara, Kazunori; Ishikawa-Takata, Kazuko; Tanaka, Shigeho

2014-01-01

The aims of our study were to examine whether a gravity-removal physical activity classification algorithm (GRPACA) is applicable for discrimination between nonlocomotive and locomotive activities for various physical activities (PAs) of children and to prove that this approach improves the estimation accuracy of a prediction model for children using an accelerometer. Japanese children (42 boys and 26 girls) attending primary school were invited to participate in this study. We used a triaxial accelerometer with a sampling interval of 32 Hz and within a measurement range of ±6 G. Participants were asked to perform 6 nonlocomotive and 5 locomotive activities. We measured raw synthetic acceleration with the triaxial accelerometer and monitored oxygen consumption and carbon dioxide production during each activity with the Douglas bag method. In addition, the resting metabolic rate (RMR) was measured with the subject sitting on a chair to calculate metabolic equivalents (METs). When the ratio of unfiltered synthetic acceleration (USA) and filtered synthetic acceleration (FSA) was 1.12, the rate of correct discrimination between nonlocomotive and locomotive activities was excellent, at 99.1% on average. As a result, a strong linear relationship was found for both nonlocomotive (METs = 0.013×synthetic acceleration +1.220, R2 = 0.772) and locomotive (METs = 0.005×synthetic acceleration +0.944, R2 = 0.880) activities, except for climbing down and up. The mean differences between the values predicted by our model and measured METs were −0.50 to 0.23 for moderate to vigorous intensity (>3.5 METs) PAs like running, ball throwing and washing the floor, which were regarded as unpredictable PAs. In addition, the difference was within 0.25 METs for sedentary to mild moderate PAs (<3.5 METs). Our specific calibration model that discriminates between nonlocomotive and locomotive activities for children can be useful to evaluate the sedentary to vigorous PAs intensity of both nonlocomotive and locomotive activities. PMID:24755646

Linear discriminant analysis based on L1-norm maximization.

PubMed

Zhong, Fujin; Zhang, Jiashu

2013-08-01

Linear discriminant analysis (LDA) is a well-known dimensionality reduction technique, which is widely used for many purposes. However, conventional LDA is sensitive to outliers because its objective function is based on the distance criterion using L2-norm. This paper proposes a simple but effective robust LDA version based on L1-norm maximization, which learns a set of local optimal projection vectors by maximizing the ratio of the L1-norm-based between-class dispersion and the L1-norm-based within-class dispersion. The proposed method is theoretically proved to be feasible and robust to outliers while overcoming the singular problem of the within-class scatter matrix for conventional LDA. Experiments on artificial datasets, standard classification datasets and three popular image databases demonstrate the efficacy of the proposed method.

Spectral discrimination of lithologic facies in the granite of the Pedra Branca Goias using LANDSAT 1 digital imagery

NASA Technical Reports Server (NTRS)

Parada, N. D. J.; Almeido, R., Jr.

1982-01-01

The applicability of LANDSAT MSS imagery for discriminating geobotanical associations observed in zones of cassiterite-rich metasomatic alterations in the granitic body of Serra da Pedra Branca was investigated. Computer compatible tapes of dry and rainy season imagery were analyzed. Image enlargement, corrections, linear contrast stretch, and ratioing of noncorrelated spectral bands were performed using the Image 100 with a grey scale of 256 levels between zero and 255. Only bands 5 and 7 were considered. Band ratioing of noncorrelated channels (5 and 7) of rainy season imagery permits distinction of areas with different vegetation coverage percentage, which corresponds to geobotanial associations in the area studied. The linear contrast stretch of channel 5, especially of the dry season image is very unsatisfactory in this area.

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.

Evaluation of Several Two-Step Scoring Functions Based on Linear Interaction Energy, Effective Ligand Size, and Empirical Pair Potentials for Prediction of Protein-Ligand Binding Geometry and Free Energy

PubMed Central

Rahaman, Obaidur; Estrada, Trilce P.; Doren, Douglas J.; Taufer, Michela; Brooks, Charles L.; Armen, Roger S.

2011-01-01

The performance of several two-step scoring approaches for molecular docking were assessed for their ability to predict binding geometries and free energies. Two new scoring functions designed for “step 2 discrimination” were proposed and compared to our CHARMM implementation of the linear interaction energy (LIE) approach using the Generalized-Born with Molecular Volume (GBMV) implicit solvation model. A scoring function S1 was proposed by considering only “interacting” ligand atoms as the “effective size” of the ligand, and extended to an empirical regression-based pair potential S2. The S1 and S2 scoring schemes were trained and five-fold cross validated on a diverse set of 259 protein-ligand complexes from the Ligand Protein Database (LPDB). The regression-based parameters for S1 and S2 also demonstrated reasonable transferability in the CSARdock 2010 benchmark using a new dataset (NRC HiQ) of diverse protein-ligand complexes. The ability of the scoring functions to accurately predict ligand geometry was evaluated by calculating the discriminative power (DP) of the scoring functions to identify native poses. The parameters for the LIE scoring function with the optimal discriminative power (DP) for geometry (step 1 discrimination) were found to be very similar to the best-fit parameters for binding free energy over a large number of protein-ligand complexes (step 2 discrimination). Reasonable performance of the scoring functions in enrichment of active compounds in four different protein target classes established that the parameters for S1 and S2 provided reasonable accuracy and transferability. Additional analysis was performed to definitively separate scoring function performance from molecular weight effects. This analysis included the prediction of ligand binding efficiencies for a subset of the CSARdock NRC HiQ dataset where the number of ligand heavy atoms ranged from 17 to 35. This range of ligand heavy atoms is where improved accuracy of predicted ligand efficiencies is most relevant to real-world drug design efforts. PMID:21644546

Nonlinear features for classification and pose estimation of machined parts from single views

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-10-01

A new nonlinear feature extraction method is presented for classification and pose estimation of objects from single views. The feature extraction method is called the maximum representation and discrimination feature (MRDF) method. The nonlinear MRDF transformations to use are obtained in closed form, and offer significant advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We consider MRDFs on image data, provide a new 2-stage nonlinear MRDF solution, and show it specializes to well-known linear and nonlinear image processing transforms under certain conditions. We show the use of MRDF in estimating the class and pose of images of rendered solid CAD models of machine parts from single views using a feature-space trajectory neural network classifier. We show new results with better classification and pose estimation accuracy than are achieved by standard principal component analysis and Fukunaga-Koontz feature extraction methods.

Do People Know I’m Poz?: Factors Associated with Knowledge of Serostatus among HIV-positive African Americans’ Social Network Members

PubMed Central

Hoover, Matthew A.; Green, Harold D.; Bogart, Laura M.; Wagner, Glenn J.; Mutchler, Matt G.; Galvan, Frank H.; McDavitt, Bryce

2015-01-01

We examined how functional social support, HIV-related discrimination, internalized HIV stigma, and social network structure and composition were cross-sectionally associated with network members’ knowledge of respondents’ serostatus among 244 HIV-positive African Americans in Los Angeles. Results of a generalized hierarchical linear model indicated people in respondents’ networks who were highly trusted, well-known to others (high degree centrality), HIV-positive, or sex partners were more likely to know respondents’ HIV serostatus; African American network members were less likely to know respondents’ serostatus, as were drug-using partners. Greater internalized stigma among respondents living with HIV was associated with less knowledge of their seropositivity within their social network whereas greater respondent-level HIV discrimination was associated with more knowledge of seropositivity within the network. Additional research is needed to understand the causal mechanisms and mediating processes associated with serostatus disclosure as well as the long-term consequences of disclosure and network members’ knowledge of respondents’ serostatus. PMID:25903505

Health service staff's attitudes towards patients with mental illness.

PubMed

Arvaniti, Aikaterini; Samakouri, Maria; Kalamara, Eleni; Bochtsou, Valentini; Bikos, Constantinos; Livaditis, Miltos

2009-08-01

Stereotypes and prejudices against patients with mental illness are widespread in many societies. The aim of the present study is to investigate such attitudes among the staff and medical students, including employees and trainees, in a general university hospital. Six hundred individuals (361 employees, 231 students) completed the following questionnaires: Level of Contact Report (LCR), Authoritarianism Scale (AS), and Opinion about Mental Illness (OMI), a scale yielding five factors (social discrimination, social restriction, social care, social integration, and aetiology). Multivariate linear regression models were applied in order to search for the simultaneous effect of many variables on the scores of OMI factors. An important part of the sample held negative attitudes especially concerning social discrimination and restriction of the patients. Women, older and less educated staff, nursing staff, and people scoring higher on authoritarianism were more prejudiced. Higher scores on familiarity were associated with less negative attitudes. The results indicate the need to develop sensitisation and training programs considering mental health topics among health service employees.

Authentication of the botanical and geographical origin of honey by mid-infrared spectroscopy.

PubMed

Ruoff, Kaspar; Luginbühl, Werner; Künzli, Raphael; Iglesias, María Teresa; Bogdanov, Stefan; Bosset, Jacques Olivier; von der Ohe, Katharina; von der Ohe, Werner; Amado, Renato

2006-09-06

The potential of Fourier transform mid-infrared spectroscopy (FT-MIR) using an attenuated total reflectance (ATR) cell was evaluated for the authentication of 11 unifloral (acacia, alpine rose, chestnut, dandelion, heather, lime, rape, fir honeydew, metcalfa honeydew, oak honeydew) and polyfloral honey types (n = 411 samples) previously classified with traditional methods such as chemical, pollen, and sensory analysis. Chemometric evaluation of the spectra was carried out by applying principal component analysis and linear discriminant analysis, the error rates of the discriminant models being calculated by using Bayes' theorem. The error rates ranged from <0.1% (polyfloral and heather honeys as well as honeydew honeys from metcalfa, oak, and fir) to 8.3% (alpine rose honey) in both jackknife classification and validation, depending on the honey type considered. This study indicates that ATR-MIR spectroscopy is a valuable tool for the authentication of the botanical origin and quality control and may also be useful for the determination of the geographical origin of honey.

Gay male attraction toward muscular men: does mating context matter?

PubMed

Varangis, Eleanna; Lanzieri, Nicholas; Hildebrandt, Tom; Feldman, Matthew

2012-03-01

The purpose of this study was to examine gay men's perceived attractiveness of male figures based on short-term and long-term partner contexts. A sample of 190 gay adult men rated the attractiveness of line-drawings depicting male figures varying systematically in muscularity and body fat percentage in both short-term and long-term dating contexts. Mixed effects modeling was used to estimate the effects of figure (muscularity and body fat), dating context (short-term vs. long-term), and individual rater characteristics on attractiveness ratings. Results indicated that figure muscularity and body-fat had significant non-linear (i.e., quadratic) relationships with attractiveness ratings, and short-term dating context was associated with more discriminating ratings of attractiveness. Interactions between individual characteristics and figure characteristics indicated that the more available the individual and lower body fat, the more discriminating they were in ratings of attractiveness. The implications for future investigations considering both object and observer characteristics of attractiveness preferences are discussed. Copyright © 2012 Elsevier Ltd. All rights reserved.

Do People Know I'm Poz?: Factors Associated with Knowledge of Serostatus Among HIV-Positive African Americans' Social Network Members.

PubMed

Hoover, Matthew A; Green, Harold D; Bogart, Laura M; Wagner, Glenn J; Mutchler, Matt G; Galvan, Frank H; McDavitt, Bryce

2016-01-01

We examined how functional social support, HIV-related discrimination, internalized HIV stigma, and social network structure and composition were cross-sectionally associated with network members' knowledge of respondents' serostatus among 244 HIV-positive African Americans in Los Angeles. Results of a generalized hierarchical linear model indicated people in respondents' networks who were highly trusted, well-known to others (high degree centrality), HIV-positive, or sex partners were more likely to know respondents' HIV serostatus; African American network members were less likely to know respondents' serostatus, as were drug-using partners. Greater internalized stigma among respondents living with HIV was associated with less knowledge of their seropositivity within their social network whereas greater respondent-level HIV discrimination was associated with more knowledge of seropositivity within the network. Additional research is needed to understand the causal mechanisms and mediating processes associated with serostatus disclosure as well as the long-term consequences of disclosure and network members' knowledge of respondents' serostatus.

Computational models of location-invariant orthographic processing

NASA Astrophysics Data System (ADS)

Dandurand, Frédéric; Hannagan, Thomas; Grainger, Jonathan

2013-03-01

We trained three topologies of backpropagation neural networks to discriminate 2000 words (lexical representations) presented at different positions of a horizontal letter array. The first topology (zero-deck) contains no hidden layer, the second (one-deck) has a single hidden layer, and for the last topology (two-deck), the task is divided in two subtasks implemented as two stacked neural networks, with explicit word-centred letters as intermediate representations. All topologies successfully simulated two key benchmark phenomena observed in skilled human reading: transposed-letter priming and relative-position priming. However, the two-deck topology most accurately simulated the ability to discriminate words from nonwords, while containing the fewest connection weights. We analysed the internal representations after training. Zero-deck networks implement a letter-based scheme with a position bias to differentiate anagrams. One-deck networks implement a holographic overlap coding in which representations are essentially letter-based and words are linear combinations of letters. Two-deck networks also implement holographic-coding.

LCFIPlus: A framework for jet analysis in linear collider studies

NASA Astrophysics Data System (ADS)

Suehara, Taikan; Tanabe, Tomohiko

2016-02-01

We report on the progress in flavor identification tools developed for a future e+e- linear collider such as the International Linear Collider (ILC) and Compact Linear Collider (CLIC). Building on the work carried out by the LCFIVertex collaboration, we employ new strategies in vertex finding and jet finding, and introduce new discriminating variables for jet flavor identification. We present the performance of the new algorithms in the conditions simulated using a detector concept designed for the ILC. The algorithms have been successfully used in ILC physics simulation studies, such as those presented in the ILC Technical Design Report.

Discriminating the Mineralogical Composition in Drill Cuttings Based on Absorption Spectra in the Terahertz Range.

PubMed

Miao, Xinyang; Li, Hao; Bao, Rima; Feng, Chengjing; Wu, Hang; Zhan, Honglei; Li, Yizhang; Zhao, Kun

2017-02-01

Understanding the geological units of a reservoir is essential to the development and management of the resource. In this paper, drill cuttings from several depths from an oilfield were studied using terahertz time domain spectroscopy (THz-TDS). Cluster analysis (CA) and principal component analysis (PCA) were employed to classify and analyze the cuttings. The cuttings were clearly classified based on CA and PCA methods, and the results were in agreement with the lithology. Moreover, calcite and dolomite have stronger absorption of a THz pulse than any other minerals, based on an analysis of the PC1 scores. Quantitative analyses of minor minerals were also realized by building a series of linear and non-linear models between contents and PC2 scores. The results prove THz technology to be a promising means for determining reservoir lithology as well as other properties, which will be a significant supplementary method in oil fields.

Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images

PubMed Central

Wang, Yu; Zhang, Yaonan; Yao, Zhaomin; Zhao, Ruixue; Zhou, Fengfeng

2016-01-01

Non-lethal macular diseases greatly impact patients’ life quality, and will cause vision loss at the late stages. Visual inspection of the optical coherence tomography (OCT) images by the experienced clinicians is the main diagnosis technique. We proposed a computer-aided diagnosis (CAD) model to discriminate age-related macular degeneration (AMD), diabetic macular edema (DME) and healthy macula. The linear configuration pattern (LCP) based features of the OCT images were screened by the Correlation-based Feature Subset (CFS) selection algorithm. And the best model based on the sequential minimal optimization (SMO) algorithm achieved 99.3% in the overall accuracy for the three classes of samples. PMID:28018716

Racial discrimination associated with higher diastolic blood pressure in a sample of American Indian adults

PubMed Central

Thayer, Zaneta M.; Blair, Irene V.; Buchwald, Dedra S.; Manson, Spero M.

2017-01-01

Objectives Hypertension prevalence is high among American Indians (AIs). AIs experience a substantial burden of interpersonal racial discrimination, which in other populations has been associated with higher blood pressure. The purpose of this study is to understand whether racial discrimination experiences are associated with higher blood pressure in AIs. Materials and Methods We used the Everyday Discrimination Scale to evaluate the relationship between discrimination and measured blood pressure among 77 AIs from two reservation communities in the Northern Plains. We used multivariate linear regression to evaluate the association of racial discrimination with systolic and diastolic blood pressure, respectively. Racial discrimination, systolic blood pressure, and diastolic blood pressure were analyzed as continuous variables. All analyses adjusted for sex, waist circumference, age, posttraumatic stress disorder status, and education. Results We found that 61% of participants experienced discrimination that they attributed to their race or ancestry. Racial discrimination was associated with significantly higher diastolic blood pressure (β = 0.22, SE = 0.09, P = 0.02), and with a similar non-significant trend toward higher systolic blood pressure (β = 0.25, SE = 0.15, P = 0.09). Conclusion The results of this analysis suggest that racial discrimination may contribute to higher diastolic blood pressure within Native communities. These findings highlight one pathway through which the social environment can shape patterns of biology and health in AI and other socially and politically marginalized groups. PMID:28198537

Multifactorial discrimination as a fundamental cause of mental health inequities.

PubMed

Khan, Mariam; Ilcisin, Misja; Saxton, Katherine

2017-03-04

The theory of fundamental causes explains why health disparities persist over time, even as risk factors, mechanisms, and diseases change. Using an intersectional framework, we evaluated multifactorial discrimination as a fundamental cause of mental health disparities. Using baseline data from the Project STRIDE: Stress, Identity, and Mental Health study, we examined the health effects of discrimination among individuals who self-identified as lesbian, gay, or bisexual. We used logistic and linear regression to assess whether multifactorial discrimination met the four criteria designating a fundamental cause, namely that the cause: 1) influences multiple health outcomes, 2) affects multiple risk factors, 3) involves access to resources that can be leveraged to reduce consequences of disease, and 4) reproduces itself in varied contexts through changing mechanisms. Multifactorial discrimination predicted high depression scores, psychological well-being, and substance use disorder diagnosis. Discrimination was positively associated with risk factors for high depression scores: chronic strain and total number of stressful life events. Discrimination was associated with significantly lower levels of mastery and self-esteem, protective factors for depressive symptomatology. Even after controlling for risk factors, discrimination remained a significant predictor for high depression scores. Among subjects with low depression scores, multifactorial discrimination also predicted anxiety and aggregate mental health scores. Multifactorial discrimination should be considered a fundamental cause of mental health inequities and may be an important cause of broad health disparities among populations with intersecting social identities.

Stereoscopic processing of crossed and uncrossed disparities in the human visual cortex.

PubMed

Li, Yuan; Zhang, Chuncheng; Hou, Chunping; Yao, Li; Zhang, Jiacai; Long, Zhiying

2017-12-21

Binocular disparity provides a powerful cue for depth perception in a stereoscopic environment. Despite increasing knowledge of the cortical areas that process disparity from neuroimaging studies, the neural mechanism underlying disparity sign processing [crossed disparity (CD)/uncrossed disparity (UD)] is still poorly understood. In the present study, functional magnetic resonance imaging (fMRI) was used to explore different neural features that are relevant to disparity-sign processing. We performed an fMRI experiment on 27 right-handed healthy human volunteers by using both general linear model (GLM) and multi-voxel pattern analysis (MVPA) methods. First, GLM was used to determine the cortical areas that displayed different responses to different disparity signs. Second, MVPA was used to determine how the cortical areas discriminate different disparity signs. The GLM analysis results indicated that shapes with UD induced significantly stronger activity in the sub-region (LO) of the lateral occipital cortex (LOC) than those with CD. The results of MVPA based on region of interest indicated that areas V3d and V3A displayed higher accuracy in the discrimination of crossed and uncrossed disparities than LOC. The results of searchlight-based MVPA indicated that the dorsal visual cortex showed significantly higher prediction accuracy than the ventral visual cortex and the sub-region LO of LOC showed high accuracy in the discrimination of crossed and uncrossed disparities. The results may suggest the dorsal visual areas are more discriminative to the disparity signs than the ventral visual areas although they are not sensitive to the disparity sign processing. Moreover, the LO in the ventral visual cortex is relevant to the recognition of shapes with different disparity signs and discriminative to the disparity sign.

Relating age and hearing loss to monaural, bilateral, and binaural temporal sensitivity1

PubMed Central

Gallun, Frederick J.; McMillan, Garnett P.; Molis, Michelle R.; Kampel, Sean D.; Dann, Serena M.; Konrad-Martin, Dawn L.

2014-01-01

Older listeners are more likely than younger listeners to have difficulties in making temporal discriminations among auditory stimuli presented to one or both ears. In addition, the performance of older listeners is often observed to be more variable than that of younger listeners. The aim of this work was to relate age and hearing loss to temporal processing ability in a group of younger and older listeners with a range of hearing thresholds. Seventy-eight listeners were tested on a set of three temporal discrimination tasks (monaural gap discrimination, bilateral gap discrimination, and binaural discrimination of interaural differences in time). To examine the role of temporal fine structure in these tasks, four types of brief stimuli were used: tone bursts, broad-frequency chirps with rising or falling frequency contours, and random-phase noise bursts. Between-subject group analyses conducted separately for each task revealed substantial increases in temporal thresholds for the older listeners across all three tasks, regardless of stimulus type, as well as significant correlations among the performance of individual listeners across most combinations of tasks and stimuli. Differences in performance were associated with the stimuli in the monaural and binaural tasks, but not the bilateral task. Temporal fine structure differences among the stimuli had the greatest impact on monaural thresholds. Threshold estimate values across all tasks and stimuli did not show any greater variability for the older listeners as compared to the younger listeners. A linear mixed model applied to the data suggested that age and hearing loss are independent factors responsible for temporal processing ability, thus supporting the increasingly accepted hypothesis that temporal processing can be impaired for older compared to younger listeners with similar hearing and/or amounts of hearing loss. PMID:25009458

Selecting predictors for discriminant analysis of species performance: an example from an amphibious softwater plant.

PubMed

Vanderhaeghe, F; Smolders, A J P; Roelofs, J G M; Hoffmann, M

2012-03-01

Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.

Associations between race-based and sex-based discrimination, health, and functioning: a longitudinal study of Marines.

PubMed

Foynes, Melissa M; Smith, Brian N; Shipherd, Jillian C

2015-04-01

Only a few studies have examined race-based discrimination (RBD) and sex-based discrimination (SBD) in military samples and all are cross-sectional. The current study examined associations between both RBD and SBD experienced during Marine recruit training and several health and functioning outcomes 11 years later in a racially/ethnically diverse sample of men and women. Linear multiple regression models were used to examine associations between sex, race/ethnicity, RBD and SBD, and later outcomes (physical health, self-esteem, and occupational/vocational functioning), accounting for baseline levels and covariates. Data were drawn from a larger longitudinal investigation of US Marine Corps recruits. The sample (N=471) was comprised of white men (34.6%), white women (37.6%), racial/ethnic minority men (12.7%), and racial/ethnic minority women (15.1%). Self-report measures of sex and race (T1), RBD and SBD (T2), social support (T2), mental health (T2), physical health (T2 and T5), self-esteem (T2 and T5), and occupational/vocational functioning (T5) were included. Over a decade later, experiences of RBD were negatively associated with physical health and self-esteem. Social support was the strongest predictor of occupational/vocational functioning. Effects of sex, SBD, and minority status were not significant in regressions after accounting for other variables. Health care providers can play a key role in tailoring care to the needs of these important subpopulations of veterans by assessing and acknowledging experiences of discrimination and remaining aware of the potential negative associations between discrimination and health and functioning above and beyond the contributions of sex and race/ethnicity.

Differentiation between pine woods according to species and growing location using FTIR-ATR.

PubMed

Traoré, Mohamed; Kaal, Joeri; Martínez Cortizas, Antonio

2018-01-01

Attenuated total reflectance-Fourier transform infrared (FTIR-ATR) spectroscopy was applied to 120 samples of heartwood rings from eight individual pine trees from different locations in Spain. Pinus sylvestris cores were collected at the Artikutza natural park (Ps-ART). Pinus nigra cores were collected in Sierra de Cazorla (Pn-LIN) and in La Sagra Mountain (Pn-LSA). Three discriminant analysis tests were performed using all bands (DF T ), lignin bands only (DF L ) and polysaccharides bands only (DF P ), to explore the ability of FTIR-ATR to separate between species and growing location. The DF L model enabled a good separation between pine species, whereas the DF P model enabled differentiation for both species and growing location. The DF T model enabled virtually perfect separation, based on two functions involving twelve FTIR bands. Discrimination between species was related to bands at 860 and 1655 cm -1 , which were more intense in P. sylvestris samples, and bands at 1425 and 1635 cm -1 , more intense in P. nigra samples. These vibrations were related to differences in lignin structure and polysaccharide linear chains. Discrimination between growing locations was mainly related to polysaccharide absorptions: at 900, 1085 and 1335 cm -1 more representative of Pn-LIN samples, and at 1105 and 1315 cm -1 mostly associated to Pn-LSA samples. These absorptions are related to β-glycosidic linkages (900 cm -1 ), cellulose and hemicellulose (C-O bonds, 1085 and 1105 cm -1 ) and content in amorphous/crystalline cellulose (1315 and 1335 cm -1 ). These results show that FTIR-ATR in combination with multivariate statistics can be a useful tool for species identification and provenancing for pine wood samples of unknown origin.

Assessment of contrast gain signature in inferred magnocellular and parvocellular pathways in patients with glaucoma.

PubMed

Sun, Hao; Swanson, William H; Arvidson, Brian; Dul, Mitchell W

2008-11-01

Contrast gain signatures of inferred magnocellular and parvocellular postreceptoral pathways were assessed for patients with glaucoma using a contrast discrimination paradigm developed by Pokorny and Smith. The potential causes for changes in contrast gain signature were investigated using model simulations of ganglion cell contrast responses. Foveal contrast discrimination thresholds were measured with a pedestal-Delta-pedestal paradigm developed by Pokorny and Smith [Pokorny, J., & Smith, V. C. (1997). Psychophysical signatures associated with magnocellular and parvocellular pathway contrast gain. Journal of the Optical Society of America A, 14(9), 2477-2486]. Stimuli were 27 ms luminance increments superimposed on 227 ms pulsed Delta-pedestals. Contrast thresholds and contrast gain signatures mediated by the inferred magnocellular (MC) and parvocellular (PC) pathways were assessed using linear fits to contrast discrimination thresholds at either lower or higher Delta-pedestal contrasts, respectively. Twenty-seven patients with glaucoma were tested, as well as 16 age-similar control subjects free of eye disease. Contrast sensitivity and contrast gain signature mediated by the inferred MC pathway were lower for the glaucoma group, and reduced contrast gain signature was correlated with reduced contrast sensitivity (r(2)=45%, p<.0005). These two parameters mediated by the inferred PC pathway were little affected for the glaucoma group. Model simulations suggest that the reduced contrast sensitivity and contrast gain signature were consistent with the hypothesis that reduced MC ganglion cell dendritic complexity can lead to reduced effective retinal illuminance, and hence increased semi-saturation contrast of the ganglion cell contrast response functions. The contrast sensitivity and contrast gain signature of the inferred MC pathway were reduced in patients with glaucoma. The results were consistent with a model of ganglion cell dysfunction due to reduced synaptic density.

Discrimination of wine from grape cultivated in Japan, imported wine, and others by multi-elemental analysis.

