An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information.

PubMed

Kumar, Shiu; Sharma, Alok; Tsunoda, Tatsuhiko

2017-12-28

Common spatial pattern (CSP) has been an effective technique for feature extraction in electroencephalography (EEG) based brain computer interfaces (BCIs). However, motor imagery EEG signal feature extraction using CSP generally depends on the selection of the frequency bands to a great extent. In this study, we propose a mutual information based frequency band selection approach. The idea of the proposed method is to utilize the information from all the available channels for effectively selecting the most discriminative filter banks. CSP features are extracted from multiple overlapping sub-bands. An additional sub-band has been introduced that cover the wide frequency band (7-30 Hz) and two different types of features are extracted using CSP and common spatio-spectral pattern techniques, respectively. Mutual information is then computed from the extracted features of each of these bands and the top filter banks are selected for further processing. Linear discriminant analysis is applied to the features extracted from each of the filter banks. The scores are fused together, and classification is done using support vector machine. The proposed method is evaluated using BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, and it outperformed all other competing methods achieving the lowest misclassification rate and the highest kappa coefficient on all three datasets. Introducing a wide sub-band and using mutual information for selecting the most discriminative sub-bands, the proposed method shows improvement in motor imagery EEG signal classification.

Remote Sensing as a Landscape Epidemiologic Tool to Identify Villages at High Risk for Malaria Transmission

NASA Technical Reports Server (NTRS)

Beck, Louisa R.; Rodriquez, Mario H.; Dister, Sheri W.; Rodriquez, Americo D.; Rejmankova, Eliska; Ulloa, Armando; Meza, Rosa A.; Roberts, Donald R.; Paris, Jack F.; Spanner, Michael A.;

A discrimination method for the detection of pneumonia using chest radiograph.

PubMed

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

2010-03-01

This paper presents a statistical method for the detection of lobar pneumonia when using digitized chest X-ray films. Each region of interest was represented by a vector of wavelet texture measures which is then multiplied by the orthogonal matrix Q(2). The first two elements of the transformed vectors were shown to have a bivariate normal distribution. Misclassification probabilities were estimated using probability ellipsoids and discriminant functions. The result of this study recommends the detection of pneumonia by constructing probability ellipsoids or discriminant function using maximum energy and maximum column sum energy texture measures where misclassification probabilities were less than 0.15. 2009 Elsevier Ltd. All rights reserved.

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

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

PubMed

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

2011-04-01

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

An information measure for class discrimination. [in remote sensing of crop observation

NASA Technical Reports Server (NTRS)

Shen, S. S.; Badhwar, G. D.

1986-01-01

This article describes a separability measure for class discrimination. This measure is based on the Fisher information measure for estimating the mixing proportion of two classes. The Fisher information measure not only provides a means to assess quantitatively the information content in the features for separating classes, but also gives the lower bound for the variance of any unbiased estimate of the mixing proportion based on observations of the features. Unlike most commonly used separability measures, this measure is not dependent on the form of the probability distribution of the features and does not imply a specific estimation procedure. This is important because the probability distribution function that describes the data for a given class does not have simple analytic forms, such as a Gaussian. Results of applying this measure to compare the information content provided by three Landsat-derived feature vectors for the purpose of separating small grains from other crops are presented.

Bubble Entropy: An Entropy Almost Free of Parameters.

PubMed

Manis, George; Aktaruzzaman, Md; Sassi, Roberto

2017-11-01

Objective : A critical point in any definition of entropy is the selection of the parameters employed to obtain an estimate in practice. We propose a new definition of entropy aiming to reduce the significance of this selection. Methods: We call the new definition Bubble Entropy . Bubble Entropy is based on permutation entropy, where the vectors in the embedding space are ranked. We use the bubble sort algorithm for the ordering procedure and count instead the number of swaps performed for each vector. Doing so, we create a more coarse-grained distribution and then compute the entropy of this distribution. Results: Experimental results with both real and synthetic HRV signals showed that bubble entropy presents remarkable stability and exhibits increased descriptive and discriminating power compared to all other definitions, including the most popular ones. Conclusion: The definition proposed is almost free of parameters. The most common ones are the scale factor r and the embedding dimension m . In our definition, the scale factor is totally eliminated and the importance of m is significantly reduced. The proposed method presents increased stability and discriminating power. Significance: After the extensive use of some entropy measures in physiological signals, typical values for their parameters have been suggested, or at least, widely used. However, the parameters are still there, application and dataset dependent, influencing the computed value and affecting the descriptive power. Reducing their significance or eliminating them alleviates the problem, decoupling the method from the data and the application, and eliminating subjective factors. Objective : A critical point in any definition of entropy is the selection of the parameters employed to obtain an estimate in practice. We propose a new definition of entropy aiming to reduce the significance of this selection. Methods: We call the new definition Bubble Entropy . Bubble Entropy is based on permutation entropy, where the vectors in the embedding space are ranked. We use the bubble sort algorithm for the ordering procedure and count instead the number of swaps performed for each vector. Doing so, we create a more coarse-grained distribution and then compute the entropy of this distribution. Results: Experimental results with both real and synthetic HRV signals showed that bubble entropy presents remarkable stability and exhibits increased descriptive and discriminating power compared to all other definitions, including the most popular ones. Conclusion: The definition proposed is almost free of parameters. The most common ones are the scale factor r and the embedding dimension m . In our definition, the scale factor is totally eliminated and the importance of m is significantly reduced. The proposed method presents increased stability and discriminating power. Significance: After the extensive use of some entropy measures in physiological signals, typical values for their parameters have been suggested, or at least, widely used. However, the parameters are still there, application and dataset dependent, influencing the computed value and affecting the descriptive power. Reducing their significance or eliminating them alleviates the problem, decoupling the method from the data and the application, and eliminating subjective factors.

Comparison of three different detectors applied to synthetic aperture radar data

NASA Astrophysics Data System (ADS)

Ranney, Kenneth I.; Khatri, Hiralal; Nguyen, Lam H.

2002-08-01

The U.S. Army Research Laboratory has investigated the relative performance of three different target detection paradigms applied to foliage penetration (FOPEN) synthetic aperture radar (SAR) data. The three detectors - a quadratic polynomial discriminator (QPD), Bayesian neural network (BNN) and a support vector machine (SVM) - utilize a common collection of statistics (feature values) calculated from the fully polarimetric FOPEN data. We describe the parametric variations required as part of the algorithm optimizations, and we present the relative performance of the detectors in terms of probability of false alarm (Pfa) and probability of detection (Pd).

Graphene Nanoplatelet-Polymer Chemiresistive Sensor Arrays for the Detection and Discrimination of Chemical Warfare Agent Simulants.

PubMed

Wiederoder, Michael S; Nallon, Eric C; Weiss, Matt; McGraw, Shannon K; Schnee, Vincent P; Bright, Collin J; Polcha, Michael P; Paffenroth, Randy; Uzarski, Joshua R

2017-11-22

A cross-reactive array of semiselective chemiresistive sensors made of polymer-graphene nanoplatelet (GNP) composite coated electrodes was examined for detection and discrimination of chemical warfare agents (CWA). The arrays employ a set of chemically diverse polymers to generate a unique response signature for multiple CWA simulants and background interferents. The developed sensors' signal remains consistent after repeated exposures to multiple analytes for up to 5 days with a similar signal magnitude across different replicate sensors with the same polymer-GNP coating. An array of 12 sensors each coated with a different polymer-GNP mixture was exposed 100 times to a cycle of single analyte vapors consisting of 5 chemically similar CWA simulants and 8 common background interferents. The collected data was vector normalized to reduce concentration dependency, z-scored to account for baseline drift and signal-to-noise ratio, and Kalman filtered to reduce noise. The processed data was dimensionally reduced with principal component analysis and analyzed with four different machine learning algorithms to evaluate discrimination capabilities. For 5 similarly structured CWA simulants alone 100% classification accuracy was achieved. For all analytes tested 99% classification accuracy was achieved demonstrating the CWA discrimination capabilities of the developed system. The novel sensor fabrication methods and data processing techniques are attractive for development of sensor platforms for discrimination of CWA and other classes of chemical vapors.

Language Recognition via Sparse Coding

DTIC Science & Technology

2016-09-08

a posteriori (MAP) adaptation scheme that further optimizes the discriminative quality of sparse-coded speech fea - tures. We empirically validate the...significantly improve the discriminative quality of sparse-coded speech fea - tures. In Section 4, we evaluate the proposed approaches against an i-vector

Forest tree species discrimination in western Himalaya using EO-1 Hyperion

NASA Astrophysics Data System (ADS)

George, Rajee; Padalia, Hitendra; Kushwaha, S. P. S.

2014-05-01

The information acquired in the narrow bands of hyperspectral remote sensing data has potential to capture plant species spectral variability, thereby improving forest tree species mapping. This study assessed the utility of spaceborne EO-1 Hyperion data in discrimination and classification of broadleaved evergreen and conifer forest tree species in western Himalaya. The pre-processing of 242 bands of Hyperion data resulted into 160 noise-free and vertical stripe corrected reflectance bands. Of these, 29 bands were selected through step-wise exclusion of bands (Wilk's Lambda). Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) algorithms were applied to the selected bands to assess their effectiveness in classification. SVM was also applied to broadband data (Landsat TM) to compare the variation in classification accuracy. All commonly occurring six gregarious tree species, viz., white oak, brown oak, chir pine, blue pine, cedar and fir in western Himalaya could be effectively discriminated. SVM produced a better species classification (overall accuracy 82.27%, kappa statistic 0.79) than SAM (overall accuracy 74.68%, kappa statistic 0.70). It was noticed that classification accuracy achieved with Hyperion bands was significantly higher than Landsat TM bands (overall accuracy 69.62%, kappa statistic 0.65). Study demonstrated the potential utility of narrow spectral bands of Hyperion data in discriminating tree species in a hilly terrain.

Robust visual tracking via multiple discriminative models with object proposals

NASA Astrophysics Data System (ADS)

Zhang, Yuanqiang; Bi, Duyan; Zha, Yufei; Li, Huanyu; Ku, Tao; Wu, Min; Ding, Wenshan; Fan, Zunlin

2018-04-01

Model drift is an important reason for tracking failure. In this paper, multiple discriminative models with object proposals are used to improve the model discrimination for relieving this problem. Firstly, the target location and scale changing are captured by lots of high-quality object proposals, which are represented by deep convolutional features for target semantics. And then, through sharing a feature map obtained by a pre-trained network, ROI pooling is exploited to wrap the various sizes of object proposals into vectors of the same length, which are used to learn a discriminative model conveniently. Lastly, these historical snapshot vectors are trained by different lifetime models. Based on entropy decision mechanism, the bad model owing to model drift can be corrected by selecting the best discriminative model. This would improve the robustness of the tracker significantly. We extensively evaluate our tracker on two popular benchmarks, the OTB 2013 benchmark and UAV20L benchmark. On both benchmarks, our tracker achieves the best performance on precision and success rate compared with the state-of-the-art trackers.

Stable Isotope Ratio and Elemental Profile Combined with Support Vector Machine for Provenance Discrimination of Oolong Tea (Wuyi-Rock Tea)

PubMed Central

Lou, Yun-xiao; Fu, Xian-shu; Yu, Xiao-ping; Zhang, Ya-fen

2017-01-01

This paper focused on an effective method to discriminate the geographical origin of Wuyi-Rock tea by the stable isotope ratio (SIR) and metallic element profiling (MEP) combined with support vector machine (SVM) analysis. Wuyi-Rock tea (n = 99) collected from nine producing areas and non-Wuyi-Rock tea (n = 33) from eleven nonproducing areas were analysed for SIR and MEP by established methods. The SVM model based on coupled data produced the best prediction accuracy (0.9773). This prediction shows that instrumental methods combined with a classification model can provide an effective and stable tool for provenance discrimination. Moreover, every feature variable in stable isotope and metallic element data was ranked by its contribution to the model. The results show that δ2H, δ18O, Cs, Cu, Ca, and Rb contents are significant indications for provenance discrimination and not all of the metallic elements improve the prediction accuracy of the SVM model. PMID:28473941

Identifying saltcedar with hyperspectral data and support vector machines

USDA-ARS?s Scientific Manuscript database

Saltcedar (Tamarix spp.) are a group of dense phreatophytic shrubs and trees that are invasive to riparian areas throughout the United States. This study determined the feasibility of using hyperspectral data and a support vector machine (SVM) classifier to discriminate saltcedar from other cover t...

Comparative decision models for anticipating shortage of food grain production in India

NASA Astrophysics Data System (ADS)

Chattopadhyay, Manojit; Mitra, Subrata Kumar

2018-01-01

This paper attempts to predict food shortages in advance from the analysis of rainfall during the monsoon months along with other inputs used for crop production, such as land used for cereal production, percentage of area covered under irrigation and fertiliser use. We used six binary classification data mining models viz., logistic regression, Multilayer Perceptron, kernel lab-Support Vector Machines, linear discriminant analysis, quadratic discriminant analysis and k-Nearest Neighbors Network, and found that linear discriminant analysis and kernel lab-Support Vector Machines are equally suitable for predicting per capita food shortage with 89.69 % accuracy in overall prediction and 92.06 % accuracy in predicting food shortage ( true negative rate). Advance information of food shortage can help policy makers to take remedial measures in order to prevent devastating consequences arising out of food non-availability.

Segmentation of magnetic resonance images using fuzzy algorithms for learning vector quantization.

PubMed

Karayiannis, N B; Pai, P I

1999-02-01

This paper evaluates a segmentation technique for magnetic resonance (MR) images of the brain based on fuzzy algorithms for learning vector quantization (FALVQ). These algorithms perform vector quantization by updating all prototypes of a competitive network through an unsupervised learning process. Segmentation of MR images is formulated as an unsupervised vector quantization process, where the local values of different relaxation parameters form the feature vectors which are represented by a relatively small set of prototypes. The experiments evaluate a variety of FALVQ algorithms in terms of their ability to identify different tissues and discriminate between normal tissues and abnormalities.

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.

Kernel-Based Discriminant Techniques for Educational Placement

ERIC Educational Resources Information Center

Lin, Miao-hsiang; Huang, Su-yun; Chang, Yuan-chin

2004-01-01

This article considers the problem of educational placement. Several discriminant techniques are applied to a data set from a survey project of science ability. A profile vector for each student consists of five science-educational indicators. The students are intended to be placed into three reference groups: advanced, regular, and remedial.…

Color measurement and discrimination

NASA Technical Reports Server (NTRS)

Wandell, B. A.

1985-01-01

Theories of color measurement attempt to provide a quantative means for predicting whether two lights will be discriminable to an average observer. All color measurement theories can be characterized as follows: suppose lights a and b evoke responses from three color channels characterized as vectors, v(a) and v(b); the vector difference v(a) - v(b) corresponds to a set of channel responses that would be generated by some real light, call it *. According to theory a and b will be discriminable when * is detectable. A detailed development and test of the classic color measurement approach are reported. In the absence of a luminance component in the test stimuli, a and b, the theory holds well. In the presence of a luminance component, the theory is clearly false. When a luminance component is present discrimination judgements depend largely on whether the lights being discriminated fall in separate, categorical regions of color space. The results suggest that sensory estimation of surface color uses different methods, and the choice of method depends upon properties of the image. When there is significant luminance variation a categorical method is used, while in the absence of significant luminance variation judgments are continuous and consistant with the measurement approach.

Polarimetric signature imaging of anisotropic bio-medical tissues

NASA Astrophysics Data System (ADS)

Wu, Stewart H.; Yang, De-Ming; Chiou, Arthur; Nee, Soe-Mie F.; Nee, Tsu-Wei

2010-02-01

Polarimetric imaging of Stokes vector (I, Q, U, V) can provide 4 independent signatures showing the linear and circular polarizations of biological tissues and cells. Using a recently developed Stokes digital imaging system, we measured the Stokes vector images of tissue samples from sections of rat livers containing normal portions and hematomas. The derived Mueller matrix elements can quantitatively provide multi-signature data of the bio-sample. This polarimetric optical technology is a new option of biosensing technology to inspect the structures of tissue samples, particularly for discriminating tumor and non-tumor biopsy. This technology is useful for critical disease discrimination and medical diagnostics applications.

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

PubMed

Wen, Zaidao; Hou, Zaidao; Jiao, Licheng

2017-11-01

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

Geographical traceability of Marsdenia tenacissima by Fourier transform infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Li, Chao; Yang, Sheng-Chao; Guo, Qiao-Sheng; Zheng, Kai-Yan; Wang, Ping-Li; Meng, Zhen-Gui

2016-01-01

A combination of Fourier transform infrared spectroscopy with chemometrics tools provided an approach for studying Marsdenia tenacissima according to its geographical origin. A total of 128 M. tenacissima samples from four provinces in China were analyzed with FTIR spectroscopy. Six pattern recognition methods were used to construct the discrimination models: support vector machine-genetic algorithms, support vector machine-particle swarm optimization, K-nearest neighbors, radial basis function neural network, random forest and support vector machine-grid search. Experimental results showed that K-nearest neighbors was superior to other mathematical algorithms after data were preprocessed with wavelet de-noising, with a discrimination rate of 100% in both the training and prediction sets. This study demonstrated that FTIR spectroscopy coupled with K-nearest neighbors could be successfully applied to determine the geographical origins of M. tenacissima samples, thereby providing reliable authentication in a rapid, cheap and noninvasive way.

Behavioural response of the malaria vector Anopheles gambiae to host plant volatiles and synthetic blends

USDA-ARS?s Scientific Manuscript database

Sugar feeding is critical for survival of malaria vectors and, although discriminative plant feeding previously has been shown to occur in Anopheles gambiae s.s., little is known about the cues mediating attraction to these plants. In this study, we investigated the role of olfaction in An. gambiae ...

Discriminant analysis for fast multiclass data classification through regularized kernel function approximation.

PubMed

Ghorai, Santanu; Mukherjee, Anirban; Dutta, Pranab K

2010-06-01

In this brief we have proposed the multiclass data classification by computationally inexpensive discriminant analysis through vector-valued regularized kernel function approximation (VVRKFA). VVRKFA being an extension of fast regularized kernel function approximation (FRKFA), provides the vector-valued response at single step. The VVRKFA finds a linear operator and a bias vector by using a reduced kernel that maps a pattern from feature space into the low dimensional label space. The classification of patterns is carried out in this low dimensional label subspace. A test pattern is classified depending on its proximity to class centroids. The effectiveness of the proposed method is experimentally verified and compared with multiclass support vector machine (SVM) on several benchmark data sets as well as on gene microarray data for multi-category cancer classification. The results indicate the significant improvement in both training and testing time compared to that of multiclass SVM with comparable testing accuracy principally in large data sets. Experiments in this brief also serve as comparison of performance of VVRKFA with stratified random sampling and sub-sampling.

Comparison of Radio Frequency Distinct Native Attribute and Matched Filtering Techniques for Device Discrimination and Operation Identification

DTIC Science & Technology

identification. URE from ten MSP430F5529 16-bit microcontrollers were analyzed using: 1) RF distinct native attributes (RF-DNA) fingerprints paired with multiple...discriminant analysis/maximum likelihood (MDA/ML) classification, 2) RF-DNA fingerprints paired with generalized relevance learning vector quantized

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

PubMed

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

2017-01-01

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

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

PubMed Central

Lim, Sa Rang; Huang, Linfang

2017-01-01

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

Photodynamic diagnosis of bladder cancer in ex vivo urine cytology

NASA Astrophysics Data System (ADS)

Fu, C. Y.; Ng, B. K.; Razul, S. Gulam; Olivo, Malini C.; Lau, Weber K. O.; Tan, P. H.; Chin, William

2006-02-01

Bladder cancer is the fourth common malignant disease worldwide, accounting for 4% of all cancer cases. In Singapore, it is the ninth most common form of cancer. The high mortality rate can be reduced by early treatment following precancerous screening. Currently, the gold standard for screening bladder tumors is histological examination of biopsy specimen, which is both invasive and time-consuming. In this study ex vivo urine fluorescence cytology is investigated to offer a timely and biopsy-free means for detecting bladder cancers. Sediments in patients' urine samples were extracted and incubated with a novel photosensitizer, hypericin. Laser confocal microscopy was used to capture the fluorescence images at an excitation wavelength of 488 nm. Images were subsequently processed to single out the exfoliated bladder cells from the other cells based on the cellular size. Intensity histogram of each targeted cell was plotted and feature vectors, derived from the histogram moments, were used to represent each sample. A difference in the distribution of the feature vectors of normal and low-grade cancerous bladder cells was observed. Diagnostic algorithm for discriminating between normal and low-grade cancerous cells is elucidated in this paper. This study suggests that the fluorescence intensity profiles of hypericin in bladder cells can potentially provide an automated quantitative means of early bladder cancer diagnosis.

Aberrant functional connectivity for diagnosis of major depressive disorder: a discriminant analysis.

PubMed

Cao, Longlong; Guo, Shuixia; Xue, Zhimin; Hu, Yong; Liu, Haihong; Mwansisya, Tumbwene E; Pu, Weidan; Yang, Bo; Liu, Chang; Feng, Jianfeng; Chen, Eric Y H; Liu, Zhening

2014-02-01

Aberrant brain functional connectivity patterns have been reported in major depressive disorder (MDD). It is unknown whether they can be used in discriminant analysis for diagnosis of MDD. In the present study we examined the efficiency of discriminant analysis of MDD by individualized computer-assisted diagnosis. Based on resting-state functional magnetic resonance imaging data, a new approach was adopted to investigate functional connectivity changes in 39 MDD patients and 37 well-matched healthy controls. By using the proposed feature selection method, we identified significant altered functional connections in patients. They were subsequently applied to our analysis as discriminant features using a support vector machine classification method. Furthermore, the relative contribution of functional connectivity was estimated. After subset selection of high-dimension features, the support vector machine classifier reached up to approximately 84% with leave-one-out training during the discrimination process. Through summarizing the classification contribution of functional connectivities, we obtained four obvious contribution modules: inferior orbitofrontal module, supramarginal gyrus module, inferior parietal lobule-posterior cingulated gyrus module and middle temporal gyrus-inferior temporal gyrus module. The experimental results demonstrated that the proposed method is effective in discriminating MDD patients from healthy controls. Functional connectivities might be useful as new biomarkers to assist clinicians in computer auxiliary diagnosis of MDD. © 2013 The Authors. Psychiatry and Clinical Neurosciences © 2013 Japanese Society of Psychiatry and Neurology.

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

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

A comparative study of linear and nonlinear anomaly detectors for hyperspectral imagery

NASA Astrophysics Data System (ADS)

Goldberg, Hirsh; Nasrabadi, Nasser M.

2007-04-01

In this paper we implement various linear and nonlinear subspace-based anomaly detectors for hyperspectral imagery. First, a dual window technique is used to separate the local area around each pixel into two regions - an inner-window region (IWR) and an outer-window region (OWR). Pixel spectra from each region are projected onto a subspace which is defined by projection bases that can be generated in several ways. Here we use three common pattern classification techniques (Principal Component Analysis (PCA), Fisher Linear Discriminant (FLD) Analysis, and the Eigenspace Separation Transform (EST)) to generate projection vectors. In addition to these three algorithms, the well-known Reed-Xiaoli (RX) anomaly detector is also implemented. Each of the four linear methods is then implicitly defined in a high- (possibly infinite-) dimensional feature space by using a nonlinear mapping associated with a kernel function. Using a common machine-learning technique known as the kernel trick all dot products in the feature space are replaced with a Mercer kernel function defined in terms of the original input data space. To determine how anomalous a given pixel is, we then project the current test pixel spectra and the spectral mean vector of the OWR onto the linear and nonlinear projection vectors in order to exploit the statistical differences between the IWR and OWR pixels. Anomalies are detected if the separation of the projection of the current test pixel spectra and the OWR mean spectra are greater than a certain threshold. Comparisons are made using receiver operating characteristics (ROC) curves.

Real-data comparison of data mining methods in prediction of diabetes in iran.

PubMed

Tapak, Lily; Mahjub, Hossein; Hamidi, Omid; Poorolajal, Jalal

2013-09-01

Diabetes is one of the most common non-communicable diseases in developing countries. Early screening and diagnosis play an important role in effective prevention strategies. This study compared two traditional classification methods (logistic regression and Fisher linear discriminant analysis) and four machine-learning classifiers (neural networks, support vector machines, fuzzy c-mean, and random forests) to classify persons with and without diabetes. The data set used in this study included 6,500 subjects from the Iranian national non-communicable diseases risk factors surveillance obtained through a cross-sectional survey. The obtained sample was based on cluster sampling of the Iran population which was conducted in 2005-2009 to assess the prevalence of major non-communicable disease risk factors. Ten risk factors that are commonly associated with diabetes were selected to compare the performance of six classifiers in terms of sensitivity, specificity, total accuracy, and area under the receiver operating characteristic (ROC) curve criteria. Support vector machines showed the highest total accuracy (0.986) as well as area under the ROC (0.979). Also, this method showed high specificity (1.000) and sensitivity (0.820). All other methods produced total accuracy of more than 85%, but for all methods, the sensitivity values were very low (less than 0.350). The results of this study indicate that, in terms of sensitivity, specificity, and overall classification accuracy, the support vector machine model ranks first among all the classifiers tested in the prediction of diabetes. Therefore, this approach is a promising classifier for predicting diabetes, and it should be further investigated for the prediction of other diseases.

Discrimination of crop and weeds on visible and visible/near-infrared spectrums using support vector machines, artificial neural network and decision tree

USDA-ARS?s Scientific Manuscript database

Weeds are regarded as farmers' natural enemy. In order to avoid excessive pesticide residues, the destruction of ecological environment, and to guarantee the quality and safety of agricultural products, it is urgent to develop highly-efficient weed management methods. Amongst, weed discrimination is...

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

Subject-based discriminative sparse representation model for detection of concealed information.

PubMed

Akhavan, Amir; Moradi, Mohammad Hassan; Vand, Safa Rafiei

2017-05-01

The use of machine learning approaches in concealed information test (CIT) plays a key role in the progress of this neurophysiological field. In this paper, we presented a new machine learning method for CIT in which each subject is considered independent of the others. The main goal of this study is to adapt the discriminative sparse models to be applicable for subject-based concealed information test. In order to provide sufficient discriminability between guilty and innocent subjects, we introduced a novel discriminative sparse representation model and its appropriate learning methods. For evaluation of the method forty-four subjects participated in a mock crime scenario and their EEG data were recorded. As the model input, in this study the recurrence plot features were extracted from single trial data of different stimuli. Then the extracted feature vectors were reduced using statistical dependency method. The reduced feature vector went through the proposed subject-based sparse model in which the discrimination power of sparse code and reconstruction error were applied simultaneously. Experimental results showed that the proposed approach achieved better performance than other competing discriminative sparse models. The classification accuracy, sensitivity and specificity of the presented sparsity-based method were about 93%, 91% and 95% respectively. Using the EEG data of a single subject in response to different stimuli types and with the aid of the proposed discriminative sparse representation model, one can distinguish guilty subjects from innocent ones. Indeed, this property eliminates the necessity of several subject EEG data in model learning and decision making for a specific subject. Copyright © 2017 Elsevier B.V. All rights reserved.

Age, class and race discrimination: their interactions and associations with mental health among Brazilian university students.

PubMed

Bastos, João Luiz; Barros, Aluisio J D; Celeste, Roger Keller; Paradies, Yin; Faerstein, Eduardo

2014-01-01

Although research on discrimination and health has progressed significantly, it has tended to focus on racial discrimination and US populations. This study explored different types of discrimination, their interactions and associations with common mental disorders among Brazilian university students, in Rio de Janeiro in 2010. Associations between discrimination and common mental disorders were examined using multiple logistic regression models, adjusted for confounders. Interactions between discrimination and socio-demographics were tested. Discrimination attributed to age, class and skin color/race were the most frequently reported. In a fully adjusted model, discrimination attributed to skin color/race and class were both independently associated with increased odds of common mental disorders. The simultaneous reporting of skin color/race, class and age discrimination was associated with the highest odds ratio. No significant interactions were found. Skin color/race and class discrimination were important, but their simultaneous reporting, in conjunction with age discrimination, were associated with the highest occurrence of common mental disorders.

[Fast discrimination of edible vegetable oil based on Raman spectroscopy].

PubMed

Zhou, Xiu-Jun; Dai, Lian-Kui; Li, Sheng

2012-07-01

A novel method to fast discriminate edible vegetable oils by Raman spectroscopy is presented. The training set is composed of different edible vegetable oils with known classes. Based on their original Raman spectra, baseline correction and normalization were applied to obtain standard spectra. Two characteristic peaks describing the unsaturated degree of vegetable oil were selected as feature vectors; then the centers of all classes were calculated. For an edible vegetable oil with unknown class, the same pretreatment and feature extraction methods were used. The Euclidian distances between the feature vector of the unknown sample and the center of each class were calculated, and the class of the unknown sample was finally determined by the minimum distance. For 43 edible vegetable oil samples from seven different classes, experimental results show that the clustering effect of each class was more obvious and the class distance was much larger with the new feature extraction method compared with PCA. The above classification model can be applied to discriminate unknown edible vegetable oils rapidly and accurately.

Attention Effects on Neural Population Representations for Shape and Location Are Stronger in the Ventral than Dorsal Stream

PubMed Central

2018-01-01

Abstract We examined how attention causes neural population representations of shape and location to change in ventral stream (AIT) and dorsal stream (LIP). Monkeys performed two identical delayed-match-to-sample (DMTS) tasks, attending either to shape or location. In AIT, shapes were more discriminable when directing attention to shape rather than location, measured by an increase in mean distance between population response vectors. In LIP, attending to location rather than shape did not increase the discriminability of different stimulus locations. Even when factoring out the change in mean vector response distance, multidimensional scaling (MDS) still showed a significant task difference in AIT, but not LIP, indicating that beyond increasing discriminability, attention also causes a nonlinear warping of representation space in AIT. Despite single-cell attentional modulations in both areas, our data show that attentional modulations of population representations are weaker in LIP, likely due to a need to maintain veridical representations for visuomotor control. PMID:29876521

Bayesian anomaly detection in monitoring data applying relevance vector machine

NASA Astrophysics Data System (ADS)

Saito, Tomoo

2011-04-01

A method for automatically classifying the monitoring data into two categories, normal and anomaly, is developed in order to remove anomalous data included in the enormous amount of monitoring data, applying the relevance vector machine (RVM) to a probabilistic discriminative model with basis functions and their weight parameters whose posterior PDF (probabilistic density function) conditional on the learning data set is given by Bayes' theorem. The proposed framework is applied to actual monitoring data sets containing some anomalous data collected at two buildings in Tokyo, Japan, which shows that the trained models discriminate anomalous data from normal data very clearly, giving high probabilities of being normal to normal data and low probabilities of being normal to anomalous data.

Financial Distress Prediction using Linear Discriminant Analysis and Support Vector Machine

NASA Astrophysics Data System (ADS)

Santoso, Noviyanti; Wibowo, Wahyu

2018-03-01

A financial difficulty is the early stages before the bankruptcy. Bankruptcies caused by the financial distress can be seen from the financial statements of the company. The ability to predict financial distress became an important research topic because it can provide early warning for the company. In addition, predicting financial distress is also beneficial for investors and creditors. This research will be made the prediction model of financial distress at industrial companies in Indonesia by comparing the performance of Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) combined with variable selection technique. The result of this research is prediction model based on hybrid Stepwise-SVM obtains better balance among fitting ability, generalization ability and model stability than the other models.

Differential autoshaping to common and distinctive elements of positive and negative discriminative stimuli1

PubMed Central

Wasserman, Edward A.; Anderson, Patricia A.

1974-01-01

The learning by hungry pigeons of a discrimination between two successively presented compound visual stimuli was investigated using a two-key autoshaping procedure. Common and distinctive stimulus elements were simultaneously presented on separate keys and either followed by food delivery, S+, or not, S−. The subjects acquired both between-trial and within-trial discriminations. On S+ trials, pigeons pecked the distinctive stimulus more than the common stimulus; before responding ceased on S− trials, they pecked the common stimulus more than the distinctive one. Mastery of the within-display discrimination during S+ trials preceded mastery of the between-trials discrimination. These findings extend the Jenkins-Sainsbury analysis of discriminations based upon a single distinguishing feature to discriminations in which common and distinctive elements are associated with both the positive and negative discriminative stimuli. The similarity of these findings to other effects found in autoshaping—approach to signals that forecast reinforcement and withdrawal from signals that forecast nonreinforcement—is also discussed. PMID:16811812

Differential autoshaping to common and distinctive elements of positive and negative discriminative stimuli.

PubMed

Wasserman, E A; Anderson, P A

1974-11-01

The learning by hungry pigeons of a discrimination between two successively presented compound visual stimuli was investigated using a two-key autoshaping procedure. Common and distinctive stimulus elements were simultaneously presented on separate keys and either followed by food delivery, S+, or not, S-. The subjects acquired both between-trial and within-trial discriminations. On S+ trials, pigeons pecked the distinctive stimulus more than the common stimulus; before responding ceased on S- trials, they pecked the common stimulus more than the distinctive one. Mastery of the within-display discrimination during S+ trials preceded mastery of the between-trials discrimination. These findings extend the Jenkins-Sainsbury analysis of discriminations based upon a single distinguishing feature to discriminations in which common and distinctive elements are associated with both the positive and negative discriminative stimuli. The similarity of these findings to other effects found in autoshaping-approach to signals that forecast reinforcement and withdrawal from signals that forecast nonreinforcement-is also discussed.

Discrimination of tooth layers and dental restorative materials using cutting sounds.

PubMed

Zakeri, Vahid; Arzanpour, Siamak; Chehroudi, Babak

2015-03-01

Dental restoration begins with removing carries and affected tissues with air-turbine rotary cutting handpieces, and later restoring the lost tissues with appropriate restorative materials to retain the functionality. Most restoration materials eventually fail as they age and need to be replaced. One of the difficulties in replacing failing restorations is discerning the boundary of restorative materials, which causes inadvertent removal of healthy tooth layers. Developing an objective and sensor-based method is a promising approach to monitor dental restorative operations and to prevent excessive tooth losses. This paper has analyzed cutting sounds of an air-turbine handpiece to discriminate between tooth layers and two commonly used restorative materials, amalgam and composite. Support vector machines were employed for classification, and the averaged short-time Fourier transform coefficients were selected as the features. The classifier performance was evaluated from different aspects such as the number of features, feature scaling methods, classification schemes, and utilized kernels. The total classification accuracies were 89% and 92% for cases included composite and amalgam materials, respectively. The obtained results indicated the feasibility and effectiveness of the proposed method.

Asynchronous arterial systolic expansion as a marker of vascular aging: assessment of the carotid artery with velocity vector imaging.

PubMed

Yang, Woo-In; Shim, Chi Y; Bang, Woo D; Oh, Chang M; Chang, Hyuk J; Chung, Namsik; Ha, Jong-Won

2011-12-01

Arterial elastic properties change with aging. Measurements of pulse wave velocity and augmentation index are useful for the evaluation of arterial stiffness. However, they likely represent only global characteristics of the arterial tree rather than local vascular alterations. The aim of this study was to evaluate whether local vascular properties assessed by velocity vector imaging differed with aging. Vascular properties of carotid arteries with ages were assessed in 100 healthy volunteers (52 men) ranging from 20 to 68 years using velocity vector imaging. The peak circumferential strain and strain rate of the six segments in left common carotid arteries were analyzed and the standard deviation of the time to peak circumferential strain and strain rate of the six segments, representing the synchronicity of the arterial expansion, were calculated. Central blood pressure, augmentation index and pulse wave velocity were assessed by commercially available radial artery tonometry, the SphygmoCor system (AtCor Medical, West Ryde, Australia). A validated generalized transfer function was used to acquire the central aortic pressures and pressure waveforms. Pulse wave velocity, augmentation index and velocity vector imaging parameters showed significant changes with age. However, the age-related changes in pulse wave velocity, augmentation index and velocity vector imaging parameters were different. The increase in pulse wave velocity was more prominent in older individuals, whereas the changes in augmentation index and carotid strain and strain rate were evident earlier, at the age of 30 years. Unlike augmentation index, which showed little change in older individuals, the standard deviation of time to peak strain and strain rate showed a steady increase from younger to older individuals. Asynchronous arterial expansion could be a useful discriminative marker of vascular aging independent of individual's age.

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.

Factor analysis of auto-associative neural networks with application in speaker verification.

PubMed

Garimella, Sri; Hermansky, Hynek

2013-04-01

Auto-associative neural network (AANN) is a fully connected feed-forward neural network, trained to reconstruct its input at its output through a hidden compression layer, which has fewer numbers of nodes than the dimensionality of input. AANNs are used to model speakers in speaker verification, where a speaker-specific AANN model is obtained by adapting (or retraining) the universal background model (UBM) AANN, an AANN trained on multiple held out speakers, using corresponding speaker data. When the amount of speaker data is limited, this adaptation procedure may lead to overfitting as all the parameters of UBM-AANN are adapted. In this paper, we introduce and develop the factor analysis theory of AANNs to alleviate this problem. We hypothesize that only the weight matrix connecting the last nonlinear hidden layer and the output layer is speaker-specific, and further restrict it to a common low-dimensional subspace during adaptation. The subspace is learned using large amounts of development data, and is held fixed during adaptation. Thus, only the coordinates in a subspace, also known as i-vector, need to be estimated using speaker-specific data. The update equations are derived for learning both the common low-dimensional subspace and the i-vectors corresponding to speakers in the subspace. The resultant i-vector representation is used as a feature for the probabilistic linear discriminant analysis model. The proposed system shows promising results on the NIST-08 speaker recognition evaluation (SRE), and yields a 23% relative improvement in equal error rate over the previously proposed weighted least squares-based subspace AANNs system. The experiments on NIST-10 SRE confirm that these improvements are consistent and generalize across datasets.

DIRProt: a computational approach for discriminating insecticide resistant proteins from non-resistant proteins.

PubMed

Meher, Prabina Kumar; Sahu, Tanmaya Kumar; Banchariya, Anjali; Rao, Atmakuri Ramakrishna

2017-03-24

Insecticide resistance is a major challenge for the control program of insect pests in the fields of crop protection, human and animal health etc. Resistance to different insecticides is conferred by the proteins encoded from certain class of genes of the insects. To distinguish the insecticide resistant proteins from non-resistant proteins, no computational tool is available till date. Thus, development of such a computational tool will be helpful in predicting the insecticide resistant proteins, which can be targeted for developing appropriate insecticides. Five different sets of feature viz., amino acid composition (AAC), di-peptide composition (DPC), pseudo amino acid composition (PAAC), composition-transition-distribution (CTD) and auto-correlation function (ACF) were used to map the protein sequences into numeric feature vectors. The encoded numeric vectors were then used as input in support vector machine (SVM) for classification of insecticide resistant and non-resistant proteins. Higher accuracies were obtained under RBF kernel than that of other kernels. Further, accuracies were observed to be higher for DPC feature set as compared to others. The proposed approach achieved an overall accuracy of >90% in discriminating resistant from non-resistant proteins. Further, the two classes of resistant proteins i.e., detoxification-based and target-based were discriminated from non-resistant proteins with >95% accuracy. Besides, >95% accuracy was also observed for discrimination of proteins involved in detoxification- and target-based resistance mechanisms. The proposed approach not only outperformed Blastp, PSI-Blast and Delta-Blast algorithms, but also achieved >92% accuracy while assessed using an independent dataset of 75 insecticide resistant proteins. This paper presents the first computational approach for discriminating the insecticide resistant proteins from non-resistant proteins. Based on the proposed approach, an online prediction server DIRProt has also been developed for computational prediction of insecticide resistant proteins, which is accessible at http://cabgrid.res.in:8080/dirprot/ . The proposed approach is believed to supplement the efforts needed to develop dynamic insecticides in wet-lab by targeting the insecticide resistant proteins.

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

Research of facial feature extraction based on MMC

NASA Astrophysics Data System (ADS)

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

2017-07-01

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

The simulation on diode-clamped five-level converters common-mode voltage suppression with zero-vector PWM strategy

NASA Astrophysics Data System (ADS)

Zhang, Yonggao; Gao, Yanli; Long, Lizhong

2012-04-01

More and more researchers have great concern on the issue of Common-mode voltage (CMV) in high voltage large power converter. A novel common-mode voltage suppression scheme based on zero-vector PWM strategy (ZVPWM) is present in this paper. Taking a diode-clamped five-level converter as example, the principle of zero vector PWM common-mode voltage (ZCMVPWM) suppression method is studied in detail. ZCMVPWM suppression strategy is including four important parts, which are locating the sector of reference voltage vector, locating the small triangular sub-sector of reference voltage vector, reference vector synthesis, and calculating the operating time of vector. The principles of four important pars are illustrated in detail and the corresponding MATLAB models are established. System simulation and experimental results are provided. It gives some consultation value for the development and research of multi-level converters.

Man-Portable Vector (MPV) EMI Sensor for UXO Contamination Assessment at Former Spencer Artillery Range, Tennessee

DTIC Science & Technology

2013-04-16

instantaneous polarization model ( Pasion and Oldenburg, 2001). The target polarizability decay parameters are the main features for the ensuing...Bell, T., Geo-location Requirements for UXO Discrimination. SERDP Geo-location Workshop, 2005. Billings, S., Pasion , L., Lhomme, N. and Oldenburg...Report. ESTCP Project MR-201005, 2011b. Pasion , L. & Oldenburg, D., A Discrimination Algorithm for UXO Using Time Domain Electromagnetics. Journal of

Near infrared spectroscopy is suitable for the classification of hazelnuts according to Protected Designation of Origin.

PubMed

Moscetti, Roberto; Radicetti, Emanuele; Monarca, Danilo; Cecchini, Massimo; Massantini, Riccardo

2015-10-01

This study investigates the possibility of using near infrared spectroscopy for the authentication of the 'Nocciola Romana' hazelnut (Corylus avellana L. cvs Tonda Gentile Romana and Nocchione) as a Protected Designation of Origin (PDO) hazelnut from central Italy. Algorithms for the selection of the optimal pretreatments were tested in combination with the following discriminant routines: k-nearest neighbour, soft independent modelling of class analogy, partial least squares discriminant analysis and support vector machine discriminant analysis. The best results were obtained using a support vector machine discriminant analysis routine. Thus, classification performance rates with specificities, sensitivities and accuracies as high as 96.0%, 95.0% and 95.5%, respectively, were achieved. Various pretreatments, such as standard normal variate, mean centring and a Savitzky-Golay filter with seven smoothing points, were used. The optimal wavelengths for classification were mainly correlated with lipids, although some contribution from minor constituents, such as proteins and carbohydrates, was also observed. Near infrared spectroscopy could classify hazelnut according to the PDO 'Nocciola Romana' designation. Thus, the experimentation lays the foundations for a rapid, online, authentication system for hazelnut. However, model robustness should be improved taking into account agro-pedo-climatic growing conditions. © 2014 Society of Chemical Industry.

Characterizing Higgs portal dark matter models at the ILC

NASA Astrophysics Data System (ADS)

Kamon, Teruki; Ko, P.; Li, Jinmian

2017-09-01

We study the dark matter (DM) discovery prospect and its spin discrimination in the theoretical framework of gauge invariant and renormalizable Higgs portal DM models at the ILC with √{s} = 500 GeV. In such models, the DM pair is produced in association with a Z boson. In the case of the singlet scalar DM, the mediator is just the SM Higgs boson, whereas for the fermion or vector DM there is an additional singlet scalar mediator that mixes with the SM Higgs boson, which produces significant observable differences. After careful investigation of the signal and backgrounds both at parton level and at detector level, we find the signal with hadronically decaying Z boson provides a better search sensitivity than the signal with leptonically decaying Z boson. Taking the fermion DM model as a benchmark scenario, when the DM-mediator coupling g_χ is relatively small, the DM signals are discoverable only for benchmark points with relatively light scalar mediator H_2. The spin discriminating from scalar DM is always promising, while it is difficult to discriminate from vector DM. As for g_χ approaching the perturbative limit, benchmark points with the mediator H_2 in the full mass region of interest are discoverable. The spin discriminating aspects from both the scalar and the fermion DM are quite promising.

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.

Highly Accurate Classification of Watson-Crick Basepairs on Termini of Single DNA Molecules

PubMed Central

Winters-Hilt, Stephen; Vercoutere, Wenonah; DeGuzman, Veronica S.; Deamer, David; Akeson, Mark; Haussler, David

2003-01-01

We introduce a computational method for classification of individual DNA molecules measured by an α-hemolysin channel detector. We show classification with better than 99% accuracy for DNA hairpin molecules that differ only in their terminal Watson-Crick basepairs. Signal classification was done in silico to establish performance metrics (i.e., where train and test data were of known type, via single-species data files). It was then performed in solution to assay real mixtures of DNA hairpins. Hidden Markov Models (HMMs) were used with Expectation/Maximization for denoising and for associating a feature vector with the ionic current blockade of the DNA molecule. Support Vector Machines (SVMs) were used as discriminators, and were the focus of off-line training. A multiclass SVM architecture was designed to place less discriminatory load on weaker discriminators, and novel SVM kernels were used to boost discrimination strength. The tuning on HMMs and SVMs enabled biophysical analysis of the captured molecule states and state transitions; structure revealed in the biophysical analysis was used for better feature selection. PMID:12547778

Speed tuning of motion segmentation and discrimination

NASA Technical Reports Server (NTRS)

Masson, G. S.; Mestre, D. R.; Stone, L. S.

1999-01-01

Motion transparency requires that the visual system distinguish different motion vectors and selectively integrate similar motion vectors over space into the perception of multiple surfaces moving through or over each other. Using large-field (7 degrees x 7 degrees) displays containing two populations of random-dots moving in the same (horizontal) direction but at different speeds, we examined speed-based segmentation by measuring the speed difference above which observers can perceive two moving surfaces. We systematically investigated this 'speed-segmentation' threshold as a function of speed and stimulus duration, and found that it increases sharply for speeds above approximately 8 degrees/s. In addition, speed-segmentation thresholds decrease with stimulus duration out to approximately 200 ms. In contrast, under matched conditions, speed-discrimination thresholds stay low at least out to 16 degrees/s and decrease with increasing stimulus duration at a faster rate than for speed segmentation. Thus, motion segmentation and motion discrimination exhibit different speed selectivity and different temporal integration characteristics. Results are discussed in terms of the speed preferences of different neuronal populations within the primate visual cortex.

Predicting complications of percutaneous coronary intervention using a novel support vector method.

PubMed

Lee, Gyemin; Gurm, Hitinder S; Syed, Zeeshan

2013-01-01

To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI). Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered. The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer-Lemeshow χ(2) value (seven cases) and the mean cross-entropy error (eight cases). The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains.

Predicting complications of percutaneous coronary intervention using a novel support vector method

PubMed Central

Lee, Gyemin; Gurm, Hitinder S; Syed, Zeeshan

2013-01-01

Objective To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI). Materials and methods Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered. Results The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer–Lemeshow χ2 value (seven cases) and the mean cross-entropy error (eight cases). Conclusions The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains. PMID:23599229

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.

Common Control by Compound Samples in Conditional Discriminations

ERIC Educational Resources Information Center

Perez-Gonzalez, Luis Antonio; Alonso-Alvarez, Benigno

2008-01-01

We tested whether teaching control by single stimulus samples in conditional discriminations would result in common control of two-stimuli compound samples, and vice versa. In Experiment 1, 5 participants were first taught four single-sample conditional discriminations. The first conditional discrimination was as follows: given sample stimulus P1,…

49 CFR 374.101 - Discrimination prohibited.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 5 2012-10-01 2012-10-01 false Discrimination prohibited. 374.101 Section 374.101... REGULATIONS Discrimination in Operations of Interstate Motor Common Carriers of Passengers § 374.101 Discrimination prohibited. No motor common carrier of passengers subject to 49 U.S.C. subtitle IV, part B shall...

49 CFR 374.101 - Discrimination prohibited.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 5 2011-10-01 2011-10-01 false Discrimination prohibited. 374.101 Section 374.101... REGULATIONS Discrimination in Operations of Interstate Motor Common Carriers of Passengers § 374.101 Discrimination prohibited. No motor common carrier of passengers subject to 49 U.S.C. subtitle IV, part B shall...

49 CFR 374.101 - Discrimination prohibited.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 5 2010-10-01 2010-10-01 false Discrimination prohibited. 374.101 Section 374.101... REGULATIONS Discrimination in Operations of Interstate Motor Common Carriers of Passengers § 374.101 Discrimination prohibited. No motor common carrier of passengers subject to 49 U.S.C. subtitle IV, part B shall...

49 CFR 374.101 - Discrimination prohibited.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 5 2014-10-01 2014-10-01 false Discrimination prohibited. 374.101 Section 374.101... REGULATIONS Discrimination in Operations of Interstate Motor Common Carriers of Passengers § 374.101 Discrimination prohibited. No motor common carrier of passengers subject to 49 U.S.C. subtitle IV, part B shall...

49 CFR 374.101 - Discrimination prohibited.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 5 2013-10-01 2013-10-01 false Discrimination prohibited. 374.101 Section 374.101... REGULATIONS Discrimination in Operations of Interstate Motor Common Carriers of Passengers § 374.101 Discrimination prohibited. No motor common carrier of passengers subject to 49 U.S.C. subtitle IV, part B shall...

Nearest Neighbor Algorithms for Pattern Classification

NASA Technical Reports Server (NTRS)

Barrios, J. O.

1972-01-01

A solution of the discrimination problem is considered by means of the minimum distance classifier, commonly referred to as the nearest neighbor (NN) rule. The NN rule is nonparametric, or distribution free, in the sense that it does not depend on any assumptions about the underlying statistics for its application. The k-NN rule is a procedure that assigns an observation vector z to a category F if most of the k nearby observations x sub i are elements of F. The condensed nearest neighbor (CNN) rule may be used to reduce the size of the training set required categorize The Bayes risk serves merely as a reference-the limit of excellence beyond which it is not possible to go. The NN rule is bounded below by the Bayes risk and above by twice the Bayes risk.

Discrimination and common mental disorders of undergraduate students of the Universidade Federal de Santa Catarina.

PubMed

de Souza, Maria Vitória Cordeiro; Lemkuhl, Isabel; Bastos, João Luiz

2015-01-01

The pathogenic and consistent effect of discrimination on mental health has been largely documented in the literature. However, there are few studies measuring multiple types of discrimination, evaluating the existence of a dose-response relationship or investigating possible effect modifiers of such an association. To investigate the association between experiences of discrimination attributed to multiple reasons and common mental disorders, including the adjustment for potential confounders, assessment of dose-response relations, and examination of effect modifiers in undergraduate students from southern Brazil. In the first semester of 2012, 1,023 students from the Universidade Federal de Santa Catarina answered a self-administered questionnaire on socio-demographic characteristics, undergraduate course, experiences of discrimination and common mental disorders. Associations were analyzed through logistic regression models, estimation of Odds Ratios and 95% confidence intervals (95%CI). The study results showed that students reporting discrimination at high frequency and intensity were 4.4 (95%CI 1.6 - 12.4) times more likely to present common mental disorders. However, the relationship between discrimination and common mental disorders was protective among Electrical Engineering students, when compared to Accounting Sciences students who did not report discrimination. The findings suggest that the dose-response relationship between experiences of discrimination and common mental disorders reinforces the hypothetical causal nature of this association. Nevertheless, the modification of effect caused by the undergraduate course should be considered in future studies for a better understanding and measurement of both phenomena.

Correlated Topic Vector for Scene Classification.

PubMed

Wei, Pengxu; Qin, Fei; Wan, Fang; Zhu, Yi; Jiao, Jianbin; Ye, Qixiang

2017-07-01

Scene images usually involve semantic correlations, particularly when considering large-scale image data sets. This paper proposes a novel generative image representation, correlated topic vector, to model such semantic correlations. Oriented from the correlated topic model, correlated topic vector intends to naturally utilize the correlations among topics, which are seldom considered in the conventional feature encoding, e.g., Fisher vector, but do exist in scene images. It is expected that the involvement of correlations can increase the discriminative capability of the learned generative model and consequently improve the recognition accuracy. Incorporated with the Fisher kernel method, correlated topic vector inherits the advantages of Fisher vector. The contributions to the topics of visual words have been further employed by incorporating the Fisher kernel framework to indicate the differences among scenes. Combined with the deep convolutional neural network (CNN) features and Gibbs sampling solution, correlated topic vector shows great potential when processing large-scale and complex scene image data sets. Experiments on two scene image data sets demonstrate that correlated topic vector improves significantly the deep CNN features, and outperforms existing Fisher kernel-based features.

A Semisupervised Support Vector Machines Algorithm for BCI Systems

PubMed Central

Qin, Jianzhao; Li, Yuanqing; Sun, Wei

2007-01-01

As an emerging technology, brain-computer interfaces (BCIs) bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP) is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm. PMID:18368141

Potential of cancer screening with serum surface-enhanced Raman spectroscopy and a support vector machine

NASA Astrophysics Data System (ADS)

Li, S. X.; Zhang, Y. J.; Zeng, Q. Y.; Li, L. F.; Guo, Z. Y.; Liu, Z. M.; Xiong, H. L.; Liu, S. H.

2014-06-01

Cancer is the most common disease to threaten human health. The ability to screen individuals with malignant tumours with only a blood sample would be greatly advantageous to early diagnosis and intervention. This study explores the possibility of discriminating between cancer patients and normal subjects with serum surface-enhanced Raman spectroscopy (SERS) and a support vector machine (SVM) through a peripheral blood sample. A total of 130 blood samples were obtained from patients with liver cancer, colonic cancer, esophageal cancer, nasopharyngeal cancer, gastric cancer, as well as 113 blood samples from normal volunteers. Several diagnostic models were built with the serum SERS spectra using SVM and principal component analysis (PCA) techniques. The results show that a diagnostic accuracy of 85.5% is acquired with a PCA algorithm, while a diagnostic accuracy of 95.8% is obtained using radial basis function (RBF), PCA-SVM methods. The results prove that a RBF kernel PCA-SVM technique is superior to PCA and conventional SVM (C-SVM) algorithms in classification serum SERS spectra. The study demonstrates that serum SERS, in combination with SVM techniques, has great potential for screening cancerous patients with any solid malignant tumour through a peripheral blood sample.

Development of a rapid method for the automatic classification of biological agents' fluorescence spectral signatures

NASA Astrophysics Data System (ADS)

Carestia, Mariachiara; Pizzoferrato, Roberto; Gelfusa, Michela; Cenciarelli, Orlando; Ludovici, Gian Marco; Gabriele, Jessica; Malizia, Andrea; Murari, Andrea; Vega, Jesus; Gaudio, Pasquale

2015-11-01

Biosecurity and biosafety are key concerns of modern society. Although nanomaterials are improving the capacities of point detectors, standoff detection still appears to be an open issue. Laser-induced fluorescence of biological agents (BAs) has proved to be one of the most promising optical techniques to achieve early standoff detection, but its strengths and weaknesses are still to be fully investigated. In particular, different BAs tend to have similar fluorescence spectra due to the ubiquity of biological endogenous fluorophores producing a signal in the UV range, making data analysis extremely challenging. The Universal Multi Event Locator (UMEL), a general method based on support vector regression, is commonly used to identify characteristic structures in arrays of data. In the first part of this work, we investigate fluorescence emission spectra of different simulants of BAs and apply UMEL for their automatic classification. In the second part of this work, we elaborate a strategy for the application of UMEL to the discrimination of different BAs' simulants spectra. Through this strategy, it has been possible to discriminate between these BAs' simulants despite the high similarity of their fluorescence spectra. These preliminary results support the use of SVR methods to classify BAs' spectral signatures.

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

NASA Astrophysics Data System (ADS)

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

2013-02-01

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

A Machine Learning Approach to the Detection of Pilot's Reaction to Unexpected Events Based on EEG Signals

PubMed Central

Cyran, Krzysztof A.

2018-01-01

This work considers the problem of utilizing electroencephalographic signals for use in systems designed for monitoring and enhancing the performance of aircraft pilots. Systems with such capabilities are generally referred to as cognitive cockpits. This article provides a description of the potential that is carried by such systems, especially in terms of increasing flight safety. Additionally, a neuropsychological background of the problem is presented. Conducted research was focused mainly on the problem of discrimination between states of brain activity related to idle but focused anticipation of visual cue and reaction to it. Especially, a problem of selecting a proper classification algorithm for such problems is being examined. For that purpose an experiment involving 10 subjects was planned and conducted. Experimental electroencephalographic data was acquired using an Emotiv EPOC+ headset. Proposed methodology involved use of a popular method in biomedical signal processing, the Common Spatial Pattern, extraction of bandpower features, and an extensive test of different classification algorithms, such as Linear Discriminant Analysis, k-nearest neighbors, and Support Vector Machines with linear and radial basis function kernels, Random Forests, and Artificial Neural Networks. PMID:29849544

Automatic assessment of mitral regurgitation severity based on extensive textural features on 2D echocardiography videos.

PubMed

Moghaddasi, Hanie; Nourian, Saeed

2016-06-01

Heart disease is the major cause of death as well as a leading cause of disability in the developed countries. Mitral Regurgitation (MR) is a common heart disease which does not cause symptoms until its end stage. Therefore, early diagnosis of the disease is of crucial importance in the treatment process. Echocardiography is a common method of diagnosis in the severity of MR. Hence, a method which is based on echocardiography videos, image processing techniques and artificial intelligence could be helpful for clinicians, especially in borderline cases. In this paper, we introduce novel features to detect micro-patterns of echocardiography images in order to determine the severity of MR. Extensive Local Binary Pattern (ELBP) and Extensive Volume Local Binary Pattern (EVLBP) are presented as image descriptors which include details from different viewpoints of the heart in feature vectors. Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Template Matching techniques are used as classifiers to determine the severity of MR based on textural descriptors. The SVM classifier with Extensive Uniform Local Binary Pattern (ELBPU) and Extensive Volume Local Binary Pattern (EVLBP) have the best accuracy with 99.52%, 99.38%, 99.31% and 99.59%, respectively, for the detection of Normal, Mild MR, Moderate MR and Severe MR subjects among echocardiography videos. The proposed method achieves 99.38% sensitivity and 99.63% specificity for the detection of the severity of MR and normal subjects. Copyright © 2016 Elsevier Ltd. All rights reserved.

Man-portable Vector Time Domain EMI Sensor and Discrimination Processing

DTIC Science & Technology

2012-04-16

points of each winding are coincident. Each receiver coil is wound helically on a set of 10 grooves etched on the surface of the cube; 36- gauge wire...subset of the data, and inject various levels of noise into the position of the MPV in order to gauge the robustness of the discrimination results...as possible. The quantity φ also provides a metric to gauge goodness of fit, being essentially an average percent error: Benjamin Barrowes, Kevin

Characterizing Higgs portal dark matter models at the ILC

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

Kamon, Teruki; Ko, P.; Li, Jinmian

For this article, we study the dark matter (DM) discovery prospect and its spin discrimination in the theoretical framework of gauge invariant and renormalizable Higgs portal DM models at the ILC withmore » $$\\sqrt{s}$$= 500 GeV. In such models, the DM pair is produced in association with a Z boson. In the case of the singlet scalar DM, the mediator is just the SM Higgs boson, whereas for the fermion or vector DM there is an additional singlet scalar mediator that mixes with the SM Higgs boson, which produces significant observable differences. After careful investigation of the signal and backgrounds both at parton level and at detector level, we find the signal with hadronically decaying Z boson provides a better search sensitivity than the signal with leptonically decaying Z boson. Taking the fermion DM model as a benchmark scenario, when the DM-mediator coupling g χ is relatively small, the DM signals are discoverable only for benchmark points with relatively light scalar mediator H 2. The spin discriminating from scalar DM is always promising, while it is difficult to discriminate from vector DM. As for g χ approaching the perturbative limit, benchmark points with the mediator H 2 in the full mass region of interest are discoverable. The spin discriminating aspects from both the scalar and the fermion DM are quite promising.« less

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.

Characterizing Higgs portal dark matter models at the ILC

DOE PAGES

Kamon, Teruki; Ko, P.; Li, Jinmian

2017-09-27

For this article, we study the dark matter (DM) discovery prospect and its spin discrimination in the theoretical framework of gauge invariant and renormalizable Higgs portal DM models at the ILC withmore » $$\\sqrt{s}$$= 500 GeV. In such models, the DM pair is produced in association with a Z boson. In the case of the singlet scalar DM, the mediator is just the SM Higgs boson, whereas for the fermion or vector DM there is an additional singlet scalar mediator that mixes with the SM Higgs boson, which produces significant observable differences. After careful investigation of the signal and backgrounds both at parton level and at detector level, we find the signal with hadronically decaying Z boson provides a better search sensitivity than the signal with leptonically decaying Z boson. Taking the fermion DM model as a benchmark scenario, when the DM-mediator coupling g χ is relatively small, the DM signals are discoverable only for benchmark points with relatively light scalar mediator H 2. The spin discriminating from scalar DM is always promising, while it is difficult to discriminate from vector DM. As for g χ approaching the perturbative limit, benchmark points with the mediator H 2 in the full mass region of interest are discoverable. The spin discriminating aspects from both the scalar and the fermion DM are quite promising.« less

Feeding profile of Mepraia spinolai, a sylvatic vector of Chagas disease in Chile.

PubMed

Chacón, F; Bacigalupo, A; Quiroga, J F; Ferreira, A; Cattan, P E; Ramírez-Toloza, G

2016-10-01

American trypanosomiasis is a chronic disease transmitted mainly by vectors. The hematophagous triatomine vectors transmit Trypanosoma cruzi to a wide variety of mammals, which usually are their food source. This study determined the feeding profile of Mepraia spinolai, a sylvatic triatomine vector, present in endemic areas of Chile. Vectors were captured in the north-central area of Chile. Samples of intestinal contents were analyzed by an Enzyme-linked immunosorbent assay (ELISA) that identifies and discriminates the presence of serum antigens from Homo sapiens and nine animal species (Canis familiaris, Felis catus, Capra hircus, Mus musculus, Gallus gallus, Octodon degus, Thylamys elegans, Phyllotis darwini and Oryctolagus cuniculus). Our data indicate the most frequent feeding source in this area was P. darwini, followed by O. degus, O. cuniculus, M. musculus, G. gallus, T. elegans, C. familiaris, F. catus and C. hircus. Mixed food sources were also identified. Copyright © 2016 Elsevier B.V. All rights reserved.

Signal peptide discrimination and cleavage site identification using SVM and NN.

PubMed

Kazemian, H B; Yusuf, S A; White, K

2014-02-01

About 15% of all proteins in a genome contain a signal peptide (SP) sequence, at the N-terminus, that targets the protein to intracellular secretory pathways. Once the protein is targeted correctly in the cell, the SP is cleaved, releasing the mature protein. Accurate prediction of the presence of these short amino-acid SP chains is crucial for modelling the topology of membrane proteins, since SP sequences can be confused with transmembrane domains due to similar composition of hydrophobic amino acids. This paper presents a cascaded Support Vector Machine (SVM)-Neural Network (NN) classification methodology for SP discrimination and cleavage site identification. The proposed method utilises a dual phase classification approach using SVM as a primary classifier to discriminate SP sequences from Non-SP. The methodology further employs NNs to predict the most suitable cleavage site candidates. In phase one, a SVM classification utilises hydrophobic propensities as a primary feature vector extraction using symmetric sliding window amino-acid sequence analysis for discrimination of SP and Non-SP. In phase two, a NN classification uses asymmetric sliding window sequence analysis for prediction of cleavage site identification. The proposed SVM-NN method was tested using Uni-Prot non-redundant datasets of eukaryotic and prokaryotic proteins with SP and Non-SP N-termini. Computer simulation results demonstrate an overall accuracy of 0.90 for SP and Non-SP discrimination based on Matthews Correlation Coefficient (MCC) tests using SVM. For SP cleavage site prediction, the overall accuracy is 91.5% based on cross-validation tests using the novel SVM-NN model. © 2013 Published by Elsevier Ltd.

Enhancement of plant metabolite fingerprinting by machine learning.

PubMed

Scott, Ian M; Vermeer, Cornelia P; Liakata, Maria; Corol, Delia I; Ward, Jane L; Lin, Wanchang; Johnson, Helen E; Whitehead, Lynne; Kular, Baldeep; Baker, John M; Walsh, Sean; Dave, Anuja; Larson, Tony R; Graham, Ian A; Wang, Trevor L; King, Ross D; Draper, John; Beale, Michael H

2010-08-01

Metabolite fingerprinting of Arabidopsis (Arabidopsis thaliana) mutants with known or predicted metabolic lesions was performed by (1)H-nuclear magnetic resonance, Fourier transform infrared, and flow injection electrospray-mass spectrometry. Fingerprinting enabled processing of five times more plants than conventional chromatographic profiling and was competitive for discriminating mutants, other than those affected in only low-abundance metabolites. Despite their rapidity and complexity, fingerprints yielded metabolomic insights (e.g. that effects of single lesions were usually not confined to individual pathways). Among fingerprint techniques, (1)H-nuclear magnetic resonance discriminated the most mutant phenotypes from the wild type and Fourier transform infrared discriminated the fewest. To maximize information from fingerprints, data analysis was crucial. One-third of distinctive phenotypes might have been overlooked had data models been confined to principal component analysis score plots. Among several methods tested, machine learning (ML) algorithms, namely support vector machine or random forest (RF) classifiers, were unsurpassed for phenotype discrimination. Support vector machines were often the best performing classifiers, but RFs yielded some particularly informative measures. First, RFs estimated margins between mutant phenotypes, whose relations could then be visualized by Sammon mapping or hierarchical clustering. Second, RFs provided importance scores for the features within fingerprints that discriminated mutants. These scores correlated with analysis of variance F values (as did Kruskal-Wallis tests, true- and false-positive measures, mutual information, and the Relief feature selection algorithm). ML classifiers, as models trained on one data set to predict another, were ideal for focused metabolomic queries, such as the distinctiveness and consistency of mutant phenotypes. Accessible software for use of ML in plant physiology is highlighted.

