Discriminant forest classification method and system

DOEpatents

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

2012-11-06

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

Classification of Fusarium-Infected Korean Hulled Barley Using Near-Infrared Reflectance Spectroscopy and Partial Least Squares Discriminant Analysis

PubMed Central

Lim, Jongguk; Kim, Giyoung; Mo, Changyeun; Oh, Kyoungmin; Yoo, Hyeonchae; Ham, Hyeonheui; Kim, Moon S.

2017-01-01

The purpose of this study is to use near-infrared reflectance (NIR) spectroscopy equipment to nondestructively and rapidly discriminate Fusarium-infected hulled barley. Both normal hulled barley and Fusarium-infected hulled barley were scanned by using a NIR spectrometer with a wavelength range of 1175 to 2170 nm. Multiple mathematical pretreatments were applied to the reflectance spectra obtained for Fusarium discrimination and the multivariate analysis method of partial least squares discriminant analysis (PLS-DA) was used for discriminant prediction. The PLS-DA prediction model developed by applying the second-order derivative pretreatment to the reflectance spectra obtained from the side of hulled barley without crease achieved 100% accuracy in discriminating the normal hulled barley and the Fusarium-infected hulled barley. These results demonstrated the feasibility of rapid discrimination of the Fusarium-infected hulled barley by combining multivariate analysis with the NIR spectroscopic technique, which is utilized as a nondestructive detection method. PMID:28974012

Authenticity of rice (Oryza sativa L.) geographical origin based on analysis of C, N, O and S stable isotope ratios: a preliminary case report in Korea, China and Philippine.

PubMed

Chung, Ill-Min; Kim, Jae-Kwang; Prabakaran, Mayakrishnan; Yang, Jin-Hee; Kim, Seung-Hyun

2016-05-01

Although rice (Oryza sativa L.) is the third largest food crop, relatively fewer studies have been reported on rice geographical origin based on light element isotope ratios in comparison with other foods such as wine, beef, juice, oil and milk. Therefore this study tries to discriminate the geographical origin of the same rice cultivars grown in different Asian countries using the analysis of C, N, O and S stable isotope ratios and chemometrics. The δ(15) NAIR , δ(18) OVSMOW and δ(34) SVCDT values of brown rice were more markedly influenced by geographical origin than was the δ(13) CVPDB value. In particular, the combination of δ(18) OVSMOW and δ(34) SVCDT more efficiently discriminated rice geographical origin than did the remaining combinations. Principal component analysis (PCA) revealed a clear discrimination between different rice geographical origins but not between rice genotypes. In particular, the first components of PCA discriminated rice cultivated in the Philippines from rice cultivated in China and Korea. The present findings suggest that analysis of the light element isotope composition combined with chemometrics can be potentially applicable to discriminate rice geographical origin and also may provide a valuable insight into the control of improper or fraudulent labeling regarding the geographical origin of rice worldwide. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

Combined use of ribotyping, PFGE typing and IS431 typing in the discrimination of nosocomial strains of methicillin-resistant Staphylococcus aureus.

PubMed

Yoshida, T; Kondo, N; Hanifah, Y A; Hiramatsu, K

1997-01-01

We have previously reported the phenotypic characterization of methicillin-resistant Staphylococcus aureus (MRSA) clinical strains isolated in Malaya University Hospital in the period 1987 to 1989 using antibiogram, coagulase typing, plasmid profiles, and phage typing. Here, we report the analysis of the same strains with three genotyping methods; ribotyping, pulsed-field gel electrophoresis (PFGE) typing, and IS431 typing (a restriction enzyme fragment length polymorphism analysis using an IS431 probe). Ribotyping could discriminate 46 clinical MRSA strains into 5 ribotypes, PFGE typing into 22 types, and IS431 typing into 15 types. Since the differences of the three genotyping patterns from strain to strain were quite independent from one another, the combined use of the three genotyping methods could discriminate 46 strains into 39 genotypes. Thus, the powerful discriminatory ability of the combination was demonstrated.

Combining 1D and 2D linear discriminant analysis for palmprint recognition

NASA Astrophysics Data System (ADS)

Zhang, Jian; Ji, Hongbing; Wang, Lei; Lin, Lin

2011-11-01

In this paper, a novel feature extraction method for palmprint recognition termed as Two-dimensional Combined Discriminant Analysis (2DCDA) is proposed. By connecting the adjacent rows of a image sequentially, the obtained new covariance matrices contain the useful information among local geometry structures in the image, which is eliminated by 2DLDA. In this way, 2DCDA combines LDA and 2DLDA for a promising recognition accuracy, but the number of coefficients of its projection matrix is lower than that of other two-dimensional methods. Experimental results on the CASIA palmprint database demonstrate the effectiveness of the proposed method.

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Hepatic MR imaging for in vivo differentiation of steatosis, iron deposition and combined storage disorder: single-ratio in/opposed phase analysis vs. dual-ratio Dixon discrimination.

PubMed

Bashir, Mustafa R; Merkle, Elmar M; Smith, Alastair D; Boll, Daniel T

2012-02-01

To assess whether in vivo dual-ratio Dixon discrimination can improve detection of diffuse liver disease, specifically steatosis, iron deposition and combined disease over traditional single-ratio in/opposed phase analysis. Seventy-one patients with biopsy-proven (17.7 ± 17.0 days) hepatic steatosis (n = 16), iron deposition (n = 11), combined deposition (n = 3) and neither disease (n = 41) underwent MR examinations. Dual-echo in/opposed-phase MR with Dixon water/fat reconstructions were acquired. Analysis consisted of: (a) single-ratio hepatic region-of-interest (ROI)-based assessment of in/opposed ratios; (b) dual-ratio hepatic ROI assessment of in/opposed and fat/water ratios; (c) computer-aided dual-ratio assessment evaluating all hepatic voxels. Disease-specific thresholds were determined; statistical analyses assessed disease-dependent voxel ratios, based on single-ratio (a) and dual-ratio (b and c) techniques. Single-ratio discrimination succeeded in identifying iron deposition (I/O(Ironthreshold)<0.88) and steatosis (I/O(Fatthreshold>1.15)) from normal parenchyma, sensitivity 70.0%; it failed to detect combined disease. Dual-ratio discrimination succeeded in identifying abnormal hepatic parenchyma (F/W(Normalthreshold)>0.05), sensitivity 96.7%; logarithmic functions for iron deposition (I/O(Irondiscriminator)e((F/W(Fat)-0.01)/0.48)) differentiated combined from isolated diseases, sensitivity 100.0%; computer-aided dual-ratio analysis was comparably sensitive but less specific, 90.2% vs. 97.6%. MR two-point-Dixon imaging using dual-ratio post-processing based on in/opposed and fat/water ratios improved in vivo detection of hepatic steatosis, iron deposition, and combined storage disease beyond traditional in/opposed analysis. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Rapid discrimination of different Apiaceae species based on HPTLC fingerprints and targeted flavonoids determination using multivariate image analysis.

PubMed

Shawky, Eman; Abou El Kheir, Rasha M

2018-02-11

Species of Apiaceae are used in folk medicine as spices and in officinal medicinal preparations of drugs. They are an excellent source of phenolics exhibiting antioxidant activity, which are of great benefit to human health. Discrimination among Apiaceae medicinal herbs remains an intricate challenge due to their morphological similarity. In this study, a combined "untargeted" and "targeted" approach to investigate different Apiaceae plants species was proposed by using the merging of high-performance thin layer chromatography (HPTLC)-image analysis and pattern recognition methods which were used for fingerprinting and classification of 42 different Apiaceae samples collected from Egypt. Software for image processing was applied for fingerprinting and data acquisition. HPTLC fingerprint assisted by principal component analysis (PCA) and hierarchical cluster analysis (HCA)-heat maps resulted in a reliable untargeted approach for discrimination and classification of different samples. The "targeted" approach was performed by developing and validating an HPTLC method allowing the quantification of eight flavonoids. The combination of quantitative data with PCA and HCA-heat-maps allowed the different samples to be discriminated from each other. The use of chemometrics tools for evaluation of fingerprints reduced expense and analysis time. The proposed method can be adopted for routine discrimination and evaluation of the phytochemical variability in different Apiaceae species extracts. Copyright © 2018 John Wiley & Sons, Ltd.

[Study on the genuineness and producing area of Panax notoginseng based on infrared spectroscopy combined with discriminant analysis].

PubMed

Liu, Fei; Wang, Yuan-zhong; Yang, Chun-yan; Jin, Hang

2015-01-01

The genuineness and producing area of Panax notoginseng were studied based on infrared spectroscopy combined with discriminant analysis. The infrared spectra of 136 taproots of P. notoginseng from 13 planting point in 11 counties were collected and the second derivate spectra were calculated by Omnic 8. 0 software. The infrared spectra and their second derivate spectra in the range 1 800 - 700 cm-1 were used to build model by stepwise discriminant analysis, which was in order to distinguish study on the genuineness of P. notoginseng. The model built based on the second derivate spectra showed the better recognition effect for the genuineness of P. notoginseng. The correct rate of returned classification reached to 100%, and the prediction accuracy was 93. 4%. The stability of model was tested by cross validation and the method was performed extrapolation validation. The second derivate spectra combined with the same discriminant analysis method were used to distinguish the producing area of P. notoginseng. The recognition effect of models built based on different range of spectrum and different numbers of samples were compared and found that when the model was built by collecting 8 samples from each planting point as training sample and the spectrum in the range 1 500 - 1 200 cm-1 , the recognition effect was better, with the correct rate of returned classification reached to 99. 0%, and the prediction accuracy was 76. 5%. The results indicated that infrared spectroscopy combined with discriminant analysis showed good recognition effect for the genuineness of P. notoginseng. The method might be a hopeful new method for identification of genuineness of P. notoginseng in practice. The method could recognize the producing area of P. notoginseng to some extent and could be a new thought for identification of the producing area of P. natoginseng.

DISCRIMINATION OF ALTERED BASALTIC ROCKS IN THE SOUTHWESTERN UNITED STATES BY ANALYSIS OF LANDSAT THEMATIC MAPPER DATA.

USGS Publications Warehouse

Davis, Philip A.; Berlin, Graydon L.; Chavez, Pat S.

1987-01-01

Landsat Thematic Mapper image data were analyzed to determine their ability to discriminate red cone basalts from gray flow basalts and sedimentary country rocks for three volcanic fields in the southwestern United States. Analyses of all of the possible three-band combinations of the six nonthermal bands indicate that the combination of bands 1, 4, and 5 best discriminates among these materials. The color-composite image of these three bands unambiguously discriminates 89 percent of the mapped red volcanic cones in the three volcanic fields. Mineralogic and chemical analyses of collected samples indicate that discrimination is facilitated by the presence of hematite as a major mineral phase in the red cone basalts (hematite is only a minor mineral phase in the gray flow basalts and red sedimentary rocks).

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

PubMed

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

2017-12-15

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

[Identification of Dendrobium varieties by infrared spectroscopy].

PubMed

Liu, Fei; Wang, Yuan-Zhong; Yang, Chun-Yan; Jin, Hang

2014-11-01

The difference of Dendrobium varieties were analyzed by Fourier transform infrared (FTIR) spectroscopy. The infrared spectra of 206 stems from 30 Dendrobium varieties were obtained, and showed that polysaccharides, especially fiber, were the main components in Dendrobium plants. FTIR combined with Wilks' Lambda stepwise discriminative analysis was used to identify Dendrobium varieties. The effects of spectral range and number of training samples on the discrimination results were also analysed. Two hundred eighty seven variables in the spectral range of 1 800-1 250 cm(-1) were studied, and showed that the return discrimination is 100% correct when the training samples number of each species was 2, 3, 4, 5, and 6, respectively, whereas for the remaining samples the correct rates of identification were equal to 79.4%, 91.3%, 93.0%, 98.2%, and 100%, respectively. The same discriminative analyses on five different training samples in the spectral range of 1 800-1 500, 1 500-1 250, 1 250-600, 1 250-950 and 950-650 cm(-1) were compared, which showed that the variables in the range of 1 800-1 250, 1 800-1 500 and 950-600 cm(-1) were more suitable for variety identification, and one can obtain the satisfactory result for discriminative analysis when the training sample is more than 3. Our results indicate that FTIR combined with stepwise discriminative analysis is an effective way to distinguish different Dendrobium varieties.

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Geographic authentication of Asian rice (Oryza sativa L.) using multi-elemental and stable isotopic data combined with multivariate analysis.

PubMed

Chung, Ill-Min; Kim, Jae-Kwang; Lee, Kyoung-Jin; Park, Sung-Kyu; Lee, Ji-Hee; Son, Na-Young; Jin, Yong-Ik; Kim, Seung-Hyun

2018-02-01

Rice (Oryza sativa L.) is the world's third largest food crop after wheat and corn. Geographic authentication of rice has recently emerged asan important issue for enhancing human health via food safety and quality assurance. Here, we aimed to discriminate rice of six Asian countries through geographic authentication using combinations of elemental/isotopic composition analysis and chemometric techniques. Principal components analysis could distinguish samples cultivated from most countries, except for those cultivated in the Philippines and Japan. Furthermore, orthogonal projection to latent structure-discriminant analysis provided clear discrimination between rice cultivated in Korea and other countries. The major common variables responsible for differentiation in these models were δ 34 S, Mn, and Mg. Our findings contribute to understanding the variations of elemental and isotopic compositions in rice depending on geographic origins, and offer valuable insight into the control of fraudulent labeling regarding the geographic origins of rice traded among Asian countries. Copyright © 2017 Elsevier Ltd. All rights reserved.

Radiation injury vs. recurrent brain metastasis: combining textural feature radiomics analysis and standard parameters may increase 18F-FET PET accuracy without dynamic scans.

PubMed

Lohmann, Philipp; Stoffels, Gabriele; Ceccon, Garry; Rapp, Marion; Sabel, Michael; Filss, Christian P; Kamp, Marcel A; Stegmayr, Carina; Neumaier, Bernd; Shah, Nadim J; Langen, Karl-Josef; Galldiks, Norbert

2017-07-01

We investigated the potential of textural feature analysis of O-(2-[ 18 F]fluoroethyl)-L-tyrosine ( 18 F-FET) PET to differentiate radiation injury from brain metastasis recurrence. Forty-seven patients with contrast-enhancing brain lesions (n = 54) on MRI after radiotherapy of brain metastases underwent dynamic 18 F-FET PET. Tumour-to-brain ratios (TBRs) of 18 F-FET uptake and 62 textural parameters were determined on summed images 20-40 min post-injection. Tracer uptake kinetics, i.e., time-to-peak (TTP) and patterns of time-activity curves (TAC) were evaluated on dynamic PET data from 0-50 min post-injection. Diagnostic accuracy of investigated parameters and combinations thereof to discriminate between brain metastasis recurrence and radiation injury was compared. Diagnostic accuracy increased from 81 % for TBR mean alone to 85 % when combined with the textural parameter Coarseness or Short-zone emphasis. The accuracy of TBR max alone was 83 % and increased to 85 % after combination with the textural parameters Coarseness, Short-zone emphasis, or Correlation. Analysis of TACs resulted in an accuracy of 70 % for kinetic pattern alone and increased to 83 % when combined with TBR max . Textural feature analysis in combination with TBRs may have the potential to increase diagnostic accuracy for discrimination between brain metastasis recurrence and radiation injury, without the need for dynamic 18 F-FET PET scans. • Textural feature analysis provides quantitative information about tumour heterogeneity • Textural features help improve discrimination between brain metastasis recurrence and radiation injury • Textural features might be helpful to further understand tumour heterogeneity • Analysis does not require a more time consuming dynamic PET acquisition.

Discriminant analysis of the metabolic effects of a new combined contraceptive vaginal ring containing Nestorone/EE vs. a second-generation oral contraceptive containing levonorgestrel/EE.

PubMed

Rad, Mandana; Burggraaf, Jacobus; de Kam, Marieke L; Cohen, Adam F; Kluft, Cornelis

2012-09-01

Discriminant analysis (DA) was performed on data of two combined hormonal contraceptives (CHC) differing in estrogen ratio to explore whether a combination of variables rather than a single variable distinguishes CHCs better. Data were used of a parallel study in premenopausal women treated for three cycles (21 days on, 7 days off) with a contraceptive vaginal ring delivering Nestorone and ethinyl estradiol (EE) or an oral contraceptive containing levonorgestrel and EE. DA was performed on the change from baseline (CFB) and the end-of-treatment values at 3 months for lipids, sex-hormone binding globulin (SHBG), C-reactive protein, angiotensinogen, blood pressure and hemostasis variables, and on the hemostasis variables only. For the complete set, the CFB for factor VII (FVII), SHBG and plasminogen (PLG), or end-of-treatment SHBG- and FVII level discriminated the treatments best. Maximal discrimination for the hemostasis data was by CFB for FVII and PLG or end-of-treatment FVII level. DA identifies differences between CHCs and may provide information on the factors associated with thrombotic risk. Copyright © 2012 Elsevier Inc. All rights reserved.

Combining features from ERP components in single-trial EEG for discriminating four-category visual objects.

PubMed

Wang, Changming; Xiong, Shi; Hu, Xiaoping; Yao, Li; Zhang, Jiacai

2012-10-01

Categorization of images containing visual objects can be successfully recognized using single-trial electroencephalograph (EEG) measured when subjects view images. Previous studies have shown that task-related information contained in event-related potential (ERP) components could discriminate two or three categories of object images. In this study, we investigated whether four categories of objects (human faces, buildings, cats and cars) could be mutually discriminated using single-trial EEG data. Here, the EEG waveforms acquired while subjects were viewing four categories of object images were segmented into several ERP components (P1, N1, P2a and P2b), and then Fisher linear discriminant analysis (Fisher-LDA) was used to classify EEG features extracted from ERP components. Firstly, we compared the classification results using features from single ERP components, and identified that the N1 component achieved the highest classification accuracies. Secondly, we discriminated four categories of objects using combining features from multiple ERP components, and showed that combination of ERP components improved four-category classification accuracies by utilizing the complementarity of discriminative information in ERP components. These findings confirmed that four categories of object images could be discriminated with single-trial EEG and could direct us to select effective EEG features for classifying visual objects.

Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms

NASA Astrophysics Data System (ADS)

Lee, Chien-Cheng; Huang, Shin-Sheng; Shih, Cheng-Yuan

2010-12-01

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

Health Consequences of Racist and Antigay Discrimination for Multiple Minority Adolescents

PubMed Central

Thoma, Brian C.; Huebner, David M.

2014-01-01

Individuals who belong to a marginalized group and who perceive discrimination based on that group membership suffer from a variety of poor health outcomes. Many people belong to more than one marginalized group, and much less is known about the influence of multiple forms of discrimination on health outcomes. Drawing on literature describing the influence of multiple stressors, three models of combined forms of discrimination are discussed: additive, prominence, and exacerbation. The current study examined the influence of multiple forms of discrimination in a sample of African American lesbian, gay, or bisexual (LGB) adolescents ages 14–19. Each of the three models of combined stressors were tested to determine which best describes how racist and antigay discrimination combine to predict depressive symptoms, suicidal ideation, and substance use. Participants were included in this analysis if they identified their ethnicity as either African American (n = 156) or African American mixed (n = 120). Mean age was 17.45 years (SD = 1.36). Results revealed both forms of mistreatment were associated with depressive symptoms and suicidal ideation among African American LGB adolescents. Racism was more strongly associated with substance use. Future intervention efforts should be targeted toward reducing discrimination and improving the social context of multiple minority adolescents, and future research with multiple minority individuals should be attuned to the multiple forms of discrimination experienced by these individuals within their environments. PMID:23731232

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…

The Raman spectrum character of skin tumor induced by UVB

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

DIFFERENTIATION OF AURANTII FRUCTUS IMMATURUS AND FRUCTUS PONICIRI TRIFOLIATAE IMMATURUS BY FLOW-INJECTION WITH ULTRAVIOLET SPECTROSCOPIC DETECTION AND PROTON NUCLEAR MAGNETIC RESONANCE USING PARTIAL LEAST-SQUARES DISCRIMINANT ANALYSIS.

PubMed

Zhang, Mengliang; Zhao, Yang; Harrington, Peter de B; Chen, Pei

2016-03-01

Two simple fingerprinting methods, flow-injection coupled to ultraviolet spectroscopy and proton nuclear magnetic resonance, were used for discriminating between Aurantii fructus immaturus and Fructus poniciri trifoliatae immaturus . Both methods were combined with partial least-squares discriminant analysis. In the flow-injection method, four data representations were evaluated: total ultraviolet absorbance chromatograms, averaged ultraviolet spectra, absorbance at 193, 205, 225, and 283 nm, and absorbance at 225 and 283 nm. Prediction rates of 100% were achieved for all data representations by partial least-squares discriminant analysis using leave-one-sample-out cross-validation. The prediction rate for the proton nuclear magnetic resonance data by partial least-squares discriminant analysis with leave-one-sample-out cross-validation was also 100%. A new validation set of data was collected by flow-injection with ultraviolet spectroscopic detection two weeks later and predicted by partial least-squares discriminant analysis models constructed by the initial data representations with no parameter changes. The classification rates were 95% with the total ultraviolet absorbance chromatograms datasets and 100% with the other three datasets. Flow-injection with ultraviolet detection and proton nuclear magnetic resonance are simple, high throughput, and low-cost methods for discrimination studies.

Spectral identification of melon seeds variety based on k-nearest neighbor and Fisher discriminant analysis

NASA Astrophysics Data System (ADS)

Li, Cuiling; Jiang, Kai; Zhao, Xueguan; Fan, Pengfei; Wang, Xiu; Liu, Chuan

2017-10-01

Impurity of melon seeds variety will cause reductions of melon production and economic benefits of farmers, this research aimed to adopt spectral technology combined with chemometrics methods to identify melon seeds variety. Melon seeds whose varieties were "Yi Te Bai", "Yi Te Jin", "Jing Mi NO.7", "Jing Mi NO.11" and " Yi Li Sha Bai "were used as research samples. A simple spectral system was developed to collect reflective spectral data of melon seeds, including a light source unit, a spectral data acquisition unit and a data processing unit, the detection wavelength range of this system was 200-1100nm with spectral resolution of 0.14 7.7nm. The original reflective spectral data was pre-treated with de-trend (DT), multiple scattering correction (MSC), first derivative (FD), normalization (NOR) and Savitzky-Golay (SG) convolution smoothing methods. Principal Component Analysis (PCA) method was adopted to reduce the dimensions of reflective spectral data and extract principal components. K-nearest neighbour (KNN) and Fisher discriminant analysis (FDA) methods were used to develop discriminant models of melon seeds variety based on PCA. Spectral data pretreatments improved the discriminant effects of KNN and FDA, FDA generated better discriminant results than KNN, both KNN and FDA methods produced discriminant accuracies reaching to 90.0% for validation set. Research results showed that using spectral technology in combination with KNN and FDA modelling methods to identify melon seeds variety was feasible.

[Discrimination among different brands of coffee by using vis-near infrared spectra].

PubMed

Wang, Yan-Yan; He, Yong; Shao, Yong-Ni; Zhang, Zhi-Fei

2007-04-01

Near infrared spectroscopy technology was used to distinguish three different brands of coffee bought from the supermarket. Two models, PCA-BP and WT-BP, were employed for the analysis and prediction of the samples. The discrimination among the different brands of coffee was based on the combination of the function of data compression in the PCA and WT technology and the ability of learning and prediction of the artificial neural network. In the experiment, 60 samples were used for model calibration and 30 for brand prediction. The result showed that both the PCA-BP and WT-BP models achieved 100% discrimination rate, and the wavelet transforms technology is superior to the principal component analysis both in time-consuming and the capability of data compression. It is indicated that the model set up by the combination of WT technology and BP neural network in the present study is rapid in analysis and precise in pattern discrimination. It can be concluded that a new approach to distinguishing pure coffee was of fered and the result of this experiment established the foundation for the determination of the raw material (coffee bean) of different brands of coffee in the market.

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

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

PubMed Central

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

2009-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2014-11-01

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

Health consequences of racist and antigay discrimination for multiple minority adolescents.

PubMed

Thoma, Brian C; Huebner, David M

2013-10-01

Individuals who belong to a marginalized group and who perceive discrimination based on that group membership suffer from a variety of poor health outcomes. Many people belong to more than one marginalized group, and much less is known about the influence of multiple forms of discrimination on health outcomes. Drawing on literature describing the influence of multiple stressors, three models of combined forms of discrimination are discussed: additive, prominence, and exacerbation. The current study examined the influence of multiple forms of discrimination in a sample of African American lesbian, gay, or bisexual (LGB) adolescents ages 14-19. Each of the three models of combined stressors were tested to determine which best describes how racist and antigay discrimination combine to predict depressive symptoms, suicidal ideation, and substance use. Participants were included in this analysis if they identified their ethnicity as either African American (n = 156) or African American mixed (n = 120). Mean age was 17.45 years (SD = 1.36). Results revealed both forms of mistreatment were associated with depressive symptoms and suicidal ideation among African American LGB adolescents. Racism was more strongly associated with substance use. Future intervention efforts should be targeted toward reducing discrimination and improving the social context of multiple minority adolescents, and future research with multiple minority individuals should be attuned to the multiple forms of discrimination experienced by these individuals within their environments. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Discrimination of healthy and osteoarthritic articular cartilages by Fourier transform infrared imaging and partial least squares-discriminant analysis

PubMed Central

Zhang, Xue-Xi; Yin, Jian-Hua; Mao, Zhi-Hua; Xia, Yang

2015-01-01

Abstract. Fourier transform infrared imaging (FTIRI) combined with chemometrics algorithm has strong potential to obtain complex chemical information from biology tissues. FTIRI and partial least squares-discriminant analysis (PLS-DA) were used to differentiate healthy and osteoarthritic (OA) cartilages for the first time. A PLS model was built on the calibration matrix of spectra that was randomly selected from the FTIRI spectral datasets of healthy and lesioned cartilage. Leave-one-out cross-validation was performed in the PLS model, and the fitting coefficient between actual and predicted categorical values of the calibration matrix reached 0.95. In the calibration and prediction matrices, the successful identifying percentages of healthy and lesioned cartilage spectra were 100% and 90.24%, respectively. These results demonstrated that FTIRI combined with PLS-DA could provide a promising approach for the categorical identification of healthy and OA cartilage specimens. PMID:26057029

Comprehensive Chemical Fingerprinting of High-Quality Cocoa at Early Stages of Processing: Effectiveness of Combined Untargeted and Targeted Approaches for Classification and Discrimination.