PubMed

Shimizu, Hideaki; Akamatsu, Fumikazu; Kamada, Aya; Koyama, Kazuya; Okuda, Masaki; Fukuda, Hisashi; Iwashita, Kazuhiro; Goto-Yamamoto, Nami

2018-04-01

Differences in mineral concentrations were examined among three types of wine in the Japanese market place: Japan wine, imported wine, and domestically produced wine mainly from foreign ingredients (DWF), where Japan wine has been recently defined by the National Tax Agency as domestically produced wine from grapes cultivated in Japan. The main objective of this study was to examine the possibility of controlling the authenticity of Japan wine. The concentrations of 18 minerals (Li, B, Na, Mg, Si, P, S, K, Ca, Mn, Co, Ni, Ga, Rb, Sr, Mo, Ba, and Pb) in 214 wine samples were determined by inductively coupled-plasma mass spectrometry (ICP-MS) and ICP-atomic emission spectrometry (ICP-AES). In general, Japan wine had a higher concentration of potassium and lower concentrations of eight elements (Li, B, Na, Si, S, Co, Sr, and Pb) as compared with the other two groups of wine. Linear discriminant analysis (LDA) models based on concentrations of the 18 minerals facilitated the identification of three wine groups: Japan wine, imported wine, and DWF with a 91.1% classification score and 87.9% prediction score. In addition, an LDA model for discrimination of wine from four domestic geographic origins (Yamanashi, Nagano, Hokkaido, and Yamagata Prefectures) using 18 elements gave a classification score of 93.1% and a prediction score of 76.4%. In summary, we have shown that an LDA model based on mineral concentrations is useful for distinguishing Japan wine from other wine groups, and can contribute to classification of the four main domestic wine-producing regions of Japan. Copyright © 2017 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data

PubMed Central

Zhao, Xin; Cheung, Leo Wang-Kit

2007-01-01

Background Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more important for our understanding of diseases at genomic level. Although many machine learning methods have been developed and applied to the area of microarray gene expression data analysis, the majority of them are based on linear models, which however are not necessarily appropriate for the underlying connection between the target disease and its associated explanatory genes. Linear model based methods usually also bring in false positive significant features more easily. Furthermore, linear model based algorithms often involve calculating the inverse of a matrix that is possibly singular when the number of potentially important genes is relatively large. This leads to problems of numerical instability. To overcome these limitations, a few non-linear methods have recently been introduced to the area. Many of the existing non-linear methods have a couple of critical problems, the model selection problem and the model parameter tuning problem, that remain unsolved or even untouched. In general, a unified framework that allows model parameters of both linear and non-linear models to be easily tuned is always preferred in real-world applications. Kernel-induced learning methods form a class of approaches that show promising potentials to achieve this goal. Results A hierarchical statistical model named kernel-imbedded Gaussian process (KIGP) is developed under a unified Bayesian framework for binary disease classification problems using microarray gene expression data. In particular, based on a probit regression setting, an adaptive algorithm with a cascading structure is designed to find the appropriate kernel, to discover the potentially significant genes, and to make the optimal class prediction accordingly. A Gibbs sampler is built as the core of the algorithm to make Bayesian inferences. Simulation studies showed that, even without any knowledge of the underlying generative model, the KIGP performed very close to the theoretical Bayesian bound not only in the case with a linear Bayesian classifier but also in the case with a very non-linear Bayesian classifier. This sheds light on its broader usability to microarray data analysis problems, especially to those that linear methods work awkwardly. The KIGP was also applied to four published microarray datasets, and the results showed that the KIGP performed better than or at least as well as any of the referred state-of-the-art methods did in all of these cases. Conclusion Mathematically built on the kernel-induced feature space concept under a Bayesian framework, the KIGP method presented in this paper provides a unified machine learning approach to explore both the linear and the possibly non-linear underlying relationship between the target features of a given binary disease classification problem and the related explanatory gene expression data. More importantly, it incorporates the model parameter tuning into the framework. The model selection problem is addressed in the form of selecting a proper kernel type. The KIGP method also gives Bayesian probabilistic predictions for disease classification. These properties and features are beneficial to most real-world applications. The algorithm is naturally robust in numerical computation. The simulation studies and the published data studies demonstrated that the proposed KIGP performs satisfactorily and consistently. PMID:17328811

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.

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

PubMed Central

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

2015-01-01

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

Taking It Like a Man: Masculine Role Norms as Moderators of the Racial Discrimination–Depressive Symptoms Association Among African American Men

PubMed Central

2012-01-01

Objectives. I examined the association between everyday racial discrimination and depressive symptoms and explored the moderating role of 2 dimensions of masculine role norms, restrictive emotionality and self-reliance. Methods. Cross-sectional survey data from 674 African American men aged 18 years and older recruited primarily from barbershops in 4 US regions (2003–2010) were used. Direct and moderated associations were assessed with multivariate linear regression analyses for the overall sample and different age groups. Models were adjusted for recruitment site, sociodemographics, masculine role norms salience, and general social stress. Results. Everyday racial discrimination was associated with more depressive symptoms across all age groups. Higher restrictive emotionality was associated with more depressive symptoms among men aged 18 to 29 and 30 to 39 years. Self-reliance was associated with fewer depressive symptoms among men aged 18 to 29 years and 40 years and older. The positive association between everyday racial discrimination and depressive symptoms was stronger among men with high restrictive emotionality, but this moderated effect was limited to men older than 30 years. Conclusions. Interventions designed to reduce African American men’s depression instigated by racism should be life-course specific and address masculine role norms that encourage emotion restriction. PMID:22401515

Feature selection from hyperspectral imaging for guava fruit defects detection

NASA Astrophysics Data System (ADS)

Mat Jafri, Mohd. Zubir; Tan, Sou Ching

2017-06-01

Development of technology makes hyperspectral imaging commonly used for defect detection. In this research, a hyperspectral imaging system was setup in lab to target for guava fruits defect detection. Guava fruit was selected as the object as to our knowledge, there is fewer attempts were made for guava defect detection based on hyperspectral imaging. The common fluorescent light source was used to represent the uncontrolled lighting condition in lab and analysis was carried out in a specific wavelength range due to inefficiency of this particular light source. Based on the data, the reflectance intensity of this specific setup could be categorized in two groups. Sequential feature selection with linear discriminant (LD) and quadratic discriminant (QD) function were used to select features that could potentially be used in defects detection. Besides the ordinary training method, training dataset in discriminant was separated in two to cater for the uncontrolled lighting condition. These two parts were separated based on the brighter and dimmer area. Four evaluation matrixes were evaluated which are LD with common training method, QD with common training method, LD with two part training method and QD with two part training method. These evaluation matrixes were evaluated using F1-score with total 48 defected areas. Experiment shown that F1-score of linear discriminant with the compensated method hitting 0.8 score, which is the highest score among all.

Perfusion Neuroimaging Abnormalities Alone Distinguish National Football League Players from a Healthy Population.

PubMed

Amen, Daniel G; Willeumier, Kristen; Omalu, Bennet; Newberg, Andrew; Raghavendra, Cauligi; Raji, Cyrus A

2016-04-25

National Football League (NFL) players are exposed to multiple head collisions during their careers. Increasing awareness of the adverse long-term effects of repetitive head trauma has raised substantial concern among players, medical professionals, and the general public. To determine whether low perfusion in specific brain regions on neuroimaging can accurately separate professional football players from healthy controls. A cohort of retired and current NFL players (n = 161) were recruited in a longitudinal study starting in 2009 with ongoing interval follow up. A healthy control group (n = 124) was separately recruited for comparison. Assessments included medical examinations, neuropsychological tests, and perfusion neuroimaging with single photon emission computed tomography (SPECT). Perfusion estimates of each scan were quantified using a standard atlas. We hypothesized that hypoperfusion particularly in the orbital frontal, anterior cingulate, anterior temporal, hippocampal, amygdala, insular, caudate, superior/mid occipital, and cerebellar sub-regions alone would reliably separate controls from NFL players. Cerebral perfusion differences were calculated using a one-way ANOVA and diagnostic separation was determined with discriminant and automatic linear regression predictive models. NFL players showed lower cerebral perfusion on average (p < 0.01) in 36 brain regions. The discriminant analysis subsequently distinguished NFL players from controls with 90% sensitivity, 86% specificity, and 94% accuracy (95% CI 95-99). Automatic linear modeling achieved similar results. Inclusion of age and clinical co-morbidities did not improve diagnostic classification. Specific brain regions commonly damaged in traumatic brain injury show abnormally low perfusion on SPECT in professional NFL players. These same regions alone can distinguish this group from healthy subjects with high diagnostic accuracy. This study carries implications for the neurological safety of NFL players.

Perfusion Neuroimaging Abnormalities Alone Distinguish National Football League Players from a Healthy Population

PubMed Central

Amen, Daniel G.; Willeumier, Kristen; Omalu, Bennet; Newberg, Andrew; Raghavendra, Cauligi; Raji, Cyrus A.

2016-01-01

Background: National Football League (NFL) players are exposed to multiple head collisions during their careers. Increasing awareness of the adverse long-term effects of repetitive head trauma has raised substantial concern among players, medical professionals, and the general public. Objective: To determine whether low perfusion in specific brain regions on neuroimaging can accurately separate professional football players from healthy controls. Method: A cohort of retired and current NFL players (n = 161) were recruited in a longitudinal study starting in 2009 with ongoing interval follow up. A healthy control group (n = 124) was separately recruited for comparison. Assessments included medical examinations, neuropsychological tests, and perfusion neuroimaging with single photon emission computed tomography (SPECT). Perfusion estimates of each scan were quantified using a standard atlas. We hypothesized that hypoperfusion particularly in the orbital frontal, anterior cingulate, anterior temporal, hippocampal, amygdala, insular, caudate, superior/mid occipital, and cerebellar sub-regions alone would reliably separate controls from NFL players. Cerebral perfusion differences were calculated using a one-way ANOVA and diagnostic separation was determined with discriminant and automatic linear regression predictive models. Results: NFL players showed lower cerebral perfusion on average (p < 0.01) in 36 brain regions. The discriminant analysis subsequently distinguished NFL players from controls with 90% sensitivity, 86% specificity, and 94% accuracy (95% CI 95-99). Automatic linear modeling achieved similar results. Inclusion of age and clinical co-morbidities did not improve diagnostic classification. Conclusion: Specific brain regions commonly damaged in traumatic brain injury show abnormally low perfusion on SPECT in professional NFL players. These same regions alone can distinguish this group from healthy subjects with high diagnostic accuracy. This study carries implications for the neurological safety of NFL players. PMID:27128374

A model for discriminating reinforcers in time and space.

PubMed

Cowie, Sarah; Davison, Michael; Elliffe, Douglas

2016-06-01

Both the response-reinforcer and stimulus-reinforcer relation are important in discrimination learning; differential responding requires a minimum of two discriminably-different stimuli and two discriminably-different associated contingencies of reinforcement. When elapsed time is a discriminative stimulus for the likely availability of a reinforcer, choice over time may be modeled by an extension of the Davison and Nevin (1999) model that assumes that local choice strictly matches the effective local reinforcer ratio. The effective local reinforcer ratio may differ from the obtained local reinforcer ratio for two reasons: Because the animal inaccurately estimates times associated with obtained reinforcers, and thus incorrectly discriminates the stimulus-reinforcer relation across time; and because of error in discriminating the response-reinforcer relation. In choice-based timing tasks, the two responses are usually highly discriminable, and so the larger contributor to differences between the effective and obtained reinforcer ratio is error in discriminating the stimulus-reinforcer relation. Such error may be modeled either by redistributing the numbers of reinforcers obtained at each time across surrounding times, or by redistributing the ratio of reinforcers obtained at each time in the same way. We assessed the extent to which these two approaches to modeling discrimination of the stimulus-reinforcer relation could account for choice in a range of temporal-discrimination procedures. The version of the model that redistributed numbers of reinforcers accounted for more variance in the data. Further, this version provides an explanation for shifts in the point of subjective equality that occur as a result of changes in the local reinforcer rate. The inclusion of a parameter reflecting error in discriminating the response-reinforcer relation enhanced the ability of each version of the model to describe data. The ability of this class of model to account for a range of data suggests that timing, like other conditional discriminations, is choice under the joint discriminative control of elapsed time and differential reinforcement. Understanding the role of differential reinforcement is therefore critical to understanding control by elapsed time. Copyright © 2016 Elsevier B.V. All rights reserved.

Ethnic identity, racial discrimination and attenuated psychotic symptoms in an urban population of emerging adults.

PubMed

Anglin, Deidre M; Lui, Florence; Espinosa, Adriana; Tikhonov, Aleksandr; Ellman, Lauren

2018-06-01

Studies suggest strong ethnic identity generally protects against negative mental health outcomes associated with racial discrimination. In light of evidence suggesting racial discrimination may enhance psychosis risk in racial and ethnic minority (REM) populations, the present study explored the relationship between ethnic identity and attenuated positive psychotic symptoms (APPS) and whether ethnic identity moderates the association between racial discrimination and these symptoms. A sample of 644 non-help-seeking REM emerging adults was administered self-report inventories for psychosis risk, experiences of discrimination and ethnic identity. Latent class analysis was applied to determine the nature and number of ethnic identity types in this population. The direct association between ethnic identity and APPS and the interaction between ethnic identity and racial discrimination on APPS were determined in linear regression analyses. Results indicated three ethnic identity classes (very low, moderate to high and very high). Ethnic identity was not directly related to APPS; however, it was related to APPS under racially discriminating conditions. Specifically, participants who experienced discrimination in the moderate to high or very high ethnic identity classes reported fewer symptoms than participants who experienced discrimination in the very low ethnic identity class. Strong ethnic group affiliation and connection may serve a protective function for psychosis risk in racially discriminating environments and contexts among REM young adults. The possible social benefits of strong ethnic identification among REM youth who face racial discrimination should be explored further in clinical high-risk studies. © 2016 John Wiley & Sons Australia, Ltd.

Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms

NASA Astrophysics Data System (ADS)

Lee, Chien-Cheng; Huang, Shin-Sheng; Shih, Cheng-Yuan

2010-12-01

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

Fluorescent polymer sensor array for detection and discrimination of explosives in water.

PubMed

Woodka, Marc D; Schnee, Vincent P; Polcha, Michael P

2010-12-01

A fluorescent polymer sensor array (FPSA) was made from commercially available fluorescent polymers coated onto glass beads and was tested to assess the ability of the array to discriminate between different analytes in aqueous solution. The array was challenged with exposures to 17 different analytes, including the explosives trinitrotoluene (TNT), tetryl, and RDX, various explosive-related compounds (ERCs), and nonexplosive electron-withdrawing compounds (EWCs). The array exhibited a natural selectivity toward EWCs, while the non-electron-withdrawing explosive 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) produced no response. Response signatures were visualized by principal component analysis (PCA), and classified by linear discriminant analysis (LDA). RDX produced the same response signature as the sampled blanks and was classified accordingly. The array exhibited excellent discrimination toward all other compounds, with the exception of the isomers of nitrotoluene and aminodinitrotoluene. Of particular note was the ability of the array to discriminate between the three isomers of dinitrobenzene. The natural selectivity of the FPSA toward EWCs, plus the ability of the FPSA to discriminate between different EWCs, could be used to design a sensor with a low false alarm rate and an excellent ability to discriminate between explosives and explosive-related compounds.

Racial Discrimination and HIV-related Risk Behaviors in Southeast Louisiana

PubMed Central

Kaplan, Kathryn C.; Hormes, Julia M.; Wallace, Maeve; Rountree, Michele; Theall, Katherine P.

2016-01-01

Objectives We examined the relationship between cumulative experiences of racial discrimination and HIV-related risk taking, and whether these relationships are mediated through alcohol use among African Americans in semi-rural southeast Louisiana. Methods Participants (N = 214) reported on experiences of discrimination, HIV sexual risk-taking, history of sexually transmitted infection (STI), and health behaviors including alcohol use in the previous 90 days. Experiences of discrimination (scaled both by frequency of occurrence and situational counts) as a predictor of a sexual risk composite score as well as a history of STI was assessed using multivariate linear and logistic regression, respectively, including tests for mediation by alcohol use. Results Discrimination was common in this cohort, with respondents confirming their experience on average 7 of the 9 potential situations and on more than 34 separate occasions. After adjustment, discrimination was significantly associated with increasing sexual risk-taking and lifetime history of STI when measured either by frequency of occurrence or number of situations, although there was no evidence that these relationships were mediated through alcohol use. Conclusions Cumulative experiences of discrimination may play a significant role in sexual risk behavior and consequently increase vulnerability to HIV and other STIs. PMID:26685822

A multiple maximum scatter difference discriminant criterion for facial feature extraction.