Discrimination of malignant lymphomas and leukemia using Radon transform based-higher order spectra

NASA Astrophysics Data System (ADS)

Luo, Yi; Celenk, Mehmet; Bejai, Prashanth

2006-03-01

A new algorithm that can be used to automatically recognize and classify malignant lymphomas and leukemia is proposed in this paper. The algorithm utilizes the morphological watersheds to obtain boundaries of cells from cell images and isolate them from the surrounding background. The areas of cells are extracted from cell images after background subtraction. The Radon transform and higher-order spectra (HOS) analysis are utilized as an image processing tool to generate class feature vectors of different type cells and to extract testing cells' feature vectors. The testing cells' feature vectors are then compared with the known class feature vectors for a possible match by computing the Euclidean distances. The cell in question is classified as belonging to one of the existing cell classes in the least Euclidean distance sense.

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

PubMed

Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

2013-01-01

The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

Detection and quantification of extra virgin olive oil adulteration by means of autofluorescence excitation-emission profiles combined with multi-way classification.

PubMed

Durán Merás, Isabel; Domínguez Manzano, Jaime; Airado Rodríguez, Diego; Muñoz de la Peña, Arsenio

2018-02-01

Within olive oils, extra virgin olive oil is the highest quality and, in consequence, the most expensive one. Because of that, it is common that some merchants attempt to take economic advantage by mixing it up with other less expensive oils, like olive oil or olive pomace oil. In consequence, the characterization and authentication of extra virgin olive oils is a subject of great interest, both for industry and consumers. This paper reports the potential of front-face total fluorescence spectroscopy combined with second-order chemometric methods for the detection of extra virgin olive oils adulteration with other olive oils. Excitation-emission matrices (EEMs) of extra virgin olive oils and extra virgin olive oils adulterated with olive oils or with olive pomace oils were recorded using front-face fluorescence spectroscopy. The full information content in these fluorescence images was analyzed with the aid of unsupervised parallel factor analysis (PARAFAC), PARAFAC supervised by linear discriminant analysis (LDA-PARAFAC), and discriminant unfolded partial least-squares (DA-UPLS). The discriminant ability of LDA-PARAFAC was studied through the tridimensional plots of the canonical vectors, defining a surface separating the established categories. For DA-UPLS, the discriminant ability was established through the bidimensional plots of predicted values of calibration and validation samples, in order to assign each sample to a given class. The models demonstrated the possibility of detecting adulterations of extra virgin olive oils with percentages of around 15% and 3% of olive and olive pomace oils, respectively. Also, UPLS regression was used to quantify the adulteration level of extra virgin olive oils with olive oils or with olive pomace oils. Copyright © 2017 Elsevier B.V. All rights reserved.

Distance learning in discriminative vector quantization.

PubMed

Schneider, Petra; Biehl, Michael; Hammer, Barbara

2009-10-01

Discriminative vector quantization schemes such as learning vector quantization (LVQ) and extensions thereof offer efficient and intuitive classifiers based on the representation of classes by prototypes. The original methods, however, rely on the Euclidean distance corresponding to the assumption that the data can be represented by isotropic clusters. For this reason, extensions of the methods to more general metric structures have been proposed, such as relevance adaptation in generalized LVQ (GLVQ) and matrix learning in GLVQ. In these approaches, metric parameters are learned based on the given classification task such that a data-driven distance measure is found. In this letter, we consider full matrix adaptation in advanced LVQ schemes. In particular, we introduce matrix learning to a recent statistical formalization of LVQ, robust soft LVQ, and we compare the results on several artificial and real-life data sets to matrix learning in GLVQ, a derivation of LVQ-like learning based on a (heuristic) cost function. In all cases, matrix adaptation allows a significant improvement of the classification accuracy. Interestingly, however, the principled behavior of the models with respect to prototype locations and extracted matrix dimensions shows several characteristic differences depending on the data sets.

The organization of conspecific face space in nonhuman primates

PubMed Central

Parr, Lisa A.; Taubert, Jessica; Little, Anthony C.; Hancock, Peter J. B.

2013-01-01

Humans and chimpanzees demonstrate numerous cognitive specializations for processing faces, but comparative studies with monkeys suggest that these may be the result of recent evolutionary adaptations. The present study utilized the novel approach of face space, a powerful theoretical framework used to understand the representation of face identity in humans, to further explore species differences in face processing. According to the theory, faces are represented by vectors in a multidimensional space, the centre of which is defined by an average face. Each dimension codes features important for describing a face’s identity, and vector length codes the feature’s distinctiveness. Chimpanzees and rhesus monkeys discriminated male and female conspecifics’ faces, rated by humans for their distinctiveness, using a computerized task. Multidimensional scaling analyses showed that the organization of face space was similar between humans and chimpanzees. Distinctive faces had the longest vectors and were the easiest for chimpanzees to discriminate. In contrast, distinctiveness did not correlate with the performance of rhesus monkeys. The feature dimensions for each species’ face space were visualized and described using morphing techniques. These results confirm species differences in the perceptual representation of conspecific faces, which are discussed within an evolutionary framework. PMID:22670823

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images.

PubMed

Zhang, Lefei; Zhang, Qian; Du, Bo; Huang, Xin; Tang, Yuan Yan; Tao, Dacheng

2018-01-01

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature, and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation has not efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient.

Tensor Rank Preserving Discriminant Analysis for Facial Recognition.

PubMed

Tao, Dapeng; Guo, Yanan; Li, Yaotang; Gao, Xinbo

2017-10-12

Facial recognition, one of the basic topics in computer vision and pattern recognition, has received substantial attention in recent years. However, for those traditional facial recognition algorithms, the facial images are reshaped to a long vector, thereby losing part of the original spatial constraints of each pixel. In this paper, a new tensor-based feature extraction algorithm termed tensor rank preserving discriminant analysis (TRPDA) for facial image recognition is proposed; the proposed method involves two stages: in the first stage, the low-dimensional tensor subspace of the original input tensor samples was obtained; in the second stage, discriminative locality alignment was utilized to obtain the ultimate vector feature representation for subsequent facial recognition. On the one hand, the proposed TRPDA algorithm fully utilizes the natural structure of the input samples, and it applies an optimization criterion that can directly handle the tensor spectral analysis problem, thereby decreasing the computation cost compared those traditional tensor-based feature selection algorithms. On the other hand, the proposed TRPDA algorithm extracts feature by finding a tensor subspace that preserves most of the rank order information of the intra-class input samples. Experiments on the three facial databases are performed here to determine the effectiveness of the proposed TRPDA algorithm.

Linear discriminant analysis with misallocation in training samples

NASA Technical Reports Server (NTRS)

Chhikara, R. (Principal Investigator); Mckeon, J.

1982-01-01

Linear discriminant analysis for a two-class case is studied in the presence of misallocation in training samples. A general appraoch to modeling of mislocation is formulated, and the mean vectors and covariance matrices of the mixture distributions are derived. The asymptotic distribution of the discriminant boundary is obtained and the asymptotic first two moments of the two types of error rate given. Certain numerical results for the error rates are presented by considering the random and two non-random misallocation models. It is shown that when the allocation procedure for training samples is objectively formulated, the effect of misallocation on the error rates of the Bayes linear discriminant rule can almost be eliminated. If, however, this is not possible, the use of Fisher rule may be preferred over the Bayes rule.

Supervised non-negative tensor factorization for automatic hyperspectral feature extraction and target discrimination

NASA Astrophysics Data System (ADS)

Anderson, Dylan; Bapst, Aleksander; Coon, Joshua; Pung, Aaron; Kudenov, Michael

2017-05-01

Hyperspectral imaging provides a highly discriminative and powerful signature for target detection and discrimination. Recent literature has shown that considering additional target characteristics, such as spatial or temporal profiles, simultaneously with spectral content can greatly increase classifier performance. Considering these additional characteristics in a traditional discriminative algorithm requires a feature extraction step be performed first. An example of such a pipeline is computing a filter bank response to extract spatial features followed by a support vector machine (SVM) to discriminate between targets. This decoupling between feature extraction and target discrimination yields features that are suboptimal for discrimination, reducing performance. This performance reduction is especially pronounced when the number of features or available data is limited. In this paper, we propose the use of Supervised Nonnegative Tensor Factorization (SNTF) to jointly perform feature extraction and target discrimination over hyperspectral data products. SNTF learns a tensor factorization and a classification boundary from labeled training data simultaneously. This ensures that the features learned via tensor factorization are optimal for both summarizing the input data and separating the targets of interest. Practical considerations for applying SNTF to hyperspectral data are presented, and results from this framework are compared to decoupled feature extraction/target discrimination pipelines.

CARSVM: a class association rule-based classification framework and its application to gene expression data.

PubMed

Kianmehr, Keivan; Alhajj, Reda

2008-09-01

In this study, we aim at building a classification framework, namely the CARSVM model, which integrates association rule mining and support vector machine (SVM). The goal is to benefit from advantages of both, the discriminative knowledge represented by class association rules and the classification power of the SVM algorithm, to construct an efficient and accurate classifier model that improves the interpretability problem of SVM as a traditional machine learning technique and overcomes the efficiency issues of associative classification algorithms. In our proposed framework: instead of using the original training set, a set of rule-based feature vectors, which are generated based on the discriminative ability of class association rules over the training samples, are presented to the learning component of the SVM algorithm. We show that rule-based feature vectors present a high-qualified source of discrimination knowledge that can impact substantially the prediction power of SVM and associative classification techniques. They provide users with more conveniences in terms of understandability and interpretability as well. We have used four datasets from UCI ML repository to evaluate the performance of the developed system in comparison with five well-known existing classification methods. Because of the importance and popularity of gene expression analysis as real world application of the classification model, we present an extension of CARSVM combined with feature selection to be applied to gene expression data. Then, we describe how this combination will provide biologists with an efficient and understandable classifier model. The reported test results and their biological interpretation demonstrate the applicability, efficiency and effectiveness of the proposed model. From the results, it can be concluded that a considerable increase in classification accuracy can be obtained when the rule-based feature vectors are integrated in the learning process of the SVM algorithm. In the context of applicability, according to the results obtained from gene expression analysis, we can conclude that the CARSVM system can be utilized in a variety of real world applications with some adjustments.

Feature Vector Construction Method for IRIS Recognition

NASA Astrophysics Data System (ADS)

Odinokikh, G.; Fartukov, A.; Korobkin, M.; Yoo, J.

2017-05-01

One of the basic stages of iris recognition pipeline is iris feature vector construction procedure. The procedure represents the extraction of iris texture information relevant to its subsequent comparison. Thorough investigation of feature vectors obtained from iris showed that not all the vector elements are equally relevant. There are two characteristics which determine the vector element utility: fragility and discriminability. Conventional iris feature extraction methods consider the concept of fragility as the feature vector instability without respect to the nature of such instability appearance. This work separates sources of the instability into natural and encodinginduced which helps deeply investigate each source of instability independently. According to the separation concept, a novel approach of iris feature vector construction is proposed. The approach consists of two steps: iris feature extraction using Gabor filtering with optimal parameters and quantization with separated preliminary optimized fragility thresholds. The proposed method has been tested on two different datasets of iris images captured under changing environmental conditions. The testing results show that the proposed method surpasses all the methods considered as a prior art by recognition accuracy on both datasets.

[Electroencephalogram Feature Selection Based on Correlation Coefficient Analysis].

PubMed

Zhou, Jinzhi; Tang, Xiaofang

2015-08-01

In order to improve the accuracy of classification with small amount of motor imagery training data on the development of brain-computer interface (BCD systems, we proposed an analyzing method to automatically select the characteristic parameters based on correlation coefficient analysis. Throughout the five sample data of dataset IV a from 2005 BCI Competition, we utilized short-time Fourier transform (STFT) and correlation coefficient calculation to reduce the number of primitive electroencephalogram dimension, then introduced feature extraction based on common spatial pattern (CSP) and classified by linear discriminant analysis (LDA). Simulation results showed that the average rate of classification accuracy could be improved by using correlation coefficient feature selection method than those without using this algorithm. Comparing with support vector machine (SVM) optimization features algorithm, the correlation coefficient analysis can lead better selection parameters to improve the accuracy of classification.

A COMPARISON OF VECTOR AND RASTER GIS METHODS FOR CALCULATING LANDSCAPE METRICS USED IN ENVIRONMENTAL ASSESSMENTS

EPA Science Inventory

GIS-based measurements that combine native raster and native vector data are commonly used to assess environmental quality. Most of these measurements can be calculated using either raster or vector data formats and processing methods. Raster processes are more commonly used beca...

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.

Pupal productivity & nutrient reserves of Aedes mosquitoes breeding in sewage drains & other habitats of Kolkata, India: Implications for habitat expansion & vector management.

PubMed

Banerjee, Soumyajit; Mohan, Sushree; Saha, Nabaneeta; Mohanty, Siba Prasad; Saha, Goutam K; Aditya, Gautam

2015-12-01

The quality of breeding sites is reflected through the pupal productivity and the life history traits of Aedes mosquitoes. Using nutrient reserves and pupal productivity of Aedes as indicators, the larval habitats including sewage drains were characterized to highlight the habitat expansion and vector management. The pupae and adults collected from the containers and sewage drains were characterized in terms of biomass and nutrient reserves and the data were subjected to three way factorial ANOVA. Discriminant function analyses were performed to highlight the differences among the habitats for sustenance of Aedes mosquitoes. Survey of larval habitats from the study area revealed significant differences (P<0.05) in the pupal productivity of Aedes among the habitats and months. Despite sewage drains being comparatively less utilized for breeding, the pupae were of higher biomass with corresponding adults having longer wings in contrast to other habitats. The nutrient reserve of the adults emerging from pupae of sewage drains was significantly higher (P<0.05), compared to other habitats, as reflected through the discriminant function analysis. The present results showed that for both Ae. aegypti and Ae. albopictus, sewage drains were equally congenial habitat as were plastic, porcelain and earthen habitats. Availability of Aedes immature in sewage drains poses increased risk of dengue, and thus vector control programme should consider inclusion of sewage drains as breeding habitat of dengue vector mosquitoes.

Discrimination attributed to mental illness or race-ethnicity by users of community psychiatric services.

PubMed

Gabbidon, Jheanell; Farrelly, Simone; Hatch, Stephani L; Henderson, Claire; Williams, Paul; Bhugra, Dinesh; Dockery, Lisa; Lassman, Francesca; Thornicroft, Graham; Clement, Sarah

2014-11-01

This study assessed participants' experienced discrimination and their causal attributions, particularly to mental illness or race-ethnicity. In a cross-sectional study, 202 service users with severe mental illnesses were interviewed to assess their reported experiences of discrimination. The Major Experiences of Discrimination Scale assessed major experiences of discrimination and their recency and frequency across 12 life domains and perceived reasons (attributions). The Everyday Experiences of Discrimination Scale assessed ten types of everyday discrimination and attributions for these experiences. Most participants (88%) reported discrimination in at least one life domain, and 94% reported ever experiencing everyday discrimination. The most common areas of major discrimination were mental health care (44%), neighbors (42%), police (33%), employment (31%), and general medical care (31%). The most common attributions for major discrimination were mental illness (57%), race-ethnicity (24%), education or income (20%), or appearance (19%). Almost half (47%) attributed experiences of major discrimination to two or more causes. No differences were found between racial-ethnic groups in overall experienced discrimination or in main attributions to mental illness. However, compared with the mixed and white groups, participants in the black group were most likely to endorse race-ethnicity as a main attribution (p<.001). Mental illness-related discrimination was found to be a common issue across racial-ethnic groups, and discrimination based on race-ethnicity was prevalent for the mixed and black groups. There is a need for antidiscrimination strategies that combine efforts to reduce the experience of discrimination attributed to mental illness and to race-ethnicity for racial-ethnic minority groups.

Recurrence quantification analysis and support vector machines for golf handicap and low back pain EMG classification.

PubMed

Silva, Luís; Vaz, João Rocha; Castro, Maria António; Serranho, Pedro; Cabri, Jan; Pezarat-Correia, Pedro

2015-08-01

The quantification of non-linear characteristics of electromyography (EMG) must contain information allowing to discriminate neuromuscular strategies during dynamic skills. There are a lack of studies about muscle coordination under motor constrains during dynamic contractions. In golf, both handicap (Hc) and low back pain (LBP) are the main factors associated with the occurrence of injuries. The aim of this study was to analyze the accuracy of support vector machines SVM on EMG-based classification to discriminate Hc (low and high handicap) and LBP (with and without LPB) in the main phases of golf swing. For this purpose recurrence quantification analysis (RQA) features of the trunk and the lower limb muscles were used to feed a SVM classifier. Recurrence rate (RR) and the ratio between determinism (DET) and RR showed a high discriminant power. The Hc accuracy for the swing, backswing, and downswing were 94.4±2.7%, 97.1±2.3%, and 95.3±2.6%, respectively. For LBP, the accuracy was 96.9±3.8% for the swing, and 99.7±0.4% in the backswing. External oblique (EO), biceps femoris (BF), semitendinosus (ST) and rectus femoris (RF) showed high accuracy depending on the laterality within the phase. RQA features and SVM showed a high muscle discriminant capacity within swing phases by Hc and by LBP. Low back pain golfers showed different neuromuscular coordination strategies when compared with asymptomatic. Copyright © 2015 Elsevier Ltd. All rights reserved.

Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images.

PubMed

Lu, Xiaobing; Yang, Yongzhe; Wu, Fengchun; Gao, Minjian; Xu, Yong; Zhang, Yue; Yao, Yongcheng; Du, Xin; Li, Chengwei; Wu, Lei; Zhong, Xiaomei; Zhou, Yanling; Fan, Ni; Zheng, Yingjun; Xiong, Dongsheng; Peng, Hongjun; Escudero, Javier; Huang, Biao; Li, Xiaobo; Ning, Yuping; Wu, Kai

2016-07-01

Structural abnormalities in schizophrenia (SZ) patients have been well documented with structural magnetic resonance imaging (MRI) data using voxel-based morphometry (VBM) and region of interest (ROI) analyses. However, these analyses can only detect group-wise differences and thus, have a poor predictive value for individuals. In the present study, we applied a machine learning method that combined support vector machine (SVM) with recursive feature elimination (RFE) to discriminate SZ patients from normal controls (NCs) using their structural MRI data. We first employed both VBM and ROI analyses to compare gray matter volume (GMV) and white matter volume (WMV) between 41 SZ patients and 42 age- and sex-matched NCs. The method of SVM combined with RFE was used to discriminate SZ patients from NCs using significant between-group differences in both GMV and WMV as input features. We found that SZ patients showed GM and WM abnormalities in several brain structures primarily involved in the emotion, memory, and visual systems. An SVM with a RFE classifier using the significant structural abnormalities identified by the VBM analysis as input features achieved the best performance (an accuracy of 88.4%, a sensitivity of 91.9%, and a specificity of 84.4%) in the discriminative analyses of SZ patients. These results suggested that distinct neuroanatomical profiles associated with SZ patients might provide a potential biomarker for disease diagnosis, and machine-learning methods can reveal neurobiological mechanisms in psychiatric diseases.

Enhancement of Plant Metabolite Fingerprinting by Machine Learning1[W

PubMed Central

Scott, Ian M.; Vermeer, Cornelia P.; Liakata, Maria; Corol, Delia I.; Ward, Jane L.; Lin, Wanchang; Johnson, Helen E.; Whitehead, Lynne; Kular, Baldeep; Baker, John M.; Walsh, Sean; Dave, Anuja; Larson, Tony R.; Graham, Ian A.; Wang, Trevor L.; King, Ross D.; Draper, John; Beale, Michael H.

2010-01-01

Metabolite fingerprinting of Arabidopsis (Arabidopsis thaliana) mutants with known or predicted metabolic lesions was performed by 1H-nuclear magnetic resonance, Fourier transform infrared, and flow injection electrospray-mass spectrometry. Fingerprinting enabled processing of five times more plants than conventional chromatographic profiling and was competitive for discriminating mutants, other than those affected in only low-abundance metabolites. Despite their rapidity and complexity, fingerprints yielded metabolomic insights (e.g. that effects of single lesions were usually not confined to individual pathways). Among fingerprint techniques, 1H-nuclear magnetic resonance discriminated the most mutant phenotypes from the wild type and Fourier transform infrared discriminated the fewest. To maximize information from fingerprints, data analysis was crucial. One-third of distinctive phenotypes might have been overlooked had data models been confined to principal component analysis score plots. Among several methods tested, machine learning (ML) algorithms, namely support vector machine or random forest (RF) classifiers, were unsurpassed for phenotype discrimination. Support vector machines were often the best performing classifiers, but RFs yielded some particularly informative measures. First, RFs estimated margins between mutant phenotypes, whose relations could then be visualized by Sammon mapping or hierarchical clustering. Second, RFs provided importance scores for the features within fingerprints that discriminated mutants. These scores correlated with analysis of variance F values (as did Kruskal-Wallis tests, true- and false-positive measures, mutual information, and the Relief feature selection algorithm). ML classifiers, as models trained on one data set to predict another, were ideal for focused metabolomic queries, such as the distinctiveness and consistency of mutant phenotypes. Accessible software for use of ML in plant physiology is highlighted. PMID:20566707

A Comparison of Vector and Raster GIS Methods for Calculating Landscape Metrics Used in Environmental Assessments

Treesearch

Timothy G. Wade; James D. Wickham; Maliha S. Nash; Anne C. Neale; Kurt H. Riitters; K. Bruce Jones

2003-01-01

AbstractGIS-based measurements that combine native raster and native vector data are commonly used in environmental assessments. Most of these measurements can be calculated using either raster or vector data formats and processing methods. Raster processes are more commonly used because they can be significantly faster computationally...

Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier.

PubMed

Li, Zheng-Wei; You, Zhu-Hong; Chen, Xing; Li, Li-Ping; Huang, De-Shuang; Yan, Gui-Ying; Nie, Ru; Huang, Yu-An

2017-04-04

Identification of protein-protein interactions (PPIs) is of critical importance for deciphering the underlying mechanisms of almost all biological processes of cell and providing great insight into the study of human disease. Although much effort has been devoted to identifying PPIs from various organisms, existing high-throughput biological techniques are time-consuming, expensive, and have high false positive and negative results. Thus it is highly urgent to develop in silico methods to predict PPIs efficiently and accurately in this post genomic era. In this article, we report a novel computational model combining our newly developed discriminative vector machine classifier (DVM) and an improved Weber local descriptor (IWLD) for the prediction of PPIs. Two components, differential excitation and orientation, are exploited to build evolutionary features for each protein sequence. The main characteristics of the proposed method lies in introducing an effective feature descriptor IWLD which can capture highly discriminative evolutionary information from position-specific scoring matrixes (PSSM) of protein data, and employing the powerful and robust DVM classifier. When applying the proposed method to Yeast and H. pylori data sets, we obtained excellent prediction accuracies as high as 96.52% and 91.80%, respectively, which are significantly better than the previous methods. Extensive experiments were then performed for predicting cross-species PPIs and the predictive results were also pretty promising. To further validate the performance of the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier on Human data set. The experimental results obtained indicate that our method is highly effective for PPIs prediction and can be taken as a supplementary tool for future proteomics research.

Face recognition using total margin-based adaptive fuzzy support vector machines.

PubMed

Liu, Yi-Hung; Chen, Yen-Ting

2007-01-01

This paper presents a new classifier called total margin-based adaptive fuzzy support vector machines (TAF-SVM) that deals with several problems that may occur in support vector machines (SVMs) when applied to the face recognition. The proposed TAF-SVM not only solves the overfitting problem resulted from the outlier with the approach of fuzzification of the penalty, but also corrects the skew of the optimal separating hyperplane due to the very imbalanced data sets by using different cost algorithm. In addition, by introducing the total margin algorithm to replace the conventional soft margin algorithm, a lower generalization error bound can be obtained. Those three functions are embodied into the traditional SVM so that the TAF-SVM is proposed and reformulated in both linear and nonlinear cases. By using two databases, the Chung Yuan Christian University (CYCU) multiview and the facial recognition technology (FERET) face databases, and using the kernel Fisher's discriminant analysis (KFDA) algorithm to extract discriminating face features, experimental results show that the proposed TAF-SVM is superior to SVM in terms of the face-recognition accuracy. The results also indicate that the proposed TAF-SVM can achieve smaller error variances than SVM over a number of tests such that better recognition stability can be obtained.

Margin-maximizing feature elimination methods for linear and nonlinear kernel-based discriminant functions.

PubMed

Aksu, Yaman; Miller, David J; Kesidis, George; Yang, Qing X

2010-05-01

Feature selection for classification in high-dimensional spaces can improve generalization, reduce classifier complexity, and identify important, discriminating feature "markers." For support vector machine (SVM) classification, a widely used technique is recursive feature elimination (RFE). We demonstrate that RFE is not consistent with margin maximization, central to the SVM learning approach. We thus propose explicit margin-based feature elimination (MFE) for SVMs and demonstrate both improved margin and improved generalization, compared with RFE. Moreover, for the case of a nonlinear kernel, we show that RFE assumes that the squared weight vector 2-norm is strictly decreasing as features are eliminated. We demonstrate this is not true for the Gaussian kernel and, consequently, RFE may give poor results in this case. MFE for nonlinear kernels gives better margin and generalization. We also present an extension which achieves further margin gains, by optimizing only two degrees of freedom--the hyperplane's intercept and its squared 2-norm--with the weight vector orientation fixed. We finally introduce an extension that allows margin slackness. We compare against several alternatives, including RFE and a linear programming method that embeds feature selection within the classifier design. On high-dimensional gene microarray data sets, University of California at Irvine (UCI) repository data sets, and Alzheimer's disease brain image data, MFE methods give promising results.

Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System.

PubMed

Jaya, T; Dheeba, J; Singh, N Albert

2015-12-01

Diabetic retinopathy is a major cause of vision loss in diabetic patients. Currently, there is a need for making decisions using intelligent computer algorithms when screening a large volume of data. This paper presents an expert decision-making system designed using a fuzzy support vector machine (FSVM) classifier to detect hard exudates in fundus images. The optic discs in the colour fundus images are segmented to avoid false alarms using morphological operations and based on circular Hough transform. To discriminate between the exudates and the non-exudates pixels, colour and texture features are extracted from the images. These features are given as input to the FSVM classifier. The classifier analysed 200 retinal images collected from diabetic retinopathy screening programmes. The tests made on the retinal images show that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and features sets, the area under the receiver operating characteristic curve reached 0.9606, which corresponds to a sensitivity of 94.1% with a specificity of 90.0%. The results suggest that detecting hard exudates using FSVM contribute to computer-assisted detection of diabetic retinopathy and as a decision support system for ophthalmologists.

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.

Machine Learning Prediction of the Energy Gap of Graphene Nanoflakes Using Topological Autocorrelation Vectors.

PubMed

Fernandez, Michael; Abreu, Jose I; Shi, Hongqing; Barnard, Amanda S

2016-11-14

The possibility of band gap engineering in graphene opens countless new opportunities for application in nanoelectronics. In this work, the energy gaps of 622 computationally optimized graphene nanoflakes were mapped to topological autocorrelation vectors using machine learning techniques. Machine learning modeling revealed that the most relevant correlations appear at topological distances in the range of 1 to 42 with prediction accuracy higher than 80%. The data-driven model can statistically discriminate between graphene nanoflakes with different energy gaps on the basis of their molecular topology.

Target discrimination method for SAR images based on semisupervised co-training

NASA Astrophysics Data System (ADS)

Wang, Yan; Du, Lan; Dai, Hui

2018-01-01

Synthetic aperture radar (SAR) target discrimination is usually performed in a supervised manner. However, supervised methods for SAR target discrimination may need lots of labeled training samples, whose acquirement is costly, time consuming, and sometimes impossible. This paper proposes an SAR target discrimination method based on semisupervised co-training, which utilizes a limited number of labeled samples and an abundant number of unlabeled samples. First, Lincoln features, widely used in SAR target discrimination, are extracted from the training samples and partitioned into two sets according to their physical meanings. Second, two support vector machine classifiers are iteratively co-trained with the extracted two feature sets based on the co-training algorithm. Finally, the trained classifiers are exploited to classify the test data. The experimental results on real SAR images data not only validate the effectiveness of the proposed method compared with the traditional supervised methods, but also demonstrate the superiority of co-training over self-training, which only uses one feature set.

Culicoides variipennis (Diptera: Ceratopogonidae) complex in Virginia.

PubMed

Schmidtmann, E T; Holbrook, F R; Day, E; Taylor, T; Tabachnick, W J

1998-09-01

Immature Culicoides variipennis (Coquillett) were sampled from aquatic habitats throughout Virginia, reared to adults, and examined by isozyme electrophoresis to assess their taxonomic status. Data from 22 counties showed that C. v. variipennis is widespread and common, the predominant taxon throughout Virginia, and genetically similar to C. v. variipennis in Maryland. Because C. v. variipennis is considered an inefficient vector of the bluetongue viruses, this observation is consistent with the low seroprevalence of bluetongue in indigenous livestock of the mid-Atlantic region. Culicoides v. sonorensis Wirth & Jones, considered to be the primary North American vector of the bluetongue viruses, was recovered in large numbers only from a wastewater lagoon at a dairy in southeastern Virginia, but also was detected at low levels in 6 other counties. Comparison of genetic distances and patterns of discriminating alleles among Virginia populations of C. v. variipennis and C. v. sonorensis showed that respective subspecies are genetically distinct and show no evidence of introgression, irrespective of geographic- and habitat-level sympatry. The persistence of a pure C. v. sonorensis population in a dairy wastewater lagoon may reflect physico-chemical factors that influence the distribution of immature C. variipennis complex populations. A better understanding of the distribution of the C. variipennis complex will benefit regionalization of U.S. exports of livestock and livestock germplasm to bluetongue-free countries.

Chemometric techniques on the analysis of Raman spectra of serum blood samples of breast cancer patients

NASA Astrophysics Data System (ADS)

Rocha-Osornio, L. N.; Pichardo-Molina, J. L.; Barbosa-Garcia, O.; Frausto-Reyes, C.; Araujo-Andrade, C.; Huerta-Franco, R.; Gutiérrez-Juárez, G.

2008-02-01

Raman spectroscopy and Multivariate methods were used to study serum blood samples of control and breast cancer patients. Blood samples were obtained from 11 patients and 12 controls from the central region of Mexico. Our results show that principal component analysis is able to discriminate serum sample of breast cancer patients from those of control group, also the loading vectors of PCA plotted as a function of Raman shift shown which bands permitted to make the maximum discrimination between both groups of samples.

An integrated analysis for determining the geographical origin of medicinal herbs using ICP-AES/ICP-MS and (1)H NMR analysis.

PubMed

Kwon, Yong-Kook; Bong, Yeon-Sik; Lee, Kwang-Sik; Hwang, Geum-Sook

2014-10-15

ICP-MS and (1)H NMR are commonly used to determine the geographical origin of food and crops. In this study, data from multielemental analysis performed by ICP-AES/ICP-MS and metabolomic data obtained from (1)H NMR were integrated to improve the reliability of determining the geographical origin of medicinal herbs. Astragalus membranaceus and Paeonia albiflora with different origins in Korea and China were analysed by (1)H NMR and ICP-AES/ICP-MS, and an integrated multivariate analysis was performed to characterise the differences between their origins. Four classification methods were applied: linear discriminant analysis (LDA), k-nearest neighbour classification (KNN), support vector machines (SVM), and partial least squares-discriminant analysis (PLS-DA). Results were compared using leave-one-out cross-validation and external validation. The integration of multielemental and metabolomic data was more suitable for determining geographical origin than the use of each individual data set alone. The integration of the two analytical techniques allowed diverse environmental factors such as climate and geology, to be considered. Our study suggests that an appropriate integration of different types of analytical data is useful for determining the geographical origin of food and crops with a high degree of reliability. Copyright © 2014 Elsevier Ltd. All rights reserved.

Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features.

PubMed

Li, Yang; Cui, Weigang; Luo, Meilin; Li, Ke; Wang, Lina

2018-01-25

The electroencephalogram (EEG) signal analysis is a valuable tool in the evaluation of neurological disorders, which is commonly used for the diagnosis of epileptic seizures. This paper presents a novel automatic EEG signal classification method for epileptic seizure detection. The proposed method first employs a continuous wavelet transform (CWT) method for obtaining the time-frequency images (TFI) of EEG signals. The processed EEG signals are then decomposed into five sub-band frequency components of clinical interest since these sub-band frequency components indicate much better discriminative characteristics. Both Gaussian Mixture Model (GMM) features and Gray Level Co-occurrence Matrix (GLCM) descriptors are then extracted from these sub-band TFI. Additionally, in order to improve classification accuracy, a compact feature selection method by combining the ReliefF and the support vector machine-based recursive feature elimination (RFE-SVM) algorithm is adopted to select the most discriminative feature subset, which is an input to the SVM with the radial basis function (RBF) for classifying epileptic seizure EEG signals. The experimental results from a publicly available benchmark database demonstrate that the proposed approach provides better classification accuracy than the recently proposed methods in the literature, indicating the effectiveness of the proposed method in the detection of epileptic seizures.

Feature selection and classification of multiparametric medical images using bagging and SVM

NASA Astrophysics Data System (ADS)

Fan, Yong; Resnick, Susan M.; Davatzikos, Christos

2008-03-01

This paper presents a framework for brain classification based on multi-parametric medical images. This method takes advantage of multi-parametric imaging to provide a set of discriminative features for classifier construction by using a regional feature extraction method which takes into account joint correlations among different image parameters; in the experiments herein, MRI and PET images of the brain are used. Support vector machine classifiers are then trained based on the most discriminative features selected from the feature set. To facilitate robust classification and optimal selection of parameters involved in classification, in view of the well-known "curse of dimensionality", base classifiers are constructed in a bagging (bootstrap aggregating) framework for building an ensemble classifier and the classification parameters of these base classifiers are optimized by means of maximizing the area under the ROC (receiver operating characteristic) curve estimated from their prediction performance on left-out samples of bootstrap sampling. This classification system is tested on a sex classification problem, where it yields over 90% classification rates for unseen subjects. The proposed classification method is also compared with other commonly used classification algorithms, with favorable results. These results illustrate that the methods built upon information jointly extracted from multi-parametric images have the potential to perform individual classification with high sensitivity and specificity.

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%.

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.

Design of Clinical Support Systems Using Integrated Genetic Algorithm and Support Vector Machine

NASA Astrophysics Data System (ADS)

Chen, Yung-Fu; Huang, Yung-Fa; Jiang, Xiaoyi; Hsu, Yuan-Nian; Lin, Hsuan-Hung

Clinical decision support system (CDSS) provides knowledge and specific information for clinicians to enhance diagnostic efficiency and improving healthcare quality. An appropriate CDSS can highly elevate patient safety, improve healthcare quality, and increase cost-effectiveness. Support vector machine (SVM) is believed to be superior to traditional statistical and neural network classifiers. However, it is critical to determine suitable combination of SVM parameters regarding classification performance. Genetic algorithm (GA) can find optimal solution within an acceptable time, and is faster than greedy algorithm with exhaustive searching strategy. By taking the advantage of GA in quickly selecting the salient features and adjusting SVM parameters, a method using integrated GA and SVM (IGS), which is different from the traditional method with GA used for feature selection and SVM for classification, was used to design CDSSs for prediction of successful ventilation weaning, diagnosis of patients with severe obstructive sleep apnea, and discrimination of different cell types form Pap smear. The results show that IGS is better than methods using SVM alone or linear discriminator.

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.

Multiple-output support vector machine regression with feature selection for arousal/valence space emotion assessment.

PubMed

Torres-Valencia, Cristian A; Álvarez, Mauricio A; Orozco-Gutiérrez, Alvaro A

2014-01-01

Human emotion recognition (HER) allows the assessment of an affective state of a subject. Until recently, such emotional states were described in terms of discrete emotions, like happiness or contempt. In order to cover a high range of emotions, researchers in the field have introduced different dimensional spaces for emotion description that allow the characterization of affective states in terms of several variables or dimensions that measure distinct aspects of the emotion. One of the most common of such dimensional spaces is the bidimensional Arousal/Valence space. To the best of our knowledge, all HER systems so far have modelled independently, the dimensions in these dimensional spaces. In this paper, we study the effect of modelling the output dimensions simultaneously and show experimentally the advantages in modeling them in this way. We consider a multimodal approach by including features from the Electroencephalogram and a few physiological signals. For modelling the multiple outputs, we employ a multiple output regressor based on support vector machines. We also include an stage of feature selection that is developed within an embedded approach known as Recursive Feature Elimination (RFE), proposed initially for SVM. The results show that several features can be eliminated using the multiple output support vector regressor with RFE without affecting the performance of the regressor. From the analysis of the features selected in smaller subsets via RFE, it can be observed that the signals that are more informative into the arousal and valence space discrimination are the EEG, Electrooculogram/Electromiogram (EOG/EMG) and the Galvanic Skin Response (GSR).

Ankle joint movements are encoded by both cutaneous and muscle afferents in humans.

PubMed

Aimonetti, Jean-Marc; Roll, Jean-Pierre; Hospod, Valérie; Ribot-Ciscar, Edith

2012-08-01

We analyzed the cutaneous encoding of two-dimensional movements by investigating the coding of movement velocity for differently oriented straight-line movements and the coding of complex trajectories describing cursive letters. The cutaneous feedback was then compared with that of the underlying muscle afferents previously recorded during the same "writing-like" movements. The unitary activity of 43 type II cutaneous afferents was recorded in the common peroneal nerve in healthy subjects during imposed ankle movements. These movements consisted first of ramp-and-hold movements imposed at two different and close velocities in seven directions and secondly of "writing-like" movements. In both cases, the responses were analyzed using the neuronal population vector model. The results show that movement velocity encoding depended on the direction of the ongoing movement. Discriminating between two velocities therefore involved processing the activity of afferent populations located in the various skin areas surrounding the moving joint, as shown by the statistically significant difference observed in the amplitude of the sum vectors. Secondly, "writing-like" movements induced cutaneous neuronal patterns of activity, which were reproducible and specific to each trajectory. Lastly, the "cutaneous neuronal trajectories," built by adding the sum vectors tip-to-tail, nearly matched both the movement trajectories and the "muscle neuronal trajectories," built from previously recorded muscle afferents. It was concluded that type II cutaneous and the underlying muscle afferents show similar encoding properties of two-dimensional movement parameters. This similarity is discussed in relation to a central gating process that would for instance increase the gain of cutaneous inputs when muscle information is altered by the fusimotor drive.

Evaluation of 4D-CT lung registration.

PubMed

Kabus, Sven; Klinder, Tobias; Murphy, Keelin; van Ginneken, Bram; van Lorenz, Cristian; Pluim, Josien P W

2009-01-01

Non-rigid registration accuracy assessment is typically performed by evaluating the target registration error at manually placed landmarks. For 4D-CT lung data, we compare two sets of landmark distributions: a smaller set primarily defined on vessel bifurcations as commonly described in the literature and a larger set being well-distributed throughout the lung volume. For six different registration schemes (three in-house schemes and three schemes frequently used by the community) the landmark error is evaluated and found to depend significantly on the distribution of the landmarks. In particular, lung regions near to the pleura show a target registration error three times larger than near-mediastinal regions. While the inter-method variability on the landmark positions is rather small, the methods show discriminating differences with respect to consistency and local volume change. In conclusion, both a well-distributed set of landmarks and a deformation vector field analysis are necessary for reliable non-rigid registration accuracy assessment.

Realistic Subsurface Anomaly Discrimination Using Electromagnetic Induction and an SVM Classifier

NASA Astrophysics Data System (ADS)

Pablo Fernández, Juan; Shubitidze, Fridon; Shamatava, Irma; Barrowes, Benjamin E.; O'Neill, Kevin

2010-12-01

The environmental research program of the United States military has set up blind tests for detection and discrimination of unexploded ordnance. One such test consists of measurements taken with the EM-63 sensor at Camp Sibert, AL. We review the performance on the test of a procedure that combines a field-potential (HAP) method to locate targets, the normalized surface magnetic source (NSMS) model to characterize them, and a support vector machine (SVM) to classify them. The HAP method infers location from the scattered magnetic field and its associated scalar potential, the latter reconstructed using equivalent sources. NSMS replaces the target with an enclosing spheroid of equivalent radial magnetization whose integral it uses as a discriminator. SVM generalizes from empirical evidence and can be adapted for multiclass discrimination using a voting system. Our method identifies all potentially dangerous targets correctly and has a false-alarm rate of about 5%.

Color discrimination across four life decades assessed by the Cambridge Colour Test.

PubMed

Paramei, Galina V

2012-02-01

Color discrimination was estimated using the Cambridge Colour Test (CCT) in 160 normal trichromats of four life decades, 20-59 years of age. For each age cohort, medians and tolerance limits of the CCT parameters are tabulated. Compared across the age cohorts (Kruskal-Wallis test), the Trivector test showed increases in the three vectors, Protan, Deutan, and Tritan, with advancing age; the Ellipses test revealed significant elongation of the major axes of all three ellipses but no changes in either the axis ratio or the angle of the ellipse major axis. Multiple comparisons (Mann-Whitney test) between the cohorts of four age decades (20+,…,50+) revealed initial benign deterioration of color discrimination in the 40+ decade, as an incremental loss of discrimination along the Deutan axis (Trivector test), and in the 50+ decade, as an elongation of the major axes of all three ellipses (Ellipses test). © 2012 Optical Society of America

Classifying adolescent attention-deficit/hyperactivity disorder (ADHD) based on functional and structural imaging.

PubMed

Iannaccone, Reto; Hauser, Tobias U; Ball, Juliane; Brandeis, Daniel; Walitza, Susanne; Brem, Silvia

2015-10-01

Attention-deficit/hyperactivity disorder (ADHD) is a common disabling psychiatric disorder associated with consistent deficits in error processing, inhibition and regionally decreased grey matter volumes. The diagnosis is based on clinical presentation, interviews and questionnaires, which are to some degree subjective and would benefit from verification through biomarkers. Here, pattern recognition of multiple discriminative functional and structural brain patterns was applied to classify adolescents with ADHD and controls. Functional activation features in a Flanker/NoGo task probing error processing and inhibition along with structural magnetic resonance imaging data served to predict group membership using support vector machines (SVMs). The SVM pattern recognition algorithm correctly classified 77.78% of the subjects with a sensitivity and specificity of 77.78% based on error processing. Predictive regions for controls were mainly detected in core areas for error processing and attention such as the medial and dorsolateral frontal areas reflecting deficient processing in ADHD (Hart et al., in Hum Brain Mapp 35:3083-3094, 2014), and overlapped with decreased activations in patients in conventional group comparisons. Regions more predictive for ADHD patients were identified in the posterior cingulate, temporal and occipital cortex. Interestingly despite pronounced univariate group differences in inhibition-related activation and grey matter volumes the corresponding classifiers failed or only yielded a poor discrimination. The present study corroborates the potential of task-related brain activation for classification shown in previous studies. It remains to be clarified whether error processing, which performed best here, also contributes to the discrimination of useful dimensions and subtypes, different psychiatric disorders, and prediction of treatment success across studies and sites.

Rapid discrimination between Anopheles gambiae s.s. and Anopheles arabiensis by High-Resolution Melt (HRM) analysis.

PubMed

Zianni, Michael R; Nikbakhtzadeh, Mahmood R; Jackson, Bryan T; Panescu, Jenny; Foster, Woodbridge A

2013-04-01

There is a need for more cost-effective options to more accurately discriminate among members of the Anopheles gambiae complex, particularly An. gambiae and Anopheles arabiensis. These species are morphologically indistinguishable in the adult stage, have overlapping distributions, but are behaviorally and ecologically different, yet both are efficient vectors of malaria in equatorial Africa. The method described here, High-Resolution Melt (HRM) analysis, takes advantage of minute differences in DNA melting characteristics, depending on the number of incongruent single nucleotide polymorphisms in an intragenic spacer region of the X-chromosome-based ribosomal DNA. The two species in question differ by an average of 13 single-nucleotide polymorphisms giving widely divergent melting curves. A real-time PCR system, Bio-Rad CFX96, was used in combination with a dsDNA-specific dye, EvaGreen, to detect and measure the melting properties of the amplicon generated from leg-extracted DNA of selected mosquitoes. Results with seven individuals from pure colonies of known species, as well as 10 field-captured individuals unambiguously identified by DNA sequencing, demonstrated that the method provided a high level of accuracy. The method was used to identify 86 field mosquitoes through the assignment of each to the two common clusters with a high degree of certainty. Each cluster was defined by individuals from pure colonies. HRM analysis is simpler to use than most other methods and provides comparable or more accurate discrimination between the two sibling species but requires a specialized melt-analysis instrument and software.

Rapid Discrimination between Anopheles gambiae s.s. and Anopheles arabiensis by High-Resolution Melt (HRM) Analysis

PubMed Central

Zianni, Michael R.; Nikbakhtzadeh, Mahmood R.; Jackson, Bryan T.; Panescu, Jenny; Foster, Woodbridge A.

2013-01-01

There is a need for more cost-effective options to more accurately discriminate among members of the Anopheles gambiae complex, particularly An. gambiae and Anopheles arabiensis. These species are morphologically indistinguishable in the adult stage, have overlapping distributions, but are behaviorally and ecologically different, yet both are efficient vectors of malaria in equatorial Africa. The method described here, High-Resolution Melt (HRM) analysis, takes advantage of minute differences in DNA melting characteristics, depending on the number of incongruent single nucleotide polymorphisms in an intragenic spacer region of the X-chromosome-based ribosomal DNA. The two species in question differ by an average of 13 single-nucleotide polymorphisms giving widely divergent melting curves. A real-time PCR system, Bio-Rad CFX96, was used in combination with a dsDNA-specific dye, EvaGreen, to detect and measure the melting properties of the amplicon generated from leg-extracted DNA of selected mosquitoes. Results with seven individuals from pure colonies of known species, as well as 10 field-captured individuals unambiguously identified by DNA sequencing, demonstrated that the method provided a high level of accuracy. The method was used to identify 86 field mosquitoes through the assignment of each to the two common clusters with a high degree of certainty. Each cluster was defined by individuals from pure colonies. HRM analysis is simpler to use than most other methods and provides comparable or more accurate discrimination between the two sibling species but requires a specialized melt-analysis instrument and software. PMID:23543777

Gender Discrimination among Medical Students in Pakistan: A Cross Sectional Survey.

PubMed

Madeeh Hashmi, Ali; Rehman, Amra; Butt, Zeeshan; Awais Aftab, Muhammad; Shahid, Aimen; Abbas Khan, Sahar

2013-04-01

To examine the prevalence and magnitude of gender discrimination experienced by undergraduate medical students, and its repercussions on their academic performance and emotional health. A cross sectional study of 500 medical and dental students studying at a private medical college in Lahore, Pakistan. Majority (78%) of students reported being victims of gender discrimination. Females were the main perpetrators (70.8%).Most common forms were denied opportunities (63%), followed by neglecting students' needs (44.3%), and unethical talk (43.6%). Most common places of gender discrimination were teachers' offices (43.7%) and lecture halls (37.2%). Most of the perpetrators were clerical staff (48%) and professors (43%).Gender discrimination did not affect the academic performance of most victims (62.6%). The most common emotional responses were anger (57.6%), frustration (46.7%) and helplessness (40.3%). 52.4% of students said that gender discrimination still continues and the majority (83.3%) did not report the problem to college authorities. RESULTS demonstrate that gender discrimination is widely prevalent in undergraduate medical education. Females are both the main victims as well as the main perpetrators. In most cases gender discrimination does not affect academic performance but does cause emotional distress.

Gender Discrimination among Medical Students in Pakistan: A Cross Sectional Survey

PubMed Central

Madeeh Hashmi, Ali; Rehman, Amra; Butt, Zeeshan; Awais Aftab, Muhammad; Shahid, Aimen; Abbas Khan, Sahar

2013-01-01

Objective: To examine the prevalence and magnitude of gender discrimination experienced by undergraduate medical students, and its repercussions on their academic performance and emotional health. Methodology: A cross sectional study of 500 medical and dental students studying at a private medical college in Lahore, Pakistan. Results: Majority (78%) of students reported being victims of gender discrimination. Females were the main perpetrators (70.8%).Most common forms were denied opportunities (63%), followed by neglecting students’ needs (44.3%), and unethical talk (43.6%). Most common places of gender discrimination were teachers’ offices (43.7%) and lecture halls (37.2%). Most of the perpetrators were clerical staff (48%) and professors (43%).Gender discrimination did not affect the academic performance of most victims (62.6%). The most common emotional responses were anger (57.6%), frustration (46.7%) and helplessness (40.3%). 52.4% of students said that gender discrimination still continues and the majority (83.3%) did not report the problem to college authorities. Conclusions: Results demonstrate that gender discrimination is widely prevalent in undergraduate medical education. Females are both the main victims as well as the main perpetrators. In most cases gender discrimination does not affect academic performance but does cause emotional distress. PMID:24353554

Selective neural pathway targeting reveals key roles of thalamostriatal projection in the control of visual discrimination.

PubMed

Kato, Shigeki; Kuramochi, Masahito; Kobayashi, Kenta; Fukabori, Ryoji; Okada, Kana; Uchigashima, Motokazu; Watanabe, Masahiko; Tsutsui, Yuji; Kobayashi, Kazuto

2011-11-23

The dorsal striatum receives converging excitatory inputs from diverse brain regions, including the cerebral cortex and the intralaminar/midline thalamic nuclei, and mediates learning processes contributing to instrumental motor actions. However, the roles of each striatal input pathway in these learning processes remain uncertain. We developed a novel strategy to target specific neural pathways and applied this strategy for studying behavioral roles of the pathway originating from the parafascicular nucleus (PF) and projecting to the dorsolateral striatum. A highly efficient retrograde gene transfer vector encoding the recombinant immunotoxin (IT) receptor was injected into the dorsolateral striatum in mice to express the receptor in neurons innervating the striatum. IT treatment into the PF of the vector-injected animals caused a selective elimination of neurons of the PF-derived thalamostriatal pathway. The elimination of this pathway impaired the response selection accuracy and delayed the motor response in the acquisition of a visual cue-dependent discrimination task. When the pathway elimination was induced after learning acquisition, it disturbed the response accuracy in the task performance with no apparent change in the response time. The elimination did not influence spontaneous locomotion, methamphetamine-induced hyperactivity, and motor skill learning that demand the function of the dorsal striatum. These results demonstrate that thalamostriatal projection derived from the PF plays essential roles in the acquisition and execution of discrimination learning in response to sensory stimulus. The temporal difference in the pathway requirement for visual discrimination suggests a stage-specific role of thalamostriatal pathway in the modulation of response time of learned motor actions.

Multistrain Infections with Lyme Borreliosis Pathogens in the Tick Vector.

PubMed

Durand, Jonas; Herrmann, Coralie; Genné, Dolores; Sarr, Anouk; Gern, Lise; Voordouw, Maarten J

2017-02-01

Mixed or multiple-strain infections are common in vector-borne diseases and have important implications for the epidemiology of these pathogens. Previous studies have mainly focused on interactions between pathogen strains in the vertebrate host, but little is known about what happens in the arthropod vector. Borrelia afzelii and Borrelia garinii are two species of spirochete bacteria that cause Lyme borreliosis in Europe and that share a tick vector, Ixodes ricinus Each of these two tick-borne pathogens consists of multiple strains that are often differentiated using the highly polymorphic ospC gene. For each Borrelia species, we studied the frequencies and abundances of the ospC strains in a wild population of I. ricinus ticks that had been sampled from the same field site over a period of 3 years. We used quantitative PCR (qPCR) and 454 sequencing to estimate the spirochete load and the strain diversity within each tick. For B. afzelii, there was a negative relationship between the two most common ospC strains, suggesting the presence of competitive interactions in the vertebrate host and possibly the tick vector. The flat relationship between total spirochete abundance and strain richness in the nymphal tick indicates that the mean abundance per strain decreases as the number of strains in the tick increases. Strains with the highest spirochete load in the nymphal tick were the most common strains in the tick population. The spirochete abundance in the nymphal tick appears to be an important life history trait that explains why some strains are more common than others in nature. Lyme borreliosis is the most common vector-borne disease in the Northern Hemisphere and is caused by spirochete bacteria that belong to the Borrelia burgdorferi sensu lato species complex. These tick-borne pathogens are transmitted among vertebrate hosts by hard ticks of the genus Ixodes Each Borrelia species can be further subdivided into genetically distinct strains. Multiple-strain infections are common in both the vertebrate host and the tick vector and can result in competitive interactions. To date, few studies on multiple-strain vector-borne pathogens have investigated patterns of cooccurrence and abundance in the arthropod vector. We demonstrate that the abundance of a given strain in the tick vector is negatively affected by the presence of coinfecting strains. In addition, our study suggests that the spirochete abundance in the tick is an important life history trait that can explain why some strains are more common in nature than others. Copyright © 2017 American Society for Microbiology.

A Shellcode Detection Method Based on Full Native API Sequence and Support Vector Machine

NASA Astrophysics Data System (ADS)

Cheng, Yixuan; Fan, Wenqing; Huang, Wei; An, Jing

2017-09-01

Dynamic monitoring the behavior of a program is widely used to discriminate between benign program and malware. It is usually based on the dynamic characteristics of a program, such as API call sequence or API call frequency to judge. The key innovation of this paper is to consider the full Native API sequence and use the support vector machine to detect the shellcode. We also use the Markov chain to extract and digitize Native API sequence features. Our experimental results show that the method proposed in this paper has high accuracy and low detection rate.

Optical vector network analyzer with improved accuracy based on polarization modulation and polarization pulling.

PubMed

Li, Wei; Liu, Jian Guo; Zhu, Ning Hua

2015-04-15

We report a novel optical vector network analyzer (OVNA) with improved accuracy based on polarization modulation and stimulated Brillouin scattering (SBS) assisted polarization pulling. The beating between adjacent higher-order optical sidebands which are generated because of the nonlinearity of an electro-optic modulator (EOM) introduces considerable error to the OVNA. In our scheme, the measurement error is significantly reduced by removing the even-order optical sidebands using polarization discrimination. The proposed approach is theoretically analyzed and experimentally verified. The experimental results show that the accuracy of the OVNA is greatly improved compared to a conventional OVNA.

Evolving discriminators for querying video sequences

NASA Astrophysics Data System (ADS)

Iyengar, Giridharan; Lippman, Andrew B.

1997-01-01

In this paper we present a framework for content based query and retrieval of information from large video databases. This framework enables content based retrieval of video sequences by characterizing the sequences using motion, texture and colorimetry cues. This characterization is biologically inspired and results in a compact parameter space where every segment of video is represented by an 8 dimensional vector. Searching and retrieval is done in real- time with accuracy in this parameter space. Using this characterization, we then evolve a set of discriminators using Genetic Programming Experiments indicate that these discriminators are capable of analyzing and characterizing video. The VideoBook is able to search and retrieve video sequences with 92% accuracy in real-time. Experiments thus demonstrate that the characterization is capable of extracting higher level structure from raw pixel values.

Fast-HPLC Fingerprinting to Discriminate Olive Oil from Other Edible Vegetable Oils by Multivariate Classification Methods.

PubMed

Jiménez-Carvelo, Ana M; González-Casado, Antonio; Pérez-Castaño, Estefanía; Cuadros-Rodríguez, Luis

2017-03-01

A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phase LC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis took only 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil were used: one input-class, two input-class, and pseudo two input-class.

Temporal signal processing of dolphin biosonar echoes from salmon prey.

PubMed

Au, Whitlow W L; Ou, Hui Helen

2014-08-01

Killer whales project short broadband biosonar clicks. The broadband nature of the clicks provides good temporal resolution of echo highlights and allows for the discriminations of salmon prey. The echoes contain many highlights as the signals reflect off different surfaces and parts of the fish body and swim bladder. The temporal characteristics of echoes from salmon are highly aspect dependent and six temporal parameters were used in a support vector machine to discriminate between species. Results suggest that killer whales can classify salmon based on their echoes and provide some insight as to which features might enable the classification.

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.

Context-Aware Local Binary Feature Learning for Face Recognition.

PubMed

Duan, Yueqi; Lu, Jiwen; Feng, Jianjiang; Zhou, Jie

2018-05-01

In this paper, we propose a context-aware local binary feature learning (CA-LBFL) method for face recognition. Unlike existing learning-based local face descriptors such as discriminant face descriptor (DFD) and compact binary face descriptor (CBFD) which learn each feature code individually, our CA-LBFL exploits the contextual information of adjacent bits by constraining the number of shifts from different binary bits, so that more robust information can be exploited for face representation. Given a face image, we first extract pixel difference vectors (PDV) in local patches, and learn a discriminative mapping in an unsupervised manner to project each pixel difference vector into a context-aware binary vector. Then, we perform clustering on the learned binary codes to construct a codebook, and extract a histogram feature for each face image with the learned codebook as the final representation. In order to exploit local information from different scales, we propose a context-aware local binary multi-scale feature learning (CA-LBMFL) method to jointly learn multiple projection matrices for face representation. To make the proposed methods applicable for heterogeneous face recognition, we present a coupled CA-LBFL (C-CA-LBFL) method and a coupled CA-LBMFL (C-CA-LBMFL) method to reduce the modality gap of corresponding heterogeneous faces in the feature level, respectively. Extensive experimental results on four widely used face datasets clearly show that our methods outperform most state-of-the-art face descriptors.

Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patterns.

PubMed

Iakovidis, Dimitris K; Keramidas, Eystratios G; Maroulis, Dimitris

2010-09-01

This paper proposes a novel approach for thyroid ultrasound pattern representation. Considering that texture and echogenicity are correlated with thyroid malignancy, the proposed approach encodes these sonographic features via a noise-resistant representation. This representation is suitable for the discrimination of nodules of high malignancy risk from normal thyroid parenchyma. The material used in this study includes a total of 250 thyroid ultrasound patterns obtained from 75 patients in Greece. The patterns are represented by fused vectors of fuzzy features. Ultrasound texture is represented by fuzzy local binary patterns, whereas echogenicity is represented by fuzzy intensity histograms. The encoded thyroid ultrasound patterns are discriminated by support vector classifiers. The proposed approach was comprehensively evaluated using receiver operating characteristics (ROCs). The results show that the proposed fusion scheme outperforms previous thyroid ultrasound pattern representation methods proposed in the literature. The best classification accuracy was obtained with a polynomial kernel support vector machine, and reached 97.5% as estimated by the area under the ROC curve. The fusion of fuzzy local binary patterns and fuzzy grey-level histogram features is more effective than the state of the art approaches for the representation of thyroid ultrasound patterns and can be effectively utilized for the detection of nodules of high malignancy risk in the context of an intelligent medical system. Copyright (c) 2010 Elsevier B.V. All rights reserved.

PREDICTION OF SOLAR FLARE SIZE AND TIME-TO-FLARE USING SUPPORT VECTOR MACHINE REGRESSION

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

Boucheron, Laura E.; Al-Ghraibah, Amani; McAteer, R. T. James

We study the prediction of solar flare size and time-to-flare using 38 features describing magnetic complexity of the photospheric magnetic field. This work uses support vector regression to formulate a mapping from the 38-dimensional feature space to a continuous-valued label vector representing flare size or time-to-flare. When we consider flaring regions only, we find an average error in estimating flare size of approximately half a geostationary operational environmental satellite (GOES) class. When we additionally consider non-flaring regions, we find an increased average error of approximately three-fourths a GOES class. We also consider thresholding the regressed flare size for the experimentmore » containing both flaring and non-flaring regions and find a true positive rate of 0.69 and a true negative rate of 0.86 for flare prediction. The results for both of these size regression experiments are consistent across a wide range of predictive time windows, indicating that the magnetic complexity features may be persistent in appearance long before flare activity. This is supported by our larger error rates of some 40 hr in the time-to-flare regression problem. The 38 magnetic complexity features considered here appear to have discriminative potential for flare size, but their persistence in time makes them less discriminative for the time-to-flare problem.« less

Combination of cytochrome b heteroduplex-assay and sequencing for identification of triatomine blood meals.

PubMed

Buitrago, Rosio; Depickère, Stéphanie; Bosseno, Marie-France; Patzi, Edda Siñani; Waleckx, Etienne; Salas, Renata; Aliaga, Claudia; Brenière, Simone Frédérique

2012-01-01

The identification of blood meals in vectors contributes greatly to the understanding of interactions between vectors, microorganisms and hosts. The aim of the current work was to complement the validation of cytochrome b (Cytb) heteroduplex assay (HDA) previously described, and to add the sequencing of the Cytb gene of some samples for the identification of blood meals in triatomines. Experimental feedings of reared triatomines helped to clarify the sensitivity of the HDA. Moreover, the sequencing coupled with the HDA, allowed the assessment of the technique's taxonomic level of discrimination. The primers used to produce DNA fragments of Cytb genes for HDA had a very high sensitivity for vertebrate DNAs, rather similar for mammals, birds and reptiles. However, the formation of heteroduplex depended on blood meal's quality rather than its quantity; a correlation was observed between blood meals' color and the positivity of HDA. HDA electrophoresis profiles were reproducible, and allowed the discrimination of blood origins at the species level. However, in some cases, intraspecific variability of Cytb gene generated different HDA profiles. The HDA based on comparison of electrophoresis profiles is a very useful tool for screening large samples to determine blood origins; the subsequent sequencing of PCR products of Cytb corresponding to different HDA profiles allowed the identification of species whatever the biotope in which the vectors were captured. Copyright © 2011. Published by Elsevier B.V.

Comparison of support vector machine classification to partial least squares dimension reduction with logistic descrimination of hyperspectral data

NASA Astrophysics Data System (ADS)

Wilson, Machelle; Ustin, Susan L.; Rocke, David

2003-03-01

Remote sensing technologies with high spatial and spectral resolution show a great deal of promise in addressing critical environmental monitoring issues, but the ability to analyze and interpret the data lags behind the technology. Robust analytical methods are required before the wealth of data available through remote sensing can be applied to a wide range of environmental problems for which remote detection is the best method. In this study we compare the classification effectiveness of two relatively new techniques on data consisting of leaf-level reflectance from plants that have been exposed to varying levels of heavy metal toxicity. If these methodologies work well on leaf-level data, then there is some hope that they will also work well on data from airborne and space-borne platforms. The classification methods compared were support vector machine classification of exposed and non-exposed plants based on the reflectance data, and partial east squares compression of the reflectance data followed by classification using logistic discrimination (PLS/LD). PLS/LD was performed in two ways. We used the continuous concentration data as the response during compression, and then used the binary response required during logistic discrimination. We also used a binary response during compression followed by logistic discrimination. The statistics we used to compare the effectiveness of the methodologies was the leave-one-out cross validation estimate of the prediction error.

Hybrid generative-discriminative approach to age-invariant face recognition

NASA Astrophysics Data System (ADS)

Sajid, Muhammad; Shafique, Tamoor

2018-03-01

Age-invariant face recognition is still a challenging research problem due to the complex aging process involving types of facial tissues, skin, fat, muscles, and bones. Most of the related studies that have addressed the aging problem are focused on generative representation (aging simulation) or discriminative representation (feature-based approaches). Designing an appropriate hybrid approach taking into account both the generative and discriminative representations for age-invariant face recognition remains an open problem. We perform a hybrid matching to achieve robustness to aging variations. This approach automatically segments the eyes, nose-bridge, and mouth regions, which are relatively less sensitive to aging variations compared with the rest of the facial regions that are age-sensitive. The aging variations of age-sensitive facial parts are compensated using a demographic-aware generative model based on a bridged denoising autoencoder. The age-insensitive facial parts are represented by pixel average vector-based local binary patterns. Deep convolutional neural networks are used to extract relative features of age-sensitive and age-insensitive facial parts. Finally, the feature vectors of age-sensitive and age-insensitive facial parts are fused to achieve the recognition results. Extensive experimental results on morphological face database II (MORPH II), face and gesture recognition network (FG-NET), and Verification Subset of cross-age celebrity dataset (CACD-VS) demonstrate the effectiveness of the proposed method for age-invariant face recognition well.

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.

Racial/Ethnic Workplace Discrimination

PubMed Central

Chavez, Laura J.; Ornelas, India J.; Lyles, Courtney R.; Williams, Emily C.