PubMed

Magagna, Federico; Guglielmetti, Alessandro; Liberto, Erica; Reichenbach, Stephen E; Allegrucci, Elena; Gobino, Guido; Bicchi, Carlo; Cordero, Chiara

2017-08-02

This study investigates chemical information of volatile fractions of high-quality cocoa (Theobroma cacao L. Malvaceae) from different origins (Mexico, Ecuador, Venezuela, Columbia, Java, Trinidad, and Sao Tomè) produced for fine chocolate. This study explores the evolution of the entire pattern of volatiles in relation to cocoa processing (raw, roasted, steamed, and ground beans). Advanced chemical fingerprinting (e.g., combined untargeted and targeted fingerprinting) with comprehensive two-dimensional gas chromatography coupled with mass spectrometry allows advanced pattern recognition for classification, discrimination, and sensory-quality characterization. The entire data set is analyzed for 595 reliable two-dimensional peak regions, including 130 known analytes and 13 potent odorants. Multivariate analysis with unsupervised exploration (principal component analysis) and simple supervised discrimination methods (Fisher ratios and linear regression trees) reveal informative patterns of similarities and differences and identify characteristic compounds related to sample origin and manufacturing step.

Discrimination of healthy and osteoarthritic articular cartilages by Fourier transform infrared imaging and partial least squares-discriminant analysis.

PubMed

Zhang, Xue-Xi; Yin, Jian-Hua; Mao, Zhi-Hua; Xia, Yang

2015-06-01

Fourier transform infrared imaging (FTIRI) combined with chemometrics algorithm has strong potential to obtain complex chemical information from biology tissues. FTIRI and partial least squares-discriminant analysis (PLS-DA) were used to differentiate healthy and osteoarthritic (OA) cartilages for the first time. A PLS model was built on the calibration matrix of spectra that was randomly selected from the FTIRI spectral datasets of healthy and lesioned cartilage. Leave-one-out cross-validation was performed in the PLS model, and the fitting coefficient between actual and predicted categorical values of the calibration matrix reached 0.95. In the calibration and prediction matrices, the successful identifying percentages of healthy and lesioned cartilage spectra were 100% and 90.24%, respectively. These results demonstrated that FTIRI combined with PLS-DA could provide a promising approach for the categorical identification of healthy and OA cartilage specimens.

The effect of Interaction Anxiousness Scale and Brief Social Phobia Scale for screening social anxiety disorder in college students: a study on discriminative validity.

PubMed

Cao, Jianqin; Yang, Jinwei; Zhou, Yuqiu; Chu, Fuliu; Zhao, Xiwu; Wang, Weiren; Wang, Yunlong; Peng, Tao

2016-12-01

Social anxiety disorder (SAD) is one of the most prevalent mental health problems, but there is little research concerning the effective screening instruments in practice. This study was designed to examine the discriminative validity of Interaction Anxiousness Scale (IAS) and Brief Social Phobia Scale (BSPS) for the screening of SAD through the compared and combined analysis. Firstly, 421 Chinese undergraduates were screened by the IAS and BSPS. Secondly, in the follow-up stage, 248 students were interviewed by the Structured Clinical Interview for DSM-IV. Receiver operating characteristic (ROC) analysis was used, and the related psychometric characters were checked. The results indicated that the ROC in these two scales demonstrated discrimination is in satisfactory level (range: 0.7-0.8). However, the highest agreement (92.17%) was identified when a cut-off point of 50 measured by the IAS and a cut-off point of 34 by the BSPS were combined, also with higher PPV, SENS, SPEC and OA than that reached when BSPS was used individually, as well as PPV, SPEC and OA in IAS. The findings indicate that the combination of these two scales is valid as the general screening instrument for SAD in maximizing the discriminative validity.

Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal.

PubMed

Hosseinifard, Behshad; Moradi, Mohammad Hassan; Rostami, Reza

2013-03-01

Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

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.

Mapping specificity landscapes of RNA-protein interactions by high throughput sequencing.

PubMed

Jankowsky, Eckhard; Harris, Michael E

2017-04-15

To function in a biological setting, RNA binding proteins (RBPs) have to discriminate between alternative binding sites in RNAs. This discrimination can occur in the ground state of an RNA-protein binding reaction, in its transition state, or in both. The extent by which RBPs discriminate at these reaction states defines RBP specificity landscapes. Here, we describe the HiTS-Kin and HiTS-EQ techniques, which combine kinetic and equilibrium binding experiments with high throughput sequencing to quantitatively assess substrate discrimination for large numbers of substrate variants at ground and transition states of RNA-protein binding reactions. We discuss experimental design, practical considerations and data analysis and outline how a combination of HiTS-Kin and HiTS-EQ allows the mapping of RBP specificity landscapes. Copyright © 2017 Elsevier Inc. All rights reserved.

Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer.

PubMed

Duan, Xiaoran; Yang, Yongli; Tan, Shanjuan; Wang, Sihua; Feng, Xiaolei; Cui, Liuxin; Feng, Feifei; Yu, Songcheng; Wang, Wei; Wu, Yongjun

2017-08-01

The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P < 0.001. The areas under the ROC curve (AUC) and 95 % CI of prediction set from Fisher discrimination analysis and BP neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.

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

PubMed

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

2017-08-01

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

A 4-gene panel as a marker at chromosome 8q in Asian gastric cancer patients.

PubMed

Cheng, Lei; Zhang, Qing; Yang, Sheng; Yang, Yanqing; Zhang, Wen; Gao, Hengjun; Deng, Xiaxing; Zhang, Qinghua

2013-10-01

A widely held viewpoint is that the use of multiple markers, combined in some type of algorithm, will be necessary to provide high enough discrimination between diseased cases and non-diseased. We applied stepwise logistic regression analysis to identify the best combination of the 32 biomarkers at chromosome 8q on an independent public microarray test set of 80 paired gastric samples. A combination of SULF1, INTS8, ATP6V1C1, and GPR172A was identified with a prediction accuracy of 98.0% for discriminating carcinomas from adjacent noncancerous tissues in our previous 25 paired samples. Interestingly, the overexpression of SULF1 was associated with tumor invasion and metastasis. Function prediction analysis revealed that the 4-marker panel was mainly associated with acidification of intracellular compartments. Taken together, we found a 4-gene panel that accurately discriminated gastric carcinomas from adjacent noncancerous tissues and these results had potential clinical significance in the early diagnosis and targeted treatment of gastric cancer. Copyright © 2013 Elsevier Inc. All rights reserved.

Investigation of varying gray scale levels for remote manipulation

NASA Technical Reports Server (NTRS)

Bierschwale, John M.; Stuart, Mark A.; Sampaio, Carlos E.

1991-01-01

A study was conducted to investigate the effects of variant monitor gray scale levels and workplace illumination levels on operators' ability to discriminate between different colors on a monochrome monitor. It was determined that 8-gray scale viewing resulted in significantly worse discrimination performance compared to 16- and 32-gray scale viewing and that there was only a negligible difference found between 16 and 32 shades of gray. Therefore, it is recommended that monitors used while performing remote manipulation tasks have 16 or above shades of gray since this evaluation has found levels lower than this to be unacceptable for color discrimination task. There was no significant performance difference found between a high and a low workplace illumination condition. Further analysis was conducted to determine which specific combinations of colors can be used in conjunction with each other to ensure errorfree color coding/brightness discrimination performance while viewing a monochrome monitor. It was found that 92 three-color combination and 9 four-color combinations could be used with 100 percent accuracy. The results can help to determine which gray scale levels should be provided on monochrome monitors as well as which colors to use to ensure the maximal performance of remotely-viewed color discrimination/coding tasks.

Differentiation of Organically and Conventionally Grown Tomatoes by Chemometric Analysis of Combined Data from Proton Nuclear Magnetic Resonance and Mid-infrared Spectroscopy and Stable Isotope Analysis.

PubMed

Hohmann, Monika; Monakhova, Yulia; Erich, Sarah; Christoph, Norbert; Wachter, Helmut; Holzgrabe, Ulrike

2015-11-04

Because the basic suitability of proton nuclear magnetic resonance spectroscopy ((1)H NMR) to differentiate organic versus conventional tomatoes was recently proven, the approach to optimize (1)H NMR classification models (comprising overall 205 authentic tomato samples) by including additional data of isotope ratio mass spectrometry (IRMS, δ(13)C, δ(15)N, and δ(18)O) and mid-infrared (MIR) spectroscopy was assessed. Both individual and combined analytical methods ((1)H NMR + MIR, (1)H NMR + IRMS, MIR + IRMS, and (1)H NMR + MIR + IRMS) were examined using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and common components and specific weight analysis (ComDim). With regard to classification abilities, fused data of (1)H NMR + MIR + IRMS yielded better validation results (ranging between 95.0 and 100.0%) than individual methods ((1)H NMR, 91.3-100%; MIR, 75.6-91.7%), suggesting that the combined examination of analytical profiles enhances authentication of organically produced tomatoes.

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

PubMed

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

2017-08-15

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

Fast discrimination of traditional Chinese medicine according to geographical origins with FTIR spectroscopy and advanced pattern recognition techniques

NASA Astrophysics Data System (ADS)

Li, Ning; Wang, Yan; Xu, Kexin

2006-08-01

Combined with Fourier transform infrared (FTIR) spectroscopy and three kinds of pattern recognition techniques, 53 traditional Chinese medicine danshen samples were rapidly discriminated according to geographical origins. The results showed that it was feasible to discriminate using FTIR spectroscopy ascertained by principal component analysis (PCA). An effective model was built by employing the Soft Independent Modeling of Class Analogy (SIMCA) and PCA, and 82% of the samples were discriminated correctly. Through use of the artificial neural network (ANN)-based back propagation (BP) network, the origins of danshen were completely classified.

Exploring Geographical Differentiation of the Hoelen Medicinal Mushroom, Wolfiporia extensa (Agaricomycetes), Using Fourier-Transform Infrared Spectroscopy Combined with Multivariate Analysis.

PubMed

Li, Yan; Zhang, Ji; Zhao, Yanli; Liu, Honggao; Wang, Yuanzhong; Jin, Hang

2016-01-01

In this study the geographical differentiation of dried sclerotia of the medicinal mushroom Wolfiporia extensa, obtained from different regions in Yunnan Province, China, was explored using Fourier-transform infrared (FT-IR) spectroscopy coupled with multivariate data analysis. The FT-IR spectra of 97 samples were obtained for wave numbers ranging from 4000 to 400 cm-1. Then, the fingerprint region of 1800-600 cm-1 of the FT-IR spectrum, rather than the full spectrum, was analyzed. Different pretreatments were applied on the spectra, and a discriminant analysis model based on the Mahalanobis distance was developed to select an optimal pretreatment combination. Two unsupervised pattern recognition procedures- principal component analysis and hierarchical cluster analysis-were applied to enhance the authenticity of discrimination of the specimens. The results showed that excellent classification could be obtained after optimizing spectral pretreatment. The tested samples were successfully discriminated according to their geographical locations. The chemical properties of dried sclerotia of W. extensa were clearly dependent on the mushroom's geographical origins. Furthermore, an interesting finding implied that the elevations of collection areas may have effects on the chemical components of wild W. extensa sclerotia. Overall, this study highlights the feasibility of FT-IR spectroscopy combined with multivariate data analysis in particular for exploring the distinction of different regional W. extensa sclerotia samples. This research could also serve as a basis for the exploitation and utilization of medicinal mushrooms.

Early discrimination of nasopharyngeal carcinoma based on tissue deoxyribose nucleic acid surface-enhanced Raman spectroscopy analysis

NASA Astrophysics Data System (ADS)

Qiu, Sufang; Li, Chao; Lin, Jinyong; Xu, Yuanji; Lu, Jun; Huang, Qingting; Zou, Changyan; Chen, Chao; Xiao, Nanyang; Lin, Duo; Chen, Rong; Pan, Jianji; Feng, Shangyuan

2016-12-01

Surface-enhanced Raman spectroscopy (SERS) was employed to detect deoxyribose nucleic acid (DNA) variations associated with the development of nasopharyngeal carcinoma (NPC). Significant SERS spectral differences between the DNA extracted from early NPC, advanced NPC, and normal nasopharyngeal tissue specimens were observed at 678, 729, 788, 1337, 1421, 1506, and 1573 cm-1, which reflects the genetic variations in NPC. Principal component analysis combined with discriminant function analysis for early NPC discrimination yielded a diagnostic accuracy of 86.8%, 92.3%, and 87.9% for early NPC, advanced NPC, and normal nasopharyngeal tissue DNA, respectively. In this exploratory study, we demonstrated the potential of SERS for early detection of NPC based on the DNA molecular study of biopsy tissues.

Terahertz time-domain attenuated total reflection spectroscopy applied to the rapid discrimination of the botanical origin of honeys

NASA Astrophysics Data System (ADS)

Liu, Wen; Zhang, Yuying; Yang, Si; Han, Donghai

2018-05-01

A new technique to identify the floral resources of honeys is demanded. Terahertz time-domain attenuated total reflection spectroscopy combined with chemometrics methods was applied to discriminate different categorizes (Medlar honey, Vitex honey, and Acacia honey). Principal component analysis (PCA), cluster analysis (CA) and partial least squares-discriminant analysis (PLS-DA) have been used to find information of the botanical origins of honeys. Spectral range also was discussed to increase the precision of PLS-DA model. The accuracy of 88.46% for validation set was obtained, using PLS-DA model in 0.5-1.5 THz. This work indicated terahertz time-domain attenuated total reflection spectroscopy was an available approach to evaluate the quality of honey rapidly.

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

PubMed

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

2004-12-01

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

Geodesic topological analysis of trabecular bone microarchitecture from high-spatial resolution magnetic resonance images.

PubMed

Carballido-Gamio, Julio; Krug, Roland; Huber, Markus B; Hyun, Ben; Eckstein, Felix; Majumdar, Sharmila; Link, Thomas M

2009-02-01

In vivo assessment of trabecular bone microarchitecture could improve the prediction of fracture risk and the efficacy of osteoporosis treatment and prevention. Geodesic topological analysis (GTA) is introduced as a novel technique to quantify the trabecular bone microarchitecture from high-spatial resolution magnetic resonance (MR) images. Trabecular bone parameters that quantify the scale, topology, and anisotropy of the trabecular bone network in terms of its junctions are the result of GTA. The reproducibility of GTA was tested with in vivo images of human distal tibiae and radii (n = 6) at 1.5 Tesla; and its ability to discriminate between subjects with and without vertebral fracture was assessed with ex vivo images of human calcanei at 1.5 and 3.0 Tesla (n = 30). GTA parameters yielded an average reproducibility of 4.8%, and their individual areas under the curve (AUC) of the receiver operating characteristic curve analysis for fracture discrimination performed better at 3.0 than at 1.5 Tesla reaching values of up to 0.78 (p < 0.001). Logistic regression analysis demonstrated that fracture discrimination was improved by combining GTA parameters, and that GTA combined with bone mineral density (BMD) allow for better discrimination than BMD alone (AUC = 0.95; p < 0.001). Results indicate that GTA can substantially contribute in studies of osteoporosis involving imaging of the trabecular bone microarchitecture. Copyright 2009 Wiley-Liss, Inc.

Multi-modal approach using Raman spectroscopy and optical coherence tomography for the discrimination of colonic adenocarcinoma from normal colon

PubMed Central

Ashok, Praveen C.; Praveen, Bavishna B.; Bellini, Nicola; Riches, Andrew; Dholakia, Kishan; Herrington, C. Simon

2013-01-01

We report a multimodal optical approach using both Raman spectroscopy and optical coherence tomography (OCT) in tandem to discriminate between colonic adenocarcinoma and normal colon. Although both of these non-invasive techniques are capable of discriminating between normal and tumour tissues, they are unable individually to provide both the high specificity and high sensitivity required for disease diagnosis. We combine the chemical information derived from Raman spectroscopy with the texture parameters extracted from OCT images. The sensitivity obtained using Raman spectroscopy and OCT individually was 89% and 78% respectively and the specificity was 77% and 74% respectively. Combining the information derived using the two techniques increased both sensitivity and specificity to 94% demonstrating that combining complementary optical information enhances diagnostic accuracy. These data demonstrate that multimodal optical analysis has the potential to achieve accurate non-invasive cancer diagnosis. PMID:24156073

Control of Origin of Sesame Oil from Various Countries by Stable Isotope Analysis and DNA Based Markers—A Pilot Study

PubMed Central

Horacek, Micha; Hansel-Hohl, Karin; Burg, Kornel; Soja, Gerhard; Okello-Anyanga, Walter; Fluch, Silvia

2015-01-01

The indication of origin of sesame seeds and sesame oil is one of the important factors influencing its price, as it is produced in many regions worldwide and certain provenances are especially sought after. We joined stable carbon and hydrogen isotope analysis with DNA based molecular marker analysis to study their combined potential for the discrimination of different origins of sesame seeds. For the stable carbon and hydrogen isotope data a positive correlation between both isotope parameters was observed, indicating a dominant combined influence of climate and water availability. This enabled discrimination between sesame samples from tropical and subtropical/moderate climatic provenances. Carbon isotope values also showed differences between oil from black and white sesame seeds from identical locations, indicating higher water use efficiency of plants producing black seeds. DNA based markers gave independent evidence for geographic variation as well as provided information on the genetic relatedness of the investigated samples. Depending on the differences in ambient environmental conditions and in the genotypic fingerprint, a combination of both analytical methods is a very powerful tool to assess the declared geographic origin. To our knowledge this is the first paper on food authenticity combining the stable isotope analysis of bio-elements with DNA based markers and their combined statistical analysis. PMID:25831054

Control of origin of sesame oil from various countries by stable isotope analysis and DNA based markers--a pilot study.

PubMed

Horacek, Micha; Hansel-Hohl, Karin; Burg, Kornel; Soja, Gerhard; Okello-Anyanga, Walter; Fluch, Silvia

2015-01-01

The indication of origin of sesame seeds and sesame oil is one of the important factors influencing its price, as it is produced in many regions worldwide and certain provenances are especially sought after. We joined stable carbon and hydrogen isotope analysis with DNA based molecular marker analysis to study their combined potential for the discrimination of different origins of sesame seeds. For the stable carbon and hydrogen isotope data a positive correlation between both isotope parameters was observed, indicating a dominant combined influence of climate and water availability. This enabled discrimination between sesame samples from tropical and subtropical/moderate climatic provenances. Carbon isotope values also showed differences between oil from black and white sesame seeds from identical locations, indicating higher water use efficiency of plants producing black seeds. DNA based markers gave independent evidence for geographic variation as well as provided information on the genetic relatedness of the investigated samples. Depending on the differences in ambient environmental conditions and in the genotypic fingerprint, a combination of both analytical methods is a very powerful tool to assess the declared geographic origin. To our knowledge this is the first paper on food authenticity combining the stable isotope analysis of bio-elements with DNA based markers and their combined statistical analysis.

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.

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

PubMed Central

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

2018-01-01

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

A Novel Graph Constructor for Semisupervised Discriminant Analysis: Combined Low-Rank and k-Nearest Neighbor Graph

PubMed Central

Pan, Yongke; Niu, Wenjia

2017-01-01

Semisupervised Discriminant Analysis (SDA) is a semisupervised dimensionality reduction algorithm, which can easily resolve the out-of-sample problem. Relative works usually focus on the geometric relationships of data points, which are not obvious, to enhance the performance of SDA. Different from these relative works, the regularized graph construction is researched here, which is important in the graph-based semisupervised learning methods. In this paper, we propose a novel graph for Semisupervised Discriminant Analysis, which is called combined low-rank and k-nearest neighbor (LRKNN) graph. In our LRKNN graph, we map the data to the LR feature space and then the kNN is adopted to satisfy the algorithmic requirements of SDA. Since the low-rank representation can capture the global structure and the k-nearest neighbor algorithm can maximally preserve the local geometrical structure of the data, the LRKNN graph can significantly improve the performance of SDA. Extensive experiments on several real-world databases show that the proposed LRKNN graph is an efficient graph constructor, which can largely outperform other commonly used baselines. PMID:28316616

Assessing Risk Prediction Models Using Individual Participant Data From Multiple Studies

PubMed Central

Pennells, Lisa; Kaptoge, Stephen; White, Ian R.; Thompson, Simon G.; Wood, Angela M.; Tipping, Robert W.; Folsom, Aaron R.; Couper, David J.; Ballantyne, Christie M.; Coresh, Josef; Goya Wannamethee, S.; Morris, Richard W.; Kiechl, Stefan; Willeit, Johann; Willeit, Peter; Schett, Georg; Ebrahim, Shah; Lawlor, Debbie A.; Yarnell, John W.; Gallacher, John; Cushman, Mary; Psaty, Bruce M.; Tracy, Russ; Tybjærg-Hansen, Anne; Price, Jackie F.; Lee, Amanda J.; McLachlan, Stela; Khaw, Kay-Tee; Wareham, Nicholas J.; Brenner, Hermann; Schöttker, Ben; Müller, Heiko; Jansson, Jan-Håkan; Wennberg, Patrik; Salomaa, Veikko; Harald, Kennet; Jousilahti, Pekka; Vartiainen, Erkki; Woodward, Mark; D'Agostino, Ralph B.; Bladbjerg, Else-Marie; Jørgensen, Torben; Kiyohara, Yutaka; Arima, Hisatomi; Doi, Yasufumi; Ninomiya, Toshiharu; Dekker, Jacqueline M.; Nijpels, Giel; Stehouwer, Coen D. A.; Kauhanen, Jussi; Salonen, Jukka T.; Meade, Tom W.; Cooper, Jackie A.; Cushman, Mary; Folsom, Aaron R.; Psaty, Bruce M.; Shea, Steven; Döring, Angela; Kuller, Lewis H.; Grandits, Greg; Gillum, Richard F.; Mussolino, Michael; Rimm, Eric B.; Hankinson, Sue E.; Manson, JoAnn E.; Pai, Jennifer K.; Kirkland, Susan; Shaffer, Jonathan A.; Shimbo, Daichi; Bakker, Stephan J. L.; Gansevoort, Ron T.; Hillege, Hans L.; Amouyel, Philippe; Arveiler, Dominique; Evans, Alun; Ferrières, Jean; Sattar, Naveed; Westendorp, Rudi G.; Buckley, Brendan M.; Cantin, Bernard; Lamarche, Benoît; Barrett-Connor, Elizabeth; Wingard, Deborah L.; Bettencourt, Richele; Gudnason, Vilmundur; Aspelund, Thor; Sigurdsson, Gunnar; Thorsson, Bolli; Kavousi, Maryam; Witteman, Jacqueline C.; Hofman, Albert; Franco, Oscar H.; Howard, Barbara V.; Zhang, Ying; Best, Lyle; Umans, Jason G.; Onat, Altan; Sundström, Johan; Michael Gaziano, J.; Stampfer, Meir; Ridker, Paul M.; Michael Gaziano, J.; Ridker, Paul M.; Marmot, Michael; Clarke, Robert; Collins, Rory; Fletcher, Astrid; Brunner, Eric; Shipley, Martin; Kivimäki, Mika; Ridker, Paul M.; Buring, Julie; Cook, Nancy; Ford, Ian; Shepherd, James; Cobbe, Stuart M.; Robertson, Michele; Walker, Matthew; Watson, Sarah; Alexander, Myriam; Butterworth, Adam S.; Angelantonio, Emanuele Di; Gao, Pei; Haycock, Philip; Kaptoge, Stephen; Pennells, Lisa; Thompson, Simon G.; Walker, Matthew; Watson, Sarah; White, Ian R.; Wood, Angela M.; Wormser, David; Danesh, John

2014-01-01

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous. PMID:24366051

Assessing risk prediction models using individual participant data from multiple studies.

PubMed

Pennells, Lisa; Kaptoge, Stephen; White, Ian R; Thompson, Simon G; Wood, Angela M

2014-03-01

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous.

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

NASA Astrophysics Data System (ADS)

Zhu, Ying; Tan, Tuck Lee

2016-04-01

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

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

NASA Astrophysics Data System (ADS)

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

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

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

PubMed

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

2013-12-15

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

Effects of Drugs and Drug Combinations in Pigeons Trained to Discriminate among Pentobarbital, Dizocilpine, a Combination of These Drugs, and Saline

ERIC Educational Resources Information Center

McMillan, D. E.; Wessinger, William D.; Li, Mi

2009-01-01

Drugs with multiple actions can have complex discriminative-stimulus properties. An approach to studying such drugs is to train subjects to discriminate among drug combinations and individual drugs in the combination so that all of the complex discriminative stimuli are present during training. In the current experiments, a four-choice procedure…

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.

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

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

PubMed

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

2003-09-10

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

Interaction geometry: an ecological perspective.

Treesearch

Rolfe A. Leary

1976-01-01

A new mathematical coordinate system results from a unique combination of two frameworks long used by ecologists: the phase plane and coaction cross-tabulation. The resulting construct combines the classifying power of the cross-tabulation and discriminating power of the phase plane. It may be used for analysis and synthesis.

Combined striatal binding and cerebral influx analysis of dynamic 11C-raclopride PET improves early differentiation between multiple-system atrophy and Parkinson disease.

PubMed

Van Laere, Koen; Clerinx, Kristien; D'Hondt, Eduard; de Groot, Tjibbe; Vandenberghe, Wim

2010-04-01

Striatal dopamine D(2) receptor (D2R) PET has been proposed to differentiate between Parkinson disease (PD) and multiple-system atrophy with predominant parkinsonism (MSA-P). However, considerable overlap in striatal D(2) binding may exist between PD and MSA-P. It has been shown that imaging of neuronal activity, as determined by metabolism or perfusion, can also help distinguish PD from MSA-P. We investigated whether the differential diagnostic value of (11)C-raclopride PET could be improved by dynamic scan analysis combining D2R binding and regional tracer influx. (11)C-raclopride PET was performed in 9 MSA-P patients (mean age +/- SD, 56.2 +/- 10.2 y; disease duration, 2.9 +/- 0.8 y; median Hoehn-Yahr score, 3), 10 PD patients (mean age +/- SD, 65.7 +/- 8.1 y; disease duration, 3.3 +/- 1.5 y; median Hoehn-Yahr score, 1.5), and 10 healthy controls (mean age +/- SD, 61.6 +/- 6.5 y). Diagnosis was obtained after prolonged follow-up (MSA-P, 5.5 +/- 2.0 y; PD, 6.0 +/- 2.3 y) using validated clinical criteria. Spatially normalized parametric images of binding potential (BP) and local influx ratio (R(1) = K(1)/K'(1)) of (11)C-raclopride were obtained using a voxelwise reference tissue model with occipital cortex as reference region. Stepwise forward discriminant analysis with cross-validation, with and without the inclusion of regional R(1) values, was performed using a predefined volume-of-interest template. Using conventional BP values, we correctly classified 65.5% (all values given with cross-validation) of 29 cases only. The combination of BP and R(1) information increased discrimination accuracy to 79.3%. When healthy controls were not included and patients only were considered, BP information alone discriminated PD and MSA-P in 84.2% of cases, but the combination with R(1) data increased accuracy to 100%. Discriminant analysis using combined striatal D2R BP and cerebral influx ratio information of a single dynamic (11)C-raclopride PET scan distinguishes MSA-P and PD patients with high accuracy and is superior to conventional methods of striatal D2R binding analysis.