PubMed

Song, Fengxi; Zhang, David; Mei, Dayong; Guo, Zhongwei

2007-12-01

Maximum scatter difference (MSD) discriminant criterion was a recently presented binary discriminant criterion for pattern classification that utilizes the generalized scatter difference rather than the generalized Rayleigh quotient as a class separability measure, thereby avoiding the singularity problem when addressing small-sample-size problems. MSD classifiers based on this criterion have been quite effective on face-recognition tasks, but as they are binary classifiers, they are not as efficient on large-scale classification tasks. To address the problem, this paper generalizes the classification-oriented binary criterion to its multiple counterpart--multiple MSD (MMSD) discriminant criterion for facial feature extraction. The MMSD feature-extraction method, which is based on this novel discriminant criterion, is a new subspace-based feature-extraction method. Unlike most other subspace-based feature-extraction methods, the MMSD computes its discriminant vectors from both the range of the between-class scatter matrix and the null space of the within-class scatter matrix. The MMSD is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the benchmark database, FERET, show that the MMSD out-performs state-of-the-art facial feature-extraction methods such as null space method, direct linear discriminant analysis (LDA), eigenface, Fisherface, and complete LDA.

Discrimination Enhancement with Transient Feature Analysis of a Graphene Chemical Sensor.

PubMed

Nallon, Eric C; Schnee, Vincent P; Bright, Collin J; Polcha, Michael P; Li, Qiliang

2016-01-19

A graphene chemical sensor is subjected to a set of structurally and chemically similar hydrocarbon compounds consisting of toluene, o-xylene, p-xylene, and mesitylene. The fractional change in resistance of the sensor upon exposure to these compounds exhibits a similar response magnitude among compounds, whereas large variation is observed within repetitions for each compound, causing a response overlap. Therefore, traditional features depending on maximum response change will cause confusion during further discrimination and classification analysis. More robust features that are less sensitive to concentration, sampling, and drift variability would provide higher quality information. In this work, we have explored the advantage of using transient-based exponential fitting coefficients to enhance the discrimination of similar compounds. The advantages of such feature analysis to discriminate each compound is evaluated using principle component analysis (PCA). In addition, machine learning-based classification algorithms were used to compare the prediction accuracies when using fitting coefficients as features. The additional features greatly enhanced the discrimination between compounds while performing PCA and also improved the prediction accuracy by 34% when using linear discrimination analysis.

Multi-class ERP-based BCI data analysis using a discriminant space self-organizing map.

PubMed

Onishi, Akinari; Natsume, Kiyohisa

2014-01-01

Emotional or non-emotional image stimulus is recently applied to event-related potential (ERP) based brain computer interfaces (BCI). Though the classification performance is over 80% in a single trial, a discrimination between those ERPs has not been considered. In this research we tried to clarify the discriminability of four-class ERP-based BCI target data elicited by desk, seal, spider images and letter intensifications. A conventional self organizing map (SOM) and newly proposed discriminant space SOM (ds-SOM) were applied, then the discriminabilites were visualized. We also classify all pairs of those ERPs by stepwise linear discriminant analysis (SWLDA) and verify the visualization of discriminabilities. As a result, the ds-SOM showed understandable visualization of the data with a shorter computational time than the traditional SOM. We also confirmed the clear boundary between the letter cluster and the other clusters. The result was coherent with the classification performances by SWLDA. The method might be helpful not only for developing a new BCI paradigm, but also for the big data analysis.

Binocular contrast discrimination needs monocular multiplicative noise

PubMed Central

Ding, Jian; Levi, Dennis M.

2016-01-01

The effects of signal and noise on contrast discrimination are difficult to separate because of a singularity in the signal-detection-theory model of two-alternative forced-choice contrast discrimination (Katkov, Tsodyks, & Sagi, 2006). In this article, we show that it is possible to eliminate the singularity by combining that model with a binocular combination model to fit monocular, dichoptic, and binocular contrast discrimination. We performed three experiments using identical stimuli to measure the perceived phase, perceived contrast, and contrast discrimination of a cyclopean sine wave. In the absence of a fixation point, we found a binocular advantage in contrast discrimination both at low contrasts (<4%), consistent with previous studies, and at high contrasts (≥34%), which has not been previously reported. However, control experiments showed no binocular advantage at high contrasts in the presence of a fixation point or for observers without accommodation. We evaluated two putative contrast-discrimination mechanisms: a nonlinear contrast transducer and multiplicative noise (MN). A binocular combination model (the DSKL model; Ding, Klein, & Levi, 2013b) was first fitted to both the perceived-phase and the perceived-contrast data sets, then combined with either the nonlinear contrast transducer or the MN mechanism to fit the contrast-discrimination data. We found that the best model combined the DSKL model with early MN. Model simulations showed that, after going through interocular suppression, the uncorrelated noise in the two eyes became anticorrelated, resulting in less binocular noise and therefore a binocular advantage in the discrimination task. Combining a nonlinear contrast transducer or MN with a binocular combination model (DSKL) provides a powerful method for evaluating the two putative contrast-discrimination mechanisms. PMID:26982370

Binocular contrast discrimination needs monocular multiplicative noise.

PubMed

Ding, Jian; Levi, Dennis M

2016-01-01

The effects of signal and noise on contrast discrimination are difficult to separate because of a singularity in the signal-detection-theory model of two-alternative forced-choice contrast discrimination (Katkov, Tsodyks, & Sagi, 2006). In this article, we show that it is possible to eliminate the singularity by combining that model with a binocular combination model to fit monocular, dichoptic, and binocular contrast discrimination. We performed three experiments using identical stimuli to measure the perceived phase, perceived contrast, and contrast discrimination of a cyclopean sine wave. In the absence of a fixation point, we found a binocular advantage in contrast discrimination both at low contrasts (<4%), consistent with previous studies, and at high contrasts (≥34%), which has not been previously reported. However, control experiments showed no binocular advantage at high contrasts in the presence of a fixation point or for observers without accommodation. We evaluated two putative contrast-discrimination mechanisms: a nonlinear contrast transducer and multiplicative noise (MN). A binocular combination model (the DSKL model; Ding, Klein, & Levi, 2013b) was first fitted to both the perceived-phase and the perceived-contrast data sets, then combined with either the nonlinear contrast transducer or the MN mechanism to fit the contrast-discrimination data. We found that the best model combined the DSKL model with early MN. Model simulations showed that, after going through interocular suppression, the uncorrelated noise in the two eyes became anticorrelated, resulting in less binocular noise and therefore a binocular advantage in the discrimination task. Combining a nonlinear contrast transducer or MN with a binocular combination model (DSKL) provides a powerful method for evaluating the two putative contrast-discrimination mechanisms.

Geometric mean for subspace selection.

PubMed

Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J

2009-02-01

Subspace selection approaches are powerful tools in pattern classification and data visualization. One of the most important subspace approaches is the linear dimensionality reduction step in the Fisher's linear discriminant analysis (FLDA), which has been successfully employed in many fields such as biometrics, bioinformatics, and multimedia information management. However, the linear dimensionality reduction step in FLDA has a critical drawback: for a classification task with c classes, if the dimension of the projected subspace is strictly lower than c - 1, the projection to a subspace tends to merge those classes, which are close together in the original feature space. If separate classes are sampled from Gaussian distributions, all with identical covariance matrices, then the linear dimensionality reduction step in FLDA maximizes the mean value of the Kullback-Leibler (KL) divergences between different classes. Based on this viewpoint, the geometric mean for subspace selection is studied in this paper. Three criteria are analyzed: 1) maximization of the geometric mean of the KL divergences, 2) maximization of the geometric mean of the normalized KL divergences, and 3) the combination of 1 and 2. Preliminary experimental results based on synthetic data, UCI Machine Learning Repository, and handwriting digits show that the third criterion is a potential discriminative subspace selection method, which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in FLDA and its several representative extensions.

Object Detection in Natural Backgrounds Predicted by Discrimination Performance and Models

NASA Technical Reports Server (NTRS)

Ahumada, A. J., Jr.; Watson, A. B.; Rohaly, A. M.; Null, Cynthia H. (Technical Monitor)

1995-01-01

In object detection, an observer looks for an object class member in a set of backgrounds. In discrimination, an observer tries to distinguish two images. Discrimination models predict the probability that an observer detects a difference between two images. We compare object detection and image discrimination with the same stimuli by: (1) making stimulus pairs of the same background with and without the target object and (2) either giving many consecutive trials with the same background (discrimination) or intermixing the stimuli (object detection). Six images of a vehicle in a natural setting were altered to remove the vehicle and mixed with the original image in various proportions. Detection observers rated the images for vehicle presence. Discrimination observers rated the images for any difference from the background image. Estimated detectabilities of the vehicles were found by maximizing the likelihood of a Thurstone category scaling model. The pattern of estimated detectabilities is similar for discrimination and object detection, and is accurately predicted by a Cortex Transform discrimination model. Predictions of a Contrast- Sensitivity- Function filter model and a Root-Mean-Square difference metric based on the digital image values are less accurate. The discrimination detectabilities averaged about twice those of object detection.

Feature and Region Selection for Visual Learning.

PubMed

Zhao, Ji; Wang, Liantao; Cabral, Ricardo; De la Torre, Fernando

2016-03-01

Visual learning problems, such as object classification and action recognition, are typically approached using extensions of the popular bag-of-words (BoWs) model. Despite its great success, it is unclear what visual features the BoW model is learning. Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questions is fundamental for understanding existing BoW models and inspiring better models for visual recognition. To answer these questions, this paper presents a method for feature selection and region selection in the visual BoW model. This allows for an intermediate visualization of the features and regions that are important for visual learning. The main idea is to assign latent weights to the features or regions, and jointly optimize these latent variables with the parameters of a classifier (e.g., support vector machine). There are four main benefits of our approach: 1) our approach accommodates non-linear additive kernels, such as the popular χ(2) and intersection kernel; 2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; 3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; and 4) we point out strong connections with multiple kernel learning and multiple instance learning approaches. Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube illustrate the benefits of our approach.

Estimation of locomotion speed and directions changes to control a vehicle using neural signals from the motor cortex of rat.

PubMed

Fukayama, Osamu; Taniguchi, Noriyuki; Suzuki, Takafumi; Mabuchi, Kunihiko

2006-01-01

We have developed a brain-machine interface (BMI) in the form of a small vehicle, which we call the RatCar. In this system, we implanted wire electrodes in the motor cortices of rat's brain to continuously record neural signals. We applied a linear model to estimate the locomotion state (e.g., speed and directions) of a rat using a weighted summation model for the neural firing rates. With this information, we then determined the approximate movement of a rat. Although the estimation is still imprecise, results suggest that our model is able to control the system to some degree. In this paper, we give an overview of our system and describe the methods used, which include continuous neural recording, spike detection and a discrimination algorithm, and a locomotion estimation model minimizes the square error of the locomotion speed and changes in direction.

Beyond δ: Tailoring marked statistics to reveal modified gravity

NASA Astrophysics Data System (ADS)

Valogiannis, Georgios; Bean, Rachel

2018-01-01

Models which attempt to explain the accelerated expansion of the universe through large-scale modifications to General Relativity (GR), must satisfy the stringent experimental constraints of GR in the solar system. Viable candidates invoke a “screening” mechanism, that dynamically suppresses deviations in high density environments, making their overall detection challenging even for ambitious future large-scale structure surveys. We present methods to efficiently simulate the non-linear properties of such theories, and consider how a series of statistics that reweight the density field to accentuate deviations from GR can be applied to enhance the overall signal-to-noise ratio in differentiating the models from GR. Our results demonstrate that the cosmic density field can yield additional, invaluable cosmological information, beyond the simple density power spectrum, that will enable surveys to more confidently discriminate between modified gravity models and ΛCDM.

Modeling the cardiovascular system using a nonlinear additive autoregressive model with exogenous input

NASA Astrophysics Data System (ADS)

Riedl, M.; Suhrbier, A.; Malberg, H.; Penzel, T.; Bretthauer, G.; Kurths, J.; Wessel, N.

2008-07-01

The parameters of heart rate variability and blood pressure variability have proved to be useful analytical tools in cardiovascular physics and medicine. Model-based analysis of these variabilities additionally leads to new prognostic information about mechanisms behind regulations in the cardiovascular system. In this paper, we analyze the complex interaction between heart rate, systolic blood pressure, and respiration by nonparametric fitted nonlinear additive autoregressive models with external inputs. Therefore, we consider measurements of healthy persons and patients suffering from obstructive sleep apnea syndrome (OSAS), with and without hypertension. It is shown that the proposed nonlinear models are capable of describing short-term fluctuations in heart rate as well as systolic blood pressure significantly better than similar linear ones, which confirms the assumption of nonlinear controlled heart rate and blood pressure. Furthermore, the comparison of the nonlinear and linear approaches reveals that the heart rate and blood pressure variability in healthy subjects is caused by a higher level of noise as well as nonlinearity than in patients suffering from OSAS. The residue analysis points at a further source of heart rate and blood pressure variability in healthy subjects, in addition to heart rate, systolic blood pressure, and respiration. Comparison of the nonlinear models within and among the different groups of subjects suggests the ability to discriminate the cohorts that could lead to a stratification of hypertension risk in OSAS patients.

An effective biometric discretization approach to extract highly discriminative, informative, and privacy-protective binary representation

NASA Astrophysics Data System (ADS)

Lim, Meng-Hui; Teoh, Andrew Beng Jin

2011-12-01

Biometric discretization derives a binary string for each user based on an ordered set of biometric features. This representative string ought to be discriminative, informative, and privacy protective when it is employed as a cryptographic key in various security applications upon error correction. However, it is commonly believed that satisfying the first and the second criteria simultaneously is not feasible, and a tradeoff between them is always definite. In this article, we propose an effective fixed bit allocation-based discretization approach which involves discriminative feature extraction, discriminative feature selection, unsupervised quantization (quantization that does not utilize class information), and linearly separable subcode (LSSC)-based encoding to fulfill all the ideal properties of a binary representation extracted for cryptographic applications. In addition, we examine a number of discriminative feature-selection measures for discretization and identify the proper way of setting an important feature-selection parameter. Encouraging experimental results vindicate the feasibility of our approach.

Development of a universal water signature for the LANDSAT-3 Multispectral Scanner, part 2 of 2

NASA Technical Reports Server (NTRS)

Schlosser, E. H.