2014-01-01

Background Experiences of discrimination are associated with tobacco and alcohol use, and work is a common setting where individuals experience racial/ethnic discrimination. Few studies have evaluated the association between workplace discrimination and these behaviors, and none have described associations across race/ethnicity. Purpose To examine the association between workplace discrimination and tobacco and alcohol use in a large, multistate sample of U.S. adult respondents to the Behavioral Risk Factor Surveillance System survey Reactions to Race Module (2004–2010). Methods Multivariable logistic regression analyses evaluated cross-sectional associations between self-reported workplace discrimination and tobacco (current and daily smoking) and alcohol use (any and heavy use, and binge drinking) among all participants and stratified by race/ethnicity, adjusting for relevant covariates. Data were analyzed in 2013. Results Among respondents, 70,080 completed the workplace discrimination measure. Discrimination was more common among black non-Hispanic (21%), Hispanic (12%), and other race respondents (11%) than white non-Hispanics (4%) (p<0.001). In the total sample, discrimination was associated with current smoking (risk ratio [RR]=1.32, 95% CI=1.19, 1.47), daily smoking (RR=1.41, 95% CI=1.24, 1.61), and heavy drinking (RR=1.11, 95% CI=1.01, 1.22), but not binge or any drinking. Among Hispanics, workplace discrimination was associated with increased heavy and binge drinking, but not any alcohol use or smoking. Workplace discrimination among black non-Hispanics and white Non-Hispanics was associated with increased current and daily smoking, but not alcohol outcomes. Conclusions Workplace discrimination is common, associated with smoking and alcohol use, and merits further policy attention given the impact of these behaviors on morbidity and mortality. PMID:25441232

Racial/ethnic workplace discrimination: association with tobacco and alcohol use.

PubMed

Chavez, Laura J; Ornelas, India J; Lyles, Courtney R; Williams, Emily C

2015-01-01

Experiences of discrimination are associated with tobacco and alcohol use, and work is a common setting where individuals experience racial/ethnic discrimination. Few studies have evaluated the association between workplace discrimination and these behaviors, and none have described associations across race/ethnicity. To examine the association between workplace discrimination and tobacco and alcohol use in a large, multistate sample of U.S. adult respondents to the Behavioral Risk Factor Surveillance System survey Reactions to Race Module (2004-2010). Multivariable logistic regression analyses evaluated cross-sectional associations between self-reported workplace discrimination and tobacco (current and daily smoking) and alcohol use (any and heavy use, and binge drinking) among all participants and stratified by race/ethnicity, adjusting for relevant covariates. Data were analyzed in 2013. Among respondents, 70,080 completed the workplace discrimination measure. Discrimination was more common among black non-Hispanic (21%), Hispanic (12%), and other race respondents (11%) than white non-Hispanics (4%) (p<0.001). In the total sample, discrimination was associated with current smoking (risk ratio [RR]=1.32, 95% CI=1.19, 1.47), daily smoking (RR=1.41, 95% CI=1.24, 1.61), and heavy drinking (RR=1.11, 95% CI=1.01, 1.22), but not binge or any drinking. Among Hispanics, workplace discrimination was associated with increased heavy and binge drinking, but not any alcohol use or smoking. Workplace discrimination among black non-Hispanics and white Non-Hispanics was associated with increased current and daily smoking, but not alcohol outcomes. Workplace discrimination is common, associated with smoking and alcohol use, and merits further policy attention, given the impact of these behaviors on morbidity and mortality. Copyright © 2015 American Journal of Preventive Medicine. All rights reserved.

Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning.

PubMed

Wang, Jinhua; Yang, Xi; Cai, Hongmin; Tan, Wanchang; Jin, Cangzheng; Li, Li

2016-06-07

Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic accuracy of microcalcifications, this study evaluates the performance of deep learning-based models on large datasets for its discrimination. A semi-automated segmentation method was used to characterize all microcalcifications. A discrimination classifier model was constructed to assess the accuracies of microcalcifications and breast masses, either in isolation or combination, for classifying breast lesions. Performances were compared to benchmark models. Our deep learning model achieved a discriminative accuracy of 87.3% if microcalcifications were characterized alone, compared to 85.8% with a support vector machine. The accuracies were 61.3% for both methods with masses alone and improved to 89.7% and 85.8% after the combined analysis with microcalcifications. Image segmentation with our deep learning model yielded 15, 26 and 41 features for the three scenarios, respectively. Overall, deep learning based on large datasets was superior to standard methods for the discrimination of microcalcifications. Accuracy was increased by adopting a combinatorial approach to detect microcalcifications and masses simultaneously. This may have clinical value for early detection and treatment of breast cancer.

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

NASA Astrophysics Data System (ADS)

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

2010-03-01

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

Discriminative Multi-View Interactive Image Re-Ranking.

PubMed

Li, Jun; Xu, Chang; Yang, Wankou; Sun, Changyin; Tao, Dacheng

2017-07-01

Given an unreliable visual patterns and insufficient query information, content-based image retrieval is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose a discriminative multi-view interactive image re-ranking (DMINTIR), which integrates user relevance feedback capturing users' intentions and multiple features that sufficiently describe the images. In DMINTIR, heterogeneous property features are incorporated in the multi-view learning scheme to exploit their complementarities. In addition, a discriminatively learned weight vector is obtained to reassign updated scores and target images for re-ranking. Compared with other multi-view learning techniques, our scheme not only generates a compact representation in the latent space from the redundant multi-view features but also maximally preserves the discriminative information in feature encoding by the large-margin principle. Furthermore, the generalization error bound of the proposed algorithm is theoretically analyzed and shown to be improved by the interactions between the latent space and discriminant function learning. Experimental results on two benchmark data sets demonstrate that our approach boosts baseline retrieval quality and is competitive with the other state-of-the-art re-ranking strategies.

Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning

PubMed Central

Wang, Jinhua; Yang, Xi; Cai, Hongmin; Tan, Wanchang; Jin, Cangzheng; Li, Li

2016-01-01

Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic accuracy of microcalcifications, this study evaluates the performance of deep learning-based models on large datasets for its discrimination. A semi-automated segmentation method was used to characterize all microcalcifications. A discrimination classifier model was constructed to assess the accuracies of microcalcifications and breast masses, either in isolation or combination, for classifying breast lesions. Performances were compared to benchmark models. Our deep learning model achieved a discriminative accuracy of 87.3% if microcalcifications were characterized alone, compared to 85.8% with a support vector machine. The accuracies were 61.3% for both methods with masses alone and improved to 89.7% and 85.8% after the combined analysis with microcalcifications. Image segmentation with our deep learning model yielded 15, 26 and 41 features for the three scenarios, respectively. Overall, deep learning based on large datasets was superior to standard methods for the discrimination of microcalcifications. Accuracy was increased by adopting a combinatorial approach to detect microcalcifications and masses simultaneously. This may have clinical value for early detection and treatment of breast cancer. PMID:27273294

Discrimination and Prejudice: An Annotated Bibliography. Second Edition.

ERIC Educational Resources Information Center

Fairchild, Halford H., Comp.; And Others

A comprehensive and comparative compilation of the social science literature pertinent to ethnic discrimination and racial discrimination and practice is presented, focusing on areas of discrimination common to these groups in economics, education, employment, health, housing, criminal justice, and political participation. To represent major…

Combining information from 3 anatomic regions in the diagnosis of glaucoma with time-domain optical coherence tomography.

PubMed

Wang, Mingwu; Lu, Ake Tzu-Hui; Varma, Rohit; Schuman, Joel S; Greenfield, David S; Huang, David

2014-03-01

To improve the diagnosis of glaucoma by combining time-domain optical coherence tomography (TD-OCT) measurements of the optic disc, circumpapillary retinal nerve fiber layer (RNFL), and macular retinal thickness. Ninety-six age-matched normal and 96 perimetric glaucoma participants were included in this observational, cross-sectional study. Or-logic, support vector machine, relevance vector machine, and linear discrimination function were used to analyze the performances of combined TD-OCT diagnostic variables. The area under the receiver-operating curve (AROC) was used to evaluate the diagnostic accuracy and to compare the diagnostic performance of single and combined anatomic variables. The best RNFL thickness variables were the inferior (AROC=0.900), overall (AROC=0.892), and superior quadrants (AROC=0.850). The best optic disc variables were horizontal integrated rim width (AROC=0.909), vertical integrated rim area (AROC=0.908), and cup/disc vertical ratio (AROC=0.890). All macular retinal thickness variables had AROCs of 0.829 or less. Combining the top 3 RNFL and optic disc variables in optimizing glaucoma diagnosis, support vector machine had the highest AROC, 0.954, followed by or-logic (AROC=0.946), linear discrimination function (AROC=0.946), and relevance vector machine (AROC=0.943). All combination diagnostic variables had significantly larger AROCs than any single diagnostic variable. There are no significant differences among the combination diagnostic indices. With TD-OCT, RNFL and optic disc variables had better diagnostic accuracy than macular retinal variables. Combining top RNFL and optic disc variables significantly improved diagnostic performance. Clinically, or-logic classification was the most practical analytical tool with sufficient accuracy to diagnose early glaucoma.

Discrimination between Alzheimer's Disease and Late Onset Bipolar Disorder Using Multivariate Analysis.

PubMed

Besga, Ariadna; Gonzalez, Itxaso; Echeburua, Enrique; Savio, Alexandre; Ayerdi, Borja; Chyzhyk, Darya; Madrigal, Jose L M; Leza, Juan C; Graña, Manuel; Gonzalez-Pinto, Ana Maria

2015-01-01

Late onset bipolar disorder (LOBD) is often difficult to distinguish from degenerative dementias, such as Alzheimer disease (AD), due to comorbidities and common cognitive symptoms. Moreover, LOBD prevalence in the elder population is not negligible and it is increasing. Both pathologies share pathophysiological neuroinflammation features. Improvements in differential diagnosis of LOBD and AD will help to select the best personalized treatment. The aim of this study is to assess the relative significance of clinical observations, neuropsychological tests, and specific blood plasma biomarkers (inflammatory and neurotrophic), separately and combined, in the differential diagnosis of LOBD versus AD. It was carried out evaluating the accuracy achieved by classification-based computer-aided diagnosis (CAD) systems based on these variables. A sample of healthy controls (HC) (n = 26), AD patients (n = 37), and LOBD patients (n = 32) was recruited at the Alava University Hospital. Clinical observations, neuropsychological tests, and plasma biomarkers were measured at recruitment time. We applied multivariate machine learning classification methods to discriminate subjects from HC, AD, and LOBD populations in the study. We analyzed, for each classification contrast, feature sets combining clinical observations, neuropsychological measures, and biological markers, including inflammation biomarkers. Furthermore, we analyzed reduced feature sets containing variables with significative differences determined by a Welch's t-test. Furthermore, a battery of classifier architectures were applied, encompassing linear and non-linear Support Vector Machines (SVM), Random Forests (RF), Classification and regression trees (CART), and their performance was evaluated in a leave-one-out (LOO) cross-validation scheme. Post hoc analysis of Gini index in CART classifiers provided a measure of each variable importance. Welch's t-test found one biomarker (Malondialdehyde) with significative differences (p < 0.001) in LOBD vs. AD contrast. Classification results with the best features are as follows: discrimination of HC vs. AD patients reaches accuracy 97.21% and AUC 98.17%. Discrimination of LOBD vs. AD patients reaches accuracy 90.26% and AUC 89.57%. Discrimination of HC vs LOBD patients achieves accuracy 95.76% and AUC 88.46%. It is feasible to build CAD systems for differential diagnosis of LOBD and AD on the basis of a reduced set of clinical variables. Clinical observations provide the greatest discrimination. Neuropsychological tests are improved by the addition of biomarkers, and both contribute significantly to improve the overall predictive performance.

Eigenanatomy on Fractional Anisotropy Imaging Provides White Matter Anatomical Features Discriminating Between Alzheimer's Disease and Late Onset Bipolar Disorder.

PubMed

Besga, Ariadna; Chyzhyk, Darya; González-Ortega, Itxaso; Savio, Alexandre; Ayerdi, Borja; Echeveste, Jon; Graña, Manuel; González-Pinto, Ana

2016-01-01

Late Onset Bipolar Disorder (LOBD) is the arousal of Bipolar Disorder (BD) at old age (>60) without any previous history of disorders. LOBD is often difficult to distinguish from degenerative dementias, such as Alzheimer Disease (AD), due to comorbidities and common cognitive symptoms. Moreover, LOBD prevalence is increasing due to population aging. Biomarkers extracted from blood plasma are not discriminant because both pathologies share pathophysiological features related to neuroinflammation, therefore we look for anatomical features highly correlated with blood biomarkers that allow accurate diagnosis prediction. This may shed some light on the basic biological mechanisms leading to one or another disease. Moreover, accurate diagnosis is needed to select the best personalized treatment. We look for white matter features which are correlated with blood plasma biomarkers (inflammatory and neurotrophic) discriminating LOBD from AD. A sample of healthy controls (HC) (n=19), AD patients (n=35), and BD patients (n=24) has been recruited at the Alava University Hospital. Plasma biomarkers have been obtained at recruitment time. Diffusion weighted (DWI) magnetic resonance imaging (MRI) are obtained for each subject. DWI is preprocessed to obtain diffusion tensor imaging (DTI) data, which is reduced to fractional anisotropy (FA) data. In the selection phase, eigenanatomy finds FA eigenvolumes maximally correlated with plasma biomarkers by partial sparse canonical correlation analysis (PSCCAN). In the analysis phase, we take the eigenvolume projection coefficients as the classification features, carrying out cross-validation of support vector machine (SVM) to obtain discrimination power of each biomarker effects. The John Hopkins Universtiy white matter atlas is used to provide anatomical localizations of the detected feature clusters. Classification results show that one specific biomarker of oxidative stress (malondialdehyde MDA) gives the best classification performance ( accuracy 85%, F-score 86%, sensitivity, and specificity 87%, ) in the discrimination of AD and LOBD. Discriminating features appear to be localized in the posterior limb of the internal capsule and superior corona radiata. It is feasible to support contrast diagnosis among LOBD and AD by means of predictive classifiers based on eigenanatomy features computed from FA imaging correlated to plasma biomarkers. In addition, white matter eigenanatomy localizations offer some new avenues to assess the differential pathophysiology of LOBD and AD.

Preliminary verification for application of a support vector machine-based cloud detection method to GOSAT-2 CAI-2

NASA Astrophysics Data System (ADS)

Oishi, Yu; Ishida, Haruma; Nakajima, Takashi Y.; Nakamura, Ryosuke; Matsunaga, Tsuneo

2018-05-01

The Greenhouse Gases Observing Satellite (GOSAT) was launched in 2009 to measure global atmospheric CO2 and CH4 concentrations. GOSAT is equipped with two sensors: the Thermal And Near infrared Sensor for carbon Observations (TANSO)-Fourier transform spectrometer (FTS) and TANSO-Cloud and Aerosol Imager (CAI). The presence of clouds in the instantaneous field of view of the FTS leads to incorrect estimates of the concentrations. Thus, the FTS data suspected to have cloud contamination must be identified by a CAI cloud discrimination algorithm and rejected. Conversely, overestimating clouds reduces the amount of FTS data that can be used to estimate greenhouse gas concentrations. This is a serious problem in tropical rainforest regions, such as the Amazon, where the amount of useable FTS data is small because of cloud cover. Preparations are continuing for the launch of the GOSAT-2 in fiscal year 2018. To improve the accuracy of the estimates of greenhouse gases concentrations, we need to refine the existing CAI cloud discrimination algorithm: Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA1). A new cloud discrimination algorithm using a support vector machine (CLAUDIA3) was developed and presented in another paper. Although the use of visual inspection of clouds as a standard for judging is not practical for screening a full satellite data set, it has the advantage of allowing for locally optimized thresholds, while CLAUDIA1 and -3 use common global thresholds. Thus, the accuracy of visual inspection is better than that of these algorithms in most regions, with the exception of snow- and ice-covered surfaces, where there is not enough spectral contrast to identify cloud. In other words, visual inspection results can be used as truth data for accuracy evaluation of CLAUDIA1 and -3. For this reason visual inspection can be used for the truth metric for the cloud discrimination verification exercise. In this study, we compared CLAUDIA1-CAI and CLAUDIA3-CAI for various land cover types, and evaluated the accuracy of CLAUDIA3-CAI by comparing both CLAUDIA1-CAI and CLAUDIA3-CAI with visual inspection (400 × 400 pixels) of the same CAI images in tropical rainforests. Comparative results between CLAUDIA1-CAI and CLAUDIA3-CAI for various land cover types indicated that CLAUDIA3-CAI had a tendency to identify bright surface and optically thin clouds. However, CLAUDIA3-CAI had a tendency to misjudge the edges of clouds compared with CLAUDIA1-CAI. The accuracy of CLAUDIA3-CAI was approximately 89.5 % in tropical rainforests, which is greater than that of CLAUDIA1-CAI (85.9 %) for the test cases presented here.

A Systematic Strategy for Screening and Application of Specific Biomarkers in Hepatotoxicity Using Metabolomics Combined With ROC Curves and SVMs.

PubMed

Li, Yubo; Wang, Lei; Ju, Liang; Deng, Haoyue; Zhang, Zhenzhu; Hou, Zhiguo; Xie, Jiabin; Wang, Yuming; Zhang, Yanjun

2016-04-01

Current studies that evaluate toxicity based on metabolomics have primarily focused on the screening of biomarkers while largely neglecting further verification and biomarker applications. For this reason, we used drug-induced hepatotoxicity as an example to establish a systematic strategy for screening specific biomarkers and applied these biomarkers to evaluate whether the drugs have potential hepatotoxicity toxicity. Carbon tetrachloride (5 ml/kg), acetaminophen (1500 mg/kg), and atorvastatin (5 mg/kg) are established as rat hepatotoxicity models. Fifteen common biomarkers were screened by multivariate statistical analysis and integration analysis-based metabolomics data. The receiver operating characteristic curve was used to evaluate the sensitivity and specificity of the biomarkers. We obtained 10 specific biomarker candidates with an area under the curve greater than 0.7. Then, a support vector machine model was established by extracting specific biomarker candidate data from the hepatotoxic drugs and nonhepatotoxic drugs; the accuracy of the model was 94.90% (92.86% sensitivity and 92.59% specificity) and the results demonstrated that those ten biomarkers are specific. 6 drugs were used to predict the hepatotoxicity by the support vector machines model; the prediction results were consistent with the biochemical and histopathological results, demonstrating that the model was reliable. Thus, this support vector machine model can be applied to discriminate the between the hepatic or nonhepatic toxicity of drugs. This approach not only presents a new strategy for screening-specific biomarkers with greater diagnostic significance but also provides a new evaluation pattern for hepatotoxicity, and it will be a highly useful tool in toxicity estimation and disease diagnoses. © The Author 2016. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Racial discrimination and relationship functioning among African American couples.

PubMed

Lavner, Justin A; Barton, Allen W; Bryant, Chalandra M; Beach, Steven R H

2018-05-21

Racial discrimination is a common stressor for African Americans, with negative consequences for mental and physical well-being. It is likely that these effects extend into the family, but little research has examined the association between racial discrimination and couple functioning. This study used dyadic data from 344 rural, predominantly low-income heterosexual African American couples with an early adolescent child to examine associations between self-reported racial discrimination, psychological and physical aggression, and relationship satisfaction and instability. Experiences of discrimination were common among men and women and were negatively associated with relationship functioning. Specifically, men reported higher levels of psychological aggression and relationship instability if they experienced higher levels of racial discrimination, and women reported higher levels of physical aggression if they experienced higher levels of racial discrimination. All results replicated when controlling for financial hardship, indicating unique effects for discrimination. Findings suggest that racial discrimination may be negatively associated with relationship functioning among African Americans and call for further research on the processes underlying these associations and their long-term consequences. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Hyperspectral Imaging and Support Vector Machine: A Powerful Combination to Differentiate Black Cohosh (Actaea racemosa) from Other Cohosh Species.

PubMed

Tankeu, Sidonie; Vermaak, Ilze; Chen, Weiyang; Sandasi, Maxleene; Kamatou, Guy; Viljoen, Alvaro

2018-04-01

Actaea racemosa (black cohosh) has a history of traditional use in the treatment of general gynecological problems. However, the plant is known to be vulnerable to adulteration with other cohosh species. This study evaluated the use of shortwave infrared hyperspectral imaging (SWIR-HSI) in tandem with chemometric data analysis as a fast alternative method for the discrimination of four cohosh species ( Actaea racemosa, Actaea podocarpa, Actaea pachypoda, Actaea cimicifuga ) and 36 commercial products labelled as black cohosh. The raw material and commercial products were analyzed using SWIR-HSI and ultra-high-performance liquid chromatography coupled to mass spectrometry (UHPLC-MS) followed by chemometric modeling. From SWIR-HSI data (920 - 2514 nm), the range containing the discriminating information of the four species was identified as 1204 - 1480 nm using Matlab software. After reduction of the data set range, partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA) models with coefficients of determination ( R2 ) of ≥ 0.8 were created. The novel SVM-DA model showed better predictions and was used to predict the commercial product content. Seven out of 36 commercial products were recognized by the SVM-DA model as being true black cohosh while 29 products indicated adulteration. Analysis of the UHPLC-MS data demonstrated that six commercial products could be authentic black cohosh. This was confirmed using the fragmentation patterns of three black cohosh markers (cimiracemoside C; 12- β ,21-dihydroxycimigenol-3- O -L-arabinoside; and 24- O -acetylhydroshengmanol-3- O - β -D-xylopyranoside). SWIR-HSI in conjunction with chemometric tools (SVM-DA) could identify 80% adulteration of commercial products labelled as black cohosh. Georg Thieme Verlag KG Stuttgart · New York.

PCR multiplexes discriminate Fusarium symbionts of invasive Euwallacea ambrosia beetles that inflict damage on numerous tree species throughout the United States

USDA-ARS?s Scientific Manuscript database

Asian Euwallacea ambrosia beetles vector Fusarium mutualists. The ambrosial fusaria are all members of the Ambrosia Fusarium Clade (AFC) within the Fusarium solani species complex (FSSC). Several Euwallacea-Fusarium mutualists have been introduced into non-native regions and have caused varying degr...

Assessing workplace discrimination among medical practitioners in Western Sydney.

PubMed

Agrawal, Ravindra; Foresti, Katia; Rajadurai, Jeremy; Zubaran, Carlos

2018-05-01

To investigate the presence of different forms of experiences of discrimination in the medical workplace. A total of 526 questionnaires were sent out, including a demographic survey form and the Everyday Discrimination Scale. Experiences of being "treated with less courtesy than other people are" and feeling as "others acted if they're better than [me]" were reported as having occurred with almost daily frequency by 4.1 % of respondents. Those whose main language was not English and classified as "non-whites" had significantly more experiences ("ever") of discrimination in the medical workplace. The most commonly reported reasons for discrimination were ancestry (31.3%), "race" (28.1%), and gender (21.1%). The results of this survey indicate that a proportion of doctors experience discrimination in the workplace in Australia. This phenomenon was more commonly reported by doctors of minority status.

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

PubMed Central

Kuzy, Jesse; Li, Changying

2017-01-01

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

Ship Detection in Optical Satellite Image Based on RX Method and PCAnet

NASA Astrophysics Data System (ADS)

Shao, Xiu; Li, Huali; Lin, Hui; Kang, Xudong; Lu, Ting

2017-12-01

In this paper, we present a novel method for ship detection in optical satellite image based on the ReedXiaoli (RX) method and the principal component analysis network (PCAnet). The proposed method consists of the following three steps. First, the spatially adjacent pixels in optical image are arranged into a vector, transforming the optical image into a 3D cube image. By taking this process, the contextual information of the spatially adjacent pixels can be integrated to magnify the discrimination between ship and background. Second, the RX anomaly detection method is adopted to preliminarily extract ship candidates from the produced 3D cube image. Finally, real ships are further confirmed among ship candidates by applying the PCAnet and the support vector machine (SVM). Specifically, the PCAnet is a simple deep learning network which is exploited to perform feature extraction, and the SVM is applied to achieve feature pooling and decision making. Experimental results demonstrate that our approach is effective in discriminating between ships and false alarms, and has a good ship detection performance.

Offline handwritten word recognition using MQDF-HMMs

NASA Astrophysics Data System (ADS)

Ramachandrula, Sitaram; Hambarde, Mangesh; Patial, Ajay; Sahoo, Dushyant; Kochar, Shaivi

2015-01-01

We propose an improved HMM formulation for offline handwriting recognition (HWR). The main contribution of this work is using modified quadratic discriminant function (MQDF) [1] within HMM framework. In an MQDF-HMM the state observation likelihood is calculated by a weighted combination of MQDF likelihoods of individual Gaussians of GMM (Gaussian Mixture Model). The quadratic discriminant function (QDF) of a multivariate Gaussian can be rewritten by avoiding the inverse of covariance matrix by using the Eigen values and Eigen vectors of it. The MQDF is derived from QDF by substituting few of badly estimated lower-most Eigen values by an appropriate constant. The estimation errors of non-dominant Eigen vectors and Eigen values of covariance matrix for which the training data is insufficient can be controlled by this approach. MQDF has been successfully shown to improve the character recognition performance [1]. The usage of MQDF in HMM improves the computation, storage and modeling power of HMM when there is limited training data. We have got encouraging results on offline handwritten character (NIST database) and word recognition in English using MQDF HMMs.

Support vector machine with a Pearson VII function kernel for discriminating halophilic and non-halophilic proteins.

PubMed

Zhang, Guangya; Ge, Huihua

2013-10-01

Understanding of proteins adaptive to hypersaline environment and identifying them is a challenging task and would help to design stable proteins. Here, we have systematically analyzed the normalized amino acid compositions of 2121 halophilic and 2400 non-halophilic proteins. The results showed that halophilic protein contained more Asp at the expense of Lys, Ile, Cys and Met, fewer small and hydrophobic residues, and showed a large excess of acidic over basic amino acids. Then, we introduce a support vector machine method to discriminate the halophilic and non-halophilic proteins, by using a novel Pearson VII universal function based kernel. In the three validation check methods, it achieved an overall accuracy of 97.7%, 91.7% and 86.9% and outperformed other machine learning algorithms. We also address the influence of protein size on prediction accuracy and found the worse performance for small size proteins might be some significant residues (Cys and Lys) were missing in the proteins. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

Multivariate pattern analysis reveals subtle brain anomalies relevant to the cognitive phenotype in neurofibromatosis type 1.

PubMed

Duarte, João V; Ribeiro, Maria J; Violante, Inês R; Cunha, Gil; Silva, Eduardo; Castelo-Branco, Miguel

2014-01-01

Neurofibromatosis Type 1 (NF1) is a common genetic condition associated with cognitive dysfunction. However, the pathophysiology of the NF1 cognitive deficits is not well understood. Abnormal brain structure, including increased total brain volume, white matter (WM) and grey matter (GM) abnormalities have been reported in the NF1 brain. These previous studies employed univariate model-driven methods preventing detection of subtle and spatially distributed differences in brain anatomy. Multivariate pattern analysis allows the combination of information from multiple spatial locations yielding a discriminative power beyond that of single voxels. Here we investigated for the first time subtle anomalies in the NF1 brain, using a multivariate data-driven classification approach. We used support vector machines (SVM) to classify whole-brain GM and WM segments of structural T1 -weighted MRI scans from 39 participants with NF1 and 60 non-affected individuals, divided in children/adolescents and adults groups. We also employed voxel-based morphometry (VBM) as a univariate gold standard to study brain structural differences. SVM classifiers correctly classified 94% of cases (sensitivity 92%; specificity 96%) revealing the existence of brain structural anomalies that discriminate NF1 individuals from controls. Accordingly, VBM analysis revealed structural differences in agreement with the SVM weight maps representing the most relevant brain regions for group discrimination. These included the hippocampus, basal ganglia, thalamus, and visual cortex. This multivariate data-driven analysis thus identified subtle anomalies in brain structure in the absence of visible pathology. Our results provide further insight into the neuroanatomical correlates of known features of the cognitive phenotype of NF1. Copyright © 2012 Wiley Periodicals, Inc.

Physical activity classification with dynamic discriminative methods.

PubMed

Ray, Evan L; Sasaki, Jeffer E; Freedson, Patty S; Staudenmayer, John

2018-06-19

A person's physical activity has important health implications, so it is important to be able to measure aspects of physical activity objectively. One approach to doing that is to use data from an accelerometer to classify physical activity according to activity type (e.g., lying down, sitting, standing, or walking) or intensity (e.g., sedentary, light, moderate, or vigorous). This can be formulated as a labeled classification problem, where the model relates a feature vector summarizing the accelerometer signal in a window of time to the activity type or intensity in that window. These data exhibit two key characteristics: (1) the activity classes in different time windows are not independent, and (2) the accelerometer features have moderately high dimension and follow complex distributions. Through a simulation study and applications to three datasets, we demonstrate that a model's classification performance is related to how it addresses these aspects of the data. Dynamic methods that account for temporal dependence achieve better performance than static methods that do not. Generative methods that explicitly model the distribution of the accelerometer signal features do not perform as well as methods that take a discriminative approach to establishing the relationship between the accelerometer signal and the activity class. Specifically, Conditional Random Fields consistently have better performance than commonly employed methods that ignore temporal dependence or attempt to model the accelerometer features. © 2018, The International Biometric Society.

Progresses towards safe and efficient gene therapy vectors.

PubMed

Chira, Sergiu; Jackson, Carlo S; Oprea, Iulian; Ozturk, Ferhat; Pepper, Michael S; Diaconu, Iulia; Braicu, Cornelia; Raduly, Lajos-Zsolt; Calin, George A; Berindan-Neagoe, Ioana

2015-10-13

The emergence of genetic engineering at the beginning of the 1970's opened the era of biomedical technologies, which aims to improve human health using genetic manipulation techniques in a clinical context. Gene therapy represents an innovating and appealing strategy for treatment of human diseases, which utilizes vehicles or vectors for delivering therapeutic genes into the patients' body. However, a few past unsuccessful events that negatively marked the beginning of gene therapy resulted in the need for further studies regarding the design and biology of gene therapy vectors, so that this innovating treatment approach can successfully move from bench to bedside. In this paper, we review the major gene delivery vectors and recent improvements made in their design meant to overcome the issues that commonly arise with the use of gene therapy vectors. At the end of the manuscript, we summarized the main advantages and disadvantages of common gene therapy vectors and we discuss possible future directions for potential therapeutic vectors.

Progresses towards safe and efficient gene therapy vectors

PubMed Central

Chira, Sergiu; Jackson, Carlo S.; Oprea, Iulian; Ozturk, Ferhat; Pepper, Michael S.; Diaconu, Iulia; Braicu, Cornelia; Raduly, Lajos-Zsolt; Calin, George A.; Berindan-Neagoe, Ioana

2015-01-01

The emergence of genetic engineering at the beginning of the 1970′s opened the era of biomedical technologies, which aims to improve human health using genetic manipulation techniques in a clinical context. Gene therapy represents an innovating and appealing strategy for treatment of human diseases, which utilizes vehicles or vectors for delivering therapeutic genes into the patients' body. However, a few past unsuccessful events that negatively marked the beginning of gene therapy resulted in the need for further studies regarding the design and biology of gene therapy vectors, so that this innovating treatment approach can successfully move from bench to bedside. In this paper, we review the major gene delivery vectors and recent improvements made in their design meant to overcome the issues that commonly arise with the use of gene therapy vectors. At the end of the manuscript, we summarized the main advantages and disadvantages of common gene therapy vectors and we discuss possible future directions for potential therapeutic vectors. PMID:26362400

Thin-layer chromatographic identification of Chinese propolis using chemometric fingerprinting.

PubMed

Tang, Tie-xin; Guo, Wei-yan; Xu, Ye; Zhang, Si-ming; Xu, Xin-jun; Wang, Dong-mei; Zhao, Zhi-min; Zhu, Long-ping; Yang, De-po

2014-01-01

Poplar tree gum has a similar chemical composition and appearance to Chinese propolis (bee glue) and has been widely used as a counterfeit propolis because Chinese propolis is typically the poplar-type propolis, the chemical composition of which is determined mainly by the resin of poplar trees. The discrimination of Chinese propolis from poplar tree gum is a challenging task. To develop a rapid thin-layer chromatographic (TLC) identification method using chemometric fingerprinting to discriminate Chinese propolis from poplar tree gum. A new TLC method using a combination of ammonia and hydrogen peroxide vapours as the visualisation reagent was developed to characterise the chemical profile of Chinese propolis. Three separate people performed TLC on eight Chinese propolis samples and three poplar tree gum samples of varying origins. Five chemometric methods, including similarity analysis, hierarchical clustering, k-means clustering, neural network and support vector machine, were compared for use in classifying the samples based on their densitograms obtained from the TLC chromatograms via image analysis. Hierarchical clustering, neural network and support vector machine analyses achieved a correct classification rate of 100% in classifying the samples. A strategy for TLC identification of Chinese propolis using chemometric fingerprinting was proposed and it provided accurate sample classification. The study has shown that the TLC identification method using chemometric fingerprinting is a rapid, low-cost method for the discrimination of Chinese propolis from poplar tree gum and may be used for the quality control of Chinese propolis. Copyright © 2014 John Wiley & Sons, Ltd.

20 years since the introduction of DNA barcoding: from theory to application.

PubMed

Fišer Pečnikar, Živa; Buzan, Elena V

2014-02-01

Traditionally, taxonomic identification has relied upon morphological characters. In the last two decades, molecular tools based on DNA sequences of short standardised gene fragments, termed DNA barcodes, have been developed for species discrimination. The most common DNA barcode used in animals is a fragment of the cytochrome c oxidase (COI) mitochondrial gene, while for plants, two chloroplast gene fragments from the RuBisCo large subunit (rbcL) and maturase K (matK) genes are widely used. Information gathered from DNA barcodes can be used beyond taxonomic studies and will have far-reaching implications across many fields of biology, including ecology (rapid biodiversity assessment and food chain analysis), conservation biology (monitoring of protected species), biosecurity (early identification of invasive pest species), medicine (identification of medically important pathogens and their vectors) and pharmacology (identification of active compounds). However, it is important that the limitations of DNA barcoding are understood and techniques continually adapted and improved as this young science matures.

Unsupervised learning of discriminative edge measures for vehicle matching between nonoverlapping cameras.

PubMed

Shan, Ying; Sawhney, Harpreet S; Kumar, Rakesh

2008-04-01

This paper proposes a novel unsupervised algorithm learning discriminative features in the context of matching road vehicles between two non-overlapping cameras. The matching problem is formulated as a same-different classification problem, which aims to compute the probability of vehicle images from two distinct cameras being from the same vehicle or different vehicle(s). We employ a novel measurement vector that consists of three independent edge-based measures and their associated robust measures computed from a pair of aligned vehicle edge maps. The weight of each measure is determined by an unsupervised learning algorithm that optimally separates the same-different classes in the combined measurement space. This is achieved with a weak classification algorithm that automatically collects representative samples from same-different classes, followed by a more discriminative classifier based on Fisher' s Linear Discriminants and Gibbs Sampling. The robustness of the match measures and the use of unsupervised discriminant analysis in the classification ensures that the proposed method performs consistently in the presence of missing/false features, temporally and spatially changing illumination conditions, and systematic misalignment caused by different camera configurations. Extensive experiments based on real data of over 200 vehicles at different times of day demonstrate promising results.

Optimal algorithm for automatic detection of microaneurysms based on receiver operating characteristic curve

NASA Astrophysics Data System (ADS)

Xu, Lili; Luo, Shuqian

2010-11-01

Microaneurysms (MAs) are the first manifestations of the diabetic retinopathy (DR) as well as an indicator for its progression. Their automatic detection plays a key role for both mass screening and monitoring and is therefore in the core of any system for computer-assisted diagnosis of DR. The algorithm basically comprises the following stages: candidate detection aiming at extracting the patterns possibly corresponding to MAs based on mathematical morphological black top hat, feature extraction to characterize these candidates, and classification based on support vector machine (SVM), to validate MAs. Feature vector and kernel function of SVM selection is very important to the algorithm. We use the receiver operating characteristic (ROC) curve to evaluate the distinguishing performance of different feature vectors and different kernel functions of SVM. The ROC analysis indicates the quadratic polynomial SVM with a combination of features as the input shows the best discriminating performance.

Optimal algorithm for automatic detection of microaneurysms based on receiver operating characteristic curve.

PubMed

Xu, Lili; Luo, Shuqian

2010-01-01

Microaneurysms (MAs) are the first manifestations of the diabetic retinopathy (DR) as well as an indicator for its progression. Their automatic detection plays a key role for both mass screening and monitoring and is therefore in the core of any system for computer-assisted diagnosis of DR. The algorithm basically comprises the following stages: candidate detection aiming at extracting the patterns possibly corresponding to MAs based on mathematical morphological black top hat, feature extraction to characterize these candidates, and classification based on support vector machine (SVM), to validate MAs. Feature vector and kernel function of SVM selection is very important to the algorithm. We use the receiver operating characteristic (ROC) curve to evaluate the distinguishing performance of different feature vectors and different kernel functions of SVM. The ROC analysis indicates the quadratic polynomial SVM with a combination of features as the input shows the best discriminating performance.

Classification of Stellar Spectra with Fuzzy Minimum Within-Class Support Vector Machine

NASA Astrophysics Data System (ADS)

Zhong-bao, Liu; Wen-ai, Song; Jing, Zhang; Wen-juan, Zhao

2017-06-01

Classification is one of the important tasks in astronomy, especially in spectra analysis. Support Vector Machine (SVM) is a typical classification method, which is widely used in spectra classification. Although it performs well in practice, its classification accuracies can not be greatly improved because of two limitations. One is it does not take the distribution of the classes into consideration. The other is it is sensitive to noise. In order to solve the above problems, inspired by the maximization of the Fisher's Discriminant Analysis (FDA) and the SVM separability constraints, fuzzy minimum within-class support vector machine (FMWSVM) is proposed in this paper. In FMWSVM, the distribution of the classes is reflected by the within-class scatter in FDA and the fuzzy membership function is introduced to decrease the influence of the noise. The comparative experiments with SVM on the SDSS datasets verify the effectiveness of the proposed classifier FMWSVM.

Structural vibration-based damage classification of delaminated smart composite laminates

NASA Astrophysics Data System (ADS)

Khan, Asif; Kim, Heung Soo; Sohn, Jung Woo

2018-03-01

Separation along the interfaces of layers (delamination) is a principal mode of failure in laminated composites and its detection is of prime importance for structural integrity of composite materials. In this work, structural vibration response is employed to detect and classify delaminations in piezo-bonded laminated composites. Improved layerwise theory and finite element method are adopted to develop the electromechanically coupled governing equation of a smart composite laminate with and without delaminations. Transient responses of the healthy and damaged structures are obtained through a surface bonded piezoelectric sensor by solving the governing equation in the time domain. Wavelet packet transform (WPT) and linear discriminant analysis (LDA) are employed to extract discriminative features from the structural vibration response of the healthy and delaminated structures. Dendrogram-based support vector machine (DSVM) is used to classify the discriminative features. The confusion matrix of the classification algorithm provided physically consistent results.

Transformation of temporal sequences in the zebra finch auditory system

PubMed Central

Lim, Yoonseob; Lagoy, Ryan; Shinn-Cunningham, Barbara G; Gardner, Timothy J

2016-01-01

This study examines how temporally patterned stimuli are transformed as they propagate from primary to secondary zones in the thalamorecipient auditory pallium in zebra finches. Using a new class of synthetic click stimuli, we find a robust mapping from temporal sequences in the primary zone to distinct population vectors in secondary auditory areas. We tested whether songbirds could discriminate synthetic click sequences in an operant setup and found that a robust behavioral discrimination is present for click sequences composed of intervals ranging from 11 ms to 40 ms, but breaks down for stimuli composed of longer inter-click intervals. This work suggests that the analog of the songbird auditory cortex transforms temporal patterns to sequence-selective population responses or ‘spatial codes', and that these distinct population responses contribute to behavioral discrimination of temporally complex sounds. DOI: http://dx.doi.org/10.7554/eLife.18205.001 PMID:27897971

Discrimination of transgenic soybean seeds by terahertz spectroscopy

NASA Astrophysics Data System (ADS)

Liu, Wei; Liu, Changhong; Chen, Feng; Yang, Jianbo; Zheng, Lei

2016-10-01

Discrimination of genetically modified organisms is increasingly demanded by legislation and consumers worldwide. The feasibility of a non-destructive discrimination of glyphosate-resistant and conventional soybean seeds and their hybrid descendants was examined by terahertz time-domain spectroscopy system combined with chemometrics. Principal component analysis (PCA), least squares-support vector machines (LS-SVM) and PCA-back propagation neural network (PCA-BPNN) models with the first and second derivative and standard normal variate (SNV) transformation pre-treatments were applied to classify soybean seeds based on genotype. Results demonstrated clear differences among glyphosate-resistant, hybrid descendants and conventional non-transformed soybean seeds could easily be visualized with an excellent classification (accuracy was 88.33% in validation set) using the LS-SVM and the spectra with SNV pre-treatment. The results indicated that THz spectroscopy techniques together with chemometrics would be a promising technique to distinguish transgenic soybean seeds from non-transformed seeds with high efficiency and without any major sample preparation.

Stigma and discrimination experienced by people living with severe and persistent mental illness in assertive community treatment settings.

PubMed

Ye, Jing; Chen, Timothy F; Paul, Diane; McCahon, Rebecca; Shankar, Sumitra; Rosen, Alan; O'Reilly, Claire L

2016-09-01

To describe the perceived experiences of stigma and discrimination among people living with severe and persistent mental illness in assertive community treatment (ACT teams) settings in New South Wales (NSW), Australia. The Discrimination and Stigma Scale (DISC) was used in this cross-sectional study with people living with severe and persistent mental illness. The DISC is a reliable and valid, quantitative and qualitative instrument used to explore and measure levels of negative, anticipated and positive discrimination. Relevant clinical history and socio-demographic information were also collected. A total of 50 clients participated, with 40 (80%) reporting experienced negative discrimination in at least one life area. Negative discrimination was most commonly experienced in being avoided or shunned (n=25, 50%), by neighbours (n=24, 48%) and family (n=23, 46%). Anticipated discrimination was common, with half of participants (n=25, 50%) feeling the need to conceal their mental health diagnosis. Discrimination was highly prevalent in everyday aspects of life. While healthcare professionals often tend to increase perceived stigma and discrimination, this was only experienced in interactions with general health professionals, while interactions with ACT team members decreased perceived stigma and increased positive discrimination. This indicates that healthcare professionals potentially have a significant role in reducing stigma and discrimination in mental health and that such an effect may be optimised in an ACT team setting. © The Author(s) 2016.

Racial/ethnic discrimination and common mental disorders among workers: findings from the EMPIRIC Study of Ethnic Minority Groups in the United Kingdom.

PubMed

Bhui, Kamaldeep; Stansfeld, Stephen; McKenzie, Kwame; Karlsen, Saffron; Nazroo, James; Weich, Scott

2005-03-01

We measured perceived discrimination and its association with common mental disorders among workers in the United Kingdom. We conducted a secondary analysis of a national sample of 6 ethnic groups (n=2054). Discrimination was measured as reports of insults; unfair treatment at work; or job denial stemming from race, religion, or language. The outcome assessed was presence of common mental disorders. The risk of mental disorders was highest among ethnic minority individuals reporting unfair treatment (odds ratio [OR]=2.0; 95% confidence interval [CI]=1.2, 3.2) and racial insults (OR=2.3; 95% CI=1.4, 3.6). The overall greatest risks were observed among Black Caribbeans exposed to unfair treatment at work (OR=2.9; 95% CI=1.2, 7.3) and Indian (OR=3.1; 95% CI=1.4, 7.2), Bangladeshi (OR=32.9; 95% CI=2.5, 436.0), and Irish (OR=2.9; 95% CI=1.1, 7.6) individuals reporting insults. Racial/ethnic discrimination shows strong associations with common mental disorders.

A unified development of several techniques for the representation of random vectors and data sets

NASA Technical Reports Server (NTRS)

Bundick, W. T.

1973-01-01

Linear vector space theory is used to develop a general representation of a set of data vectors or random vectors by linear combinations of orthonormal vectors such that the mean squared error of the representation is minimized. The orthonormal vectors are shown to be the eigenvectors of an operator. The general representation is applied to several specific problems involving the use of the Karhunen-Loeve expansion, principal component analysis, and empirical orthogonal functions; and the common properties of these representations are developed.

Transdiagnostic Factors and Mediation of the Relationship Between Perceived Racial Discrimination and Mental Disorders.

PubMed

Rodriguez-Seijas, Craig; Stohl, Malki; Hasin, Deborah S; Eaton, Nicholas R

2015-07-01

Multivariable comorbidity research indicates that many common mental disorders are manifestations of 2 latent transdiagnostic factors, internalizing and externalizing. Environmental stressors are known to increase the risk for experiencing particular mental disorders, but their relationships with transdiagnostic disorder constructs are unknown. The present study investigated one such stressor, perceived racial discrimination, which is robustly associated with a variety of mental disorders. To examine the direct and indirect associations between perceived racial discrimination and common forms of psychopathology. Quantitative analysis of 12 common diagnoses that were previously assessed in a nationally representative sample (N = 5191) of African American and Afro-Caribbean adults in the United States, taken from the National Survey of American Life, and used to test the possibility that transdiagnostic factors mediate the effects of discrimination on disorders. The data were obtained from February 2001 to March 2003. Latent variable measurement models, including factor analysis, and indirect effect models were used in the study. Mental health diagnoses from reliable and valid structured interviews and perceived race-based discrimination. While perceived discrimination was positively associated with all examined forms of psychopathology and substance use disorders, latent variable indirect effects modeling revealed that almost all of these associations were significantly mediated by the transdiagnostic factors. For social anxiety disorder and attention-deficit/hyperactivity disorder, complete mediation was found. The pathways linking perceived discrimination to psychiatric disorders were not direct but indirect (via transdiagnostic factors). Therefore, perceived discrimination may be associated with risk for myriad psychiatric disorders due to its association with transdiagnostic factors.

S parameter and pseudo Nambu-Goldstone boson mass from lattice QCD.

PubMed

Shintani, E; Aoki, S; Fukaya, H; Hashimoto, S; Kaneko, T; Matsufuru, H; Onogi, T; Yamada, N

2008-12-12

We present a lattice calculation of L10, one of the low-energy constants in chiral perturbation theory, and the charged-neutral pion squared-mass splitting, using dynamical overlap fermion. The exact chiral symmetry of the overlap fermion allows us to reliably extract these quantities from the difference of the vacuum polarization functions for vector and axial-vector currents. In the context of the technicolor models, these two quantities are read as the S parameter and the pseudo Nambu-Goldstone boson mass, respectively, and play an important role in discriminating the models from others. This calculation can serve as a feasibility study of the lattice techniques for more general technicolor gauge theories.

Rapid discrimination of pork in Halal and non-Halal Chinese ham sausages by Fourier transform infrared (FTIR) spectroscopy and chemometrics.

PubMed

Xu, L; Cai, C B; Cui, H F; Ye, Z H; Yu, X P

2012-12-01

Rapid discrimination of pork in Halal and non-Halal Chinese ham sausages was developed by Fourier transform infrared (FTIR) spectrometry combined with chemometrics. Transmittance spectra ranging from 400 to 4000 cm⁻¹ of 73 Halal and 78 non-Halal Chinese ham sausages were measured. Sample preparation involved finely grinding of samples and formation of KBr disks (under 10 MPa for 5 min). The influence of data preprocessing methods including smoothing, taking derivatives and standard normal variate (SNV) on partial least squares discriminant analysis (PLSDA) and least squares support vector machine (LS-SVM) was investigated. The results indicate removal of spectral background and baseline plays an important role in discrimination. Taking derivatives, SNV can improve classification accuracy and reduce the complexity of PLSDA. Possibly due to the loss of detailed high-frequency spectral information, smoothing degrades the model performance. For the best models, the sensitivity and specificity was 0.913 and 0.929 for PLSDA with SNV spectra, 0.957 and 0.929 for LS-SVM with second derivative spectra, respectively. Copyright © 2012 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.

Review of Temephos Discriminating Concentration for Monitoring the Susceptibility of Anopheles labranchiae (Falleroni, 1926), Malaria Vector in Morocco

PubMed Central

Faraj, C.; Adlaoui, E.; Elkohli, M.; Herrak, T.; Ameur, B.; Chandre, F.

2010-01-01

In Morocco, the resistance monitoring of Anopheles labranchiae larvae to temephos is done using discriminating concentration of 0.125 mg, which is half of the WHO recommended dose for Anopheles. However, this dosage seemed to be too high to allow an early detection of the resistance and its revision was found necessary. The present study was carried out during May-June 2008 and 2009 in nine provinces from the north-west of the country. The aim was to determine the lethal concentrations LC100 of temephos for the most susceptible populations and to define the discriminating dosage as the double of this value. The bioassays were conducted according to WHO standard operating protocol to establish the dose-mortality relationship and deduct the LC50 and LC95. The results of this study indicated that the LC100 obtained on the most susceptible populations was close to 0.05 mg/L. Therefore, the temephos discriminating dosage for susceptibility monitoring of An. labranchiae larvae in Morocco was set to be 0.1 mg/L. PMID:22332019

(13)C NMR pattern recognition techniques for the classification of Atlantic salmon (Salmo salar L.) according to their wild, farmed, and geographical origin.

PubMed

Aursand, Marit; Standal, Inger B; Praël, Angelika; McEvoy, Lesley; Irvine, Joe; Axelson, David E

2009-05-13

(13)C nuclear magnetic resonance (NMR) in combination with multivariate data analysis was used to (1) discriminate between farmed and wild Atlantic salmon ( Salmo salar L.), (2) discriminate between different geographical origins, and (3) verify the origin of market samples. Muscle lipids from 195 Atlantic salmon of known origin (wild and farmed salmon from Norway, Scotland, Canada, Iceland, Ireland, the Faroes, and Tasmania) in addition to market samples were analyzed by (13)C NMR spectroscopy and multivariate analysis. Both probabilistic neural networks (PNN) and support vector machines (SVM) provided excellent discrimination (98.5 and 100.0%, respectively) between wild and farmed salmon. Discrimination with respect to geographical origin was somewhat more difficult, with correct classification rates ranging from 82.2 to 99.3% by PNN and SVM, respectively. In the analysis of market samples, five fish labeled and purchased as wild salmon were classified as farmed salmon (indicating mislabeling), and there were also some discrepancies between the classification and the product declaration with regard to geographical origin.

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.

SOLAR FLARE PREDICTION USING SDO/HMI VECTOR MAGNETIC FIELD DATA WITH A MACHINE-LEARNING ALGORITHM

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

Bobra, M. G.; Couvidat, S., E-mail: couvidat@stanford.edu

2015-01-10

We attempt to forecast M- and X-class solar flares using a machine-learning algorithm, called support vector machine (SVM), and four years of data from the Solar Dynamics Observatory's Helioseismic and Magnetic Imager, the first instrument to continuously map the full-disk photospheric vector magnetic field from space. Most flare forecasting efforts described in the literature use either line-of-sight magnetograms or a relatively small number of ground-based vector magnetograms. This is the first time a large data set of vector magnetograms has been used to forecast solar flares. We build a catalog of flaring and non-flaring active regions sampled from a databasemore » of 2071 active regions, comprised of 1.5 million active region patches of vector magnetic field data, and characterize each active region by 25 parameters. We then train and test the machine-learning algorithm and we estimate its performances using forecast verification metrics with an emphasis on the true skill statistic (TSS). We obtain relatively high TSS scores and overall predictive abilities. We surmise that this is partly due to fine-tuning the SVM for this purpose and also to an advantageous set of features that can only be calculated from vector magnetic field data. We also apply a feature selection algorithm to determine which of our 25 features are useful for discriminating between flaring and non-flaring active regions and conclude that only a handful are needed for good predictive abilities.« less

Study of The Vector Product using Three Dimensions Vector Card of Engineering in Pathumwan Institute of Technology

NASA Astrophysics Data System (ADS)

Mueanploy, Wannapa

2015-06-01

The objective of this research was to offer the way to improve engineering students in Physics topic of vector product. The sampling of this research was the engineering students at Pathumwan Institute of Technology during the first semester of academic year 2013. 1) Select 120 students by random sampling are asked to fill in a satisfaction questionnaire scale, to select size of three dimensions vector card in order to apply in the classroom. 2) Select 60 students by random sampling to do achievement test and take the test to be used in the classroom. The methods used in analysis of achievement test by the Kuder-Richardson Method (KR- 20). The results show that 12 items of achievement test are appropriate to be applied in the classroom. The achievement test gets Difficulty (P) = 0.40-0.67, Discrimination = 0.33-0.73 and Reliability (r) = 0.70.The experimental in the classroom. 3) Select 60 students by random sampling divide into two groups; group one (the controlled group) with 30 students was chosen to study in the vector product lesson by the regular teaching method. Group two (the experimental group) with 30 students was chosen to learn the vector product lesson with three dimensions vector card. 4) Analyzed data between the controlled group and the experimental group, the result showed that experimental group got higher achievement test than the controlled group significant at .01 level.

Malaria-induced changes in host odors enhance mosquito attraction

PubMed Central

De Moraes, Consuelo M.; Stanczyk, Nina M.; Betz, Heike S.; Pulido, Hannier; Sim, Derek G.; Read, Andrew F.; Mescher, Mark C.

2014-01-01

Vector-borne pathogens may alter traits of their primary hosts in ways that influence the frequency and nature of interactions between hosts and vectors. Previous work has reported enhanced mosquito attraction to host organisms infected with malaria parasites but did not address the mechanisms underlying such effects. Here we document malaria-induced changes in the odor profiles of infected mice (relative to healthy individuals) over the course of infection, as well as effects on the attractiveness of infected hosts to mosquito vectors. We observed enhanced mosquito attraction to infected mice during a key period after the subsidence of acute malaria symptoms, but during which mice remained highly infectious. This attraction corresponded to an overall elevation in the volatile emissions of infected mice observed during this period. Furthermore, data analyses—using discriminant analysis of principal components and random forest approaches—revealed clear differences in the composition of the volatile blends of infected and healthy individuals. Experimental manipulation of individual compounds that exhibited altered emission levels during the period when differential vector attraction was observed also elicited enhanced mosquito attraction, indicating that compounds being influenced by malaria infection status also mediate vector host-seeking behavior. These findings provide important insights into the cues that mediate vector attraction to hosts infected with transmissible stages of malaria parasites, as well as documenting characteristic changes in the odors of infected individuals that may have potential value as diagnostic biomarkers of infection. PMID:24982164

Malaria-induced changes in host odors enhance mosquito attraction.

PubMed

De Moraes, Consuelo M; Stanczyk, Nina M; Betz, Heike S; Pulido, Hannier; Sim, Derek G; Read, Andrew F; Mescher, Mark C

2014-07-29

Vector-borne pathogens may alter traits of their primary hosts in ways that influence the frequency and nature of interactions between hosts and vectors. Previous work has reported enhanced mosquito attraction to host organisms infected with malaria parasites but did not address the mechanisms underlying such effects. Here we document malaria-induced changes in the odor profiles of infected mice (relative to healthy individuals) over the course of infection, as well as effects on the attractiveness of infected hosts to mosquito vectors. We observed enhanced mosquito attraction to infected mice during a key period after the subsidence of acute malaria symptoms, but during which mice remained highly infectious. This attraction corresponded to an overall elevation in the volatile emissions of infected mice observed during this period. Furthermore, data analyses--using discriminant analysis of principal components and random forest approaches--revealed clear differences in the composition of the volatile blends of infected and healthy individuals. Experimental manipulation of individual compounds that exhibited altered emission levels during the period when differential vector attraction was observed also elicited enhanced mosquito attraction, indicating that compounds being influenced by malaria infection status also mediate vector host-seeking behavior. These findings provide important insights into the cues that mediate vector attraction to hosts infected with transmissible stages of malaria parasites, as well as documenting characteristic changes in the odors of infected individuals that may have potential value as diagnostic biomarkers of infection.

Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification.

PubMed

d'Alessandro, Brian; O'Neil, Cathy; LaGatta, Tom

2017-06-01

Recent research has helped to cultivate growing awareness that machine-learning systems fueled by big data can create or exacerbate troubling disparities in society. Much of this research comes from outside of the practicing data science community, leaving its members with little concrete guidance to proactively address these concerns. This article introduces issues of discrimination to the data science community on its own terms. In it, we tour the familiar data-mining process while providing a taxonomy of common practices that have the potential to produce unintended discrimination. We also survey how discrimination is commonly measured, and suggest how familiar development processes can be augmented to mitigate systems' discriminatory potential. We advocate that data scientists should be intentional about modeling and reducing discriminatory outcomes. Without doing so, their efforts will result in perpetuating any systemic discrimination that may exist, but under a misleading veil of data-driven objectivity.

matK-QR classifier: a patterns based approach for plant species identification.

PubMed

More, Ravi Prabhakar; Mane, Rupali Chandrashekhar; Purohit, Hemant J

2016-01-01

DNA barcoding is widely used and most efficient approach that facilitates rapid and accurate identification of plant species based on the short standardized segment of the genome. The nucleotide sequences of maturaseK ( matK ) and ribulose-1, 5-bisphosphate carboxylase ( rbcL ) marker loci are commonly used in plant species identification. Here, we present a new and highly efficient approach for identifying a unique set of discriminating nucleotide patterns to generate a signature (i.e. regular expression) for plant species identification. In order to generate molecular signatures, we used matK and rbcL loci datasets, which encompass 125 plant species in 52 genera reported by the CBOL plant working group. Initially, we performed Multiple Sequence Alignment (MSA) of all species followed by Position Specific Scoring Matrix (PSSM) for both loci to achieve a percentage of discrimination among species. Further, we detected Discriminating Patterns (DP) at genus and species level using PSSM for the matK dataset. Combining DP and consecutive pattern distances, we generated molecular signatures for each species. Finally, we performed a comparative assessment of these signatures with the existing methods including BLASTn, Support Vector Machines (SVM), Jrip-RIPPER, J48 (C4.5 algorithm), and the Naïve Bayes (NB) methods against NCBI-GenBank matK dataset. Due to the higher discrimination success obtained with the matK as compared to the rbcL , we selected matK gene for signature generation. We generated signatures for 60 species based on identified discriminating patterns at genus and species level. Our comparative assessment results suggest that a total of 46 out of 60 species could be correctly identified using generated signatures, followed by BLASTn (34 species), SVM (18 species), C4.5 (7 species), NB (4 species) and RIPPER (3 species) methods As a final outcome of this study, we converted signatures into QR codes and developed a software matK -QR Classifier (http://www.neeri.res.in/matk_classifier/index.htm), which search signatures in the query matK gene sequences and predict corresponding plant species. This novel approach of employing pattern-based signatures opens new avenues for the classification of species. In addition to existing methods, we believe that matK -QR Classifier would be a valuable tool for molecular taxonomists enabling precise identification of plant species.

Fast label-free detection of Legionella spp. in biofilms by applying immunomagnetic beads and Raman spectroscopy.

PubMed

Kusić, Dragana; Rösch, Petra; Popp, Jürgen

2016-03-01

Legionellae colonize biofilms, can form a biofilm by itself and multiply intracellularly within the protozoa commonly found in water distribution systems. Approximately half of the known species are pathogenic and have been connected to severe multisystem Legionnaires' disease. The detection methods for Legionella spp. in water samples are still based on cultivation, which is time consuming due to the slow growth of this bacterium. Here, we developed a cultivation-independent, label-free and fast detection method for legionellae in a biofilm matrix based on the Raman spectroscopic analysis of isolated single cells via immunomagnetic separation (IMS). A database comprising the Raman spectra of single bacterial cells captured and separated from the biofilms formed by each species was used to build the identification method based on a support vector machine (SVM) discriminative classifier. The complete method allows the detection of Legionella spp. in 100 min. Cross-reactivity of Legionella spp. specific immunomagnetic beads to the other studied genera was tested, where only small cell amounts of Pseudomonas aeruginosa, Klebsiella pneumoniae and Escherichia coli compared to the initial number of cells were isolated by the immunobeads. Nevertheless, the Raman spectra collected from isolated non-targeted bacteria were well-discriminated from the Raman spectra collected from isolated Legionella cells, whereby the Raman spectra of the independent dataset of Legionella strains were assigned with an accuracy of 98.6%. In addition, Raman spectroscopy was also used to differentiate between isolated Legionella species. Copyright © 2016 Elsevier GmbH. All rights reserved.

Direct model-based predictive control scheme without cost function for voltage source inverters with reduced common-mode voltage

NASA Astrophysics Data System (ADS)

Kim, Jae-Chang; Moon, Sung-Ki; Kwak, Sangshin

2018-04-01

This paper presents a direct model-based predictive control scheme for voltage source inverters (VSIs) with reduced common-mode voltages (CMVs). The developed method directly finds optimal vectors without using repetitive calculation of a cost function. To adjust output currents with the CMVs in the range of -Vdc/6 to +Vdc/6, the developed method uses voltage vectors, as finite control resources, excluding zero voltage vectors which produce the CMVs in the VSI within ±Vdc/2. In a model-based predictive control (MPC), not using zero voltage vectors increases the output current ripples and the current errors. To alleviate these problems, the developed method uses two non-zero voltage vectors in one sampling step. In addition, the voltage vectors scheduled to be used are directly selected at every sampling step once the developed method calculates the future reference voltage vector, saving the efforts of repeatedly calculating the cost function. And the two non-zero voltage vectors are optimally allocated to make the output current approach the reference current as close as possible. Thus, low CMV, rapid current-following capability and sufficient output current ripple performance are attained by the developed method. The results of a simulation and an experiment verify the effectiveness of the developed method.

Japanese International Female Students' Experience of Discrimination, Prejudice, and Stereotypes

ERIC Educational Resources Information Center

Bonazzo, Claude; Wong, Y. Joel

2007-01-01

This qualitative study examined four Japanese international female college students' experience of discrimination, prejudice, and stereotypes in a predominately white university. Four themes emerged from the analysis of data: (1) overt forms of prejudice and discrimination; (2) stereotypes common to Asians; (3) stereotypes unique to the Japanese;…

Perceived Discrimination and Personality Development in Adulthood

ERIC Educational Resources Information Center

Sutin, Angelina R.; Stephan, Yannick; Terracciano, Antonio

2016-01-01

Perceived discrimination is common and a significant source of stress that may have implications for personality development across adulthood. In this study, we examined whether experiences with discrimination were associated with maladaptive changes in the 5 major dimensions of personality using 2 longitudinal samples that differed in age and…

Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C.

PubMed

Stoean, Ruxandra; Stoean, Catalin; Lupsor, Monica; Stefanescu, Horia; Badea, Radu

2011-01-01

Hepatic fibrosis, the principal pointer to the development of a liver disease within chronic hepatitis C, can be measured through several stages. The correct evaluation of its degree, based on recent different non-invasive procedures, is of current major concern. The latest methodology for assessing it is the Fibroscan and the effect of its employment is impressive. However, the complex interaction between its stiffness indicator and the other biochemical and clinical examinations towards a respective degree of liver fibrosis is hard to be manually discovered. In this respect, the novel, well-performing evolutionary-powered support vector machines are proposed towards an automated learning of the relationship between medical attributes and fibrosis levels. The traditional support vector machines have been an often choice for addressing hepatic fibrosis, while the evolutionary option has been validated on many real-world tasks and proven flexibility and good performance. The evolutionary approach is simple and direct, resulting from the hybridization of the learning component within support vector machines and the optimization engine of evolutionary algorithms. It discovers the optimal coefficients of surfaces that separate instances of distinct classes. Apart from a detached manner of establishing the fibrosis degree for new cases, a resulting formula also offers insight upon the correspondence between the medical factors and the respective outcome. What is more, a feature selection genetic algorithm can be further embedded into the method structure, in order to dynamically concentrate search only on the most relevant attributes. The data set refers 722 patients with chronic hepatitis C infection and 24 indicators. The five possible degrees of fibrosis range from F0 (no fibrosis) to F4 (cirrhosis). Since the standard support vector machines are among the most frequently used methods in recent artificial intelligence studies for hepatic fibrosis staging, the evolutionary method is viewed in comparison to the traditional one. The multifaceted discrimination into all five degrees of fibrosis and the slightly less difficult common separation into solely three related stages are both investigated. The resulting performance proves the superiority over the standard support vector classification and the attained formula is helpful in providing an immediate calculation of the liver stage for new cases, while establishing the presence/absence and comprehending the weight of each medical factor with respect to a certain fibrosis level. The use of the evolutionary technique for fibrosis degree prediction triggers simplicity and offers a direct expression of the influence of dynamically selected indicators on the corresponding stage. Perhaps most importantly, it significantly surpasses the classical support vector machines, which are both widely used and technically sound. All these therefore confirm the promise of the new methodology towards a dependable support within the medical decision-making. Copyright © 2010 Elsevier B.V. All rights reserved.