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

PubMed

Zhu, Ying; Tan, Tuck Lee

2016-04-15

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

Discrimination of Temperature and Strain in Brillouin Optical Time Domain Analysis Using a Multicore Optical Fiber

PubMed Central

Zaghloul, Mohamed A. S.; Wang, Mohan; Milione, Giovanni; Li, Ming-Jun; Li, Shenping; Huang, Yue-Kai; Wang, Ting; Chen, Kevin P.

2018-01-01

Brillouin optical time domain analysis is the sensing of temperature and strain changes along an optical fiber by measuring the frequency shift changes of Brillouin backscattering. Because frequency shift changes are a linear combination of temperature and strain changes, their discrimination is a challenge. Here, a multicore optical fiber that has two cores is fabricated. The differences between the cores’ temperature and strain coefficients are such that temperature (strain) changes can be discriminated with error amplification factors of 4.57 °C/MHz (69.11 μϵ/MHz), which is 2.63 (3.67) times lower than previously demonstrated. As proof of principle, using the multicore optical fiber and a commercial Brillouin optical time domain analyzer, the temperature (strain) changes of a thermally expanding metal cylinder are discriminated with an error of 0.24% (3.7%). PMID:29649148

DISCRIMINATION OF GRANITOIDS AND MINERALIZED GRANITOIDS IN THE MIDYAN REGION, NORTHWESTERN ARABIAN SHIELD, SAUDI ARABIA, BY LANDSAT MSS DATA-ANALYSIS.

USGS Publications Warehouse

Davis, Philip A.; Grolier, Maurice J.

1984-01-01

Landsat multispectral scanner (MSS) band and band-ratio databases of two scenes covering the Midyan region of northwestern Saudi Arabia were examined quantitatively and qualitatively to determine which databases best discriminate the geologic units of this semi-arid and arid region. Unsupervised, linear-discriminant cluster-analysis was performed on these two band-ratio combinations and on the MSS bands for both scenes. The results for granitoid-rock discrimination indicated that the classification images using the MSS bands are superior to the band-ratio classification images for two reasons, discussed in the paper. Yet, the effects of topography and material type (including desert varnish) on the MSS-band data produced ambiguities in the MSS-band classification results. However, these ambiguities were clarified by using a simulated natural-color image in conjunction with the MSS-band classification image.

Discrimination of genetically modified sugar beets based on terahertz spectroscopy

NASA Astrophysics Data System (ADS)

Chen, Tao; Li, Zhi; Yin, Xianhua; Hu, Fangrong; Hu, Cong

2016-01-01

The objective of this paper was to apply terahertz (THz) spectroscopy combined with chemometrics techniques for discrimination of genetically modified (GM) and non-GM sugar beets. In this paper, the THz spectra of 84 sugar beet samples (36 GM sugar beets and 48 non-GM ones) were obtained by using terahertz time-domain spectroscopy (THz-TDS) system in the frequency range from 0.2 to 1.2 THz. Three chemometrics methods, principal component analysis (PCA), discriminant analysis (DA) and discriminant partial least squares (DPLS), were employed to classify sugar beet samples into two groups: genetically modified organisms (GMOs) and non-GMOs. The DPLS method yielded the best classification result, and the percentages of successful classification for GM and non-GM sugar beets were both 100%. Results of the present study demonstrate the usefulness of THz spectroscopy together with chemometrics methods as a powerful tool to distinguish GM and non-GM sugar beets.

Discrimination of Temperature and Strain in Brillouin Optical Time Domain Analysis Using a Multicore Optical Fiber.

PubMed

Zaghloul, Mohamed A S; Wang, Mohan; Milione, Giovanni; Li, Ming-Jun; Li, Shenping; Huang, Yue-Kai; Wang, Ting; Chen, Kevin P

2018-04-12

Brillouin optical time domain analysis is the sensing of temperature and strain changes along an optical fiber by measuring the frequency shift changes of Brillouin backscattering. Because frequency shift changes are a linear combination of temperature and strain changes, their discrimination is a challenge. Here, a multicore optical fiber that has two cores is fabricated. The differences between the cores' temperature and strain coefficients are such that temperature (strain) changes can be discriminated with error amplification factors of 4.57 °C/MHz (69.11 μ ϵ /MHz), which is 2.63 (3.67) times lower than previously demonstrated. As proof of principle, using the multicore optical fiber and a commercial Brillouin optical time domain analyzer, the temperature (strain) changes of a thermally expanding metal cylinder are discriminated with an error of 0.24% (3.7%).

The discrimination of geoforensic trace material from close proximity locations by organic profiling using HPLC and plant wax marker analysis by GC.

PubMed

McCulloch, G; Dawson, L A; Ross, J M; Morgan, R M

2018-07-01

There is a need to develop a wider empirical research base to expand the scope for utilising the organic fraction of soil in forensic geoscience, and to demonstrate the capability of the analytical techniques used in forensic geoscience to discriminate samples at close proximity locations. The determination of wax markers from soil samples by GC analysis has been used extensively in court and is known to be effective in discriminating samples from different land use types. A new HPLC method for the analysis of the organic fraction of forensic sediment samples has also been shown recently to add value in conjunction with existing inorganic techniques for the discrimination of samples derived from close proximity locations. This study compares the ability of these two organic techniques to discriminate samples derived from close proximity locations and finds the GC technique to provide good discrimination at this scale, providing quantification of known compounds, whilst the HPLC technique offered a shorter and simpler sample preparation method and provided very good discrimination between groups of samples of different provenance in most cases. The use of both data sets together gave further improved accuracy rates in some cases, suggesting that a combined organic approach can provide added benefits in certain case scenarios and crime reconstruction contexts. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

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.

Sex determination based on a thoracic vertebra and ribs evaluation using clinical chest radiography.

PubMed

Tsubaki, Shun; Morishita, Junji; Usumoto, Yosuke; Sakaguchi, Kyoko; Matsunobu, Yusuke; Kawazoe, Yusuke; Okumura, Miki; Ikeda, Noriaki

2017-07-01

Our aim was to investigate whether sex can be determined from a combination of geometric features obtained from the 10th thoracic vertebra, 6th rib, and 7th rib. Six hundred chest radiographs (300 males and 300 females) were randomly selected to include patients of six age groups (20s, 30s, 40s, 50s, 60s, and 70s). Each group included 100 images (50 males and 50 females). A total of 14 features, including 7 lengths, 5 indices for the vertebra, and 2 types of widths for ribs, were utilized and analyzed for sex determination. Dominant features contributing to sex determination were selected by stepwise discriminant analysis after checking the variance inflation factors for multicollinearity. The accuracy of sex determination using a combination of the vertebra and ribs was evaluated from the selected features by the stepwise discriminant analysis. The accuracies in each age group were also evaluated in this study. The accuracy of sex determination based on a combination of features of the vertebra and ribs was 88.8% (533/600). This performance was superior to that of the vertebra or ribs only. Moreover, sex determination of subjects in their 20s demonstrated the highest accuracy (96.0%, 96/100). The features selected in the stepwise discriminant analysis included some features in both the vertebra and ribs. These results indicate the usefulness of combined information obtained from the vertebra and ribs for sex determination. We conclude that a combination of geometric characteristics obtained from the vertebra and ribs could be useful for determining sex. Copyright © 2017 Elsevier B.V. All rights reserved.

Metabolomics of Ulva lactuca Linnaeus (Chlorophyta) exposed to oil fuels: Fourier transform infrared spectroscopy and multivariate analysis as tools for metabolic fingerprint.

PubMed

Pilatti, Fernanda Kokowicz; Ramlov, Fernanda; Schmidt, Eder Carlos; Costa, Christopher; Oliveira, Eva Regina de; Bauer, Claudia M; Rocha, Miguel; Bouzon, Zenilda Laurita; Maraschin, Marcelo

2017-01-30

Fossil fuels, e.g. gasoline and diesel oil, account for substantial share of the pollution that affects marine ecosystems. Environmental metabolomics is an emerging field that may help unravel the effect of these xenobiotics on seaweeds and provide methodologies for biomonitoring coastal ecosystems. In the present study, FTIR and multivariate analysis were used to discriminate metabolic profiles of Ulva lactuca after in vitro exposure to diesel oil and gasoline, in combinations of concentrations (0.001%, 0.01%, 0.1%, and 1.0% - v/v) and times of exposure (30min, 1h, 12h, and 24h). PCA and HCA performed on entire mid-infrared spectral window were able to discriminate diesel oil-exposed thalli from the gasoline-exposed ones. HCA performed on spectral window related to the protein absorbance (1700-1500cm -1 ) enabled the best discrimination between gasoline-exposed samples regarding the time of exposure, and between diesel oil-exposed samples according to the concentration. The results indicate that the combination of FTIR with multivariate analysis is a simple and efficient methodology for metabolic profiling with potential use for biomonitoring strategies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Testing of complementarity of PDA and MS detectors using chromatographic fingerprinting of genuine and counterfeit samples containing sildenafil citrate.

PubMed

Custers, Deborah; Krakowska, Barbara; De Beer, Jacques O; Courselle, Patricia; Daszykowski, Michal; Apers, Sandra; Deconinck, Eric

2016-02-01

Counterfeit medicines are a global threat to public health. High amounts enter the European market, which is why characterization of these products is a very important issue. In this study, a high-performance liquid chromatography-photodiode array (HPLC-PDA) and high-performance liquid chromatography-mass spectrometry (HPLC-MS) method were developed for the analysis of genuine Viagra®, generic products of Viagra®, and counterfeit samples in order to obtain different types of fingerprints. These data were included in the chemometric data analysis, aiming to test whether PDA and MS are complementary detection techniques. The MS data comprise both MS1 and MS2 fingerprints; the PDA data consist of fingerprints measured at three different wavelengths, i.e., 254, 270, and 290 nm, and all possible combinations of these wavelengths. First, it was verified if both groups of fingerprints can discriminate between genuine, generic, and counterfeit medicines separately; next, it was studied if the obtained results could be ameliorated by combining both fingerprint types. This data analysis showed that MS1 does not provide suitable classification models since several genuines and generics are classified as counterfeits and vice versa. However, when analyzing the MS1_MS2 data in combination with partial least squares-discriminant analysis (PLS-DA), a perfect discrimination was obtained. When only using data measured at 254 nm, good classification models can be obtained by k nearest neighbors (kNN) and soft independent modelling of class analogy (SIMCA), which might be interesting for the characterization of counterfeit drugs in developing countries. However, in general, the combination of PDA and MS data (254 nm_MS1) is preferred due to less classification errors between the genuines/generics and counterfeits compared to PDA and MS data separately.

Near-infrared confocal micro-Raman spectroscopy combined with PCA-LDA multivariate analysis for detection of esophageal cancer

NASA Astrophysics Data System (ADS)

Chen, Long; Wang, Yue; Liu, Nenrong; Lin, Duo; Weng, Cuncheng; Zhang, Jixue; Zhu, Lihuan; Chen, Weisheng; Chen, Rong; Feng, Shangyuan

2013-06-01

The diagnostic capability of using tissue intrinsic micro-Raman signals to obtain biochemical information from human esophageal tissue is presented in this paper. Near-infrared micro-Raman spectroscopy combined with multivariate analysis was applied for discrimination of esophageal cancer tissue from normal tissue samples. Micro-Raman spectroscopy measurements were performed on 54 esophageal cancer tissues and 55 normal tissues in the 400-1750 cm-1 range. The mean Raman spectra showed significant differences between the two groups. Tentative assignments of the Raman bands in the measured tissue spectra suggested some changes in protein structure, a decrease in the relative amount of lactose, and increases in the percentages of tryptophan, collagen and phenylalanine content in esophageal cancer tissue as compared to those of a normal subject. The diagnostic algorithms based on principal component analysis (PCA) and linear discriminate analysis (LDA) achieved a diagnostic sensitivity of 87.0% and specificity of 70.9% for separating cancer from normal esophageal tissue samples. The result demonstrated that near-infrared micro-Raman spectroscopy combined with PCA-LDA analysis could be an effective and sensitive tool for identification of esophageal cancer.

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.

Can early hepatic fibrosis stages be discriminated by combining ultrasonic parameters?

PubMed

Bouzitoune, Razika; Meziri, Mahmoud; Machado, Christiano Bittencourt; Padilla, Frédéric; Pereira, Wagner Coelho de Albuquerque

2016-05-01

In this study, we put forward a new approach to classify early stages of fibrosis based on a multiparametric characterization using backscatter ultrasonic signals. Ultrasonic parameters, such as backscatter coefficient (Bc), speed of sound (SoS), attenuation coefficient (Ac), mean scatterer spacing (MSS), and spectral slope (SS), have shown their potential to differentiate between healthy and pathologic samples in different organs (eye, breast, prostate, liver). Recently, our group looked into the characterization of stages of hepatic fibrosis using the parameters cited above. The results showed that none of them could individually distinguish between the different stages. Therefore, we explored a multiparametric approach by combining these parameters in two and three, to test their potential to discriminate between the stages of liver fibrosis: F0 (normal), F1, F3, and/without F4 (cirrhosis), according to METAVIR Score. Discriminant analysis showed that the most relevant individual parameter was Bc, followed by SoS, SS, MSS, and Ac. The combination of (Bc, SoS) along with the four stages was the best in differentiating between the stages of fibrosis and correctly classified 85% of the liver samples with a high level of significance (p<0.0001). Nevertheless, when taking into account only stages F0, F1, and F3, the discriminant analysis showed that the parameters (Bc, SoS) and (Bc, Ac) had a better classification (93%) with a high level of significance (p<0.0001). The combination of the three parameters (Bc, SoS, and Ac) led to a 100% correct classification. In conclusion, the current findings show that the multiparametric approach has great potential in differentiating between the stages of fibrosis, and thus could play an important role in the diagnosis and follow-up of hepatic fibrosis. Copyright © 2016 Elsevier B.V. All rights reserved.

A comparative analysis of alternative approaches for quantifying nonlinear dynamics in cardiovascular system.

PubMed

Chen, Yun; Yang, Hui

2013-01-01

Heart rate variability (HRV) analysis has emerged as an important research topic to evaluate autonomic cardiac function. However, traditional time and frequency-domain analysis characterizes and quantify only linear and stationary phenomena. In the present investigation, we made a comparative analysis of three alternative approaches (i.e., wavelet multifractal analysis, Lyapunov exponents and multiscale entropy analysis) for quantifying nonlinear dynamics in heart rate time series. Note that these extracted nonlinear features provide information about nonlinear scaling behaviors and the complexity of cardiac systems. To evaluate the performance, we used 24-hour HRV recordings from 54 healthy subjects and 29 heart failure patients, available in PhysioNet. Three nonlinear methods are evaluated not only individually but also in combination using three classification algorithms, i.e., linear discriminate analysis, quadratic discriminate analysis and k-nearest neighbors. Experimental results show that three nonlinear methods capture nonlinear dynamics from different perspectives and the combined feature set achieves the best performance, i.e., sensitivity 97.7% and specificity 91.5%. Collectively, nonlinear HRV features are shown to have the promise to identify the disorders in autonomic cardiovascular function.

Capability of AVHRR data in discriminating rangeland cover mixtures

USGS Publications Warehouse

Senay, Gabriel B.; Elliott, R.L.

2002-01-01

A combination of high temporal resolution Advanced Very High Resolution Radiometer (AVHRR) data and high spatial information Map Information Analysis and Display System (MIADS) landuse/landcover data from the United States Department of Agriculture-Natural Resources Conservation Service (USDA-NRCS) were used to investigate the feasibility of using the combined dataset for regional evapotranspiration (ET) studies. It was shown that the biweekly maximum Normalized Difference Vegetation Index (NDVI) composite AVHRR data were capable of discriminating rangelands with different types of trees and shrubs species. AVHRR data also showed a potential to distinguish canopy cover differences within a mix of similar species. The combination of MIADS data and AVHRR data can be used to study temporal dynamics of various cover types for use in regional ET estimates.

Combining markers with and without the limit of detection

PubMed Central

Dong, Ting; Liu, Catherine Chunling; Petricoin, Emanuel F.; Tang, Liansheng Larry

2014-01-01

In this paper, we consider the combination of markers with and without the limit of detection (LOD). LOD is often encountered when measuring proteomic markers. Because of the limited detecting ability of an equipment or instrument, it is difficult to measure markers at a relatively low level. Suppose that after some monotonic transformation, the marker values approximately follow multivariate normal distributions. We propose to estimate distribution parameters while taking the LOD into account, and then combine markers using the results from the linear discriminant analysis. Our simulation results show that the ROC curve parameter estimates generated from the proposed method are much closer to the truth than simply using the linear discriminant analysis to combine markers without considering the LOD. In addition, we propose a procedure to select and combine a subset of markers when many candidate markers are available. The procedure based on the correlation among markers is different from a common understanding that a subset of the most accurate markers should be selected for the combination. The simulation studies show that the accuracy of a combined marker can be largely impacted by the correlation of marker measurements. Our methods are applied to a protein pathway dataset to combine proteomic biomarkers to distinguish cancer patients from non-cancer patients. PMID:24132938

Combination of Face Regions in Forensic Scenarios.

PubMed

Tome, Pedro; Fierrez, Julian; Vera-Rodriguez, Ruben; Ortega-Garcia, Javier

2015-07-01

This article presents an experimental analysis of the combination of different regions of the human face on various forensic scenarios to generate scientific knowledge useful for the forensic experts. Three scenarios of interest at different distances are considered comparing mugshot and CCTV face images using MORPH and SC face databases. One of the main findings is that inner facial regions combine better in mugshot and close CCTV scenarios and outer facial regions combine better in far CCTV scenarios. This means, that depending of the acquisition distance, the discriminative power of the facial regions change, having in some cases better performance than the full face. This effect can be exploited by considering the fusion of facial regions which results in a very significant improvement of the discriminative performance compared to just using the full face. © 2015 American Academy of Forensic Sciences.

Discrimination of Radix Polygoni Multiflori from different geographical areas by UPLC-QTOF/MS combined with chemometrics.

PubMed

Tang, Jin-Fa; Li, Wei-Xia; Zhang, Fan; Li, Yu-Hui; Cao, Ying-Jie; Zhao, Ya; Li, Xue-Lin; Ma, Zhi-Jie

2017-01-01

Nowadays, Radix Polygoni Multiflori (RPM, Heshouwu in Chinese) from different geographical origins were used in clinic. In order to characterize the chemical profiles of different geographical origins of RPM samples, ultra-high performance liquid chromatography quadrupole time of flight mass spectrometry (UPLC-QTOF/MS) combined with chemometrics (partial least squared discriminant analysis, PLS‑DA) method was applied in the present study. The chromatography, chemical composition and MS information of RPM samples from 18 geographical origins were acquired and profiled by UPLC-QTOF/MS. The chemical markers contributing the differentiation of RPM samples were observed and characterized by supervised PLS‑DA method of chemometrics. The chemical composition differences of RPM samples derived from 18 different geographical origins were observed. Nine chemical markers were tentatively identified which could be used as specific chemical markers for the differentiation of geographical RPM samples. UPLC-QTOF/MS method coupled with chemometrics analysis has potential to be used for discriminating different geographical TCMs. Results will help to develop strategies for conservation and utilization of RPM samples.

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

PubMed

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

2015-12-01

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

A new discriminative kernel from probabilistic models.

PubMed

Tsuda, Koji; Kawanabe, Motoaki; Rätsch, Gunnar; Sonnenburg, Sören; Müller, Klaus-Robert

2002-10-01

Recently, Jaakkola and Haussler (1999) proposed a method for constructing kernel functions from probabilistic models. Their so-called Fisher kernel has been combined with discriminative classifiers such as support vector machines and applied successfully in, for example, DNA and protein analysis. Whereas the Fisher kernel is calculated from the marginal log-likelihood, we propose the TOP kernel derived; from tangent vectors of posterior log-odds. Furthermore, we develop a theoretical framework on feature extractors from probabilistic models and use it for analyzing the TOP kernel. In experiments, our new discriminative TOP kernel compares favorably to the Fisher kernel.

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

PubMed

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

2018-02-05

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

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

PubMed

Yudthavorasit, Soparat; Wongravee, Kanet; Leepipatpiboon, Natchanun

2014-09-01

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

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

PubMed

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

2016-01-01

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

Significance of serum CA125 and TPS antigen levels for determination of overall survival after three chemotherapy courses in ovarian cancer patients during long-term follow-up.

PubMed

van Dalen, A; Favier, J; Hallensleben, E; Burges, A; Stieber, P; de Bruijn, H W A; Fink, D; Ferrero, A; McGing, P; Harlozinska, A; Kainz, Ch; Markowska, J; Molina, R; Sturgeon, C; Bowman, A; Einarsson, R; Goike, H

2009-01-01

To evaluate the prognostic significance for overall survival rate for the marker combination TPS and CA125 in ovarian cancer patients after three chemotherapy courses during long-term clinical follow-up. The overall survival of 212 (out of 213) ovarian cancer patients (FIGO Stages I-IV) was analyzed in a prospective multicenter study during a 10-year clinical follow-up by univariate and multivariate analysis. In patients with ovarian cancer FIGO Stage I (34 patients) or FIGO Stage II (30 patients) disease, the univariate and multivariate analysis of the 10-year overall survival data showed that CA125 and TPS serum levels were not independent prognostic factors. In the FIGO Stage III group (112 patients), the 10-year overall survival was 15.2%; while in the FIGO Stage IV group (36 patients) a 10-year overall survival of 5.6% was seen. Here, the tumor markers CA125 and TPS levels were significant prognostic factors in both univariate and multivariate analysis (p < 0.0001). In a combined FIGO Stage III + FIGO Stage IV group (60 patients with optimal debulking surgery), multivariate analysis demonstrated that CA125 and TPS levels were independent prognostic factors. For patients in this combined FIGO Stage III + IV group having both markers below respective discrimination level, 35.3% survived for more than ten years, as opposed to patients having one marker above the discrimination level where the 10-year survival was reduced to 10% of the patients. For patients showing both markers above the respective discrimination level, none of the patients survived for the 10-year follow-up time. In FIGO III and IV ovarian cancer patients, only patients with CA 125 and TPS markers below the discrimination level after three chemotherapy courses indicated a favorable prognosis. Patients with an elevated level of CA 125 or TPS or both markers after three chemotherapy courses showed unfavorable prognosis.

Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria

NASA Astrophysics Data System (ADS)

Prochazka, D.; Mazura, M.; Samek, O.; Rebrošová, K.; Pořízka, P.; Klus, J.; Prochazková, P.; Novotný, J.; Novotný, K.; Kaiser, J.

2018-01-01

In this work, we investigate the impact of data provided by complementary laser-based spectroscopic methods on multivariate classification accuracy. Discrimination and classification of five Staphylococcus bacterial strains and one strain of Escherichia coli is presented. The technique that we used for measurements is a combination of Raman spectroscopy and Laser-Induced Breakdown Spectroscopy (LIBS). Obtained spectroscopic data were then processed using Multivariate Data Analysis algorithms. Principal Components Analysis (PCA) was selected as the most suitable technique for visualization of bacterial strains data. To classify the bacterial strains, we used Neural Networks, namely a supervised version of Kohonen's self-organizing maps (SOM). We were processing results in three different ways - separately from LIBS measurements, from Raman measurements, and we also merged data from both mentioned methods. The three types of results were then compared. By applying the PCA to Raman spectroscopy data, we observed that two bacterial strains were fully distinguished from the rest of the data set. In the case of LIBS data, three bacterial strains were fully discriminated. Using a combination of data from both methods, we achieved the complete discrimination of all bacterial strains. All the data were classified with a high success rate using SOM algorithm. The most accurate classification was obtained using a combination of data from both techniques. The classification accuracy varied, depending on specific samples and techniques. As for LIBS, the classification accuracy ranged from 45% to 100%, as for Raman Spectroscopy from 50% to 100% and in case of merged data, all samples were classified correctly. Based on the results of the experiments presented in this work, we can assume that the combination of Raman spectroscopy and LIBS significantly enhances discrimination and classification accuracy of bacterial species and strains. The reason is the complementarity in obtained chemical information while using these two methods.

Binocular contrast discrimination needs monocular multiplicative noise

PubMed Central

Ding, Jian; Levi, Dennis M.

2016-01-01

The effects of signal and noise on contrast discrimination are difficult to separate because of a singularity in the signal-detection-theory model of two-alternative forced-choice contrast discrimination (Katkov, Tsodyks, & Sagi, 2006). In this article, we show that it is possible to eliminate the singularity by combining that model with a binocular combination model to fit monocular, dichoptic, and binocular contrast discrimination. We performed three experiments using identical stimuli to measure the perceived phase, perceived contrast, and contrast discrimination of a cyclopean sine wave. In the absence of a fixation point, we found a binocular advantage in contrast discrimination both at low contrasts (<4%), consistent with previous studies, and at high contrasts (≥34%), which has not been previously reported. However, control experiments showed no binocular advantage at high contrasts in the presence of a fixation point or for observers without accommodation. We evaluated two putative contrast-discrimination mechanisms: a nonlinear contrast transducer and multiplicative noise (MN). A binocular combination model (the DSKL model; Ding, Klein, & Levi, 2013b) was first fitted to both the perceived-phase and the perceived-contrast data sets, then combined with either the nonlinear contrast transducer or the MN mechanism to fit the contrast-discrimination data. We found that the best model combined the DSKL model with early MN. Model simulations showed that, after going through interocular suppression, the uncorrelated noise in the two eyes became anticorrelated, resulting in less binocular noise and therefore a binocular advantage in the discrimination task. Combining a nonlinear contrast transducer or MN with a binocular combination model (DSKL) provides a powerful method for evaluating the two putative contrast-discrimination mechanisms. PMID:26982370

Binocular contrast discrimination needs monocular multiplicative noise.