1980-01-01

A generalized four-channel hyperplane to discriminate water from non-water was developed using LANDSAT-3 multispectral scanner (MSS) scences and matching same/next-day color infrared aerial photography. The MSS scenes over upstate New York, eastern Washington, Montana and Louisiana taken between May and October 1978 varied in Sun elevation angle from 40 to 58 degrees. The 28 matching air photo frames selected for analysis contained over 1400 water bodies larger than one surface acre. A preliminary water discriminant was used to screen the data and eliminate from further consideration all pixels distant from water in MSS spectral space. Approximately 1300 pixels, half of them non-edge water pixels and half non-water pixels spectrally close to water, were labelled. A linear discriminant was iteratively fitted to the labelled pixels, giving more weight to those pixels that were difficult to discriminate. This discriminant correctly classified 98.7 percent of the water pixels and 98.6 percent of the non-water pixels.

Discrimination of almonds (Prunus dulcis) geographical origin by minerals and fatty acids profiling.

PubMed

Amorello, Diana; Orecchio, Santino; Pace, Andrea; Barreca, Salvatore

2016-09-01

Twenty-one almond samples from three different geographical origins (Sicily, Spain and California) were investigated by determining minerals and fatty acids compositions. Data were used to discriminate by chemometry almond origin by linear discriminant analysis. With respect to previous PCA profiling studies, this work provides a simpler analytical protocol for the identification of almonds geographical origin. Classification by using mineral contents data only was correct in 77% of the samples, while, by using fatty acid profiles, the percentages of samples correctly classified reached 82%. The coupling of mineral contents and fatty acid profiles lead to an increased efficiency of the classification with 87% of samples correctly classified.

Probing thyroglobulin in undiluted human serum based on pattern recognition and competitive adsorption of proteins

NASA Astrophysics Data System (ADS)

Wang, Ran; Huang, Shuai; Li, Jing; Chae, Junseok

2014-10-01

Thyroglobulin (Tg) is a sensitive indicator of persistent or recurrent differentiated thyroid cancer of follicular cell origin. Detection of Tg in human serum is challenging as bio-receptors, such as anti-Tg, used in immunoassay have relatively weak binding affinity. We engineer sensing surfaces using the competitive adsorption of proteins, termed the Vroman Effect. Coupled with Surface Plasmon Resonance, the "cross-responsive" interactions of Tg on the engineered surfaces produce uniquely distinguishable multiple signature patterns, which are discriminated using Linear Discriminant Analysis. Tg-spiked samples, down to 2 ng/ml Tg in undiluted human serum, are sensitively and selectively discriminated from the control (undiluted human serum).

Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission

USGS Publications Warehouse

Mariotto, Isabella; Thenkabail, Prasad S.; Huete, Alfredo; Slonecker, E. Terrence; Platonov, Alexander

2013-01-01

Precise monitoring of agricultural crop biomass and yield quantities is critical for crop production management and prediction. The goal of this study was to compare hyperspectral narrowband (HNB) versus multispectral broadband (MBB) reflectance data in studying irrigated cropland characteristics of five leading world crops (cotton, wheat, maize, rice, and alfalfa) with the objectives of: 1. Modeling crop productivity, and 2. Discriminating crop types. HNB data were obtained from Hyperion hyperspectral imager and field ASD spectroradiometer, and MBB data were obtained from five broadband sensors: Landsat-7 Enhanced Thematic Mapper Plus (ETM +), Advanced Land Imager (ALI), Indian Remote Sensing (IRS), IKONOS, and QuickBird. A large collection of field spectral and biophysical variables were gathered for the 5 crops in Central Asia throughout the growing seasons of 2006 and 2007. Overall, the HNB and hyperspectral vegetation index (HVI) crop biophysical models explained about 25% greater variability when compared with corresponding MBB models. Typically, 3 to 7 HNBs, in multiple linear regression models of a given crop variable, explained more than 93% of variability in crop models. The evaluation of λ1 (400–2500 nm) versus λ2 (400–2500 nm) plots of various crop biophysical variables showed that the best two-band normalized difference HVIs involved HNBs centered at: (i) 742 nm and 1175 nm (HVI742-1175), (ii) 1296 nm and 1054 nm (HVI1296-1054), (iii) 1225 nm and 697 nm (HVI1225-697), and (iv) 702 nm and 1104 nm (HVI702-1104). Among the most frequently occurring HNBs in various crop biophysical models, 74% were located in the 1051–2331 nm spectral range, followed by 10% in the moisture sensitive 970 nm, 6% in the red and red-edge (630–752 nm), and the remaining 10% distributed between blue (400–500 nm), green (501–600 nm), and NIR (760–900 nm).Discriminant models, used for discriminating 3 or 4 or 5 crop types, showed significantly higher accuracies when using HNBs (> 90%) over MBBs data (varied between 45 and 84%).Finally, the study highlighted 29 HNBs of Hyperion that are optimal in the study of agricultural crops and potentially significant to the upcoming NASA HyspIRI mission. Determining optimal and redundant bands for a given application will help overcoming the Hughes' phenomenon (or curse of high dimensionality of data).

Advanced microwave soil moisture studies. [Big Sioux River Basin, Iowa

NASA Technical Reports Server (NTRS)

Dalsted, K. J.; Harlan, J. C.

1983-01-01

Comparisons of low level L-band brightness temperature (TB) and thermal infrared (TIR) data as well as the following data sets: soil map and land cover data; direct soil moisture measurement; and a computer generated contour map were statistically evaluated using regression analysis and linear discriminant analysis. Regression analysis of footprint data shows that statistical groupings of ground variables (soil features and land cover) hold promise for qualitative assessment of soil moisture and for reducing variance within the sampling space. Dry conditions appear to be more conductive to producing meaningful statistics than wet conditions. Regression analysis using field averaged TB and TIR data did not approach the higher sq R values obtained using within-field variations. The linear discriminant analysis indicates some capacity to distinguish categories with the results being somewhat better on a field basis than a footprint basis.

Local structure-based image decomposition for feature extraction with applications to face recognition.

PubMed

Qian, Jianjun; Yang, Jian; Xu, Yong

2013-09-01

This paper presents a robust but simple image feature extraction method, called image decomposition based on local structure (IDLS). It is assumed that in the local window of an image, the macro-pixel (patch) of the central pixel, and those of its neighbors, are locally linear. IDLS captures the local structural information by describing the relationship between the central macro-pixel and its neighbors. This relationship is represented with the linear representation coefficients determined using ridge regression. One image is actually decomposed into a series of sub-images (also called structure images) according to a local structure feature vector. All the structure images, after being down-sampled for dimensionality reduction, are concatenated into one super-vector. Fisher linear discriminant analysis is then used to provide a low-dimensional, compact, and discriminative representation for each super-vector. The proposed method is applied to face recognition and examined using our real-world face image database, NUST-RWFR, and five popular, publicly available, benchmark face image databases (AR, Extended Yale B, PIE, FERET, and LFW). Experimental results show the performance advantages of IDLS over state-of-the-art algorithms.

Power spectrum estimation from peculiar velocity catalogues

NASA Astrophysics Data System (ADS)

Macaulay, E.; Feldman, H. A.; Ferreira, P. G.; Jaffe, A. H.; Agarwal, S.; Hudson, M. J.; Watkins, R.

2012-09-01

The peculiar velocities of galaxies are an inherently valuable cosmological probe, providing an unbiased estimate of the distribution of matter on scales much larger than the depth of the survey. Much research interest has been motivated by the high dipole moment of our local peculiar velocity field, which suggests a large-scale excess in the matter power spectrum and can appear to be in some tension with the Λ cold dark matter (ΛCDM) model. We use a composite catalogue of 4537 peculiar velocity measurements with a characteristic depth of 33 h-1 Mpc to estimate the matter power spectrum. We compare the constraints with this method, directly studying the full peculiar velocity catalogue, to results by Macaulay et al., studying minimum variance moments of the velocity field, as calculated by Feldman, Watkins & Hudson. We find good agreement with the ΛCDM model on scales of k > 0.01 h Mpc-1. We find an excess of power on scales of k < 0.01 h Mpc-1 with a 1σ uncertainty which includes the ΛCDM model. We find that the uncertainty in excess at these scales is larger than an alternative result studying only moments of the velocity field, which is due to the minimum variance weights used to calculate the moments. At small scales, we are able to clearly discriminate between linear and non-linear clustering in simulated peculiar velocity catalogues and find some evidence (although less clear) for linear clustering in the real peculiar velocity data.

[The influence of perceived discrimination on health in migrants].

PubMed

Igel, Ulrike; Brähler, Elmar; Grande, Gesine

2010-05-01

The aim of the study was to investigate the influence of racial discrimination on subjective health in migrants. The sample included 1.844 migrants from the SOEP. Discrimination was assessed by two items. Socioeconomic status, country of origin, and health behavior were included in multivariate regression models to control for effects on health. Differential models with regard to gender and origin were analysed. Migrants who experienced discrimination report a worse health status. Discrimination determines mental and physical health of migrants. There are differences in models due to gender and origin. In addition to socioeconomic factors experienced discrimination should be taken into account as a psycho-social stressor of migrants.

The Longitudinal Associations Between Discrimination, Depressive Symptoms, and Prosocial Behaviors in U.S. Latino/a Recent Immigrant Adolescents.

PubMed

Davis, Alexandra N; Carlo, Gustavo; Schwartz, Seth J; Unger, Jennifer B; Zamboanga, Byron L; Lorenzo-Blanco, Elma I; Cano, Miguel Ángel; Baezconde-Garbanati, Lourdes; Oshri, Assaf; Streit, Cara; Martinez, Miriam M; Piña-Watson, Brandy; Lizzi, Karina; Soto, Daniel

2016-03-01

The links between discrimination and adjustment in U.S. Latino/a immigrant adolescents is an important but understudied phenomenon. We aimed to investigate the longitudinal associations (across 1 year) among discrimination, prosocial behaviors, and depressive symptoms in U.S. Latino immigrant adolescents using two competing models: associations between discrimination and prosocial behaviors via depressive symptoms (mental health strain model), and associations between discrimination and depressive symptoms via prosocial behaviors (prosociality strain model). Participants were 302 Latino/a recent immigrant adolescents (53.3 % boys, M age = 14.51 years at Time 1, SD = .88 years) who completed measures of discrimination, depressive symptoms, and prosocial behaviors at 6-month intervals. The results provided support for both proposed models. The discussion examines the importance of prosocial behaviors in understanding adjustment and effects of discrimination among recently immigrated U.S. Latino adolescents.

A Feasibility Study of Nonlinear Spectroscopic Measurement of Magnetic Nanoparticles Targeted to Cancer Cells.

PubMed

Ficko, Bradley W; NDong, Christian; Giacometti, Paolo; Griswold, Karl E; Diamond, Solomon G

2017-05-01

Magnetic nanoparticles (MNPs) are an emerging platform for targeted diagnostics in cancer. An important component needed for translation of MNPs is the detection and quantification of targeted MNPs bound to tumor cells. This study explores the feasibility of a multifrequency nonlinear magnetic spectroscopic method that uses excitation and pickup coils and is capable of discriminating between quantities of bound and unbound MNPs in 0.5 ml samples of KB and Igrov human cancer cell lines. The method is tested over a range of five concentrations of MNPs from 0 to 80 μg/ml and five concentrations of cells from 50 to 400 000 count per ml. A linear model applied to the magnetic spectroscopy data was able to simultaneously measure bound and unbound MNPs with agreement between the model-fit and lab assay measurements (p < 0.001). The detectable iron of the presented method to bound and unbound MNPs was < 2 μg in a 0.5 ml sample. The linear model parameters used to determine the quantities of bound and unbound nanoparticles in KB cells were also used to measure the bound and unbound MNP in the Igrov cell line and vice versa. Nonlinear spectroscopic measurement of MNPs may be a useful method for studying targeted MNPs in oncology. Determining the quantity of bound and unbound MNP in an unknown sample using a linear model represents an exciting opportunity to translate multifrequency nonlinear spectroscopy methods to in vivo applications where MNPs could be targeted to cancer cells.

Linear Quantitative Profiling Method Fast Monitors Alkaloids of Sophora Flavescens That Was Verified by Tri-Marker Analyses

PubMed Central

Hou, Zhifei; Sun, Guoxiang; Guo, Yong

2016-01-01

The present study demonstrated the use of the Linear Quantitative Profiling Method (LQPM) to evaluate the quality of Alkaloids of Sophora flavescens (ASF) based on chromatographic fingerprints in an accurate, economical and fast way. Both linear qualitative and quantitative similarities were calculated in order to monitor the consistency of the samples. The results indicate that the linear qualitative similarity (LQLS) is not sufficiently discriminating due to the predominant presence of three alkaloid compounds (matrine, sophoridine and oxymatrine) in the test samples; however, the linear quantitative similarity (LQTS) was shown to be able to obviously identify the samples based on the difference in the quantitative content of all the chemical components. In addition, the fingerprint analysis was also supported by the quantitative analysis of three marker compounds. The LQTS was found to be highly correlated to the contents of the marker compounds, indicating that quantitative analysis of the marker compounds may be substituted with the LQPM based on the chromatographic fingerprints for the purpose of quantifying all chemicals of a complex sample system. Furthermore, once reference fingerprint (RFP) developed from a standard preparation in an immediate detection way and the composition similarities calculated out, LQPM could employ the classical mathematical model to effectively quantify the multiple components of ASF samples without any chemical standard. PMID:27529425

Bone and cartilage characteristics in postmenopausal women with mild knee radiographic osteoarthritis and those without radiographic osteoarthritis

PubMed Central

Multanen, J.; Heinonen, A.; Häkkinen, A.; Kautiainen, H.; Kujala, U.M.; Lammentausta, E.; Jämsä, T.; Kiviranta, I.; Nieminen, M.T.

2015-01-01

Objectives: To evaluate the association between radiographically-assessed knee osteoarthritis and femoral neck bone characteristics in women with mild knee radiographic osteoarthritis and those without radiographic osteoarthritis. Methods: Ninety postmenopausal women (mean age [SD], 58 [4] years; height, 163 [6] cm; weight, 71 [11] kg) participated in this cross-sectional study. The severity of radiographic knee osteoarthritis was defined using Kellgren-Lawrence grades 0=normal (n=12), 1=doubtful (n=25) or 2=minimal (n=53). Femoral neck bone mineral content (BMC), section modulus (Z), and cross-sectional area (CSA) were measured with DXA. The biochemical composition of ipsilateral knee cartilage was estimated using quantitative MRI measures, T2 mapping and dGEMRIC. The associations between radiographic knee osteoarthritis grades and bone and cartilage characteristics were analyzed using generalized linear models. Results: Age-, height-, and weight-adjusted femoral neck BMC (p for linearity=0.019), Z (p for linearity=0.033), and CSA (p for linearity=0.019) increased significantly with higher knee osteoarthritis grades. There was no linear relationship between osteoarthritis grades and knee cartilage indices. Conclusions: Increased DXA assessed hip bone strength is related to knee osteoarthritis severity. These results are hypothesis driven that there is an inverse relationship between osteoarthritis and osteoporosis. However, MRI assessed measures of cartilage do not discriminate mild radiographic osteoarthritis severity. PMID:25730654

Self-reported discrimination and mental health among Asian Indians: Cultural beliefs and coping style as moderators

PubMed Central

Nadimpalli, Sarah B.; Kanaya, Alka M.; McDade, Thomas W.; Kandula, Namratha R.