Support Vector Machine-based classification of protein folds using the structural properties of amino acid residues and amino acid residue pairs.

PubMed

Shamim, Mohammad Tabrez Anwar; Anwaruddin, Mohammad; Nagarajaram, H A

2007-12-15

Fold recognition is a key step in the protein structure discovery process, especially when traditional sequence comparison methods fail to yield convincing structural homologies. Although many methods have been developed for protein fold recognition, their accuracies remain low. This can be attributed to insufficient exploitation of fold discriminatory features. We have developed a new method for protein fold recognition using structural information of amino acid residues and amino acid residue pairs. Since protein fold recognition can be treated as a protein fold classification problem, we have developed a Support Vector Machine (SVM) based classifier approach that uses secondary structural state and solvent accessibility state frequencies of amino acids and amino acid pairs as feature vectors. Among the individual properties examined secondary structural state frequencies of amino acids gave an overall accuracy of 65.2% for fold discrimination, which is better than the accuracy by any method reported so far in the literature. Combination of secondary structural state frequencies with solvent accessibility state frequencies of amino acids and amino acid pairs further improved the fold discrimination accuracy to more than 70%, which is approximately 8% higher than the best available method. In this study we have also tested, for the first time, an all-together multi-class method known as Crammer and Singer method for protein fold classification. Our studies reveal that the three multi-class classification methods, namely one versus all, one versus one and Crammer and Singer method, yield similar predictions. Dataset and stand-alone program are available upon request.

Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis.

PubMed

Möller, Christiane; Pijnenburg, Yolande A L; van der Flier, Wiesje M; Versteeg, Adriaan; Tijms, Betty; de Munck, Jan C; Hafkemeijer, Anne; Rombouts, Serge A R B; van der Grond, Jeroen; van Swieten, John; Dopper, Elise; Scheltens, Philip; Barkhof, Frederik; Vrenken, Hugo; Wink, Alle Meije

2016-06-01

Purpose To investigate the diagnostic accuracy of an image-based classifier to distinguish between Alzheimer disease (AD) and behavioral variant frontotemporal dementia (bvFTD) in individual patients by using gray matter (GM) density maps computed from standard T1-weighted structural images obtained with multiple imagers and with independent training and prediction data. Materials and Methods The local institutional review board approved the study. Eighty-four patients with AD, 51 patients with bvFTD, and 94 control subjects were divided into independent training (n = 115) and prediction (n = 114) sets with identical diagnosis and imager type distributions. Training of a support vector machine (SVM) classifier used diagnostic status and GM density maps and produced voxelwise discrimination maps. Discriminant function analysis was used to estimate suitability of the extracted weights for single-subject classification in the prediction set. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for image-based classifiers and neuropsychological z scores. Results Training accuracy of the SVM was 85% for patients with AD versus control subjects, 72% for patients with bvFTD versus control subjects, and 79% for patients with AD versus patients with bvFTD (P ≤ .029). Single-subject diagnosis in the prediction set when using the discrimination maps yielded accuracies of 88% for patients with AD versus control subjects, 85% for patients with bvFTD versus control subjects, and 82% for patients with AD versus patients with bvFTD, with a good to excellent AUC (range, 0.81-0.95; P ≤ .001). Machine learning-based categorization of AD versus bvFTD based on GM density maps outperforms classification based on neuropsychological test results. Conclusion The SVM can be used in single-subject discrimination and can help the clinician arrive at a diagnosis. The SVM can be used to distinguish disease-specific GM patterns in patients with AD and those with bvFTD as compared with normal aging by using common T1-weighted structural MR imaging. (©) RSNA, 2015.

The effect of combining two echo times in automatic brain tumor classification by MRS.

PubMed

García-Gómez, Juan M; Tortajada, Salvador; Vidal, César; Julià-Sapé, Margarida; Luts, Jan; Moreno-Torres, Angel; Van Huffel, Sabine; Arús, Carles; Robles, Montserrat

2008-11-01

(1)H MRS is becoming an accurate, non-invasive technique for initial examination of brain masses. We investigated if the combination of single-voxel (1)H MRS at 1.5 T at two different (TEs), short TE (PRESS or STEAM, 20-32 ms) and long TE (PRESS, 135-136 ms), improves the classification of brain tumors over using only one echo TE. A clinically validated dataset of 50 low-grade meningiomas, 105 aggressive tumors (glioblastoma and metastasis), and 30 low-grade glial tumors (astrocytomas grade II, oligodendrogliomas and oligoastrocytomas) was used to fit predictive models based on the combination of features from short-TEs and long-TE spectra. A new approach that combines the two consecutively was used to produce a single data vector from which relevant features of the two TE spectra could be extracted by means of three algorithms: stepwise, reliefF, and principal components analysis. Least squares support vector machines and linear discriminant analysis were applied to fit the pairwise and multiclass classifiers, respectively. Significant differences in performance were found when short-TE, long-TE or both spectra combined were used as input. In our dataset, to discriminate meningiomas, the combination of the two TE acquisitions produced optimal performance. To discriminate aggressive tumors from low-grade glial tumours, the use of short-TE acquisition alone was preferable. The classifier development strategy used here lends itself to automated learning and test performance processes, which may be of use for future web-based multicentric classifier development studies. Copyright (c) 2008 John Wiley & Sons, Ltd.

A Simple Colorimetric Assay for Specific Detection of Glutathione-S Transferase Activity Associated with DDT Resistance in Mosquitoes

PubMed Central

Rajatileka, Shavanti; Steven, Andrew; Hemingway, Janet; Ranson, Hilary; Paine, Mark; Vontas, John

2010-01-01

Background Insecticide-based methods represent the most effective means of blocking the transmission of vector borne diseases. However, insecticide resistance poses a serious threat and there is a need for tools, such as diagnostic tests for resistance detection, that will improve the sustainability of control interventions. The development of such tools for metabolism-based resistance in mosquito vectors lags behind those for target site resistance mutations. Methodology/Principal Findings We have developed and validated a simple colorimetric assay for the detection of Epsilon class Glutathione transferases (GST)-based DDT resistance in mosquito species, such as Aedes aegypti, the major vector of dengue and yellow fever worldwide. The colorimetric assay is based on the specific alkyl transferase activity of Epsilon GSTs for the haloalkene substrate iodoethane, which produces a dark blue colour highly correlated with AaGSTE2-2-overexpression in individual mosquitoes. The colour can be measured visually and spectrophotometrically. Conclusions/Significance The novel assay is substantially more sensitive compared to the gold standard CDNB assay and allows the discrimination of moderate resistance phenotypes. We anticipate that it will have direct application in routine vector monitoring as a resistance indicator and possibly an important impact on disease vector control. PMID:20824165

Impression Management in the Job Interview: An Effective Way of Mitigating Discrimination against Older Applicants?

PubMed

Gioaba, Irina; Krings, Franciska

2017-01-01

The increasingly aging population in most industrialized societies, coupled with the rather age-diverse current workforce makes discrimination against older employees a prevalent issue, especially in employment contexts. This renders research on ways for reducing this type of discrimination a particularly pressing concern. Drawing on theories of social identity and impression management, our research examines the role of impression management, aimed at refuting common older worker stereotypes, in diminishing bias against older job applicants during the job interview. The study consisted in an experimental hiring simulation conducted on a sample of 515 undergraduate students. Results show that older applicants who used impression management to contradict common older worker stereotypes were perceived as more hirable than those who did not. However, despite this positive effect, discrimination persisted: older applicants were consistently rated as less hirable than their younger counterparts when displaying the same IM behavior. Taken together, this research demonstrates that older job seekers can indeed ameliorate biased interview outcomes by engaging in impression management targeting common age stereotypes; however, it also shows that this strategy is insufficient for overcoming age discrimination entirely. The current study has important implications for theory, by expanding research on the use of impression management in mitigating age discrimination, as well as for practice, by offering older employees a hands-on strategy to reduce bias and stereotyping against them.

Impression Management in the Job Interview: An Effective Way of Mitigating Discrimination against Older Applicants?

PubMed Central

Gioaba, Irina; Krings, Franciska

2017-01-01

The increasingly aging population in most industrialized societies, coupled with the rather age-diverse current workforce makes discrimination against older employees a prevalent issue, especially in employment contexts. This renders research on ways for reducing this type of discrimination a particularly pressing concern. Drawing on theories of social identity and impression management, our research examines the role of impression management, aimed at refuting common older worker stereotypes, in diminishing bias against older job applicants during the job interview. The study consisted in an experimental hiring simulation conducted on a sample of 515 undergraduate students. Results show that older applicants who used impression management to contradict common older worker stereotypes were perceived as more hirable than those who did not. However, despite this positive effect, discrimination persisted: older applicants were consistently rated as less hirable than their younger counterparts when displaying the same IM behavior. Taken together, this research demonstrates that older job seekers can indeed ameliorate biased interview outcomes by engaging in impression management targeting common age stereotypes; however, it also shows that this strategy is insufficient for overcoming age discrimination entirely. The current study has important implications for theory, by expanding research on the use of impression management in mitigating age discrimination, as well as for practice, by offering older employees a hands-on strategy to reduce bias and stereotyping against them. PMID:28559869

Exploitation of RF-DNA for Device Classification and Verification Using GRLVQI Processing

DTIC Science & Technology

2012-12-01

5 FLD Fisher’s Linear Discriminant . . . . . . . . . . . . . . . . . . . 6 kNN K-Nearest Neighbor...Neighbor ( kNN ), Support Vector Machine (SVM), and simple cross-correlation techniques [40, 57, 82, 88, 94, 95]. The RF-DNA fingerprinting research in...Expansion and the Dis- crete Gabor Transform on a Non-Separable Lattice”. 2000 IEEE Int’l Conf on Acoustics, Speech , and Signal Processing (ICASSP00

Altering the selection capabilities of common cloning vectors via restriction enzyme mediated gene disruption

PubMed Central

2013-01-01

Background The cloning of gene sequences forms the basis for many molecular biological studies. One important step in the cloning process is the isolation of bacterial transformants carrying vector DNA. This involves a vector-encoded selectable marker gene, which in most cases, confers resistance to an antibiotic. However, there are a number of circumstances in which a different selectable marker is required or may be preferable. Such situations can include restrictions to host strain choice, two phase cloning experiments and mutagenesis experiments, issues that result in additional unnecessary cloning steps, in which the DNA needs to be subcloned into a vector with a suitable selectable marker. Results We have used restriction enzyme mediated gene disruption to modify the selectable marker gene of a given vector by cloning a different selectable marker gene into the original marker present in that vector. Cloning a new selectable marker into a pre-existing marker was found to change the selection phenotype conferred by that vector, which we were able to demonstrate using multiple commonly used vectors and multiple resistance markers. This methodology was also successfully applied not only to cloning vectors, but also to expression vectors while keeping the expression characteristics of the vector unaltered. Conclusions Changing the selectable marker of a given vector has a number of advantages and applications. This rapid and efficient method could be used for co-expression of recombinant proteins, optimisation of two phase cloning procedures, as well as multiple genetic manipulations within the same host strain without the need to remove a pre-existing selectable marker in a previously genetically modified strain. PMID:23497512

Gender Discrimination and Women's Development in India

ERIC Educational Resources Information Center

Sivakumar, M.

2008-01-01

Gender is a common term where as gender discrimination is meant only for women, because females are the only victims of gender discrimination. Females are nearly 50 percent of the total population but their representation in public life is very low. Recognizing women's right and believing their ability are essential for women's empowerment and…

Effect of sample preparation on the discrimination of bacterial isolates cultured in liquid nutrient media using laser induced breakdown spectroscopy

USDA-ARS?s Scientific Manuscript database

Laser induced breakdown spectroscopy (LIBS) is used as the basis for discrimination between 2 genera of gram-negative bacteria and 2 genera of gram-positive bacteria representing pathogenic threats commonly found in poultry processing rinse waters. Because LIBS-based discrimination relies primarily ...

Overlap in the Seasonal Infection Patterns of Avian Malaria Parasites and West Nile Virus in Vectors and Hosts

PubMed Central

Medeiros, Matthew C. I.; Ricklefs, Robert E.; Brawn, Jeffrey D.; Ruiz, Marilyn O.; Goldberg, Tony L.; Hamer, Gabriel L.

2016-01-01

Multiple vector-borne pathogens often circulate in the same vector and host communities, and seasonal infection dynamics influence the potential for pathogen interactions. Here, we explore the seasonal infection patterns of avian malaria (Haemosporida) parasites (Plasmodium and Haemoproteus) and West Nile virus (WNV) in birds and mosquitoes in suburban Chicago. We show that both pathogens vary seasonally in Culex mosquitoes and avian hosts, but that patterns of covariation are complex. Different putative Plasmodium species varied asynchronously across the season in mosquitoes and birds, suggesting that different forces may govern their transmission. Infections of Culex mosquitoes with Plasmodium parasites were positively associated with WNV infections in pools of individuals aggregated from the same time and site, suggesting that these pathogens respond to common environmental drivers and co-circulate among the same host and vector populations. Future research should focus on these common drivers, and whether these pathogens interact in vectors and hosts. PMID:27621305

Common Host-Derived Chemicals Increase Catches of Disease-Transmitting Mosquitoes and Can Improve Early Warning Systems for Rift Valley Fever Virus

PubMed Central

Tchouassi, David P.; Sang, Rosemary; Sole, Catherine L.; Bastos, Armanda D. S.; Teal, Peter E. A.; Borgemeister, Christian; Torto, Baldwyn

2013-01-01

Rift Valley fever (RVF), a mosquito-borne zoonosis, is a major public health and veterinary problem in sub-Saharan Africa. Surveillance to monitor mosquito populations during the inter-epidemic period (IEP) and viral activity in these vectors is critical to informing public health decisions for early warning and control of the disease. Using a combination of field bioassays, electrophysiological and chemical analyses we demonstrated that skin-derived aldehydes (heptanal, octanal, nonanal, decanal) common to RVF virus (RVFV) hosts including sheep, cow, donkey, goat and human serve as potent attractants for RVFV mosquito vectors. Furthermore, a blend formulated from the four aldehydes and combined with CO2-baited CDC trap without a light bulb doubled to tripled trap captures compared to control traps baited with CO2 alone. Our results reveal that (a) because of the commonality of the host chemical signature required for attraction, the host-vector interaction appears to favor the mosquito vector allowing it to find and opportunistically feed on a wide range of mammalian hosts of the disease, and (b) the sensitivity, specificity and superiority of this trapping system offers the potential for its wider use in surveillance programs for RVFV mosquito vectors especially during the IEP. PMID:23326620

Face recognition algorithm using extended vector quantization histogram features.

PubMed

Yan, Yan; Lee, Feifei; Wu, Xueqian; Chen, Qiu

2018-01-01

In this paper, we propose a face recognition algorithm based on a combination of vector quantization (VQ) and Markov stationary features (MSF). The VQ algorithm has been shown to be an effective method for generating features; it extracts a codevector histogram as a facial feature representation for face recognition. Still, the VQ histogram features are unable to convey spatial structural information, which to some extent limits their usefulness in discrimination. To alleviate this limitation of VQ histograms, we utilize Markov stationary features (MSF) to extend the VQ histogram-based features so as to add spatial structural information. We demonstrate the effectiveness of our proposed algorithm by achieving recognition results superior to those of several state-of-the-art methods on publicly available face databases.

Large General Purpose Frame for Studying Force Vectors

ERIC Educational Resources Information Center

Heid, Christy; Rampolla, Donald

2011-01-01

Many illustrations and problems on the vector nature of forces have weights and forces in a vertical plane. One of the common devices for studying the vector nature of forces is a horizontal "force table," in which forces are produced by weights hanging vertically and transmitted to cords in a horizontal plane. Because some students have…

Recombination of the steering vector of the triangle grid array in quaternions and the reduction of the MUSIC algorithm

NASA Astrophysics Data System (ADS)

Bai, Chen; Han, Dongjuan

2018-04-01

MUSIC is widely used on DOA estimation. Triangle grid is a common kind of the arrangement of array, but it is more complicated than rectangular array in calculation of steering vector. In this paper, the quaternions algorithm can reduce dimension of vector and make the calculation easier.

Spatially-Explicit Simulation Modeling of Ecological Response to Climate Change: Methodological Considerations in Predicting Shifting Population Dynamics of Infectious Disease Vectors.

PubMed

Dhingra, Radhika; Jimenez, Violeta; Chang, Howard H; Gambhir, Manoj; Fu, Joshua S; Liu, Yang; Remais, Justin V

2013-09-01

Poikilothermic disease vectors can respond to altered climates through spatial changes in both population size and phenology. Quantitative descriptors to characterize, analyze and visualize these dynamic responses are lacking, particularly across large spatial domains. In order to demonstrate the value of a spatially explicit, dynamic modeling approach, we assessed spatial changes in the population dynamics of Ixodes scapularis , the Lyme disease vector, using a temperature-forced population model simulated across a grid of 4 × 4 km cells covering the eastern United States, using both modeled (Weather Research and Forecasting (WRF) 3.2.1) baseline/current (2001-2004) and projected (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5; 2057-2059) climate data. Ten dynamic population features (DPFs) were derived from simulated populations and analyzed spatially to characterize the regional population response to current and future climate across the domain. Each DPF under the current climate was assessed for its ability to discriminate observed Lyme disease risk and known vector presence/absence, using data from the US Centers for Disease Control and Prevention. Peak vector population and month of peak vector population were the DPFs that performed best as predictors of current Lyme disease risk. When examined under baseline and projected climate scenarios, the spatial and temporal distributions of DPFs shift and the seasonal cycle of key questing life stages is compressed under some scenarios. Our results demonstrate the utility of spatial characterization, analysis and visualization of dynamic population responses-including altered phenology-of disease vectors to altered climate.

Spatially-Explicit Simulation Modeling of Ecological Response to Climate Change: Methodological Considerations in Predicting Shifting Population Dynamics of Infectious Disease Vectors

PubMed Central

Dhingra, Radhika; Jimenez, Violeta; Chang, Howard H.; Gambhir, Manoj; Fu, Joshua S.; Liu, Yang; Remais, Justin V.

2014-01-01

Poikilothermic disease vectors can respond to altered climates through spatial changes in both population size and phenology. Quantitative descriptors to characterize, analyze and visualize these dynamic responses are lacking, particularly across large spatial domains. In order to demonstrate the value of a spatially explicit, dynamic modeling approach, we assessed spatial changes in the population dynamics of Ixodes scapularis, the Lyme disease vector, using a temperature-forced population model simulated across a grid of 4 × 4 km cells covering the eastern United States, using both modeled (Weather Research and Forecasting (WRF) 3.2.1) baseline/current (2001–2004) and projected (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5; 2057–2059) climate data. Ten dynamic population features (DPFs) were derived from simulated populations and analyzed spatially to characterize the regional population response to current and future climate across the domain. Each DPF under the current climate was assessed for its ability to discriminate observed Lyme disease risk and known vector presence/absence, using data from the US Centers for Disease Control and Prevention. Peak vector population and month of peak vector population were the DPFs that performed best as predictors of current Lyme disease risk. When examined under baseline and projected climate scenarios, the spatial and temporal distributions of DPFs shift and the seasonal cycle of key questing life stages is compressed under some scenarios. Our results demonstrate the utility of spatial characterization, analysis and visualization of dynamic population responses—including altered phenology—of disease vectors to altered climate. PMID:24772388

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…

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

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

Effects of changing canopy directional reflectance on feature selection

NASA Technical Reports Server (NTRS)

Smith, J. A.; Oliver, R. E.; Kilpela, O. E.

1973-01-01

The use of a Monte Carlo model for generating sample directional reflectance data for two simplified target canopies at two different solar positions is reported. Successive iterations through the model permit the calculation of a mean vector and covariance matrix for canopy reflectance for varied sensor view angles. These data may then be used to calculate the divergence between the target distributions for various wavelength combinations and for these view angles. Results of a feature selection analysis indicate that different sets of wavelengths are optimum for target discrimination depending on sensor view angle and that the targets may be more easily discriminated for some scan angles than others. The time-varying behavior of these results is also pointed out.

Real-Time Visual Tracking through Fusion Features

PubMed Central

Ruan, Yang; Wei, Zhenzhong

2016-01-01

Due to their high-speed, correlation filters for object tracking have begun to receive increasing attention. Traditional object trackers based on correlation filters typically use a single type of feature. In this paper, we attempt to integrate multiple feature types to improve the performance, and we propose a new DD-HOG fusion feature that consists of discriminative descriptors (DDs) and histograms of oriented gradients (HOG). However, fusion features as multi-vector descriptors cannot be directly used in prior correlation filters. To overcome this difficulty, we propose a multi-vector correlation filter (MVCF) that can directly convolve with a multi-vector descriptor to obtain a single-channel response that indicates the location of an object. Experiments on the CVPR2013 tracking benchmark with the evaluation of state-of-the-art trackers show the effectiveness and speed of the proposed method. Moreover, we show that our MVCF tracker, which uses the DD-HOG descriptor, outperforms the structure-preserving object tracker (SPOT) in multi-object tracking because of its high-speed and ability to address heavy occlusion. PMID:27347951

Barriers and Opportunities to Advancing Women in Leadership Roles in Vector Control: Perspectives from a Stakeholder Survey.

PubMed

Hayden, Mary H; Barrett, Erika; Bernard, Guyah; Toko, Eunice N; Agawo, Maurice; Okello, Amanda M; Gunn, Jayleen K L; Ernst, Kacey C

2018-05-01

Increasing the active participation of professional women in vector control (VC) activities may help promote greater gender equity in the workplace and reduce the burden of vector-borne diseases. This stakeholder survey examined the current roles and perspective of professionals employed in the VC sector in Kenya, Indonesia, India, and other countries. The largest barriers that women face in pursuing leadership roles in the VC sector include lack of awareness of career opportunities, limitations based on cultural norms, and the belief that VC is men's work. These barriers could be addressed through improving education and recruitment campaigns, as well as supporting higher education and mentoring programs. Females were almost six times more likely to be encouraged to pursue leadership positions in their organization compared with male respondents (odds ratio = 5.9, P > 0.03, 95% confidence interval: 1.19, 29.42). These findings suggest that once women are recruited into the VC workforce, they face minimal discrimination and have increased leadership opportunities.

Discrimination against HIV-Infected People and the Spread of HIV: Some Evidence from France

PubMed Central

Peretti-Watel, Patrick; Spire, Bruno; Obadia, Yolande; Moatti, Jean-Paul

2007-01-01

Background Many people living with HIV/AIDS (PLWHA) suffer from stigma and discrimination. There is an ongoing debate, however, about whether stigma, fear and discrimination actually fuel the persisting spread of HIV, or slow it down by reducing contacts between the whole population and high-risk minorities. To contribute to this debate, we analysed the relationship between perceived discrimination and unsafe sex in a large sample of French PLWHAs. Methodology/Principal Findings In 2003, we conducted a national cross-sectional survey among a random sample of HIV-infected patients. The analysis was restricted to sexually active respondents (N = 2,136). Unsafe sex was defined as sexual intercourse without a condom with a seronegative/unknown serostatus partner during the prior 12 months. Separate analyses were performed for each transmission group (injecting drug use (IDU), homosexual contact, heterosexual contact). Overall, 24% of respondents reported experiences of discrimination in their close social environment (relatives, friends and colleagues) and 18% reported unsafe sex during the previous 12 months. Both prevalences were higher in the IDU group (32% for perceived discrimination, 23% for unsafe sex). In multivariate analyses, experience of discrimination in the close social environment was associated with an increase in unsafe sex for both PLWHAs infected through IDU and heterosexual contact (OR = 1.65 and 1.80 respectively). Conclusions Our study clearly confirms a relationship between discrimination and unsafe sex among PLWHAs infected through either IDU or heterosexual contact. This relationship was especially strong in the heterosexual group that has become the main vector of HIV transmission in France, and who is the more likely of sexual mixing with the general population. These results seriously question the hypothesis that HIV-stigma has no effect or could even reduce the infection spread of HIV. PMID:17476333

Adaptation and Evaluation of a Multi-Criteria Decision Analysis Model for Lyme Disease Prevention

PubMed Central

Aenishaenslin, Cécile; Gern, Lise; Michel, Pascal; Ravel, André; Hongoh, Valérie; Waaub, Jean-Philippe; Milord, François; Bélanger, Denise

2015-01-01

Designing preventive programs relevant to vector-borne diseases such as Lyme disease (LD) can be complex given the need to include multiple issues and perspectives into prioritizing public health actions. A multi-criteria decision aid (MCDA) model was previously used to rank interventions for LD prevention in Quebec, Canada, where the disease is emerging. The aim of the current study was to adapt and evaluate the decision model constructed in Quebec under a different epidemiological context, in Switzerland, where LD has been endemic for the last thirty years. The model adaptation was undertaken with a group of Swiss stakeholders using a participatory approach. The PROMETHEE method was used for multi-criteria analysis. Key elements and results of the MCDA model are described and contrasted with the Quebec model. All criteria and most interventions of the MCDA model developed for LD prevention in Quebec were directly transferable to the Swiss context. Four new decision criteria were added, and the list of proposed interventions was modified. Based on the overall group ranking, interventions targeting human populations were prioritized in the Swiss model, with the top ranked action being the implementation of a large communication campaign. The addition of criteria did not significantly alter the intervention rankings, but increased the capacity of the model to discriminate between highest and lowest ranked interventions. The current study suggests that beyond the specificity of the MCDA models developed for Quebec and Switzerland, their general structure captures the fundamental and common issues that characterize the complexity of vector-borne disease prevention. These results should encourage public health organizations to adapt, use and share MCDA models as an effective and functional approach to enable the integration of multiple perspectives and considerations in the prevention and control of complex public health issues such as Lyme disease or other vector-borne and zoonotic diseases. PMID:26295344

Granular support vector machines with association rules mining for protein homology prediction.

PubMed

Tang, Yuchun; Jin, Bo; Zhang, Yan-Qing

2005-01-01

Protein homology prediction between protein sequences is one of critical problems in computational biology. Such a complex classification problem is common in medical or biological information processing applications. How to build a model with superior generalization capability from training samples is an essential issue for mining knowledge to accurately predict/classify unseen new samples and to effectively support human experts to make correct decisions. A new learning model called granular support vector machines (GSVM) is proposed based on our previous work. GSVM systematically and formally combines the principles from statistical learning theory and granular computing theory and thus provides an interesting new mechanism to address complex classification problems. It works by building a sequence of information granules and then building support vector machines (SVM) in some of these information granules on demand. A good granulation method to find suitable granules is crucial for modeling a GSVM with good performance. In this paper, we also propose an association rules-based granulation method. For the granules induced by association rules with high enough confidence and significant support, we leave them as they are because of their high "purity" and significant effect on simplifying the classification task. For every other granule, a SVM is modeled to discriminate the corresponding data. In this way, a complex classification problem is divided into multiple smaller problems so that the learning task is simplified. The proposed algorithm, here named GSVM-AR, is compared with SVM by KDDCUP04 protein homology prediction data. The experimental results show that finding the splitting hyperplane is not a trivial task (we should be careful to select the association rules to avoid overfitting) and GSVM-AR does show significant improvement compared to building one single SVM in the whole feature space. Another advantage is that the utility of GSVM-AR is very good because it is easy to be implemented. More importantly and more interestingly, GSVM provides a new mechanism to address complex classification problems.

Adaptation and Evaluation of a Multi-Criteria Decision Analysis Model for Lyme Disease Prevention.

PubMed

Aenishaenslin, Cécile; Gern, Lise; Michel, Pascal; Ravel, André; Hongoh, Valérie; Waaub, Jean-Philippe; Milord, François; Bélanger, Denise

2015-01-01

Designing preventive programs relevant to vector-borne diseases such as Lyme disease (LD) can be complex given the need to include multiple issues and perspectives into prioritizing public health actions. A multi-criteria decision aid (MCDA) model was previously used to rank interventions for LD prevention in Quebec, Canada, where the disease is emerging. The aim of the current study was to adapt and evaluate the decision model constructed in Quebec under a different epidemiological context, in Switzerland, where LD has been endemic for the last thirty years. The model adaptation was undertaken with a group of Swiss stakeholders using a participatory approach. The PROMETHEE method was used for multi-criteria analysis. Key elements and results of the MCDA model are described and contrasted with the Quebec model. All criteria and most interventions of the MCDA model developed for LD prevention in Quebec were directly transferable to the Swiss context. Four new decision criteria were added, and the list of proposed interventions was modified. Based on the overall group ranking, interventions targeting human populations were prioritized in the Swiss model, with the top ranked action being the implementation of a large communication campaign. The addition of criteria did not significantly alter the intervention rankings, but increased the capacity of the model to discriminate between highest and lowest ranked interventions. The current study suggests that beyond the specificity of the MCDA models developed for Quebec and Switzerland, their general structure captures the fundamental and common issues that characterize the complexity of vector-borne disease prevention. These results should encourage public health organizations to adapt, use and share MCDA models as an effective and functional approach to enable the integration of multiple perspectives and considerations in the prevention and control of complex public health issues such as Lyme disease or other vector-borne and zoonotic diseases.

Rapid characterization of transgenic and non-transgenic soybean oils by chemometric methods using NIR spectroscopy

NASA Astrophysics Data System (ADS)

Luna, Aderval S.; da Silva, Arnaldo P.; Pinho, Jéssica S. A.; Ferré, Joan; Boqué, Ricard

Near infrared (NIR) spectroscopy and multivariate classification were applied to discriminate soybean oil samples into non-transgenic and transgenic. Principal Component Analysis (PCA) was applied to extract relevant features from the spectral data and to remove the anomalous samples. The best results were obtained when with Support Vectors Machine-Discriminant Analysis (SVM-DA) and Partial Least Squares-Discriminant Analysis (PLS-DA) after mean centering plus multiplicative scatter correction. For SVM-DA the percentage of successful classification was 100% for the training group and 100% and 90% in validation group for non transgenic and transgenic soybean oil samples respectively. For PLS-DA the percentage of successful classification was 95% and 100% in training group for non transgenic and transgenic soybean oil samples respectively and 100% and 80% in validation group for non transgenic and transgenic respectively. The results demonstrate that NIR spectroscopy can provide a rapid, nondestructive and reliable method to distinguish non-transgenic and transgenic soybean oils.

Intra-regional classification of grape seeds produced in Mendoza province (Argentina) by multi-elemental analysis and chemometrics tools.

PubMed

Canizo, Brenda V; Escudero, Leticia B; Pérez, María B; Pellerano, Roberto G; Wuilloud, Rodolfo G

2018-03-01

The feasibility of the application of chemometric techniques associated with multi-element analysis for the classification of grape seeds according to their provenance vineyard soil was investigated. Grape seed samples from different localities of Mendoza province (Argentina) were evaluated. Inductively coupled plasma mass spectrometry (ICP-MS) was used for the determination of twenty-nine elements (Ag, As, Ce, Co, Cs, Cu, Eu, Fe, Ga, Gd, La, Lu, Mn, Mo, Nb, Nd, Ni, Pr, Rb, Sm, Te, Ti, Tl, Tm, U, V, Y, Zn and Zr). Once the analytical data were collected, supervised pattern recognition techniques such as linear discriminant analysis (LDA), partial least square discriminant analysis (PLS-DA), k-nearest neighbors (k-NN), support vector machine (SVM) and Random Forest (RF) were applied to construct classification/discrimination rules. The results indicated that nonlinear methods, RF and SVM, perform best with up to 98% and 93% accuracy rate, respectively, and therefore are excellent tools for classification of grapes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Application of Hyperspectral Imaging to Detect Sclerotinia sclerotiorum on Oilseed Rape Stems

PubMed Central

Kong, Wenwen; Zhang, Chu; Huang, Weihao

2018-01-01

Hyperspectral imaging covering the spectral range of 384–1034 nm combined with chemometric methods was used to detect Sclerotinia sclerotiorum (SS) on oilseed rape stems by two sample sets (60 healthy and 60 infected stems for each set). Second derivative spectra and PCA loadings were used to select the optimal wavelengths. Discriminant models were built and compared to detect SS on oilseed rape stems, including partial least squares-discriminant analysis, radial basis function neural network, support vector machine and extreme learning machine. The discriminant models using full spectra and optimal wavelengths showed good performance with classification accuracies of over 80% for the calibration and prediction set. Comparing all developed models, the optimal classification accuracies of the calibration and prediction set were over 90%. The similarity of selected optimal wavelengths also indicated the feasibility of using hyperspectral imaging to detect SS on oilseed rape stems. The results indicated that hyperspectral imaging could be used as a fast, non-destructive and reliable technique to detect plant diseases on stems. PMID:29300315

SVM-Based Synthetic Fingerprint Discrimination Algorithm and Quantitative Optimization Strategy

PubMed Central

Chen, Suhang; Chang, Sheng; Huang, Qijun; He, Jin; Wang, Hao; Huang, Qiangui

2014-01-01

Synthetic fingerprints are a potential threat to automatic fingerprint identification systems (AFISs). In this paper, we propose an algorithm to discriminate synthetic fingerprints from real ones. First, four typical characteristic factors—the ridge distance features, global gray features, frequency feature and Harris Corner feature—are extracted. Then, a support vector machine (SVM) is used to distinguish synthetic fingerprints from real fingerprints. The experiments demonstrate that this method can achieve a recognition accuracy rate of over 98% for two discrete synthetic fingerprint databases as well as a mixed database. Furthermore, a performance factor that can evaluate the SVM's accuracy and efficiency is presented, and a quantitative optimization strategy is established for the first time. After the optimization of our synthetic fingerprint discrimination task, the polynomial kernel with a training sample proportion of 5% is the optimized value when the minimum accuracy requirement is 95%. The radial basis function (RBF) kernel with a training sample proportion of 15% is a more suitable choice when the minimum accuracy requirement is 98%. PMID:25347063

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.

Dimensional feature weighting utilizing multiple kernel learning for single-channel talker location discrimination using the acoustic transfer function.

PubMed

Takashima, Ryoichi; Takiguchi, Tetsuya; Ariki, Yasuo

2013-02-01

This paper presents a method for discriminating the location of the sound source (talker) using only a single microphone. In a previous work, the single-channel approach for discriminating the location of the sound source was discussed, where the acoustic transfer function from a user's position is estimated by using a hidden Markov model of clean speech in the cepstral domain. In this paper, each cepstral dimension of the acoustic transfer function is newly weighted, in order to obtain the cepstral dimensions having information that is useful for classifying the user's position. Then, this paper proposes a feature-weighting method for the cepstral parameter using multiple kernel learning, defining the base kernels for each cepstral dimension of the acoustic transfer function. The user's position is trained and classified by support vector machine. The effectiveness of this method has been confirmed by sound source (talker) localization experiments performed in different room environments.

Recognizing human activities using appearance metric feature and kinematics feature

NASA Astrophysics Data System (ADS)

Qian, Huimin; Zhou, Jun; Lu, Xinbiao; Wu, Xinye

2017-05-01

The problem of automatically recognizing human activities from videos through the fusion of the two most important cues, appearance metric feature and kinematics feature, is considered. And a system of two-dimensional (2-D) Poisson equations is introduced to extract the more discriminative appearance metric feature. Specifically, the moving human blobs are first detected out from the video by background subtraction technique to form a binary image sequence, from which the appearance feature designated as the motion accumulation image and the kinematics feature termed as centroid instantaneous velocity are extracted. Second, 2-D discrete Poisson equations are employed to reinterpret the motion accumulation image to produce a more differentiated Poisson silhouette image, from which the appearance feature vector is created through the dimension reduction technique called bidirectional 2-D principal component analysis, considering the balance between classification accuracy and time consumption. Finally, a cascaded classifier based on the nearest neighbor classifier and two directed acyclic graph support vector machine classifiers, integrated with the fusion of the appearance feature vector and centroid instantaneous velocity vector, is applied to recognize the human activities. Experimental results on the open databases and a homemade one confirm the recognition performance of the proposed algorithm.

Search for heavy resonances decaying into a vector boson and a Higgs boson in final states with charged leptons, neutrinos, and b quarks

NASA Astrophysics Data System (ADS)

Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Asilar, E.; Bergauer, T.; Brandstetter, J.; Brondolin, E.; Dragicevic, M.; Erö, J.; Flechl, M.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hartl, C.; Hörmann, N.; Hrubec, J.; Jeitler, M.; König, A.; Krätschmer, I.; Liko, D.; Matsushita, T.; Mikulec, I.; Rabady, D.; Rad, N.; Rahbaran, B.; Rohringer, H.; Schieck, J.; Strauss, J.; Waltenberger, W.; Wulz, C.-E.; Dvornikov, O.; Makarenko, V.; Zykunov, V.; Mossolov, V.; Shumeiko, N.; Suarez Gonzalez, J.; Alderweireldt, S.; De Wolf, E. A.; Janssen, X.; Lauwers, J.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Abu Zeid, S.; Blekman, F.; D'Hondt, J.; Daci, N.; De Bruyn, I.; Deroover, K.; Heracleous, N.; Lowette, S.; Moortgat, S.; Moreels, L.; Olbrechts, A.; Python, Q.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Parijs, I.; Brun, H.; Clerbaux, B.; De Lentdecker, G.; Delannoy, H.; Fasanella, G.; Favart, L.; Goldouzian, R.; Grebenyuk, A.; Karapostoli, G.; Lenzi, T.; Léonard, A.; Luetic, J.; Maerschalk, T.; Marinov, A.; Randle-conde, A.; Seva, T.; Vander Velde, C.; Vanlaer, P.; Yonamine, R.; Zenoni, F.; Zhang, F.; Cimmino, A.; Cornelis, T.; Dobur, D.; Fagot, A.; Garcia, G.; Gul, M.; Poyraz, D.; Salva, S.; Schöfbeck, R.; Sharma, A.; Tytgat, M.; Van Driessche, W.; Yazgan, E.; Zaganidis, N.; Bakhshiansohi, H.; Beluffi, C.; Bondu, O.; Brochet, S.; Bruno, G.; Caudron, A.; De Visscher, S.; Delaere, C.; Delcourt, M.; Francois, B.; Giammanco, A.; Jafari, A.; Jez, P.; Komm, M.; Lemaitre, V.; Magitteri, A.; Mertens, A.; Musich, M.; Nuttens, C.; Piotrzkowski, K.; Quertenmont, L.; Selvaggi, M.; Vidal Marono, M.; Wertz, S.; Beliy, N.; Aldá Júnior, W. L.; Alves, F. L.; Alves, G. A.; Brito, L.; Hensel, C.; Moraes, A.; Pol, M. E.; Rebello Teles, P.; Belchior Batista Das Chagas, E.; Carvalho, W.; Chinellato, J.; Custódio, A.; Da Costa, E. M.; Da Silveira, G. G.; De Jesus Damiao, D.; De Oliveira Martins, C.; Fonseca De Souza, S.; Huertas Guativa, L. M.; Malbouisson, H.; Matos Figueiredo, D.; Mora Herrera, C.; Mundim, L.; Nogima, H.; Prado Da Silva, W. L.; Santoro, A.; Sznajder, A.; Tonelli Manganote, E. J.; Vilela Pereira, A.; Ahuja, S.; Bernardes, C. A.; Dogra, S.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Mercadante, P. G.; Moon, C. S.; Novaes, S. F.; Padula, Sandra S.; Romero Abad, D.; Ruiz Vargas, J. C.; Aleksandrov, A.; Hadjiiska, R.; Iaydjiev, P.; Rodozov, M.; Stoykova, S.; Sultanov, G.; Vutova, M.; Dimitrov, A.; Glushkov, I.; Litov, L.; Pavlov, B.; Petkov, P.; Fang, W.; Ahmad, M.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Chen, Y.; Cheng, T.; Jiang, C. H.; Leggat, D.; Liu, Z.; Romeo, F.; Shaheen, S. M.; Spiezia, A.; Tao, J.; Wang, C.; Wang, Z.; Zhang, H.; Zhao, J.; Ban, Y.; Chen, G.; Li, Q.; Liu, S.; Mao, Y.; Qian, S. J.; Wang, D.; Xu, Z.; Avila, C.; Cabrera, A.; Chaparro Sierra, L. F.; Florez, C.; Gomez, J. P.; González Hernández, C. F.; Ruiz Alvarez, J. D.; Sanabria, J. C.; Godinovic, N.; Lelas, D.; Puljak, I.; Ribeiro Cipriano, P. M.; Sculac, T.; Antunovic, Z.; Kovac, M.; Brigljevic, V.; Ferencek, D.; Kadija, K.; Micanovic, S.; Sudic, L.; Susa, T.; Attikis, A.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Rykaczewski, H.; Tsiakkouri, D.; Finger, M.; Finger, M.; Carrera Jarrin, E.; El-khateeb, E.; Elgammal, S.; Mohamed, A.; Calpas, B.; Kadastik, M.; Murumaa, M.; Perrini, L.; Raidal, M.; Tiko, A.; Veelken, C.; Eerola, P.; Pekkanen, J.; Voutilainen, M.; Härkönen, J.; Järvinen, T.; Karimäki, V.; Kinnunen, R.; Lampén, T.; Lassila-Perini, K.; Lehti, S.; Lindén, T.; Luukka, P.; Tuominiemi, J.; Tuovinen, E.; Wendland, L.; Talvitie, J.; Tuuva, T.; Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Fabbro, B.; Faure, J. L.; Favaro, C.; Ferri, F.; Ganjour, S.; Ghosh, S.; Givernaud, A.; Gras, P.; Hamel de Monchenault, G.; Jarry, P.; Kucher, I.; Locci, E.; Machet, M.; Malcles, J.; Rander, J.; Rosowsky, A.; Titov, M.; Zghiche, A.; Abdulsalam, A.; Antropov, I.; Baffioni, S.; Beaudette, F.; Busson, P.; Cadamuro, L.; Chapon, E.; Charlot, C.; Davignon, O.; Granier de Cassagnac, R.; Jo, M.; Lisniak, S.; Miné, P.; Nguyen, M.; Ochando, C.; Ortona, G.; Paganini, P.; Pigard, P.; Regnard, S.; Salerno, R.; Sirois, Y.; Strebler, T.; Yilmaz, Y.; Zabi, A.; Agram, J.-L.; Andrea, J.; Aubin, A.; Bloch, D.; Brom, J.-M.; Buttignol, M.; Chabert, E. C.; Chanon, N.; Collard, C.; Conte, E.; Coubez, X.; Fontaine, J.-C.; Gelé, D.; Goerlach, U.; Le Bihan, A.-C.; Skovpen, K.; Van Hove, P.; Gadrat, S.; Beauceron, S.; Bernet, C.; Boudoul, G.; Bouvier, E.; Carrillo Montoya, C. A.; Chierici, R.; Contardo, D.; Courbon, B.; Depasse, P.; El Mamouni, H.; Fan, J.; Fay, J.; Gascon, S.; Gouzevitch, M.; Grenier, G.; Ille, B.; Lagarde, F.; Laktineh, I. B.; Lethuillier, M.; Mirabito, L.; Pequegnot, A. L.; Perries, S.; Popov, A.; Sabes, D.; Sordini, V.; Vander Donckt, M.; Verdier, P.; Viret, S.; Khvedelidze, A.; Tsamalaidze, Z.; Autermann, C.; Beranek, S.; Feld, L.; Heister, A.; Kiesel, M. K.; Klein, K.; Lipinski, M.; Ostapchuk, A.; Preuten, M.; Raupach, F.; Schael, S.; Schomakers, C.; Schulz, J.; Verlage, T.; Weber, H.; Zhukov, V.; Albert, A.; Brodski, M.; Dietz-Laursonn, E.; Duchardt, D.; Endres, M.; Erdmann, M.; Erdweg, S.; Esch, T.; Fischer, R.; Güth, A.; Hamer, M.; Hebbeker, T.; Heidemann, C.; Hoepfner, K.; Knutzen, S.; Merschmeyer, M.; Meyer, A.; Millet, P.; Mukherjee, S.; Olschewski, M.; Padeken, K.; Pook, T.; Radziej, M.; Reithler, H.; Rieger, M.; Scheuch, F.; Sonnenschein, L.; Teyssier, D.; Thüer, S.; Cherepanov, V.; Flügge, G.; Hoehle, F.; Kargoll, B.; Kress, T.; Künsken, A.; Lingemann, J.; Müller, T.; Nehrkorn, A.; Nowack, A.; Nugent, I. M.; Pistone, C.; Pooth, O.; Stahl, A.; Aldaya Martin, M.; Arndt, T.; Asawatangtrakuldee, C.; Beernaert, K.; Behnke, O.; Behrens, U.; Bin Anuar, A. A.; Borras, K.; Campbell, A.; Connor, P.; Contreras-Campana, C.; Costanza, F.; Diez Pardos, C.; Dolinska, G.; Eckerlin, G.; Eckstein, D.; Eichhorn, T.; Eren, E.; Gallo, E.; Garay Garcia, J.; Geiser, A.; Gizhko, A.; Grados Luyando, J. M.; Gunnellini, P.; Harb, A.; Hauk, J.; Hempel, M.; Jung, H.; Kalogeropoulos, A.; Karacheban, O.; Kasemann, M.; Keaveney, J.; Kleinwort, C.; Korol, I.; Krücker, D.; Lange, W.; Lelek, A.; Leonard, J.; Lipka, K.; Lobanov, A.; Lohmann, W.; Mankel, R.; Melzer-Pellmann, I.-A.; Meyer, A. B.; Mittag, G.; Mnich, J.; Mussgiller, A.; Ntomari, E.; Pitzl, D.; Placakyte, R.; Raspereza, A.; Roland, B.; Sahin, M. Ö.; Saxena, P.; Schoerner-Sadenius, T.; Seitz, C.; Spannagel, S.; Stefaniuk, N.; Van Onsem, G. P.; Walsh, R.; Wissing, C.; Blobel, V.; Centis Vignali, M.; Draeger, A. R.; Dreyer, T.; Garutti, E.; Gonzalez, D.; Haller, J.; Hoffmann, M.; Junkes, A.; Klanner, R.; Kogler, R.; Kovalchuk, N.; Lapsien, T.; Lenz, T.; Marchesini, I.; Marconi, D.; Meyer, M.; Niedziela, M.; Nowatschin, D.; Pantaleo, F.; Peiffer, T.; Perieanu, A.; Poehlsen, J.; Sander, C.; Scharf, C.; Schleper, P.; Schmidt, A.; Schumann, S.; Schwandt, J.; Stadie, H.; Steinbrück, G.; Stober, F. M.; Stöver, M.; Tholen, H.; Troendle, D.; Usai, E.; Vanelderen, L.; Vanhoefer, A.; Vormwald, B.; Akbiyik, M.; Barth, C.; Baur, S.; Baus, C.; Berger, J.; Butz, E.; Caspart, R.; Chwalek, T.; Colombo, F.; De Boer, W.; Dierlamm, A.; Fink, S.; Friese, R.; Giffels, M.; Gilbert, A.; Goldenzweig, P.; Haitz, D.; Hartmann, F.; Heindl, S. M.; Husemann, U.; Katkov, I.; Kudella, S.; Lobelle Pardo, P.; Mildner, H.; Mozer, M. U.; Müller, Th.; Plagge, M.; Quast, G.; Rabbertz, K.; Röcker, S.; Roscher, F.; Schröder, M.; Shvetsov, I.; Sieber, G.; Simonis, H. J.; Ulrich, R.; Wagner-Kuhr, J.; Wayand, S.; Weber, M.; Weiler, T.; Williamson, S.; Wöhrmann, C.; Wolf, R.; Anagnostou, G.; Daskalakis, G.; Geralis, T.; Giakoumopoulou, V. A.; Kyriakis, A.; Loukas, D.; Topsis-Giotis, I.; Kesisoglou, S.; Panagiotou, A.; Saoulidou, N.; Tziaferi, E.; Evangelou, I.; Flouris, G.; Foudas, C.; Kokkas, P.; Loukas, N.; Manthos, N.; Papadopoulos, I.; Paradas, E.; Filipovic, N.; Bencze, G.; Hajdu, C.; Hidas, P.; Horvath, D.; Sikler, F.; Veszpremi, V.; Vesztergombi, G.; Zsigmond, A. J.; Beni, N.; Czellar, S.; Karancsi, J.; Makovec, A.; Molnar, J.; Szillasi, Z.; Bartók, M.; Raics, P.; Trocsanyi, Z. L.; Ujvari, B.; Bahinipati, S.; Choudhury, S.; Mal, P.; Mandal, K.; Nayak, A.; Sahoo, D. K.; Sahoo, N.; Swain, S. K.; Bansal, S.; Beri, S. B.; Bhatnagar, V.; Chawla, R.; Bhawandeep, U.; Kalsi, A. K.; Kaur, A.; Kaur, M.; Kumar, R.; Kumari, P.; Mehta, A.; Mittal, M.; Singh, J. B.; Walia, G.; Kumar, Ashok; Bhardwaj, A.; Choudhary, B. C.; Garg, R. 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M.; Lanza, G.; Lista, L.; Meola, S.; Paolucci, P.; Sciacca, C.; Thyssen, F.; Azzi, P.; Bacchetta, N.; Benato, L.; Bisello, D.; Boletti, A.; Carlin, R.; Carvalho Antunes De Oliveira, A.; Checchia, P.; Dall'Osso, M.; De Castro Manzano, P.; Dorigo, T.; Dosselli, U.; Gasparini, F.; Gasparini, U.; Gozzelino, A.; Lacaprara, S.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Pozzobon, N.; Ronchese, P.; Simonetto, F.; Torassa, E.; Zanetti, M.; Zotto, P.; Zumerle, G.; Braghieri, A.; Magnani, A.; Montagna, P.; Ratti, S. P.; Re, V.; Riccardi, C.; Salvini, P.; Vai, I.; Vitulo, P.; Alunni Solestizi, L.; Bilei, G. M.; Ciangottini, D.; Fanò, L.; Lariccia, P.; Leonardi, R.; Mantovani, G.; Menichelli, M.; Saha, A.; Santocchia, A.; Androsov, K.; Azzurri, P.; Bagliesi, G.; Bernardini, J.; Boccali, T.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Donato, S.; Fedi, G.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Lomtadze, T.; Martini, L.; Messineo, A.; Palla, F.; Rizzi, A.; Savoy-Navarro, A.; Spagnolo, P.; Tenchini, R.; Tonelli, G.; Venturi, A.; Verdini, P. G.; Barone, L.; Cavallari, F.; Cipriani, M.; D'imperio, G.; Del Re, D.; Diemoz, M.; Gelli, S.; Longo, E.; Margaroli, F.; Marzocchi, B.; Meridiani, P.; Organtini, G.; Paramatti, R.; Preiato, F.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.; Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bartosik, N.; Bellan, R.; Biino, C.; Cartiglia, N.; Cenna, F.; Costa, M.; Covarelli, R.; Degano, A.; Demaria, N.; Finco, L.; Kiani, B.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V.; Monteil, E.; Obertino, M. M.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Pinna Angioni, G. L.; Ravera, F.; Romero, A.; Ruspa, M.; Sacchi, R.; Shchelina, K.; Sola, V.; Solano, A.; Staiano, A.; Traczyk, P.; Belforte, S.; Casarsa, M.; Cossutti, F.; Della Ricca, G.; Zanetti, A.; Kim, D. H.; Kim, G. N.; Kim, M. S.; Lee, S.; Lee, S. W.; Oh, Y. 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V.; Terkulov, A.; Baskakov, A.; Belyaev, A.; Boos, E.; Bunichev, V.; Dubinin, M.; Dudko, L.; Klyukhin, V.; Kodolova, O.; Lokhtin, I.; Miagkov, I.; Obraztsov, S.; Perfilov, M.; Petrushanko, S.; Savrin, V.; Snigirev, A.; Blinov, V.; Skovpen, Y.; Azhgirey, I.; Bayshev, I.; Bitioukov, S.; Elumakhov, D.; Kachanov, V.; Kalinin, A.; Konstantinov, D.; Krychkine, V.; Petrov, V.; Ryutin, R.; Sobol, A.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.; Adzic, P.; Cirkovic, P.; Devetak, D.; Dordevic, M.; Milosevic, J.; Rekovic, V.; Alcaraz Maestre, J.; Barrio Luna, M.; Calvo, E.; Cerrada, M.; Chamizo Llatas, M.; Colino, N.; De La Cruz, B.; Delgado Peris, A.; Escalante Del Valle, A.; Fernandez Bedoya, C.; Fernández Ramos, J. P.; Flix, J.; Fouz, M. C.; Garcia-Abia, P.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M. I.; Navarro De Martino, E.; Pérez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Quintario Olmeda, A.; Redondo, I.; Romero, L.; Soares, M. S.; de Trocóniz, J. 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R.; Williams, T.; Baber, M.; Bainbridge, R.; Buchmuller, O.; Bundock, A.; Burton, D.; Casasso, S.; Citron, M.; Colling, D.; Corpe, L.; Dauncey, P.; Davies, G.; De Wit, A.; Della Negra, M.; Di Maria, R.; Dunne, P.; Elwood, A.; Futyan, D.; Haddad, Y.; Hall, G.; Iles, G.; James, T.; Lane, R.; Laner, C.; Lucas, R.; Lyons, L.; Magnan, A.-M.; Malik, S.; Mastrolorenzo, L.; Nash, J.; Nikitenko, A.; Pela, J.; Penning, B.; Pesaresi, M.; Raymond, D. M.; Richards, A.; Rose, A.; Seez, C.; Summers, S.; Tapper, A.; Uchida, K.; Vazquez Acosta, M.; Virdee, T.; Wright, J.; Zenz, S. C.; Cole, J. E.; Hobson, P. R.; Khan, A.; Kyberd, P.; Leslie, D.; Reid, I. D.; Symonds, P.; Teodorescu, L.; Turner, M.; Borzou, A.; Call, K.; Dittmann, J.; Hatakeyama, K.; Liu, H.; Pastika, N.; Charaf, O.; Cooper, S. 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B.; Azzolini, V.; Ferguson, T.; Paulini, M.; Russ, J.; Sun, M.; Vogel, H.; Vorobiev, I.; Cumalat, J. P.; Ford, W. T.; Jensen, F.; Johnson, A.; Krohn, M.; Mulholland, T.; Stenson, K.; Wagner, S. R.; Alexander, J.; Chaves, J.; Chu, J.; Dittmer, S.; Mcdermott, K.; Mirman, N.; Nicolas Kaufman, G.; Patterson, J. R.; Rinkevicius, A.; Ryd, A.; Skinnari, L.; Soffi, L.; Tan, S. M.; Tao, Z.; Thom, J.; Tucker, J.; Wittich, P.; Zientek, M.; Winn, D.; Abdullin, S.; Albrow, M.; Apollinari, G.; Banerjee, S.; Bauerdick, L. A. T.; Beretvas, A.; Berryhill, J.; Bhat, P. C.; Bolla, G.; Burkett, K.; Butler, J. N.; Cheung, H. W. K.; Chlebana, F.; Cihangir, S.; Cremonesi, M.; Elvira, V. D.; Fisk, I.; Freeman, J.; Gottschalk, E.; Gray, L.; Green, D.; Grünendahl, S.; Gutsche, O.; Hare, D.; Harris, R. M.; Hasegawa, S.; Hirschauer, J.; Hu, Z.; Jayatilaka, B.; Jindariani, S.; Johnson, M.; Joshi, U.; Klima, B.; Kreis, B.; Lammel, S.; Linacre, J.; Lincoln, D.; Lipton, R.; Liu, T.; Lopes De Sá, R.; Lykken, J.; Maeshima, K.; Magini, N.; Marraffino, J. M.; Maruyama, S.; Mason, D.; McBride, P.; Merkel, P.; Mrenna, S.; Nahn, S.; Newman-Holmes, C.; O'Dell, V.; Pedro, K.; Prokofyev, O.; Rakness, G.; Ristori, L.; Sexton-Kennedy, E.; Soha, A.; Spalding, W. J.; Spiegel, L.; Stoynev, S.; Strobbe, N.; Taylor, L.; Tkaczyk, S.; Tran, N. V.; Uplegger, L.; Vaandering, E. W.; Vernieri, C.; Verzocchi, M.; Vidal, R.; Wang, M.; Weber, H. A.; Whitbeck, A.; Acosta, D.; Avery, P.; Bortignon, P.; Bourilkov, D.; Brinkerhoff, A.; Carnes, A.; Carver, M.; Curry, D.; Das, S.; Field, R. D.; Furic, I. K.; Konigsberg, J.; Korytov, A.; Ma, P.; Matchev, K.; Mei, H.; Milenovic, P.; Mitselmakher, G.; Rank, D.; Shchutska, L.; Sperka, D.; Thomas, L.; Wang, J.; Wang, S.; Yelton, J.; Linn, S.; Markowitz, P.; Martinez, G.; Rodriguez, J. L.; Ackert, A.; Adams, J. R.; Adams, T.; Askew, A.; Bein, S.; Diamond, B.; Hagopian, S.; Hagopian, V.; Johnson, K. F.; Khatiwada, A.; Prosper, H.; Santra, A.; Weinberg, M.; Baarmand, M. M.; Bhopatkar, V.; Colafranceschi, S.; Hohlmann, M.; Noonan, D.; Roy, T.; Yumiceva, F.; Adams, M. R.; Apanasevich, L.; Berry, D.; Betts, R. R.; Bucinskaite, I.; Cavanaugh, R.; Evdokimov, O.; Gauthier, L.; Gerber, C. E.; Hofman, D. J.; Jung, K.; Kurt, P.; O'Brien, C.; Sandoval Gonzalez, I. D.; Turner, P.; Varelas, N.; Wang, H.; Wu, Z.; Zakaria, M.; Zhang, J.; Bilki, B.; Clarida, W.; Dilsiz, K.; Durgut, S.; Gandrajula, R. P.; Haytmyradov, M.; Khristenko, V.; Merlo, J.-P.; Mermerkaya, H.; Mestvirishvili, A.; Moeller, A.; Nachtman, J.; Ogul, H.; Onel, Y.; Ozok, F.; Penzo, A.; Snyder, C.; Tiras, E.; Wetzel, J.; Yi, K.; Anderson, I.; Blumenfeld, B.; Cocoros, A.; Eminizer, N.; Fehling, D.; Feng, L.; Gritsan, A. V.; Maksimovic, P.; Martin, C.; Osherson, M.; Roskes, J.; Sarica, U.; Swartz, M.; Xiao, M.; Xin, Y.; You, C.; Al-bataineh, A.; Baringer, P.; Bean, A.; Boren, S.; Bowen, J.; Bruner, C.; Castle, J.; Forthomme, L.; Kenny, R. P., III; Kropivnitskaya, A.; Majumder, D.; Mcbrayer, W.; Murray, M.; Sanders, S.; Stringer, R.; Tapia Takaki, J. D.; Wang, Q.; Ivanov, A.; Kaadze, K.; Khalil, S.; Maravin, Y.; Mohammadi, A.; Saini, L. K.; Skhirtladze, N.; Toda, S.; Rebassoo, F.; Wright, D.; Anelli, C.; Baden, A.; Baron, O.; Belloni, A.; Calvert, B.; Eno, S. C.; Ferraioli, C.; Gomez, J. A.; Hadley, N. J.; Jabeen, S.; Kellogg, R. G.; Kolberg, T.; Kunkle, J.; Lu, Y.; Mignerey, A. C.; Ricci-Tam, F.; Shin, Y. H.; Skuja, A.; Tonjes, M. B.; Tonwar, S. C.; Abercrombie, D.; Allen, B.; Apyan, A.; Barbieri, R.; Baty, A.; Bi, R.; Bierwagen, K.; Brandt, S.; Busza, W.; Cali, I. A.; Demiragli, Z.; Di Matteo, L.; Gomez Ceballos, G.; Goncharov, M.; Hsu, D.; Iiyama, Y.; Innocenti, G. M.; Klute, M.; Kovalskyi, D.; Krajczar, K.; Lai, Y. S.; Lee, Y.-J.; Levin, A.; Luckey, P. D.; Maier, B.; Marini, A. C.; Mcginn, C.; Mironov, C.; Narayanan, S.; Niu, X.; Paus, C.; Roland, C.; Roland, G.; Salfeld-Nebgen, J.; Stephans, G. S. F.; Sumorok, K.; Tatar, K.; Varma, M.; Velicanu, D.; Veverka, J.; Wang, J.; Wang, T. W.; Wyslouch, B.; Yang, M.; Zhukova, V.; Benvenuti, A. C.; Chatterjee, R. M.; Evans, A.; Finkel, A.; Gude, A.; Hansen, P.; Kalafut, S.; Kao, S. C.; Kubota, Y.; Lesko, Z.; Mans, J.; Nourbakhsh, S.; Ruckstuhl, N.; Rusack, R.; Tambe, N.; Turkewitz, J.; Acosta, J. G.; Oliveros, S.; Avdeeva, E.; Bartek, R.; Bloom, K.; Claes, D. R.; Dominguez, A.; Fangmeier, C.; Gonzalez Suarez, R.; Kamalieddin, R.; Kravchenko, I.; Malta Rodrigues, A.; Meier, F.; Monroy, J.; Siado, J. E.; Snow, G. R.; Stieger, B.; Alyari, M.; Dolen, J.; George, J.; Godshalk, A.; Harrington, C.; Iashvili, I.; Kaisen, J.; Kharchilava, A.; Kumar, A.; Parker, A.; Rappoccio, S.; Roozbahani, B.; Alverson, G.; Barberis, E.; Hortiangtham, A.; Massironi, A.; Morse, D. M.; Nash, D.; Orimoto, T.; Teixeira De Lima, R.; Trocino, D.; Wang, R.-J.; Wood, D.; Bhattacharya, S.; Hahn, K. A.; Kubik, A.; Kumar, A.; Low, J. F.; Mucia, N.; Odell, N.; Pollack, B.; Schmitt, M. H.; Sung, K.; Trovato, M.; Velasco, M.; Dev, N.; Hildreth, M.; Hurtado Anampa, K.; Jessop, C.; Karmgard, D. J.; Kellams, N.; Lannon, K.; Marinelli, N.; Meng, F.; Mueller, C.; Musienko, Y.; Planer, M.; Reinsvold, A.; Ruchti, R.; Smith, G.; Taroni, S.; Wayne, M.; Wolf, M.; Woodard, A.; Alimena, J.; Antonelli, L.; Brinson, J.; Bylsma, B.; Durkin, L. S.; Flowers, S.; Francis, B.; Hart, A.; Hill, C.; Hughes, R.; Ji, W.; Liu, B.; Luo, W.; Puigh, D.; Winer, B. L.; Wulsin, H. W.; Cooperstein, S.; Driga, O.; Elmer, P.; Hardenbrook, J.; Hebda, P.; Lange, D.; Luo, J.; Marlow, D.; Mc Donald, J.; Medvedeva, T.; Mei, K.; Mooney, M.; Olsen, J.; Palmer, C.; Piroué, P.; Stickland, D.; Tully, C.; Zuranski, A.; Malik, S.; Barker, A.; Barnes, V. E.; Folgueras, S.; Gutay, L.; Jha, M. K.; Jones, M.; Jung, A. W.; Miller, D. H.; Neumeister, N.; Schulte, J. F.; Shi, X.; Sun, J.; Svyatkovskiy, A.; Wang, F.; Xie, W.; Xu, L.; Parashar, N.; Stupak, J.; Adair, A.; Akgun, B.; Chen, Z.; Ecklund, K. M.; Geurts, F. J. M.; Guilbaud, M.; Li, W.; Michlin, B.; Northup, M.; Padley, B. P.; Redjimi, R.; Roberts, J.; Rorie, J.; Tu, Z.; Zabel, J.; Betchart, B.; Bodek, A.; de Barbaro, P.; Demina, R.; Duh, Y. t.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Han, J.; Hindrichs, O.; Khukhunaishvili, A.; Lo, K. H.; Tan, P.; Verzetti, M.; Agapitos, A.; Chou, J. P.; Contreras-Campana, E.; Gershtein, Y.; Gómez Espinosa, T. A.; Halkiadakis, E.; Heindl, M.; Hidas, D.; Hughes, E.; Kaplan, S.; Kunnawalkam Elayavalli, R.; Kyriacou, S.; Lath, A.; Nash, K.; Saka, H.; Salur, S.; Schnetzer, S.; Sheffield, D.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Delannoy, A. G.; Foerster, M.; Heideman, J.; Riley, G.; Rose, K.; Spanier, S.; Thapa, K.; Bouhali, O.; Celik, A.; Dalchenko, M.; De Mattia, M.; Delgado, A.; Dildick, S.; Eusebi, R.; Gilmore, J.; Huang, T.; Juska, E.; Kamon, T.; Mueller, R.; Pakhotin, Y.; Patel, R.; Perloff, A.; Perniè, L.; Rathjens, D.; Rose, A.; Safonov, A.; Tatarinov, A.; Ulmer, K. A.; Akchurin, N.; Cowden, C.; Damgov, J.; De Guio, F.; Dragoiu, C.; Dudero, P. R.; Faulkner, J.; Gurpinar, E.; Kunori, S.; Lamichhane, K.; Lee, S. W.; Libeiro, T.; Peltola, T.; Undleeb, S.; Volobouev, I.; Wang, Z.; Greene, S.; Gurrola, A.; Janjam, R.; Johns, W.; Maguire, C.; Melo, A.; Ni, H.; Sheldon, P.; Tuo, S.; Velkovska, J.; Xu, Q.; Arenton, M. W.; Barria, P.; Cox, B.; Goodell, J.; Hirosky, R.; Ledovskoy, A.; Li, H.; Neu, C.; Sinthuprasith, T.; Sun, X.; Wang, Y.; Wolfe, E.; Xia, F.; Clarke, C.; Harr, R.; Karchin, P. E.; Sturdy, J.; Belknap, D. A.; Caillol, C.; Dasu, S.; Dodd, L.; Duric, S.; Gomber, B.; Grothe, M.; Herndon, M.; Hervé, A.; Klabbers, P.; Lanaro, A.; Levine, A.; Long, K.; Loveless, R.; Ojalvo, I.; Perry, T.; Pierro, G. A.; Polese, G.; Ruggles, T.; Savin, A.; Smith, N.; Smith, W. H.; Taylor, D.; Woods, N.; CMS Collaboration

2017-05-01

A search for heavy resonances decaying to a Higgs boson and a vector boson is presented. The analysis is performed using data samples collected in 2015 by the CMS experiment at the LHC in proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to integrated luminosities of 2.2-2.5 fb-1. The search is performed in channels in which the vector boson decays into leptonic final states (Z → νν, W → ℓν, and Z → ℓℓ, with ℓ = e , μ), while the Higgs boson decays to collimated b quark pairs detected as a single massive jet. The discriminating power of a jet mass requirement and a b jet tagging algorithm are exploited to suppress the standard model backgrounds. The event yields observed in data are consistent with the background expectation. In the context of a theoretical model with a heavy vector triplet, a resonance with mass less than 2 TeV is excluded at 95% confidence level. The results are also interpreted in terms of limits on the parameters of the model, improving on the reach of previous searches.