PubMed

Ding, Jian; Levi, Dennis M

2016-01-01

The effects of signal and noise on contrast discrimination are difficult to separate because of a singularity in the signal-detection-theory model of two-alternative forced-choice contrast discrimination (Katkov, Tsodyks, & Sagi, 2006). In this article, we show that it is possible to eliminate the singularity by combining that model with a binocular combination model to fit monocular, dichoptic, and binocular contrast discrimination. We performed three experiments using identical stimuli to measure the perceived phase, perceived contrast, and contrast discrimination of a cyclopean sine wave. In the absence of a fixation point, we found a binocular advantage in contrast discrimination both at low contrasts (<4%), consistent with previous studies, and at high contrasts (≥34%), which has not been previously reported. However, control experiments showed no binocular advantage at high contrasts in the presence of a fixation point or for observers without accommodation. We evaluated two putative contrast-discrimination mechanisms: a nonlinear contrast transducer and multiplicative noise (MN). A binocular combination model (the DSKL model; Ding, Klein, & Levi, 2013b) was first fitted to both the perceived-phase and the perceived-contrast data sets, then combined with either the nonlinear contrast transducer or the MN mechanism to fit the contrast-discrimination data. We found that the best model combined the DSKL model with early MN. Model simulations showed that, after going through interocular suppression, the uncorrelated noise in the two eyes became anticorrelated, resulting in less binocular noise and therefore a binocular advantage in the discrimination task. Combining a nonlinear contrast transducer or MN with a binocular combination model (DSKL) provides a powerful method for evaluating the two putative contrast-discrimination mechanisms.

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

PubMed

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

2014-03-28

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

Evaluating the capacity of plant DNA barcodes to discriminate species of cotton (Gossypium: Malvaceae).

PubMed

Ashfaq, Muhammad; Asif, Muhammad; Anjum, Zahid Iqbal; Zafar, Yusuf

2013-07-01

Although two plastid regions have been adopted as the standard markers for plant DNA barcoding, their limited resolution has provoked the consideration of other gene regions, especially in taxonomically diverse genera. The genus Gossypium (cotton) includes eight diploid genome groups (A-G, and K) and five allotetraploid species which are difficult to discriminate morphologically. In this study, we tested the effectiveness of three widely used markers (matK, rbcL, and ITS2) in the discrimination of 20 diploid and five tetraploid species of cotton. Sequences were analysed locus-wise and in combinations to determine the most effective strategy for species identification. Sequence recovery was high, ranging from 92% to 100% with mean pairwise interspecific distance highest for ITS2 (3.68%) and lowest for rbcL (0.43%). At a 0.5% threshold, the combination of matK+ITS2 produced the greatest number of species clusters. Based on 'best match' analysis, the combination of matK+ITS2 was best, while based on 'all species barcodes' analysis, ITS2 gave the highest percentage of correct species identifications (98.93%). The combination of sequences for all three markers produced the best resolved tree. The disparity index test based on matK+rbcL+ITS2 was significant (P < 0.05) for a higher number of species pairs than the individual gene sequences. Although all three barcodes separated the species with respect to their genome type, no single combination of barcodes could differentiate all the Gossypium species, and tetraploid species were particularly difficult. © 2013 John Wiley & Sons Ltd.

Analysis of chemical signals in red fire ants by gas chromatography and pattern recognition techniques

USDA-ARS?s Scientific Manuscript database

The combination of gas chromatography and pattern recognition (GC/PR) analysis is a powerful tool for investigating complicated biological problems. Clustering, mapping, discriminant development, etc. are necessary to analyze realistically large chromatographic data sets and to seek meaningful relat...

(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.

Analysis of Active Sensor Discrimination Requirements for Various Defense Missile Defense Scenarios Final Report 1999(99-ERD-080)

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

Ledebuhr, A.G.; Ng, L.C.; Gaughan, R.J.

2000-02-15

During FY99, we have explored and analyzed a combined passive/active sensor concept to support the advanced discrimination requirements for various missile defense scenario. The idea is to combine multiple IR spectral channels with an imaging LIDAR (Light Detection and Ranging) behind a common optical system. The imaging LIDAR would itself consist of at least two channels; one at the fundamental laser wavelength (e.g., the 1.064 {micro}m for Nd:YAG) and one channel at the frequency doubled (at 532 nm for Nd:YAG). two-color laser output would, for example, allow the longer wavelength for a direct detection time of flight ranger and anmore » active imaging channel at the shorter wavelength. The LIDAR can function as a high-resolution 2D spatial image either passively or actively with laser illumination. Advances in laser design also offer three color (frequency tripled) systems, high rep-rate operation, better pumping efficiencies that can provide longer distance acquisition, and ranging for enhanced discrimination phenomenology. New detector developments can enhance the performance and operation of both LIDAR channels. A real time data fusion approach that combines multi-spectral IR phenomenology with LIDAR imagery can improve both discrimination and aim-point selection capability.« less

[Support vector machine?assisted diagnosis of human malignant gastric tissues based on dielectric properties].

PubMed

Zhang, Sa; Li, Zhou; Xin, Xue-Gang

2017-12-20

To achieve differential diagnosis of normal and malignant gastric tissues based on discrepancies in their dielectric properties using support vector machine. The dielectric properties of normal and malignant gastric tissues at the frequency ranging from 42.58 to 500 MHz were measured by coaxial probe method, and the Cole?Cole model was used to fit the measured data. Receiver?operating characteristic (ROC) curve analysis was used to evaluate the discrimination capability with respect to permittivity, conductivity, and Cole?Cole fitting parameters. Support vector machine was used for discriminating normal and malignant gastric tissues, and the discrimination accuracy was calculated using k?fold cross? The area under the ROC curve was above 0.8 for permittivity at the 5 frequencies at the lower end of the measured frequency range. The combination of the support vector machine with the permittivity at all these 5 frequencies combined achieved the highest discrimination accuracy of 84.38% with a MATLAB runtime of 3.40 s. The support vector machine?assisted diagnosis is feasible for human malignant gastric tissues based on the dielectric properties.

Temperature-strain discrimination in distributed optical fiber sensing using phase-sensitive optical time-domain reflectometry.

PubMed

Lu, Xin; Soto, Marcelo A; Thévenaz, Luc

2017-07-10

A method based on coherent Rayleigh scattering distinctly evaluating temperature and strain is proposed and experimentally demonstrated for distributed optical fiber sensing. Combining conventional phase-sensitive optical time-domain domain reflectometry (ϕOTDR) and ϕOTDR-based birefringence measurements, independent distributed temperature and strain profiles are obtained along a polarization-maintaining fiber. A theoretical analysis, supported by experimental data, indicates that the proposed system for temperature-strain discrimination is intrinsically better conditioned than an equivalent existing approach that combines classical Brillouin sensing with Brillouin dynamic gratings. This is due to the higher sensitivity of coherent Rayleigh scatting compared to Brillouin scattering, thus offering better performance and lower temperature-strain uncertainties in the discrimination. Compared to the Brillouin-based approach, the ϕOTDR-based system here proposed requires access to only one fiber-end, and a much simpler experimental layout. Experimental results validate the full discrimination of temperature and strain along a 100 m-long elliptical-core polarization-maintaining fiber with measurement uncertainties of ~40 mK and ~0.5 με, respectively. These values agree very well with the theoretically expected measurand resolutions.

Automated palpation for breast tissue discrimination based on viscoelastic biomechanical properties.

PubMed

Tsukune, Mariko; Kobayashi, Yo; Miyashita, Tomoyuki; Fujie, G Masakatsu

2015-05-01

Accurate, noninvasive methods are sought for breast tumor detection and diagnosis. In particular, a need for noninvasive techniques that measure both the nonlinear elastic and viscoelastic properties of breast tissue has been identified. For diagnostic purposes, it is important to select a nonlinear viscoelastic model with a small number of parameters that highly correlate with histological structure. However, the combination of conventional viscoelastic models with nonlinear elastic models requires a large number of parameters. A nonlinear viscoelastic model of breast tissue based on a simple equation with few parameters was developed and tested. The nonlinear viscoelastic properties of soft tissues in porcine breast were measured experimentally using fresh ex vivo samples. Robotic palpation was used for measurements employed in a finite element model. These measurements were used to calculate nonlinear viscoelastic parameters for fat, fibroglandular breast parenchyma and muscle. The ability of these parameters to distinguish the tissue types was evaluated in a two-step statistical analysis that included Holm's pairwise [Formula: see text] test. The discrimination error rate of a set of parameters was evaluated by the Mahalanobis distance. Ex vivo testing in porcine breast revealed significant differences in the nonlinear viscoelastic parameters among combinations of three tissue types. The discrimination error rate was low among all tested combinations of three tissue types. Although tissue discrimination was not achieved using only a single nonlinear viscoelastic parameter, a set of four nonlinear viscoelastic parameters were able to reliably and accurately discriminate fat, breast fibroglandular tissue and muscle.

Metabolic fingerprint of Brazilian maize landraces silk (stigma/styles) using NMR spectroscopy and chemometric methods.

PubMed

Kuhnen, Shirley; Bernardi Ogliari, Juliana; Dias, Paulo Fernando; da Silva Santos, Maiara; Ferreira, Antônio Gilberto; Bonham, Connie C; Wood, Karl Vernon; Maraschin, Marcelo

2010-02-24

Aqueous extract from maize silks is used by traditional medicine for the treatment of several ailments, mainly related to the urinary system. This work focuses on the application of NMR spectroscopy and chemometric analysis for the determination of metabolic fingerprint and pattern recognition of silk extracts from seven maize landraces cultivated in southern Brazil. Principal component analysis (PCA) of the (1)H NMR data set showed clear discrimination among the maize varieties by PC1 and PC2, pointing out three distinct metabolic profiles. Target compounds analysis showed significant differences (p < 0.05) in the contents of protocatechuic acid, gallic acid, t-cinnamic acid, and anthocyanins, corroborating the discrimination of the genotypes in this study as revealed by PCA analysis. Thus the combination of (1)H NMR and PCA is a useful tool for the discrimination of maize silks in respect to their chemical composition, including rapid authentication of the raw material of current pharmacological interest.

Characterization and Differentiation of Petroleum-Derived Products by E-Nose Fingerprints

PubMed Central

Ferreiro-González, Marta; Palma, Miguel; Ayuso, Jesús; Álvarez, José A.; Barroso, Carmelo G.

2017-01-01

Characterization of petroleum-derived products is an area of continuing importance in environmental science, mainly related to fuel spills. In this study, a non-separative analytical method based on E-Nose (Electronic Nose) is presented as a rapid alternative for the characterization of several different petroleum-derived products including gasoline, diesel, aromatic solvents, and ethanol samples, which were poured onto different surfaces (wood, cork, and cotton). The working conditions about the headspace generation were 145 °C and 10 min. Mass spectroscopic data (45–200 m/z) combined with chemometric tools such as hierarchical cluster analysis (HCA), later principal component analysis (PCA), and finally linear discriminant analysis (LDA) allowed for a full discrimination of the samples. A characteristic fingerprint for each product can be used for discrimination or identification. The E-Nose can be considered as a green technique, and it is rapid and easy to use in routine analysis, thus providing a good alternative to currently used methods. PMID:29113069

A biomarker panel and psychological morbidity differentiates the irritable bowel syndrome from health and provides novel pathophysiological leads.

PubMed

Jones, M P; Chey, W D; Singh, S; Gong, H; Shringarpure, R; Hoe, N; Chuang, E; Talley, N J

2014-02-01

The development of a reliable biomarker for irritable bowel syndrome (IBS) remains one of the major aims of research in functional gastrointestinal disorders (FGIDs) and is complicated by the absence of a perfect reference standard. Previous efforts based on genetic and immune markers have showed promise, but have not been robust. To evaluate an extensive panel of gene expression and serology markers combined with psychological measures in differentiating IBS from health and between subtypes of IBS. Of subjects eligible for analysis (N = 244), 168 met criteria for IBS (60 IBS-C, 57 IBS-D and 51 mixed), while 76 were free of any FGID. A total of 34 markers were selected based on pathways implicated in pathophysiology of IBS or whole human genome screening. Psychological measures were recorded that covered anxiety, depression and somatisation. Models differentiating disease and health were based on unconditional logistic regression and performance assessed through area under the receiver-operator characteristic curve (AUC), sensitivity and specificity. The performance of a combination of 34 markers was good in differentiating IBS from health (AUC = 0.81) and was improved considerably with the addition of four psychological markers (combined AUC = 0.93). Of the 34 markers considered, discrimination was derived largely from a small subset. Good discrimination was also obtained between IBS subtypes with the best being observed for IBS-C vs. IBS-D (AUC = 0.92); however, psychological variables provided almost no incremental discrimination subtypes over biological markers (combined AUC = 0.94). A combination of gene expression and serological markers in combination with psychological measures shows exciting progress towards a diagnostic test for IBS compared with healthy subjects, and to discriminate IBS-C from IBS-D. © 2014 John Wiley & Sons Ltd.

Multivariate Analysis of Combined Fourier Transform Near-Infrared Spectrometry (FT-NIR) and Raman Datasets for Improved Discrimination of Drying Oils.

PubMed

Carlesi, Serena; Ricci, Marilena; Cucci, Costanza; La Nasa, Jacopo; Lofrumento, Cristiana; Picollo, Marcello; Becucci, Maurizio

2015-07-01

This work explores the application of chemometric techniques to the analysis of lipidic paint binders (i.e., drying oils) by means of Raman and near-infrared spectroscopy. These binders have been widely used by artists throughout history, both individually and in mixtures. We prepared various model samples of the pure binders (linseed, poppy seed, and walnut oils) obtained from different manufacturers. These model samples were left to dry and then characterized by Raman and reflectance near-infrared spectroscopy. Multivariate analysis was performed by applying principal component analysis (PCA) on the first derivative of the corresponding Raman spectra (1800-750 cm(-1)), near-infrared spectra (6000-3900 cm(-1)), and their combination to test whether spectral differences could enable samples to be distinguished on the basis of their composition. The vibrational bands we found most useful to discriminate between the different products we studied are the fundamental ν(C=C) stretching and methylenic stretching and bending combination bands. The results of the multivariate analysis demonstrated the potential of chemometric approaches for characterizing and identifying drying oils, and also for gaining a deeper insight into the aging process. Comparison with high-performance liquid chromatography data was conducted to check the PCA results.

Component-based subspace linear discriminant analysis method for face recognition with one training sample

NASA Astrophysics Data System (ADS)

Huang, Jian; Yuen, Pong C.; Chen, Wen-Sheng; Lai, J. H.

2005-05-01

Many face recognition algorithms/systems have been developed in the last decade and excellent performances have also been reported when there is a sufficient number of representative training samples. In many real-life applications such as passport identification, only one well-controlled frontal sample image is available for training. Under this situation, the performance of existing algorithms will degrade dramatically or may not even be implemented. We propose a component-based linear discriminant analysis (LDA) method to solve the one training sample problem. The basic idea of the proposed method is to construct local facial feature component bunches by moving each local feature region in four directions. In this way, we not only generate more samples with lower dimension than the original image, but also consider the face detection localization error while training. After that, we propose a subspace LDA method, which is tailor-made for a small number of training samples, for the local feature projection to maximize the discrimination power. Theoretical analysis and experiment results show that our proposed subspace LDA is efficient and overcomes the limitations in existing LDA methods. Finally, we combine the contributions of each local component bunch with a weighted combination scheme to draw the recognition decision. A FERET database is used for evaluating the proposed method and results are encouraging.

Root Cause Analysis of Quality Defects Using HPLC-MS Fingerprint Knowledgebase for Batch-to-batch Quality Control of Herbal Drugs.

PubMed

Yan, Binjun; Fang, Zhonghua; Shen, Lijuan; Qu, Haibin

2015-01-01

The batch-to-batch quality consistency of herbal drugs has always been an important issue. To propose a methodology for batch-to-batch quality control based on HPLC-MS fingerprints and process knowledgebase. The extraction process of Compound E-jiao Oral Liquid was taken as a case study. After establishing the HPLC-MS fingerprint analysis method, the fingerprints of the extract solutions produced under normal and abnormal operation conditions were obtained. Multivariate statistical models were built for fault detection and a discriminant analysis model was built using the probabilistic discriminant partial-least-squares method for fault diagnosis. Based on multivariate statistical analysis, process knowledge was acquired and the cause-effect relationship between process deviations and quality defects was revealed. The quality defects were detected successfully by multivariate statistical control charts and the type of process deviations were diagnosed correctly by discriminant analysis. This work has demonstrated the benefits of combining HPLC-MS fingerprints, process knowledge and multivariate analysis for the quality control of herbal drugs. Copyright © 2015 John Wiley & Sons, Ltd.

The prediction of human exons by oligonucleotide composition and discriminant analysis of spliceable open reading frames

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

Solovyev, V.V.; Salamov, A.A.; Lawrence, C.B.

1994-12-31

Discriminant analysis is applied to the problem of recognition 5`-, internal and 3`-exons in human DNA sequences. Specific recognition functions were developed for revealing exons of particular types. The method based on a splice site prediction algorithm that uses the linear Fisher discriminant to combine the information about significant triplet frequencies of various functional parts of splice site regions and preferences of oligonucleotide in protein coding and nation regions. The accuracy of our splice site recognition function is about 97%. A discriminant function for 5`-exon prediction includes hexanucleotide composition of upstream region, triplet composition around the ATG codon, ORF codingmore » potential, donor splice site potential and composition of downstream introit region. For internal exon prediction, we combine in a discriminant function the characteristics describing the 5`- intron region, donor splice site, coding region, acceptor splice site and Y-intron region for each open reading frame flanked by GT and AG base pairs. The accuracy of precise internal exon recognition on a test set of 451 exon and 246693 pseudoexon sequences is 77% with a specificity of 79% and a level of pseudoexon ORF prediction of 99.96%. The recognition quality computed at the level of individual nucleotides is 89%, for exon sequences and 98% for intron sequences. A discriminant function for 3`-exon prediction includes octanucleolide composition of upstream nation region, triplet composition around the stop codon, ORF coding potential, acceptor splice site potential and hexanucleotide composition of downstream region. We unite these three discriminant functions in exon predicting program FEX (find exons). FEX exactly predicts 70% of 1016 exons from the test of 181 complete genes with specificity 73%, and 89% exons are exactly or partially predicted. On the average, 85% of nucleotides were predicted accurately with specificity 91%.« less

Accuracy of Acoustic Analysis Measurements in the Evaluation of Patients With Different Laryngeal Diagnoses.

PubMed

Lopes, Leonardo Wanderley; Batista Simões, Layssa; Delfino da Silva, Jocélio; da Silva Evangelista, Deyverson; da Nóbrega E Ugulino, Ana Celiane; Oliveira Costa Silva, Priscila; Jefferson Dias Vieira, Vinícius

2017-05-01

This study aims to investigate the accuracy of acoustic measures in discriminating between patients with different laryngeal diagnoses. The study design is descriptive, cross-sectional, and retrospective. A total of 279 female patients participated in the research. Acoustic measures of the mean and standard deviation (SD) values of the fundamental frequency (F 0 ), jitter, shimmer, and glottal to noise excitation (GNE) were extracted from the emission of the vowel /ε/. Isolated acoustic measures do not demonstrate adequate performance in discriminating patients with and without laryngeal alteration. The combination of GNE, SD of the F 0 , jitter, and shimmer improved the ability to classify patients with and without laryngeal alteration. In isolation, the SD of the F 0 , shimmer, and GNE presented acceptable performance in discriminating individuals with different laryngeal diagnoses. The combination of acoustic measurements caused discrete improvement in performance of the classifier to discriminate healthy larynx vs vocal polyp (SD of the F 0 , shimmer, and GNE), healthy larynx vs unilateral vocal fold paralysis (SD of the F 0 and jitter), healthy larynx vs vocal nodules (SD of the F 0 and jitter), healthy larynx vs sulcus vocalis (SD of the F 0 and shimmer), and healthy larynx vs voice disorder due to gastroesophageal reflux (F 0 mean, jitter, and shimmer). Isolated acoustic measures do not demonstrate adequate performance in discriminating patients with and without laryngeal alteration, although they present acceptable performance in classifying different laryngeal diagnoses. Combined acoustic measures present an acceptable capacity to discriminate between the presence and the absence of laryngeal alteration and to differentiate several laryngeal diagnoses. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Discrimination of bullet types using analysis of lead isotopes deposited in gunshot entry wounds.

PubMed

Wunnapuk, Klintean; Minami, Takeshi; Durongkadech, Piya; Tohno, Setsuko; Ruangyuttikarn, Werawan; Moriwake, Yumi; Vichairat, Karnda; Sribanditmongkol, Pongruk; Tohno, Yoshiyuki

2009-01-01

In order to discriminate bullet types used in firearms, of which the victims died, the authors investigated lead isotope ratios in gunshot entry wounds from nine lead (unjacketed) bullets, 15 semi-jacketed bullets, and 14 full-jacketed bullets by inductively coupled plasma-mass spectrometry. It was found that the lead isotope ratio of 207/206 in gunshot entry wounds was the highest with lead bullets, and it decreased in order from full-jacketed to semi-jacketed bullets. Lead isotope ratios of 208/206 or 208/207 to 207/206 at the gunshot entry wound were able to discriminate semi-jacketed bullets from lead and full-jacketed ones, but it was difficult to discriminate between lead and full-jacketed bullets. However, a combination of element and lead isotope ratio analyses in gunshot entry wounds enabled discrimination between lead, semi-jacketed, and full-jacketed bullets.

Evaluation of removal of the size effect using data scaling and elliptic Fourier descriptors in otolith shape analysis, exemplified by the discrimination of two yellow croaker stocks along the Chinese coast

NASA Astrophysics Data System (ADS)

Zhao, Bo; Liu, Jinhu; Song, Junjie; Cao, Liang; Dou, Shuozeng

2017-11-01

Removal of the length effect in otolith shape analysis for stock identification using length scaling is an important issue; however, few studies have attempted to investigate the effectiveness or weakness of this methodology in application. The aim of this study was to evaluate whether commonly used size scaling methods and normalized elliptic Fourier descriptors (NEFDs) could effectively remove the size effect of fish in stock discrimination. To achieve this goal, length groups from two known geographical stocks of yellow croaker, Larimichthys polyactis, along the Chinese coast (five groups from the Changjiang River estuary of the East China Sea and three groups from the Bohai Sea) were subjected to otolith shape analysis. The results indicated that the variation of otolith shape caused by intra-stock fish length might exceed that due to inter-stock geographical separation, even when otolith shape variables are standardized with length scaling methods. This variation could easily result in misleading stock discrimination through otolith shape analysis. Therefore, conclusions about fish stock structure should be carefully drawn from otolith shape analysis because the observed discrimination may primarily be due to length effects, rather than differences among stocks. The application of multiple methods, such as otoliths shape analysis combined with elemental fingering, tagging or genetic analysis, is recommended for sock identification.

Forensic Comparison of Soil Samples Using Nondestructive Elemental Analysis.

PubMed

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

2017-07-01

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

Texture-Based Analysis of 100 MR Examinations of Head and Neck Tumors - Is It Possible to Discriminate Between Benign and Malignant Masses in a Multicenter Trial?

PubMed

Fruehwald-Pallamar, J; Hesselink, J R; Mafee, M F; Holzer-Fruehwald, L; Czerny, C; Mayerhoefer, M E

2016-02-01

To evaluate whether texture-based analysis of standard MRI sequences can help in the discrimination between benign and malignant head and neck tumors. The MR images of 100 patients with a histologically clarified head or neck mass, from two different institutions, were analyzed. Texture-based analysis was performed using texture analysis software, with region of interest measurements for 2 D and 3 D evaluation independently for all axial sequences. COC, RUN, GRA, ARM, and WAV features were calculated for all ROIs. 10 texture feature subsets were used for a linear discriminant analysis, in combination with k-nearest-neighbor classification. Benign and malignant tumors were compared with regard to texture-based values. There were differences in the images from different field-strength scanners, as well as from different vendors. For the differentiation of benign and malignant tumors, we found differences on STIR and T2-weighted images for 2 D, and on contrast-enhanced T1-TSE with fat saturation for 3 D evaluation. In a separate analysis of the subgroups 1.5 and 3 Tesla, more discriminating features were found. Texture-based analysis is a useful tool in the discrimination of benign and malignant tumors when performed on one scanner with the same protocol. We cannot recommend this technique for the use of multicenter studies with clinical data. 2 D/3 D texture-based analysis can be performed in head and neck tumors. Texture-based analysis can differentiate between benign and malignant masses. Analyzed MR images should originate from one scanner with an identical protocol. © Georg Thieme Verlag KG Stuttgart · New York.

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.

Micro-Raman spectroscopy and chemometrical analysis for the distinction of copper phthalocyanine polymorphs in paint layers.

PubMed

Defeyt, C; Van Pevenage, J; Moens, L; Strivay, D; Vandenabeele, P

2013-11-01

In art analysis, copper phthalocyanine (CuPc) is often identified as an important pigment (PB15) in 20th century artworks. Raman spectroscopy is a very valuable technique for the detection of this pigment in paint systems. However, PB15 is used in different polymorphic forms and identification of the polymorph could retrieve information on the production process of the pigment at the moment. Raman spectroscopy, being a molecular spectroscopic method of analysis, is able to discriminate between polymorphs of crystals. However, in the case of PB15, spectral interpretation is not straightforward, and Raman data treatment requires some improvements concerning the PB15 polymorphic discrimination in paints. Here, Raman spectroscopy is combined with chemometrical analysis in order to develop a procedure allowing us to identify the PB15 crystalline structure in painted layers and in artworks. The results obtained by Linear Discriminant Analysis (LDA), using intensity ratios as variables, demonstrate the ability of this procedure to predict the crystalline structure of a PB15 pigment in unknown paint samples. Copyright © 2013 Elsevier B.V. All rights reserved.

Local connected fractal dimension analysis in gill of fish experimentally exposed to toxicants.

PubMed

Manera, Maurizio; Giari, Luisa; De Pasquale, Joseph A; Sayyaf Dezfuli, Bahram

2016-06-01

An operator-neutral method was implemented to objectively assess European seabass, Dicentrarchus labrax (Linnaeus, 1758) gill pathology after experimental exposure to cadmium (Cd) and terbuthylazine (TBA) for 24 and 48h. An algorithm-derived local connected fractal dimension (LCFD) frequency measure was used in this comparative analysis. Canonical variates (CVA) and linear discriminant analysis (LDA) were used to evaluate the discrimination power of the method among exposure classes (unexposed, Cd exposed, TBA exposed). Misclassification, sensitivity and specificity, both with original and cross-validated cases, were determined. LCFDs frequencies enhanced the differences among classes which were visually selected after their means, respective variances and the differences between Cd and TBA exposed means, with respect to unexposed mean, were analyzed by scatter plots. Selected frequencies were then scanned by means of LDA, stepwise analysis, and Mahalanobis distance to detect the most discriminative frequencies out of ten originally selected. Discrimination resulted in 91.7% of cross-validated cases correctly classified (22 out of 24 total cases), with sensitivity and specificity, respectively, of 95.5% (1 false negative with respect to 21 really positive cases) and 75% (1 false positive with respect to 3 really negative cases). CVA with convex hull polygons ensured prompt, visually intuitive discrimination among exposure classes and graphically supported the false positive case. The combined use of semithin sections, which enhanced the visual evaluation of the overall lamellar structure; of LCFD analysis, which objectively detected local variation in complexity, without the possible bias connected to human personnel; and of CVA/LDA, could be an objective, sensitive and specific approach to study fish gill lamellar pathology. Furthermore this approach enabled discrimination with sufficient confidence between exposure classes or pathological states and avoided misdiagnosis. Copyright © 2016 Elsevier B.V. All rights reserved.