2016-01-01

The South Asian (SA) population has been underrepresented in research linking discrimination with health indicators; studies that focus on the unique cultural and psychosocial experiences of different SA subgroups are needed. The purpose of this study was to examine associations between self-reported discrimination and mental health among Asian Indians (AIs), and whether traditional cultural beliefs (believing that South Asian cultural traditions should be practiced in the US), coping style, and social support moderated these relationships. Asian Indians (N = 733) had been recruited from community-based sampling frames for the Mediators of Atherosclerosis in South Asians Living in America (MASALA) study were included in this analysis. Multiple linear regression analyses were employed to evaluate relationships between discrimination and depressive symptoms, anger, and anxiety. Participants (men = 54%) were on average 55 years of age and had high levels of English proficiency, education, and income. Higher reports of discrimination were significantly associated with higher depressive symptoms, B = .27 (.05) p < .001, anger, B = .08 (.01), p < .001, and anxiety, B = .10 (.01), p < .001. Associations between discrimination and anger, B = −.005 (.002), p = .02, were weakest among those with stronger cultural beliefs. The link between discrimination and anxiety was attenuated by an active coping style, B = −.05 (.03), p = .04. In sum, self-reported discrimination appeared to adversely impact the mental health of AIs. Discrimination may be better coped with by having strong traditional cultural beliefs and actively managing experiences of discrimination. PMID:27668066

Chronic exposure to everyday discrimination and sleep in a multiethnic sample of middle-aged women.

PubMed

Lewis, Tené T; Troxel, Wendy M; Kravitz, Howard M; Bromberger, Joyce T; Matthews, Karen A; Hall, Martica H

2013-07-01

Researchers have suggested that poor sleep may play a role in the association between discrimination and health, but studies linking experiences of discrimination to sleep are limited. The authors examined associations between reports of everyday discrimination over 4 years (chronic everyday discrimination) and subjective and objective indicators of poor sleep. Participants were 368 African American, Caucasian, and Chinese women from the Study of Women's Health Across the Nation Sleep Study. Everyday discrimination was assessed each year from baseline through the third follow-up exam via questionnaire with the Everyday Discrimination Scale (intraclass correlation coefficient over 4 years = .90). Subjective sleep complaints were measured beginning in Year 5 with the Pittsburgh Sleep Quality Index. Objective indices of sleep continuity, duration, and architecture were assessed via in-home polysomnography, beginning in Year 5. In linear regression analyses adjusted for age, race/ethnicity, and financial strain, chronic everyday discrimination was associated with more subjective sleep complaints (Estimate = 1.52, p < .001) and polysomnography-assessed wakefulness after sleep onset (Estimate = .19, p < .02), a marker of sleep continuity. Findings did not differ by race/ethnicity and remained significant after adjusting for menopausal status, body mass index, medication use, and depressive symptoms. Experiences of chronic everyday discrimination are independently associated with both subjective and objective indices of poor sleep. Findings add to the growing literature linking discrimination to key markers of biobehavioral health. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Racial discrimination associated with higher diastolic blood pressure in a sample of American Indian adults.

PubMed

Thayer, Zaneta M; Blair, Irene V; Buchwald, Dedra S; Manson, Spero M

2017-05-01

Hypertension prevalence is high among American Indians (AIs). AIs experience a substantial burden of interpersonal racial discrimination, which in other populations has been associated with higher blood pressure. The purpose of this study is to understand whether racial discrimination experiences are associated with higher blood pressure in AIs. We used the Everyday Discrimination Scale to evaluate the relationship between discrimination and measured blood pressure among 77 AIs from two reservation communities in the Northern Plains. We used multivariate linear regression to evaluate the association of racial discrimination with systolic and diastolic blood pressure, respectively. Racial discrimination, systolic blood pressure, and diastolic blood pressure were analyzed as continuous variables. All analyses adjusted for sex, waist circumference, age, posttraumatic stress disorder status, and education. We found that 61% of participants experienced discrimination that they attributed to their race or ancestry. Racial discrimination was associated with significantly higher diastolic blood pressure (β = 0.22, SE = 0.09, p = .02), and with a similar non-significant trend toward higher systolic blood pressure (β = 0.25, SE = 0.15, p = .09). The results of this analysis suggest that racial discrimination may contribute to higher diastolic blood pressure within Native communities. These findings highlight one pathway through which the social environment can shape patterns of biology and health in AI and other socially and politically marginalized groups. © 2017 Wiley Periodicals, Inc.

Interpreting incremental value of markers added to risk prediction models.

PubMed

Pencina, Michael J; D'Agostino, Ralph B; Pencina, Karol M; Janssens, A Cecile J W; Greenland, Philip

2012-09-15

The discrimination of a risk prediction model measures that model's ability to distinguish between subjects with and without events. The area under the receiver operating characteristic curve (AUC) is a popular measure of discrimination. However, the AUC has recently been criticized for its insensitivity in model comparisons in which the baseline model has performed well. Thus, 2 other measures have been proposed to capture improvement in discrimination for nested models: the integrated discrimination improvement and the continuous net reclassification improvement. In the present study, the authors use mathematical relations and numerical simulations to quantify the improvement in discrimination offered by candidate markers of different strengths as measured by their effect sizes. They demonstrate that the increase in the AUC depends on the strength of the baseline model, which is true to a lesser degree for the integrated discrimination improvement. On the other hand, the continuous net reclassification improvement depends only on the effect size of the candidate variable and its correlation with other predictors. These measures are illustrated using the Framingham model for incident atrial fibrillation. The authors conclude that the increase in the AUC, integrated discrimination improvement, and net reclassification improvement offer complementary information and thus recommend reporting all 3 alongside measures characterizing the performance of the final model.

Integrated InP frequency discriminator for Phase-modulated microwave photonic links.

PubMed

Fandiño, J S; Doménech, J D; Muñoz, P; Capmany, J

2013-02-11

We report the design, fabrication and characterization of an integrated frequency discriminator on InP technology for microwave photonic phase modulated links. The optical chip is, to the best of our knowledge, the first reported in an active platform and the first to include the optical detectors. The discriminator, designed as a linear filter in intensity, features preliminary SFDR values the range between 67 and 79 dB.Hz(2/3) for signal frequencies in the range of 5-9 GHz limited, in principle, by the high value of the optical losses arising from the use of several free space coupling devices in our experimental setup. As discussed, these losses can be readily reduced by the use of integrated spot-size converters improving the SFDR by 17.3 dB (84-96 dB.Hz(2/3)). Further increase up to a range of (104-116 dB.Hz(2/3)) is possible by reducing the system noise eliminating the EDFA employed in the setup and using a commercially available laser source providing higher output power and lower relative intensity noise. Other paths for improvement requiring a filter redesign to be linear in the optical field are also discussed.

Kernel-based discriminant feature extraction using a representative dataset

NASA Astrophysics Data System (ADS)

Li, Honglin; Sancho Gomez, Jose-Luis; Ahalt, Stanley C.

2002-07-01

Discriminant Feature Extraction (DFE) is widely recognized as an important pre-processing step in classification applications. Most DFE algorithms are linear and thus can only explore the linear discriminant information among the different classes. Recently, there has been several promising attempts to develop nonlinear DFE algorithms, among which is Kernel-based Feature Extraction (KFE). The efficacy of KFE has been experimentally verified by both synthetic data and real problems. However, KFE has some known limitations. First, KFE does not work well for strongly overlapped data. Second, KFE employs all of the training set samples during the feature extraction phase, which can result in significant computation when applied to very large datasets. Finally, KFE can result in overfitting. In this paper, we propose a substantial improvement to KFE that overcomes the above limitations by using a representative dataset, which consists of critical points that are generated from data-editing techniques and centroid points that are determined by using the Frequency Sensitive Competitive Learning (FSCL) algorithm. Experiments show that this new KFE algorithm performs well on significantly overlapped datasets, and it also reduces computational complexity. Further, by controlling the number of centroids, the overfitting problem can be effectively alleviated.

Local linear discriminant analysis framework using sample neighbors.

PubMed

Fan, Zizhu; Xu, Yong; Zhang, David

2011-07-01

The linear discriminant analysis (LDA) is a very popular linear feature extraction approach. The algorithms of LDA usually perform well under the following two assumptions. The first assumption is that the global data structure is consistent with the local data structure. The second assumption is that the input data classes are Gaussian distributions. However, in real-world applications, these assumptions are not always satisfied. In this paper, we propose an improved LDA framework, the local LDA (LLDA), which can perform well without needing to satisfy the above two assumptions. Our LLDA framework can effectively capture the local structure of samples. According to different types of local data structure, our LLDA framework incorporates several different forms of linear feature extraction approaches, such as the classical LDA and principal component analysis. The proposed framework includes two LLDA algorithms: a vector-based LLDA algorithm and a matrix-based LLDA (MLLDA) algorithm. MLLDA is directly applicable to image recognition, such as face recognition. Our algorithms need to train only a small portion of the whole training set before testing a sample. They are suitable for learning large-scale databases especially when the input data dimensions are very high and can achieve high classification accuracy. Extensive experiments show that the proposed algorithms can obtain good classification results.

Classification of sodium MRI data of cartilage using machine learning.

PubMed

Madelin, Guillaume; Poidevin, Frederick; Makrymallis, Antonios; Regatte, Ravinder R

2015-11-01

To assess the possible utility of machine learning for classifying subjects with and subjects without osteoarthritis using sodium magnetic resonance imaging data. Theory: Support vector machine, k-nearest neighbors, naïve Bayes, discriminant analysis, linear regression, logistic regression, neural networks, decision tree, and tree bagging were tested. Sodium magnetic resonance imaging with and without fluid suppression by inversion recovery was acquired on the knee cartilage of 19 controls and 28 osteoarthritis patients. Sodium concentrations were measured in regions of interests in the knee for both acquisitions. Mean (MEAN) and standard deviation (STD) of these concentrations were measured in each regions of interest, and the minimum, maximum, and mean of these two measurements were calculated over all regions of interests for each subject. The resulting 12 variables per subject were used as predictors for classification. Either Min [STD] alone, or in combination with Mean [MEAN] or Min [MEAN], all from fluid suppressed data, were the best predictors with an accuracy >74%, mainly with linear logistic regression and linear support vector machine. Other good classifiers include discriminant analysis, linear regression, and naïve Bayes. Machine learning is a promising technique for classifying osteoarthritis patients and controls from sodium magnetic resonance imaging data. © 2014 Wiley Periodicals, Inc.

Image Discrimination Predictions of a Single Channel Model with Contrast Gain Control

NASA Technical Reports Server (NTRS)

Ahumada, Albert J., Jr.; Null, Cynthia H.

1995-01-01

Image discrimination models predict the number of just-noticeable-differences between two images. We report the predictions of a single channel model with contrast masking for a range of standard discrimination experiments. Despite its computational simplicity, this model has performed as well as a multiple channel model in an object detection task.

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology

PubMed Central

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease. PMID:27977767

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.

PubMed

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease.

Nonlinearities of heart rate variability in animal models of impaired cardiac control: contribution of different time scales.

PubMed

Silva, Luiz Eduardo Virgilio; Lataro, Renata Maria; Castania, Jaci Airton; Silva, Carlos Alberto Aguiar; Salgado, Helio Cesar; Fazan, Rubens; Porta, Alberto

2017-08-01

Heart rate variability (HRV) has been extensively explored by traditional linear approaches (e.g., spectral analysis); however, several studies have pointed to the presence of nonlinear features in HRV, suggesting that linear tools might fail to account for the complexity of the HRV dynamics. Even though the prevalent notion is that HRV is nonlinear, the actual presence of nonlinear features is rarely verified. In this study, the presence of nonlinear dynamics was checked as a function of time scales in three experimental models of rats with different impairment of the cardiac control: namely, rats with heart failure (HF), spontaneously hypertensive rats (SHRs), and sinoaortic denervated (SAD) rats. Multiscale entropy (MSE) and refined MSE (RMSE) were chosen as the discriminating statistic for the surrogate test utilized to detect nonlinearity. Nonlinear dynamics is less present in HF animals at both short and long time scales compared with controls. A similar finding was found in SHR only at short time scales. SAD increased the presence of nonlinear dynamics exclusively at short time scales. Those findings suggest that a working baroreflex contributes to linearize HRV and to reduce the likelihood to observe nonlinear components of the cardiac control at short time scales. In addition, an increased sympathetic modulation seems to be a source of nonlinear dynamics at long time scales. Testing nonlinear dynamics as a function of the time scales can provide a characterization of the cardiac control complementary to more traditional markers in time, frequency, and information domains. NEW & NOTEWORTHY Although heart rate variability (HRV) dynamics is widely assumed to be nonlinear, nonlinearity tests are rarely used to check this hypothesis. By adopting multiscale entropy (MSE) and refined MSE (RMSE) as the discriminating statistic for the nonlinearity test, we show that nonlinear dynamics varies with time scale and the type of cardiac dysfunction. Moreover, as complexity metrics and nonlinearities provide complementary information, we strongly recommend using the test for nonlinearity as an additional index to characterize HRV. Copyright © 2017 the American Physiological Society.

Self-reported experiences of discrimination and inflammation among men and women: The multi-ethnic study of atherosclerosis.

PubMed

Kershaw, Kiarri N; Lewis, Tené T; Diez Roux, Ana V; Jenny, Nancy S; Liu, Kiang; Penedo, Frank J; Carnethon, Mercedes R

2016-04-01

To examine associations of lifetime and everyday discrimination with inflammation independent of sociodemographic characteristics. Cross-sectional associations of self-reported experiences of everyday discrimination and lifetime discrimination with interleukin-6 (IL-6) and C-reactive protein (CRP) were examined by gender in a multiethnic sample of 3,099 men and 3,468 women aged 45-84 years. Everyday discrimination, lifetime discrimination due to any attribution, and lifetime discrimination attributed to race/ethnicity were based on self-report, and IL-6 and CRP were assayed from blood samples. Among women, higher levels of all 3 discrimination measures were significantly associated with higher IL-6 in models adjusted for sociodemographic characteristics, recent infection, anti-inflammatory medication use, and hormone replacement therapy use. All associations were attenuated with adjustment for body mass index (BMI). For men, everyday discrimination was inversely associated with IL-6 in all adjusted models. Lifetime discrimination was not related to IL-6 among men. Discrimination was unassociated with CRP in all models for both men and women. The association between discrimination and inflammation varied by gender and marker of inflammation. These findings highlight the complex relationship between discrimination and cardiovascular disease (CVD) risk and point to areas in need of further research. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Relationship of plasma N-terminal pro-brain natriuretic peptide concentrations to heart failure classification and cause of respiratory distress in dogs using a 2nd generation ELISA assay.