Appearance and characterization of fruit image textures for quality sorting using wavelet transform and genetic algorithms.

PubMed

Khoje, Suchitra

2018-02-01

Images of four qualities of mangoes and guavas are evaluated for color and textural features to characterize and classify them, and to model the fruit appearance grading. The paper discusses three approaches to identify most discriminating texture features of both the fruits. In the first approach, fruit's color and texture features are selected using Mahalanobis distance. A total of 20 color features and 40 textural features are extracted for analysis. Using Mahalanobis distance and feature intercorrelation analyses, one best color feature (mean of a* [L*a*b* color space]) and two textural features (energy a*, contrast of H*) are selected as features for Guava while two best color features (R std, H std) and one textural features (energy b*) are selected as features for mangoes with the highest discriminate power. The second approach studies some common wavelet families for searching the best classification model for fruit quality grading. The wavelet features extracted from five basic mother wavelets (db, bior, rbior, Coif, Sym) are explored to characterize fruits texture appearance. In third approach, genetic algorithm is used to select only those color and wavelet texture features that are relevant to the separation of the class, from a large universe of features. The study shows that image color and texture features which were identified using a genetic algorithm can distinguish between various qualities classes of fruits. The experimental results showed that support vector machine classifier is elected for Guava grading with an accuracy of 97.61% and artificial neural network is elected from Mango grading with an accuracy of 95.65%. The proposed method is nondestructive fruit quality assessment method. The experimental results has proven that Genetic algorithm along with wavelet textures feature has potential to discriminate fruit quality. Finally, it can be concluded that discussed method is an accurate, reliable, and objective tool to determine fruit quality namely Mango and Guava, and might be applicable to in-line sorting systems. © 2017 Wiley Periodicals, Inc.

Introduction to multivariate discrimination

NASA Astrophysics Data System (ADS)

Kégl, Balázs

2013-07-01

Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either relevant to or even motivated by certain unorthodox applications of multivariate discrimination in experimental physics.

Discrimination and Measurements of Three Flavonols with Similar Structure Using Terahertz Spectroscopy and Chemometrics

NASA Astrophysics Data System (ADS)

Yan, Ling; Liu, Changhong; Qu, Hao; Liu, Wei; Zhang, Yan; Yang, Jianbo; Zheng, Lei

2018-03-01

Terahertz (THz) technique, a recently developed spectral method, has been researched and used for the rapid discrimination and measurements of food compositions due to its low-energy and non-ionizing characteristics. In this study, THz spectroscopy combined with chemometrics has been utilized for qualitative and quantitative analysis of myricetin, quercetin, and kaempferol with concentrations of 0.025, 0.05, and 0.1 mg/mL. The qualitative discrimination was achieved by KNN, ELM, and RF models with the spectra pre-treatments. An excellent discrimination (100% CCR in the prediction set) could be achieved using the RF model. Furthermore, the quantitative analyses were performed by partial least square regression (PLSR) and least squares support vector machine (LS-SVM). Comparing to the PLSR models, the LS-SVM yielded better results with low RMSEP (0.0044, 0.0039, and 0.0048), higher Rp (0.9601, 0.9688, and 0.9359), and higher RPD (8.6272, 9.6333, and 7.9083) for myricetin, quercetin, and kaempferol, respectively. Our results demonstrate that THz spectroscopy technique is a powerful tool for identification of three flavonols with similar chemical structures and quantitative determination of their concentrations.

A Non-destructive Terahertz Spectroscopy-Based Method for Transgenic Rice Seed Discrimination via Sparse Representation

NASA Astrophysics Data System (ADS)

Hu, Xiaohua; Lang, Wenhui; Liu, Wei; Xu, Xue; Yang, Jianbo; Zheng, Lei

2017-08-01

Terahertz (THz) spectroscopy technique has been researched and developed for rapid and non-destructive detection of food safety and quality due to its low-energy and non-ionizing characteristics. The objective of this study was to develop a flexible identification model to discriminate transgenic and non-transgenic rice seeds based on terahertz (THz) spectroscopy. To extract THz spectral features and reduce the feature dimension, sparse representation (SR) is employed in this work. A sufficient sparsity level is selected to train the sparse coding of the THz data, and the random forest (RF) method is then applied to obtain a discrimination model. The results show that there exist differences between transgenic and non-transgenic rice seeds in THz spectral band and, comparing with Least squares support vector machines (LS-SVM) method, SR-RF is a better model for discrimination (accuracy is 95% in prediction set, 100% in calibration set, respectively). The conclusion is that SR may be more useful in the application of THz spectroscopy to reduce dimension and the SR-RF provides a new, effective, and flexible method for detection and identification of transgenic and non-transgenic rice seeds with THz spectral system.

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

Perception of Racial Discrimination and Psychopathology Across Three U.S. Ethnic Minority Groups

PubMed Central

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

2012-01-01

To examine the association between the perception of racial discrimination and the lifetime prevalence rates of psychological disorders in the three most common ethnic minorities in the U.S., we analyzed data from a sample consisting of 793 Asian Americans, 951 Hispanic Americans, and 2,795 African Americans who received the Composite International Diagnostic Interview through the Collaborative Psychiatric Epidemiology Studies. The perception of racial discrimination was associated with the endorsement of major depressive disorder, panic disorder with agoraphobia, agoraphobia without history of panic disorder, post-traumatic stress disorder, and substance use disorders in varying degrees amongst the three minority groups, independent of the socioeconomic status, level of education, age, and gender of participants. The results suggest that the perception of racial discrimination is associated with psychopathology in the three most common U.S. minority groups. PMID:21967527

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

PubMed

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

2012-01-01

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

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

PubMed Central

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

2018-01-01

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

Time-frequency optimization for discrimination between imagination of right and left hand movements based on two bipolar electroencephalography channels

NASA Astrophysics Data System (ADS)

Yang, Yuan; Chevallier, Sylvain; Wiart, Joe; Bloch, Isabelle

2014-12-01

To enforce a widespread use of efficient and easy to use brain-computer interfaces (BCIs), the inter-subject robustness should be increased and the number of electrodes should be reduced. These two key issues are addressed in this contribution, proposing a novel method to identify subject-specific time-frequency characteristics with a minimal number of electrodes. In this method, two alternative criteria, time-frequency discrimination factor ( TFDF) and F score, are proposed to evaluate the discriminative power of time-frequency regions. Distinct from classical measures (e.g., Fisher criterion, r 2 coefficient), the TFDF is based on the neurophysiologic phenomena, on which the motor imagery BCI paradigm relies, rather than only from statistics. F score is based on the popular Fisher's discriminant and purely data driven; however, it differs from traditional measures since it provides a simple and effective measure for quantifying the discriminative power of a multi-dimensional feature vector. The proposed method is tested on BCI competition IV datasets IIa and IIb for discriminating right and left hand motor imagery. Compared to state-of-the-art methods, our method based on both criteria led to comparable or even better classification results, while using fewer electrodes (i.e., only two bipolar channels, C3 and C4). This work indicates that time-frequency optimization can not only improve the classification performance but also contribute to reducing the number of electrodes required in motor imagery BCIs.

Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath

PubMed Central

Machado, Roberto F.; Laskowski, Daniel; Deffenderfer, Olivia; Burch, Timothy; Zheng, Shuo; Mazzone, Peter J.; Mekhail, Tarek; Jennings, Constance; Stoller, James K.; Pyle, Jacqueline; Duncan, Jennifer; Dweik, Raed A.; Erzurum, Serpil C.

2005-01-01

Rationale: Electronic noses are successfully used in commercial applications, including detection and analysis of volatile organic compounds in the food industry. Objectives: We hypothesized that the electronic nose could identify and discriminate between lung diseases, especially bronchogenic carcinoma. Methods: In a discovery and training phase, exhaled breath of 14 individuals with bronchogenic carcinoma and 45 healthy control subjects or control subjects without cancer was analyzed. Principal components and canonic discriminant analysis of the sensor data was used to determine whether exhaled gases could discriminate between cancer and noncancer. Discrimination between classes was performed using Mahalanobis distance. Support vector machine analysis was used to create and apply a cancer prediction model prospectively in a separate group of 76 individuals, 14 with and 62 without cancer. Main Results: Principal components and canonic discriminant analysis demonstrated discrimination between samples from patients with lung cancer and those from other groups. In the validation study, the electronic nose had 71.4% sensitivity and 91.9% specificity for detecting lung cancer; positive and negative predictive values were 66.6 and 93.4%, respectively. In this population with a lung cancer prevalence of 18%, positive and negative predictive values were 66.6 and 94.5%, respectively. Conclusion: The exhaled breath of patients with lung cancer has distinct characteristics that can be identified with an electronic nose. The results provide feasibility to the concept of using the electronic nose for managing and detecting lung cancer. PMID:15750044

[Are pathological (or vital) anxiety and common fear the same experiences? A controlled study].

PubMed

Ramos Brieva, J A; Montejo Iglesias, M L; Ponce de León, C; del Valle López, P; Lafuente López, R; Cordero Villafáfila, A; Baca García, E

1996-01-01

Some authors affirm that the anxiety of panic attacks is endogenous and of different quality that the common fear. But objective clinical data don't exist that confirm that observation. The authors study by means of a discriminant analysis the description of the anxiety made by subject with panic attacks and normal subjects with common fear. It meet a discriminant function that shows the existence of qualitative differences between both experiences. It seem to have achieved enough empirical support in order to sustain that the anxiety of panic attacks and the common fear are two qualitatively different experiences; presumably due to their several origin.

Demonstration of Advanced EMI Models for Live-Site UXO Discrimination at Waikoloa, Hawaii

DTIC Science & Technology

2015-12-01

magnetic source models PNN Probabilistic Neural Network SERDP Strategic Environmental Research and Development Program SLO San Luis Obispo...SNR Signal to noise ratio SVM Support vector machine TD Time Domain TEMTADS Time Domain Electromagnetic Towed Array Detection System TOI... intrusive procedure, which was used by Parsons at WMA, failed to document accurately all intrusive results, or failed to detect and clear all UXO like

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

Man Portable Vector EMI Sensor for Full UXO Characterization

DTIC Science & Technology

2012-05-01

with project management and coordination. Drs. Laurens Beran, Leonard Pasion , and Stephen Billings advised on technical aspects and Dr. Gregory Schultz...approximated as a point dipole (e.g., Bell et al., 2001; Pasion and Oldenburg, 2001; Gasperikova et al., 2009). The process of estimating the target...39, 1286–1293. Bell, T. 2005. Geo-location Requirements for UXO Discrimination. SERDP Geo-location Workshop. Billings, S., L. Pasion , N. Lhomme

Voltammetric Electronic Tongue and Support Vector Machines for Identification of Selected Features in Mexican Coffee

PubMed Central

Domínguez, Rocio Berenice; Moreno-Barón, Laura; Muñoz, Roberto; Gutiérrez, Juan Manuel

2014-01-01

This paper describes a new method based on a voltammetric electronic tongue (ET) for the recognition of distinctive features in coffee samples. An ET was directly applied to different samples from the main Mexican coffee regions without any pretreatment before the analysis. The resulting electrochemical information was modeled with two different mathematical tools, namely Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Growing conditions (i.e., organic or non-organic practices and altitude of crops) were considered for a first classification. LDA results showed an average discrimination rate of 88% ± 6.53% while SVM successfully accomplished an overall accuracy of 96.4% ± 3.50% for the same task. A second classification based on geographical origin of samples was carried out. Results showed an overall accuracy of 87.5% ± 7.79% for LDA and a superior performance of 97.5% ± 3.22% for SVM. Given the complexity of coffee samples, the high accuracy percentages achieved by ET coupled with SVM in both classification problems suggested a potential applicability of ET in the assessment of selected coffee features with a simpler and faster methodology along with a null sample pretreatment. In addition, the proposed method can be applied to authentication assessment while improving cost, time and accuracy of the general procedure. PMID:25254303

Voltammetric electronic tongue and support vector machines for identification of selected features in Mexican coffee.

PubMed

Domínguez, Rocio Berenice; Moreno-Barón, Laura; Muñoz, Roberto; Gutiérrez, Juan Manuel

2014-09-24

This paper describes a new method based on a voltammetric electronic tongue (ET) for the recognition of distinctive features in coffee samples. An ET was directly applied to different samples from the main Mexican coffee regions without any pretreatment before the analysis. The resulting electrochemical information was modeled with two different mathematical tools, namely Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Growing conditions (i.e., organic or non-organic practices and altitude of crops) were considered for a first classification. LDA results showed an average discrimination rate of 88% ± 6.53% while SVM successfully accomplished an overall accuracy of 96.4% ± 3.50% for the same task. A second classification based on geographical origin of samples was carried out. Results showed an overall accuracy of 87.5% ± 7.79% for LDA and a superior performance of 97.5% ± 3.22% for SVM. Given the complexity of coffee samples, the high accuracy percentages achieved by ET coupled with SVM in both classification problems suggested a potential applicability of ET in the assessment of selected coffee features with a simpler and faster methodology along with a null sample pretreatment. In addition, the proposed method can be applied to authentication assessment while improving cost, time and accuracy of the general procedure.

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.

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.

Robust Lentiviral Gene Delivery But Limited Transduction Capacity of Commonly Used Adeno-Associated Viral Serotypes in Xenotransplanted Human Skin.

PubMed

Jakobsen, Maria; Askou, Anne Louise; Stenderup, Karin; Rosada, Cecilia; Dagnæs-Hansen, Frederik; Jensen, Thomas G; Corydon, Thomas J; Mikkelsen, Jacob Giehm; Aagaard, Lars

2015-08-01

Skin is an easily accessible organ, and therapeutic gene transfer to skin remains an attractive alternative for the treatment of skin diseases. Although we have previously documented potent lentiviral gene delivery to human skin, vectors based on adeno-associated virus (AAV) rank among the most promising gene delivery tools for in vivo purposes. Thus, we compared the potential usefulness of various serotypes of recombinant AAV vectors and lentiviral vectors for gene transfer to human skin in a xenotransplanted mouse model. Vector constructs encoding firefly luciferase were packaged in AAV capsids of serotype 1, 2, 5, 6, 8, and 9 and separately administered by intradermal injection in human skin transplants. For all serotypes, live bioimaging demonstrated low levels of transgene expression in the human skin graft, and firefly luciferase expression was observed primarily in neighboring tissue outside of the graft. In contrast, gene delivery by intradermally injected lentiviral vectors was efficient and led to extensive and persistent firefly luciferase expression within the human skin graft only. The study demonstrates the limited capacity of single-stranded AAV vectors of six commonly used serotypes for gene delivery to human skin in vivo.

Robust Lentiviral Gene Delivery But Limited Transduction Capacity of Commonly Used Adeno-Associated Viral Serotypes in Xenotransplanted Human Skin

PubMed Central

Jakobsen, Maria; Askou, Anne Louise; Stenderup, Karin; Rosada, Cecilia; Dagnæs-Hansen, Frederik; Jensen, Thomas G.; Corydon, Thomas J.; Mikkelsen, Jacob Giehm; Aagaard, Lars

2015-01-01

Skin is an easily accessible organ, and therapeutic gene transfer to skin remains an attractive alternative for the treatment of skin diseases. Although we have previously documented potent lentiviral gene delivery to human skin, vectors based on adeno-associated virus (AAV) rank among the most promising gene delivery tools for in vivo purposes. Thus, we compared the potential usefulness of various serotypes of recombinant AAV vectors and lentiviral vectors for gene transfer to human skin in a xenotransplanted mouse model. Vector constructs encoding firefly luciferase were packaged in AAV capsids of serotype 1, 2, 5, 6, 8, and 9 and separately administered by intradermal injection in human skin transplants. For all serotypes, live bioimaging demonstrated low levels of transgene expression in the human skin graft, and firefly luciferase expression was observed primarily in neighboring tissue outside of the graft. In contrast, gene delivery by intradermally injected lentiviral vectors was efficient and led to extensive and persistent firefly luciferase expression within the human skin graft only. The study demonstrates the limited capacity of single-stranded AAV vectors of six commonly used serotypes for gene delivery to human skin in vivo. PMID:26204415

Microchip solid-state cylindrical vector lasers with orthogonally polarized dual laser-diode end pumping.

PubMed

Otsuka, Kenju; Chu, Shu-Chun

2013-05-01

We report a simple method for generating cylindrical vector beams directly from laser-diode (LD)-pumped microchip solid-state lasers by using dual end-pumping beams. Radially as well as azimuthally polarized vector field emissions have been generated from the common c-cut Nd:GdVO4 laser cavity merely by controlling the focus positions of orthogonally polarized LD off-axis pump beams. Hyperbolically polarized vector fields have also been observed, in which the cylindrical symmetry of vector fields is broken. Experimental results have been well reproduced by numerical simulations.

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

PubMed

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

2016-05-01

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

Perceived Discrimination and Privilege in Health Care: The Role of Socioeconomic Status and Race.

PubMed

Stepanikova, Irena; Oates, Gabriela R

2017-01-01

This study examined how perceived racial privilege and perceived racial discrimination in health care varied with race and socioeconomic status (SES). The sample consisted of white, black, and Native American respondents to the Behavioral Risk Factor Surveillance System (2005-2013) who had sought health care in the past 12 months. Multiple logistic regression models of perceived racial privilege and perceived discrimination were estimated. Analyses were performed in 2016. Perceptions of racial privilege were less common among blacks and Native Americans compared with whites, while perceptions of racial discrimination were more common among these minorities. In whites, higher income and education contributed to increased perceptions of privileged treatment and decreased perceptions of discrimination. The pattern was reversed in blacks, who reported more discrimination and less privilege at higher income and education levels. Across racial groups, respondents who reported foregone medical care due to cost had higher risk of perceived racial discrimination. Health insurance contributed to less perceived racial discrimination and more perceived privilege only among whites. SES is an important social determinant of perceived privilege and perceived discrimination in health care, but its role varies by indicator and racial group. Whites with low education or no health insurance, well-educated blacks, and individuals who face cost-related barriers to care are at increased risk of perceived discrimination. Policies and interventions to reduce these perceptions should target structural and systemic factors, including society-wide inequalities in income, education, and healthcare access, and should be tailored to account for racially specific healthcare experiences. Copyright © 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Lightweight Biometric Sensing for Walker Classification Using Narrowband RF Links

PubMed Central

Liang, Zhuo-qian

2017-01-01

This article proposes a lightweight biometric sensing system using ubiquitous narrowband radio frequency (RF) links for path-dependent walker classification. The fluctuated received signal strength (RSS) sequence generated by human motion is used for feature representation. To capture the most discriminative characteristics of individuals, a three-layer RF sensing network is organized for building multiple sampling links at the most common heights of upper limbs, thighs, and lower legs. The optimal parameters of sensing configuration, such as the height of link location and number of fused links, are investigated to improve sensory data distinctions among subjects, and the experimental results suggest that the synergistic sensing by using multiple links can contribute a better performance. This is the new consideration of using RF links in building a biometric sensing system. In addition, two types of classification methods involving vector quantization (VQ) and hidden Markov models (HMMs) are developed and compared for closed-set walker recognition and verification. Experimental studies in indoor line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios are conducted to validate the proposed method. PMID:29206188

Lightweight Biometric Sensing for Walker Classification Using Narrowband RF Links.

PubMed

Liu, Tong; Liang, Zhuo-Qian

2017-12-05

This article proposes a lightweight biometric sensing system using ubiquitous narrowband radio frequency (RF) links for path-dependent walker classification. The fluctuated received signal strength (RSS) sequence generated by human motion is used for feature representation. To capture the most discriminative characteristics of individuals, a three-layer RF sensing network is organized for building multiple sampling links at the most common heights of upper limbs, thighs, and lower legs. The optimal parameters of sensing configuration, such as the height of link location and number of fused links, are investigated to improve sensory data distinctions among subjects, and the experimental results suggest that the synergistic sensing by using multiple links can contribute a better performance. This is the new consideration of using RF links in building a biometric sensing system. In addition, two types of classification methods involving vector quantization (VQ) and hidden Markov models (HMMs) are developed and compared for closed-set walker recognition and verification. Experimental studies in indoor line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios are conducted to validate the proposed method.

A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis.

PubMed

Liu, Bin; Wang, Xiaolong; Lin, Lei; Dong, Qiwen; Wang, Xuan

2008-12-01

Protein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. A key step to improve the performance of the SVM-based methods is to find a suitable representation of protein sequences. In this paper, a novel building block of proteins called Top-n-grams is presented, which contains the evolutionary information extracted from the protein sequence frequency profiles. The protein sequence frequency profiles are calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into Top-n-grams. The protein sequences are transformed into fixed-dimension feature vectors by the occurrence times of each Top-n-gram. The training vectors are evaluated by SVM to train classifiers which are then used to classify the test protein sequences. We demonstrate that the prediction performance of remote homology detection and fold recognition can be improved by combining Top-n-grams and latent semantic analysis (LSA), which is an efficient feature extraction technique from natural language processing. When tested on superfamily and fold benchmarks, the method combining Top-n-grams and LSA gives significantly better results compared to related methods. The method based on Top-n-grams significantly outperforms the methods based on many other building blocks including N-grams, patterns, motifs and binary profiles. Therefore, Top-n-gram is a good building block of the protein sequences and can be widely used in many tasks of the computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the prediction of protein binding sites.

Emotion Recognition from Single-Trial EEG Based on Kernel Fisher's Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine

PubMed Central

Liu, Yi-Hung; Wu, Chien-Te; Cheng, Wei-Teng; Hsiao, Yu-Tsung; Chen, Po-Ming; Teng, Jyh-Tong

2014-01-01

Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods. PMID:25061837

Emotion recognition from single-trial EEG based on kernel Fisher's emotion pattern and imbalanced quasiconformal kernel support vector machine.

PubMed

Liu, Yi-Hung; Wu, Chien-Te; Cheng, Wei-Teng; Hsiao, Yu-Tsung; Chen, Po-Ming; Teng, Jyh-Tong

2014-07-24

Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods.

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.

Bed Bugs (Cimex lectularius) as Vectors of Trypanosoma cruzi

PubMed Central

Salazar, Renzo; Castillo-Neyra, Ricardo; Tustin, Aaron W.; Borrini-Mayorí, Katty; Náquira, César; Levy, Michael Z.

2015-01-01

Populations of the common bed bug, Cimex lectularius, have recently undergone explosive growth. Bed bugs share many important traits with triatomine insects, but it remains unclear whether these similarities include the ability to transmit Trypanosoma cruzi, the etiologic agent of Chagas disease. Here, we show efficient and bidirectional transmission of T. cruzi between hosts and bed bugs in a laboratory environment. Most bed bugs that fed on experimentally infected mice acquired the parasite. A majority of previously uninfected mice became infected after a period of cohabitation with exposed bed bugs. T. cruzi was also transmitted to mice after the feces of infected bed bugs were applied directly to broken host skin. Quantitative bed bug defecation measures were similar to those of important triatomine vectors. Our findings suggest that the common bed bug may be a competent vector of T. cruzi and could pose a risk for vector-borne transmission of Chagas disease. PMID:25404068

Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

PubMed Central

Wang, Deng; Miao, Duoqian; Blohm, Gunnar

2012-01-01

Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications. PMID:23087607

Early identification of MCI converting to AD: a FDG PET study.

PubMed

Pagani, Marco; Nobili, Flavio; Morbelli, Silvia; Arnaldi, Dario; Giuliani, Alessandro; Öberg, Johanna; Girtler, Nicola; Brugnolo, Andrea; Picco, Agnese; Bauckneht, Matteo; Piva, Roberta; Chincarini, Andrea; Sambuceti, Gianmario; Jonsson, Cathrine; De Carli, Fabrizio

2017-11-01

Mild cognitive impairment (MCI) is a transitional pathological stage between normal ageing (NA) and Alzheimer's disease (AD). Although subjects with MCI show a decline at different rates, some individuals remain stable or even show an improvement in their cognitive level after some years. We assessed the accuracy of FDG PET in discriminating MCI patients who converted to AD from those who did not. FDG PET was performed in 42 NA subjects, 27 MCI patients who had not converted to AD at 5 years (nc-MCI; mean follow-up time 7.5 ± 1.5 years), and 95 MCI patients who converted to AD within 5 years (MCI-AD; mean conversion time 1.8 ± 1.1 years). Relative FDG uptake values in 26 meta-volumes of interest were submitted to ANCOVA and support vector machine analyses to evaluate regional differences and discrimination accuracy. The MCI-AD group showed significantly lower FDG uptake values in the temporoparietal cortex than the other two groups. FDG uptake values in the nc-MCI group were similar to those in the NA group. Support vector machine analysis discriminated nc-MCI from MCI-AD patients with an accuracy of 89% (AUC 0.91), correctly detecting 93% of the nc-MCI patients. In MCI patients not converting to AD within a minimum follow-up time of 5 years and MCI patients converting within 5 years, baseline FDG PET and volume-based analysis identified those who converted with an accuracy of 89%. However, further analysis is needed in patients with amnestic MCI who convert to a dementia other than AD.

Multilevel image recognition using discriminative patches and kernel covariance descriptor

NASA Astrophysics Data System (ADS)

Lu, Le; Yao, Jianhua; Turkbey, Evrim; Summers, Ronald M.

2014-03-01

Computer-aided diagnosis of medical images has emerged as an important tool to objectively improve the performance, accuracy and consistency for clinical workflow. To computerize the medical image diagnostic recognition problem, there are three fundamental problems: where to look (i.e., where is the region of interest from the whole image/volume), image feature description/encoding, and similarity metrics for classification or matching. In this paper, we exploit the motivation, implementation and performance evaluation of task-driven iterative, discriminative image patch mining; covariance matrix based descriptor via intensity, gradient and spatial layout; and log-Euclidean distance kernel for support vector machine, to address these three aspects respectively. To cope with often visually ambiguous image patterns for the region of interest in medical diagnosis, discovery of multilabel selective discriminative patches is desired. Covariance of several image statistics summarizes their second order interactions within an image patch and is proved as an effective image descriptor, with low dimensionality compared with joint statistics and fast computation regardless of the patch size. We extensively evaluate two extended Gaussian kernels using affine-invariant Riemannian metric or log-Euclidean metric with support vector machines (SVM), on two medical image classification problems of degenerative disc disease (DDD) detection on cortical shell unwrapped CT maps and colitis detection on CT key images. The proposed approach is validated with promising quantitative results on these challenging tasks. Our experimental findings and discussion also unveil some interesting insights on the covariance feature composition with or without spatial layout for classification and retrieval, and different kernel constructions for SVM. This will also shed some light on future work using covariance feature and kernel classification for medical image analysis.

Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors.

PubMed

Rodriguez Gutierrez, D; Awwad, A; Meijer, L; Manita, M; Jaspan, T; Dineen, R A; Grundy, R G; Auer, D P

2014-05-01

Qualitative radiologic MR imaging review affords limited differentiation among types of pediatric posterior fossa brain tumors and cannot detect histologic or molecular subtypes, which could help to stratify treatment. This study aimed to improve current posterior fossa discrimination of histologic tumor type by using support vector machine classifiers on quantitative MR imaging features. This retrospective study included preoperative MRI in 40 children with posterior fossa tumors (17 medulloblastomas, 16 pilocytic astrocytomas, and 7 ependymomas). Shape, histogram, and textural features were computed from contrast-enhanced T2WI and T1WI and diffusivity (ADC) maps. Combinations of features were used to train tumor-type-specific classifiers for medulloblastoma, pilocytic astrocytoma, and ependymoma types in separation and as a joint posterior fossa classifier. A tumor-subtype classifier was also produced for classic medulloblastoma. The performance of different classifiers was assessed and compared by using randomly selected subsets of training and test data. ADC histogram features (25th and 75th percentiles and skewness) yielded the best classification of tumor type (on average >95.8% of medulloblastomas, >96.9% of pilocytic astrocytomas, and >94.3% of ependymomas by using 8 training samples). The resulting joint posterior fossa classifier correctly assigned >91.4% of the posterior fossa tumors. For subtype classification, 89.4% of classic medulloblastomas were correctly classified on the basis of ADC texture features extracted from the Gray-Level Co-Occurence Matrix. Support vector machine-based classifiers using ADC histogram features yielded very good discrimination among pediatric posterior fossa tumor types, and ADC textural features show promise for further subtype discrimination. These findings suggest an added diagnostic value of quantitative feature analysis of diffusion MR imaging in pediatric neuro-oncology. © 2014 by American Journal of Neuroradiology.

Improving semi-text-independent method of writer verification using difference vector

NASA Astrophysics Data System (ADS)

Li, Xin; Ding, Xiaoqing

2009-01-01

The semi-text-independent method of writer verification based on the linear framework is a method that can use all characters of two handwritings to discriminate the writers in the condition of knowing the text contents. The handwritings are allowed to just have small numbers of even totally different characters. This fills the vacancy of the classical text-dependent methods and the text-independent methods of writer verification. Moreover, the information, what every character is, is used for the semi-text-independent method in this paper. Two types of standard templates, generated from many writer-unknown handwritten samples and printed samples of each character, are introduced to represent the content information of each character. The difference vectors of the character samples are gotten by subtracting the standard templates from the original feature vectors and used to replace the original vectors in the process of writer verification. By removing a large amount of content information and remaining the style information, the verification accuracy of the semi-text-independent method is improved. On a handwriting database involving 30 writers, when the query handwriting and the reference handwriting are composed of 30 distinct characters respectively, the average equal error rate (EER) of writer verification reaches 9.96%. And when the handwritings contain 50 characters, the average EER falls to 6.34%, which is 23.9% lower than the EER of not using the difference vectors.

Research on bearing fault diagnosis of large machinery based on mathematical morphology

NASA Astrophysics Data System (ADS)

Wang, Yu

2018-04-01

To study the automatic diagnosis of large machinery fault based on support vector machine, combining the four common faults of the large machinery, the support vector machine is used to classify and identify the fault. The extracted feature vectors are entered. The feature vector is trained and identified by multi - classification method. The optimal parameters of the support vector machine are searched by trial and error method and cross validation method. Then, the support vector machine is compared with BP neural network. The results show that the support vector machines are short in time and high in classification accuracy. It is more suitable for the research of fault diagnosis in large machinery. Therefore, it can be concluded that the training speed of support vector machines (SVM) is fast and the performance is good.

A scheme of quantum state discrimination over specified states via weak-value measurement

NASA Astrophysics Data System (ADS)

Chen, Xi; Dai, Hong-Yi; Liu, Bo-Yang; Zhang, Ming

2018-04-01

The commonly adopted projective measurements are invalid in the specified task of quantum state discrimination when the discriminated states are superposition of planar-position basis states whose complex-number probability amplitudes have the same magnitude but different phases. Therefore we propose a corresponding scheme via weak-value measurement and examine the feasibility of this scheme. Furthermore, the role of the weak-value measurement in quantum state discrimination is analyzed and compared with one in quantum state tomography in this Letter.

Automatic Recognition of Acute Myelogenous Leukemia in Blood Microscopic Images Using K-means Clustering and Support Vector Machine.

PubMed

Kazemi, Fatemeh; Najafabadi, Tooraj Abbasian; Araabi, Babak Nadjar

2016-01-01

Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is characterized by the accumulation of myeloid blasts in the bone marrow. Careful microscopic examination of stained blood smear or bone marrow aspirate is still the most significant diagnostic methodology for initial AML screening and considered as the first step toward diagnosis. It is time-consuming and due to the elusive nature of the signs and symptoms of AML; wrong diagnosis may occur by pathologists. Therefore, the need for automation of leukemia detection has arisen. In this paper, an automatic technique for identification and detection of AML and its prevalent subtypes, i.e., M2-M5 is presented. At first, microscopic images are acquired from blood smears of patients with AML and normal cases. After applying image preprocessing, color segmentation strategy is applied for segmenting white blood cells from other blood components and then discriminative features, i.e., irregularity, nucleus-cytoplasm ratio, Hausdorff dimension, shape, color, and texture features are extracted from the entire nucleus in the whole images containing multiple nuclei. Images are classified to cancerous and noncancerous images by binary support vector machine (SVM) classifier with 10-fold cross validation technique. Classifier performance is evaluated by three parameters, i.e., sensitivity, specificity, and accuracy. Cancerous images are also classified into their prevalent subtypes by multi-SVM classifier. The results show that the proposed algorithm has achieved an acceptable performance for diagnosis of AML and its common subtypes. Therefore, it can be used as an assistant diagnostic tool for pathologists.

Arthropods: Vectors of Disease Agents

DTIC Science & Technology

1994-07-01

vector or glands in the tick legs. Filarid in some tick species, individuals of be passively regurgitated during worms (eg, dog heartworm) devel- which...Australia and an arthropod serves as the vector passively regurgitated during blood- Antarctica. An estimated 50 million transferring the etiologic agent...opossums, Biological ɝ More common in racoons, armadillos dogs than people Garbage, sewage, etc Mechanical NA I akwoator% %ledionc %.j. 2i, \\.I

Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers

NASA Astrophysics Data System (ADS)

Maier, Oskar; Wilms, Matthias; von der Gablentz, Janina; Krämer, Ulrike; Handels, Heinz

2014-03-01

Automatic segmentation of ischemic stroke lesions in magnetic resonance (MR) images is important in clinical practice and for neuroscientific trials. The key problem is to detect largely inhomogeneous regions of varying sizes, shapes and locations. We present a stroke lesion segmentation method based on local features extracted from multi-spectral MR data that are selected to model a human observer's discrimination criteria. A support vector machine classifier is trained on expert-segmented examples and then used to classify formerly unseen images. Leave-one-out cross validation on eight datasets with lesions of varying appearances is performed, showing our method to compare favourably with other published approaches in terms of accuracy and robustness. Furthermore, we compare a number of feature selectors and closely examine each feature's and MR sequence's contribution.

Polymerase chain reaction identification of three members of the Anopheles sundaicus (Diptera: Culicidae) complex, malaria vectors in Southeast Asia.

PubMed

Dusfour, Isabelle; Blondeau, Johanna; Harbach, Ralph E; Vythilingham, Indra; Baimai, Visut; Trung, Ho D; Sochanta, Tho; Bangs, Michael J; Manguin, Sylvie

2007-09-01

Anopheles sundaicus s.l., a major malaria vector taxon, occurs primarily along coastal areas and on islands in Southeast Asia. Our previous studies using cytochrome oxidase I, cytochrome-b, and internal transcribed spacer 2 markers discriminated three allopatric species: An. sundaicus s.s. in northern Borneo, An. epiroticus in Southeast Asia, and An. sundaicus E on Sumatra and Java, Indonesia. Morphological comparisons of three developmental stages did not reveal unique diagnostic characters that could reliably distinguish the three species. Therefore, we developed a multiplex polymerase chain reaction (PCR) assay based on two mitochondrial DNA markers to unambiguously identify them. This PCR was tested on 374 specimens from 24 different geographical populations, expanding our knowledge of the distribution of these species.

Sound Processing Features for Speaker-Dependent and Phrase-Independent Emotion Recognition in Berlin Database

NASA Astrophysics Data System (ADS)

Anagnostopoulos, Christos Nikolaos; Vovoli, Eftichia

An emotion recognition framework based on sound processing could improve services in human-computer interaction. Various quantitative speech features obtained from sound processing of acting speech were tested, as to whether they are sufficient or not to discriminate between seven emotions. Multilayered perceptrons were trained to classify gender and emotions on the basis of a 24-input vector, which provide information about the prosody of the speaker over the entire sentence using statistics of sound features. Several experiments were performed and the results were presented analytically. Emotion recognition was successful when speakers and utterances were “known” to the classifier. However, severe misclassifications occurred during the utterance-independent framework. At least, the proposed feature vector achieved promising results for utterance-independent recognition of high- and low-arousal emotions.

Deformation Invariant Attribute Vector for Deformable Registration of Longitudinal Brain MR Images

PubMed Central

Li, Gang; Guo, Lei; Liu, Tianming

2009-01-01

This paper presents a novel approach to define deformation invariant attribute vector (DIAV) for each voxel in 3D brain image for the purpose of anatomic correspondence detection. The DIAV method is validated by using synthesized deformation in 3D brain MRI images. Both theoretic analysis and experimental studies demonstrate that the proposed DIAV is invariant to general nonlinear deformation. Moreover, our experimental results show that the DIAV is able to capture rich anatomic information around the voxels and exhibit strong discriminative ability. The DIAV has been integrated into a deformable registration algorithm for longitudinal brain MR images, and the results on both simulated and real brain images are provided to demonstrate the good performance of the proposed registration algorithm based on matching of DIAVs. PMID:19369031

Motor Oil Classification using Color Histograms and Pattern Recognition Techniques.

PubMed

Ahmadi, Shiva; Mani-Varnosfaderani, Ahmad; Habibi, Biuck

2018-04-20

Motor oil classification is important for quality control and the identification of oil adulteration. In thiswork, we propose a simple, rapid, inexpensive and nondestructive approach based on image analysis and pattern recognition techniques for the classification of nine different types of motor oils according to their corresponding color histograms. For this, we applied color histogram in different color spaces such as red green blue (RGB), grayscale, and hue saturation intensity (HSI) in order to extract features that can help with the classification procedure. These color histograms and their combinations were used as input for model development and then were statistically evaluated by using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM) techniques. Here, two common solutions for solving a multiclass classification problem were applied: (1) transformation to binary classification problem using a one-against-all (OAA) approach and (2) extension from binary classifiers to a single globally optimized multilabel classification model. In the OAA strategy, LDA, QDA, and SVM reached up to 97% in terms of accuracy, sensitivity, and specificity for both the training and test sets. In extension from binary case, despite good performances by the SVM classification model, QDA and LDA provided better results up to 92% for RGB-grayscale-HSI color histograms and up to 93% for the HSI color map, respectively. In order to reduce the numbers of independent variables for modeling, a principle component analysis algorithm was used. Our results suggest that the proposed method is promising for the identification and classification of different types of motor oils.

Assessment Tools for Identifying Functional Limitations Associated With Functional Ankle Instability

PubMed Central

Ross, Scott E; Guskiewicz, Kevin M; Gross, Michael T; Yu, Bing

2008-01-01

Context: Assessment tools should identify functional limitations associated with functional ankle instability (FAI) by discriminating unstable from stable ankles. Objective: To identify assessment tools that discriminated FAI from stable ankles and determine the most accurate assessment tool for discriminating between FAI and stable ankles. Design: Case-control study. Setting: Research laboratory. Patients or Other Participants: Fifteen individuals with FAI and 15 healthy individuals; participants with unilateral FAI reported “giving-way” sensations and ankle sprains, whereas healthy participants did not. Intervention(s): Participants answered 12 questions on the Ankle Joint Functional Assessment Tool (AJFAT). They also performed a single-leg jump landing, which required them to jump to half their maximum jump height, land on a single leg, and stabilize quickly on a force plate. Main Outcome Measure(s): Receiver operating characteristic curves determined cutoff scores for discriminating between ankle groups for AJFAT total score and resultant vector (RV) time to stabilization. Accuracy values for discriminating between groups were determined by calculating the area under the receiver operating characteristic curves. Results: The cutoff score for discriminating between FAI and stable ankles was ≥26 (sensitivity  =  1, specificity  =  1) and ≥1.58 seconds (sensitivity  =  0.67, specificity  =  0.73) for the AJFAT total score and RV time to stabilization, respectively. The area under the curve for the AJFAT was 1.0 (asymptotic significance <.05), whereas the RV time to stabilization had an area under the curve of 0.72 (asymptotic significance <.05). Conclusions: The AJFAT was an excellent assessment tool for discriminating between ankle groups, whereas RV time to stabilization was a fair assessment tool. Although both assessments discriminated between ankle groups, the AJFAT more accurately discriminated between groups than the RV time to stabilization did. Future researchers should confirm these findings using a prospective research design. PMID:18335012

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.

Mutual information-based facial expression recognition

NASA Astrophysics Data System (ADS)

Hazar, Mliki; Hammami, Mohamed; Hanêne, Ben-Abdallah

2013-12-01

This paper introduces a novel low-computation discriminative regions representation for expression analysis task. The proposed approach relies on interesting studies in psychology which show that most of the descriptive and responsible regions for facial expression are located around some face parts. The contributions of this work lie in the proposition of new approach which supports automatic facial expression recognition based on automatic regions selection. The regions selection step aims to select the descriptive regions responsible or facial expression and was performed using Mutual Information (MI) technique. For facial feature extraction, we have applied Local Binary Patterns Pattern (LBP) on Gradient image to encode salient micro-patterns of facial expressions. Experimental studies have shown that using discriminative regions provide better results than using the whole face regions whilst reducing features vector dimension.

A nonlinear discriminant algorithm for feature extraction and data classification.

PubMed

Santa Cruz, C; Dorronsoro, J R

1998-01-01

This paper presents a nonlinear supervised feature extraction algorithm that combines Fisher's criterion function with a preliminary perceptron-like nonlinear projection of vectors in pattern space. Its main motivation is to combine the approximation properties of multilayer perceptrons (MLP's) with the target free nature of Fisher's classical discriminant analysis. In fact, although MLP's provide good classifiers for many problems, there may be some situations, such as unequal class sizes with a high degree of pattern mixing among them, that may make difficult the construction of good MLP classifiers. In these instances, the features extracted by our procedure could be more effective. After the description of its construction and the analysis of its complexity, we will illustrate its use over a synthetic problem with the above characteristics.

Discrimination of common Mediterranean plant species using field spectroradiometry

NASA Astrophysics Data System (ADS)

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

2011-12-01

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

An introduction to kernel-based learning algorithms.

PubMed

Müller, K R; Mika, S; Rätsch, G; Tsuda, K; Schölkopf, B

2001-01-01

This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by discussing applications such as optical character recognition and DNA analysis.

Vaxvec: The first web-based recombinant vaccine vector database and its data analysis

PubMed Central

Deng, Shunzhou; Martin, Carly; Patil, Rasika; Zhu, Felix; Zhao, Bin; Xiang, Zuoshuang; He, Yongqun

2015-01-01

A recombinant vector vaccine uses an attenuated virus, bacterium, or parasite as the carrier to express a heterologous antigen(s). Many recombinant vaccine vectors and related vaccines have been developed and extensively investigated. To compare and better understand recombinant vectors and vaccines, we have generated Vaxvec (http://www.violinet.org/vaxvec), the first web-based database that stores various recombinant vaccine vectors and those experimentally verified vaccines that use these vectors. Vaxvec has now included 59 vaccine vectors that have been used in 196 recombinant vector vaccines against 66 pathogens and cancers. These vectors are classified to 41 viral vectors, 15 bacterial vectors, 1 parasitic vector, and 1 fungal vector. The most commonly used viral vaccine vectors are double-stranded DNA viruses, including herpesviruses, adenoviruses, and poxviruses. For example, Vaxvec includes 63 poxvirus-based recombinant vaccines for over 20 pathogens and cancers. Vaxvec collects 30 recombinant vector influenza vaccines that use 17 recombinant vectors and were experimentally tested in 7 animal models. In addition, over 60 protective antigens used in recombinant vector vaccines are annotated and analyzed. User-friendly web-interfaces are available for querying various data in Vaxvec. To support data exchange, the information of vaccine vectors, vaccines, and related information is stored in the Vaccine Ontology (VO). Vaxvec is a timely and vital source of vaccine vector database and facilitates efficient vaccine vector research and development. PMID:26403370

Prevalence of harassment and discrimination among residents in three training hospitals in Saudi Arabia.

PubMed

Fnais, Naif; al-Nasser, Muhammad; Zamakhshary, Mohammad; Abuznadah, Wesam; Dhukair, Shahla Al; Saadeh, Mayssa; Al-Qarni, Ali; Bokhari, Bayan; Alshaeri, Taqreed; Aboalsamh, Nouf; Binahmed, AbdulAziz

2013-01-01

Multiple surveys of medical residents have shown a high incidence of harassment and discrimination in academic health centers. Harassment has a negative effects on residents' health and on their ability to function. No previous study has documented the prevalence of harassment and discrimination among residents in Saudi Arabia. We aimed in this study to assess the prevalence of harassment and discrimination among residents at a tertiary care academic hospitals in Saudi Arabia. Cross-sectional survey conducted at National Guard Hospitals in Riyadh, Jeddah and Al-Ahsa'a from 27 July to 20 August 2010. The survey included questions on the prevalence of harassment of different types, inlcuding verbal, academic, physical and sexual harassment, as well as discrimination on the basis of gender, region of origin or physical appearance. Of 380 residents, 213 (56%) returned a completed questionnaire (123 male, 57.8%). At least one of type of harassment and discrimination was reported by 83.6% of respondents. The most frequently reported forms were verbal harassment and gender discrimination (61.5% and 58.3%, respectively). Sexual harassment was commonly reported (19.3%) and was experienced significantly more often by female residents than by male residents (P=.0061). Harassment and discrimination of Saudi residents is common with more than three-quarters reporting having had such an experience. Identification of the risk factors is a necessary first step in clarifying this issue and could be used when planning strategies for prevention.

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.

Identification of Human Semiochemicals Attractive to the Major Vectors of Onchocerciasis

PubMed Central

Young, Ryan M.; Burkett-Cadena, Nathan D.; McGaha, Tommy W.; Rodriguez-Perez, Mario A.; Toé, Laurent D.; Adeleke, Monsuru A.; Sanfo, Moussa; Soungalo, Traore; Katholi, Charles R.; Noblet, Raymond; Fadamiro, Henry; Torres-Estrada, Jose L.; Salinas-Carmona, Mario C.; Baker, Bill; Unnasch, Thomas R.; Cupp, Eddie W.

2015-01-01

Background Entomological indicators are considered key metrics to document the interruption of transmission of Onchocerca volvulus, the etiological agent of human onchocerciasis. Human landing collection is the standard employed for collection of the vectors for this parasite. Recent studies reported the development of traps that have the potential for replacing humans for surveillance of O. volvulus in the vector population. However, the key chemical components of human odor that are attractive to vector black flies have not been identified. Methodology/Principal Findings Human sweat compounds were analyzed using GC-MS analysis and compounds common to three individuals identified. These common compounds, with others previously identified as attractive to other hematophagous arthropods were evaluated for their ability to stimulate and attract the major onchocerciasis vectors in Africa (Simulium damnosum sensu lato) and Latin America (Simulium ochraceum s. l.) using electroantennography and a Y tube binary choice assay. Medium chain length carboxylic acids and aldehydes were neurostimulatory for S. damnosum s.l. while S. ochraceum s.l. was stimulated by short chain aliphatic alcohols and aldehydes. Both species were attracted to ammonium bicarbonate and acetophenone. The compounds were shown to be attractive to the relevant vector species in field studies, when incorporated into a formulation that permitted a continuous release of the compound over time and used in concert with previously developed trap platforms. Conclusions/Significance The identification of compounds attractive to the major vectors of O. volvulus will permit the development of optimized traps. Such traps may replace the use of human vector collectors for monitoring the effectiveness of onchocerciasis elimination programs and could find use as a contributing component in an integrated vector control/drug program aimed at eliminating river blindness in Africa. PMID:25569240

Identification of human semiochemicals attractive to the major vectors of onchocerciasis.

PubMed

Young, Ryan M; Burkett-Cadena, Nathan D; McGaha, Tommy W; Rodriguez-Perez, Mario A; Toé, Laurent D; Adeleke, Monsuru A; Sanfo, Moussa; Soungalo, Traore; Katholi, Charles R; Noblet, Raymond; Fadamiro, Henry; Torres-Estrada, Jose L; Salinas-Carmona, Mario C; Baker, Bill; Unnasch, Thomas R; Cupp, Eddie W

2015-01-01

Entomological indicators are considered key metrics to document the interruption of transmission of Onchocerca volvulus, the etiological agent of human onchocerciasis. Human landing collection is the standard employed for collection of the vectors for this parasite. Recent studies reported the development of traps that have the potential for replacing humans for surveillance of O. volvulus in the vector population. However, the key chemical components of human odor that are attractive to vector black flies have not been identified. Human sweat compounds were analyzed using GC-MS analysis and compounds common to three individuals identified. These common compounds, with others previously identified as attractive to other hematophagous arthropods were evaluated for their ability to stimulate and attract the major onchocerciasis vectors in Africa (Simulium damnosum sensu lato) and Latin America (Simulium ochraceum s. l.) using electroantennography and a Y tube binary choice assay. Medium chain length carboxylic acids and aldehydes were neurostimulatory for S. damnosum s.l. while S. ochraceum s.l. was stimulated by short chain aliphatic alcohols and aldehydes. Both species were attracted to ammonium bicarbonate and acetophenone. The compounds were shown to be attractive to the relevant vector species in field studies, when incorporated into a formulation that permitted a continuous release of the compound over time and used in concert with previously developed trap platforms. The identification of compounds attractive to the major vectors of O. volvulus will permit the development of optimized traps. Such traps may replace the use of human vector collectors for monitoring the effectiveness of onchocerciasis elimination programs and could find use as a contributing component in an integrated vector control/drug program aimed at eliminating river blindness in Africa.

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

NASA Astrophysics Data System (ADS)

Leka, K. D.; Barnes, G.

2003-10-01

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

Search for heavy resonances decaying into a vector boson and a Higgs boson in final states with charged leptons, neutrinos, and b quarks

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

Khachatryan, Vardan

A search for heavy resonances decaying to a Higgs boson and a vector boson is presented. The analysis is performed using data samples collected in 2015 by the CMS experiment at the LHC in proton–proton collisions at a center-of-mass energy of 13 TeV, corresponding to integrated luminosities of 2.2–2.5 fb –1. The search is performed in channels in which the vector boson decays into leptonic final states (Z→νν, W→ℓν, and Z→ℓℓ, with ℓ=e,μ), while the Higgs boson decays to collimated b quark pairs detected as a single massive jet. The discriminating power of a jet mass requirement and a bmore » jet tagging algorithm are exploited to suppress the standard model backgrounds. The event yields observed in data are consistent with the background expectation. In the context of a theoretical model with a heavy vector triplet, a resonance with mass less than 2 TeV is excluded at 95% confidence level. Finally, the results are also interpreted in terms of limits on the parameters of the model, improving on the reach of previous searches.« less

Search for heavy resonances decaying into a vector boson and a Higgs boson in final states with charged leptons, neutrinos, and b quarks

DOE PAGES

Khachatryan, Vardan

2017-02-22

A search for heavy resonances decaying to a Higgs boson and a vector boson is presented. The analysis is performed using data samples collected in 2015 by the CMS experiment at the LHC in proton–proton collisions at a center-of-mass energy of 13 TeV, corresponding to integrated luminosities of 2.2–2.5 fb –1. The search is performed in channels in which the vector boson decays into leptonic final states (Z→νν, W→ℓν, and Z→ℓℓ, with ℓ=e,μ), while the Higgs boson decays to collimated b quark pairs detected as a single massive jet. The discriminating power of a jet mass requirement and a bmore » jet tagging algorithm are exploited to suppress the standard model backgrounds. The event yields observed in data are consistent with the background expectation. In the context of a theoretical model with a heavy vector triplet, a resonance with mass less than 2 TeV is excluded at 95% confidence level. Finally, the results are also interpreted in terms of limits on the parameters of the model, improving on the reach of previous searches.« less

On the sparseness of 1-norm support vector machines.

PubMed

Zhang, Li; Zhou, Weida

2010-04-01

There is some empirical evidence available showing that 1-norm Support Vector Machines (1-norm SVMs) have good sparseness; however, both how good sparseness 1-norm SVMs can reach and whether they have a sparser representation than that of standard SVMs are not clear. In this paper we take into account the sparseness of 1-norm SVMs. Two upper bounds on the number of nonzero coefficients in the decision function of 1-norm SVMs are presented. First, the number of nonzero coefficients in 1-norm SVMs is at most equal to the number of only the exact support vectors lying on the +1 and -1 discriminating surfaces, while that in standard SVMs is equal to the number of support vectors, which implies that 1-norm SVMs have better sparseness than that of standard SVMs. Second, the number of nonzero coefficients is at most equal to the rank of the sample matrix. A brief review of the geometry of linear programming and the primal steepest edge pricing simplex method are given, which allows us to provide the proof of the two upper bounds and evaluate their tightness by experiments. Experimental results on toy data sets and the UCI data sets illustrate our analysis. Copyright 2009 Elsevier Ltd. All rights reserved.

Generation of arbitrary vector fields based on a pair of orthogonal elliptically polarized base vectors.

PubMed

Xu, Danfeng; Gu, Bing; Rui, Guanghao; Zhan, Qiwen; Cui, Yiping

2016-02-22

We present an arbitrary vector field with hybrid polarization based on the combination of a pair of orthogonal elliptically polarized base vectors on the Poincaré sphere. It is shown that the created vector field is only dependent on the latitude angle 2χ but is independent on the longitude angle 2ψ on the Poincaré sphere. By adjusting the latitude angle 2χ, which is related to two identical waveplates in a common path interferometric arrangement, one could obtain arbitrary type of vector fields. Experimentally, we demonstrate the generation of such kind of vector fields and confirm the distribution of state of polarization by the measurement of Stokes parameters. Besides, we investigate the tight focusing properties of these vector fields. It is found that the additional degree of freedom 2χ provided by arbitrary vector field with hybrid polarization allows one to control the spatial structure of polarization and to engineer the focusing field.

Patterns and correlates of self-reported racial discrimination among Australian Aboriginal and Torres Strait Islander adults, 2008–09: analysis of national survey data

PubMed Central

2013-01-01

Background There is now considerable evidence that racism is a pernicious and enduring social problem with a wide range of detrimental outcomes for individuals, communities and societies. Although indigenous people worldwide are subjected to high levels of racism, there is a paucity of population-based, quantitative data about the factors associated with their reporting of racial discrimination, about the settings in which such discrimination takes place, and about the frequency with which it is experienced. Such information is essential in efforts to reduce both exposure to racism among indigenous people and the harms associated with such exposure. Methods Weighted data on self-reported racial discrimination from over 7,000 Indigenous Australian adults participating in the 2008–09 National Aboriginal and Torres Strait Islander Survey, a nationally representative survey conducted by the Australian Bureau of Statistics, were analysed by socioeconomic, demographic and cultural factors. Results More than one in four respondents (27%) reported experiencing racial discrimination in the past year. Racial discrimination was most commonly reported in public (41% of those reporting any racial discrimination), legal (40%) and work (30%) settings. Among those reporting any racial discrimination, about 40% experienced this discrimination most or all of the time (as opposed to a little or some of the time) in at least one setting. Reporting of racial discrimination peaked in the 35–44 year age group and then declined. Higher reporting of racial discrimination was associated with removal from family, low trust, unemployment, having a university degree, and indicators of cultural identity and participation. Lower reporting of racial discrimination was associated with home ownership, remote residence and having relatively few Indigenous friends. Conclusions These data indicate that racial discrimination is commonly experienced across a wide variety of settings, with public, legal and work settings identified as particularly salient. The observed relationships, while not necessarily causal, help to build a detailed picture of self-reported racial discrimination experienced by Indigenous people in contemporary Australia, providing important evidence to inform anti-racism policy. PMID:23816052

Patterns and correlates of self-reported racial discrimination among Australian Aboriginal and Torres Strait Islander adults, 2008-09: analysis of national survey data.

PubMed

Cunningham, Joan; Paradies, Yin C

2013-07-01

There is now considerable evidence that racism is a pernicious and enduring social problem with a wide range of detrimental outcomes for individuals, communities and societies. Although indigenous people worldwide are subjected to high levels of racism, there is a paucity of population-based, quantitative data about the factors associated with their reporting of racial discrimination, about the settings in which such discrimination takes place, and about the frequency with which it is experienced. Such information is essential in efforts to reduce both exposure to racism among indigenous people and the harms associated with such exposure. Weighted data on self-reported racial discrimination from over 7,000 Indigenous Australian adults participating in the 2008-09 National Aboriginal and Torres Strait Islander Survey, a nationally representative survey conducted by the Australian Bureau of Statistics, were analysed by socioeconomic, demographic and cultural factors. More than one in four respondents (27%) reported experiencing racial discrimination in the past year. Racial discrimination was most commonly reported in public (41% of those reporting any racial discrimination), legal (40%) and work (30%) settings. Among those reporting any racial discrimination, about 40% experienced this discrimination most or all of the time (as opposed to a little or some of the time) in at least one setting. Reporting of racial discrimination peaked in the 35-44 year age group and then declined. Higher reporting of racial discrimination was associated with removal from family, low trust, unemployment, having a university degree, and indicators of cultural identity and participation. Lower reporting of racial discrimination was associated with home ownership, remote residence and having relatively few Indigenous friends. These data indicate that racial discrimination is commonly experienced across a wide variety of settings, with public, legal and work settings identified as particularly salient. The observed relationships, while not necessarily causal, help to build a detailed picture of self-reported racial discrimination experienced by Indigenous people in contemporary Australia, providing important evidence to inform anti-racism policy.

Habitat discrimination by gravid Anopheles gambiae sensu lato – a push-pull system

PubMed Central

2014-01-01

Background The non-random distribution of anopheline larvae in natural habitats suggests that gravid females discriminate between habitats of different quality. Whilst physical and chemical cues used by Culex and Aedes vector mosquitoes for selecting an oviposition site have been extensively studied, those for Anopheles remain poorly explored. Here the habitat selection by Anopheles gambiae sensu lato (s.l.), the principal African malaria vector, was investigated when presented with a choice of two infusions made from rabbit food pellets, or soil. Methods Natural colonization and larval survival was evaluated in artificial ponds filled randomly with either infusion. Dual-choice, egg-count bioassays evaluated the responses of caged gravid females to (1) two- to six-day old infusions versus lake water; (2) autoclaved versus non-autoclaved soil infusions; and assessed (3) the olfactory memory of gravid females conditioned in pellet infusion as larvae. Results Wild Anopheles exclusively colonized ponds with soil infusion and avoided those with pellet infusion. When the individual infusions were tested in comparison with lake water, caged An. gambiae sensu stricto (s.s.) showed a dose response: females increasingly avoided the pellet infusion with increasing infusion age (six-day versus lake water: odds ratio (OR) 0.22; 95% confidence interval (CI) 0.1-0.5) and showed increasing preference to lay eggs as soil infusion age increased (six-day versus lake water: OR 2.1; 95% CI 1.4-3.3). Larvae survived in soil infusions equally well as in lake water but died in pellet infusions. Anopheles gambiae s.s. preferred to lay eggs in the non-autoclaved soil (OR 2.6; 95% CI 1.8-3.7) compared with autoclaved soil. There was no change in the avoidance of pellet infusion by individuals reared in the infusion compared with those reared in lake water. Conclusion Wild and caged An. gambiae s.l. females discriminate between potential aquatic habitats for oviposition. These choices benefit the survival of the offspring. Although the study was not designed to distinguish between stimuli that acted over a distance or on contact, it could be demonstrated that the choice of habitat is mediated by chemical cues based on both preference and avoidance. These cues, if identified, might be developed for ‘push-pull’ strategies to improve malaria vector monitoring and control. PMID:24693951

LANDMARK-BASED SPEECH RECOGNITION: REPORT OF THE 2004 JOHNS HOPKINS SUMMER WORKSHOP.

PubMed

Hasegawa-Johnson, Mark; Baker, James; Borys, Sarah; Chen, Ken; Coogan, Emily; Greenberg, Steven; Juneja, Amit; Kirchhoff, Katrin; Livescu, Karen; Mohan, Srividya; Muller, Jennifer; Sonmez, Kemal; Wang, Tianyu

2005-01-01

Three research prototype speech recognition systems are described, all of which use recently developed methods from artificial intelligence (specifically support vector machines, dynamic Bayesian networks, and maximum entropy classification) in order to implement, in the form of an automatic speech recognizer, current theories of human speech perception and phonology (specifically landmark-based speech perception, nonlinear phonology, and articulatory phonology). All three systems begin with a high-dimensional multiframe acoustic-to-distinctive feature transformation, implemented using support vector machines trained to detect and classify acoustic phonetic landmarks. Distinctive feature probabilities estimated by the support vector machines are then integrated using one of three pronunciation models: a dynamic programming algorithm that assumes canonical pronunciation of each word, a dynamic Bayesian network implementation of articulatory phonology, or a discriminative pronunciation model trained using the methods of maximum entropy classification. Log probability scores computed by these models are then combined, using log-linear combination, with other word scores available in the lattice output of a first-pass recognizer, and the resulting combination score is used to compute a second-pass speech recognition output.

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.

Laser-Induced Breakdown Spectroscopy Coupled with Multivariate Chemometrics for Variety Discrimination of Soil

PubMed Central

Yu, Ke-Qiang; Zhao, Yan-Ru; Liu, Fei; He, Yong

2016-01-01

The aim of this work was to analyze the variety of soil by laser-induced breakdown spectroscopy (LIBS) coupled with chemometrics methods. 6 certified reference materials (CRMs) of soil samples were selected and their LIBS spectra were captured. Characteristic emission lines of main elements were identified based on the LIBS curves and corresponding contents. From the identified emission lines, LIBS spectra in 7 lines with high signal-to-noise ratio (SNR) were chosen for further analysis. Principal component analysis (PCA) was carried out using the LIBS spectra at 7 selected lines and an obvious cluster of 6 soils was observed. Soft independent modeling of class analogy (SIMCA) and least-squares support vector machine (LS-SVM) were introduced to establish discriminant models for classifying the 6 types of soils, and they offered the correct discrimination rates of 90% and 100%, respectively. Receiver operating characteristic (ROC) curve was used to evaluate the performance of models and the results demonstrated that the LS-SVM model was promising. Lastly, 8 types of soils from different places were gathered to conduct the same experiments for verifying the selected 7 emission lines and LS-SVM model. The research revealed that LIBS technology coupled with chemometrics could conduct the variety discrimination of soil. PMID:27279284

Task-specific image partitioning.

PubMed

Kim, Sungwoong; Nowozin, Sebastian; Kohli, Pushmeet; Yoo, Chang D

2013-02-01

Image partitioning is an important preprocessing step for many of the state-of-the-art algorithms used for performing high-level computer vision tasks. Typically, partitioning is conducted without regard to the task in hand. We propose a task-specific image partitioning framework to produce a region-based image representation that will lead to a higher task performance than that reached using any task-oblivious partitioning framework and existing supervised partitioning framework, albeit few in number. The proposed method partitions the image by means of correlation clustering, maximizing a linear discriminant function defined over a superpixel graph. The parameters of the discriminant function that define task-specific similarity/dissimilarity among superpixels are estimated based on structured support vector machine (S-SVM) using task-specific training data. The S-SVM learning leads to a better generalization ability while the construction of the superpixel graph used to define the discriminant function allows a rich set of features to be incorporated to improve discriminability and robustness. We evaluate the learned task-aware partitioning algorithms on three benchmark datasets. Results show that task-aware partitioning leads to better labeling performance than the partitioning computed by the state-of-the-art general-purpose and supervised partitioning algorithms. We believe that the task-specific image partitioning paradigm is widely applicable to improving performance in high-level image understanding tasks.

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.

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

PubMed

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

2012-08-01

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

Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning.