A Review of Classical Methods of Item Analysis.

ERIC Educational Resources Information Center

French, Christine L.

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

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

PubMed

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

2016-12-01

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

Effects of Amphetamine-CNS Depressant Combinations and of Other CNS Stimulants in Four-Choice Drug Discriminations

ERIC Educational Resources Information Center

Li, Mi; Wessinger, William D.; McMillan, D. E.

2005-01-01

Three pigeons were trained to discriminate among 5 mg/kg pentobarbital, 2 mg/kg amphetamine, a combination of these two drugs at these doses, and saline using a four-choice procedure (amphetamine--pentobarbital group). Three other pigeons were trained to discriminate among 5 mg/kg morphine, 2 mg/kg methamphetamine, a combination of these two drugs…

Comparative analysis of the volatile composition of honeys from Brazilian stingless bees by static headspace GC-MS.

PubMed

de Lima Morais da Silva, Patricia; de Lima, Liliane Schier; Caetano, Ísis Kaminski; Torres, Yohandra Reyes

2017-12-01

The volatile composition of honeys produced by eight species of stingless bees collected in three municipalities in the state of Paraná (Brazil) was compared by combining static headspace GC-MS and chemometrics methods. Forty-four compounds were identified using NIST library and linear retention index relative to n-alkanes (C 8 -C 40 ). Linalool derivatives were the most abundant peaks in most honeys regardless geographical or entomological origin. However, Principal Component Analysis discriminated honeys from different geographical origins considering their distinctive minor volatile components. Honey samples from Guaraqueçaba were characterized by the presence of hotrienol while those from Cambará showed epoxylinalol, benzaldehyde and TDN as minor discriminating compounds. Punctual species such as Borá showed similar fingerprints regardless geographical origin, with ethyl octanoate and ethyl decanoate as characteristic intense chromatographic peaks, which may suggest a specialized behavior for nectar collection. Discriminant Analysis allowed correct geographic discrimination of most honeys produced in the three spots tested. We concluded that volatile profile of stingless bee honeys can be used to attest authenticity related to regional origin of honeys. Copyright © 2017. Published by Elsevier Ltd.

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

PubMed

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

2017-03-01

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

Compound-Specific δ¹⁵N and δ¹³C Analyses of Amino Acids for Potential Discrimination between Organically and Conventionally Grown Wheat.

PubMed

Paolini, Mauro; Ziller, Luca; Laursen, Kristian Holst; Husted, Søren; Camin, Federica

2015-07-01

We present a study deploying compound-specific nitrogen and carbon isotope analysis of amino acids to discriminate between organically and conventionally grown plants. We focused on grain samples of common wheat and durum wheat grown using synthetic nitrogen fertilizers, animal manures, or green manures from nitrogen-fixing legumes. The measurement of amino acid δ(15)N and δ(13)C values, after protein hydrolysis and derivatization, was carried out using gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS). Our results demonstrated that δ(13)C of glutamic acid and glutamine in particular, but also the combination of δ(15)N and δ(13)C of 10 amino acids, can improve the discrimination between conventional and organic wheat compared to stable isotope bulk tissue analysis. We concluded that compound-specific stable isotope analysis of amino acids represents a novel analytical tool with the potential to support and improve the certification and control procedures in the organic sector.

Determination of Chinese rice wine from different wineries by near-infrared spectroscopy combined with chemometrics methods

NASA Astrophysics Data System (ADS)

Niu, Xiaoying; Ying, Yibin; Yu, Haiyan; Xie, Lijuan; Fu, Xiaping; Zhou, Ying; Jiang, Xuesong

2007-09-01

In this paper, 104 samples of Chinese rice wines of the same variety (Shaoxing rice wine), collected in three winery ("guyuelongshan", "pagoda" brand, "kuaijishan"), three brewed years (2002, 2004, 2004-2006) were analyzed by near-infrared transmission spectroscopy between 800 and 2500 nm. The spectral differences were studied by principal components analysis (PCA), and Classifications, according the brand, were carried out by discriminant analysis (DA) and partial least squares discriminant analysis (PLSDA). The DA model gained a total accuracy of 94.23% and when used to predict the brand of the validation set samples, a better result, correctly classified all of the three kinds of Chinese rice wine up to 100%, are obtained by PLSDA model. The work reported here is a feasibility study and requires further development with considerable samples of more different brands. Further studies are needed in order to improve the accuracy and robustness, and to extend the discrimination to other Chinese rice wine varieties or brands.

Unique volatolomic signatures of TP53 and KRAS in lung cells

PubMed Central

Davies, M P A; Barash, O; Jeries, R; Peled, N; Ilouze, M; Hyde, R; Marcus, M W; Field, J K; Haick, H

2014-01-01

Background: Volatile organic compounds (VOCs) are potential biomarkers for cancer detection in breath, but it is unclear if they reflect specific mutations. To test this, we have compared human bronchial epithelial cell (HBEC) cell lines carrying the KRASV12 mutation, knockdown of TP53 or both with parental HBEC cells. Methods: VOC from headspace above cultured cells were collected by passive sampling and analysed by thermal desorption gas chromatography mass spectrometry (TD-GC–MS) or sensor array with discriminant factor analysis (DFA). Results: In TD-GC–MS analysis, individual compounds had limited ability to discriminate between cell lines, but by applying DFA analysis combinations of 20 VOCs successfully discriminated between all cell types (accuracies 80–100%, with leave-one-out cross validation). Sensor array detection DFA demonstrated the ability to discriminate samples based on their cell type for all comparisons with accuracies varying between 77% and 93%. Conclusions: Our results demonstrate that minimal genetic changes in bronchial airway cells lead to detectable differences in levels of specific VOCs identified by TD-GC–MS or of patterns of VOCs identified by sensor array output. From the clinical aspect, these results suggest the possibility of breath analysis for detection of minimal genetic changes for earlier diagnosis or for genetic typing of lung cancers. PMID:25051409

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2017-10-01

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

Multimodal discrimination of immune cells using a combination of Raman spectroscopy and digital holographic microscopy

NASA Astrophysics Data System (ADS)

McReynolds, Naomi; Cooke, Fiona G. M.; Chen, Mingzhou; Powis, Simon J.; Dholakia, Kishan

2017-03-01

The ability to identify and characterise individual cells of the immune system under label-free conditions would be a significant advantage in biomedical and clinical studies where untouched and unmodified cells are required. We present a multi-modal system capable of simultaneously acquiring both single point Raman spectra and digital holographic images of single cells. We use this combined approach to identify and discriminate between immune cell populations CD4+ T cells, B cells and monocytes. We investigate several approaches to interpret the phase images including signal intensity histograms and texture analysis. Both modalities are independently able to discriminate between cell subsets and dual-modality may therefore be used a means for validation. We demonstrate here sensitivities achieved in the range of 86.8% to 100%, and specificities in the range of 85.4% to 100%. Additionally each modality provides information not available from the other providing both a molecular and a morphological signature of each cell.

Selecting predictors for discriminant analysis of species performance: an example from an amphibious softwater plant.

PubMed

Vanderhaeghe, F; Smolders, A J P; Roelofs, J G M; Hoffmann, M

2012-03-01

Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.

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

PubMed

Ding, Hang

2014-01-01

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

Assessment and forensic application of laser-induced breakdown spectroscopy (LIBS) for the discrimination of Australian window glass.

PubMed

El-Deftar, Moteaa M; Speers, Naomi; Eggins, Stephen; Foster, Simon; Robertson, James; Lennard, Chris

2014-08-01

A commercially available laser-induced breakdown spectroscopy (LIBS) instrument was evaluated for the determination of elemental composition of twenty Australian window glass samples, consisting of 14 laminated samples and 6 non-laminated samples (or not otherwise specified) collected from broken windows at crime scenes. In this study, the LIBS figures of merit were assessed in terms of accuracy, limits of detection and precision using three standard reference materials (NIST 610, 612, and 1831). The discrimination potential of LIBS was compared to that obtained using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), X-ray microfluorescence spectroscopy (μXRF) and scanning electron microscopy energy dispersive X-ray spectrometry (SEM-EDX) for the analysis of architectural window glass samples collected from crime scenes in the Canberra region, Australia. Pairwise comparisons were performed using a three-sigma rule, two-way ANOVA and Tukey's HSD test at 95% confidence limit in order to investigate the discrimination power for window glass analysis. The results show that the elemental analysis of glass by LIBS provides a discrimination power greater than 97% (>98% when combined with refractive index data), which was comparable to the discrimination powers obtained by LA-ICP-MS and μXRF. These results indicate that LIBS is a feasible alternative to the more expensive LA-ICP-MS and μXRF options for the routine forensic analysis of window glass samples. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

Discrimination of Clover and Citrus Honeys from Egypt According to Floral Type Using Easily Assessable Physicochemical Parameters and Discriminant Analysis: An External Validation of the Chemometric Approach.

PubMed

Karabagias, Ioannis K; Karabournioti, Sofia

2018-05-03

Twenty-two honey samples, namely clover and citrus honeys, were collected from the greater Cairo area during the harvesting year 2014⁻2015. The main purpose of the present study was to characterize the aforementioned honey types and to investigate whether the use of easily assessable physicochemical parameters, including color attributes in combination with chemometrics, could differentiate honey floral origin. Parameters taken into account were: pH, electrical conductivity, ash, free acidity, lactonic acidity, total acidity, moisture content, total sugars (degrees Brix-°Bx), total dissolved solids and their ratio to total acidity, salinity, CIELAB color parameters, along with browning index values. Results showed that all honey samples analyzed met the European quality standards set for honey and had variations in the aforementioned physicochemical parameters depending on floral origin. Application of linear discriminant analysis showed that eight physicochemical parameters, including color, could classify Egyptian honeys according to floral origin ( p < 0.05). Correct classification rate was 95.5% using the original method and 90.9% using the cross validation method. The discriminatory ability of the developed model was further validated using unknown honey samples. The overall correct classification rate was not affected. Specific physicochemical parameter analysis in combination with chemometrics has the potential to enhance the differences in floral honeys produced in a given geographical zone.

Discrimination of Clover and Citrus Honeys from Egypt According to Floral Type Using Easily Assessable Physicochemical Parameters and Discriminant Analysis: An External Validation of the Chemometric Approach

PubMed Central

Karabournioti, Sofia

2018-01-01

Twenty-two honey samples, namely clover and citrus honeys, were collected from the greater Cairo area during the harvesting year 2014–2015. The main purpose of the present study was to characterize the aforementioned honey types and to investigate whether the use of easily assessable physicochemical parameters, including color attributes in combination with chemometrics, could differentiate honey floral origin. Parameters taken into account were: pH, electrical conductivity, ash, free acidity, lactonic acidity, total acidity, moisture content, total sugars (degrees Brix-°Bx), total dissolved solids and their ratio to total acidity, salinity, CIELAB color parameters, along with browning index values. Results showed that all honey samples analyzed met the European quality standards set for honey and had variations in the aforementioned physicochemical parameters depending on floral origin. Application of linear discriminant analysis showed that eight physicochemical parameters, including color, could classify Egyptian honeys according to floral origin (p < 0.05). Correct classification rate was 95.5% using the original method and 90.9% using the cross validation method. The discriminatory ability of the developed model was further validated using unknown honey samples. The overall correct classification rate was not affected. Specific physicochemical parameter analysis in combination with chemometrics has the potential to enhance the differences in floral honeys produced in a given geographical zone. PMID:29751543

A Biomimetic Sensor for the Classification of Honeys of Different Floral Origin and the Detection of Adulteration

PubMed Central

Zakaria, Ammar; Shakaff, Ali Yeon Md; Masnan, Maz Jamilah; Ahmad, Mohd Noor; Adom, Abdul Hamid; Jaafar, Mahmad Nor; Ghani, Supri A.; Abdullah, Abu Hassan; Aziz, Abdul Hallis Abdul; Kamarudin, Latifah Munirah; Subari, Norazian; Fikri, Nazifah Ahmad

2011-01-01

The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose) and electronic tongue (e-tongue) measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused. PMID:22164046

A biomimetic sensor for the classification of honeys of different floral origin and the detection of adulteration.

PubMed

Zakaria, Ammar; Shakaff, Ali Yeon Md; Masnan, Maz Jamilah; Ahmad, Mohd Noor; Adom, Abdul Hamid; Jaafar, Mahmad Nor; Ghani, Supri A; Abdullah, Abu Hassan; Aziz, Abdul Hallis Abdul; Kamarudin, Latifah Munirah; Subari, Norazian; Fikri, Nazifah Ahmad

2011-01-01

The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose) and electronic tongue (e-tongue) measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused.

Hard and soft age discrimination: the dual nature of workplace discrimination.

PubMed

Stypinska, Justyna; Turek, Konrad

2017-03-01

The paper concentrates on the problem of age discrimination in the labour market and the way it can be conceptualised and measured in a multi-disciplinary way. The approach proposed here combines two understandings of age discrimination-a sociological and legal one, what allows for a fuller and expanded understanding of ageism in the workplace. At the heart of the study is a survey carried out in Poland with a sample of 1000 men and women aged 45-65 years. The study takes a deeper and innovative look into the issue of age discrimination in employment. Confirmatory factor analysis with WLSMV estimation and logistic regressions were used to test the hypotheses. The study shows that age discrimination in labour market can take on different forms: hard and soft, where the hard type of age discrimination mirrors the legally prohibited types of behaviours and those which relate to the actual decisions of employers which can impact on the employee's career development. The soft discrimination corresponds with those occurrences, which are not inscribed in the legal system per se, are occurring predominantly in the interpersonal sphere, but can nevertheless have negative consequences. Soft discrimination was experienced more often (28.6% of respondents) than hard discrimination (15.7%) with higher occurrences among women, persons in precarious job situation or residents of urban areas. The role of education was not confirmed to influence the levels of perceived age discrimination.

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.

Analysis of salivary phenotypes of generalized aggressive and chronic periodontitis through nuclear magnetic resonance-based metabolomics.

PubMed

Romano, Federica; Meoni, Gaia; Manavella, Valeria; Baima, Giacomo; Tenori, Leonardo; Cacciatore, Stefano; Aimetti, Mario

2018-06-07

Recent findings about the differential gene expression signature of periodontal lesions have raised the hypothesis of distinctive biological phenotypes expressed by generalized chronic periodontitis (GCP) and generalized aggressive periodontitis (GAgP) patients. Therefore, this cross-sectional investigation was planned, primarily, to determine the ability of nuclear magnetic resonance (NMR) spectroscopic analysis of unstimulated whole saliva to discriminate GCP and GAgP disease-specific metabolomic fingerprint and, secondarily, to assess potential metabolites discriminating periodontitis patients from periodontally healthy individuals (HI). NMR-metabolomics spectra were acquired from salivary samples of patients with a clinical diagnosis of GCP (n = 33) or GAgP (n = 28) and from HI (n = 39). The clustering of HI, GCP and GAgP patients was achieved by using a combination of the Principal Component Analysis and Canonical Correlation Analysis on the NMR profiles. These analyses revealed a significant predictive accuracy discriminating HI from GCP, and discriminating HI from GAgP patients (both 81%). In contrast, the GAgP and GCP saliva samples seem to belong to the same metabolic space (60% predictive accuracy). Significantly lower levels (P < 0.05) of pyruvate, N-acetyl groups and lactate and higher levels (P < 0.05) of proline, phenylalanine, and tyrosine were found in GCP and GAgP patients compared with HI. Within the limitations of this study, CGP and GAgP metabolomic profiles were not unequivocally discriminated through a NMR-based spectroscopic analysis of saliva. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing

PubMed Central

Wen, Tailai; Huang, Daoyu; Lu, Kun; Deng, Changjian; Zeng, Tanyue; Yu, Song; He, Zhiyi

2018-01-01

The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors’ responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose’s classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods. PMID:29382146

Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing.

PubMed

Wen, Tailai; Yan, Jia; Huang, Daoyu; Lu, Kun; Deng, Changjian; Zeng, Tanyue; Yu, Song; He, Zhiyi

2018-01-29

The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors' responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose's classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods.

Using color histograms and SPA-LDA to classify bacteria.

PubMed

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

2014-09-01

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

Population genetic analysis of insertion-deletion polymorphisms in a Brazilian population using the Investigator DIPplex kit.

PubMed

Ferreira Palha, Teresinha de Jesus Brabo; Ribeiro Rodrigues, Elzemar Martins; Cavalcante, Giovanna Chaves; Marrero, Andrea; de Souza, Ilíada Rainha; Seki Uehara, Clineu Julien; Silveira da Motta, Carlos Henrique Ares; Koshikene, Daniela; da Silva, Dayse Aparecida; de Carvalho, Elizeu Fagundes; Chemale, Gustavo; Freitas, Jorge M; Alexandre, Lídia; Paranaiba, Renato T F; Soler, Mirella Perruccio; Santos, Sidney

2015-11-01

The aim of this study was to estimate the diversity of 30 insertion/deletion (INDEL) markers (Investigator(®) DIPplex kit) in a sample of 519 individuals from six Brazilian states and to evaluate their applicability in forensic genetics. All INDEL markers were found to be highly polymorphic in the Brazilian population and were in Hardy-Weinberg equilibrium. To determine their forensic suitability in the Brazilian population, the markers were evaluated for discrimination power, match probability and exclusion power. The combined discrimination power (CDP), combined match power (CMP) and combined power of exclusion (CPE) were higher than 0.999999, 3.4 × 10(-13) and 0.9973, respectively. Further comparison of 29 worldwide populations revealed significant genetic differences between continental populations and a closer relationship between the Brazilian and European populations. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

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

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

PubMed Central

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

2015-01-01

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

Discrimination theory of rule-governed behavior

PubMed Central

Cerutti, Daniel T.

1989-01-01

In rule-governed behavior, previously established elementary discriminations are combined in complex instructions and thus result in complex behavior. Discriminative combining and recombining of responses produce behavior with characteristics differing from those of behavior that is established through the effects of its direct consequences. For example, responding in instructed discrimination may be occasioned by discriminative stimuli that are temporally and situationally removed from the circumstances under which the discrimination is instructed. The present account illustrates properties of rule-governed behavior with examples from research in instructional control and imitation learning. Units of instructed behavior, circumstances controlling compliance with instructions, and rule-governed problem solving are considered. PMID:16812579

Non-destructive profiling of volatile organic compounds using HS-SPME/GC-MS and its application for the geographical discrimination of white rice.

PubMed

Lim, Dong Kyu; Mo, Changyeun; Lee, Dong-Kyu; Long, Nguyen Phuoc; Lim, Jongguk; Kwon, Sung Won

2018-01-01

The authenticity determination of white rice is crucial to prevent deceptive origin labeling and dishonest trading. However, a non-destructive and comprehensive method for rapidly discriminating the geographical origins of white rice between countries is still lacking. In the current study, we developed a volatile organic compound based geographical discrimination method using headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME/GC-MS) to discriminate rice samples from Korea and China. A partial least squares discriminant analysis (PLS-DA) model exhibited a good classification of white rice between Korea and China (accuracy = 0.958, goodness of fit = 0.937, goodness of prediction = 0.831, and permutation test p-value = 0.043). Combining the PLS-DA based feature selection with the differentially expressed features from the unpaired t-test and significance analysis of microarrays, 12 discriminatory biomarkers were found. Among them, hexanal and 1-hexanol have been previously known to be associated with the cultivation environment and storage conditions. Other hydrocarbon biomarkers are novel, and their impact on rice production and storage remains to be elucidated. In conclusion, our findings highlight the ability to rapidly discriminate white rice from Korea and China. The developed method maybe useful for the authenticity and quality control of white rice. Copyright © 2017. Published by Elsevier B.V.

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

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

Forensic analysis of explosives using isotope ratio mass spectrometry (IRMS)--discrimination of ammonium nitrate sources.

PubMed

Benson, Sarah J; Lennard, Christopher J; Maynard, Philip; Hill, David M; Andrew, Anita S; Roux, Claude

2009-06-01

An evaluation was undertaken to determine if isotope ratio mass spectrometry (IRMS) could assist in the investigation of complex forensic cases by providing a level of discrimination not achievable utilising traditional forensic techniques. The focus of the research was on ammonium nitrate (AN), a common oxidiser used in improvised explosive mixtures. The potential value of IRMS to attribute Australian AN samples to the manufacturing source was demonstrated through the development of a preliminary AN classification scheme based on nitrogen isotopes. Although the discrimination utilising nitrogen isotopes alone was limited and only relevant to samples from the three Australian manufacturers during the evaluated time period, the classification scheme has potential as an investigative aid. Combining oxygen and hydrogen stable isotope values permitted the differentiation of AN prills from three different Australian manufacturers. Samples from five different overseas sources could be differentiated utilising a combination of the nitrogen, oxygen and hydrogen isotope values. Limited differentiation between Australian and overseas prills was achieved for the samples analysed. The comparison of nitrogen isotope values from intact AN prill samples with those from post-blast AN prill residues highlighted that the nitrogen isotopic composition of the prills was not maintained post-blast; hence, limiting the technique to analysis of un-reacted explosive material.

A computerized method for automated identification of erect posteroanterior and supine anteroposterior chest radiographs

NASA Astrophysics Data System (ADS)

Kao, E.-Fong; Lin, Wei-Chen; Hsu, Jui-Sheng; Chou, Ming-Chung; Jaw, Twei-Shiun; Liu, Gin-Chung

2011-12-01

A computerized scheme was developed for automated identification of erect posteroanterior (PA) and supine anteroposterior (AP) chest radiographs. The method was based on three features, the tilt angle of the scapula superior border, the tilt angle of the clavicle and the extent of radiolucence in lung fields, to identify the view of a chest radiograph. The three indices Ascapula, Aclavicle and Clung were determined from a chest image for the three features. Linear discriminant analysis was used to classify PA and AP chest images based on the three indices. The performance of the method was evaluated by receiver operating characteristic analysis. The proposed method was evaluated using a database of 600 PA and 600 AP chest radiographs. The discriminant performances Az of Ascapula, Aclavicle and Clung were 0.878 ± 0.010, 0.683 ± 0.015 and 0.962 ± 0.006, respectively. The combination of the three indices obtained an Az value of 0.979 ± 0.004. The results indicate that the combination of the three indices could yield high discriminant performance. The proposed method could provide radiologists with information about the view of chest radiographs for interpretation or could be used as a preprocessing step for analyzing chest images.

Micro-Raman spectroscopy of natural and synthetic indigo samples.

PubMed

Vandenabeele, Peter; Moens, Luc

2003-02-01

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

Rapid detection of milk adulteration using intact protein flow injection mass spectrometric fingerprints combined with chemometrics.

PubMed

Du, Lijuan; Lu, Weiying; Cai, Zhenzhen Julia; Bao, Lei; Hartmann, Christoph; Gao, Boyan; Yu, Liangli Lucy

2018-02-01

Flow injection mass spectrometry (FIMS) combined with chemometrics was evaluated for rapidly detecting economically motivated adulteration (EMA) of milk. Twenty-two pure milk and thirty-five counterparts adulterated with soybean, pea, and whey protein isolates at 0.5, 1, 3, 5, and 10% (w/w) levels were analyzed. The principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA), and support vector machine (SVM) classification models indicated that the adulterated milks could successfully be classified from the pure milks. FIMS combined with chemometrics might be an effective method to detect possible EMA in milk. Copyright © 2017 Elsevier Ltd. All rights reserved.

A manual and an automatic TERS based virus discrimination

NASA Astrophysics Data System (ADS)

Olschewski, Konstanze; Kämmer, Evelyn; Stöckel, Stephan; Bocklitz, Thomas; Deckert-Gaudig, Tanja; Zell, Roland; Cialla-May, Dana; Weber, Karina; Deckert, Volker; Popp, Jürgen

2015-02-01

Rapid techniques for virus identification are more relevant today than ever. Conventional virus detection and identification strategies generally rest upon various microbiological methods and genomic approaches, which are not suited for the analysis of single virus particles. In contrast, the highly sensitive spectroscopic technique tip-enhanced Raman spectroscopy (TERS) allows the characterisation of biological nano-structures like virions on a single-particle level. In this study, the feasibility of TERS in combination with chemometrics to discriminate two pathogenic viruses, Varicella-zoster virus (VZV) and Porcine teschovirus (PTV), was investigated. In a first step, chemometric methods transformed the spectral data in such a way that a rapid visual discrimination of the two examined viruses was enabled. In a further step, these methods were utilised to perform an automatic quality rating of the measured spectra. Spectra that passed this test were eventually used to calculate a classification model, through which a successful discrimination of the two viral species based on TERS spectra of single virus particles was also realised with a classification accuracy of 91%.Rapid techniques for virus identification are more relevant today than ever. Conventional virus detection and identification strategies generally rest upon various microbiological methods and genomic approaches, which are not suited for the analysis of single virus particles. In contrast, the highly sensitive spectroscopic technique tip-enhanced Raman spectroscopy (TERS) allows the characterisation of biological nano-structures like virions on a single-particle level. In this study, the feasibility of TERS in combination with chemometrics to discriminate two pathogenic viruses, Varicella-zoster virus (VZV) and Porcine teschovirus (PTV), was investigated. In a first step, chemometric methods transformed the spectral data in such a way that a rapid visual discrimination of the two examined viruses was enabled. In a further step, these methods were utilised to perform an automatic quality rating of the measured spectra. Spectra that passed this test were eventually used to calculate a classification model, through which a successful discrimination of the two viral species based on TERS spectra of single virus particles was also realised with a classification accuracy of 91%. Electronic supplementary information (ESI) available. See DOI: 10.1039/c4nr07033j

Paintings discrimination by mice: Different strategies for different paintings.