PubMed

Fox, P R; Oyama, M A; Hezzell, M J; Rush, J E; Nguyenba, T P; DeFrancesco, T C; Lehmkuhl, L B; Kellihan, H B; Bulmer, B; Gordon, S G; Cunningham, S M; MacGregor, J; Stepien, R L; Lefbom, B; Adin, D; Lamb, K

2015-01-01

Cardiac biomarkers provide objective data that augments clinical assessment of heart disease (HD). Determine the utility of plasma N-terminal pro-brain natriuretic peptide concentration [NT-proBNP] measured by a 2nd generation canine ELISA assay to discriminate cardiac from noncardiac respiratory distress and evaluate HD severity. Client-owned dogs (n = 291). Multicenter, cross-sectional, prospective investigation. Medical history, physical examination, echocardiography, and thoracic radiography classified 113 asymptomatic dogs (group 1, n = 39 without HD; group 2, n = 74 with HD), and 178 with respiratory distress (group 3, n = 104 respiratory disease, either with or without concurrent HD; group 4, n = 74 with congestive heart failure [CHF]). HD severity was graded using International Small Animal Cardiac Health Council (ISACHC) and ACVIM Consensus (ACVIM-HD) schemes without knowledge of [NT-proBNP] results. Receiver-operating characteristic curve analysis assessed the capacity of [NT-proBNP] to discriminate between dogs with cardiac and noncardiac respiratory distress. Multivariate general linear models containing key clinical variables tested associations between [NT-proBNP] and HD severity. Plasma [NT-proBNP] (median; IQR) was higher in CHF dogs (5,110; 2,769-8,466 pmol/L) compared to those with noncardiac respiratory distress (1,287; 672-2,704 pmol/L; P < .0001). A cut-off >2,447 pmol/L discriminated CHF from noncardiac respiratory distress (81.1% sensitivity; 73.1% specificity; area under curve, 0.84). A multivariate model comprising left atrial to aortic ratio, heart rate, left ventricular diameter, end-systole, and ACVIM-HD scheme most accurately associated average plasma [NT-proBNP] with HD severity. Plasma [NT-proBNP] was useful for discriminating CHF from noncardiac respiratory distress. Average plasma [NT-BNP] increased significantly as a function of HD severity using the ACVIM-HD classification scheme. Copyright © 2014 by the American College of Veterinary Internal Medicine.

Association of perceived ethnic discrimination with general and abdominal obesity in ethnic minority groups: the HELIUS study.

PubMed

Schmengler, Heiko; Ikram, Umar Z; Snijder, Marieke B; Kunst, Anton E; Agyemang, Charles

2017-05-01

Discrimination is associated with obesity, but this may differ according to the type of obesity and ethnic group. This study examines the association of perceived ethnic discrimination (PED) with general and abdominal obesity in 5 ethnic minority groups. We used cross-sectional data from the HELIUS study, collected from 2011 to 2015. The study sample included 2297 Ghanaians, 4110 African Surinamese, 3021 South-Asian Surinamese, 3562 Turks and 3868 Moroccans aged 18-70 years residing in Amsterdam, the Netherlands. Body mass index (BMI) was used as a measure for general obesity, and waist circumference (WC) for abdominal obesity. PED was measured using the Everyday Discrimination Scale. We used linear regression models adjusted for sociodemographics, psychosocial stressors and health behaviours. In additional analysis, we used standardised variables to compare the strength of the associations. In adjusted models, PED was significantly, positively associated with BMI in the South-Asian Surinamese (β coefficient 0.338; 95% CI 0.106 to 0.570), African Surinamese (0.394; 0.171 to 0.618) and Turks (0.269; 0.027 to 0.510). For WC, a similar pattern was seen: positive associations in the South-Asian Surinamese (0.759; 0.166 to 1.353), African Surinamese (0.833; 0.278 to 1.388) and Turks (0.870; 0.299 to 1.440). When stratified by sex, we found positive associations in Surinamese women, Turkish men and Moroccan men. The strength of the associations with BMI and WC was comparable in the groups. Among the Ghanaians, no significant associations were observed. Ethnic and sex variations are observed in the association of PED with both general and abdominal obesity. Further research on psychosocial buffers and underlying biological mechanisms might help in understanding these variations. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

The Emotion Recognition System Based on Autoregressive Model and Sequential Forward Feature Selection of Electroencephalogram Signals

PubMed Central

Hatamikia, Sepideh; Maghooli, Keivan; Nasrabadi, Ali Motie

2014-01-01

Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emotion recognition system using autoregressive (AR) model, sequential forward feature selection (SFS) and K-nearest neighbor (KNN) classifier using EEG signals during emotional audio-visual inductions. The main purpose of this paper is to investigate the performance of AR features in the classification of emotional states. To achieve this goal, a distinguished AR method (Burg's method) based on Levinson-Durbin's recursive algorithm is used and AR coefficients are extracted as feature vectors. In the next step, two different feature selection methods based on SFS algorithm and Davies–Bouldin index are used in order to decrease the complexity of computing and redundancy of features; then, three different classifiers include KNN, quadratic discriminant analysis and linear discriminant analysis are used to discriminate two and three different classes of valence and arousal levels. The proposed method is evaluated with EEG signals of available database for emotion analysis using physiological signals, which are recorded from 32 participants during 40 1 min audio visual inductions. According to the results, AR features are efficient to recognize emotional states from EEG signals, and KNN performs better than two other classifiers in discriminating of both two and three valence/arousal classes. The results also show that SFS method improves accuracies by almost 10-15% as compared to Davies–Bouldin based feature selection. The best accuracies are %72.33 and %74.20 for two classes of valence and arousal and %61.10 and %65.16 for three classes, respectively. PMID:25298928

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.

Discrimination of Temperature and Strain in Brillouin Optical Time Domain Analysis Using a Multicore Optical Fiber

PubMed Central

Zaghloul, Mohamed A. S.; Wang, Mohan; Milione, Giovanni; Li, Ming-Jun; Li, Shenping; Huang, Yue-Kai; Wang, Ting; Chen, Kevin P.

2018-01-01

Brillouin optical time domain analysis is the sensing of temperature and strain changes along an optical fiber by measuring the frequency shift changes of Brillouin backscattering. Because frequency shift changes are a linear combination of temperature and strain changes, their discrimination is a challenge. Here, a multicore optical fiber that has two cores is fabricated. The differences between the cores’ temperature and strain coefficients are such that temperature (strain) changes can be discriminated with error amplification factors of 4.57 °C/MHz (69.11 μϵ/MHz), which is 2.63 (3.67) times lower than previously demonstrated. As proof of principle, using the multicore optical fiber and a commercial Brillouin optical time domain analyzer, the temperature (strain) changes of a thermally expanding metal cylinder are discriminated with an error of 0.24% (3.7%). PMID:29649148

DISCRIMINATION OF GRANITOIDS AND MINERALIZED GRANITOIDS IN THE MIDYAN REGION, NORTHWESTERN ARABIAN SHIELD, SAUDI ARABIA, BY LANDSAT MSS DATA-ANALYSIS.

USGS Publications Warehouse

Davis, Philip A.; Grolier, Maurice J.

1984-01-01

Landsat multispectral scanner (MSS) band and band-ratio databases of two scenes covering the Midyan region of northwestern Saudi Arabia were examined quantitatively and qualitatively to determine which databases best discriminate the geologic units of this semi-arid and arid region. Unsupervised, linear-discriminant cluster-analysis was performed on these two band-ratio combinations and on the MSS bands for both scenes. The results for granitoid-rock discrimination indicated that the classification images using the MSS bands are superior to the band-ratio classification images for two reasons, discussed in the paper. Yet, the effects of topography and material type (including desert varnish) on the MSS-band data produced ambiguities in the MSS-band classification results. However, these ambiguities were clarified by using a simulated natural-color image in conjunction with the MSS-band classification image.

Discrimination of Temperature and Strain in Brillouin Optical Time Domain Analysis Using a Multicore Optical Fiber.

PubMed

Zaghloul, Mohamed A S; Wang, Mohan; Milione, Giovanni; Li, Ming-Jun; Li, Shenping; Huang, Yue-Kai; Wang, Ting; Chen, Kevin P

2018-04-12

Brillouin optical time domain analysis is the sensing of temperature and strain changes along an optical fiber by measuring the frequency shift changes of Brillouin backscattering. Because frequency shift changes are a linear combination of temperature and strain changes, their discrimination is a challenge. Here, a multicore optical fiber that has two cores is fabricated. The differences between the cores' temperature and strain coefficients are such that temperature (strain) changes can be discriminated with error amplification factors of 4.57 °C/MHz (69.11 μ ϵ /MHz), which is 2.63 (3.67) times lower than previously demonstrated. As proof of principle, using the multicore optical fiber and a commercial Brillouin optical time domain analyzer, the temperature (strain) changes of a thermally expanding metal cylinder are discriminated with an error of 0.24% (3.7%).

Toward optimal feature and time segment selection by divergence method for EEG signals classification.

PubMed

Wang, Jie; Feng, Zuren; Lu, Na; Luo, Jing

2018-06-01

Feature selection plays an important role in the field of EEG signals based motor imagery pattern classification. It is a process that aims to select an optimal feature subset from the original set. Two significant advantages involved are: lowering the computational burden so as to speed up the learning procedure and removing redundant and irrelevant features so as to improve the classification performance. Therefore, feature selection is widely employed in the classification of EEG signals in practical brain-computer interface systems. In this paper, we present a novel statistical model to select the optimal feature subset based on the Kullback-Leibler divergence measure, and automatically select the optimal subject-specific time segment. The proposed method comprises four successive stages: a broad frequency band filtering and common spatial pattern enhancement as preprocessing, features extraction by autoregressive model and log-variance, the Kullback-Leibler divergence based optimal feature and time segment selection and linear discriminate analysis classification. More importantly, this paper provides a potential framework for combining other feature extraction models and classification algorithms with the proposed method for EEG signals classification. Experiments on single-trial EEG signals from two public competition datasets not only demonstrate that the proposed method is effective in selecting discriminative features and time segment, but also show that the proposed method yields relatively better classification results in comparison with other competitive methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia.

PubMed

Sui, Jing; Adali, Tülay; Pearlson, Godfrey; Yang, Honghui; Sponheim, Scott R; White, Tonya; Calhoun, Vince D

2010-05-15

Collection of multiple-task brain imaging data from the same subject has now become common practice in medical imaging studies. In this paper, we propose a simple yet effective model, "CCA+ICA", as a powerful tool for multi-task data fusion. This joint blind source separation (BSS) model takes advantage of two multivariate methods: canonical correlation analysis and independent component analysis, to achieve both high estimation accuracy and to provide the correct connection between two datasets in which sources can have either common or distinct between-dataset correlation. In both simulated and real fMRI applications, we compare the proposed scheme with other joint BSS models and examine the different modeling assumptions. The contrast images of two tasks: sensorimotor (SM) and Sternberg working memory (SB), derived from a general linear model (GLM), were chosen to contribute real multi-task fMRI data, both of which were collected from 50 schizophrenia patients and 50 healthy controls. When examining the relationship with duration of illness, CCA+ICA revealed a significant negative correlation with temporal lobe activation. Furthermore, CCA+ICA located sensorimotor cortex as the group-discriminative regions for both tasks and identified the superior temporal gyrus in SM and prefrontal cortex in SB as task-specific group-discriminative brain networks. In summary, we compared the new approach to some competitive methods with different assumptions, and found consistent results regarding each of their hypotheses on connecting the two tasks. Such an approach fills a gap in existing multivariate methods for identifying biomarkers from brain imaging data.

Racial and ethnic health disparities: evidence of discrimination's effects across the SEP spectrum.

PubMed

D'Anna, Laura Hoyt; Ponce, Ninez A; Siegel, Judith M

2010-04-01

Perceived discrimination is a psychosocial stressor that plays a role in explaining racial/ethnic disparities in self-reported physical and mental health. The purpose of this paper is: (1) to investigate the association between perceived discrimination in receiving healthcare and racial/ethnic disparities in self-rated health status, physical, and emotional functional limitations among a diverse sample of California adults; (2) to assess whether discrimination effects vary by racial/ethnic group and gender; and (3) to evaluate how the effects of discrimination on health are manifest across the socioeconomic position (SEP) spectrum. Data were drawn from the 2001 California Health Interview Survey adult file (n=55,428). The analytic approach employed multivariate linear and logistic regressions. Discrimination is qualitatively identified into two types: (1) discrimination due to race/ethnicity, language, or accent, and (2) other discrimination. Findings show that both types of discrimination negatively influenced self-rated health, and were associated with a two to three-fold odds of limitations in physical and emotional health. Further, these effects varied by racial/ethnic group and gender, and the effects were mixed. Most notably, for emotional health, racial/ethnic discrimination penalized Latinas more than non-Latina Whites, but for physical health, other discrimination was less detrimental to Latinas than it was to non-Latina Whites. At higher levels of SEP, the effects of racial/ethnic discrimination on self-rated health and other discriminations' effects on physical health were attenuated. Higher SEP may serve as an important mitigator, particularly when comparing the medium to the low SEP categories. It is also possible that SEP effects cannot be extracted from the relationships of interest in that SEP is an expression of social discrimination. In fact, negative health effects associated with discrimination are evident across the SEP spectrum. This study highlights the complexity of the relationships between discrimination and racial/ethnic identity, gender, and SEP.

A discriminant function model as an alternative method to spirometry for COPD screening in primary care settings in China.