PubMed

Zhou, Tao; Liu, Fanghui; Bhaskar, Harish; Yang, Jie

2017-09-12

In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank dictionaries that can provide good discriminative representations of both target and background. We accomplish this by exploiting the recovery ability of low-rank matrices. That is if we assume that the data from the same class are linearly correlated, then the corresponding basis vectors learned from the training set of each class shall render the dictionary to become approximately low-rank. The proposed dictionary learning technique incorporates a reconstruction error that improves the reliability of classification. Also, a multiconstraint objective function is designed to enable active learning of a discriminative and robust dictionary. Further, an optimal solution is obtained by iteratively computing the dictionary, coefficients, and by simultaneously learning the classifier parameters. Finally, a simple yet effective likelihood function is implemented to estimate the optimal state of the target during tracking. Moreover, to make the dictionary adaptive to the variations of the target and background during tracking, an online update criterion is employed while learning the new dictionary. Experimental results on a publicly available benchmark dataset have demonstrated that the proposed tracking algorithm performs better than other state-of-the-art trackers.

Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods.

PubMed

Georgiadis, Pantelis; Cavouras, Dionisis; Kalatzis, Ioannis; Glotsos, Dimitris; Athanasiadis, Emmanouil; Kostopoulos, Spiros; Sifaki, Koralia; Malamas, Menelaos; Nikiforidis, George; Solomou, Ekaterini

2009-01-01

Three-dimensional (3D) texture analysis of volumetric brain magnetic resonance (MR) images has been identified as an important indicator for discriminating among different brain pathologies. The purpose of this study was to evaluate the efficiency of 3D textural features using a pattern recognition system in the task of discriminating benign, malignant and metastatic brain tissues on T1 postcontrast MR imaging (MRI) series. The dataset consisted of 67 brain MRI series obtained from patients with verified and untreated intracranial tumors. The pattern recognition system was designed as an ensemble classification scheme employing a support vector machine classifier, specially modified in order to integrate the least squares features transformation logic in its kernel function. The latter, in conjunction with using 3D textural features, enabled boosting up the performance of the system in discriminating metastatic, malignant and benign brain tumors with 77.14%, 89.19% and 93.33% accuracy, respectively. The method was evaluated using an external cross-validation process; thus, results might be considered indicative of the generalization performance of the system to "unseen" cases. The proposed system might be used as an assisting tool for brain tumor characterization on volumetric MRI series.

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.

Sensory interactions between six common aroma vectors explain four main red wine aroma nuances.

PubMed

Ferreira, Vicente; Sáenz-Navajas, María-Pilar; Campo, Eva; Herrero, Paula; de la Fuente, Arancha; Fernández-Zurbano, Purificación

2016-05-15

This work aims at assessing the aromatic sensory dimensions linked to 6 common wine aroma vectors (N, norisoprenoids; A, branched acids; F, enolones; E, branched ethyl esters; L, fusel alcohols, M, wood compounds) varying in their natural range of occurrence. Wine models were built by adding the vectors at two levels (fractional factorial design 2(VI)) to a de-aromatised aged red wine. Twenty other different models were evaluated by descriptive analysis. Red, black and dried fruits and woody notes were satisfactorily reproduced. Individual vectors explained just 15% of the sensory space, mostly dependent on perceptual interactions. N influences dried and black fruits and suppresses red fruits. A suppresses black fruits and enhances red and dried fruits. F exerts a major role on red fruits. E suppresses dried fruits and modulates black fruits. L is revealed as a strong suppressor of red fruits and particularly of woody notes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Data-Rate Performance and Coverage of the Sub-Band Vectoring for VDSL 35b Profile

NASA Astrophysics Data System (ADS)

Giuliano, Romeo; Mazzenga, Franco; Vatalaro, Francesco

2017-05-01

Vectoring, used in VDSL2 to counteract FEXT, becomes less effective, or even ineffective, when users belong to different vectoring groups. This situation is common when the Regulator imposes sub-loop unbundling and users of uncoordinated service providers cause alien-FEXT. The sub-band vectoring (SBV) technique introduced here, avoids this situation and retains the vectoring benefits. We show SBV allows achieving up to 150 Mbit/s per user in downstream at 200 m from cabinet for VDSL2 profile-35b, with two concurrent service providers. We also introduce the concept of data-rate coverage representing the users' percentage served at a given data-rate in the area.

Vaxvec: The first web-based recombinant vaccine vector database and its data analysis.

PubMed

Deng, Shunzhou; Martin, Carly; Patil, Rasika; Zhu, Felix; Zhao, Bin; Xiang, Zuoshuang; He, Yongqun

2015-11-27

A recombinant vector vaccine uses an attenuated virus, bacterium, or parasite as the carrier to express a heterologous antigen(s). Many recombinant vaccine vectors and related vaccines have been developed and extensively investigated. To compare and better understand recombinant vectors and vaccines, we have generated Vaxvec (http://www.violinet.org/vaxvec), the first web-based database that stores various recombinant vaccine vectors and those experimentally verified vaccines that use these vectors. Vaxvec has now included 59 vaccine vectors that have been used in 196 recombinant vector vaccines against 66 pathogens and cancers. These vectors are classified to 41 viral vectors, 15 bacterial vectors, 1 parasitic vector, and 1 fungal vector. The most commonly used viral vaccine vectors are double-stranded DNA viruses, including herpesviruses, adenoviruses, and poxviruses. For example, Vaxvec includes 63 poxvirus-based recombinant vaccines for over 20 pathogens and cancers. Vaxvec collects 30 recombinant vector influenza vaccines that use 17 recombinant vectors and were experimentally tested in 7 animal models. In addition, over 60 protective antigens used in recombinant vector vaccines are annotated and analyzed. User-friendly web-interfaces are available for querying various data in Vaxvec. To support data exchange, the information of vaccine vectors, vaccines, and related information is stored in the Vaccine Ontology (VO). Vaxvec is a timely and vital source of vaccine vector database and facilitates efficient vaccine vector research and development. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Feature Selection Method Based on Fisher's Discriminant Ratio for Text Sentiment Classification

NASA Astrophysics Data System (ADS)

Wang, Suge; Li, Deyu; Wei, Yingjie; Li, Hongxia

With the rapid growth of e-commerce, product reviews on the Web have become an important information source for customers' decision making when they intend to buy some product. As the reviews are often too many for customers to go through, how to automatically classify them into different sentiment orientation categories (i.e. positive/negative) has become a research problem. In this paper, based on Fisher's discriminant ratio, an effective feature selection method is proposed for product review text sentiment classification. In order to validate the validity of the proposed method, we compared it with other methods respectively based on information gain and mutual information while support vector machine is adopted as the classifier. In this paper, 6 subexperiments are conducted by combining different feature selection methods with 2 kinds of candidate feature sets. Under 1006 review documents of cars, the experimental results indicate that the Fisher's discriminant ratio based on word frequency estimation has the best performance with F value 83.3% while the candidate features are the words which appear in both positive and negative texts.

Vector-like quarks coupling discrimination at the LHC and future hadron colliders

NASA Astrophysics Data System (ADS)

Barducci, D.; Panizzi, L.

2017-12-01

The existence of new coloured states with spin one-half, i.e. extra-quarks, is a striking prediction of various classes of new physics models. Should one of these states be discovered during the 13 TeV runs of the LHC or at future high energy hadron colliders, understanding its properties will be crucial in order to shed light on the underlying model structure. Depending on the extra-quarks quantum number under SU(2) L , their coupling to Standard Model quarks and bosons have either a dominant left- or right-handed chiral component. By exploiting the polarisation properties of the top quarks arising from the decay of pair-produced extra quarks, we show how it is possible to discriminate among the two hypothesis in the whole discovery range currently accessible at the LHC, thus effectively narrowing down the possible interpretations of a discovered state in terms of new physics scenarios. Moreover, we estimate the discovery and discrimination power of future prototype hadron colliders with centre of mass energies of 33 and 100 TeV.

Laser-Induced Breakdown Spectroscopy for Rapid Discrimination of Heavy-Metal-Contaminated Seafood Tegillarca granosa

PubMed Central

Ji, Guoli; Ye, Pengchao; Shi, Yijian; Yuan, Leiming; Chen, Xiaojing; Yuan, Mingshun; Zhu, Dehua; Chen, Xi; Hu, Xinyu; Jiang, Jing

2017-01-01

Tegillarca granosa samples contaminated artificially by three kinds of toxic heavy metals including zinc (Zn), cadmium (Cd), and lead (Pb) were attempted to be distinguished using laser-induced breakdown spectroscopy (LIBS) technology and pattern recognition methods in this study. The measured spectra were firstly processed by a wavelet transform algorithm (WTA), then the generated characteristic information was subsequently expressed by an information gain algorithm (IGA). As a result, 30 variables obtained were used as input variables for three classifiers: partial least square discriminant analysis (PLS-DA), support vector machine (SVM), and random forest (RF), among which the RF model exhibited the best performance, with 93.3% discrimination accuracy among those classifiers. Besides, the extracted characteristic information was used to reconstruct the original spectra by inverse WTA, and the corresponding attribution of the reconstructed spectra was then discussed. This work indicates that the healthy shellfish samples of Tegillarca granosa could be distinguished from the toxic heavy-metal-contaminated ones by pattern recognition analysis combined with LIBS technology, which only requires minimal pretreatments. PMID:29149053

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

PubMed

Qiu, Shanshan; Wang, Jun; Gao, Liping

2014-07-09

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

Optical computing and image processing using photorefractive gallium arsenide

NASA Technical Reports Server (NTRS)

Cheng, Li-Jen; Liu, Duncan T. H.

1990-01-01

Recent experimental results on matrix-vector multiplication and multiple four-wave mixing using GaAs are presented. Attention is given to a simple concept of using two overlapping holograms in GaAs to do two matrix-vector multiplication processes operating in parallel with a common input vector. This concept can be used to construct high-speed, high-capacity, reconfigurable interconnection and multiplexing modules, important for optical computing and neural-network applications.

Justice foundations for the Comprehensive Law Movement.

PubMed

Dewhurst, Dale

2010-01-01

Authors examining the developing dispute resolution alternatives to the adversarial system have identified nine converging "vectors" or alternatives in what has been termed the Comprehensive Law Movement. These authors have sought to understand how the developing vectors can remain separate and vibrant movements while sharing common ground. Some analyze these developments as being within law and legal practice, others see them as alternative approaches to law, and still others take a combined approach. It will be impossible to understand how these vectors have meaningful differences from law and legal practice if the search is limited to looking within law and legal practice. It will be impossible to understand how these vectors have meaningful commonalities with law and legal practice if the search is limited to looking external to law and legal practice. Instead of comparing the vectors with the adversarial system, higher order criteria are required. What is needed is a comprehensive and internally consistent super-system of norms; one that can be used to evaluate the adversarial system and the evolving vectors on an equal footing. An Aristotelian natural law virtue theory of justice can: (a) provide a functional guiding definition of justice; (b) serve as a comprehensive and internally consistent super-system of norms; and (c) provide the theoretical and evaluative foundation required to clarify the relationships among the adversarial system and the developing vectors. Finally, it will become clear why the Comprehensive Law Movement might be more appropriately conceptualized as the Comprehensive Justice Movement. Copyright © 2010 Elsevier Ltd. All rights reserved.

Schools as Potential Risk Sites for Vector-Borne Disease Transmission: Mosquito Vectors in Rural Schools in Two Municipalities in Colombia.

PubMed

Olano, Víctor Alberto; Matiz, María Inés; Lenhart, Audrey; Cabezas, Laura; Vargas, Sandra Lucía; Jaramillo, Juan Felipe; Sarmiento, Diana; Alexander, Neal; Stenström, Thor Axel; Overgaard, Hans J

2015-09-01

Dengue and other vector-borne diseases are of great public health importance in Colombia. Vector surveillance and control activities are often focused at the household level. Little is known about the importance of nonhousehold sites, including schools, in maintaining vector-borne disease transmission. The objectives of this paper were to determine the mosquito species composition in rural schools in 2 municipalities in Colombia and to assess the potential risk of vector-borne disease transmission in school settings. Entomological surveys were carried out in rural schools during the dry and rainy seasons of 2011. A total of 12 mosquito species were found: Aedes aegypti, Anopheles pseudopunctipennis, Culex coronator, Cx. quinquefasciatus, and Limatus durhamii in both immature and adult forms; Ae. fluviatilis, Cx. nigripalpus, Cx. corniger, and Psorophora ferox in immature forms only; and Ae. angustivittatus, Haemagogus equinus, and Trichoprosopon lampropus in adult forms only. The most common mosquito species was Cx. quinquefasciatus. Classrooms contained the greatest abundance of adult female Ae. aegypti and Cx. quinquefasciatus. The most common Ae. aegypti breeding sites were containers classified as "others" (e.g., cans), followed by containers used for water storage. A high level of Ae. aegypti infestation was found during the wet season. Our results suggest that rural schools are potentially important foci for the transmission of dengue and other mosquito-borne diseases. We propose that public health programs should be implemented in rural schools to prevent vector-borne diseases.

Advanced Waveform Research Methods for Geress Recordings

DTIC Science & Technology

1993-04-15

spect to their standard deviation. Tierefore it is not useful, to correct the observed p and BAZ values with these mislocation vectors. Fig. 3-I1...became vl on the arry beam. But it is very doubtful to claim this onset as an observation. Therefore the observed pa- rameters of tbis onset are given as...fired explosions in seismology poses an analogous problem to the discrimination between consonants and vowels in speech processing. In a voiced portion of

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.

Studying DDT Susceptibility at Discriminating Time Intervals Focusing on Maximum Limit of Exposure Time Survived by DDT Resistant Phlebotomus argentipes (Diptera: Psychodidae): an Investigative Report.

PubMed

Rama, Aarti; Kesari, Shreekant; Das, Pradeep; Kumar, Vijay

2017-07-24

Extensive application of routine insecticide i.e., dichlorodiphenyltrichloroethane (DDT) to control Phlebotomus argentipes (Diptera: Psychodidae), the proven vector of visceral leishmaniasis in India, had evoked the problem of resistance/tolerance against DDT, eventually nullifying the DDT dependent strategies to control this vector. Because tolerating an hour-long exposure to DDT is not challenging enough for the resistant P. argentipes, estimating susceptibility by exposing sand flies to insecticide for just an hour becomes a trivial and futile task.Therefore, this bioassay study was carried out to investigate the maximum limit of exposure time to which DDT resistant P. argentipes can endure the effect of DDT for their survival. The mortality rate of laboratory-reared DDT resistant strain P. argentipes exposed to DDT was studied at discriminating time intervals of 60 min and it was concluded that highly resistant sand flies could withstand up to 420 min of exposure to this insecticide. Additionally, the lethal time for female P. argentipes was observed to be higher than for males suggesting that they are highly resistant to DDT's toxicity. Our results support the monitoring of tolerance limit with respect to time and hence points towards an urgent need to change the World Health Organization's protocol for susceptibility identification in resistant P. argentipes.

Comparative evaluation of support vector machine classification for computer aided detection of breast masses in mammography

NASA Astrophysics Data System (ADS)

Lesniak, J. M.; Hupse, R.; Blanc, R.; Karssemeijer, N.; Székely, G.

2012-08-01

False positive (FP) marks represent an obstacle for effective use of computer-aided detection (CADe) of breast masses in mammography. Typically, the problem can be approached either by developing more discriminative features or by employing different classifier designs. In this paper, the usage of support vector machine (SVM) classification for FP reduction in CADe is investigated, presenting a systematic quantitative evaluation against neural networks, k-nearest neighbor classification, linear discriminant analysis and random forests. A large database of 2516 film mammography examinations and 73 input features was used to train the classifiers and evaluate for their performance on correctly diagnosed exams as well as false negatives. Further, classifier robustness was investigated using varying training data and feature sets as input. The evaluation was based on the mean exam sensitivity in 0.05-1 FPs on normals on the free-response receiver operating characteristic curve (FROC), incorporated into a tenfold cross validation framework. It was found that SVM classification using a Gaussian kernel offered significantly increased detection performance (P = 0.0002) compared to the reference methods. Varying training data and input features, SVMs showed improved exploitation of large feature sets. It is concluded that with the SVM-based CADe a significant reduction of FPs is possible outperforming other state-of-the-art approaches for breast mass CADe.

Stable Defects in Semiconductor Nanowires.

PubMed

Sanchez, A M; Gott, J A; Fonseka, H A; Zhang, Y; Liu, H; Beanland, R

2018-05-09

Semiconductor nanowires are commonly described as being defect-free due to their ability to expel mobile defects with long-range strain fields. Here, we describe previously undiscovered topologically protected line defects with null Burgers vector that, unlike dislocations, are stable in nanoscale crystals. We analyze the defects present in semiconductor nanowires in regions of imperfect crystal growth, i.e., at the nanowire tip formed during consumption of the droplet in self-catalyzed vapor-liquid-solid growth and subsequent vapor-solid shell growth. We use a form of the Burgers circuit method that can be applied to multiply twinned material without difficulty. Our observations show that the nanowire microstructure is very different from bulk material, with line defects either (a) trapped by locks or other defects, (b) arranged as dipoles or groups with a zero total Burgers vector, or (c) have a zero Burgers vector. We find two new line defects with a null Burgers vector, formed from the combination of partial dislocations in twinned material. The most common defect is the three-monolayer high twin facet with a zero Burgers vector. Studies of individual nanowires using cathodoluminescence show that optical emission is quenched in defective regions, showing that they act as strong nonradiative recombination centers.

A Prototype SSVEP Based Real Time BCI Gaming System

PubMed Central

Martišius, Ignas

2016-01-01

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel. PMID:27051414

A Prototype SSVEP Based Real Time BCI Gaming System.

PubMed

Martišius, Ignas; Damaševičius, Robertas

2016-01-01

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.

Pre-existing immunity against vaccine vectors – friend or foe?

PubMed Central

Saxena, Manvendra; Van, Thi Thu Hao; Baird, Fiona J.; Coloe, Peter J.

2013-01-01

Over the last century, the successful attenuation of multiple bacterial and viral pathogens has led to an effective, robust and safe form of vaccination. Recently, these vaccines have been evaluated as delivery vectors for heterologous antigens, as a means of simultaneous vaccination against two pathogens. The general consensus from published studies is that these vaccine vectors have the potential to be both safe and efficacious. However, some of the commonly employed vectors, for example Salmonella and adenovirus, often have pre-existing immune responses in the host and this has the potential to modify the subsequent immune response to a vectored antigen. This review examines the literature on this topic, and concludes that for bacterial vectors there can in fact, in some cases, be an enhancement in immunogenicity, typically humoral, while for viral vectors pre-existing immunity is a hindrance for subsequent induction of cell-mediated responses. PMID:23175507

Rapid differentiation of Ghana cocoa beans by FT-NIR spectroscopy coupled with multivariate classification

NASA Astrophysics Data System (ADS)

Teye, Ernest; Huang, Xingyi; Dai, Huang; Chen, Quansheng

2013-10-01

Quick, accurate and reliable technique for discrimination of cocoa beans according to geographical origin is essential for quality control and traceability management. This current study presents the application of Near Infrared Spectroscopy technique and multivariate classification for the differentiation of Ghana cocoa beans. A total of 194 cocoa bean samples from seven cocoa growing regions were used. Principal component analysis (PCA) was used to extract relevant information from the spectral data and this gave visible cluster trends. The performance of four multivariate classification methods: Linear discriminant analysis (LDA), K-nearest neighbors (KNN), Back propagation artificial neural network (BPANN) and Support vector machine (SVM) were compared. The performances of the models were optimized by cross validation. The results revealed that; SVM model was superior to all the mathematical methods with a discrimination rate of 100% in both the training and prediction set after preprocessing with Mean centering (MC). BPANN had a discrimination rate of 99.23% for the training set and 96.88% for prediction set. While LDA model had 96.15% and 90.63% for the training and prediction sets respectively. KNN model had 75.01% for the training set and 72.31% for prediction set. The non-linear classification methods used were superior to the linear ones. Generally, the results revealed that NIR Spectroscopy coupled with SVM model could be used successfully to discriminate cocoa beans according to their geographical origins for effective quality assurance.

Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics

PubMed Central

Belo, David; Gamboa, Hugo

2017-01-01

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

Image classification at low light levels

NASA Astrophysics Data System (ADS)

Wernick, Miles N.; Morris, G. Michael

1986-12-01

An imaging photon-counting detector is used to achieve automatic sorting of two image classes. The classification decision is formed on the basis of the cross correlation between a photon-limited input image and a reference function stored in computer memory. Expressions for the statistical parameters of the low-light-level correlation signal are given and are verified experimentally. To obtain a correlation-based system for two-class sorting, it is necessary to construct a reference function that produces useful information for class discrimination. An expression for such a reference function is derived using maximum-likelihood decision theory. Theoretically predicted results are used to compare on the basis of performance the maximum-likelihood reference function with Fukunaga-Koontz basis vectors and average filters. For each method, good class discrimination is found to result in milliseconds from a sparse sampling of the input image.

Discrimination of Mediterranean mussel (Mytilus galloprovincialis) feces in deposited materials by fecal morphology.

PubMed

Akiyama, Yoshihiro B; Iseri, Erina; Kataoka, Tomoya; Tanaka, Makiko; Katsukoshi, Kiyonori; Moki, Hirotada; Naito, Ryoji; Hem, Ramrav; Okada, Tomonari

2017-02-15

In the present study, we determined the common morphological characteristics of the feces of Mytilus galloprovincialis to develop a method for visually discriminating the feces of this mussel in deposited materials. This method can be used to assess the effect of mussel feces on benthic environments. The accuracy of visual morphology-based discrimination of mussel feces in deposited materials was confirmed by DNA analysis. Eighty-nine percent of mussel feces shared five common morphological characteristics. Of the 372 animal species investigated, only four species shared all five of these characteristics. More than 96% of the samples were visually identified as M. galloprovincialis feces on the basis of morphology of the particles containing the appropriate mitochondrial DNA. These results suggest that mussel feces can be discriminated with high accuracy on the basis of their morphological characteristics. Thus, our method can be used to quantitatively assess the effect of mussel feces on local benthic environments. Copyright © 2016 Elsevier Ltd. All rights reserved.

Y,REE,Nb,Ta,Ti-oxide (AB 2O 6) minerals from REL-REE euxenite-subtype pegmatites of the Třebíč Pluton, Czech Republic; substitutions and fractionation trends

NASA Astrophysics Data System (ADS)

Škoda, Radek; Novák, Milan

2007-04-01

Aeschynite-group minerals (AGM) and euxenite-group minerals (EGM) occur in REL-REE euxenite-subtype pegmatites from the Třebíč Pluton, Czech Republic. They form strongly metamictized, light brown to black, equigranular to needle-like, subhedral to anhedral grains enclosed in blocky K-feldspar and less commonly in albite, and blocky quartz, and in the graphic unit (quartz and K-feldspar). Both AGM and EGM are homogeneous to slightly heterogeneous in BSE images. They are not commonly associated with the other primary Y,REE,Ti,Nb-bearing minerals, i.e. allanite-(Ce), monazite-(Ce), titanite, and ilmenite, which occur within the same textural-paragenetic unit. Aeschynite-(Y), aeschynite-(Ce), aeschynite-(Nd), nioboaeschynite-(Ce), tantalaeschynite-(Ce), vigezzite and polycrase-(Y) were identified using EMP and canonical discrimination analysis [Ercit, T.S., 2005a. Identification and alteration trends of granitic-pegmatite-hosted (Y,REE,U,Th)-(Nb,Ta,Ti) oxide minerals: a statistical approach. Can. Mineral. 43, 4 1291-1303.]. The exchange vector ACa B(Nb,Ta) A(Y,REE) - 1 BTi - 1 or its combination with the exchange vector ACa 2B(Nb,Ta) 3A(U,Th) - 1 A(Y,REE) - 1 BTi - 3 have been elucidated for the AGM. The exchange vector ACa A(U,Th) A(Y,REE) - 2 is predominant in the EGM. The AGM are enriched in HREE, whereas LREE are concentrated in the EGM. Weak to none-existent geochemical fractionations, as expressed by the U/(U + Th), Y/(Y + REE), Ta/(Ta + Nb) and (Nb + Ta)/(Ti + Nb + Ta) ratios, were noted for single grains from both the AGM and EGM, as well as in grains of polycrase-(Y) from four different textural-paragenetic units located in the Vladislav pegmatite. Simultaneous increase of U/(U + Th) and Y/(Y + REE) in the AGM during fractionation is typical. The Ta/(Ta + Nb) fractionation is usually weak and contradicts the Y/(Y + REE) and U/(U + Th) fractionation trends. This unusual behavior of Nb and Ta may be controlled by associated Ti-rich minerals (titanite, ilmenite, rutile), the composition of parental melt and/or by elevated F activity. The AGM and EGM from pegmatites of the Třebíč Pluton are quite similar in composition to those from REL-REE euxenite-subtype pegmatites in the Trout Creek Pass, Chaffee County, Colorado, USA, which are generally Ca,U,Th-depleted, show lower Ta/(Ta+Nb), and lower variation in HREE/LREE.

AIDS-Related Stigma and Mental Disorders among People Living with HIV: A Cross-Sectional Study in Cambodia

PubMed Central

Yi, Siyan; Chhoun, Pheak; Suong, Samedy; Thin, Kouland; Brody, Carinne; Tuot, Sovannary

2015-01-01

Background AIDS-related stigma and mental disorders are the most common conditions in people living with HIV (PLHIV). We therefore conducted this study to examine the association of AIDS-related stigma and discrimination with mental disorders among PLHIV in Cambodia. Methods A two-stage cluster sampling method was used to select 1,003 adult PLHIV from six provinces. The People Living with HIV Stigma Index was used to measure stigma and discrimination, and a short version of general health questionnaire (GHQ-12) was used to measure mental disorders. Multivariate logistic regression analysis was conducted. Results The reported experiences of discrimination in communities in the past 12 months ranged from 0.8% for reports of being denied health services to 42.3% for being aware of being gossiped about. Internal stigma was also common ranging from 2.8% for avoiding going to a local clinic and/or hospital to 59.6% for deciding not to have (more) children. The proportions of PLHIV who reported fear of stigma and discrimination ranged from 13.9% for fear of being physically assaulted to 34.5% for fear of being gossiped about. The mean score of GHQ-12 was 3.2 (SD = 2.4). After controlling for several potential confounders, higher levels of mental disorders (GHQ-12≥ 4) remained significantly associated with higher levels of experiences of stigma and discrimination in family and communities (AOR = 1.9, 95% CI = 1.4–2.6), higher levels of internal stigma (AOR = 1.7, 95% CI = 1.2–2.3), and higher levels of fear of stigma and discrimination in family and communities (AOR = 1.5, 95% CI = 1.1–2.2). Conclusions AIDS-related stigma and discrimination among PLHIV in Cambodia are common and may have potential impacts on their mental health conditions. These findings indicate a need for community-based interventions to reduce stigma and discrimination in the general public and to help PLHIV to cope with this situation. PMID:25806534

Perceived and anticipated discrimination in people with mental illness--an interview study.

PubMed

Hansson, Lars; Stjernswärd, Sigrid; Svensson, Bengt

2014-02-01

Studies on perceived discrimination of people with mental illness are largely lacking. The purpose of the study was to investigate perceived discrimination in a sample of users in contact with mental health services in Sweden. Interviews were conducted with 156 users, asking for perceived and anticipated discrimination during the last 2 years. Background characteristics were also collected. Perceived discrimination was common. Highest frequencies were reported regarding family (54%), avoidance by people who knew about the mental illness (53%) and in making or keeping friends (50%). A majority of those anticipating discrimination regarding job or education seeking, or starting a close relationship did not report having been discriminated in these areas. Previous hospitalizations were associated with discrimination, and age with anticipated discrimination. Public stigma and self-stigma have been reported to have a number of negative consequences for people with mental illness. Discrimination is part of this complex situation and this study showed that this largely affects a number of individual life areas posing an obstacle for social integration. Anticipated discrimination or self-stigma was also prevalent and it is pointed out that this to a great extent is an obstacle on its own without being promoted by actual experiences of discrimination.

A Language-Independent Approach to Automatic Text Difficulty Assessment for Second-Language Learners

DTIC Science & Technology

2013-08-01

best-suited for regression. Our baseline uses z-normalized shallow length features and TF -LOG weighted vectors on bag-of-words for Arabic, Dari...length features and TF -LOG weighted vectors on bag-of-words for Arabic, Dari, English and Pashto. We compare Support Vector Machines and the Margin...football, whereas they are much less common in documents about opera). We used TF -LOG weighted word frequencies on bag-of-words for each document

Searching for transcription factor binding sites in vector spaces

PubMed Central

2012-01-01

Background Computational approaches to transcription factor binding site identification have been actively researched in the past decade. Learning from known binding sites, new binding sites of a transcription factor in unannotated sequences can be identified. A number of search methods have been introduced over the years. However, one can rarely find one single method that performs the best on all the transcription factors. Instead, to identify the best method for a particular transcription factor, one usually has to compare a handful of methods. Hence, it is highly desirable for a method to perform automatic optimization for individual transcription factors. Results We proposed to search for transcription factor binding sites in vector spaces. This framework allows us to identify the best method for each individual transcription factor. We further introduced two novel methods, the negative-to-positive vector (NPV) and optimal discriminating vector (ODV) methods, to construct query vectors to search for binding sites in vector spaces. Extensive cross-validation experiments showed that the proposed methods significantly outperformed the ungapped likelihood under positional background method, a state-of-the-art method, and the widely-used position-specific scoring matrix method. We further demonstrated that motif subtypes of a TF can be readily identified in this framework and two variants called the k NPV and k ODV methods benefited significantly from motif subtype identification. Finally, independent validation on ChIP-seq data showed that the ODV and NPV methods significantly outperformed the other compared methods. Conclusions We conclude that the proposed framework is highly flexible. It enables the two novel methods to automatically identify a TF-specific subspace to search for binding sites. Implementations are available as source code at: http://biogrid.engr.uconn.edu/tfbs_search/. PMID:23244338

High density recombinant AAV particles are competent vectors for in vivo transduction

USDA-ARS?s Scientific Manuscript database

Recombinant adeno-associated viral (rAAV) vectors have recently achieved clinical successes in human gene therapy. However, the commonly observed heavier particles found in AAV preparations have traditionally been ignored due to its low in vitro infectivity. In this study, we systemically compared t...

Contributions of hydrology to Vesicular Stomatitis Virus emergence in the Western United States

USDA-ARS?s Scientific Manuscript database

Relationships between environmental variables associated with the spread of vector-borne pathogens, such as RNA viruses transmitted to humans and animals, remain poorly understood. Vesicular stomatitis (VS) is caused by a vector-borne, zoonotic RNA virus (VSV), and is the most common vesicular dise...

Perceived Discrimination During Prenatal Care, Labor, and Delivery: An Examination of Data From the Oregon Pregnancy Risk Assessment Monitoring System, 1998–1999, 2000, and 2001

PubMed Central

Thorburn, Sheryl; Zhao, Weiyi

2008-01-01

Although recent research has examined discrimination in health care, no studies have investigated women's experiences during prenatal or obstetrical care. Analyses of data from the Oregon Pregnancy Risk Assessment Monitoring System showed that 18.53% of mothers reported discrimination by providers during prenatal care, labor, or delivery, most commonly because of age or insurance status. Perceived discrimination was associated with maternal characteristics such as age, marital status, and type of insurance, but not with number of subsequent well-baby visits. PMID:18703444

Lesbians still face job discrimination.

PubMed

Ryniker, Margaret R

2008-01-01

This article examines continued discrimination against lesbians in the workplace. A number of cases from various jurisdictions in the United States are highlighted. The paper studies two common forms of discrimination: denial of employment benefits to same sex partners, and sexual harassment. On the first front, the case law suggests that health insurance coverage for one's partner is becoming the norm. On the question of sexual harassment in the workplace, the case law did not provide protection for lesbians. Finally, U.S. employment policies related to sexual orientation are contrasted with those in Israel, which provides much greater protection from discrimination.

CARBON ISOTOPE DISCRIMINATION AND GROWTH RESPONSE TO STAND DENSITY REDUCTIONS IN OLD PINUS PONDEROSA TREES

EPA Science Inventory

Carbon isotope ratios ( 13C) of tree rings are commonly used for paleoclimatic reconstruction and for inferring canopy water-use efficiency (WUE). However, the responsiveness of carbon isotope discrimination ( ) to site disturbance and resource availability has only rarely been ...

Habitat suitability and ecological niche profile of major malaria vectors in Cameroon

PubMed Central

2009-01-01

Background Suitability of environmental conditions determines a species distribution in space and time. Understanding and modelling the ecological niche of mosquito disease vectors can, therefore, be a powerful predictor of the risk of exposure to the pathogens they transmit. In Africa, five anophelines are responsible for over 95% of total malaria transmission. However, detailed knowledge of the geographic distribution and ecological requirements of these species is to date still inadequate. Methods Indoor-resting mosquitoes were sampled from 386 villages covering the full range of ecological settings available in Cameroon, Central Africa. Using a predictive species distribution modeling approach based only on presence records, habitat suitability maps were constructed for the five major malaria vectors Anopheles gambiae, Anopheles funestus, Anopheles arabiensis, Anopheles nili and Anopheles moucheti. The influence of 17 climatic, topographic, and land use variables on mosquito geographic distribution was assessed by multivariate regression and ordination techniques. Results Twenty-four anopheline species were collected, of which 17 are known to transmit malaria in Africa. Ecological Niche Factor Analysis, Habitat Suitability modeling and Canonical Correspondence Analysis revealed marked differences among the five major malaria vector species, both in terms of ecological requirements and niche breadth. Eco-geographical variables (EGVs) related to human activity had the highest impact on habitat suitability for the five major malaria vectors, with areas of low population density being of marginal or unsuitable habitat quality. Sunlight exposure, rainfall, evapo-transpiration, relative humidity, and wind speed were among the most discriminative EGVs separating "forest" from "savanna" species. Conclusions The distribution of major malaria vectors in Cameroon is strongly affected by the impact of humans on the environment, with variables related to proximity to human settings being among the best predictors of habitat suitability. The ecologically more tolerant species An. gambiae and An. funestus were recorded in a wide range of eco-climatic settings. The other three major vectors, An. arabiensis, An. moucheti, and An. nili, were more specialized. Ecological niche and species distribution modelling should help improve malaria vector control interventions by targeting places and times where the impact on vector populations and disease transmission can be optimized. PMID:20028559

Habitat suitability and ecological niche profile of major malaria vectors in Cameroon.

PubMed

Ayala, Diego; Costantini, Carlo; Ose, Kenji; Kamdem, Guy C; Antonio-Nkondjio, Christophe; Agbor, Jean-Pierre; Awono-Ambene, Parfait; Fontenille, Didier; Simard, Frédéric

2009-12-23

Suitability of environmental conditions determines a species distribution in space and time. Understanding and modelling the ecological niche of mosquito disease vectors can, therefore, be a powerful predictor of the risk of exposure to the pathogens they transmit. In Africa, five anophelines are responsible for over 95% of total malaria transmission. However, detailed knowledge of the geographic distribution and ecological requirements of these species is to date still inadequate. Indoor-resting mosquitoes were sampled from 386 villages covering the full range of ecological settings available in Cameroon, Central Africa. Using a predictive species distribution modeling approach based only on presence records, habitat suitability maps were constructed for the five major malaria vectors Anopheles gambiae, Anopheles funestus, Anopheles arabiensis, Anopheles nili and Anopheles moucheti. The influence of 17 climatic, topographic, and land use variables on mosquito geographic distribution was assessed by multivariate regression and ordination techniques. Twenty-four anopheline species were collected, of which 17 are known to transmit malaria in Africa. Ecological Niche Factor Analysis, Habitat Suitability modeling and Canonical Correspondence Analysis revealed marked differences among the five major malaria vector species, both in terms of ecological requirements and niche breadth. Eco-geographical variables (EGVs) related to human activity had the highest impact on habitat suitability for the five major malaria vectors, with areas of low population density being of marginal or unsuitable habitat quality. Sunlight exposure, rainfall, evapo-transpiration, relative humidity, and wind speed were among the most discriminative EGVs separating "forest" from "savanna" species. The distribution of major malaria vectors in Cameroon is strongly affected by the impact of humans on the environment, with variables related to proximity to human settings being among the best predictors of habitat suitability. The ecologically more tolerant species An. gambiae and An. funestus were recorded in a wide range of eco-climatic settings. The other three major vectors, An. arabiensis, An. moucheti, and An. nili, were more specialized. Ecological niche and species distribution modelling should help improve malaria vector control interventions by targeting places and times where the impact on vector populations and disease transmission can be optimized.

Imer-product array processor for retrieval of stored images represented by bipolar binary (+1,-1) pixels using partial input trinary pixels represented by (+1,-1)

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Inventor); Awwal, Abdul A. S. (Inventor); Karim, Mohammad A. (Inventor)

1993-01-01

An inner-product array processor is provided with thresholding of the inner product during each iteration to make more significant the inner product employed in estimating a vector to be used as the input vector for the next iteration. While stored vectors and estimated vectors are represented in bipolar binary (1,-1), only those elements of an initial partial input vector that are believed to be common with those of a stored vector are represented in bipolar binary; the remaining elements of a partial input vector are set to 0. This mode of representation, in which the known elements of a partial input vector are in bipolar binary form and the remaining elements are set equal to 0, is referred to as trinary representation. The initial inner products corresponding to the partial input vector will then be equal to the number of known elements. Inner-product thresholding is applied to accelerate convergence and to avoid convergence to a negative input product.

Gaussian Discriminant Analysis for Optimal Delineation of Mild Cognitive Impairment in Alzheimer's Disease.

PubMed

Fang, Chen; Li, Chunfei; Cabrerizo, Mercedes; Barreto, Armando; Andrian, Jean; Rishe, Naphtali; Loewenstein, David; Duara, Ranjan; Adjouadi, Malek

2018-04-12

Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer's disease (AD) and its prodromal stage of mild cognitive impairment (MCI). Using multimodal biomarkers for this high-dimensional classification problem, the widely used algorithms include Support Vector Machines (SVM), Sparse Representation-based classification (SRC), Deep Belief Networks (DBN) and Random Forest (RF). These widely used algorithms continue to yield unsatisfactory performance for delineating the MCI participants from the cognitively normal control (CN) group. A novel Gaussian discriminant analysis-based algorithm is thus introduced to achieve a more effective and accurate classification performance than the aforementioned state-of-the-art algorithms. This study makes use of magnetic resonance imaging (MRI) data uniquely as input to two separate high-dimensional decision spaces that reflect the structural measures of the two brain hemispheres. The data used include 190 CN, 305 MCI and 133 AD subjects as part of the AD Big Data DREAM Challenge #1. Using 80% data for a 10-fold cross-validation, the proposed algorithm achieved an average F1 score of 95.89% and an accuracy of 96.54% for discriminating AD from CN; and more importantly, an average F1 score of 92.08% and an accuracy of 90.26% for discriminating MCI from CN. Then, a true test was implemented on the remaining 20% held-out test data. For discriminating MCI from CN, an accuracy of 80.61%, a sensitivity of 81.97% and a specificity of 78.38% were obtained. These results show significant improvement over existing algorithms for discriminating the subtle differences between MCI participants and the CN group.

Regional autonomy changes in resting-state functional MRI in patients with HIV associated neurocognitive disorder

NASA Astrophysics Data System (ADS)

DSouza, Adora M.; Abidin, Anas Z.; Chockanathan, Udaysankar; Wismüller, Axel

2018-03-01

In this study, we investigate whether there are discernable changes in influence that brain regions have on themselves once patients show symptoms of HIV Associated Neurocognitive Disorder (HAND) using functional MRI (fMRI). Simple functional connectivity measures, such as correlation cannot reveal such information. To this end, we use mutual connectivity analysis (MCA) with Local Models (LM), which reveals a measure of influence in terms of predictability. Once such measures of interaction are obtained, we train two classifiers to characterize difference in patterns of regional self-influence between healthy subjects and subjects presenting with HAND symptoms. The two classifiers we use are Support Vector Machines (SVM) and Localized Generalized Matrix Learning Vector Quantization (LGMLVQ). Performing machine learning on fMRI connectivity measures is popularly known as multi-voxel pattern analysis (MVPA). By performing such an analysis, we are interested in studying the impact HIV infection has on an individual's brain. The high area under receiver operating curve (AUC) and accuracy values for 100 different train/test separations using MCA-LM self-influence measures (SVM: mean AUC=0.86, LGMLVQ: mean AUC=0.88, SVM and LGMLVQ: mean accuracy=0.78) compared with standard MVPA analysis using cross-correlation between fMRI time-series (SVM: mean AUC=0.58, LGMLVQ: mean AUC=0.57), demonstrates that self-influence features can be more discriminative than measures of interaction between time-series pairs. Furthermore, our results suggest that incorporating measures of self-influence in MVPA analysis used commonly in fMRI analysis has the potential to provide a performance boost and indicate important changes in dynamics of regions in the brain as a consequence of HIV infection.

orco mutant mosquitoes lose strong preference for humans and are not repelled by volatile DEET.

PubMed

DeGennaro, Matthew; McBride, Carolyn S; Seeholzer, Laura; Nakagawa, Takao; Dennis, Emily J; Goldman, Chloe; Jasinskiene, Nijole; James, Anthony A; Vosshall, Leslie B

2013-06-27

Female mosquitoes of some species are generalists and will blood-feed on a variety of vertebrate hosts, whereas others display marked host preference. Anopheles gambiae and Aedes aegypti have evolved a strong preference for humans, making them dangerously efficient vectors of malaria and Dengue haemorrhagic fever. Specific host odours probably drive this strong preference because other attractive cues, including body heat and exhaled carbon dioxide (CO2), are common to all warm-blooded hosts. Insects sense odours via several chemosensory receptor families, including the odorant receptors (ORs), membrane proteins that form heteromeric odour-gated ion channels comprising a variable ligand-selective subunit and an obligate co-receptor called Orco (ref. 6). Here we use zinc-finger nucleases to generate targeted mutations in the orco gene of A. aegypti to examine the contribution of Orco and the odorant receptor pathway to mosquito host selection and sensitivity to the insect repellent DEET (N,N-diethyl-meta-toluamide). orco mutant olfactory sensory neurons have greatly reduced spontaneous activity and lack odour-evoked responses. Behaviourally, orco mutant mosquitoes have severely reduced attraction to honey, an odour cue related to floral nectar, and do not respond to human scent in the absence of CO2. However, in the presence of CO2, female orco mutant mosquitoes retain strong attraction to both human and animal hosts, but no longer strongly prefer humans. orco mutant females are attracted to human hosts even in the presence of DEET, but are repelled upon contact, indicating that olfactory- and contact-mediated effects of DEET are mechanistically distinct. We conclude that the odorant receptor pathway is crucial for an anthropophilic vector mosquito to discriminate human from non-human hosts and to be effectively repelled by volatile DEET.

On-Time 3D Time-Domain EMI and Tensor Magnetic Gradiometry for UXO Detection and Discrimination

DTIC Science & Technology

2008-06-04

three-axis fluxgate magnetometers mounted on a tetrahedral structure as shown in figure 5.1.3.1. TMGS was intended to measure gradients of vector...manufacturer of the fluxgate magnetometers supplied specifications for each of the triaxial sensors (figure 5.3.2.25). Figure 5.3.2.25...Manufacturer’s specifications for the four triaxial fluxgate magnetometers used in the TMGS planar array. As shown previously in figure 5.3.2.4

System for information discovery

DOEpatents

Pennock, Kelly A [Richland, WA; Miller, Nancy E [Kennewick, WA

2002-11-19

A sequence of word filters are used to eliminate terms in the database which do not discriminate document content, resulting in a filtered word set and a topic word set whose members are highly predictive of content. These two word sets are then formed into a two dimensional matrix with matrix entries calculated as the conditional probability that a document will contain a word in a row given that it contains the word in a column. The matrix representation allows the resultant vectors to be utilized to interpret document contents.

Hexons from adenovirus serotypes 5 and 48 differentially protect adenovirus vectors from neutralization by mouse and human serum

PubMed Central

Harmon, Andrew W.; Moitra, Rituparna; Xu, Zhili

2018-01-01

Adenovirus vectors are widely used in gene therapy clinical trials, and preclinical studies with these vectors are often conducted in mice. It is therefore critical to understand whether mouse studies adequately predict the behavior of adenovirus vectors in humans. The most commonly-used adenovirus vectors are derived from adenovirus serotype 5 (Ad5). The Ad5 hexon protein can bind coagulation factor X (FX), and binding of FX has a major impact on vector interactions with other blood proteins. In mouse serum, FX protects Ad5 vectors from neutralization by natural antibodies and complement. In the current study, we similarly find that human FX inhibits neutralization of Ad5 vectors by human serum, and this finding is consistent among individual human sera. We show that human IgM and human IgG can each induce complement-mediated neutralization when Ad5 vectors are not protected by FX. Although mouse and human serum had similar effects on Ad5 vectors, we found that this was not true for a chimeric Ad5 vector that incorporated hexon regions from adenovirus serotype 48. Interestingly, this hexon-chimeric vector was neutralized by human serum, but not by mouse serum. These findings indicate that studies in mouse serum accurately predict the behavior of Ad5 vectors in human serum, but mouse serum is not an accurate model system for all adenovirus vectors. PMID:29401488

Gene therapy using retrovirus vectors: vector development and biosafety at clinical trials.

PubMed

Doi, Knayo; Takeuchi, Yasuhiro

2015-01-01

Retrovirus vectors (gammaretroviral and lentiviral vectors) have been considered as promising tools to transfer therapeutic genes into patient cells because they can permanently integrate into host cellular genome. To treat monogenic, inherited diseases, retroviral vectors have been used to add correct genes into patient cells. Conventional gammaretroviral vectors achieved successful results in clinical trials: treated patients had therapeutic gene expression in target cells and had improved symptoms of diseases. However, serious side-effects of leukemia occurred, caused by retroviral insertional mutagenesis (IM). These incidences stressed the importance of monitoring vector integration sites in patient cells as well as of re-consideration on safer vectors. More recently lentiviral vectors which can deliver genes into non-dividing cells started to be used in clinical trials including neurological disorders, showing their efficacy. Vector integration site analysis revealed that lentiviruses integrate less likely to near promoter regions of oncogenes than gammaretroviruses and no adverse events have been reported in lentiviral vector-mediated gene therapy clinical trials. Therefore lentiviral vectors have promises to be applied to a wide range of common diseases in near future. For example, T cells from cancer patients were transduced to express chimeric T cell receptors recognizing their tumour cells enhancing patients' anti-cancer immunity.

Classification of autistic individuals and controls using cross-task characterization of fMRI activity

PubMed Central

Chanel, Guillaume; Pichon, Swann; Conty, Laurence; Berthoz, Sylvie; Chevallier, Coralie; Grèzes, Julie

2015-01-01

Multivariate pattern analysis (MVPA) has been applied successfully to task-based and resting-based fMRI recordings to investigate which neural markers distinguish individuals with autistic spectrum disorders (ASD) from controls. While most studies have focused on brain connectivity during resting state episodes and regions of interest approaches (ROI), a wealth of task-based fMRI datasets have been acquired in these populations in the last decade. This calls for techniques that can leverage information not only from a single dataset, but from several existing datasets that might share some common features and biomarkers. We propose a fully data-driven (voxel-based) approach that we apply to two different fMRI experiments with social stimuli (faces and bodies). The method, based on Support Vector Machines (SVMs) and Recursive Feature Elimination (RFE), is first trained for each experiment independently and each output is then combined to obtain a final classification output. Second, this RFE output is used to determine which voxels are most often selected for classification to generate maps of significant discriminative activity. Finally, to further explore the clinical validity of the approach, we correlate phenotypic information with obtained classifier scores. The results reveal good classification accuracy (range between 69% and 92.3%). Moreover, we were able to identify discriminative activity patterns pertaining to the social brain without relying on a priori ROI definitions. Finally, social motivation was the only dimension which correlated with classifier scores, suggesting that it is the main dimension captured by the classifiers. Altogether, we believe that the present RFE method proves to be efficient and may help identifying relevant biomarkers by taking advantage of acquired task-based fMRI datasets in psychiatric populations. PMID:26793434

Sequentially distant but structurally similar proteins exhibit fold specific patterns based on their biophysical properties.

PubMed

Rajendran, Senthilnathan; Jothi, Arunachalam

2018-05-16

The Three-dimensional structure of a protein depends on the interaction between their amino acid residues. These interactions are in turn influenced by various biophysical properties of the amino acids. There are several examples of proteins that share the same fold but are very dissimilar at the sequence level. For proteins to share a common fold some crucial interactions should be maintained despite insignificant sequence similarity. Since the interactions are because of the biophysical properties of the amino acids, we should be able to detect descriptive patterns for folds at such a property level. In this line, the main focus of our research is to analyze such proteins and to characterize them in terms of their biophysical properties. Protein structures with sequence similarity lesser than 40% were selected for ten different subfolds from three different mainfolds (according to CATH classification) and were used for this analysis. We used the normalized values of the 49 physio-chemical, energetic and conformational properties of amino acids. We characterize the folds based on the average biophysical property values. We also observed a fold specific correlational behavior of biophysical properties despite a very low sequence similarity in our data. We further trained three different binary classification models (Naive Bayes-NB, Support Vector Machines-SVM and Bayesian Generalized Linear Model-BGLM) which could discriminate mainfold based on the biophysical properties. We also show that among the three generated models, the BGLM classifier model was able to discriminate protein sequences coming under all beta category with 81.43% accuracy and all alpha, alpha-beta proteins with 83.37% accuracy. Copyright © 2018 Elsevier Ltd. All rights reserved.

The association between discrimination and health: findings on Russian, Somali and Kurdish origin populations in Finland.

PubMed

Rask, Shadia; Elo, Irma T; Koskinen, Seppo; Lilja, Eero; Koponen, Päivikki; Castaneda, Anu E

2018-06-06

The Second European Union Minorities and Discrimination Survey recently demonstrated widespread discrimination across EU countries, with high discrimination rates observed in countries like Finland. Discrimination is known to negatively impact health, but fewer studies have examined how different types of perceived discrimination are related to health. This study examines (i) the prevalence of different types of perceived discrimination among Russian, Somali and Kurdish origin populations in Finland, and (ii) the association between different types of perceived discrimination (no experiences; subtle discrimination only; overt or subtle and overt discrimination) and health (self-rated health; limiting long-term illness (LLTI) or disability; mental health symptoms). Data are from the Finnish Migrant Health and Wellbeing Study (n = 1795). Subtle discrimination implies reporting being treated with less courtesy and/or treated with less respect than others, and overt discrimination being called names or insulted and/or threatened or harassed. The prevalence of discrimination and the associations between discrimination and health were calculated with predicted margins and logistic regression. Experiences of subtle discrimination were more common than overt discrimination in all the studied groups. Subtle discrimination was reported by 29% of Somali origin persons and 35% Russian and Kurdish origin persons. The prevalence of overt discrimination ranged between 22% and 24%. Experiences of discrimination increased the odds for poor self-reported health, LLTI and mental health symptoms, particularly among those reporting subtle discrimination only. To promote the health of diverse populations, actions against racism and discrimination are highly needed, including initiatives that promote shared belonging.

An emergence of coordinated communication in populations of agents.

PubMed

Kvasnicka, V; Pospichal, J

1999-01-01

The purpose of this article is to demonstrate that coordinated communication spontaneously emerges in a population composed of agents that are capable of specific cognitive activities. Internal states of agents are characterized by meaning vectors. Simple neural networks composed of one layer of hidden neurons perform cognitive activities of agents. An elementary communication act consists of the following: (a) two agents are selected, where one of them is declared the speaker and the other the listener; (b) the speaker codes a selected meaning vector onto a sequence of symbols and sends it to the listener as a message; and finally, (c) the listener decodes this message into a meaning vector and adapts his or her neural network such that the differences between speaker and listener meaning vectors are decreased. A Darwinian evolution enlarged by ideas from the Baldwin effect and Dawkins' memes is simulated by a simple version of an evolutionary algorithm without crossover. The agent fitness is determined by success of the mutual pairwise communications. It is demonstrated that agents in the course of evolution gradually do a better job of decoding received messages (they are closer to meaning vectors of speakers) and all agents gradually start to use the same vocabulary for the common communication. Moreover, if agent meaning vectors contain regularities, then these regularities are manifested also in messages created by agent speakers, that is, similar parts of meaning vectors are coded by similar symbol substrings. This observation is considered a manifestation of the emergence of a grammar system in the common coordinated communication.

Sound Effects

ERIC Educational Resources Information Center

Wolfram, Walt

2013-01-01

Linguist Rosina Lippi-Green concludes in her book, "English with an Accent: Language, Ideology, and Discrimination in the United States," "Accent discrimination can be found everywhere in our daily lives. In fact, such behavior is so commonly accepted, so widely perceived as appropriate, that it must be seen as the last back door to…

Validity of the American Sign Language Discrimination Test

ERIC Educational Resources Information Center

Bochner, Joseph H.; Samar, Vincent J.; Hauser, Peter C.; Garrison, Wayne M.; Searls, J. Matt; Sanders, Cynthia A.

2016-01-01

American Sign Language (ASL) is one of the most commonly taught languages in North America. Yet, few assessment instruments for ASL proficiency have been developed, none of which have adequately demonstrated validity. We propose that the American Sign Language Discrimination Test (ASL-DT), a recently developed measure of learners' ability to…

The effect of nutrient media water purity on LIBS based identification of bacteria

USDA-ARS?s Scientific Manuscript database

Single pulse laser induced breakdown spectroscopy (LIBS) is used as the basis for discrimination between 3 genera of Gram-negative bacteria and 2 genera of gram-positive bacteria representing pathogenic threats commonly found in poultry processing rinse waters. Because LIBS-based discrimination reli...

Teaching Third-Degree Price Discrimination

ERIC Educational Resources Information Center

Round, David K.; McIver, Ron P.

2006-01-01

Third-degree price discrimination is taught in almost every intermediate microeconomics class. The theory, geometry, and the algebra behind the concept are simple, and the phenomenon is commonly associated with the sale of many of the goods and services used frequently by students. Classroom discussion is usually vibrant as students can relate…

Saccharomyces cerevisiae Shuttle vectors.

PubMed

Gnügge, Robert; Rudolf, Fabian

2017-05-01

Yeast shuttle vectors are indispensable tools in yeast research. They enable cloning of defined DNA sequences in Escherichia coli and their direct transfer into Saccharomyces cerevisiae cells. There are three types of commonly used yeast shuttle vectors: centromeric plasmids, episomal plasmids and integrating plasmids. In this review, we discuss the different plasmid systems and their characteristic features. We focus on their segregational stability and copy number and indicate how to modify these properties. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Simple modification of Oja rule limits L1-norm of weight vector and leads to sparse connectivity.

PubMed

Aparin, Vladimir

2012-03-01

This letter describes a simple modification of the Oja learning rule, which asymptotically constrains the L1-norm of an input weight vector instead of the L2-norm as in the original rule. This constraining is local as opposed to commonly used instant normalizations, which require the knowledge of all input weights of a neuron to update each one of them individually. The proposed rule converges to a weight vector that is sparser (has more zero weights) than the vector learned by the original Oja rule with or without the zero bound, which could explain the developmental synaptic pruning.

Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: A support vector machine learning approach

PubMed Central

Gould, Ian C.; Shepherd, Alana M.; Laurens, Kristin R.; Cairns, Murray J.; Carr, Vaughan J.; Green, Melissa J.

2014-01-01

Heterogeneity in the structural brain abnormalities associated with schizophrenia has made identification of reliable neuroanatomical markers of the disease difficult. The use of more homogenous clinical phenotypes may improve the accuracy of predicting psychotic disorder/s on the basis of observable brain disturbances. Here we investigate the utility of cognitive subtypes of schizophrenia – ‘cognitive deficit’ and ‘cognitively spared’ – in determining whether multivariate patterns of volumetric brain differences can accurately discriminate these clinical subtypes from healthy controls, and from each other. We applied support vector machine classification to grey- and white-matter volume data from 126 schizophrenia patients previously allocated to the cognitive spared subtype, 74 cognitive deficit schizophrenia patients, and 134 healthy controls. Using this method, cognitive subtypes were distinguished from healthy controls with up to 72% accuracy. Cross-validation analyses between subtypes achieved an accuracy of 71%, suggesting that some common neuroanatomical patterns distinguish both subtypes from healthy controls. Notably, cognitive subtypes were best distinguished from one another when the sample was stratified by sex prior to classification analysis: cognitive subtype classification accuracy was relatively low (<60%) without stratification, and increased to 83% for females with sex stratification. Distinct neuroanatomical patterns predicted cognitive subtype status in each sex: sex-specific multivariate patterns did not predict cognitive subtype status in the other sex above chance, and weight map analyses demonstrated negative correlations between the spatial patterns of weights underlying classification for each sex. These results suggest that in typical mixed-sex samples of schizophrenia patients, the volumetric brain differences between cognitive subtypes are relatively minor in contrast to the large common disease-associated changes. Volumetric differences that distinguish between cognitive subtypes on a case-by-case basis appear to occur in a sex-specific manner that is consistent with previous evidence of disrupted relationships between brain structure and cognition in male, but not female, schizophrenia patients. Consideration of sex-specific differences in brain organization is thus likely to assist future attempts to distinguish subgroups of schizophrenia patients on the basis of neuroanatomical features. PMID:25379435

Structure, Spatial and Temporal Distribution of the Culex pipiens Complex in Shanghai, China

PubMed Central

Gao, Qiang; Xiong, Chenglong; Su, Fei; Cao, Hui; Zhou, Jianjun; Jiang, Qingwu

2016-01-01

Background: Culex pipiens molestus was first reported in Shanghai in 2010. The population structures and seasonal distributions of Culex pipiens subspecies C. p. molestus, Culex pipiens pallens, and Culex pipiens quinquefasciatus are not well known. Methods: From late February to November 2013, we conducted daily field surveillance of mosquitoes at eight sites at two green lands and three residential areas in downtown Shanghai. Morphological comparison and DV/D ratios (DV/D is an indicator of mosquito taxonomy) were used to identify adult mosquitoes. Results: The distribution curves of the Culex pipiens complex members indicated seasonal fluctuations. The temperature range of 20–25 °C was the most suitable for adult activity. Micro-environmental factors may differentiate the complex population structures. Hybridization between C. p. pallens and C. p. quinquefasciatus was common and neither “DV/D = 0.40” nor “DV/D = 0.50” can distinguish these subspecies and their hybrids. Conclusion: the population structure of the Culex pipiens complex is complex and characterized by significant hybridization. Measures other than DV/D ratios are needed for the discrimination of subspecies. The C. p. molestus invasion might result in the transmission of novel vector-borne diseases in Shanghai. PMID:27869687

Quantification of localized vertebral deformities using a sparse wavelet-based shape model.

PubMed

Zewail, R; Elsafi, A; Durdle, N

2008-01-01

Medical experts often examine hundreds of spine x-ray images to determine existence of various pathologies. Common pathologies of interest are anterior osteophites, disc space narrowing, and wedging. By careful inspection of the outline shapes of the vertebral bodies, experts are able to identify and assess vertebral abnormalities with respect to the pathology under investigation. In this paper, we present a novel method for quantification of vertebral deformation using a sparse shape model. Using wavelets and Independent component analysis (ICA), we construct a sparse shape model that benefits from the approximation power of wavelets and the capability of ICA to capture higher order statistics in wavelet space. The new model is able to capture localized pathology-related shape deformations, hence it allows for quantification of vertebral shape variations. We investigate the capability of the model to predict localized pathology related deformations. Next, using support-vector machines, we demonstrate the diagnostic capabilities of the method through the discrimination of anterior osteophites in lumbar vertebrae. Experiments were conducted using a set of 150 contours from digital x-ray images of lumbar spine. Each vertebra is labeled as normal or abnormal. Results reported in this work focus on anterior osteophites as the pathology of interest.

Metabolic Differentiation of Early Lyme Disease from Southern Tick-Associated Rash Illness (STARI)

PubMed Central

Molins, C. R.; Ashton, L. V.; Wormser, G. P.; Andre, B. G.; Hess, A. M.; Delorey, M. J.; Pilgard, M. A.; Johnson, B. J.; Webb, K.; Islam, M. N.; Pegalajar-Jurado, A; Molla, I.; Jewett, M. W.; Belisle, J. T.

2017-01-01

Lyme disease, the most commonly reported vector-borne disease in the United States, results from infection with Borrelia burgdorferi. Early clinical diagnosis of this disease is largely based on the presence of an erythematous skin lesion for individuals in high-risk regions. This, however, can be confused with other illnesses including southern tick-associated rash illness (STARI), an illness that lacks a defined etiological agent or laboratory diagnostic test, and is co-prevalent with Lyme disease in portions of the Eastern United States. By applying an unbiased metabolomics approach with sera retrospectively obtained from well-characterized patients we defined biochemical and diagnostic differences between early Lyme disease and STARI. Specifically, a metabolic biosignature consisting of 261 molecular features (MFs) revealed that altered N-acyl ethanolamine and primary fatty acid amide metabolism discriminated early Lyme disease from STARI. More importantly, development of classification models with the 261 MF biosignature and testing against validation samples differentiated early Lyme disease from STARI with an accuracy of 85 to 98%. These findings revealed metabolic dissimilarity between early Lyme disease and STARI, and provide a powerful and new approach to objectively distinguish early Lyme disease from an illness with nearly identical symptoms. PMID:28814545

Understandability of Patient Information Booklets for Patients with Cancer.

PubMed

Keinki, Christian; Zowalla, Richard; Wiesner, Martin; Koester, Marie Jolin; Huebner, Jutta

2018-06-01

The improvement of health literacy in general and the information of individual patient is a major concern of the German national cancer plan and similar initiatives in other western countries. The aim of our study was to assess the readability and understandability of information booklets for cancer patients available at German Web sites. A support vector machine (SVM) was used to discriminate between laymen- and expert-centric patient information booklets about nine most common tumor types. All booklets had to be available for free at the Internet. A total of 52 different patient booklets were downloaded and assessed. Overall, the assessment of all booklets showed that an understandability level L of 4.6 and therefore increased medical background knowledge is required to understand a random text selected from the sample. The assessed information booklets on cancer show very limited suitability for laymen. We were able to demonstrate that a medical background is necessary to understand the examined booklets. The current study highlights the need to create information material adjusted to the needs of laymen. Assessing understandability before publication, especially for laymen with low health literacy, could ensure the suitability and thus quality of the information material.

A Negative Selection Immune System Inspired Methodology for Fault Diagnosis of Wind Turbines.

PubMed

Alizadeh, Esmaeil; Meskin, Nader; Khorasani, Khashayar

2017-11-01

High operational and maintenance costs represent as major economic constraints in the wind turbine (WT) industry. These concerns have made investigation into fault diagnosis of WT systems an extremely important and active area of research. In this paper, an immune system (IS) inspired methodology for performing fault detection and isolation (FDI) of a WT system is proposed and developed. The proposed scheme is based on a self nonself discrimination paradigm of a biological IS. Specifically, the negative selection mechanism [negative selection algorithm (NSA)] of the human body is utilized. In this paper, a hierarchical bank of NSAs are designed to detect and isolate both individual as well as simultaneously occurring faults common to the WTs. A smoothing moving window filter is then utilized to further improve the reliability and performance of the FDI scheme. Moreover, the performance of our proposed scheme is compared with another state-of-the-art data-driven technique, namely the support vector machines (SVMs) to demonstrate and illustrate the superiority and advantages of our proposed NSA-based FDI scheme. Finally, a nonparametric statistical comparison test is implemented to evaluate our proposed methodology with that of the SVM under various fault severities.

Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution.

PubMed

Trakoolwilaiwan, Thanawin; Behboodi, Bahareh; Lee, Jaeseok; Kim, Kyungsoo; Choi, Ji-Woong

2018-01-01

The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.

Weight Discrimination and Risk of Mortality

PubMed Central

Sutin, Angelina R.; Stephan, Yannick; Terracciano, Antonio

2015-01-01

Discrimination based on weight is a stressful social experience linked to declines in physical and mental health. We examine whether this harmful association extends to risk of mortality. Participants in the Health and Retirement Study (HRS; N=13,692) and the Midlife in the United States Study (MIDUS; N=5,079) reported on discriminatory experiences and attributed those experiences to personal characteristics, including weight. Weight discrimination was associated with a nearly 60% increased mortality risk in both HRS (HR=1.57, 95% CI=1.34-1.84) and MIDUS (HR=1.59, 95% CI=1.09-2.31) that was not accounted for by common physical and psychological risk factors. The association between weight discrimination and mortality was generally stronger than for other attributions for discrimination. In addition to poor health outcomes, weight discrimination may shorten life expectancy. PMID:26420442

[Discrimination of varieties of borneol using terahertz spectra based on principal component analysis and support vector machine].