PubMed

Watanabe, Shigeru

2017-09-01

C57BL/6 mice were trained on simultaneous discrimination of paintings with multiple exemplars, using an operant chamber with a touch screen. The number of exemplars was successively increased up to six. Those mice trained in Kandinsky/Mondrian discrimination showed improved learning and generalization, whereas those trained in Picasso/Renoir discrimination showed no improvements in learning or generalization. These results suggest category-like discrimination in the Kandinsky/Mondrian task, but item-to-item discrimination in the Picasso/Renoir task. Mice maintained their discriminative behavior in a pixelization test with various paintings; however, mice in the Picasso/Renoir task showed poor performance in a test that employed scrambling processing. These results do not indicate that discrimination strategy for any Kandinsky/Mondrian combinations differed from that for any Picasso/Monet combinations but suggest the mice employed different strategies of discrimination tasks depending upon stimuli. Copyright © 2017 Elsevier B.V. All rights reserved.

Evaluation of facial expression in acute pain in cats.

PubMed

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

2014-12-01

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

Kennard-Stone combined with least square support vector machine method for noncontact discriminating human blood species

NASA Astrophysics Data System (ADS)

Zhang, Linna; Li, Gang; Sun, Meixiu; Li, Hongxiao; Wang, Zhennan; Li, Yingxin; Lin, Ling

2017-11-01

Identifying whole bloods to be either human or nonhuman is an important responsibility for import-export ports and inspection and quarantine departments. Analytical methods and DNA testing methods are usually destructive. Previous studies demonstrated that visible diffuse reflectance spectroscopy method can realize noncontact human and nonhuman blood discrimination. An appropriate method for calibration set selection was very important for a robust quantitative model. In this paper, Random Selection (RS) method and Kennard-Stone (KS) method was applied in selecting samples for calibration set. Moreover, proper stoichiometry method can be greatly beneficial for improving the performance of classification model or quantification model. Partial Least Square Discrimination Analysis (PLSDA) method was commonly used in identification of blood species with spectroscopy methods. Least Square Support Vector Machine (LSSVM) was proved to be perfect for discrimination analysis. In this research, PLSDA method and LSSVM method was used for human blood discrimination. Compared with the results of PLSDA method, this method could enhance the performance of identified models. The overall results convinced that LSSVM method was more feasible for identifying human and animal blood species, and sufficiently demonstrated LSSVM method was a reliable and robust method for human blood identification, and can be more effective and accurate.

Similar discriminative-stimulus effects of D-amphetamine in women and men.

PubMed

Vansickel, Andrea R; Lile, Joshua A; Stoops, William W; Rush, Craig R

2007-01-01

The results of controlled non-human animal and human laboratory studies are mixed regarding whether women and men respond differently to stimulant drugs. In order to assess potential gender differences in the effects of D-amphetamine, we conducted a retrospective analysis of six studies conducted in our laboratory that used identical procedures and measures. Thirteen women and fourteen men learned to discriminate 15 mg oral D-amphetamine. After acquiring the discrimination (i.e., >or=80% correct responding on 4 consecutive sessions), the effects of a range of doses of D-amphetamine (0, 2.5, 5, 10, and 15 mg) alone and in combination with other drugs, were assessed. Only data from sessions in which D-amphetamine was administered alone were included in this analysis. D-amphetamine functioned as a discriminative stimulus and dose-dependently increased drug-appropriate responding. Women and men did not differ in their ability to discriminate D-amphetamine. Women and men differed on participant-ratings of high (womenmen) and sluggish (women

Determination of authenticity, regional origin, and vintage of Slovenian wines using a combination of IRMS and SNIF-NMR analyses.

PubMed

Ogrinc, N; Kosir, I J; Kocjancic, M; Kidric, J

2001-03-01

The authenticity and geographical origin of wines produced in Slovenia were investigated by a combination of IRMS and SNIF-NMR methods. A total of 102 grape samples of selected wines were carefully collected in three different wine-growing regions of Slovenia in 1996, 1997, and 1998. The stable isotope data were evaluated using principal component analysis (PCA) and linear discriminant analysis (LDA). The isotopic ratios to discriminate between coastal and continental regions are the deuterium/hydrogen isotopic ratio of the methylene site in the ethanol molecule (D/H)(II) and delta(13)C values; including also delta(18)O values in the PCA and LDA made possible separation between the two continental regions Drava and Sava. It was found that delta(18)O values are modified by the meteorological events during grape ripening and harvest. The usefulness of isotopic parameters for detecting adulteration or watering and to assess the geographical origin of wines is improved only when they are used concurrently.

Authentication of virgin olive oil by a novel curve resolution approach combined with visible spectroscopy.

PubMed

Ferreiro-González, Marta; Barbero, Gerardo F; Álvarez, José A; Ruiz, Antonio; Palma, Miguel; Ayuso, Jesús

2017-04-01

Adulteration of olive oil is not only a major economic fraud but can also have major health implications for consumers. In this study, a combination of visible spectroscopy with a novel multivariate curve resolution method (CR), principal component analysis (PCA) and linear discriminant analysis (LDA) is proposed for the authentication of virgin olive oil (VOO) samples. VOOs are well-known products with the typical properties of a two-component system due to the two main groups of compounds that contribute to the visible spectra (chlorophylls and carotenoids). Application of the proposed CR method to VOO samples provided the two pure-component spectra for the aforementioned families of compounds. A correlation study of the real spectra and the resolved component spectra was carried out for different types of oil samples (n=118). LDA using the correlation coefficients as variables to discriminate samples allowed the authentication of 95% of virgin olive oil samples. Copyright © 2016 Elsevier Ltd. All rights reserved.

Performance of brain-damaged and non-brain-damaged institutionalized children on the Minnesota Percepto-Diagnostic Test.

PubMed

Holland, J M; Fuller, G B; Barth, C E

1982-01-01

Examined the performance of 64 children on the Minnesota Percepto-Diagnostic test (MPD) who were diagnosed as either Brain-Damaged (BD) or emotionally impaired Non-Brain-Damaged (NBD). There were 31 children in the NBD group and 33 in the BD group. The MPD T-score and Actuarial Table significantly differentiated between the two groups. Seventy-four percent of the combined BD-NBD groups were identified correctly. Additional discriminant analysis on this sample yielded combined BD-NBD groups classification rates that ranged from 77% with the MPD variables Separation of Circle-Diamond (SPCD), Distortion of Circle-Diamond (DCD) and Distortion of Dots (DD) to 83% with the WISC-R three IQ scores plus the MPD T-score, SPCD and DD. The MPD T-score and Actuarial Table (MPD Two-Step Diagnosis) appeared to generalize to other populations more readily than discriminant analysis formulae, which tend to be sensitive to the samples from which they are derived.

Differential gene expression profiles of peripheral blood mononuclear cells in childhood asthma.

PubMed

Kong, Qian; Li, Wen-Jing; Huang, Hua-Rong; Zhong, Ying-Qiang; Fang, Jian-Pei

2015-05-01

Asthma is a common childhood disease with strong genetic components. This study compared whole-genome expression differences between asthmatic young children and healthy controls to identify gene signatures of childhood asthma. Total RNA extracted from peripheral blood mononuclear cells (PBMC) was subjected to microarray analysis. QRT-PCR was performed to verify the microarray results. Classification and functional characterization of differential genes were illustrated by hierarchical clustering and gene ontology analysis. Multiple logistic regression (MLR) analysis, receiver operating characteristic (ROC) curve analysis, and discriminate power were used to scan asthma-specific diagnostic markers. For fold-change>2 and p < 0.05, there were 758 named differential genes. The results of QRT-PCR confirmed successfully the array data. Hierarchical clustering divided 29 highly possible genes into seven categories and the genes in the same cluster were likely to possess similar expression patterns or functions. Gene ontology analysis presented that differential genes primarily enriched in immune response, response to stress or stimulus, and regulation of apoptosis in biological process. MLR and ROC curve analysis revealed that the combination of ADAM33, Smad7, and LIGHT possessed excellent discriminating power. The combination of ADAM33, Smad7, and LIGHT would be a reliable and useful childhood asthma model for prediction and diagnosis.

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

PubMed

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

2015-08-01

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

Identification and DUS Testing of Rice Varieties through Microsatellite Markers.

PubMed

Pourabed, Ehsan; Jazayeri Noushabadi, Mohammad Reza; Jamali, Seyed Hossein; Moheb Alipour, Naser; Zareyan, Abbas; Sadeghi, Leila

2015-01-01

Identification and registration of new rice varieties are very important to be free from environmental effects and using molecular markers that are more reliable. The objectives of this study were, first, the identification and distinction of 40 rice varieties consisting of local varieties of Iran, improved varieties, and IRRI varieties using PIC, and discriminating power, second, cluster analysis based on Dice similarity coefficient and UPGMA algorithm, and, third, determining the ability of microsatellite markers to separate varieties utilizing the best combination of markers. For this research, 12 microsatellite markers were used. In total, 83 polymorphic alleles (6.91 alleles per locus) were found. In addition, the variation of PIC was calculated from 0.52 to 0.9. The results of cluster analysis showed the complete discrimination of varieties from each other except for IR58025A and IR58025B. Moreover, cluster analysis could detect the most of the improved varieties from local varieties. Based on the best combination of markers analysis, five pair primers together have shown the same results of all markers for detection among all varieties. Considering the results of this research, we can propose that microsatellite markers can be used as a complementary tool for morphological characteristics in DUS tests.

Discriminant analysis for predictor of falls in stroke patients by using the Berg Balance Scale.

PubMed

Maeda, Noriaki; Urabe, Yukio; Murakami, Masahito; Itotani, Keisuke; Kato, Junichi

2015-05-01

An observational study was carried out to estimate the strength of the relationships among balance, mobility and falls in hemiplegic stroke inpatients. The objective was to examine factors that may aid in the prediction of the likelihood of falls in stroke patients. A total of 53 stroke patients (30 male, 23 female) aged 67.0 ± 11.1 years were interviewed regarding their fall history. Physical performance was assessed using the Berg Balance Scale (BBS) and the Functional Independence Measure (FIM) scale. Variables that differed between fallers and non-fallers were identified, and a discriminant function analysis was carried out to determine the combination of variables that effectively predicted fall status. Of the 53 stroke patients, 19 were fallers. Compared with the non-fallers, the fallers scored low on the FIM, and differed with respect to age, time from stroke onset, length of hospital stay, Brunnstrom recovery stage and admission BBS score. Discriminant analysis for predicting falls in stroke patients showed that admission BBS score was significantly related to the likelihood of falls. Moreover, discriminant analysis showed that the use of a significant BBS score to classify fallers and non-fallers had an accuracy of 81.1%. The discriminating criterion between the two groups was a score of 31 points on the BBS. The results of this study suggest that BBS score is a strong predictor of falls in stroke patients. As balance is closely related to the risk of falls in hospitalised stroke patients, BBS might be useful in the prediction of falls.

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.

Photoacoustic spectroscopy based investigatory approach to discriminate breast cancer from normal: a pilot study

NASA Astrophysics Data System (ADS)

Priya, Mallika; Rao, Bola Sadashiva Satish; Chandra, Subhash; Ray, Satadru; Mathew, Stanley; Datta, Anirbit; Nayak, Subramanya G.; Mahato, Krishna Kishore

2016-02-01

In spite of many efforts for early detection of breast cancer, there is still lack of technology for immediate implementation. In the present study, the potential photoacoustic spectroscopy was evaluated in discriminating breast cancer from normal, involving blood serum samples seeking early detection. Three photoacoustic spectra in time domain were recorded from each of 20 normal and 20 malignant samples at 281nm pulsed laser excitations and a total of 120 spectra were generated. The time domain spectra were then Fast Fourier Transformed into frequency domain and 116.5625 - 206.875 kHz region was selected for further analysis using a combinational approach of wavelet, PCA and logistic regression. Initially, wavelet analysis was performed on the FFT data and seven features (mean, median, area under the curve, variance, standard deviation, skewness and kurtosis) from each were extracted. PCA was then performed on the feature matrix (7x120) for discriminating malignant samples from the normal by plotting a decision boundary using logistic regression analysis. The unsupervised mode of classification used in the present study yielded specificity and sensitivity values of 100% in each respectively with a ROC - AUC value of 1. The results obtained have clearly demonstrated the capability of photoacoustic spectroscopy in discriminating cancer from the normal, suggesting its possible clinical implications.

[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.

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.

Discrimination of Gastrodia elata from Different Geographical Origin for Quality Evaluation Using Newly-Build Near Infrared Spectrum Coupled with Multivariate Analysis.

PubMed

Zuo, Yamin; Deng, Xuehua; Wu, Qing

2018-05-04

Discrimination of Gastrodia elata ( G. elata ) geographical origin is of great importance to pharmaceutical companies and consumers in China. this paper focuses on the feasibility of near infrared spectrum (NIRS) combined multivariate analysis as a rapid and non-destructive method to prove its fit for this purpose. Firstly, 16 batches of G. elata samples from four main-cultivation regions in China were quantified by traditional HPLC method. It showed that samples from different origins could not be efficiently differentiated by the contents of four phenolic compounds in this study. Secondly, the raw near infrared (NIR) spectra of those samples were acquired and two different pattern recognition techniques were used to classify the geographical origins. The results showed that with spectral transformation optimized, discriminant analysis (DA) provided 97% and 99% correct classification for the calibration and validation sets of samples from discriminating of four different main-cultivation regions, and provided 98% and 99% correct classifications for the calibration and validation sets of samples from eight different cities, respectively, which all performed better than the principal component analysis (PCA) method. Thirdly, as phenolic compounds content (PCC) is highly related with the quality of G. elata , synergy interval partial least squares (Si-PLS) was applied to build the PCC prediction model. The coefficient of determination for prediction (R p ²) of the Si-PLS model was 0.9209, and root mean square error for prediction (RMSEP) was 0.338. The two regions (4800 cm −1 ⁻5200 cm −1 , and 5600 cm −1 ⁻6000 cm −1 ) selected by Si-PLS corresponded to the absorptions of aromatic ring in the basic phenolic structure. It can be concluded that NIR spectroscopy combined with PCA, DA and Si-PLS would be a potential tool to provide a reference for the quality control of G. elata.

Sex determination from the mandibular ramus flexure of Koreans by discrimination function analysis using three-dimensional mandible models.

PubMed

Lin, Chenghe; Jiao, Benzheng; Liu, Shanshan; Guan, Feng; Chung, Nak-Eun; Han, Seung-Ho; Lee, U-Young

2014-03-01

It has been known that mandible ramus flexure is an important morphologic trait for sex determination. However, it will be unavailable when mandible is incomplete or fragmented. Therefore, the anthropometric analysis on incomplete or fragmented mandible becomes more important. The aim of this study is to investigate the sex-discriminant potential of mandible ramus flexure on the Korean three-dimensional (3D) mandible models with anthropometric analysis. The sample consists of 240 three dimensional mandibular models obtained from Korean population (M:F; 120:120, mean age 46.2 y), collected by The Catholic Institute for Applied Anatomy, The Catholic University of Korea. Anthropometric information about 11 metric was taken with Mimics, anthropometry libraries toolkit. These parameters were subjected to different discriminant function analyses using SPSS 17.0. Univariate analyses showed that the resubstitution accuracies for sex determination range from 50.4 to 77.1%. Mandibular flexure upper border (MFUB), maximum ramus vertical height (MRVH), and upper ramus vertical height (URVH) expressed the greatest dimorphism, 72.1 to 77.1%. Bivariate analyses indicated that the combination of MFUB and MRVH hold even higher resubstitution accuracy of 81.7%. Furthermore, the direct and stepwise discriminant analyses with the variables on the upper ramus above flexure could predict sex in 83.3 and 85.0%, respectively. When all variables of mandibular ramus flexure were input in stepwise discriminant analysis, the resubstitution accuracy arrived as high as 88.8%. Therefore, we concluded that the upper ramus above flexure hold the larger potentials than the mandibular ramus flexure itself to predict sexes, and that the equations in bivariate and multivariate analysis from our study will be helpful for sex determination on Korean population in forensic science and law. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Transgender women of color: discrimination and depression symptoms

PubMed Central

Jefferson, Kevin; Neilands, Torsten B.; Sevelius, Jae

2014-01-01

Purpose Trans women of color contend with multiple marginalizations; the purpose of this study is to examine associations between experiencing discriminatory (racist/transphobic) events and depression symptoms. It uses a categorical measure of combined discrimination, and examines a protective association of transgender identity on depression symptoms. Design/methodology/approach Data from a subset of trans women of color participants in the Sheroes study were analyzed with linear and logistic regression. Associations of depression symptoms with racist and transphobic events, combined discrimination, coping self-efficacy, and transgender identity were assessed with odds ratios. Findings Exposure to discriminatory events and combined discrimination positively associated with depression symptom odds. Increased transgender identity associated with increased coping self-efficacy, which negatively associated with depression symptom odds. Research limitations/implications Cross-sectional study data prohibits inferring causality; results support conducting longitudinal research on discrimination’s health effects, and research on transgender identity. Results also support operationalizing intersectionality in health research. The study’s categorical approach to combined discrimination may be replicable in studies with hard to reach populations and small sample sizes. Practical implications Health programs could pursue psychosocial interventions and anti-discrimination campaigns. Interventions might advocate increasing participants’ coping self-efficacy while providing space to explore and develop social identity. Social implications There is a need for policy and health programs to center trans women of color concerns. Originality/value This study examines combined discrimination and identity in relation to depression symptoms among trans women of color, an underserved population. Paper type Research paper PMID:25346778

Source-Type Identification Analysis Using Regional Seismic Moment Tensors

NASA Astrophysics Data System (ADS)

Chiang, A.; Dreger, D. S.; Ford, S. R.; Walter, W. R.

2012-12-01

Waveform inversion to determine the seismic moment tensor is a standard approach in determining the source mechanism of natural and manmade seismicity, and may be used to identify, or discriminate different types of seismic sources. The successful applications of the regional moment tensor method at the Nevada Test Site (NTS) and the 2006 and 2009 North Korean nuclear tests (Ford et al., 2009a, 2009b, 2010) show that the method is robust and capable for source-type discrimination at regional distances. The well-separated populations of explosions, earthquakes and collapses on a Hudson et al., (1989) source-type diagram enables source-type discrimination; however the question remains whether or not the separation of events is universal in other regions, where we have limited station coverage and knowledge of Earth structure. Ford et al., (2012) have shown that combining regional waveform data and P-wave first motions removes the CLVD-isotropic tradeoff and uniquely discriminating the 2009 North Korean test as an explosion. Therefore, including additional constraints from regional and teleseismic P-wave first motions enables source-type discrimination at regions with limited station coverage. We present moment tensor analysis of earthquakes and explosions (M6) from Lop Nor and Semipalatinsk test sites for station paths crossing Kazakhstan and Western China. We also present analyses of smaller events from industrial sites. In these sparse coverage situations we combine regional long-period waveforms, and high-frequency P-wave polarity from the same stations, as well as from teleseismic arrays to constrain the source type. Discrimination capability with respect to velocity model and station coverage is examined, and additionally we investigate the velocity model dependence of vanishing free-surface traction effects on seismic moment tensor inversion of shallow sources and recovery of explosive scalar moment. Our synthetic data tests indicate that biases in scalar seismic moment and discrimination for shallow sources are small and can be understood in a systematic manner. We are presently investigating the frequency dependence of vanishing traction of a very shallow (10m depth) M2+ chemical explosion recorded at several kilometer distances, and preliminary results indicate at the typical frequency passband we employ the bias does not affect our ability to retrieve the correct source mechanism but may affect the retrieval of the correct scalar seismic moment. Finally, we assess discrimination capability in a composite P-value statistical framework.

Discrimination and chemical phylogenetic study of seven species of Dendrobium using infrared spectroscopy combined with cluster analysis

NASA Astrophysics Data System (ADS)

Luo, Congpei; He, Tao; Chun, Ze

2013-04-01

Dendrobium is a commonly used and precious herb in Traditional Chinese Medicine. The high biodiversity of Dendrobium and the therapeutic needs require tools for the correct and fast discrimination of different Dendrobium species. This study investigates Fourier transform infrared spectroscopy followed by cluster analysis for discrimination and chemical phylogenetic study of seven Dendrobium species. Despite the general pattern of the IR spectra, different intensities, shapes, peak positions were found in the IR spectra of these samples, especially in the range of 1800-800 cm-1. The second derivative transformation and alcoholic extracting procedure obviously enlarged the tiny spectral differences among these samples. The results indicated each Dendrobium species had a characteristic IR spectra profile, which could be used to discriminate them. The similarity coefficients among the samples were analyzed based on their second derivative IR spectra, which ranged from 0.7632 to 0.9700, among the seven Dendrobium species, and from 0.5163 to 0.9615, among the ethanol extracts. A dendrogram was constructed based on cluster analysis the IR spectra for studying the chemical phylogenetic relationships among the samples. The results indicated that D. denneanum and D. crepidatum could be the alternative resources to substitute D. chrysotoxum, D. officinale and D. nobile which were officially recorded in Chinese Pharmacopoeia. In conclusion, with the advantages of high resolution, speediness and convenience, the experimental approach can successfully discriminate and construct the chemical phylogenetic relationships of the seven Dendrobium species.

Lithological mapping of Kanjamalai hill using hyperspectral remote sensing tools in Salem district, Tamil Nadu, India

NASA Astrophysics Data System (ADS)

Arulbalaji, Palanisamy; Balasubramanian, Gurugnanam

2017-07-01

This study uses advanced spaceborne thermal emission and reflection radiometer (ASTER) hyperspectral remote sensing techniques to discriminate rock types composing Kanjamalai hill located in the Salem district of Tamil Nadu, India. Kanjamalai hill is of particular interest because it contains economically viable iron ore deposits. ASTER hyperspectral data were subjected to principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF) to improve identification of lithologies remotely and to compare these digital data results with published geologic maps. Hyperspectral remote sensing analysis indicates that PCA (R∶G∶B=2∶1∶3), MNF (R∶G∶B=3∶2∶1), and ICA (R∶G∶B=1∶3∶2) provide the best band combination for effective discrimination of lithological rock types composing Kanjamalai hill. The remote sensing-derived lithological map compares favorably with a published geological map from Geological Survey of India and has been verified with ground truth field investigations. Therefore, ASTER data-based lithological mapping provides fast, cost-effective, and accurate geologic data useful for lithological discrimination and identification of ore deposits.

Prostate lesion detection and localization based on locality alignment discriminant analysis

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

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

PubMed

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

2017-05-10

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

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

PubMed

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

2007-12-01

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

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

Combined Raman spectroscopy and autofluoresence imaging method for in vivo skin tumor diagnosis

NASA Astrophysics Data System (ADS)

Zakharov, V. P.; Bratchenko, I. A.; Myakinin, O. O.; Artemyev, D. N.; Khristoforova, Y. A.; Kozlov, S. V.; Moryatov, A. A.

2014-09-01

The fluorescence and Raman spectroscopy (RS) combined method of in vivo detection of malignant human skin cancer was demonstrated. The fluorescence analysis was used for detection of abnormalities during fast scanning of large tissue areas. In suspected cases of malignancy the Raman spectrum analysis of biological tissue was performed to determine the type of neoplasm. A special RS phase method was proposed for in vivo identification of skin tumor. Quadratic Discriminant Analysis was used for tumor type classification on phase planes. It was shown that the application of phase method provides a diagnosis of malignant melanoma with a sensitivity of 89% and a specificity of 87%.

Modeling the impact of social discrimination and financial hardship on the sexual risk of HIV among Latino and Black men who have sex with men.

PubMed

Ayala, George; Bingham, Trista; Kim, Junyeop; Wheeler, Darrell P; Millett, Gregorio A

2012-05-01

We examined the impact of social discrimination and financial hardship on unprotected anal intercourse with a male sex partner of serodiscordant or unknown HIV status in the past 3 months among 1081 Latino and 1154 Black men who have sex with men (MSM; n = 2235) residing in Los Angeles County, California; New York, New York; and Philadelphia, Pennsylvania. We administered HIV testing and a questionnaire assessing 6 explanatory variables. We combined traditional mediation analysis with the results of a path analysis to simultaneously examine the direct, indirect, and total effects of these variables on the outcome variable. Bivariate analysis showed that homophobia, racism, financial hardship, and lack of social support were associated with unprotected anal intercourse with a serodiscordant or sero-unknown partner. Path analysis determined that these relations were mediated by participation in risky sexual situations and lack of social support. However, paths between the explanatory variable and 2 mediating variables varied by participants' serostatus. Future prevention research and program designs should specifically address the differential impact of social discrimination and financial hardship on lack of social support and risky sexual situations among Latino and Black MSM.

Glycomics meets artificial intelligence - Potential of glycan analysis for identification of seropositive and seronegative rheumatoid arthritis patients revealed.

PubMed

Chocholova, Erika; Bertok, Tomas; Jane, Eduard; Lorencova, Lenka; Holazova, Alena; Belicka, Ludmila; Belicky, Stefan; Mislovicova, Danica; Vikartovska, Alica; Imrich, Richard; Kasak, Peter; Tkac, Jan

2018-06-01

In this study, one hundred serum samples from healthy people and patients with rheumatoid arthritis (RA) were analyzed. Standard immunoassays for detection of 10 different RA markers and analysis of glycan markers on antibodies in 10 different assay formats with several lectins were applied for each serum sample. A dataset containing 2000 data points was data mined using artificial neural networks (ANN). We identified key RA markers, which can discriminate between healthy people and seropositive RA patients (serum containing autoantibodies) with accuracy of 83.3%. Combination of RA markers with glycan analysis provided much better discrimination accuracy of 92.5%. Immunoassays completely failed to identify seronegative RA patients (serum not containing autoantibodies), while glycan analysis correctly identified 43.8% of these patients. Further, we revealed other critical parameters for successful glycan analysis such as type of a sample, format of analysis and orientation of captured antibodies for glycan analysis. Copyright © 2018 Elsevier B.V. All rights reserved.

A novel method for qualitative analysis of edible oil oxidation using an electronic nose.

PubMed

Xu, Lirong; Yu, Xiuzhu; Liu, Lei; Zhang, Rui

2016-07-01

An electronic nose (E-nose) was used for rapid assessment of the degree of oxidation in edible oils. Peroxide and acid values of edible oil samples were analyzed using data obtained by the American Oil Chemists' Society (AOCS) Official Method for reference. Qualitative discrimination between non-oxidized and oxidized oils was conducted using the E-nose technique developed in combination with cluster analysis (CA), principal component analysis (PCA), and linear discriminant analysis (LDA). The results from CA, PCA and LDA indicated that the E-nose technique could be used for differentiation of non-oxidized and oxidized oils. LDA produced slightly better results than CA and PCA. The proposed approach can be used as an alternative to AOCS Official Method as an innovative tool for rapid detection of edible oil oxidation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Principle component analysis and linear discriminant analysis of multi-spectral autofluorescence imaging data for differentiating basal cell carcinoma and healthy skin

NASA Astrophysics Data System (ADS)

Chernomyrdin, Nikita V.; Zaytsev, Kirill I.; Lesnichaya, Anastasiya D.; Kudrin, Konstantin G.; Cherkasova, Olga P.; Kurlov, Vladimir N.; Shikunova, Irina A.; Perchik, Alexei V.; Yurchenko, Stanislav O.; Reshetov, Igor V.