PubMed

Cui, Jiangyu; Zhou, Yumin; Tian, Jia; Wang, Xinwang; Zheng, Jingping; Zhong, Nanshan; Ran, Pixin

2012-12-01

COPD is often underdiagnosed in a primary care setting where the spirometry is unavailable. This study was aimed to develop a simple, economical and applicable model for COPD screening in those settings. First we established a discriminant function model based on Bayes' Rule by stepwise discriminant analysis, using the data from 243 COPD patients and 112 non-COPD subjects from our COPD survey in urban and rural communities and local primary care settings in Guangdong Province, China. We then used this model to discriminate COPD in additional 150 subjects (50 non-COPD and 100 COPD ones) who had been recruited by the same methods as used to have established the model. All participants completed pre- and post-bronchodilator spirometry and questionnaires. COPD was diagnosed according to the Global Initiative for Chronic Obstructive Lung Disease criteria. The sensitivity and specificity of the discriminant function model was assessed. THE ESTABLISHED DISCRIMINANT FUNCTION MODEL INCLUDED NINE VARIABLES: age, gender, smoking index, body mass index, occupational exposure, living environment, wheezing, cough and dyspnoea. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, accuracy and error rate of the function model to discriminate COPD were 89.00%, 82.00%, 4.94, 0.13, 86.66% and 13.34%, respectively. The accuracy and Kappa value of the function model to predict COPD stages were 70% and 0.61 (95% CI, 0.50 to 0.71). This discriminant function model may be used for COPD screening in primary care settings in China as an alternative option instead of spirometry.

Study of blur discrimination for 3D stereo viewing

NASA Astrophysics Data System (ADS)

Subedar, Mahesh; Karam, Lina J.

2014-03-01

Blur is an important attribute in the study and modeling of the human visual system. Blur discrimination was studied extensively using 2D test patterns. In this study, we present the details of subjective tests performed to measure blur discrimination thresholds using stereoscopic 3D test patterns. Specifically, the effect of disparity on the blur discrimination thresholds is studied on a passive stereoscopic 3D display. The blur discrimination thresholds are measured using stereoscopic 3D test patterns with positive, negative and zero disparity values, at multiple reference blur levels. A disparity value of zero represents the 2D viewing case where both the eyes will observe the same image. The subjective test results indicate that the blur discrimination thresholds remain constant as we vary the disparity value. This further indicates that binocular disparity does not affect blur discrimination thresholds and the models developed for 2D blur discrimination thresholds can be extended to stereoscopic 3D blur discrimination thresholds. We have presented fitting of the Weber model to the 3D blur discrimination thresholds measured from the subjective experiments.

Error quantification of a high-resolution coupled hydrodynamic-ecosystem coastal-ocean model: Part 2. Chlorophyll-a, nutrients and SPM

NASA Astrophysics Data System (ADS)

Allen, J. Icarus; Holt, Jason T.; Blackford, Jerry; Proctor, Roger

2007-12-01

Marine systems models are becoming increasingly complex and sophisticated, but far too little attention has been paid to model errors and the extent to which model outputs actually relate to ecosystem processes. Here we describe the application of summary error statistics to a complex 3D model (POLCOMS-ERSEM) run for the period 1988-1989 in the southern North Sea utilising information from the North Sea Project, which collected a wealth of observational data. We demonstrate that to understand model data misfit and the mechanisms creating errors, we need to use a hierarchy of techniques, including simple correlations, model bias, model efficiency, binary discriminator analysis and the distribution of model errors to assess model errors spatially and temporally. We also demonstrate that a linear cost function is an inappropriate measure of misfit. This analysis indicates that the model has some skill for all variables analysed. A summary plot of model performance indicates that model performance deteriorates as we move through the ecosystem from the physics, to the nutrients and plankton.

Discounting of reward sequences: a test of competing formal models of hyperbolic discounting

PubMed Central

Zarr, Noah; Alexander, William H.; Brown, Joshua W.

2014-01-01

Humans are known to discount future rewards hyperbolically in time. Nevertheless, a formal recursive model of hyperbolic discounting has been elusive until recently, with the introduction of the hyperbolically discounted temporal difference (HDTD) model. Prior to that, models of learning (especially reinforcement learning) have relied on exponential discounting, which generally provides poorer fits to behavioral data. Recently, it has been shown that hyperbolic discounting can also be approximated by a summed distribution of exponentially discounted values, instantiated in the μAgents model. The HDTD model and the μAgents model differ in one key respect, namely how they treat sequences of rewards. The μAgents model is a particular implementation of a Parallel discounting model, which values sequences based on the summed value of the individual rewards whereas the HDTD model contains a non-linear interaction. To discriminate among these models, we observed how subjects discounted a sequence of three rewards, and then we tested how well each candidate model fit the subject data. The results show that the Parallel model generally provides a better fit to the human data. PMID:24639662

Highly sensitive molecular diagnosis of prostate cancer using surplus material washed off from biopsy needles

PubMed Central

Bermudo, R; Abia, D; Mozos, A; García-Cruz, E; Alcaraz, A; Ortiz, Á R; Thomson, T M; Fernández, P L

2011-01-01

Introduction: Currently, final diagnosis of prostate cancer (PCa) is based on histopathological analysis of needle biopsies, but this process often bears uncertainties due to small sample size, tumour focality and pathologist's subjective assessment. Methods: Prostate cancer diagnostic signatures were generated by applying linear discriminant analysis to microarray and real-time RT–PCR (qRT–PCR) data from normal and tumoural prostate tissue samples. Additionally, after removal of biopsy tissues, material washed off from transrectal biopsy needles was used for molecular profiling and discriminant analysis. Results: Linear discriminant analysis applied to microarray data for a set of 318 genes differentially expressed between non-tumoural and tumoural prostate samples produced 26 gene signatures, which classified the 84 samples used with 100% accuracy. To identify signatures potentially useful for the diagnosis of prostate biopsies, surplus material washed off from routine biopsy needles from 53 patients was used to generate qRT–PCR data for a subset of 11 genes. This analysis identified a six-gene signature that correctly assigned the biopsies as benign or tumoural in 92.6% of the cases, with 88.8% sensitivity and 96.1% specificity. Conclusion: Surplus material from prostate needle biopsies can be used for minimal-size gene signature analysis for sensitive and accurate discrimination between non-tumoural and tumoural prostates, without interference with current diagnostic procedures. This approach could be a useful adjunct to current procedures in PCa diagnosis. PMID:22009027

Linear high-boost fusion of Stokes vector imagery for effective discrimination and recognition of real targets in the presence of multiple identical decoys

NASA Astrophysics Data System (ADS)

El-Saba, Aed; Sakla, Wesam A.

2010-04-01

Recently, the use of imaging polarimetry has received considerable attention for use in automatic target recognition (ATR) applications. In military remote sensing applications, there is a great demand for sensors that are capable of discriminating between real targets and decoys. Accurate discrimination of decoys from real targets is a challenging task and often requires the fusion of various sensor modalities that operate simultaneously. In this paper, we use a simple linear fusion technique known as the high-boost fusion method for effective discrimination of real targets in the presence of multiple decoys. The HBF assigns more weight to the polarization-based imagery in forming the final fused image that is used for detection. We have captured both intensity and polarization-based imagery from an experimental laboratory arrangement containing a mixture of sand/dirt, rocks, vegetation, and other objects for the purpose of simulating scenery that would be acquired in a remote sensing military application. A target object and three decoys that are identical in physical appearance (shape, surface structure and color) and different in material composition have also been placed in the scene. We use the wavelet-filter joint transform correlation (WFJTC) technique to perform detection between input scenery and the target object. Our results show that use of the HBF method increases the correlation performance metrics associated with the WFJTC-based detection process when compared to using either the traditional intensity or polarization-based images.

Geometry-based ensembles: toward a structural characterization of the classification boundary.

PubMed

Pujol, Oriol; Masip, David

2009-06-01

This paper introduces a novel binary discriminative learning technique based on the approximation of the nonlinear decision boundary by a piecewise linear smooth additive model. The decision border is geometrically defined by means of the characterizing boundary points-points that belong to the optimal boundary under a certain notion of robustness. Based on these points, a set of locally robust linear classifiers is defined and assembled by means of a Tikhonov regularized optimization procedure in an additive model to create a final lambda-smooth decision rule. As a result, a very simple and robust classifier with a strong geometrical meaning and nonlinear behavior is obtained. The simplicity of the method allows its extension to cope with some of today's machine learning challenges, such as online learning, large-scale learning or parallelization, with linear computational complexity. We validate our approach on the UCI database, comparing with several state-of-the-art classification techniques. Finally, we apply our technique in online and large-scale scenarios and in six real-life computer vision and pattern recognition problems: gender recognition based on face images, intravascular ultrasound tissue classification, speed traffic sign detection, Chagas' disease myocardial damage severity detection, old musical scores clef classification, and action recognition using 3D accelerometer data from a wearable device. The results are promising and this paper opens a line of research that deserves further attention.

Luminescent screen composition and apparatus

NASA Technical Reports Server (NTRS)

Hilborn, E. H.

1970-01-01

Ultraviolet light projects photographically produced images on a screen composed of a mixture of linear and nonlinear phosphors whose spectral emissions are different. This allows the display of polychromatic luminescent images, which gives better discrimination of the objects being viewed.

Electronic circuit delivers pulse of high interval stability

NASA Technical Reports Server (NTRS)

Fisher, B.

1966-01-01

Circuit generates a pulse of high interval stability with a complexity level considerably below systems of comparable stability. This circuit is being used as a linear frequency discriminator in the signal conditioner of the Apollo command module.

Automated discrimination of dementia spectrum disorders using extreme learning machine and structural T1 MRI features.

PubMed

Jongin Kim; Boreom Lee

2017-07-01

The classification of neuroimaging data for the diagnosis of Alzheimer's Disease (AD) is one of the main research goals of the neuroscience and clinical fields. In this study, we performed extreme learning machine (ELM) classifier to discriminate the AD, mild cognitive impairment (MCI) from normal control (NC). We compared the performance of ELM with that of a linear kernel support vector machine (SVM) for 718 structural MRI images from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The data consisted of normal control, MCI converter (MCI-C), MCI non-converter (MCI-NC), and AD. We employed SVM-based recursive feature elimination (RFE-SVM) algorithm to find the optimal subset of features. In this study, we found that the RFE-SVM feature selection approach in combination with ELM shows the superior classification accuracy to that of linear kernel SVM for structural T1 MRI data.

Principle component analysis and linear discriminant analysis of multi-spectral autofluorescence imaging data for differentiating basal cell carcinoma and healthy skin

NASA Astrophysics Data System (ADS)

Chernomyrdin, Nikita V.; Zaytsev, Kirill I.; Lesnichaya, Anastasiya D.; Kudrin, Konstantin G.; Cherkasova, Olga P.; Kurlov, Vladimir N.; Shikunova, Irina A.; Perchik, Alexei V.; Yurchenko, Stanislav O.; Reshetov, Igor V.

2016-09-01

In present paper, an ability to differentiate basal cell carcinoma (BCC) and healthy skin by combining multi-spectral autofluorescence imaging, principle component analysis (PCA), and linear discriminant analysis (LDA) has been demonstrated. For this purpose, the experimental setup, which includes excitation and detection branches, has been assembled. The excitation branch utilizes a mercury arc lamp equipped with a 365-nm narrow-linewidth excitation filter, a beam homogenizer, and a mechanical chopper. The detection branch employs a set of bandpass filters with the central wavelength of spectral transparency of λ = 400, 450, 500, and 550 nm, and a digital camera. The setup has been used to study three samples of freshly excised BCC. PCA and LDA have been implemented to analyze the data of multi-spectral fluorescence imaging. Observed results of this pilot study highlight the advantages of proposed imaging technique for skin cancer diagnosis.

Geographical identification of saffron (Crocus sativus L.) by linear discriminant analysis applied to the UV-visible spectra of aqueous extracts.

PubMed

D'Archivio, Angelo Antonio; Maggi, Maria Anna

2017-03-15

We attempted geographical classification of saffron using UV-visible spectroscopy, conventionally adopted for quality grading according to the ISO Normative 3632. We investigated 81 saffron samples produced in L'Aquila, Città della Pieve, Cascia, and Sardinia (Italy) and commercial products purchased in various supermarkets. Exploratory principal component analysis applied to the UV-vis spectra of saffron aqueous extracts revealed a clear differentiation of the samples belonging to different quality categories, but a poor separation according to the geographical origin of the spices. On the other hand, linear discriminant analysis based on 8 selected absorbance values, concentrated near 279, 305 and 328nm, allowed a good distinction of the spices coming from different sites. Under severe validation conditions (30% and 50% of saffron samples in the evaluation set), correct predictions were 85 and 83%, respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

Combining features from ERP components in single-trial EEG for discriminating four-category visual objects.

PubMed

Wang, Changming; Xiong, Shi; Hu, Xiaoping; Yao, Li; Zhang, Jiacai

2012-10-01

Categorization of images containing visual objects can be successfully recognized using single-trial electroencephalograph (EEG) measured when subjects view images. Previous studies have shown that task-related information contained in event-related potential (ERP) components could discriminate two or three categories of object images. In this study, we investigated whether four categories of objects (human faces, buildings, cats and cars) could be mutually discriminated using single-trial EEG data. Here, the EEG waveforms acquired while subjects were viewing four categories of object images were segmented into several ERP components (P1, N1, P2a and P2b), and then Fisher linear discriminant analysis (Fisher-LDA) was used to classify EEG features extracted from ERP components. Firstly, we compared the classification results using features from single ERP components, and identified that the N1 component achieved the highest classification accuracies. Secondly, we discriminated four categories of objects using combining features from multiple ERP components, and showed that combination of ERP components improved four-category classification accuracies by utilizing the complementarity of discriminative information in ERP components. These findings confirmed that four categories of object images could be discriminated with single-trial EEG and could direct us to select effective EEG features for classifying visual objects.

Perceived discrimination and social networks among older African Americans and Caribbean blacks.

PubMed

Marshall, Gillian L; Rue, Tessa C

2012-01-01

The relationship between perceived discrimination and depressive symptoms among older black American populations is poorly understood. Although a small number of studies have examined the relationship between stress and social support, few have examined the association between perceived discrimination, social networks, and depressive symptoms among a representative sample of older racial and ethnic groups. This study examines (a) the relationship between sociodemographic factors, perceived discrimination and depressive symptoms and (b) social networks as a potential moderator in the perceived discrimination and depressive symptom relationship between 2 groups of older black Americans. This was a cross-sectional study using data from the National Survey of American Life with a sample of older African Americans (N = 837) and Caribbean blacks (N = 271). Depressive symptoms were assessed using the 12-item Center for Epidemiological Studies Depression scale. Linear regression analyses were used to predict depressive symptoms. The relationship between perceived discrimination and depressive symptoms was significant in both groups. Social networks contributed as a protective factor for depressive symptoms for both groups. However, there was no significant moderation effect. Results suggest that regardless of ethnic affiliation, the experience of perceived discrimination is similar in both groups and is a risk factor for depressive symptoms. Future research is needed in this area to better understand the associations between sociodemographic factors, perceived discrimination, social networks, and their impact on depressive symptoms.