PubMed

Li, Wu; Hu, Bing; Wang, Ming-wei

2014-12-01

In the present paper, the terahertz time-domain spectroscopy (THz-TDS) identification model of borneol based on principal component analysis (PCA) and support vector machine (SVM) was established. As one Chinese common agent, borneol needs a rapid, simple and accurate detection and identification method for its different source and being easily confused in the pharmaceutical and trade links. In order to assure the quality of borneol product and guard the consumer's right, quickly, efficiently and correctly identifying borneol has significant meaning to the production and transaction of borneol. Terahertz time-domain spectroscopy is a new spectroscopy approach to characterize material using terahertz pulse. The absorption terahertz spectra of blumea camphor, borneol camphor and synthetic borneol were measured in the range of 0.2 to 2 THz with the transmission THz-TDS. The PCA scores of 2D plots (PC1 X PC2) and 3D plots (PC1 X PC2 X PC3) of three kinds of borneol samples were obtained through PCA analysis, and both of them have good clustering effect on the 3 different kinds of borneol. The value matrix of the first 10 principal components (PCs) was used to replace the original spectrum data, and the 60 samples of the three kinds of borneol were trained and then the unknown 60 samples were identified. Four kinds of support vector machine model of different kernel functions were set up in this way. Results show that the accuracy of identification and classification of SVM RBF kernel function for three kinds of borneol is 100%, and we selected the SVM with the radial basis kernel function to establish the borneol identification model, in addition, in the noisy case, the classification accuracy rates of four SVM kernel function are above 85%, and this indicates that SVM has strong generalization ability. This study shows that PCA with SVM method of borneol terahertz spectroscopy has good classification and identification effects, and provides a new method for species identification of borneol in Chinese medicine.

Identification of DNA-binding proteins by combining auto-cross covariance transformation and ensemble learning.

PubMed

Liu, Bin; Wang, Shanyi; Dong, Qiwen; Li, Shumin; Liu, Xuan

2016-04-20

DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. With the rapid development of next generation of sequencing technique, the number of protein sequences is unprecedentedly increasing. Thus it is necessary to develop computational methods to identify the DNA-binding proteins only based on the protein sequence information. In this study, a novel method called iDNA-KACC is presented, which combines the Support Vector Machine (SVM) and the auto-cross covariance transformation. The protein sequences are first converted into profile-based protein representation, and then converted into a series of fixed-length vectors by the auto-cross covariance transformation with Kmer composition. The sequence order effect can be effectively captured by this scheme. These vectors are then fed into Support Vector Machine (SVM) to discriminate the DNA-binding proteins from the non DNA-binding ones. iDNA-KACC achieves an overall accuracy of 75.16% and Matthew correlation coefficient of 0.5 by a rigorous jackknife test. Its performance is further improved by employing an ensemble learning approach, and the improved predictor is called iDNA-KACC-EL. Experimental results on an independent dataset shows that iDNA-KACC-EL outperforms all the other state-of-the-art predictors, indicating that it would be a useful computational tool for DNA binding protein identification. .

Risk Factors for the Presence of Chikungunya and Dengue Vectors (Aedes aegypti and Aedes albopictus), Their Altitudinal Distribution and Climatic Determinants of Their Abundance in Central Nepal

PubMed Central

Dhimal, Meghnath; Gautam, Ishan; Joshi, Hari Datt; O’Hara, Robert B.; Ahrens, Bodo; Kuch, Ulrich

2015-01-01

Background The presence of the recently introduced primary dengue virus vector mosquito Aedes aegypti in Nepal, in association with the likely indigenous secondary vector Aedes albopictus, raises public health concerns. Chikungunya fever cases have also been reported in Nepal, and the virus causing this disease is also transmitted by these mosquito species. Here we report the results of a study on the risk factors for the presence of chikungunya and dengue virus vectors, their elevational ceiling of distribution, and climatic determinants of their abundance in central Nepal. Methodology/Principal Findings We collected immature stages of mosquitoes during six monthly cross-sectional surveys covering six administrative districts along an altitudinal transect in central Nepal that extended from Birgunj (80 m above sea level [asl]) to Dhunche (highest altitude sampled: 2,100 m asl). The dengue vectors Ae. aegypti and Ae. albopictus were commonly found up to 1,350 m asl in Kathmandu valley and were present but rarely found from 1,750 to 2,100 m asl in Dhunche. The lymphatic filariasis vector Culex quinquefasciatus was commonly found throughout the study transect. Physiographic region, month of collection, collection station and container type were significant predictors of the occurrence and co-occurrence of Ae. aegypti and Ae. albopictus. The climatic variables rainfall, temperature, and relative humidity were significant predictors of chikungunya and dengue virus vectors abundance. Conclusions/Significance We conclude that chikungunya and dengue virus vectors have already established their populations up to the High Mountain region of Nepal and that this may be attributed to the environmental and climate change that has been observed over the decades in Nepal. The rapid expansion of the distribution of these important disease vectors in the High Mountain region, previously considered to be non-endemic for dengue and chikungunya fever, calls for urgent actions to protect the health of local people and tourists travelling in the central Himalayas. PMID:25774518

Amblyomma imitator Ticks as Vectors of Rickettsia rickettsii, Mexico

PubMed Central

Oliveira, Karla A.; Pinter, Adriano; Medina-Sanchez, Aaron; Boppana, Venkata D.; Wikel, Stephen K.; Saito, Tais B.; Shelite, Thomas; Blanton, Lucas; Popov, Vsevolod; Teel, Pete D.; Walker, David H.; Galvao, Marcio A.M.; Mafra, Claudio

2010-01-01

Real-time PCR of Amblyomma imitator tick egg masses obtained in Nuevo Leon State, Mexico, identified a Rickettsia species. Sequence analyses of 17-kD common antigen and outer membrane protein A and B gene fragments showed to it to be R. rickettsii, which suggested a potential new vector for this bacterium. PMID:20678325

Monograph On Tensor Notations

NASA Technical Reports Server (NTRS)

Sirlin, Samuel W.

1993-01-01

Eight-page report describes systems of notation used most commonly to represent tensors of various ranks, with emphasis on tensors in Cartesian coordinate systems. Serves as introductory or refresher text for scientists, engineers, and others familiar with basic concepts of coordinate systems, vectors, and partial derivatives. Indicial tensor, vector, dyadic, and matrix notations, and relationships among them described.

Elderly fall risk prediction using static posturography

PubMed Central

2017-01-01

Maintaining and controlling postural balance is important for activities of daily living, with poor postural balance being predictive of future falls. This study investigated eyes open and eyes closed standing posturography with elderly adults to identify differences and determine appropriate outcome measure cut-off scores for prospective faller, single-faller, multi-faller, and non-faller classifications. 100 older adults (75.5 ± 6.7 years) stood quietly with eyes open and then eyes closed while Wii Balance Board data were collected. Range in anterior-posterior (AP) and medial-lateral (ML) center of pressure (CoP) motion; AP and ML CoP root mean square distance from mean (RMS); and AP, ML, and vector sum magnitude (VSM) CoP velocity were calculated. Romberg Quotients (RQ) were calculated for all parameters. Participants reported six-month fall history and six-month post-assessment fall occurrence. Groups were retrospective fallers (24), prospective all fallers (42), prospective fallers (22 single, 6 multiple), and prospective non-fallers (47). Non-faller RQ AP range and RQ AP RMS differed from prospective all fallers, fallers, and single fallers. Non-faller eyes closed AP velocity, eyes closed VSM velocity, RQ AP velocity, and RQ VSM velocity differed from multi-fallers. RQ calculations were particularly relevant for elderly fall risk assessments. Cut-off scores from Clinical Cut-off Score, ROC curves, and discriminant functions were clinically viable for multi-faller classification and provided better accuracy than single-faller classification. RQ AP range with cut-off score 1.64 could be used to screen for older people who may fall once. Prospective multi-faller classification with a discriminant function (-1.481 + 0.146 x Eyes Closed AP Velocity—0.114 x Eyes Closed Vector Sum Magnitude Velocity—2.027 x RQ AP Velocity + 2.877 x RQ Vector Sum Magnitude Velocity) and cut-off score 0.541 achieved an accuracy of 84.9% and is viable as a screening tool for older people at risk of multiple falls. PMID:28222191

Elderly fall risk prediction using static posturography.

PubMed

Howcroft, Jennifer; Lemaire, Edward D; Kofman, Jonathan; McIlroy, William E

2017-01-01

Maintaining and controlling postural balance is important for activities of daily living, with poor postural balance being predictive of future falls. This study investigated eyes open and eyes closed standing posturography with elderly adults to identify differences and determine appropriate outcome measure cut-off scores for prospective faller, single-faller, multi-faller, and non-faller classifications. 100 older adults (75.5 ± 6.7 years) stood quietly with eyes open and then eyes closed while Wii Balance Board data were collected. Range in anterior-posterior (AP) and medial-lateral (ML) center of pressure (CoP) motion; AP and ML CoP root mean square distance from mean (RMS); and AP, ML, and vector sum magnitude (VSM) CoP velocity were calculated. Romberg Quotients (RQ) were calculated for all parameters. Participants reported six-month fall history and six-month post-assessment fall occurrence. Groups were retrospective fallers (24), prospective all fallers (42), prospective fallers (22 single, 6 multiple), and prospective non-fallers (47). Non-faller RQ AP range and RQ AP RMS differed from prospective all fallers, fallers, and single fallers. Non-faller eyes closed AP velocity, eyes closed VSM velocity, RQ AP velocity, and RQ VSM velocity differed from multi-fallers. RQ calculations were particularly relevant for elderly fall risk assessments. Cut-off scores from Clinical Cut-off Score, ROC curves, and discriminant functions were clinically viable for multi-faller classification and provided better accuracy than single-faller classification. RQ AP range with cut-off score 1.64 could be used to screen for older people who may fall once. Prospective multi-faller classification with a discriminant function (-1.481 + 0.146 x Eyes Closed AP Velocity-0.114 x Eyes Closed Vector Sum Magnitude Velocity-2.027 x RQ AP Velocity + 2.877 x RQ Vector Sum Magnitude Velocity) and cut-off score 0.541 achieved an accuracy of 84.9% and is viable as a screening tool for older people at risk of multiple falls.

Identification of central Kenyan Rift Valley Fever virus vector habitats with Landsat TM and evaluation of their flooding status with airborne imaging radar

NASA Technical Reports Server (NTRS)

Pope, K. O.; Sheffner, E. J.; Linthicum, K. J.; Bailey, C. L.; Logan, T. M.; Kasischke, E. S.; Birney, K.; Njogu, A. R.; Roberts, C. R.

1992-01-01

Rift Valley Fever (RVF) is a mosquito-borne virus that affects livestock and humans in Africa. Landsat TM data are shown to be effective in identifying dambos, intermittently flooded areas that are potential mosquite breeding sites, in an area north of Nairobi, Kenya. Positive results were obtained from a limited test of flood detection in dambos with airborne high resolution L, C, and X band multipolarization SAR imagery. L and C bands were effective in detecting flooded dambos, but LHH was by far the best channel for discrimination between flooded and nonflooded sites in both sedge and short-grass environments. This study demonstrates the feasibility of a combined passive and active remote sensing program for monitoring the location and condition of RVF vector habitats, thus making future control of the disease more promising.

More than apples and oranges - Detecting cancer with a fruit fly's antenna

NASA Astrophysics Data System (ADS)

Strauch, Martin; Lüdke, Alja; Münch, Daniel; Laudes, Thomas; Galizia, C. Giovanni; Martinelli, Eugenio; Lavra, Luca; Paolesse, Roberto; Ulivieri, Alessandra; Catini, Alexandro; Capuano, Rosamaria; di Natale, Corrado

2014-01-01

Cancer cells and non-cancer cells differ in their metabolism and they emit distinct volatile compound profiles, allowing to recognise cancer cells by their scent. Insect odorant receptors are excellent chemosensors with high sensitivity and a broad receptive range unmatched by current gas sensors. We thus investigated the potential of utilising the fruit fly's olfactory system to detect cancer cells. Using in vivo calcium imaging, we recorded an array of olfactory receptor neurons on the fruit fly's antenna. We performed multidimensional analysis of antenna responses, finding that cell volatiles from different cell types lead to characteristic response vectors. The distances between these response vectors are conserved across flies and can be used to discriminate healthy mammary epithelial cells from different types of breast cancer cells. This may expand the repertoire of clinical diagnostics, and it is the first step towards electronic noses equipped with biological sensors, integrating artificial and biological olfaction.

Method and apparatus for enhanced detection of toxic agents

DOEpatents

Greenbaum, Elias; Rodriguez, Jr., Miguel; Wu, Jie Jayne; Qi, Hairong

2013-10-01

A biosensor based detection of toxins includes enhancing a fluorescence signal by concentrating a plurality of photosynthetic organisms in a fluid into a concentrated region using biased AC electro-osmosis. A measured photosynthetic activity of the photosynthetic organisms is obtained in the concentrated region, where chemical, biological or radiological agents reduce a nominal photosynthetic activity of the photosynthetic organisms. A presence of the chemical, biological and/or radiological agents or precursors thereof, is determined in the fluid based on the measured photosynthetic activity of the concentrated plurality of photosynthetic organisms. A lab-on-a-chip system is used for the concentrating step. The presence of agents is determined from feature vectors, obtained from processing a time dependent signal using amplitude statistics and/or time-frequency analysis, relative to a control signal. A linear discriminant method including support vector machine classification (SVM) is used to identify the agents.

Spatial Pyramid Covariance based Compact Video Code for Robust Face Retrieval in TV-series.

PubMed

Li, Yan; Wang, Ruiping; Cui, Zhen; Shan, Shiguang; Chen, Xilin

2016-10-10

We address the problem of face video retrieval in TV-series which searches video clips based on the presence of specific character, given one face track of his/her. This is tremendously challenging because on one hand, faces in TV-series are captured in largely uncontrolled conditions with complex appearance variations, and on the other hand retrieval task typically needs efficient representation with low time and space complexity. To handle this problem, we propose a compact and discriminative representation for the huge body of video data, named Compact Video Code (CVC). Our method first models the face track by its sample (i.e., frame) covariance matrix to capture the video data variations in a statistical manner. To incorporate discriminative information and obtain more compact video signature suitable for retrieval, the high-dimensional covariance representation is further encoded as a much lower-dimensional binary vector, which finally yields the proposed CVC. Specifically, each bit of the code, i.e., each dimension of the binary vector, is produced via supervised learning in a max margin framework, which aims to make a balance between the discriminability and stability of the code. Besides, we further extend the descriptive granularity of covariance matrix from traditional pixel-level to more general patchlevel, and proceed to propose a novel hierarchical video representation named Spatial Pyramid Covariance (SPC) along with a fast calculation method. Face retrieval experiments on two challenging TV-series video databases, i.e., the Big Bang Theory and Prison Break, demonstrate the competitiveness of the proposed CVC over state-of-the-art retrieval methods. In addition, as a general video matching algorithm, CVC is also evaluated in traditional video face recognition task on a standard Internet database, i.e., YouTube Celebrities, showing its quite promising performance by using an extremely compact code with only 128 bits.

Vectors a Fortran 90 module for 3-dimensional vector and dyadic arithmetic

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

Brock, B.C.

1998-02-01

A major advance contained in the new Fortran 90 language standard is the ability to define new data types and the operators associated with them. Writing computer code to implement computations with real and complex three-dimensional vectors and dyadics is greatly simplified if the equations can be implemented directly, without the need to code the vector arithmetic explicitly. The Fortran 90 module described here defines new data types for real and complex 3-dimensional vectors and dyadics, along with the common operations needed to work with these objects. Routines to allow convenient initialization and output of the new types are alsomore » included. In keeping with the philosophy of data abstraction, the details of the implementation of the data types are maintained private, and the functions and operators are made generic to simplify the combining of real, complex, single- and double-precision vectors and dyadics.« less

Deterministic binary vectors for efficient automated indexing of MEDLINE/PubMed abstracts.

PubMed

Wahle, Manuel; Widdows, Dominic; Herskovic, Jorge R; Bernstam, Elmer V; Cohen, Trevor

2012-01-01

The need to maintain accessibility of the biomedical literature has led to development of methods to assist human indexers by recommending index terms for newly encountered articles. Given the rapid expansion of this literature, it is essential that these methods be scalable. Document vector representations are commonly used for automated indexing, and Random Indexing (RI) provides the means to generate them efficiently. However, RI is difficult to implement in real-world indexing systems, as (1) efficient nearest-neighbor search requires retaining all document vectors in RAM, and (2) it is necessary to maintain a store of randomly generated term vectors to index future documents. Motivated by these concerns, this paper documents the development and evaluation of a deterministic binary variant of RI. The increased capacity demonstrated by binary vectors has implications for information retrieval, and the elimination of the need to retain term vectors facilitates distributed implementations, enhancing the scalability of RI.

Deterministic Binary Vectors for Efficient Automated Indexing of MEDLINE/PubMed Abstracts

PubMed Central

Wahle, Manuel; Widdows, Dominic; Herskovic, Jorge R.; Bernstam, Elmer V.; Cohen, Trevor

2012-01-01

The need to maintain accessibility of the biomedical literature has led to development of methods to assist human indexers by recommending index terms for newly encountered articles. Given the rapid expansion of this literature, it is essential that these methods be scalable. Document vector representations are commonly used for automated indexing, and Random Indexing (RI) provides the means to generate them efficiently. However, RI is difficult to implement in real-world indexing systems, as (1) efficient nearest-neighbor search requires retaining all document vectors in RAM, and (2) it is necessary to maintain a store of randomly generated term vectors to index future documents. Motivated by these concerns, this paper documents the development and evaluation of a deterministic binary variant of RI. The increased capacity demonstrated by binary vectors has implications for information retrieval, and the elimination of the need to retain term vectors facilitates distributed implementations, enhancing the scalability of RI. PMID:23304369

Manipulation of dielectric Rayleigh particles using highly focused elliptically polarized vector fields.

PubMed

Gu, Bing; Xu, Danfeng; Rui, Guanghao; Lian, Meng; Cui, Yiping; Zhan, Qiwen

2015-09-20

Generation of vectorial optical fields with arbitrary polarization distribution is of great interest in areas where exotic optical fields are desired. In this work, we experimentally demonstrate the versatile generation of linearly polarized vector fields, elliptically polarized vector fields, and circularly polarized vortex beams through introducing attenuators in a common-path interferometer. By means of Richards-Wolf vectorial diffraction method, the characteristics of the highly focused elliptically polarized vector fields are studied. The optical force and torque on a dielectric Rayleigh particle produced by these tightly focused vector fields are calculated and exploited for the stable trapping of dielectric Rayleigh particles. It is shown that the additional degree of freedom provided by the elliptically polarized vector field allows one to control the spatial structure of polarization, to engineer the focusing field, and to tailor the optical force and torque on a dielectric Rayleigh particle.

Novel strategies to construct complex synthetic vectors to produce DNA molecular weight standards.

PubMed

Chen, Zhe; Wu, Jianbing; Li, Xiaojuan; Ye, Chunjiang; Wenxing, He

2009-05-01

DNA molecular weight standards (DNA markers, nucleic acid ladders) are commonly used in molecular biology laboratories as references to estimate the size of various DNA samples in electrophoresis process. One method of DNA marker production is digestion of synthetic vectors harboring multiple DNA fragments of known sizes by restriction enzymes. In this article, we described three novel strategies-sequential DNA fragment ligation, screening of ligation products by polymerase chain reaction (PCR) with end primers, and "small fragment accumulation"-for constructing complex synthetic vectors and minimizing the mass differences between DNA fragments produced from restrictive digestion of synthetic vectors. The strategy could be applied to construct various complex synthetic vectors to produce any type of low-range DNA markers, usually available commercially. In addition, the strategy is useful for single-step ligation of multiple DNA fragments for construction of complex synthetic vectors and other applications in molecular biology field.

Classification of lung cancer histology by gold nanoparticle sensors

PubMed Central

Barash, Orna; Peled, Nir; Tisch, Ulrike; Bunn, Paul A.; Hirsch, Fred R.; Haick, Hossam

2016-01-01

We propose a nanomedical device for the classification of lung cancer (LC) histology. The device profiles volatile organic compounds (VOCs) in the headspace of (subtypes of) LC cells, using gold nanoparticle (GNP) sensors that are suitable for detecting LC-specific patterns of VOC profiles, as determined by gas chromatography–mass spectrometry analysis. Analyzing the GNP sensing signals by support vector machine allowed significant discrimination between (i) LC and healthy cells; (ii) small cell LC and non–small cell LC; and between (iii) two subtypes of non–small cell LC: adenocarcinoma and squamous cell carcinoma. The discriminative power of the GNP sensors was then linked with the chemical nature and composition of the headspace VOCs of each LC state. These proof-of-concept findings could totally revolutionize LC screening and diagnosis, and might eventually allow early and differential diagnosis of LC subtypes with detectable or unreachable lung nodules. PMID:22033081

Detection and classification of subject-generated artifacts in EEG signals using autoregressive models.

PubMed

Lawhern, Vernon; Hairston, W David; McDowell, Kaleb; Westerfield, Marissa; Robbins, Kay

2012-07-15

We examine the problem of accurate detection and classification of artifacts in continuous EEG recordings. Manual identification of artifacts, by means of an expert or panel of experts, can be tedious, time-consuming and infeasible for large datasets. We use autoregressive (AR) models for feature extraction and characterization of EEG signals containing several kinds of subject-generated artifacts. AR model parameters are scale-invariant features that can be used to develop models of artifacts across a population. We use a support vector machine (SVM) classifier to discriminate among artifact conditions using the AR model parameters as features. Results indicate reliable classification among several different artifact conditions across subjects (approximately 94%). These results suggest that AR modeling can be a useful tool for discriminating among artifact signals both within and across individuals. Copyright © 2012 Elsevier B.V. All rights reserved.

Diverse Region-Based CNN for Hyperspectral Image Classification.

PubMed

Zhang, Mengmeng; Li, Wei; Du, Qian

2018-06-01

Convolutional neural network (CNN) is of great interest in machine learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, we propose a classification framework, called diverse region-based CNN, which can encode semantic context-aware representation to obtain promising features. With merging a diverse set of discriminative appearance factors, the resulting CNN-based representation exhibits spatial-spectral context sensitivity that is essential for accurate pixel classification. The proposed method exploiting diverse region-based inputs to learn contextual interactional features is expected to have more discriminative power. The joint representation containing rich spectral and spatial information is then fed to a fully connected network and the label of each pixel vector is predicted by a softmax layer. Experimental results with widely used hyperspectral image data sets demonstrate that the proposed method can surpass any other conventional deep learning-based classifiers and other state-of-the-art classifiers.

Discriminant locality preserving projections based on L1-norm maximization.

PubMed

Zhong, Fujin; Zhang, Jiashu; Li, Defang

2014-11-01

Conventional discriminant locality preserving projection (DLPP) is a dimensionality reduction technique based on manifold learning, which has demonstrated good performance in pattern recognition. However, because its objective function is based on the distance criterion using L2-norm, conventional DLPP is not robust to outliers which are present in many applications. This paper proposes an effective and robust DLPP version based on L1-norm maximization, which learns a set of local optimal projection vectors by maximizing the ratio of the L1-norm-based locality preserving between-class dispersion and the L1-norm-based locality preserving within-class dispersion. The proposed method is proven to be feasible and also robust to outliers while overcoming the small sample size problem. The experimental results on artificial datasets, Binary Alphadigits dataset, FERET face dataset and PolyU palmprint dataset have demonstrated the effectiveness of the proposed method.

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.

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

PubMed

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

2000-01-01

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

Limited Rank Matrix Learning, discriminative dimension reduction and visualization.

PubMed

Bunte, Kerstin; Schneider, Petra; Hammer, Barbara; Schleif, Frank-Michael; Villmann, Thomas; Biehl, Michael

2012-02-01

We present an extension of the recently introduced Generalized Matrix Learning Vector Quantization algorithm. In the original scheme, adaptive square matrices of relevance factors parameterize a discriminative distance measure. We extend the scheme to matrices of limited rank corresponding to low-dimensional representations of the data. This allows to incorporate prior knowledge of the intrinsic dimension and to reduce the number of adaptive parameters efficiently. In particular, for very large dimensional data, the limitation of the rank can reduce computation time and memory requirements significantly. Furthermore, two- or three-dimensional representations constitute an efficient visualization method for labeled data sets. The identification of a suitable projection is not treated as a pre-processing step but as an integral part of the supervised training. Several real world data sets serve as an illustration and demonstrate the usefulness of the suggested method. Copyright © 2011 Elsevier Ltd. All rights reserved.

Metabolomics combined with chemometric tools (PCA, HCA, PLS-DA and SVM) for screening cassava (Manihot esculenta Crantz) roots during postharvest physiological deterioration.

PubMed

Uarrota, Virgílio Gavicho; Moresco, Rodolfo; Coelho, Bianca; Nunes, Eduardo da Costa; Peruch, Luiz Augusto Martins; Neubert, Enilto de Oliveira; Rocha, Miguel; Maraschin, Marcelo

2014-10-15

Cassava roots are an important source of dietary and industrial carbohydrates and suffer markedly from postharvest physiological deterioration (PPD). This paper deals with metabolomics combined with chemometric tools for screening the chemical and enzymatic composition in several genotypes of cassava roots during PPD. Metabolome analyses showed increases in carotenoids, flavonoids, anthocyanins, phenolics, reactive scavenging species, and enzymes (superoxide dismutase family, hydrogen peroxide, and catalase) until 3-5days postharvest. PPD correlated negatively with phenolics and carotenoids and positively with anthocyanins and flavonoids. Chemometric tools such as principal component analysis, partial least squares discriminant analysis, and support vector machines discriminated well cassava samples and enabled a good prediction of samples. Hierarchical clustering analyses grouped samples according to their levels of PPD and chemical compositions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Classification of Hand Grasp Kinetics and Types Using Movement-Related Cortical Potentials and EEG Rhythms.

PubMed

Jochumsen, Mads; Rovsing, Cecilie; Rovsing, Helene; Niazi, Imran Khan; Dremstrup, Kim; Kamavuako, Ernest Nlandu

2017-01-01

Detection of single-trial movement intentions from EEG is paramount for brain-computer interfacing in neurorehabilitation. These movement intentions contain task-related information and if this is decoded, the neurorehabilitation could potentially be optimized. The aim of this study was to classify single-trial movement intentions associated with two levels of force and speed and three different grasp types using EEG rhythms and components of the movement-related cortical potential (MRCP) as features. The feature importance was used to estimate encoding of discriminative information. Two data sets were used. 29 healthy subjects executed and imagined different hand movements, while EEG was recorded over the contralateral sensorimotor cortex. The following features were extracted: delta, theta, mu/alpha, beta, and gamma rhythms, readiness potential, negative slope, and motor potential of the MRCP. Sequential forward selection was performed, and classification was performed using linear discriminant analysis and support vector machines. Limited classification accuracies were obtained from the EEG rhythms and MRCP-components: 0.48 ± 0.05 (grasp types), 0.41 ± 0.07 (kinetic profiles, motor execution), and 0.39 ± 0.08 (kinetic profiles, motor imagination). Delta activity contributed the most but all features provided discriminative information. These findings suggest that information from the entire EEG spectrum is needed to discriminate between task-related parameters from single-trial movement intentions.

Multiantenna Relay Beamforming Design for QoS Discrimination in Two-Way Relay Networks

PubMed Central

Xiong, Ke; Zhang, Yu; Li, Dandan; Zhong, Zhangdui

2013-01-01

This paper investigates the relay beamforming design for quality of service (QoS) discrimination in two-way relay networks. The purpose is to keep legitimate two-way relay users exchange their information via a helping multiantenna relay with QoS guarantee while avoiding the exchanged information overhearing by unauthorized receiver. To this end, we propose a physical layer method, where the relay beamforming is jointly designed with artificial noise (AN) which is used to interfere in the unauthorized user's reception. We formulate the joint beamforming and AN (BFA) design into an optimization problem such that the received signal-to-interference-ratio (SINR) at the two legitimate users is over a predefined QoS threshold while limiting the received SINR at the unauthorized user which is under a certain secure threshold. The objective of the optimization problem is to seek the optimal AN and beamforming vectors to minimize the total power consumed by the relay node. Since the optimization problem is nonconvex, we solve it by using semidefinite program (SDP) relaxation. For comparison, we also study the optimal relay beamforming without using AN (BFO) under the same QoS discrimination constraints. Simulation results show that both the proposed BFA and BFO can achieve the QoS discrimination of the two-way transmission. However, the proposed BFA yields significant power savings and lower infeasible rates compared with the BFO method. PMID:24391459

Object detection in natural backgrounds predicted by discrimination performance and models

NASA Technical Reports Server (NTRS)

Rohaly, A. M.; Ahumada, A. J. Jr; Watson, A. B.

1997-01-01

Many models of visual performance predict image discriminability, the visibility of the difference between a pair of images. We compared the ability of three image discrimination models to predict the detectability of objects embedded in natural backgrounds. The three models were: a multiple channel Cortex transform model with within-channel masking; a single channel contrast sensitivity filter model; and a digital image difference metric. Each model used a Minkowski distance metric (generalized vector magnitude) to summate absolute differences between the background and object plus background images. For each model, this summation was implemented with three different exponents: 2, 4 and infinity. In addition, each combination of model and summation exponent was implemented with and without a simple contrast gain factor. The model outputs were compared to measures of object detectability obtained from 19 observers. Among the models without the contrast gain factor, the multiple channel model with a summation exponent of 4 performed best, predicting the pattern of observer d's with an RMS error of 2.3 dB. The contrast gain factor improved the predictions of all three models for all three exponents. With the factor, the best exponent was 4 for all three models, and their prediction errors were near 1 dB. These results demonstrate that image discrimination models can predict the relative detectability of objects in natural scenes.

Rate determination from vector observations

NASA Technical Reports Server (NTRS)

Weiss, Jerold L.

1993-01-01

Vector observations are a common class of attitude data provided by a wide variety of attitude sensors. Attitude determination from vector observations is a well-understood process and numerous algorithms such as the TRIAD algorithm exist. These algorithms require measurement of the line of site (LOS) vector to reference objects and knowledge of the LOS directions in some predetermined reference frame. Once attitude is determined, it is a simple matter to synthesize vehicle rate using some form of lead-lag filter, and then, use it for vehicle stabilization. Many situations arise, however, in which rate knowledge is required but knowledge of the nominal LOS directions are not available. This paper presents two methods for determining spacecraft angular rates from vector observations without a priori knowledge of the vector directions. The first approach uses an extended Kalman filter with a spacecraft dynamic model and a kinematic model representing the motion of the observed LOS vectors. The second approach uses a 'differential' TRIAD algorithm to compute the incremental direction cosine matrix, from which vehicle rate is then derived.

The Adenovirus Genome Contributes to the Structural Stability of the Virion

PubMed Central

Saha, Bratati; Wong, Carmen M.; Parks, Robin J.

2014-01-01

Adenovirus (Ad) vectors are currently the most commonly used platform for therapeutic gene delivery in human gene therapy clinical trials. Although these vectors are effective, many researchers seek to further improve the safety and efficacy of Ad-based vectors through detailed characterization of basic Ad biology relevant to its function as a vector system. Most Ad vectors are deleted of key, or all, viral protein coding sequences, which functions to not only prevent virus replication but also increase the cloning capacity of the vector for foreign DNA. However, radical modifications to the genome size significantly decreases virion stability, suggesting that the virus genome plays a role in maintaining the physical stability of the Ad virion. Indeed, a similar relationship between genome size and virion stability has been noted for many viruses. This review discusses the impact of the genome size on Ad virion stability and emphasizes the need to consider this aspect of virus biology in Ad-based vector design. PMID:25254384

Herpes simplex virus type 1 (HSV-1)-derived recombinant vectors for gene transfer and gene therapy.

PubMed

Marconi, Peggy; Fraefel, Cornel; Epstein, Alberto L

2015-01-01

Herpes simplex virus type 1 (HSV-1 ) is a human pathogen whose lifestyle is based on a long-term dual interaction with the infected host, being able to establish both lytic and latent infections. The virus genome is a 153-kilobase pair (kbp) double-stranded DNA molecule encoding more than 80 genes. The interest of HSV-1 as gene transfer vector stems from its ability to infect many different cell types, both quiescent and proliferating cells, the very high packaging capacity of the virus capsid, the outstanding neurotropic adaptations that this virus has evolved, and the fact that it never integrates into the cellular chromosomes, thus avoiding the risk of insertional mutagenesis. Two types of vectors can be derived from HSV-1, recombinant vectors and amplicon vectors, and different methodologies have been developed to prepare large stocks of each type of vector. This chapter summarizes the approach most commonly used to prepare recombinant HSV-1 vectors through homologous recombination, either in eukaryotic cells or in bacteria.

Harassment and discrimination in medical training: a systematic review and meta-analysis.

PubMed

Fnais, Naif; Soobiah, Charlene; Chen, Maggie Hong; Lillie, Erin; Perrier, Laure; Tashkhandi, Mariam; Straus, Sharon E; Mamdani, Muhammad; Al-Omran, Mohammed; Tricco, Andrea C

2014-05-01

Harassment and discrimination include a wide range of behaviors that medical trainees perceive as being humiliating, hostile, or abusive. To understand the significance of such mistreatment and to explore potential preventive strategies, the authors conducted a systematic review and meta-analysis to examine the prevalence, risk factors, and sources of harassment and discrimination among medical trainees. In 2011, the authors identified relevant studies by searching MEDLINE and EMBASE, scanning reference lists of relevant studies, and contacting experts. They included studies that reported the prevalence, risk factors, and sources of harassment and discrimination among medical trainees. Two reviewers independently screened all articles and abstracted study and participant characteristics and study results. The authors assessed the methodological quality in individual studies using the Newcastle-Ottawa Scale. They also conducted a meta-analysis. The authors included 57 cross-sectional and 2 cohort studies in their review. The meta-analysis of 51 studies demonstrated that 59.4% of medical trainees had experienced at least one form of harassment or discrimination during their training (95% confidence interval [CI]: 52.0%-66.7%). Verbal harassment was the most commonly cited form of harassment (prevalence: 63.0%; 95% CI: 54.8%-71.2%). Consultants were the most commonly cited source of harassment and discrimination, followed by patients or patients' families (34.4% and 21.9%, respectively). This review demonstrates the surprisingly high prevalence of harassment and discrimination among medical trainees that has not declined over time. The authors recommend both drafting policies and promoting cultural change within academic institutions to prevent future abuse.

Optical filter selection for high confidence discrimination of strongly overlapping infrared chemical spectra.

PubMed

Major, Kevin J; Poutous, Menelaos K; Ewing, Kenneth J; Dunnill, Kevin F; Sanghera, Jasbinder S; Aggarwal, Ishwar D

2015-09-01

Optical filter-based chemical sensing techniques provide a new avenue to develop low-cost infrared sensors. These methods utilize multiple infrared optical filters to selectively measure different response functions for various chemicals, dependent on each chemical's infrared absorption. Rather than identifying distinct spectral features, which can then be used to determine the identity of a target chemical, optical filter-based approaches rely on measuring differences in the ensemble response between a given filter set and specific chemicals of interest. Therefore, the results of such methods are highly dependent on the original optical filter choice, which will dictate the selectivity, sensitivity, and stability of any filter-based sensing method. Recently, a method has been developed that utilizes unique detection vector operations defined by optical multifilter responses, to discriminate between volatile chemical vapors. This method, comparative-discrimination spectral detection (CDSD), is a technique which employs broadband optical filters to selectively discriminate between chemicals with highly overlapping infrared absorption spectra. CDSD has been shown to correctly distinguish between similar chemicals in the carbon-hydrogen stretch region of the infrared absorption spectra from 2800-3100 cm(-1). A key challenge to this approach is how to determine which optical filter sets should be utilized to achieve the greatest discrimination between target chemicals. Previous studies used empirical approaches to select the optical filter set; however this is insufficient to determine the optimum selectivity between strongly overlapping chemical spectra. Here we present a numerical approach to systematically study the effects of filter positioning and bandwidth on a number of three-chemical systems. We describe how both the filter properties, as well as the chemicals in each set, affect the CDSD results and subsequent discrimination. These results demonstrate the importance of choosing the proper filter set and chemicals for comparative discrimination, in order to identify the target chemical of interest in the presence of closely matched chemical interferents. These findings are an integral step in the development of experimental prototype sensors, which will utilize CDSD.

Process for structural geologic analysis of topography and point data

DOEpatents

Eliason, Jay R.; Eliason, Valerie L. C.

1987-01-01

A quantitative method of geologic structural analysis of digital terrain data is described for implementation on a computer. Assuming selected valley segments are controlled by the underlying geologic structure, topographic lows in the terrain data, defining valley bottoms, are detected, filtered and accumulated into a series line segments defining contiguous valleys. The line segments are then vectorized to produce vector segments, defining valley segments, which may be indicative of the underlying geologic structure. Coplanar analysis is performed on vector segment pairs to determine which vectors produce planes which represent underlying geologic structure. Point data such as fracture phenomena which can be related to fracture planes in 3-dimensional space can be analyzed to define common plane orientation and locations. The vectors, points, and planes are displayed in various formats for interpretation.

Zika mosquito vectors: the jury is still out.

PubMed

Leal, Walter S

2016-01-01

After a 40-year hiatus, the International Congress of Entomology (ICE 2016) convened in Orlando, Florida (September 25-30, 2016). One of the symposia at ICE 2016, the Zika Symposium, covered multiple aspects of the Zika epidemic, including epidemiology, sexual transmission, genetic tools for reducing transmission, and particularly vector competence. While there was a consensus among participants that the yellow fever mosquito, Aedes aegypti , is a vector of the Zika virus, there is growing evidence indicating that the range of mosquito vectors might be wider than anticipated. In particular, three independent groups from Canada, China, and Brazil presented and discussed laboratory and field data strongly suggesting that the southern house mosquito, Culex quinquefasciatus , also known as the common mosquito, is highly likely to be a vector in certain environments.

Discrimination and support from friends and family members experienced by people with mental health problems: findings from an Australian national survey.

PubMed

Morgan, Amy J; Reavley, N J; Jorm, A F; Beatson, R

2017-11-01

To investigate the scope and nature of discrimination and positive treatment experienced by adults with mental health problems from their friends and family in a population-based survey. An Australian telephone-survey of 5220 adults included 1381 individuals who reported a mental health problem or scored high on a screening questionnaire. Respondents were interviewed about their experience of discrimination and positive treatment from their friends, spouse and other family members. Descriptions of experiences were content-analysed to identify key characteristics. Mental health diagnoses were primarily depression or anxiety disorders, and just over half had received treatment in the last 12 months. Positive treatment from family and friends was far more common than discrimination, reported by 74.1% of respondents. This was primarily characterised by providing emotional support and maintaining contact, as well as checking on their mental health and being a good listener. Nevertheless, discriminatory behaviours from friends and family were reported by 25.8% of respondents, with reducing or cutting contact being by far the most common. Friends and family also commonly dismissed that mental illness was real or caused suffering and showed a lack of understanding about mental health problems or treatments and how they can impact behaviour and functioning. This nationally representative study of real life experiences highlights the potential for harm or benefit from a person's social support network. Despite positive experiences being common, there is an ongoing need to reduce mental illness stigma and improve understanding of how to support a loved one with a mental health problem.

Rule-based fuzzy vector median filters for 3D phase contrast MRI segmentation

NASA Astrophysics Data System (ADS)

Sundareswaran, Kartik S.; Frakes, David H.; Yoganathan, Ajit P.

2008-02-01

Recent technological advances have contributed to the advent of phase contrast magnetic resonance imaging (PCMRI) as standard practice in clinical environments. In particular, decreased scan times have made using the modality more feasible. PCMRI is now a common tool for flow quantification, and for more complex vector field analyses that target the early detection of problematic flow conditions. Segmentation is one component of this type of application that can impact the accuracy of the final product dramatically. Vascular segmentation, in general, is a long-standing problem that has received significant attention. Segmentation in the context of PCMRI data, however, has been explored less and can benefit from object-based image processing techniques that incorporate fluids specific information. Here we present a fuzzy rule-based adaptive vector median filtering (FAVMF) algorithm that in combination with active contour modeling facilitates high-quality PCMRI segmentation while mitigating the effects of noise. The FAVMF technique was tested on 111 synthetically generated PC MRI slices and on 15 patients with congenital heart disease. The results were compared to other multi-dimensional filters namely the adaptive vector median filter, the adaptive vector directional filter, and the scalar low pass filter commonly used in PC MRI applications. FAVMF significantly outperformed the standard filtering methods (p < 0.0001). Two conclusions can be drawn from these results: a) Filtering should be performed after vessel segmentation of PC MRI; b) Vector based filtering methods should be used instead of scalar techniques.

Racial discrimination as race-based trauma, coping strategies, and dissociative symptoms among emerging adults.

PubMed

Polanco-Roman, Lillian; Danies, Ashley; Anglin, Deidre M

2016-09-01

The race-based traumatic stress theory (Carter, 2007) suggests that some racial- and ethnic-minority individuals experience racial discrimination as psychological trauma, as it may elicit a response comparable to posttraumatic stress. In the present study, we examined this further by determining the relation between racial discrimination and dissociation, a common response to trauma exposure. Further, we examined whether active coping strategies specifically employed to cope with racial discrimination related to less dissociative symptomatology. The predominant racial- and ethnic-minority sample (N = 743) of emerging adults, ages 18-29, recruited from a public university in northeastern United States completed a battery of self-report measures on racial discrimination, responses to racial discrimination, traumatic life events, and dissociative symptoms. Frequency of racial discrimination was positively associated with dissociative symptoms in regression analyses adjusted for demographics and other traumatic life events. In addition, more active coping strategies in response to racial discrimination were negatively associated with dissociative symptoms. Racial- and ethnic-minority emerging adults who experience racial discrimination, possibly as traumatic, may be more vulnerable to dissociative symptoms. However, different strategies of coping with racial discrimination may differentially impact risk for dissociation. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Racial discrimination as race-based trauma, coping strategies and dissociative symptoms among emerging adults

PubMed Central

Polanco-Roman, Lillian; Danies, Ashley; Anglin, Deidre M.

2016-01-01

Objective The race-based traumatic stress theory (Carter, 2007) suggests that some racial and ethnic minority individuals may experience racial discrimination as a psychological trauma, as it may elicit a response comparable to posttraumatic stress. The present study examined this further by determining the relation between racial discrimination and dissociation, a common response to trauma exposure. Further, we examined whether active coping strategies specifically employed to cope with racial discrimination related to less dissociative symptomatology. Methods The predominant racial and ethnic minority sample (N=743) of emerging adults (i.e., ages 18-29) recruited from a public university in Northeastern U.S. completed a battery of self-report measures on racial discrimination, responses to racial discrimination, traumatic life events and dissociative symptoms. Results Frequency of racial discrimination was positively associated with dissociative symptoms in regression analyses adjusted for demographics and other traumatic life events. Additionally, more active coping strategies in response to racial discrimination were negatively associated with dissociative symptoms. Conclusion Racial and ethnic minority emerging adults who experience racial discrimination, possibly as traumatic, may be more vulnerable to dissociative symptoms. However, different strategies of coping with racial discrimination may differentially impact risk for dissociation. PMID:26963957

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Reciprocity relationships in vector acoustics and their application to vector field calculations.

PubMed

Deal, Thomas J; Smith, Kevin B

2017-08-01

The reciprocity equation commonly stated in underwater acoustics relates pressure fields and monopole sources. It is often used to predict the pressure measured by a hydrophone for multiple source locations by placing a source at the hydrophone location and calculating the field everywhere for that source. A similar equation that governs the orthogonal components of the particle velocity field is needed to enable this computational method to be used for acoustic vector sensors. This paper derives a general reciprocity equation that accounts for both monopole and dipole sources. This vector-scalar reciprocity equation can be used to calculate individual components of the received vector field by altering the source type used in the propagation calculation. This enables a propagation model to calculate the received vector field components for an arbitrary number of source locations with a single model run for each vector field component instead of requiring one model run for each source location. Application of the vector-scalar reciprocity principle is demonstrated with analytic solutions for a range-independent environment and with numerical solutions for a range-dependent environment using a parabolic equation model.

Breast cancer risk assessment and diagnosis model using fuzzy support vector machine based expert system

NASA Astrophysics Data System (ADS)

Dheeba, J.; Jaya, T.; Singh, N. Albert

2017-09-01

Classification of cancerous masses is a challenging task in many computerised detection systems. Cancerous masses are difficult to detect because these masses are obscured and subtle in mammograms. This paper investigates an intelligent classifier - fuzzy support vector machine (FSVM) applied to classify the tissues containing masses on mammograms for breast cancer diagnosis. The algorithm utilises texture features extracted using Laws texture energy measures and a FSVM to classify the suspicious masses. The new FSVM treats every feature as both normal and abnormal samples, but with different membership. By this way, the new FSVM have more generalisation ability to classify the masses in mammograms. The classifier analysed 219 clinical mammograms collected from breast cancer screening laboratory. The tests made on the real clinical mammograms shows that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and Laws texture features, the area under the Receiver operating characteristic curve reached .95, which corresponds to a sensitivity of 93.27% with a specificity of 87.17%. The results suggest that detecting masses using FSVM contribute to computer-aided detection of breast cancer and as a decision support system for radiologists.

Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms.

PubMed

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

2018-01-01

Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug distribution properties should be considered at the initial phases of the drug design and development. Therefore, PPB prediction models are receiving an increased attention. In the current study, we present a systematic approach using Support vector machine, Artificial neural network, k- nearest neighbor, Probabilistic neural network, Partial least square and Linear discriminant analysis to relate various in vitro and in silico molecular descriptors to a diverse dataset of 736 drugs/drug-like compounds. The overall accuracy of Support vector machine with Radial basis function kernel came out to be comparatively better than the rest of the applied algorithms. The training set accuracy, validation set accuracy, precision, sensitivity, specificity and F1 score for the Suprort vector machine was found to be 89.73%, 89.97%, 92.56%, 87.26%, 91.97% and 0.898, respectively. This model can potentially be useful in screening of relevant drug candidates at the preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Feature selection gait-based gender classification under different circumstances

NASA Astrophysics Data System (ADS)

Sabir, Azhin; Al-Jawad, Naseer; Jassim, Sabah

2014-05-01

This paper proposes a gender classification based on human gait features and investigates the problem of two variations: clothing (wearing coats) and carrying bag condition as addition to the normal gait sequence. The feature vectors in the proposed system are constructed after applying wavelet transform. Three different sets of feature are proposed in this method. First, Spatio-temporal distance that is dealing with the distance of different parts of the human body (like feet, knees, hand, Human Height and shoulder) during one gait cycle. The second and third feature sets are constructed from approximation and non-approximation coefficient of human body respectively. To extract these two sets of feature we divided the human body into two parts, upper and lower body part, based on the golden ratio proportion. In this paper, we have adopted a statistical method for constructing the feature vector from the above sets. The dimension of the constructed feature vector is reduced based on the Fisher score as a feature selection method to optimize their discriminating significance. Finally k-Nearest Neighbor is applied as a classification method. Experimental results demonstrate that our approach is providing more realistic scenario and relatively better performance compared with the existing approaches.

Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition.

PubMed

Ibrahim, Wisam; Abadeh, Mohammad Saniee

2017-05-21

Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid sequences to obtain better classifiers. In this paper, we have proposed six descriptors to extract features from protein sequences. These descriptors are applied in the first stage of a three-stage framework PCA-DELM-LDA to extract feature vectors from the amino-acid sequences. Principal Component Analysis PCA has been implemented to reduce the number of extracted features. The extracted feature vectors have been used with original features to improve the performance of the Deep Extreme Learning Machine DELM in the second stage. Four new features have been extracted from the second stage and used in the third stage by Linear Discriminant Analysis LDA to classify the instances into 27 folds. The proposed framework is implemented on the independent and combined feature sets in SCOP datasets. The experimental results show that extracted feature vectors in the first stage could improve the performance of DELM in extracting new useful features in second stage. Copyright © 2017 Elsevier Ltd. All rights reserved.

Estimating high mosquito-producing rice fields using spectral and spatial data

NASA Technical Reports Server (NTRS)

Wood, B. L.; Beck, L. R.; Washino, R. K.; Hibbard, K. A.; Salute, J. S.

1992-01-01

The cultivation of irrigated rice provides ideal larval habitat for a number of anopheline vectors of malaria throughout the world. Anopheles freeborni, a potential vector of human malaria, is associated with the nearly 240,000 hectares of irrigated rice grown annually in Northern and Central California; therefore, this species can serve as a model for the study of rice field anopheline population dynamics. Analysis of field data revealed that rice fields with early season canopy development, that are located near bloodmeal sources (i.e., pastures with livestock) were more likely to produce anopheline larvae than fields with less developed canopies located further from pastures. Remote sensing reflectance measurements of early-season canopy development and geographic information system (GIS) measurements of distanes between rice fields and pastures with livestock were combined to distinguish between high and low mosquito-producing rice fields. Using spectral and distance measures in either a discriminant or Bayesian analysis, the identification of high mosquito-producing fields was made with 85 percent accuracy nearly two months before anopheline larval populations peaked. Since omission errors were also minimized by these approaches, they could provide a new basis for directing abatement techniques for the control of malaria vectors.

DNA barcoding for identification of sand fly species (Diptera: Psychodidae) from leishmaniasis-endemic areas of Peru.

PubMed

Nzelu, Chukwunonso O; Cáceres, Abraham G; Arrunátegui-Jiménez, Martín J; Lañas-Rosas, Máximo F; Yañez-Trujillano, Henrry H; Luna-Caipo, Deysi V; Holguín-Mauricci, Carlos E; Katakura, Ken; Hashiguchi, Yoshihisa; Kato, Hirotomo

2015-05-01

Phlebotomine sand flies are the only proven vectors of leishmaniases, a group of human and animal diseases. Accurate knowledge of sand fly species identification is essential in understanding the epidemiology of leishmaniasis and vector control in endemic areas. Classical identification of sand fly species based on morphological characteristics often remains difficult and requires taxonomic expertise. Here, we generated DNA barcodes of the cytochrome c oxidase subunit 1 (COI) gene using 159 adult specimens morphologically identified to be 19 species of sand flies, belonging to 6 subgenera/species groups circulating in Peru, including the vector species. Neighbor-joining (NJ) analysis based on Kimura 2-Parameter genetic distances formed non-overlapping clusters for all species. The levels of intraspecific genetic divergence ranged from 0 to 5.96%, whereas interspecific genetic divergence among different species ranged from 8.39 to 19.08%. The generated COI barcodes could discriminate between all the sand fly taxa. Besides its success in separating known species, we found that DNA barcoding is useful in revealing population differentiation and cryptic diversity, and thus promises to be a valuable tool for epidemiological studies of leishmaniasis. Copyright © 2015 Elsevier B.V. All rights reserved.

Signal-Detection Analyses of Conditional Discrimination and Delayed Matching-to-Sample Performance

ERIC Educational Resources Information Center

Alsop, Brent

2004-01-01

Quantitative analyses of stimulus control and reinforcer control in conditional discriminations and delayed matching-to-sample procedures often encounter a problem; it is not clear how to analyze data when subjects have not made errors. The present article examines two common methods for overcoming this problem. Monte Carlo simulations of…

Establishing the discriminating concentration for permethrin and fipronil resistance in Rhipicephalus sanguineus (Latreille) (Acari:Ixodidae), the brown dog tick

USDA-ARS?s Scientific Manuscript database

Rhipicephalus sanguineus (Latreille), the brown dog tick, is a veterinary canine and urban pest. These ticks have been found to develop permethrin resistance and fipronil tolerance, two commonly used acaricides. We developed a discriminating concentration that can be used to rapidly detect permethri...

Color discrimination with broadband photoreceptors.

PubMed

Schnaitmann, Christopher; Garbers, Christian; Wachtler, Thomas; Tanimoto, Hiromu

2013-12-02

Color vision is commonly assumed to rely on photoreceptors tuned to narrow spectral ranges. In the ommatidium of Drosophila, the four types of so-called inner photoreceptors express different narrow-band opsins. In contrast, the outer photoreceptors have a broadband spectral sensitivity and were thought to exclusively mediate achromatic vision. Using computational models and behavioral experiments, we demonstrate that the broadband outer photoreceptors contribute to color vision in Drosophila. The model of opponent processing that includes the opsin of the outer photoreceptors scored the best fit to wavelength discrimination data. To experimentally uncover the contribution of individual photoreceptor types, we restored phototransduction of targeted photoreceptor combinations in a blind mutant. Dichromatic flies with only broadband photoreceptors and one additional receptor type can discriminate different colors, indicating the existence of a specific output comparison of the outer and inner photoreceptors. Furthermore, blocking interneurons postsynaptic to the outer photoreceptors specifically impaired color but not intensity discrimination. Our findings show that receptors with a complex and broad spectral sensitivity can contribute to color vision and reveal that chromatic and achromatic circuits in the fly share common photoreceptors. Copyright © 2013 Elsevier Ltd. All rights reserved.

Evaluation of a Machine-Learning Classifier for Keratoconus Detection Based on Scheimpflug Tomography.

PubMed

Ruiz Hidalgo, Irene; Rodriguez, Pablo; Rozema, Jos J; Ní Dhubhghaill, Sorcha; Zakaria, Nadia; Tassignon, Marie-José; Koppen, Carina

2016-06-01

To evaluate the performance of a support vector machine algorithm that automatically and objectively identifies corneal patterns based on a combination of 22 parameters obtained from Pentacam measurements and to compare this method with other known keratoconus (KC) classification methods. Pentacam data from 860 eyes were included in the study and divided into 5 groups: 454 KC, 67 forme fruste (FF), 28 astigmatic, 117 after refractive surgery (PR), and 194 normal eyes (N). Twenty-two parameters were used for classification using a support vector machine algorithm developed in Weka, a machine-learning computer software. The cross-validation accuracy for 3 different classification tasks (KC vs. N, FF vs. N and all 5 groups) was calculated and compared with other known classification methods. The accuracy achieved in the KC versus N discrimination task was 98.9%, with 99.1% sensitivity and 98.5% specificity for KC detection. The accuracy in the FF versus N task was 93.1%, with 79.1% sensitivity and 97.9% specificity for the FF discrimination. Finally, for the 5-groups classification, the accuracy was 88.8%, with a weighted average sensitivity of 89.0% and specificity of 95.2%. Despite using the strictest definition for FF KC, the present study obtained comparable or better results than the single-parameter methods and indices reported in the literature. In some cases, direct comparisons with the literature were not possible because of differences in the compositions and definitions of the study groups, especially the FF KC.

Assessing the druggability of protein-protein interactions by a supervised machine-learning method.

PubMed

Sugaya, Nobuyoshi; Ikeda, Kazuyoshi

2009-08-25

Protein-protein interactions (PPIs) are challenging but attractive targets of small molecule drugs for therapeutic interventions of human diseases. In this era of rapid accumulation of PPI data, there is great need for a methodology that can efficiently select drug target PPIs by holistically assessing the druggability of PPIs. To address this need, we propose here a novel approach based on a supervised machine-learning method, support vector machine (SVM). To assess the druggability of the PPIs, 69 attributes were selected to cover a wide range of structural, drug and chemical, and functional information on the PPIs. These attributes were used as feature vectors in the SVM-based method. Thirty PPIs known to be druggable were carefully selected from previous studies; these were used as positive instances. Our approach was applied to 1,295 human PPIs with tertiary structures of their protein complexes already solved. The best SVM model constructed discriminated the already-known target PPIs from others at an accuracy of 81% (sensitivity, 82%; specificity, 79%) in cross-validation. Among the attributes, the two with the greatest discriminative power in the best SVM model were the number of interacting proteins and the number of pathways. Using the model, we predicted several promising candidates for druggable PPIs, such as SMAD4/SKI. As more PPI data are accumulated in the near future, our method will have increased ability to accelerate the discovery of druggable PPIs.

Identification of six New World Leishmania species through the implementation of a High-Resolution Melting (HRM) genotyping assay.

PubMed

Hernández, Carolina; Alvarez, Catalina; González, Camila; Ayala, Martha Stella; León, Cielo Maritza; Ramírez, Juan David

2014-11-14

Leishmaniases are tropical zoonotic diseases, caused by parasites from the genus Leishmania. New World (NW) species are related to sylvatic cycles although urbanization processes have been reported in some South American Countries such as Colombia. This eco-epidemiological complexity imposes a challenge to the detection of circulating parasite species, not only related to human cases but also infecting vectors and reservoirs. Currently, no harmonized methods have been deployed to discriminate the NW Leishmania species. Herein, we conducted a systematic and mechanistic High-Resolution Melting (HRM) assay targeted to HSP70 and ITS1. Specific primers were designed that coupled with a HRM analyses permitted to discriminate six NW Leishmania species. In order to validate the herein described algorithm, we included 35 natural isolates obtained from human cases, insect vectors and mammals. Our genotyping assay allowed the correct assignment of the six NW Leishmania species (L. mexicana, L. infantum (chagasi), L. amazonensis, L. panamensis, L. guyanensis and L. braziliensis) based on reference strains. When the algorithm was applied to a set of well-characterized strains by means of PCR-RFLP, MLEE and monoclonal antibodies (MA) we observed a tailored concordance between the HRM and PCR-RFLP/MLEE/MA (KI = 1.0). Additionally, we tested the limit of detection for the HRM method showing that this is able to detect at least 10 equivalent-parasites per mL. This is a rapid and reliable method to conduct molecular epidemiology and host-parasite association studies in endemic areas.

The Influence of Discrimination on Smoking Cessation among Latinos

PubMed Central

Kendzor, Darla E.; Businelle, Michael S.; Reitzel, Lorraine R.; Castro, Yessenia; Vidrine, Jennifer I.; Mazas, Carlos A.; Cinciripini, Paul M.; Lam, Cho Y.; Adams, Claire E.; Correa-Fernández, Virmarie; Cano, Miguel Ángel; Wetter, David W.

2014-01-01

Background: Although studies have shown a cross-sectional link between discrimination and smoking, the prospective influence of discrimination on smoking cessation has yet to be evaluated. Thus, the purpose of the current study was to determine the influence of everyday and major discrimination on smoking cessation among Latinos making a quit attempt. Methods: Participants were 190 Spanish speaking smokers of Mexican Heritage recruited from the Houston, TX metropolitan area who participated in the study between 2009 and 2012. Logistic regression analyses were conducted to evaluate the associations of everyday and major discrimination with smoking abstinence at 26 weeks post-quit. Results: Most participants reported at least some everyday discrimination (64.4%), and at least one major discrimination event (56%) in their lifetimes. Race/ethnicity/nationality was the most commonly perceived reason for both everyday and major discrimination. Everyday discrimination was not associated with post-quit smoking status. However, experiencing a greater number of major discrimination events was associated with a reduced likelihood of achieving 7-day point prevalence smoking abstinence, OR = .51, p = .004, and continuous smoking abstinence, OR = .29, p = .018, at 26 weeks post-quit. Conclusions: Findings highlight the high frequency of exposure to discrimination among Latinos, and demonstrate the negative impact of major discrimination events on a smoking cessation attempt. Efforts are needed to attenuate the detrimental effects of major discrimination events on smoking cessation outcomes. PMID:24485880

Predators indirectly control vector-borne disease: linking predator-prey and host-pathogen models.

PubMed

Moore, Sean M; Borer, Elizabeth T; Hosseini, Parviez R

2010-01-06

Pathogens transmitted by arthropod vectors are common in human populations, agricultural systems and natural communities. Transmission of these vector-borne pathogens depends on the population dynamics of the vector species as well as its interactions with other species within the community. In particular, predation may be sufficient to control pathogen prevalence indirectly via the vector. To examine the indirect effect of predators on vectored-pathogen dynamics, we developed a theoretical model that integrates predator-prey and host-pathogen theory. We used this model to determine whether predation can prevent pathogen persistence or alter the stability of host-pathogen dynamics. We found that, in the absence of predation, pathogen prevalence in the host increases with vector fecundity, whereas predation on the vector causes pathogen prevalence to decline, or even become extinct, with increasing vector fecundity. We also found that predation on a vector may drastically slow the initial spread of a pathogen. The predator can increase host abundance indirectly by reducing or eliminating infection in the host population. These results highlight the importance of studying interactions that, within the greater community, may alter our predictions when studying disease dynamics. From an applied perspective, these results also suggest situations where an introduced predator or the natural enemies of a vector may slow the rate of spread of an emerging vector-borne pathogen.

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

PubMed

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

2014-08-01

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

[Can discrimination ban guarantee equality of opportunities for disabled persons?].

PubMed

Günther, H

1997-11-01

Embodied in Germany's Basic Law since 1994, the discrimination ban on behalf of disabled persons--in line with similar provisions introduced in other countries--highlights the fact that disabled persons, as individuals and with their lawful rights, have become the point of departure of disability policies, as opposed to the hitherto prevailing social welfare perspective. Banning discrimination alone, though, will enable equal opportunities to be achieved only for some areas of social life; positive discriminations in favour of disabled persons are possible and necessary in addition. When it comes to integration into worklife, banning discrimination against disabled persons will achieve but little. Rather, in a market economy, employers and the general public share a common responsibility, for one, by respecting employers' rights of disposal over their jobs and, for the other, by changing present labour market conditions in favour of disabled persons, i.e., taking measures of positive discrimination.

Discrimination and mental health among Somali refugee adolescents: the role of acculturation and gender.

PubMed

Ellis, B Heidi; MacDonald, Helen Z; Klunk-Gillis, Julie; Lincoln, Alisa; Strunin, Lee; Cabral, Howard J

2010-10-01

This study examines the role of social identity (acculturation and gender) in moderating the association between discrimination and Somali adolescent refugees' mental health. Participants were English-speaking Somali adolescent refugees between the ages of 11 and 20 (N = 135). Perceived discrimination, trauma history, posttraumatic stress disorder (PTSD), depressive symptoms, and behavioral acculturation were assessed in structured interviews. Fourteen in-depth qualitative interviews and 3 focus groups were also conducted. Results indicated that discrimination was common and associated with worse mental health. For girls, greater Somali acculturation was associated with better mental health. Also, the association between discrimination and PTSD was less strong for girls who showed higher levels of Somali acculturation. For boys, greater American acculturation was associated with better mental health, and the association between discrimination and depression was less strong for boys with higher levels of American acculturation. © 2010 American Orthopsychiatric Association.

Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review.

PubMed

Maione, Camila; Barbosa, Rommel Melgaço

2018-01-24

Rice is one of the most important staple foods around the world. Authentication of rice is one of the most addressed concerns in the present literature, which includes recognition of its geographical origin and variety, certification of organic rice and many other issues. Good results have been achieved by multivariate data analysis and data mining techniques when combined with specific parameters for ascertaining authenticity and many other useful characteristics of rice, such as quality, yield and others. This paper brings a review of the recent research projects on discrimination and authentication of rice using multivariate data analysis and data mining techniques. We found that data obtained from image processing, molecular and atomic spectroscopy, elemental fingerprinting, genetic markers, molecular content and others are promising sources of information regarding geographical origin, variety and other aspects of rice, being widely used combined with multivariate data analysis techniques. Principal component analysis and linear discriminant analysis are the preferred methods, but several other data classification techniques such as support vector machines, artificial neural networks and others are also frequently present in some studies and show high performance for discrimination of rice.

Common themes in changing vector-borne disease scenarios.

PubMed

Molyneux, David H

2003-01-01

The impact of climate change on disease patterns is controversial. However, global burden of disease studies suggest that infectious diseases will contribute a proportionately smaller burden of disease over the next 2 decades as non-communicable diseases emerge as public health problems. However, infectious diseases contribute proportionately more in the poorest quintile of the population. Notwithstanding the different views of the impact of global warming on vector-borne infections this paper reviews the conditions which drive the changing epidemiology of these infections and suggests that such change is linked by common themes including interactions of generalist vectors and reservoir hosts at interfaces with humans, reduced biodiversity associated with anthropogenic environmental changes, increases in Plasmodium falciparum: P. vivax ratios and well-described land use changes such as hydrological, urbanization, agricultural, mining and forest-associated impacts (extractive activities, road building, deforestation and migration) which are seen on a global scale.

SVPWM Technique with Varying DC-Link Voltage for Common Mode Voltage Reduction in a Matrix Converter and Analytical Estimation of its Output Voltage Distortion

NASA Astrophysics Data System (ADS)

Padhee, Varsha

Common Mode Voltage (CMV) in any power converter has been the major contributor to premature motor failures, bearing deterioration, shaft voltage build up and electromagnetic interference. Intelligent control methods like Space Vector Pulse Width Modulation (SVPWM) techniques provide immense potential and flexibility to reduce CMV, thereby targeting all the afore mentioned problems. Other solutions like passive filters, shielded cables and EMI filters add to the volume and cost metrics of the entire system. Smart SVPWM techniques therefore, come with a very important advantage of being an economical solution. This thesis discusses a modified space vector technique applied to an Indirect Matrix Converter (IMC) which results in the reduction of common mode voltages and other advanced features. The conventional indirect space vector pulse-width modulation (SVPWM) method of controlling matrix converters involves the usage of two adjacent active vectors and one zero vector for both rectifying and inverting stages of the converter. By suitable selection of space vectors, the rectifying stage of the matrix converter can generate different levels of virtual DC-link voltage. This capability can be exploited for operation of the converter in different ranges of modulation indices for varying machine speeds. This results in lower common mode voltage and improves the harmonic spectrum of the output voltage, without increasing the number of switching transitions as compared to conventional modulation. To summarize it can be said that the responsibility of formulating output voltages with a particular magnitude and frequency has been transferred solely to the rectifying stage of the IMC. Estimation of degree of distortion in the three phase output voltage is another facet discussed in this thesis. An understanding of the SVPWM technique and the switching sequence of the space vectors in detail gives the potential to estimate the RMS value of the switched output voltage of any converter. This conceivably aids the sizing and design of output passive filters. An analytical estimation method has been presented to achieve this purpose for am IMC. Knowledge of the fundamental component in output voltage can be utilized to calculate its Total Harmonic Distortion (THD). The effectiveness of the proposed SVPWM algorithms and the analytical estimation technique is substantiated by simulations in MATLAB / Simulink and experiments on a laboratory prototype of the IMC. Proper comparison plots have been provided to contrast the performance of the proposed methods with the conventional SVPWM method. The behavior of output voltage distortion and CMV with variation in operating parameters like modulation index and output frequency has also been analyzed.