2016-09-01

In present paper, an ability to differentiate basal cell carcinoma (BCC) and healthy skin by combining multi-spectral autofluorescence imaging, principle component analysis (PCA), and linear discriminant analysis (LDA) has been demonstrated. For this purpose, the experimental setup, which includes excitation and detection branches, has been assembled. The excitation branch utilizes a mercury arc lamp equipped with a 365-nm narrow-linewidth excitation filter, a beam homogenizer, and a mechanical chopper. The detection branch employs a set of bandpass filters with the central wavelength of spectral transparency of λ = 400, 450, 500, and 550 nm, and a digital camera. The setup has been used to study three samples of freshly excised BCC. PCA and LDA have been implemented to analyze the data of multi-spectral fluorescence imaging. Observed results of this pilot study highlight the advantages of proposed imaging technique for skin cancer diagnosis.

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.

Quantitative determination and evaluation of Paris polyphylla var. yunnanensis with different harvesting times using UPLC-UV-MS and FT-IR spectroscopy in combination with partial least squares discriminant analysis.

PubMed

Yang, Yuan-Gui; Zhang, Ji; Zhao, Yan-Li; Zhang, Jin-Yu; Wang, Yuan-Zhong

2017-07-01

A rapid method was developed and validated by ultra-performance liquid chromatography-triple quadrupole mass spectroscopy with ultraviolet detection (UPLC-UV-MS) for simultaneous determination of paris saponin I, paris saponin II, paris saponin VI and paris saponin VII. Partial least squares discriminant analysis (PLS-DA) based on UPLC and Fourier transform infrared (FT-IR) spectroscopy was employed to evaluate Paris polyphylla var. yunnanensis (PPY) at different harvesting times. Quantitative determination implied that the various contents of bioactive compounds with different harvesting times may lead to different pharmacological effects; the average content of total saponins for PPY harvested at 8 years was higher than that from other samples. The PLS-DA of FT-IR spectra had a better performance than that of UPLC for discrimination of PPY from different harvesting times. Copyright © 2016 John Wiley & Sons, Ltd.

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

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

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

1993-10-01

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

Paper spray mass spectrometry and chemometric tools for a fast and reliable identification of counterfeit blended Scottish whiskies.

PubMed

Teodoro, Janaína Aparecida Reis; Pereira, Hebert Vinicius; Sena, Marcelo Martins; Piccin, Evandro; Zacca, Jorge Jardim; Augusti, Rodinei

2017-12-15

A direct method based on the application of paper spray mass spectrometry (PS-MS) combined with a chemometric supervised method (partial least square discriminant analysis, PLS-DA) was developed and applied to the discrimination of authentic and counterfeit samples of blended Scottish whiskies. The developed methodology employed the negative ion mode MS, included 44 authentic whiskies from diverse brands and batches and 44 counterfeit samples of the same brands seized during operations of the Brazilian Federal Police, totalizing 88 samples. An exploratory principal component analysis (PCA) model showed a reasonable discrimination of the counterfeit whiskies in PC2. In spite of the samples heterogeneity, a robust, reliable and accurate PLS-DA model was generated and validated, which was able to correctly classify the samples with nearly 100% success rate. The use of PS-MS also allowed the identification of the main marker compounds associated with each type of sample analyzed: authentic or counterfeit. Copyright © 2017 Elsevier Ltd. All rights reserved.

Genotypic and environmental effects on coffee (Coffea arabica L.) bean fatty acid profile: impact on variety and origin chemometric determination.

PubMed

Villarreal, Diana; Laffargue, Andreina; Posada, Huver; Bertrand, Benoit; Lashermes, Philippe; Dussert, Stephane

2009-12-09

In a previous study, the effectiveness of chlorogenic acids, fatty acids (FA), and elements was compared for the discrimination of Arabica varieties and growing terroirs. Since FA provided the best results, the aim of the present work was to validate their discrimination ability using an extended experimental design, including twice the number of location x variety combinations and 2 years of study. It also aimed at understanding how the environment influences FA composition through correlation analysis using different climatic parameters. Percentages of correct classification of known samples remained very high, independent of the classification criterion. However, cross-validation tests across years indicated that prediction of unknown locations was less efficient than that of unknown genotypes. Environmental temperature during the development of coffee beans had a dramatic influence on their FA composition. Analysis of climate patterns over years enabled us to understand the efficient location discrimination within a single year but only moderate efficiency across years.

Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis.

PubMed

Yang, Yan-Qin; Yin, Hong-Xu; Yuan, Hai-Bo; Jiang, Yong-Wen; Dong, Chun-Wang; Deng, Yu-Liang

2018-01-01

In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP) values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA) allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties.

Two-photon optical imaging, spectral and fluorescence lifetime analysis to discriminate urothelial carcinoma grades.

PubMed

Pradère, B; Poulon, F; Compérat, E; Lucas, I; Bazin, D; Doizi, S; Cussenot, O; Traxer, O; Abi Haidar, D

2018-05-28

In the framework of urologic oncology, mini-invasive procedures have increased in the last few decades particularly for urothelial carcinoma. One of the essential elements in the management of this disease is still the diagnosis, which strongly influences the choice of treatment. The histopathologic evaluation of the tumor grade is a keystone of diagnosis, and tumor characterization is not possible with just a macroscopic evaluation. Even today intraoperative evaluation remains difficult despite the emergence of new technologies which use exogenous fluorophore. This study assessed an optical multimodal technique based on endogenous fluorescence, combining qualitative and quantitative analysis, for the diagnostic of urothelial carcinoma. It was found that the combination of two photon fluorescence, second harmonic generation microscopy, spectral analysis and fluorescence lifetime imaging were all able to discriminate tumor from healthy tissue, and to determine the grade of tumors. Spectral analysis of fluorescence intensity and the redox ratio used as quantitative evaluations showed statistical differences between low grade and high grade tumors. These results showed that multimodal optical analysis is a promising technology for the development of an optical fiber setup designed for an intraoperative diagnosis of urothelial carcinoma in the area of endourology. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis

PubMed Central

Yin, Hong-Xu; Yuan, Hai-Bo; Jiang, Yong-Wen; Dong, Chun-Wang; Deng, Yu-Liang

2018-01-01

In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP) values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA) allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties. PMID:29494626

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

PubMed

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

2015-02-01

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

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

A composite sensor array impedentiometric electronic tongue Part II. Discrimination of basic tastes.

PubMed

Pioggia, G; Di Francesco, F; Marchetti, A; Ferro, M; Leardi, R; Ahluwalia, A

2007-05-15

An impedentiometric electronic tongue based on the combination of a composite sensor array and chemometric techniques aimed at the discrimination of soluble compounds able to elicit different gustative perceptions is presented. A composite array consisting of chemo-sensitive layers based on carbon nanotubes or carbon black dispersed in polymeric matrices and doped polythiophenes was used. The electrical impedance of the sensor array was measured at a frequency of 150 Hz by means of an impedance meter. The experimental set-up was designed in order to allow the automatic selection of a test solution and dipping of the sensor array following a dedicated measurement protocol. Measurements were carried out on 15 different solutions eliciting 5 different tastes (sodium chloride, citric acid, glucose, glutamic acid and sodium dehydrocholate for salty, sour, sweet, umami and bitter, respectively) at 3 concentration levels comprising the human perceptive range. In order to avoid over-fitting, more than 100 repetitions for each sample were carried in a 4-month period. Principal component analysis (PCA) was used to detect and remove outliers. Classification was performed by linear discriminant analysis (LDA). A fairly good degree of discrimination was obtained.

Vineland-II adaptive behavior profile of children with attention-deficit/hyperactivity disorder or specific learning disorders.

PubMed

Balboni, Giulia; Incognito, Oriana; Belacchi, Carmen; Bonichini, Sabrina; Cubelli, Roberto

2017-02-01

The evaluation of adaptive behavior is informative in children with attention-deficit/hyperactivity disorder (ADHD) or specific learning disorders (SLD). However, the few investigations available have focused only on the gross level of domains of adaptive behavior. To investigate which item subsets of the Vineland-II can discriminate children with ADHD or SLD from peers with typical development. Student's t-tests, ROC analysis, logistic regression, and linear discriminant function analysis were used to compare 24 children with ADHD, 61 elementary students with SLD, and controls matched on age, sex, school level attended, and both parents' education level. Several item subsets that address not only ADHD core symptoms, but also understanding in social context and development of interpersonal relationships, allowed discrimination of children with ADHD from controls. The combination of four item subsets (Listening and attending, Expressing complex ideas, Social communication, and Following instructions) classified children with ADHD with both sensitivity and specificity of 87.5%. Only Reading skills, Writing skills, and Time and dates discriminated children with SLD from controls. Evaluation of Vineland-II scores at the level of item content categories is a useful procedure for an efficient clinical description. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

PubMed

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

2017-05-08

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

Accurate palm vein recognition based on wavelet scattering and spectral regression kernel discriminant analysis

NASA Astrophysics Data System (ADS)

Elnasir, Selma; Shamsuddin, Siti Mariyam; Farokhi, Sajad

2015-01-01

Palm vein recognition (PVR) is a promising new biometric that has been applied successfully as a method of access control by many organizations, which has even further potential in the field of forensics. The palm vein pattern has highly discriminative features that are difficult to forge because of its subcutaneous position in the palm. Despite considerable progress and a few practical issues, providing accurate palm vein readings has remained an unsolved issue in biometrics. We propose a robust and more accurate PVR method based on the combination of wavelet scattering (WS) with spectral regression kernel discriminant analysis (SRKDA). As the dimension of WS generated features is quite large, SRKDA is required to reduce the extracted features to enhance the discrimination. The results based on two public databases-PolyU Hyper Spectral Palmprint public database and PolyU Multi Spectral Palmprint-show the high performance of the proposed scheme in comparison with state-of-the-art methods. The proposed approach scored a 99.44% identification rate and a 99.90% verification rate [equal error rate (EER)=0.1%] for the hyperspectral database and a 99.97% identification rate and a 99.98% verification rate (EER=0.019%) for the multispectral database.

Detection of Lipitor counterfeits: a comparison of NIR and Raman spectroscopy in combination with chemometrics.

PubMed

de Peinder, P; Vredenbregt, M J; Visser, T; de Kaste, D

2008-08-05

Research has been carried on the feasibility of near infrared (NIR) and Raman spectroscopy as rapid screening methods to discriminate between genuine and counterfeits of the cholesterol-lowering medicine Lipitor. Classification, based on partial least squares discriminant analysis (PLS-DA) models, appears to be successful for both spectroscopic techniques, irrespective of whether atorvastatine or lovastatine has been used as the active pharmaceutical ingredient (API). The discriminative power of the NIR model, in particular, largely relies on the spectral differences of the tablet matrix. This is due to the relative large sample volume that is probed with NIR and the strong spectroscopic activity of the excipients. PLS-DA models based on NIR or Raman spectra can also be applied to distinguish between atorvastatine and lovastatine as the API used in the counterfeits tested in this study. A disadvantage of Raman microscopy for this type of analysis is that it is primarily a surface technique. As a consequence spectra of the coating and the tablet core might differ. Besides, spectra may change with the position of the laser in case the sample is inhomogeneous. However, the robustness of the PLS-DA models turned out to be sufficiently large to allow a reliable discrimination. Principal component analysis (PCA) of the spectra revealed that the conditions, at which tablets have been stored, affect the NIR data. This effect is attributed to the adsorption of water from the atmosphere after unpacking from the blister. It implies that storage conditions should be taken into account when the NIR technique is used for discriminating purposes. However, in this study both models based on NIR spectra and Raman data enabled reliable discrimination between genuine and counterfeited Lipitor tablets, regardless of their storage conditions.

Predicting nature-based tourist roles: a life span perspective

Treesearch

James J. Murdy; Heather J. Gibson; Andrew Yiannakis

2003-01-01

The concept of stable, clearly identifiable patterns of tourist behavior, or roles, is a relatively recent development. Yiannakis and Gibson (1988, 1992) identified fifteen tourist roles based on leisure travelers' vacation behaviors. Building on this work, Gibson (1994) used discriminant analysis to determine the combination of needs and demographics are...

Forensic analysis of tire rubbers based on their sulfur chemical states.

PubMed

Funatsuki, Atsushi; Shiota, Kenji; Takaoka, Masaki; Tamenori, Yusuke

2015-05-01

The chemical states of sulfur in 11 tires were analyzed using X-ray absorption near-edge structure (XANES) in order to discriminate between various tire rubbers. All tires had peaks around 2471.5 and 2480.5eV, and the shapes and heights of these peaks differed among tires, suggesting that the sulfur chemical state could be used for discrimination between tire rubbers. Based on t-tests on the results of XANES, 43 of 55 combinations were different at a significance level of 5%. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

The Discriminant Value of Phase-Dependent Local Dynamic Stability of Daily Life Walking in Older Adult Community-Dwelling Fallers and Nonfallers

PubMed Central

Ihlen, Espen A. F.; Weiss, Aner; Helbostad, Jorunn L.; Hausdorff, Jeffrey M.

2015-01-01

The present study compares phase-dependent measures of local dynamic stability of daily life walking with 35 conventional gait features in their ability to discriminate between community-dwelling older fallers and nonfallers. The study reanalyzes 3D-acceleration data of 3-day daily life activity from 39 older people who reported less than 2 falls during one year and 31 who reported two or more falls. Phase-dependent local dynamic stability was defined for initial perturbation at 0%, 20%, 40%, 60%, and 80% of the step cycle. A partial least square discriminant analysis (PLS-DA) was used to compare the discriminant abilities of phase-dependent local dynamic stability with the discriminant abilities of 35 conventional gait features. The phase-dependent local dynamic stability λ at 0% and 60% of the step cycle discriminated well between fallers and nonfallers (AUC = 0.83) and was significantly larger (p < 0.01) for the nonfallers. Furthermore, phase-dependent λ discriminated as well between fallers and nonfallers as all other gait features combined. The present result suggests that phase-dependent measures of local dynamic stability of daily life walking might be of importance for further development in early fall risk screening tools. PMID:26491669

PERCEIVED DISCRIMINATION AND PSYCHOLOGICAL WELL-BEING: THE MEDIATING AND MODERATING ROLE OF SENSE OF CONTROL*

PubMed Central

Jang, Yuri; Chiriboga, David A.; Small, Brent J.

2010-01-01

Being discriminated against is an unpleasant and stressful experience, and its connection to reduced psychological well-being is well-documented. The present study hypothesized that a sense of control would serve as both mediator and moderator in the dynamics of perceived discrimination and psychological well-being. In addition, variations by age, gender, and race in the effects of perceived discrimination were explored. Data from the Midlife Development in the United States (MIDUS) survey (N = 1,554; age range = 45 to 74) provided supportive evidence for the hypotheses. The relationships between perceived discrimination and positive and negative affect were reduced when sense of control was controlled, demonstrating the role of sense of control as a mediator. The moderating role of sense of control was also supported, but only in the analysis for negative affect: the combination of a discriminatory experience and low sense of control markedly increased negative affect. In addition, age and gender variations were observed: the negative impact of perceived discrimination on psychological well-being was more pronounced among younger adults and females compared to their counterparts. The findings elucidated the mechanisms by which perceived discrimination manifested its psychological outcomes, and suggest ways to reduce adverse consequences associated with discriminatory experiences. PMID:18459602

Perceived discrimination and psychological well-being: the mediating and moderating role of sense of control.

PubMed

Jang, Yuri; Chiriboga, David A; Small, Brent J

2008-01-01

Being discriminated against is an unpleasant and stressful experience, and its connection to reduced psychological well-being is well-documented. The present study hypothesized that a sense of control would serve as both mediator and moderator in the dynamics of perceived discrimination and psychological well-being. In addition, variations by age, gender, and race in the effects of perceived discrimination were explored. Data from the Midlife Development in the United States (MIDUS) survey (N=1554; age range = 45 to 74) provided supportive evidence for the hypotheses. The relationships between perceived discrimination and positive and negative affect were reduced when sense of control was controlled, demonstrating the role of sense of control as a mediator. The moderating role of sense of control was also supported, but only in the analysis for negative affect: the combination of a discriminatory experience and low sense of control markedly increased negative affect. In addition, age and gender variations were observed: the negative impact of perceived discrimination on psychological well-being was more pronounced among younger adults and females compared to their counterparts. The findings elucidated the mechanisms by which perceived discrimination manifested its psychological outcomes, and suggest ways to reduce adverse consequences associated with discriminatory experiences.

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

PubMed

Yang, Jun-Ho; Yoh, Jack J

2018-01-01

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

Separate and combined effects of gabapentin and Δ9-THC in humans discriminating Δ9-THC

PubMed Central

Lile, Joshua A.; Wesley, Michael J.; Kelly, Thomas H.; Hays, Lon R.

2015-01-01

The aim of the present study was to examine a potential mechanism of action of gabapentin to manage cannabis-use disorders by determining the interoceptive effects of gabapentin in cannabis users discriminating Δ9-THC using a pharmacologically selective drug-discrimination procedure. Eight cannabis users learned to discriminate 30 mg oral Δ9-THC from placebo and then received gabapentin (600 and 1200 mg), Δ9-THC (5, 15 and 30 mg) and placebo, alone and in combination. Self-report, task performance and physiological measures were also collected. Δ9-THC served as a discriminative stimulus, produced positive subjective effects, elevated heart rate and impaired psychomotor performance. Both doses of gabapentin substituted for the Δ9-THC discriminative stimulus and engendered subjective and performance-impairing effects that overlapped with those of Δ9-THC when administered alone. When administered concurrently, gabapentin shifted the discriminative-stimulus effects of Δ9-THC leftward/upward, and combinations of Δ9-THC and gabapentin generally produced larger effects on cannabinoid-sensitive outcomes relative to Δ9-THC alone. These results suggest that one mechanism by which gabapentin might facilitate cannabis abstinence is by producing effects that overlap with those of cannabinoids. PMID:26313650

Rapid discrimination of sea buckthorn berries from different H. rhamnoides subspecies by multi-step IR spectroscopy coupled with multivariate data analysis

NASA Astrophysics Data System (ADS)

Liu, Yue; Zhang, Ying; Zhang, Jing; Fan, Gang; Tu, Ya; Sun, Suqin; Shen, Xudong; Li, Qingzhu; Zhang, Yi

2018-03-01

As an important ethnic medicine, sea buckthorn was widely used to prevent and treat various diseases due to its nutritional and medicinal properties. According to the Chinese Pharmacopoeia, sea buckthorn was originated from H. rhamnoides, which includes five subspecies distributed in China. Confusion and misidentification usually occurred due to their similar morphology, especially in dried and powdered forms. Additionally, these five subspecies have vital differences in quality and physiological efficacy. This paper focused on the quick classification and identification method of sea buckthorn berry powders from five H. rhamnoides subspecies using multi-step IR spectroscopy coupled with multivariate data analysis. The holistic chemical compositions revealed by the FT-IR spectra demonstrated that flavonoids, fatty acids and sugars were the main chemical components. Further, the differences in FT-IR spectra regarding their peaks, positions and intensities were used to identify H. rhamnoides subspecies samples. The discrimination was achieved using principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). The results showed that the combination of multi-step IR spectroscopy and chemometric analysis offered a simple, fast and reliable method for the classification and identification of the sea buckthorn berry powders from different H. rhamnoides subspecies.

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

PubMed

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

2014-11-01

Plastic packaging wastes increased considerably in recent decades, raising a major and serious public concern on political, economical and environmental levels. Dealing with this kind of problems is generally done by landfilling and energy recovery. However, these two methods are becoming more and more expensive, hazardous to the public health and the environment. Therefore, recycling is gaining worldwide consideration as a solution to decrease the growing volume of plastic packaging wastes and simultaneously reduce the consumption of oil required to produce virgin resin. Nevertheless, a major shortage is encountered in recycling which is related to the sorting of plastic wastes. In this paper, a feasibility study was performed in order to test the potential of an innovative approach combining mid infrared (MIR) spectroscopy with independent components analysis (ICA), as a simple and fast approach which could achieve high separation rates. This approach (MIR-ICA) gave 100% discrimination rates in the separation of all studied plastics: polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), polystyrene (PS) and polylactide (PLA). In addition, some more specific discriminations were obtained separating plastic materials belonging to the same polymer family e.g. high density polyethylene (HDPE) from low density polyethylene (LDPE). High discrimination rates were obtained despite the heterogeneity among samples especially differences in colors, thicknesses and surface textures. The reproducibility of the proposed approach was also tested using two spectrometers with considerable differences in their sensitivities. Discrimination rates were not affected proving that the developed approach could be extrapolated to different spectrometers. MIR combined with ICA is a promising tool for plastic waste separation that can help improve performance in this field; however further technological improvements and developments are required before it can be applied at an industrial level given that all tests presented here were performed under laboratory conditions. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

PubMed

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

2017-07-15

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

Combined Raman and autofluorescence ex vivo diagnostics of skin cancer in near-infrared and visible regions

NASA Astrophysics Data System (ADS)

Bratchenko, Ivan A.; Artemyev, Dmitry N.; Myakinin, Oleg O.; Khristoforova, Yulia A.; Moryatov, Alexander A.; Kozlov, Sergey V.; Zakharov, Valery P.

2017-02-01

The differentiation of skin melanomas and basal cell carcinomas (BCCs) was demonstrated based on combined analysis of Raman and autofluorescence spectra stimulated by visible and NIR lasers. It was ex vivo tested on 39 melanomas and 40 BCCs. Six spectroscopic criteria utilizing information about alteration of melanin, porphyrins, flavins, lipids, and collagen content in tumor with a comparison to healthy skin were proposed. The measured correlation between the proposed criteria makes it possible to define weakly correlated criteria groups for discriminant analysis and principal components analysis application. It was shown that the accuracy of cancerous tissues classification reaches 97.3% for a combined 6-criteria multimodal algorithm, while the accuracy determined separately for each modality does not exceed 79%. The combined 6-D method is a rapid and reliable tool for malignant skin detection and classification.

Discriminative analysis of lip motion features for speaker identification and speech-reading.

PubMed

Cetingül, H Ertan; Yemez, Yücel; Erzin, Engin; Tekalp, A Murat

2006-10-01

There have been several studies that jointly use audio, lip intensity, and lip geometry information for speaker identification and speech-reading applications. This paper proposes using explicit lip motion information, instead of or in addition to lip intensity and/or geometry information, for speaker identification and speech-reading within a unified feature selection and discrimination analysis framework, and addresses two important issues: 1) Is using explicit lip motion information useful, and, 2) if so, what are the best lip motion features for these two applications? The best lip motion features for speaker identification are considered to be those that result in the highest discrimination of individual speakers in a population, whereas for speech-reading, the best features are those providing the highest phoneme/word/phrase recognition rate. Several lip motion feature candidates have been considered including dense motion features within a bounding box about the lip, lip contour motion features, and combination of these with lip shape features. Furthermore, a novel two-stage, spatial, and temporal discrimination analysis is introduced to select the best lip motion features for speaker identification and speech-reading applications. Experimental results using an hidden-Markov-model-based recognition system indicate that using explicit lip motion information provides additional performance gains in both applications, and lip motion features prove more valuable in the case of speech-reading application.

Combining Raman spectroscopy and digital holographic microscopy for label-free classification of human immune cells (Conference Presentation)

NASA Astrophysics Data System (ADS)

McReynolds, Naomi; Cooke, Fiona G. M.; Chen, Mingzhou; Powis, Simon J.; Dholakia, Kishan

2017-02-01

Moving towards label-free techniques for cell identification is essential for many clinical and research applications. Raman spectroscopy and digital holographic microscopy (DHM) are both label-free, non-destructive optical techniques capable of providing complimentary information. We demonstrate a multi-modal system which may simultaneously take Raman spectra and DHM images to provide both a molecular and a morphological description of our sample. In this study we use Raman spectroscopy and DHM to discriminate between three immune cell populations CD4+ T cells, B cells, and monocytes, which together comprise key functional immune cell subsets in immune responses to invading pathogens. Various parameters that may be used to describe the phase images are also examined such as pixel value histograms or texture analysis. Using our system it is possible to consider each technique individually or in combination. Principal component analysis is used on the data set to discriminate between cell types and leave-one-out cross-validation is used to estimate the efficiency of our method. Raman spectroscopy provides specific chemical information but requires relatively long acquisition times, combining this with a faster modality such as DHM could help achieve faster throughput rates. The combination of these two complimentary optical techniques provides a wealth of information for cell characterisation which is a step towards achieving label free technology for the identification of human immune cells.

Discriminative and Reinforcing Stimulus Effects of Nicotine, Cocaine, and Cocaine + Nicotine Combinations in Rhesus Monkeys

PubMed Central

Mello, Nancy K.; Newman, Jennifer L.

2011-01-01

Concurrent cigarette smoking and cocaine use is well documented. However, the behavioral pharmacology of cocaine and nicotine combinations is poorly understood, and there is a need for animal models to examine this form of polydrug abuse. The purpose of this study was two-fold: first to assess the effects of nicotine on the discriminative stimulus effects of cocaine, and second, to study self-administration of nicotine/cocaine combinations in a novel polydrug abuse model. In drug discrimination experiments, nicotine increased the discriminative stimulus effects of low cocaine doses in two of three monkeys, but nicotine did not substitute for cocaine in any monkey. Self-administration of cocaine and nicotine alone, and cocaine + nicotine combinations was studied under a second-order fixed ratio 2, variable ratio 16 (FR2[VR16:S]) schedule of reinforcement. Cocaine and nicotine alone were self-administered in a dose-dependent manner. The combination of marginally reinforcing doses of cocaine and nicotine increased drug self-administration behavior above levels observed with the same dose of either cocaine or nicotine alone. These findings indicate that nicotine may increase cocaine’s discriminative stimulus and reinforcing effects in rhesus monkeys, and illustrate the feasibility of combining cocaine and nicotine in a preclinical model of polydrug abuse. Further studies of the behavioral effects of nicotine + cocaine combinations will contribute to our understanding the pharmacology of dual nicotine and cocaine dependence, and will be useful for evaluation of new treatment medications. PMID:21480727

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.