Social Context of Depressive Distress in Aging Transgender Adults

PubMed Central

White Hughto, Jaclyn M.; Reisner, Sari L.

2016-01-01

This study investigates the relationship between discrimination and mental health in aging transgender adults. Survey responses from 61 transgender adults above 50 (Mage = 57.7, SD = 5.8; 77.1% male-to-female; 78.7% White non-Hispanic) were analyzed. Multivariable logistic regression models examined the relationship between gender- and age-related discrimination, number of everyday discrimination experiences, and past-week depressive distress, adjusting for social support, sociodemographics, and other forms of discrimination. The most commonly attributed reasons for experiencing discrimination were related to gender (80.3%) and age (34.4%). More than half of participants (55.5%) met criteria for past-week depressive distress. In an adjusted multivariable model, gender-related discrimination and a greater number of everyday discrimination experiences were associated with increased odds of past-week depressive distress. Additional research is needed to understand the effects of aging and gender identity on depressive symptoms and develop interventions to safeguard the mental health of this vulnerable aging population. PMID:28380703

Social Context of Depressive Distress in Aging Transgender Adults.

PubMed

White Hughto, Jaclyn M; Reisner, Sari L

2016-11-01

This study investigates the relationship between discrimination and mental health in aging transgender adults. Survey responses from 61 transgender adults above 50 ( M age = 57.7, SD = 5.8; 77.1% male-to-female; 78.7% White non-Hispanic) were analyzed. Multivariable logistic regression models examined the relationship between gender- and age-related discrimination, number of everyday discrimination experiences, and past-week depressive distress, adjusting for social support, sociodemographics, and other forms of discrimination. The most commonly attributed reasons for experiencing discrimination were related to gender (80.3%) and age (34.4%). More than half of participants (55.5%) met criteria for past-week depressive distress. In an adjusted multivariable model, gender-related discrimination and a greater number of everyday discrimination experiences were associated with increased odds of past-week depressive distress. Additional research is needed to understand the effects of aging and gender identity on depressive symptoms and develop interventions to safeguard the mental health of this vulnerable aging population.

Meta-analysis of field experiments shows no change in racial discrimination in hiring over time.

PubMed

Quillian, Lincoln; Pager, Devah; Hexel, Ole; Midtbøen, Arnfinn H

2017-10-10

This study investigates change over time in the level of hiring discrimination in US labor markets. We perform a meta-analysis of every available field experiment of hiring discrimination against African Americans or Latinos ( n = 28). Together, these studies represent 55,842 applications submitted for 26,326 positions. We focus on trends since 1989 ( n = 24 studies), when field experiments became more common and improved methodologically. Since 1989, whites receive on average 36% more callbacks than African Americans, and 24% more callbacks than Latinos. We observe no change in the level of hiring discrimination against African Americans over the past 25 years, although we find modest evidence of a decline in discrimination against Latinos. Accounting for applicant education, applicant gender, study method, occupational groups, and local labor market conditions does little to alter this result. Contrary to claims of declining discrimination in American society, our estimates suggest that levels of discrimination remain largely unchanged, at least at the point of hire.

The optical analogy for vector fields

NASA Technical Reports Server (NTRS)

Parker, E. N. (Editor)

1991-01-01

This paper develops the optical analogy for a general vector field. The optical analogy allows the examination of certain aspects of a vector field that are not otherwise readily accessible. In particular, in the cases of a stationary Eulerian flow v of an ideal fluid and a magnetostatic field B, the vectors v and B have surface loci in common with their curls. The intrinsic discontinuities around local maxima in absolute values of v and B take the form of vortex sheets and current sheets, respectively, the former playing a fundamental role in the development of hydrodyamic turbulence and the latter playing a major role in heating the X-ray coronas of stars and galaxies.

Cost Study of Educational Media Systems and Their Equipment Components. Volume II, Technical Report. Final Report.

ERIC Educational Resources Information Center

General Learning Corp., Washington, DC.

A common instructional task and a set of educational environments are hypothesized for analysis of media cost data. The analytic structure may be conceptulized as a three-dimensional matrix: the first vector separates costs into production, distribution, and reception; the second vector delineates capital (initial) and operating (annual) costs;…

A web-server of cell type discrimination system.

PubMed

Wang, Anyou; Zhong, Yan; Wang, Yanhua; He, Qianchuan

2014-01-01

Discriminating cell types is a daily request for stem cell biologists. However, there is not a user-friendly system available to date for public users to discriminate the common cell types, embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and somatic cells (SCs). Here, we develop WCTDS, a web-server of cell type discrimination system, to discriminate the three cell types and their subtypes like fetal versus adult SCs. WCTDS is developed as a top layer application of our recent publication regarding cell type discriminations, which employs DNA-methylation as biomarkers and machine learning models to discriminate cell types. Implemented by Django, Python, R, and Linux shell programming, run under Linux-Apache web server, and communicated through MySQL, WCTDS provides a friendly framework to efficiently receive the user input and to run mathematical models for analyzing data and then to present results to users. This framework is flexible and easy to be expended for other applications. Therefore, WCTDS works as a user-friendly framework to discriminate cell types and subtypes and it can also be expended to detect other cell types like cancer cells.

A Web-Server of Cell Type Discrimination System

PubMed Central

Zhong, Yan

2014-01-01

Discriminating cell types is a daily request for stem cell biologists. However, there is not a user-friendly system available to date for public users to discriminate the common cell types, embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and somatic cells (SCs). Here, we develop WCTDS, a web-server of cell type discrimination system, to discriminate the three cell types and their subtypes like fetal versus adult SCs. WCTDS is developed as a top layer application of our recent publication regarding cell type discriminations, which employs DNA-methylation as biomarkers and machine learning models to discriminate cell types. Implemented by Django, Python, R, and Linux shell programming, run under Linux-Apache web server, and communicated through MySQL, WCTDS provides a friendly framework to efficiently receive the user input and to run mathematical models for analyzing data and then to present results to users. This framework is flexible and easy to be expended for other applications. Therefore, WCTDS works as a user-friendly framework to discriminate cell types and subtypes and it can also be expended to detect other cell types like cancer cells. PMID:24578634

Coping with discrimination among HIV-positive Black men who have sex with men.

PubMed

Bogart, Laura M; Dale, Sannisha K; Christian, Jana; Patel, Kinjal; Daffin, Gary K; Mayer, Kenneth H; Pantalone, David W

2017-07-01

In the USA, HIV-positive Black men who have sex with men show large disparities in disease outcomes compared to other racial/ethnic and risk groups. This study examined the strategies that HIV-positive Black men who have sex with men use to cope with different types of discrimination. A total of 27 HIV-positive Black men who have sex with men participated in semi-structured interviews, which were transcribed verbatim and coded using thematic analysis by multiple raters. Major coping themes included reactive avoidance (using behaviours, cognitions and emotions to escape from discrimination), a common reaction to racism; proactive avoidance (avoiding situations in which discrimination is anticipated), manifested as selective disclosure of HIV-serostatus; external attribution for discrimination (versus self-blame), used more for sexual orientation and HIV discrimination; and social support-seeking, which most often emerged in response to racism. Active coping strategies, such as self-advocacy (countering discrimination directly or indirectly), were infrequently reported. Findings suggest a need for structural anti-discrimination interventions, in tandem with culturally congruent individual- or group-level interventions that aim to enhance men's existing adaptive coping strategies.

The Inside Out of Lentiviral Vectors

PubMed Central

Durand, Stéphanie; Cimarelli, Andrea

2011-01-01

Lentiviruses induce a wide variety of pathologies in different animal species. A common feature of the replicative cycle of these viruses is their ability to target non-dividing cells, a property that constitutes an extremely attractive asset in gene therapy. In this review, we shall describe the main basic aspects of the virology of lentiviruses that were exploited to obtain efficient gene transfer vectors. In addition, we shall discuss some of the hurdles that oppose the efficient genetic modification mediated by lentiviral vectors and the strategies that are being developed to circumvent them. PMID:22049307

ANALYSIS OF CLINICAL AND DERMOSCOPIC FEATURES FOR BASAL CELL CARCINOMA NEURAL NETWORK CLASSIFICATION

PubMed Central

Cheng, Beibei; Stanley, R. Joe; Stoecker, William V; Stricklin, Sherea M.; Hinton, Kristen A.; Nguyen, Thanh K.; Rader, Ryan K.; Rabinovitz, Harold S.; Oliviero, Margaret; Moss, Randy H.

2012-01-01

Background Basal cell carcinoma (BCC) is the most commonly diagnosed cancer in the United States. In this research, we examine four different feature categories used for diagnostic decisions, including patient personal profile (patient age, gender, etc.), general exam (lesion size and location), common dermoscopic (blue-gray ovoids, leaf-structure dirt trails, etc.), and specific dermoscopic lesion (white/pink areas, semitranslucency, etc.). Specific dermoscopic features are more restricted versions of the common dermoscopic features. Methods Combinations of the four feature categories are analyzed over a data set of 700 lesions, with 350 BCCs and 350 benign lesions, for lesion discrimination using neural network-based techniques, including Evolving Artificial Neural Networks and Evolving Artificial Neural Network Ensembles. Results Experiment results based on ten-fold cross validation for training and testing the different neural network-based techniques yielded an area under the receiver operating characteristic curve as high as 0.981 when all features were combined. The common dermoscopic lesion features generally yielded higher discrimination results than other individual feature categories. Conclusions Experimental results show that combining clinical and image information provides enhanced lesion discrimination capability over either information source separately. This research highlights the potential of data fusion as a model for the diagnostic process. PMID:22724561

Vector-Borne Bacterial Plant Pathogens: Interactions with Hemipteran Insects and Plants

PubMed Central

Perilla-Henao, Laura M.; Casteel, Clare L.

2016-01-01

Hemipteran insects are devastating pests of crops due to their wide host range, rapid reproduction, and ability to transmit numerous plant-infecting pathogens as vectors. While the field of plant–virus–vector interactions has flourished in recent years, plant–bacteria–vector interactions remain poorly understood. Leafhoppers and psyllids are by far the most important vectors of bacterial pathogens, yet there are still significant gaps in our understanding of their feeding behavior, salivary secretions, and plant responses as compared to important viral vectors, such as whiteflies and aphids. Even with an incomplete understanding of plant–bacteria–vector interactions, some common themes have emerged: (1) all known vector-borne bacteria share the ability to propagate in the plant and insect host; (2) particular hemipteran families appear to be incapable of transmitting vector-borne bacteria; (3) all known vector-borne bacteria have highly reduced genomes and coding capacity, resulting in host-dependence; and (4) vector-borne bacteria encode proteins that are essential for colonization of specific hosts, though only a few types of proteins have been investigated. Here, we review the current knowledge on important vector-borne bacterial pathogens, including Xylella fastidiosa, Spiroplasma spp., Liberibacter spp., and ‘Candidatus Phytoplasma spp.’. We then highlight recent approaches used in the study of vector-borne bacteria. Finally, we discuss the application of this knowledge for control and future directions that will need to be addressed in the field of vector–plant–bacteria interactions. PMID:27555855

Host-agent-vector-environment measures for electronic cigarette research used in NIH grants.

PubMed

Garcia-Cazarin, Mary L; Mandal, Rachel J; Grana, Rachel; Wanke, Kay L; Meissner, Helen I

2018-01-13

The purpose of this study is to describe the focus and comprehensiveness of domains measured in e-cigarette research. A portfolio analysis of National Institutes of Health grants focusing on e-cigarette research and funded between the fiscal years 2007 and 2015 was conducted. Grant proposals were retrieved using a government database and coded using the Host-Agent-Vector-Environment (HAVE) model as a framework to characterise the measures proposed. Eighty-one projects met the criteria for inclusion in the analysis. The primary HAVE focus most commonly found was Host (73%), followed by Agent (21%), Vector (6%) and Environment (0%). Intrapersonal measures and use trajectories were the most common measures in studies that include Host measures (n=59 and n=51, respectively). Product composition was the most common area of measurement in Agent studies (n=24), whereas Marketing (n=21) was the most common (n=21) area of Vector measurement. When Environment measures were examined as secondary measures in studies, they primarily focused on measuring Peer, Occupation and Social Networks (n=18). Although all studies mentioned research on e-cigarettes, most (n=52; 64%) did not specify the type of e-cigarette device or liquid solution under study. This analysis revealed a heavy focus on Host measures (73%) and a lack of focus on Environment measures. The predominant focus on Host measures may have the unintended effect of limiting the evidence base for tobacco control and regulatory science. Further, a lack of specificity about the e-cigarette product under study will make comparing results across studies and using the outcomes to inform tobacco policy difficult. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

A hybrid structure for the storage and manipulation of very large spatial data sets

USGS Publications Warehouse

Peuquet, Donna J.

1982-01-01

The map data input and output problem for geographic information systems is rapidly diminishing with the increasing availability of mass digitizing, direct spatial data capture and graphics hardware based on raster technology. Although a large number of efficient raster-based algorithms exist for performing a wide variety of common tasks on these data, there are a number of procedures which are more efficiently performed in vector mode or for which raster mode equivalents of current vector-based techniques have not yet been developed. This paper presents a hybrid spatial data structure, named the ?vaster' structure, which can utilize the advantages of both raster and vector structures while potentially eliminating, or greatly reducing, the need for raster-to-vector and vector-to-raster conversion. Other advantages of the vaster structure are also discussed.

Generation of a new Gateway-compatible inducible lentiviral vector platform allowing easy derivation of co-transduced cells.

PubMed

De Groote, Philippe; Grootjans, Sasker; Lippens, Saskia; Eichperger, Chantal; Leurs, Kirsten; Kahr, Irene; Tanghe, Giel; Bruggeman, Inge; De Schamphelaire, Wouter; Urwyler, Corinne; Vandenabeele, Peter; Haustraete, Jurgen; Declercq, Wim

2016-01-01

In contrast to most common gene delivery techniques, lentiviral vectors allow targeting of almost any mammalian cell type, even non-dividing cells, and they stably integrate in the genome. Therefore, these vectors are a very powerful tool for biomedical research. Here we report the generation of a versatile new set of 22 lentiviral vectors with broad applicability in multiple research areas. In contrast to previous systems, our platform provides a choice between constitutive and/or conditional expression and six different C-terminal fusions. Furthermore, two compatible selection markers enable the easy derivation of stable cell lines co-expressing differently tagged transgenes in a constitutive or inducible manner. We show that all of the vector features are functional and that they contribute to transgene overexpression in proof-of-principle experiments.

The evolution of heart gene delivery vectors.

PubMed

Wasala, Nalinda B; Shin, Jin-Hong; Duan, Dongsheng

2011-10-01

Gene therapy holds promise for treating numerous heart diseases. A key premise for the success of cardiac gene therapy is the development of powerful gene transfer vehicles that can achieve highly efficient and persistent gene transfer specifically in the heart. Other features of an ideal vector include negligible toxicity, minimal immunogenicity and easy manufacturing. Rapid progress in the fields of molecular biology and virology has offered great opportunities to engineer various genetic materials for heart gene delivery. Several nonviral vectors (e.g. naked plasmids, plasmid lipid/polymer complexes and oligonucleotides) have been tested. Commonly used viral vectors include lentivirus, adenovirus and adeno-associated virus. Among these, adeno-associated virus has shown many attractive features for pre-clinical experimentation in animal models of heart diseases. We review the history and evolution of these vectors for heart gene transfer. Copyright © 2011 John Wiley & Sons, Ltd.

The evolution of heart gene delivery vectors

PubMed Central

Wasala, Nalinda B.; Shin, Jin-Hong; Duan, Dongsheng

2012-01-01

Gene therapy holds promise for treating numerous heart diseases. A key premise for the success of cardiac gene therapy is the development of powerful gene transfer vehicles that can achieve highly efficient and persistent gene transfer specifically in the heart. Other features of an ideal vector include negligible toxicity, minimal immunogenicity and easy manufacturing. Rapid progress in the fields of molecular biology and virology has offered great opportunities to engineer various genetic materials for heart gene delivery. Several nonviral vectors (e.g. naked plasmids, plasmid lipid/polymer complexes and oligonucleotides) have been tested. Commonly used viral vectors include lentivirus, adenovirus and adeno-associated virus. Among these, adeno-associated virus has shown many attractive features for pre-clinical experimentation in animal models of heart diseases. We review the history and evolution of these vectors for heart gene transfer. PMID:21837689

Trauma exposure, discrimination, and romantic relationship functioning: A longitudinal investigation among LGB young adults

PubMed Central

Sullivan, Timothy J.; Feinstein, Brian A.; Marshall, Amy D.; Mustanski, Brian

2017-01-01

Sexual orientation-related discrimination is common among sexual minority individuals, but its influence on romantic relationship functioning remains unclear. Further, exposure to potentially traumatic events may influence the association between discrimination and relationship functioning, but this has not been tested among sexual minority couples to date. The current study examines breadth of lifetime trauma exposure as a moderator of the associations between recent discrimination and changes in relationship functioning (satisfaction, commitment, and trust) over twelve months among 86 racially/ethnically diverse sexual minority young adults in relationships. For those with low trauma exposure, discrimination was associated with increases in satisfaction and commitment, but not trust. In contrast, for those with high trauma exposure, discrimination was not associated with changes in relationship functioning. Thus, some partnered sexual minority young adults may experience resilience in the face of discrimination, such that discrimination may promote positive relationship functioning. However, this does not appear to extend to those with more extensive trauma exposure histories. With an eye toward informing interventions, these findings call for additional research on individual differences in responses to discrimination, such as support seeking and dyadic coping. PMID:29527540

Development of a new vector using Soybean yellow common mosaic virus for gene function study or heterologous protein expression in soybeans.

PubMed

Lim, Seungmo; Nam, Moon; Kim, Kil Hyun; Lee, Su-Heon; Moon, Jung-Kyung; Lim, Hyoun-Sub; Choung, Myoung-Gun; Kim, Sang-Mok; Moon, Jae Sun

2016-02-01

A new vector using Soybean yellow common mosaic virus (SYCMV) was constructed for gene function study or heterologous protein expression in soybeans. The in vitro transcript with a 5' cap analog m7GpppG from an SYCMV full-length infectious vector driven by a T7 promoter infected soybeans (pSYCMVT7-full). The symptoms observed in the soybeans infected with either the sap from SYCMV-infected leaves or pSYCMVT7-full were indistinguishable, suggesting that the vector exhibits equivalent biological activity as the virus itself. To utilize the vector further, a DNA-based vector driven by the Cauliflower mosaic virus (CaMV) 35S promoter was constructed. The complete sequence of the SYCMV genome was inserted into a binary vector flanked by a CaMV 35S promoter at the 5' terminus of the SYCMV genome and a cis-cleaving ribozyme sequence followed by a nopaline synthase terminator at the 3' terminus of the SYCMV genome (pSYCMV-full). The SYCMV-derived vector was tested for use as a virus-induced gene silencing (VIGS) vector for the functional analysis of soybean genes. VIGS constructs containing either a fragment of the Phytoene desaturase (PDS) gene (pSYCMV-PDS1) or a fragment of the small subunit of ribulose-1,5-bisphosphate carboxylase/oxygenase (RbcS) gene (pSYCMV-RbcS2) were constructed. Plants infiltrated with each vector using the Agrobacterium-mediated inoculation method exhibited distinct symptoms, such as photo-bleaching in plants infiltrated with pSYCMV-PDS1 and yellow or pale green coloring in plants infiltrated with pSYCMV-RbcS2. In addition, down-regulation of the transcripts of the two target genes was confirmed via northern blot analysis. Particle bombardment and direct plasmid DNA rubbing were also confirmed as alternative inoculation methods. To determine if the SYCMV vector can be used for the expression of heterologous proteins in soybean plants, the vector encoding amino acids 135-160 of VP1 of Foot-and-mouth disease virus (FMDV) serotype O1 Campos (O1C) was constructed (pSYCMV-FMDV). Plants infiltrated with pSYCMV-FMDV were only detected via western blotting using the O1C antibody. Based on these results, we propose that the SYCMV-derived vector can be used for gene function study or expression of useful heterologous proteins in soybeans. Copyright © 2015 Elsevier B.V. All rights reserved.

Training with Differential Outcomes Enhances Discriminative Learning and Visuospatial Recognition Memory in Children Born Prematurely

ERIC Educational Resources Information Center

Martinez, Lourdes; Mari-Beffa, Paloma; Roldan-Tapia, Dolores; Ramos-Lizana, Julio; Fuentes, Luis J.; Estevez, Angeles F.

2012-01-01

Previous studies have demonstrated that discriminative learning is facilitated when a particular outcome is associated with each relation to be learned. When this training procedure is applied (the differential outcome procedure; DOP), learning is faster and more accurate than when the more common non-differential outcome procedure is used. This…

Input from the Medial Geniculate Nucleus Modulates Amygdala Encoding of Fear Memory Discrimination

ERIC Educational Resources Information Center

Ferrara, Nicole C.; Cullen, Patrick K.; Pullins, Shane P.; Rotondo, Elena K.; Helmstetter, Fred J.

2017-01-01

Generalization of fear can involve abnormal responding to cues that signal safety and is common in people diagnosed with post-traumatic stress disorder. Differential auditory fear conditioning can be used as a tool to measure changes in fear discrimination and generalization. Most prior work in this area has focused on elevated amygdala activity…

"Human Immunodeficiency Virus serostatus disclosure-Rate, reactions, and discrimination": a cross-sectional study at a rural tertiary care hospital.

PubMed

Joge, Umesh S; Deo, Deepali S; Choudhari, Sonali G; Malkar, Vilas R; Ughade, Harshada M

2013-01-01

From the moment scientists identified Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome (HIV/AIDS), social responses of fear, denial, stigma, and discrimination have accompanied the epidemic. To assess the rate of disclosure of HIV serostatus, reactions by the HIV/AIDS patients and their spouse, and discrimination faced by the patients. The present cross-sectional study was conducted at Antiretroviral Therapy (ART) center of a rural tertiary care hospital, situated in Marathawada region of Maharashtra state from November 2008 to October 2010. Totally, 801 HIV-positive patients coming to ART center for treatment were included after ensuring confidentiality and taking informed consent. A preformed questionnaire was used to enquire about reaction after diagnosis, disclosure, and discrimination faced by the patients. The data analyzed using descriptive statistics and Chi-square test. The most common immediate reaction by the HIV patients after getting diagnosed as seropositive was fear (593, 74.03%) followed by depression (385, 48.06%) and suicidal thoughts (98, 12.25%). Out of 801 patients, 769 (96%) had spouse and of these maximum number of patients (653, 84.92%) had disclosed HIV status to their spouses. Most common immediate reaction by spouse after disclosure was crime (324, 42.13%) followed by horror (294, 38.23%) and anger (237, 36.29%). Maximum number of patients were discriminated by friends (120, 71.01%) followed by discrimination at workplace (49, 67.12%), by neighbors (32, 56.14%), and by relatives (53, 43.80%). Male positives were granted greater acceptance, care, and support by their spouses. More percentage of females discriminated by neighbors, relatives, and friends and at workplace which might be due to factors like customs, morals, and taboos.

Studies of neutron-γ pulse shape discrimination in EJ-309 liquid scintillator using charge integration method

NASA Astrophysics Data System (ADS)

Pawełczak, I. A.; Ouedraogo, S. A.; Glenn, A. M.; Wurtz, R. E.; Nakae, L. F.

2013-05-01

Pulse shape discrimination capability based on the charge integration has been investigated for liquid scintillator EJ-309. The effectiveness of neutron-γ discrimination in 4-in. diameter and 3-in. thick EJ-309 cells coupled with 3-in. photomultiplier tubes has been carefully studied in the laboratory environment and compared to the commonly used EJ-301 liquid scintillator formulation. Influences of distortions in pulse shape caused by 13.7-m long cables necessary for some remote operations have been examined. The parameter space for an effective neutron-γ discrimination for these assays, such as position and width of a gate used for integration of the delayed light, has been explored.

New ΦBT1 site-specific integrative vectors with neutral phenotype in Streptomyces.

PubMed

Gonzalez-Quiñonez, Nathaly; López-García, María Teresa; Yagüe, Paula; Rioseras, Beatriz; Pisciotta, Annalisa; Alduina, Rosa; Manteca, Ángel

2016-03-01

Integrative plasmids are one of the best options to introduce genes in low copy and in a stable form into bacteria. The ΦC31-derived plasmids constitute the most common integrative vectors used in Streptomyces. They integrate at different positions (attB and pseudo-attB sites) generating different mutations. The less common ΦBT1-derived vectors integrate at the unique attB site localized in the SCO4848 gene (S. coelicolor genome) or their orthologues in other streptomycetes. This work demonstrates that disruption of SCO4848 generates a delay in spore germination. SCO4848 is co-transcribed with SCO4849, and the spore germination phenotype is complemented by SCO4849. Plasmids pNG1-4 were created by modifying the ΦBT1 integrative vector pMS82 by introducing a copy of SCO4849 under the control of the promoter region of SCO4848. pNG2 and pNG4 also included a copy of the P ermE * in order to facilitate gene overexpression. pNG3 and pNG4 harboured a copy of the bla gene (ampicillin resistance) to facilitate selection in E. coli. pNG1-4 are the only integrative vectors designed to produce a neutral phenotype when they are integrated into the Streptomyces genome. The experimental approach developed in this work can be applied to create phenotypically neutral integrative plasmids in other bacteria.

Host group formation decreases exposure to vector-borne disease: a field experiment in a 'hotspot' of West Nile virus transmission.

PubMed

Krebs, Bethany L; Anderson, Tavis K; Goldberg, Tony L; Hamer, Gabriel L; Kitron, Uriel D; Newman, Christina M; Ruiz, Marilyn O; Walker, Edward D; Brawn, Jeffrey D

2014-12-07

Animals can decrease their individual risk of predation by forming groups. The encounter-dilution hypothesis extends the potential benefits of gregariousness to biting insects and vector-borne disease by predicting that the per capita number of insect bites should decrease within larger host groups. Although vector-borne diseases are common and can exert strong selective pressures on hosts, there have been few tests of the encounter-dilution effect in natural systems. We conducted an experimental test of the encounter-dilution hypothesis using the American robin (Turdus migratorius), a common host species for the West Nile virus (WNV), a mosquito-borne pathogen. By using sentinel hosts (house sparrows, Passer domesticus) caged in naturally occurring communal roosts in the suburbs of Chicago, we assessed sentinel host risk of WNV exposure inside and outside of roosts. We also estimated per capita host exposure to infected vectors inside roosts and outside of roosts. Sentinel birds caged inside roosts seroconverted to WNV more slowly than those outside of roosts, suggesting that social groups decrease per capita exposure to infected mosquitoes. These results therefore support the encounter-dilution hypothesis in a vector-borne disease system. Our results suggest that disease-related selective pressures on sociality may depend on the mode of disease transmission. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Disability-based discrimination and health: findings from an Australian-based population study.

PubMed

Krnjacki, Lauren; Priest, Naomi; Aitken, Zoe; Emerson, Eric; Llewellyn, Gwynnyth; King, Tania; Kavanagh, Anne

2018-04-01

Among working-age Australian adults with a disability, we assess the association between disability-based discrimination and both overall health and psychological distress. Using data from the 2015 Australian Bureau of Statistics Survey of Disability, Ageing and Carers we estimated the proportion of working-age women and men (15-64 years) with disability who report disability-based discrimination by socio-demographic characteristics and assessed the association between disability-based discrimination and self-reported health and psychological distress. Nearly 14% of Australians with disability reported disability-based discrimination in the previous year. Disability-based discrimination was more common among people living in more disadvantaged circumstances (unemployed, low income, lower-status occupations), younger people and people born in English-speaking countries. Disability-based discrimination was associated with higher levels of psychological distress (OR: 2.53, 95%CI: 2.11, 3.02) and poorer self-reported health (OR: 1.63, 95%CI: 1.37, 1.95). Disability-based discrimination is a prevalent, important determinant of health for Australians with disability. Implications for public health: Disability-based discrimination is an under-recognised public health problem that is likely to contribute to disability-based health inequities. Public health policy, research and practice needs to concentrate efforts on developing policy and programs that reduce discrimination experienced by Australians with disability. © 2017 The Authors.

Differential experiences of discrimination among ethnoracially diverse persons experiencing mental illness and homelessness.

PubMed

Zerger, Suzanne; Bacon, Sarah; Corneau, Simon; Skosireva, Anna; McKenzie, Kwame; Gapka, Susan; O'Campo, Patricia; Sarang, Aseefa; Stergiopoulos, Vicky

2014-12-14

This mixed methods study explored the characteristics of and experiences with perceived discrimination in an ethnically diverse urban sample of adults experiencing homelessness and mental illness. Data were collected in Toronto, Ontario, as part of a 4-year national randomized field trial of the Housing First treatment model. Rates of perceived discrimination were captured from survey questions regarding perceived discrimination among 231 ethnoracially diverse participants with moderate mental health needs. The qualitative component included thirty six in-depth interviews which explored how individuals who bear these multiple identities of oppression navigate stigma and discrimination, and what affects their capacity to do so. Quantitative analysis revealed very high rates of perceived discrimination related to: homelessness/poverty (61.5%), race/ethnicity/skin colour (50.6%) and mental illness/substance use (43.7%). Immigrants and those who had been homeless three or more years reported higher perceived discrimination on all three domains. Analysis of qualitative interviews revealed three common themes related to navigating these experiences of discrimination among participants: 1) social distancing; 2) old and new labels/identities; and, 3) 'homeland' cultures. These study findings underscore poverty and homelessness as major sources of perceived discrimination, and expose underlying complexities in the navigation of multiple identities in responding to stigma and discrimination. Current Controlled Trials ISRCTN42520374 . Registered 18 August 2009.

Active and Passive 3D Vector Radiative Transfer with Preferentially-Aligned Ice Particles

NASA Technical Reports Server (NTRS)

Adams, Ian S.; Munchak, Stephen J.; Pelissier, Craig S.; Kuo, Kwo-Sen; Heymsfield, Gerald M.

2017-01-01

For the purposes of interpreting active (radar) and passive (radiometer) microwave and millimeter wave remote sensing data, we have constructed a consistent radiative transfer modeling framework to simulate the responses for arbitrary sensors with differing sensing geometries and hardware configurations. As part of this work, we have implemented a recent method for calculating the electromagnetic properties of individual ice crystals and snow flakes. These calculations will allow us to exploit polarized remote sensing observations to discriminate different particles types and elucidate dynamics of cloud and precipitating systems.

Evolutionary emergence and maintenance of horizontally transmitted mutualism that do not rely on the supply of standing variation in symbiont quality.

PubMed

Uchiumi, Y; Ohtsuki, H; Sasaki, A

2017-12-01

Mutualism based on reciprocal exchange of costly services must avoid exploitation by 'free-rides'. Accordingly, hosts discriminate against free-riding symbionts in many mutualistic relationships. However, as the selective advantage of discriminators comes from the presence of variability in symbiont quality that they eliminate, discrimination and thus mutualism have been considered to be maintained with exogenous supply of free-riders. In this study, we tried to resolve the 'paradoxical' co-evolution of discrimination by hosts and cooperation by symbionts, by comparing two different types of discrimination: 'one-shot' discrimination, where a host does not reacquire new symbionts after evicting free-riders, and 'resampling' discrimination, where a host does from the environment. Our study shows that this apparently minor difference in discrimination types leads to qualitatively different evolutionary outcomes. First, although it has been usually considered that the benefit of discriminators is derived from the variability of symbiont quality, the benefit of a certain type of discriminators (e.g. one-shot discrimination) is proportional to the frequency of free-riders, which is in stark contrast to the case of resampling discrimination. As a result, one-shot discriminators can invade the free-rider/nondiscriminator population, even if standing variation for symbiont quality is absent. Second, our one-shot discriminators can also be maintained without exogenous supply of free-riders and hence is free from the paradox of discrimination. Therefore, our result indicates that the paradox is not a common feature of evolution of discrimination but is a problem of specific types of discrimination. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Disease Vector Ecology Profile: Ecuador

DTIC Science & Technology

1998-12-01

fleas, has not been reported since 1985; however, extensive outbreaks in neighboring Peru occurred in 1992-94. Avoid rodent -infested areas and use PPM...throughout the year ( Peru ). Heavy shade in permanent bodies of water with abundant floatage. Commonly bites humans but apparently prefers rodents ...Vectors of Arboviruses other than Dengue or Yellow Fever in the Amazon Basin and Associated Northwestern Regions of South America

Orthonormal vector polynomials in a unit circle, Part I: Basis set derived from gradients of Zernike polynomials.

PubMed

Zhao, Chunyu; Burge, James H

2007-12-24

Zernike polynomials provide a well known, orthogonal set of scalar functions over a circular domain, and are commonly used to represent wavefront phase or surface irregularity. A related set of orthogonal functions is given here which represent vector quantities, such as mapping distortion or wavefront gradient. These functions are generated from gradients of Zernike polynomials, made orthonormal using the Gram- Schmidt technique. This set provides a complete basis for representing vector fields that can be defined as a gradient of some scalar function. It is then efficient to transform from the coefficients of the vector functions to the scalar Zernike polynomials that represent the function whose gradient was fit. These new vector functions have immediate application for fitting data from a Shack-Hartmann wavefront sensor or for fitting mapping distortion for optical testing. A subsequent paper gives an additional set of vector functions consisting only of rotational terms with zero divergence. The two sets together provide a complete basis that can represent all vector distributions in a circular domain.

What is the 'true' effect of Trypanosoma rangeli on its triatomine bug vector?

PubMed

Peterson, Jennifer K; Graham, Andrea L

2016-06-01

The phrase, "T. rangeli is pathogenic to its insect vector," is commonly found in peer-reviewed publications on the matter, such that it has become the orthodox view of this interaction. In a literature survey, we identified over 20 papers with almost the exact phrase and several others alluding to it. The idea is of particular importance in triatomine population dynamics and the study of vector-borne T. cruzi transmission, as it could mean that triatomines infected with T. rangeli have lower fitness than uninfected insects. Trypanosoma rangeli pathogenicity was first observed in a series of studies carried out over fifty years ago using the triatomine species Rhodnius prolixus. However, there are few studies of the effect of T. rangeli on its other vector species, and several of the studies were carried out with R. prolixus under non-physiological conditions. Here, we re-evaluate the published studies that led to the conclusion that T. rangeli is pathogenic to its vector, to determine whether or not this indeed is the "true" effect of T. rangeli on its triatomine vector. © 2016 The Society for Vector Ecology.

Herpes simplex virus type 1-derived recombinant and amplicon vectors.

PubMed

Fraefel, Cornel; Marconi, Peggy; Epstein, Alberto L

2011-01-01

Herpes simplex virus type 1 (HSV-1) is a human pathogen whose lifestyle is based on a long-term dual interaction with the infected host, being able to establish both lytic and latent infections. The virus genome is a 153 kbp double-stranded DNA molecule encoding more than 80 genes. The interest of HSV-1 as gene transfer vector stems from its ability to infect many different cell types, both quiescent and proliferating cells, the very high packaging capacity of the virus capsid, the outstanding neurotropic adaptations that this virus has evolved, and the fact that it never integrates into the cellular chromosomes, thus avoiding the risk of insertional mutagenesis. Two types of vectors can be derived from HSV-1, recombinant vectors and amplicon vectors, and different methodologies have been developed to prepare large stocks of each type of vector. This chapter summarizes (1) the two approaches most commonly used to prepare recombinant vectors through homologous recombination, either in eukaryotic cells or in bacteria, and (2) the two methodologies currently used to generate helper-free amplicon vectors, either using a bacterial artificial chromosome (BAC)-based approach or a Cre/loxP site-specific recombination strategy.

Initialization of Formation Flying Using Primer Vector Theory

NASA Technical Reports Server (NTRS)

Mailhe, Laurie; Schiff, Conrad; Folta, David

2002-01-01

In this paper, we extend primer vector analysis to formation flying. Optimization of the classical rendezvous or free-time transfer problem between two orbits using primer vector theory has been extensively studied for one spacecraft. However, an increasing number of missions are now considering flying a set of spacecraft in close formation. Missions such as the Magnetospheric MultiScale (MMS) and Leonardo-BRDF (Bidirectional Reflectance Distribution Function) need to determine strategies to transfer each spacecraft from the common launch orbit to their respective operational orbit. In addition, all the spacecraft must synchronize their states so that they achieve the same desired formation geometry over each orbit. This periodicity requirement imposes constraints on the boundary conditions that can be used for the primer vector algorithm. In this work we explore the impact of the periodicity requirement in optimizing each spacecraft transfer trajectory using primer vector theory. We first present our adaptation of primer vector theory to formation flying. Using this method, we then compute the AV budget for each spacecraft subject to different formation endpoint constraints.

Left ventricular hypertrophy index based on a combination of frontal and transverse planes in the ECG and VCG: Diagnostic utility of cardiac vectors

NASA Astrophysics Data System (ADS)

Bonomini, Maria Paula; Juan Ingallina, Fernando; Barone, Valeria; Antonucci, Ricardo; Valentinuzzi, Max; Arini, Pedro David

2016-04-01

The changes that left ventricular hypertrophy (LVH) induces in depolarization and repolarization vectors are well known. We analyzed the performance of the electrocardiographic and vectorcardiographic transverse planes (TP in the ECG and XZ in the VCG) and frontal planes (FP in the ECG and XY in the VCG) to discriminate LVH patients from control subjects. In an age-balanced set of 58 patients, the directions and amplitudes of QRS-complexes and T-wave vectors were studied. The repolarization vector significantly decreased in modulus from controls to LVH in the transverse plane (TP: 0.45±0.17mV vs. 0.24±0.13mV, p<0.0005 XZ: 0.43±0.16mV vs. 0.26±0.11mV, p<0.005) while the depolarization vector significantly changed in angle in the electrocardiographic frontal plane (Controls vs. LVH, FP: 48.24±33.66° vs. 46.84±35.44°, p<0.005, XY: 20.28±35.20° vs. 19.35±12.31°, NS). Several LVH indexes were proposed combining such information in both ECG and VCG spaces. A subset of all those indexes with AUC values greater than 0.7 was further studied. This subset comprised four indexes, with three of them belonging to the ECG space. Two out of the four indexes presented the best ROC curves (AUC values: 0.78 and 0.75, respectively). One index belonged to the ECG space and the other one to the VCG space. Both indexes showed a sensitivity of 86% and a specificity of 70%. In conclusion, the proposed indexes can favorably complement LVH diagnosis

Vector magnetic field and vector current density in and around the δ-spot NOAA 10808†

NASA Astrophysics Data System (ADS)

Bommier, Véronique; Landi Degl'Innocenti, Egidio; Schmieder, Brigitte; Gelly, Bernard

2011-08-01

The context is that of the so-called ``fundamental ambiguity'' (also azimuth ambiguity, or 180° ambiguity) in magnetic field vector measurements: two field vectors symmetrical with respect to the line-of-sight have the same polarimetric signature, so that they cannot be discriminated. We propose a method to solve this ambiguity by applying the ``simulated annealing'' algorithm to the minimization of the field divergence, added to the longitudinal current absolute value, the line-of-sight derivative of the magnetic field being inferred by the interpretation of the Zeeman effect observed by spectropolarimetry in two lines formed at different depths. We find that the line pair Fe I λ 6301.5 and Fe I λ 6302.5 is appropriate for this purpose. We treat the example case of the δ-spot of NOAA 10808 observed on 13 September 2005 between 14:25 and 15:25 UT with the THEMIS telescope. Besides the magnetic field resolved map, the electric current density vector map is also obtained. A strong horizontal current density flow is found surrounding each spot inside its penumbra, associated to a non-zero Lorentz force centripetal with respect to the spot center (i.e., oriented towards the spot center). The current wrapping direction is found to depend on the spot polarity: clockwise for the positive polarity, counterclockwise for the negative one. This analysis is made possible thanks to the UNNOFIT2 Milne-Eddington inversion code, where the usual theory is generalized to the case of a line (Fe I λ 6301.5) that is not a normal Zeeman triplet line (like Fe I λ 6302.5).

Identification of Wolbachia Strains in Mosquito Disease Vectors

PubMed Central

Osei-Poku, Jewelna; Han, Calvin; Mbogo, Charles M.; Jiggins, Francis M.

2012-01-01

Wolbachia bacteria are common endosymbionts of insects, and some strains are known to protect their hosts against RNA viruses and other parasites. This has led to the suggestion that releasing Wolbachia-infected mosquitoes could prevent the transmission of arboviruses and other human parasites. We have identified Wolbachia in Kenyan populations of the yellow fever vector Aedes bromeliae and its relative Aedes metallicus, and in Mansonia uniformis and Mansonia africana, which are vectors of lymphatic filariasis. These Wolbachia strains cluster together on the bacterial phylogeny, and belong to bacterial clades that have recombined with other unrelated strains. These new Wolbachia strains may be affecting disease transmission rates of infected mosquito species, and could be transferred into other mosquito vectors as part of control programs. PMID:23185484

Retrovirus-based vectors for transient and permanent cell modification.

PubMed

Schott, Juliane W; Hoffmann, Dirk; Schambach, Axel

2015-10-01

Retroviral vectors are commonly employed for long-term transgene expression via integrating vector technology. However, three alternative retrovirus-based platforms are currently available that allow transient cell modification. Gene expression can be mediated from either episomal DNA or RNA templates, or selected proteins can be directly transferred through retroviral nanoparticles. The different technologies are functionally graded with respect to safety, expression magnitude and expression duration. Improvement of the initial technologies, including modification of vector designs, targeted increase in expression strength and duration as well as improved safety characteristics, has allowed maturation of retroviral systems into efficient and promising tools that meet the technological demands of a wide variety of potential application areas. Copyright © 2015 Elsevier Ltd. All rights reserved.

Impact of Vector Dispersal and Host-Plant Fidelity on the Dissemination of an Emerging Plant Pathogen

PubMed Central

Johannesen, Jes; Foissac, Xavier; Kehrli, Patrik; Maixner, Michael

2012-01-01

Dissemination of vector-transmitted pathogens depend on the survival and dispersal of the vector and the vector's ability to transmit the pathogen, while the host range of vector and pathogen determine the breath of transmission possibilities. In this study, we address how the interaction between dispersal and plant fidelities of a pathogen (stolbur phytoplasma tuf-a) and its vector (Hyalesthes obsoletus: Cixiidae) affect the emergence of the pathogen. Using genetic markers, we analysed the geographic origin and range expansion of both organisms in Western Europe and, specifically, whether the pathogen's dissemination in the northern range is caused by resident vectors widening their host-plant use from field bindweed to stinging nettle, and subsequent host specialisation. We found evidence for common origins of pathogen and vector south of the European Alps. Genetic patterns in vector populations show signals of secondary range expansion in Western Europe leading to dissemination of tuf-a pathogens, which might be newly acquired and of hybrid origin. Hence, the emergence of stolbur tuf-a in the northern range was explained by secondary immigration of vectors carrying stinging nettle-specialised tuf-a, not by widening the host-plant spectrum of resident vectors with pathogen transmission from field bindweed to stinging nettle nor by primary co-migration from the resident vector's historical area of origin. The introduction of tuf-a to stinging nettle in the northern range was therefore independent of vector's host-plant specialisation but the rapid pathogen dissemination depended on the vector's host shift, whereas the general dissemination elsewhere was linked to plant specialisation of the pathogen but not of the vector. PMID:23284774

Impact of vector dispersal and host-plant fidelity on the dissemination of an emerging plant pathogen.

PubMed

Johannesen, Jes; Foissac, Xavier; Kehrli, Patrik; Maixner, Michael

2012-01-01

Dissemination of vector-transmitted pathogens depend on the survival and dispersal of the vector and the vector's ability to transmit the pathogen, while the host range of vector and pathogen determine the breath of transmission possibilities. In this study, we address how the interaction between dispersal and plant fidelities of a pathogen (stolbur phytoplasma tuf-a) and its vector (Hyalesthes obsoletus: Cixiidae) affect the emergence of the pathogen. Using genetic markers, we analysed the geographic origin and range expansion of both organisms in Western Europe and, specifically, whether the pathogen's dissemination in the northern range is caused by resident vectors widening their host-plant use from field bindweed to stinging nettle, and subsequent host specialisation. We found evidence for common origins of pathogen and vector south of the European Alps. Genetic patterns in vector populations show signals of secondary range expansion in Western Europe leading to dissemination of tuf-a pathogens, which might be newly acquired and of hybrid origin. Hence, the emergence of stolbur tuf-a in the northern range was explained by secondary immigration of vectors carrying stinging nettle-specialised tuf-a, not by widening the host-plant spectrum of resident vectors with pathogen transmission from field bindweed to stinging nettle nor by primary co-migration from the resident vector's historical area of origin. The introduction of tuf-a to stinging nettle in the northern range was therefore independent of vector's host-plant specialisation but the rapid pathogen dissemination depended on the vector's host shift, whereas the general dissemination elsewhere was linked to plant specialisation of the pathogen but not of the vector.

Faculty perceptions of gender discrimination and sexual harassment in academic medicine.

PubMed

Carr, P L; Ash, A S; Friedman, R H; Szalacha, L; Barnett, R C; Palepu, A; Moskowitz, M M

2000-06-06

Gender-based discrimination and sexual harassment are common in medical practice and may be even more prevalent in academic medicine. To examine the prevalence of gender-based discrimination and sexual harassment among medical school faculty and the associations of gender-based discrimination with number of publications, career satisfaction, and perceptions of career advancement. A self-administered mailed questionnaire of U.S. medical school faculty that covered a broad range of topics relating to academic life. 24 randomly selected medical schools in the contiguous United States. A random sample of 3332 full-time faculty, stratified by specialty, graduation cohort, and sex. Prevalence of self-reported experiences of discrimination and harassment, number of peer-reviewed publications, career satisfaction, and perception of career advancement. Female faculty were more than 2.5 times more likely than male faculty to perceive gender-based discrimination in the academic environment (P < 0.001). Among women, rates of reported discrimination ranged from 47% for the youngest faculty to 70% for the oldest faculty. Women who reported experiencing negative gender bias had similar productivity but lower career satisfaction scores than did other women (P< 0.001). About half of female faculty but few male faculty experienced some form of sexual harassment. These experiences were similarly prevalent across the institutions in the sample and in all regions of the United States. Female faculty who reported being sexually harassed perceived gender-specific bias in the academic environment more often than did other women (80% compared with 61 %) and more often reported experiencing gender bias in professional advancement (72% compared with 47%). Publications, career satisfaction, and professional confidence were not affected by sexual harassment, and self-assessed career advancement was only marginally lower for female faculty who had experienced sexual harassment (P = 0.06). Despite substantial increases in the number of female faculty, reports of gender-based discrimination and sexual harassment remain common.

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

PubMed Central

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

2015-01-01

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

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

PubMed

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

2015-01-01

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

Personal-Level and Group-Level Discrimination and Mental Health: the Role of Skin Color.

PubMed

Fattore, Gisel Lorena; Amorim, Leila D; Dos Santos, Letícia Marques; Dos Santos, Darci Neves; Barreto, Mauricio Lima

2017-12-21

This study investigates the association between personal-level and group-level discrimination and common mental disorders (CMDs) among Afro-Brazilian women, aiming to explore the role of skin color on this association. This is a cross-sectional study involving 1130 women who were participating in the Social Change, Asthma and Allergy in Latin America (SCAALA) study, whose children were recruited from 24 geographical micro-regions representative of the population without sanitation. Measures of discrimination were defined by: experiences (personal-level) and concern about discrimination (group-level) using the Experiences of Discrimination Scale. Skin color was registered by self-declaration, being classified as white, brown, and black. The association between "self-reported" discrimination and CMDs was evaluated using Poisson regression analysis. Prevalence of CMDs was high (38.3%), especially in the group exposed to discriminatory experiences and black women. Experiences and concern about discrimination were positive and significantly associated with mental health, before and after adjustment for potential confounders. The effect of discrimination on CMDs was lower among black women, suggesting the development of other strategies to confront racism. This study emphasizes the use of both personal- and group-level discrimination measures, as well as skin color, for the evaluation of mental disorders in public health research. Further studies of health consequences of discrimination will require investigation of protective factors for mental disorders in the population suffering discrimination and racism.

Newcastle Disease Virus as a Vaccine Vector for Development of Human and Veterinary Vaccines

PubMed Central

Kim, Shin-Hee; Samal, Siba K.

2016-01-01

Viral vaccine vectors have shown to be effective in inducing a robust immune response against the vaccine antigen. Newcastle disease virus (NDV), an avian paramyxovirus, is a promising vaccine vector against human and veterinary pathogens. Avirulent NDV strains LaSota and B1 have long track records of safety and efficacy. Therefore, use of these strains as vaccine vectors is highly safe in avian and non-avian species. NDV replicates efficiently in the respiratory track of the host and induces strong local and systemic immune responses against the foreign antigen. As a vaccine vector, NDV can accommodate foreign sequences with a good degree of stability and as a RNA virus, there is limited possibility for recombination with host cell DNA. Using NDV as a vaccine vector in humans offers several advantages over other viral vaccine vectors. NDV is safe in humans due to host range restriction and there is no pre-existing antibody to NDV in the human population. NDV is antigenically distinct from common human pathogens. NDV replicates to high titer in a cell line acceptable for human vaccine development. Therefore, NDV is an attractive vaccine vector for human pathogens for which vaccines are currently not available. NDV is also an attractive vaccine vector for animal pathogens. PMID:27384578

A Novel Vaccine Approach for Chagas Disease Using Rare Adenovirus Serotype 48 Vectors

PubMed Central

Farrow, Anitra L.; Peng, Binghao J.; Gu, Linlin; Krendelchtchikov, Alexandre; Matthews, Qiana L.

2016-01-01

Due to the increasing amount of people afflicted worldwide with Chagas disease and an increasing prevalence in the United States, there is a greater need to develop a safe and effective vaccine for this neglected disease. Adenovirus serotype 5 (Ad5) is the most common adenovirus vector used for gene therapy and vaccine approaches, but its efficacy is limited by preexisting vector immunity in humans resulting from natural infections. Therefore, we have employed rare serotype adenovirus 48 (Ad48) as an alternative choice for adenovirus/Chagas vaccine therapy. In this study, we modified Ad5 and Ad48 vectors to contain T. cruzi’s amastigote surface protein 2 (ASP-2) in the adenoviral early gene. We also modified Ad5 and Ad48 vectors to utilize the “Antigen Capsid-Incorporation” strategy by adding T. cruzi epitopes to protein IX (pIX). Mice that were immunized with the modified vectors were able to elicit T. cruzi-specific humoral and cellular responses. This study indicates that Ad48-modified vectors function comparable to or even premium to Ad5-modified vectors. This study provides novel data demonstrating that Ad48 can be used as a potential adenovirus vaccine vector against Chagas disease. PMID:26978385

Effects of Cucumber mosaic virus infection on vector and non-vector herbivores of squash.

PubMed

Mauck, Kerry E; De Moraes, Consuelo M; Mescher, Mark C

2010-11-01

Plant chemicals mediating interactions with insect herbivores seem a likely target for manipulation by insectvectored plant pathogens. Yet, little is currently known about the chemical ecology of insect-vectored diseases or their effects on the ecology of vector and nonvector insects. We recently reported that a widespread plant pathogen, Cucumber mosaic virus (CMV), greatly reduces the quality of host-plants (squash) for aphid vectors, but that aphids are nevertheless attracted to the odors of infected plants-which exhibit elevated emissions of a volatile blend otherwise similar to the odor of healthy plants. This finding suggests that exaggerating existing host-location cues can be a viable vector attraction strategy for pathogens that otherwise reduce host quality for vectors. Here we report additional data regarding the effects of CMV infection on plant interactions with a common nonvector herbivore, the squash bug, Anasa tristis, which is a pest in this system. We found that adult A. tristis females preferred to oviposit on healthy plants in the field, and that healthy plants supported higher populations of nymphs. Collectively, our recent findings suggest that CMV-induced changes in host plant chemistry influence the behavior of both vector and non-vector herbivores, with significant implications both for disease spread and for broader community-level interactions.

Newcastle Disease Virus as a Vaccine Vector for Development of Human and Veterinary Vaccines.

PubMed

Kim, Shin-Hee; Samal, Siba K

2016-07-04

Viral vaccine vectors have shown to be effective in inducing a robust immune response against the vaccine antigen. Newcastle disease virus (NDV), an avian paramyxovirus, is a promising vaccine vector against human and veterinary pathogens. Avirulent NDV strains LaSota and B1 have long track records of safety and efficacy. Therefore, use of these strains as vaccine vectors is highly safe in avian and non-avian species. NDV replicates efficiently in the respiratory track of the host and induces strong local and systemic immune responses against the foreign antigen. As a vaccine vector, NDV can accommodate foreign sequences with a good degree of stability and as a RNA virus, there is limited possibility for recombination with host cell DNA. Using NDV as a vaccine vector in humans offers several advantages over other viral vaccine vectors. NDV is safe in humans due to host range restriction and there is no pre-existing antibody to NDV in the human population. NDV is antigenically distinct from common human pathogens. NDV replicates to high titer in a cell line acceptable for human vaccine development. Therefore, NDV is an attractive vaccine vector for human pathogens for which vaccines are currently not available. NDV is also an attractive vaccine vector for animal pathogens.

In vivo production of recombinant proteins using occluded recombinant AcMNPV-derived baculovirus vectors.

PubMed

Guijarro-Pardo, Eva; Gómez-Sebastián, Silvia; Escribano, José M

2017-12-01

Trichoplusia ni insect larvae infected with vectors derived from the Autographa californica multiple nucleopolyhedrovirus (AcMNPV), are an excellent alternative to insect cells cultured in conventional bioreactors to produce recombinant proteins because productivity and cost-efficiency reasons. However, there is still a lot of work to do to reduce the manual procedures commonly required in this production platform that limit its scalability. To increase the scalability of this platform technology, a current bottleneck to be circumvented in the future is the need of injection for the inoculation of larvae with polyhedrin negative baculovirus vectors (Polh-) because of the lack of oral infectivity of these viruses, which are commonly used for production in insect cell cultures. In this work we have developed a straightforward alternative to obtain orally infective vectors derived from AcMNPV and expressing recombinant proteins that can be administered to the insect larvae (Trichoplusia ni) by feeding, formulated in the insect diet. The approach developed was based on the use of a recombinant polyhedrin protein expressed by a recombinant vector (Polh+), able to co-occlude any recombinant Polh- baculovirus vector expressing a recombinant protein. A second alternative was developed by the generation of a dual vector co-expressing the recombinant polyhedrin protein and the foreign gene of interest to obtain the occluded viruses. Additionally, by the incorporation of a reporter gene into the helper Polh+ vector, it was possible the follow-up visualization of the co-occluded viruses infection in insect larvae and will help to homogenize infection conditions. By using these methodologies, the production of recombinant proteins in per os infected larvae, without manual infection procedures, was very similar in yield to that obtained by manual injection of recombinant Polh- AcMNPV-based vectors expressing the same proteins. However, further analyses will be required for a detailed comparison of production yields reached by injection vs oral infections for different recombinant proteins. In conclusion, these results open the possibility of future industrial scaling-up production of recombinant proteins in insect larvae by reducing manual operations. Copyright © 2017 Elsevier B.V. All rights reserved.

Visual discrimination predicts naming and semantic association accuracy in Alzheimer disease.

PubMed

Harnish, Stacy M; Neils-Strunjas, Jean; Eliassen, James; Reilly, Jamie; Meinzer, Marcus; Clark, John Greer; Joseph, Jane

2010-12-01

Language impairment is a common symptom of Alzheimer disease (AD), and is thought to be related to semantic processing. This study examines the contribution of another process, namely visual perception, on measures of confrontation naming and semantic association abilities in persons with probable AD. Twenty individuals with probable mild-moderate Alzheimer disease and 20 age-matched controls completed a battery of neuropsychologic measures assessing visual perception, naming, and semantic association ability. Visual discrimination tasks that varied in the degree to which they likely accessed stored structural representations were used to gauge whether structural processing deficits could account for deficits in naming and in semantic association in AD. Visual discrimination abilities of nameable objects in AD strongly predicted performance on both picture naming and semantic association ability, but lacked the same predictive value for controls. Although impaired, performance on visual discrimination tests of abstract shapes and novel faces showed no significant relationship with picture naming and semantic association. These results provide additional evidence to support that structural processing deficits exist in AD, and may contribute to object recognition and naming deficits. Our findings suggest that there is a common deficit in discrimination of pictures using nameable objects, picture naming, and semantic association of pictures in AD. Disturbances in structural processing of pictured items may be associated with lexical-semantic impairment in AD, owing to degraded internal storage of structural knowledge.

Perceptions of weight discrimination: prevalence and comparison to race and gender discrimination in America.

PubMed

Puhl, R M; Andreyeva, T; Brownell, K D

2008-06-01

Limited data are available on the prevalence and patterns of body weight discrimination from representative samples. This study examined experiences of weight/height discrimination in a nationally representative sample of US adults and compared their prevalence and patterns with discrimination experiences based on race and gender. Data were from the National Survey of Midlife Development in the United States, a 1995-1996 community-based survey of English-speaking adults aged 25-74 (N=2290). Reported experiences of weight/height discrimination included a variety of institutional settings and interpersonal relationships. Multivariate regression analyses were used to predict weight/height discrimination controlling for sociodemographic characteristics and body weight status. The prevalence of weight/height discrimination ranged from 5% among men to 10% among women, but these average percentages obscure the much higher risk of weight discrimination among heavier individuals (40% for adults with body mass index (BMI) of 35 and above). Younger individuals with a higher BMI had a particularly high risk of weight/height discrimination regardless of their race, education and weight status. Women were at greater risk for weight/height discrimination than men, especially women with a BMI of 30-35 who were three times more likely to report weight/height discrimination compared to male peers of a similar weight. Weight/height discrimination is prevalent in American society and is relatively close to reported rates of racial discrimination, particularly among women. Both institutional forms of weight/height discrimination (for example, in employment settings) and interpersonal mistreatment due to weight/height (for example, being called names) were common, and in some cases were even more prevalent than discrimination due to gender and race.

Rapid and accurate detection of urinary pathogens by mobile IMS-based electronic nose: a proof-of-principle study.

PubMed

Roine, Antti; Saviauk, Taavi; Kumpulainen, Pekka; Karjalainen, Markus; Tuokko, Antti; Aittoniemi, Janne; Vuento, Risto; Lekkala, Jukka; Lehtimäki, Terho; Tammela, Teuvo L; Oksala, Niku K J

2014-01-01

Urinary tract infection (UTI) is a common disease with significant morbidity and economic burden, accounting for a significant part of the workload in clinical microbiology laboratories. Current clinical chemisty point-of-care diagnostics rely on imperfect dipstick analysis which only provides indirect and insensitive evidence of urinary bacterial pathogens. An electronic nose (eNose) is a handheld device mimicking mammalian olfaction that potentially offers affordable and rapid analysis of samples without preparation at athmospheric pressure. In this study we demonstrate the applicability of ion mobility spectrometry (IMS) -based eNose to discriminate the most common UTI pathogens from gaseous headspace of culture plates rapidly and without sample preparation. We gathered a total of 101 culture samples containing four most common UTI bacteries: E. coli, S. saprophyticus, E. faecalis, Klebsiella spp and sterile culture plates. The samples were analyzed using ChemPro 100i device, consisting of IMS cell and six semiconductor sensors. Data analysis was conducted by linear discriminant analysis (LDA) and logistic regression (LR). The results were validated by leave-one-out and 5-fold cross validation analysis. In discrimination of sterile and bacterial samples sensitivity of 95% and specificity of 97% were achieved. The bacterial species were identified with sensitivity of 95% and specificity of 96% using eNose as compared to urine bacterial cultures. These findings strongly demonstrate the ability of our eNose to discriminate bacterial cultures and provides a proof of principle to use this method in urinanalysis of UTI.

Label-free haemogram using wavelength modulated Raman spectroscopy for identifying immune-cell subset

NASA Astrophysics Data System (ADS)

Ashok, Praveen C.; Praveen, Bavishna B.; Campbell, Elaine C.; Dholakia, Kishan; Powis, Simon J.

2014-03-01

Leucocytes in the blood of mammals form a powerful protective system against a wide range of dangerous pathogens. There are several types of immune cells that has specific role in the whole immune system. The number and type of immune cells alter in the disease state and identifying the type of immune cell provides information about a person's state of health. There are several immune cell subsets that are essentially morphologically identical and require external labeling to enable discrimination. Here we demonstrate the feasibility of using Wavelength Modulated Raman Spectroscopy (WMRS) with suitable machine learning algorithms as a label-free method to distinguish between different closely lying immune cell subset. Principal Component Analysis (PCA) was performed on WMRS data from single cells, obtained using confocal Raman microscopy for feature reduction, followed by Support Vector Machine (SVM) for binary discrimination of various cell subset, which yielded an accuracy >85%. The method was successful in discriminating between untouched and unfixed purified populations of CD4+CD3+ and CD8+CD3+ T lymphocyte subsets, and CD56+CD3- natural killer cells with a high degree of specificity. It was also proved sensitive enough to identify unique Raman signatures that allow clear discrimination between dendritic cell subsets, comprising CD303+CD45+ plasmacytoid and CD1c+CD141+ myeloid dendritic cells. The results of this study clearly show that WMRS is highly sensitive and can distinguish between cell types that are morphologically identical.

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

PubMed

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

2018-05-16

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

Face-selective regions differ in their ability to classify facial expressions

PubMed Central

Zhang, Hui; Japee, Shruti; Nolan, Rachel; Chu, Carlton; Liu, Ning; Ungerleider, Leslie G

2016-01-01

Recognition of facial expressions is crucial for effective social interactions. Yet, the extent to which the various face-selective regions in the human brain classify different facial expressions remains unclear. We used functional magnetic resonance imaging (fMRI) and support vector machine pattern classification analysis to determine how well face-selective brain regions are able to decode different categories of facial expression. Subjects participated in a slow event-related fMRI experiment in which they were shown 32 face pictures, portraying four different expressions: neutral, fearful, angry, and happy and belonging to eight different identities. Our results showed that only the amygdala and the posterior superior temporal sulcus (STS) were able to accurately discriminate between these expressions, albeit in different ways: The amygdala discriminated fearful faces from non-fearful faces, whereas STS discriminated neutral from emotional (fearful, angry and happy) faces. In contrast to these findings on the classification of emotional expression, only the fusiform face area (FFA) and anterior inferior temporal cortex (aIT) could discriminate among the various facial identities. Further, the amygdala and STS were better than FFA and aIT at classifying expression, while FFA and aIT were better than the amygdala and STS at classifying identity. Taken together, our findings indicate that the decoding of facial emotion and facial identity occurs in different neural substrates: the amygdala and STS for the former and FFA and aIT for the latter. PMID:26826513

Face-selective regions differ in their ability to classify facial expressions.

PubMed

Zhang, Hui; Japee, Shruti; Nolan, Rachel; Chu, Carlton; Liu, Ning; Ungerleider, Leslie G

2016-04-15

Recognition of facial expressions is crucial for effective social interactions. Yet, the extent to which the various face-selective regions in the human brain classify different facial expressions remains unclear. We used functional magnetic resonance imaging (fMRI) and support vector machine pattern classification analysis to determine how well face-selective brain regions are able to decode different categories of facial expression. Subjects participated in a slow event-related fMRI experiment in which they were shown 32 face pictures, portraying four different expressions: neutral, fearful, angry, and happy and belonging to eight different identities. Our results showed that only the amygdala and the posterior superior temporal sulcus (STS) were able to accurately discriminate between these expressions, albeit in different ways: the amygdala discriminated fearful faces from non-fearful faces, whereas STS discriminated neutral from emotional (fearful, angry and happy) faces. In contrast to these findings on the classification of emotional expression, only the fusiform face area (FFA) and anterior inferior temporal cortex (aIT) could discriminate among the various facial identities. Further, the amygdala and STS were better than FFA and aIT at classifying expression, while FFA and aIT were better than the amygdala and STS at classifying identity. Taken together, our findings indicate that the decoding of facial emotion and facial identity occurs in different neural substrates: the amygdala and STS for the former and FFA and aIT for the latter. Published by Elsevier Inc.

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.