Identification and DUS Testing of Rice Varieties through Microsatellite Markers

PubMed Central

Pourabed, Ehsan; Jazayeri Noushabadi, Mohammad Reza; Jamali, Seyed Hossein; Moheb Alipour, Naser; Zareyan, Abbas; Sadeghi, Leila

2015-01-01

Identification and registration of new rice varieties are very important to be free from environmental effects and using molecular markers that are more reliable. The objectives of this study were, first, the identification and distinction of 40 rice varieties consisting of local varieties of Iran, improved varieties, and IRRI varieties using PIC, and discriminating power, second, cluster analysis based on Dice similarity coefficient and UPGMA algorithm, and, third, determining the ability of microsatellite markers to separate varieties utilizing the best combination of markers. For this research, 12 microsatellite markers were used. In total, 83 polymorphic alleles (6.91 alleles per locus) were found. In addition, the variation of PIC was calculated from 0.52 to 0.9. The results of cluster analysis showed the complete discrimination of varieties from each other except for IR58025A and IR58025B. Moreover, cluster analysis could detect the most of the improved varieties from local varieties. Based on the best combination of markers analysis, five pair primers together have shown the same results of all markers for detection among all varieties. Considering the results of this research, we can propose that microsatellite markers can be used as a complementary tool for morphological characteristics in DUS tests. PMID:25755666

Ultra-high-performance liquid chromatography/tandem high-resolution mass spectrometry analysis of sixteen red beverages containing carminic acid: identification of degradation products by using principal component analysis/discriminant analysis.

PubMed

Gosetti, Fabio; Chiuminatto, Ugo; Mazzucco, Eleonora; Mastroianni, Rita; Marengo, Emilio

2015-01-15

The study investigates the sunlight photodegradation process of carminic acid, a natural red colourant used in beverages. For this purpose, both carminic acid aqueous standard solutions and sixteen different commercial beverages, ten containing carminic acid and six containing E120 dye, were subjected to photoirradiation. The results show different patterns of degradation, not only between the standard solutions and the beverages, but also from beverage to beverage. Due to the different beverage recipes, unpredictable reactions take place between the dye and the other ingredients. To identify the dye degradation products in a very complex scenario, a methodology was used, based on the combined use of principal component analysis with discriminant analysis and ultra-high-performance liquid chromatography coupled with tandem high resolution mass spectrometry. The methodology is unaffected by beverage composition and allows the degradation products of carminic acid dye to be identified for each beverage. Copyright © 2014 Elsevier Ltd. All rights reserved.

Comparison of three-dimensional optical coherence tomography and combining a rotating Scheimpflug camera with a Placido topography system for forme fruste keratoconus diagnosis.

PubMed

Fukuda, Shinichi; Beheregaray, Simone; Hoshi, Sujin; Yamanari, Masahiro; Lim, Yiheng; Hiraoka, Takahiro; Yasuno, Yoshiaki; Oshika, Tetsuro

2013-12-01

To evaluate the ability of parameters measured by three-dimensional (3D) corneal and anterior segment optical coherence tomography (CAS-OCT) and a rotating Scheimpflug camera combined with a Placido topography system (Scheimpflug camera with topography) to discriminate between normal eyes and forme fruste keratoconus. Forty-eight eyes of 48 patients with keratoconus, 25 eyes of 25 patients with forme fruste keratoconus and 128 eyes of 128 normal subjects were evaluated. Anterior and posterior keratometric parameters (steep K, flat K, average K), elevation, topographic parameters, regular and irregular astigmatism (spherical, asymmetry, regular and higher-order astigmatism) and five pachymetric parameters (minimum, minimum-median, inferior-superior, inferotemporal-superonasal, vertical thinnest location of the cornea) were measured using 3D CAS-OCT and a Scheimpflug camera with topography. The area under the receiver operating curve (AUROC) was calculated to assess the discrimination ability. Compatibility and repeatability of both devices were evaluated. Posterior surface elevation showed higher AUROC values in discrimination analysis of forme fruste keratoconus using both devices. Both instruments showed significant linear correlations (p<0.05, Pearson's correlation coefficient) and good repeatability (ICCs: 0.885-0.999) for normal and forme fruste keratoconus. Posterior elevation was the best discrimination parameter for forme fruste keratoconus. Both instruments presented good correlation and repeatability for this condition.

The use of δ(2)H and δ(18)O isotopic analyses combined with chemometrics as a traceability tool for the geographical origin of bell peppers.

PubMed

de Rijke, E; Schoorl, J C; Cerli, C; Vonhof, H B; Verdegaal, S J A; Vivó-Truyols, G; Lopatka, M; Dekter, R; Bakker, D; Sjerps, M J; Ebskamp, M; de Koster, C G

2016-08-01

Two approaches were investigated to discriminate between bell peppers of different geographic origins. Firstly, δ(18)O fruit water and corresponding source water were analyzed and correlated to the regional GNIP (Global Network of Isotopes in Precipitation) values. The water and GNIP data showed good correlation with the pepper data, with constant isotope fractionation of about -4. Secondly, compound-specific stable hydrogen isotope data was used for classification. Using n-alkane fingerprinting data, both linear discriminant analysis (LDA) and a likelihood-based classification, using the kernel-density smoothed data, were developed to discriminate between peppers from different origins. Both methods were evaluated using the δ(2)H values and n-alkanes relative composition as variables. Misclassification rates were calculated using a Monte-Carlo 5-fold cross-validation procedure. Comparable overall classification performance was achieved, however, the two methods showed sensitivity to different samples. The combined values of δ(2)H IRMS, and complimentary information regarding the relative abundance of four main alkanes in bell pepper fruit water, has proven effective for geographic origin discrimination. Evaluation of the rarity of observing particular ranges for these characteristics could be used to make quantitative assertions regarding geographic origin of bell peppers and, therefore, have a role in verifying compliance with labeling of geographical origin. Copyright © 2016 Elsevier Ltd. All rights reserved.

Distinguishing fibromyalgia from rheumatoid arthritis and systemic lupus in clinical questionnaires: an analysis of the revised Fibromyalgia Impact Questionnaire (FIQR) and its variant, the Symptom Impact Questionnaire (SIQR), along with pain locations

PubMed Central

2011-01-01

Introduction The purpose of this study was to explore a data set of patients with fibromyalgia (FM), rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) who completed the Revised Fibromyalgia Impact Questionnaire (FIQR) and its variant, the Symptom Impact Questionnaire (SIQR), for discriminating features that could be used to differentiate FM from RA and SLE in clinical surveys. Methods The frequency and means of comparing FM, RA and SLE patients on all pain sites and SIQR variables were calculated. Multiple regression analysis was then conducted to identify the significant pain sites and SIQR predictors of group membership. Thereafter stepwise multiple regression analysis was performed to identify the order of variables in predicting their maximal statistical contribution to group membership. Partial correlations assessed their unique contribution, and, last, two-group discriminant analysis provided a classification table. Results The data set contained information on the SIQR and also pain locations in 202 FM, 31 RA and 20 SLE patients. As the SIQR and pain locations did not differ much between the RA and SLE patients, they were grouped together (RA/SLE) to provide a more robust analysis. The combination of eight SIQR items and seven pain sites correctly classified 99% of FM and 90% of RA/SLE patients in a two-group discriminant analysis. The largest reported SIQR differences (FM minus RA/SLE) were seen for the parameters "tenderness to touch," "difficulty cleaning floors" and "discomfort on sitting for 45 minutes." Combining the SIQR and pain locations in a stepwise multiple regression analysis revealed that the seven most important predictors of group membership were mid-lower back pain (29%; 79% vs. 16%), tenderness to touch (11.5%; 6.86 vs. 3.02), neck pain (6.8%; 91% vs. 39%), hand pain (5%; 64% vs. 77%), arm pain (3%; 69% vs. 18%), outer lower back pain (1.7%; 80% vs. 22%) and sitting for 45 minutes (1.4%; 5.56 vs. 1.49). Conclusions A combination of two SIQR questions ("tenderness to touch" and "difficulty sitting for 45 minutes") plus pain in the lower back, neck, hands and arms may be useful in the construction of clinical questionnaires designed for patients with musculoskeletal pain. This combination provided the correct diagnosis in 97% of patients, with only 7 of 253 patients misclassified. PMID:21477308

Differentiation of whole grain and refined wheat (T. aestivum) flour using a fuzzy mass spectrometric fingerprinting and chemometric approaches

USDA-ARS?s Scientific Manuscript database

A fuzzy mass spectrometric (MS) fingerprinting method combined with chemometric analysis was established to provide rapid discrimination between whole grain and refined wheat flour. Twenty one samples, including thirteen samples from three cultivars and eight from local grocery store, were studied....

Interpretation of sedimentological processes of coarse-grained deposits applying a novel combined cluster and discriminant analysis

NASA Astrophysics Data System (ADS)

Farics, Éva; Farics, Dávid; Kovács, József; Haas, János

2017-10-01

The main aim of this paper is to determine the depositional environments of an Upper-Eocene coarse-grained clastic succession in the Buda Hills, Hungary. First of all, we measured some commonly used parameters of samples (size, amount, roundness and sphericity) in a much more objective overall and faster way than with traditional measurement approaches, using the newly developed Rock Analyst application. For the multivariate data obtained, we applied Combined Cluster and Discriminant Analysis (CCDA) in order to determine homogeneous groups of the sampling locations based on the quantitative composition of the conglomerate as well as the shape parameters (roundness and sphericity). The result is the spatial pattern of these groups, which assists with the interpretation of the depositional processes. According to our concept, those sampling sites which belong to the same homogeneous groups were likely formed under similar geological circumstances and by similar geological processes. In the Buda Hills, we were able to distinguish various sedimentological environments within the area based on the results: fan, intermittent stream or marine.

Temporal Lobe and “Default” Hemodynamic Brain Modes Discriminate Between Schizophrenia and Bipolar Disorder

PubMed Central

Calhoun, Vince D.; Maciejewski, Paul K.; Pearlson, Godfrey D.; Kiehl, Kent A.

2009-01-01

Schizophrenia and bipolar disorder are currently diagnosed on the basis of psychiatric symptoms and longitudinal course. The determination of a reliable, biologically-based diagnostic indicator of these diseases (a biomarker) could provide the groundwork for developing more rigorous tools for differential diagnosis and treatment assignment. Recently, methods have been used to identify distinct sets of brain regions or “spatial modes” exhibiting temporally coherent brain activity. Using functional magnetic resonance imaging (fMRI) data and a multivariate analysis method, independent component analysis, we combined the temporal lobe and the default modes to discriminate subjects with bipolar disorder, chronic schizophrenia, and healthy controls. Temporal lobe and default mode networks were reliably identified in all participants. Classification results on an independent set of individuals revealed an average sensitivity and specificity of 90 and 95%, respectively. The use of coherent brain networks such as the temporal lobe and default mode networks may provide a more reliable measure of disease state than task-correlated fMRI activity. A combination of two such hemodynamic brain networks shows promise as a biomarker for schizophrenia and bipolar disorder. PMID:17894392

Temporal lobe and "default" hemodynamic brain modes discriminate between schizophrenia and bipolar disorder.

PubMed

Calhoun, Vince D; Maciejewski, Paul K; Pearlson, Godfrey D; Kiehl, Kent A

2008-11-01

Schizophrenia and bipolar disorder are currently diagnosed on the basis of psychiatric symptoms and longitudinal course. The determination of a reliable, biologically-based diagnostic indicator of these diseases (a biomarker) could provide the groundwork for developing more rigorous tools for differential diagnosis and treatment assignment. Recently, methods have been used to identify distinct sets of brain regions or "spatial modes" exhibiting temporally coherent brain activity. Using functional magnetic resonance imaging (fMRI) data and a multivariate analysis method, independent component analysis, we combined the temporal lobe and the default modes to discriminate subjects with bipolar disorder, chronic schizophrenia, and healthy controls. Temporal lobe and default mode networks were reliably identified in all participants. Classification results on an independent set of individuals revealed an average sensitivity and specificity of 90 and 95%, respectively. The use of coherent brain networks such as the temporal lobe and default mode networks may provide a more reliable measure of disease state than task-correlated fMRI activity. A combination of two such hemodynamic brain networks shows promise as a biomarker for schizophrenia and bipolar disorder.

Information on co-morbidities collected by history is useful for assigning Otitis Media risk to children.

PubMed

Casselbrant, Margaretha L; Mandel, Ellen M; Doyle, William J

2016-06-01

Determine if a 2-Step multivariate analysis of historical symptom/sign data for comorbid diseases can abstract high-level constructs useful in assigning a child's "risk" for different Otitis Media expressions. Seventeen items related to the symptom/sign expression of hypothesized Otitis Media comorbidities were collected by history on 141 3-year-old children. Using established criteria, the children were assigned to 1 of 3 groups: Control (no significant past Otitis Media, n=45), Chronic Otitis Media with Effusion (n=45) and Recurrent Acute Otitis Media (n=51). Principal Component Analysis was used to identify factors representing the non-redundant shared information among related items and Discriminant Analysis operating on those factors was used to estimate the best predictor equation for pairwise group assignments. Six multivariate factors representing the assignable comorbidities of frequent colds, nasal allergy, gastroesophageal disease (specific and general), nasal congestion and asthma were identified and explained 81% of the variance in the 17 items. Discriminant Analysis showed that, for the Control-Chronic Otitis Media with Effusion comparison, a combination of 3 factors and, for the Control-Recurrent Acute Otitis Media comparison, a combination of 2 factors had assignment accuracies of 74% and 68%, respectively. For the contrast between the two disease expressions, a 2-factor combination had an assignment accuracy of 61%. These results show that this analytic methodology can abstract high-level constructs, comorbidities, from low-level data, symptom/sign scores, support a linkage between certain comorbidities and Otitis Media risk and suggest that specific comorbidity combinations contain information relevant to assigning the risk for different Otitis Media expressions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Identification of anisodamine tablets by Raman and near-infrared spectroscopy with chemometrics.

PubMed

Li, Lian; Zang, Hengchang; Li, Jun; Chen, Dejun; Li, Tao; Wang, Fengshan

2014-06-05

Vibrational spectroscopy including Raman and near-infrared (NIR) spectroscopy has become an attractive tool for pharmaceutical analysis. In this study, effective calibration models for the identification of anisodamine tablet and its counterfeit and the distinguishment of manufacturing plants, based on Raman and NIR spectroscopy, were built, respectively. Anisodamine counterfeit tablets were identified by Raman spectroscopy with correlation coefficient method, and the results showed that the predictive accuracy was 100%. The genuine anisodamine tablets from 5 different manufacturing plants were distinguished by NIR spectroscopy using partial least squares discriminant analysis (PLS-DA) models based on interval principal component analysis (iPCA) method. And the results showed the recognition rate and rejection rate were 100% respectively. In conclusion, Raman spectroscopy and NIR spectroscopy combined with chemometrics are feasible and potential tools for rapid pharmaceutical tablet discrimination. Copyright © 2014 Elsevier B.V. All rights reserved.

Surface-enhanced Raman spectra of hemoglobin for esophageal cancer diagnosis

NASA Astrophysics Data System (ADS)

Zhou, Xue; Diao, Zhenqi; Fan, Chunzhen; Guo, Huiqiang; Xiong, Yang; Tang, Weiyue

2014-03-01

Surface-enhanced Raman scattering (SERS) spectra of hemoglobin from 30 esophageal cancer patients and 30 healthy persons have been detected and analyzed. The results indicate that, there are more iron ions in low spin state and less in high for the hemoglobin of esophageal cancer patients than normal persons, which is consistent with the fact that it is easier to hemolyze for the blood of cancer patients. By using principal component analysis (PCA) and discriminate analysis, we can get a three-dimensional scatter plot of PC scores from the SERS spectra of healthy persons and cancer patients, from which the two groups can be discriminated. The total accuracy of this method is 90%, while the diagnostic specificity is 93.3% and sensitivity is 86.7%. Thus SERS spectra of hemoglobin analysis combined with PCA may be a new technique for the early diagnose of esophageal cancer.

The use of multicomponent statistical analysis in hydrogeological environmental research.

PubMed

Lambrakis, Nicolaos; Antonakos, Andreas; Panagopoulos, George

2004-04-01

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

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

PubMed

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

2017-06-20

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

Botanical discrimination of Greek unifloral honeys with physico-chemical and chemometric analyses.

PubMed

Karabagias, Ioannis K; Badeka, Anastasia V; Kontakos, Stavros; Karabournioti, Sofia; Kontominas, Michael G

2014-12-15

The aim of the present study was to investigate the possibility of characterisation and classification of Greek unifloral honeys (pine, thyme, fir and orange blossom) according to botanical origin using volatile compounds, conventional physico-chemical parameters and chemometric analyses (MANOVA and Linear Discriminant Analysis). For this purpose, 119 honey samples were collected during the harvesting period 2011 from 14 different regions in Greece known to produce unifloral honey of good quality. Physico-chemical analysis included the identification and semi quantification of fifty five volatile compounds performed by Headspace Solid Phase Microextraction coupled to gas chromatography/mass spectroscopy and the determination of conventional quality parameters such as pH, free, lactonic, total acidity, electrical conductivity, moisture, ash, lactonic/free acidity ratio and colour parameters L, a, b. Results showed that using 40 diverse variables (30 volatile compounds of different classes and 10 physico-chemical parameters) the honey samples were satisfactorily classified according to botanical origin using volatile compounds (84.0% correct prediction), physicochemical parameters (97.5% correct prediction), and the combination of both (95.8% correct prediction) indicating that multi element analysis comprises a powerful tool for honey discrimination purposes. Copyright © 2014 Elsevier Ltd. All rights reserved.

An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques

PubMed Central

Sui, Jing; Adali, Tülay; Pearlson, Godfrey D.; Calhoun, Vince D.

2013-01-01

Extraction of relevant features from multitask functional MRI (fMRI) data in order to identify potential biomarkers for disease, is an attractive goal. In this paper, we introduce a novel feature-based framework, which is sensitive and accurate in detecting group differences (e.g. controls vs. patients) by proposing three key ideas. First, we integrate two goal-directed techniques: coefficient-constrained independent component analysis (CC-ICA) and principal component analysis with reference (PCA-R), both of which improve sensitivity to group differences. Secondly, an automated artifact-removal method is developed for selecting components of interest derived from CC-ICA, with an average accuracy of 91%. Finally, we propose a strategy for optimal feature/component selection, aiming to identify optimal group-discriminative brain networks as well as the tasks within which these circuits are engaged. The group-discriminating performance is evaluated on 15 fMRI feature combinations (5 single features and 10 joint features) collected from 28 healthy control subjects and 25 schizophrenia patients. Results show that a feature from a sensorimotor task and a joint feature from a Sternberg working memory (probe) task and an auditory oddball (target) task are the top two feature combinations distinguishing groups. We identified three optimal features that best separate patients from controls, including brain networks consisting of temporal lobe, default mode and occipital lobe circuits, which when grouped together provide improved capability in classifying group membership. The proposed framework provides a general approach for selecting optimal brain networks which may serve as potential biomarkers of several brain diseases and thus has wide applicability in the neuroimaging research community. PMID:19457398

Aging time and brand determination of pasteurized milk using a multisensor e-nose combined with a voltammetric e-tongue.

PubMed

Bougrini, Madiha; Tahri, Khalid; Haddi, Zouhair; El Bari, Nezha; Llobet, Eduard; Jaffrezic-Renault, Nicole; Bouchikhi, Benachir

2014-12-01

A combined approach based on a multisensor system to get additional chemical information from liquid samples through the analysis of the solution and its headspace is illustrated and commented. In the present work, innovative analytical techniques, such as a hybrid e-nose and a voltammetric e-tongue were elaborated to differentiate between different pasteurized milk brands and for the exact recognition of their storage days through the data fusion technique of the combined system. The Principal Component Analysis (PCA) has shown an acceptable discrimination of the pasteurized milk brands on the first day of storage, when the two instruments were used independently. Contrariwise, PCA indicated that no clear storage day's discrimination can be drawn when the two instruments are applied separately. Mid-level of abstraction data fusion approach has demonstrated that results obtained by the data fusion approach outperformed the classification results of the e-nose and e-tongue taken individually. Furthermore, the Support Vector Machine (SVM) supervised method was applied to the new subset and confirmed that all storage days were correctly identified. This study can be generalized to several beverage and food products where their quality is based on the perception of odor and flavor. Copyright © 2014 Elsevier B.V. All rights reserved.

Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition.

PubMed

Janousova, Eva; Schwarz, Daniel; Kasparek, Tomas

2015-06-30

We investigated a combination of three classification algorithms, namely the modified maximum uncertainty linear discriminant analysis (mMLDA), the centroid method, and the average linkage, with three types of features extracted from three-dimensional T1-weighted magnetic resonance (MR) brain images, specifically MR intensities, grey matter densities, and local deformations for distinguishing 49 first episode schizophrenia male patients from 49 healthy male subjects. The feature sets were reduced using intersubject principal component analysis before classification. By combining the classifiers, we were able to obtain slightly improved results when compared with single classifiers. The best classification performance (81.6% accuracy, 75.5% sensitivity, and 87.8% specificity) was significantly better than classification by chance. We also showed that classifiers based on features calculated using more computation-intensive image preprocessing perform better; mMLDA with classification boundary calculated as weighted mean discriminative scores of the groups had improved sensitivity but similar accuracy compared to the original MLDA; reducing a number of eigenvectors during data reduction did not always lead to higher classification accuracy, since noise as well as the signal important for classification were removed. Our findings provide important information for schizophrenia research and may improve accuracy of computer-aided diagnostics of neuropsychiatric diseases. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Identification of microbes from the surfaces of food-processing lines based on the flow cytometric evaluation of cellular metabolic activity combined with cell sorting.

PubMed

Juzwa, W; Duber, A; Myszka, K; Białas, W; Czaczyk, K

2016-09-01

In this study the design of a flow cytometry-based procedure to facilitate the detection of adherent bacteria from food-processing surfaces was evaluated. The measurement of the cellular redox potential (CRP) of microbial cells was combined with cell sorting for the identification of microorganisms. The procedure enhanced live/dead cell discrimination owing to the measurement of the cell physiology. The microbial contamination of the surface of a stainless steel conveyor used to process button mushrooms was evaluated in three independent experiments. The flow cytometry procedure provided a step towards monitoring of contamination and enabled the assessment of microbial food safety hazards by the discrimination of active, mid-active and non-active bacterial sub-populations based on determination of their cellular vitality and subsequently single cell sorting to isolate microbial strains from discriminated sub-populations. There was a significant correlation (r = 0.97; p < 0.05) between the bacterial cell count estimated by the pour plate method and flow cytometry, despite there being differences in the absolute number of cells detected. The combined approach of flow cytometric CRP measurement and cell sorting allowed an in situ analysis of microbial cell vitality and the identification of species from defined sub-populations, although the identified microbes were limited to culturable cells.

Machine-learning techniques for geochemical discrimination of 2011 Tohoku tsunami deposits

PubMed Central

Kuwatani, Tatsu; Nagata, Kenji; Okada, Masato; Watanabe, Takahiro; Ogawa, Yasumasa; Komai, Takeshi; Tsuchiya, Noriyoshi

2014-01-01

Geochemical discrimination has recently been recognised as a potentially useful proxy for identifying tsunami deposits in addition to classical proxies such as sedimentological and micropalaeontological evidence. However, difficulties remain because it is unclear which elements best discriminate between tsunami and non-tsunami deposits. Herein, we propose a mathematical methodology for the geochemical discrimination of tsunami deposits using machine-learning techniques. The proposed method can determine the appropriate combinations of elements and the precise discrimination plane that best discerns tsunami deposits from non-tsunami deposits in high-dimensional compositional space through the use of data sets of bulk composition that have been categorised as tsunami or non-tsunami sediments. We applied this method to the 2011 Tohoku tsunami and to background marine sedimentary rocks. After an exhaustive search of all 262,144 (= 218) combinations of the 18 analysed elements, we observed several tens of combinations with discrimination rates higher than 99.0%. The analytical results show that elements such as Ca and several heavy-metal elements are important for discriminating tsunami deposits from marine sedimentary rocks. These elements are considered to reflect the formation mechanism and origin of the tsunami deposits. The proposed methodology has the potential to aid in the identification of past tsunamis by using other tsunami proxies. PMID:25399750

Predicting stress urinary incontinence during pregnancy: combination of pelvic floor ultrasound parameters and clinical factors.

PubMed

Chen, Ling; Luo, Dan; Yu, Xiajuan; Jin, Mei; Cai, Wenzhi

2018-05-12

The aim of this study was to develop and validate a predictive tool that combining pelvic floor ultrasound parameters and clinical factors for stress urinary incontinence during pregnancy. A total of 535 women in first or second trimester were included for an interview and transperineal ultrasound assessment from two hospitals. Imaging data sets were analyzed offline to assess for bladder neck vertical position, urethra angles (α, β, and γ angles), hiatal area and bladder neck funneling. All significant continuous variables at univariable analysis were analyzed by receiver-operating characteristics. Three multivariable logistic models were built on clinical factor, and combined with ultrasound parameters. The final predictive model with best performance and fewest variables was selected to establish a nomogram. Internal and external validation of the nomogram were performed by both discrimination represented by C-index and calibration measured by Hosmer-Lemeshow test. A decision curve analysis was conducted to determine the clinical utility of the nomogram. After excluding 14 women with invalid data, 521 women were analyzed. β angle, γ angle and hiatal area had limited predictive value for stress urinary incontinence during pregnancy, with area under curves of 0.558-0.648. The final predictive model included body mass index gain since pregnancy, constipation, previous delivery mode, β angle at rest, and bladder neck funneling. The nomogram based on the final model showed good discrimination with a C-index of 0.789 and satisfactory calibration (P=0.828), both of which were supported by external validation. Decision curve analysis showed that the nomogram was clinical useful. The nomogram incorporating both the pelvic floor ultrasound parameters and clinical factors has been validated to show good discrimination and calibration, and could be an important tool for stress urinary incontinence risk prediction at an early stage of pregnancy. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Differential Lipid Profiles of Normal Human Brain Matter and Gliomas by Positive and Negative Mode Desorption Electrospray Ionization – Mass Spectrometry Imaging

PubMed Central

Pirro, Valentina; Hattab, Eyas M.; Cohen-Gadol, Aaron A.; Cooks, R. Graham

2016-01-01

Desorption electrospray ionization—mass spectrometry (DESI-MS) imaging was used to analyze unmodified human brain tissue sections from 39 subjects sequentially in the positive and negative ionization modes. Acquisition of both MS polarities allowed more complete analysis of the human brain tumor lipidome as some phospholipids ionize preferentially in the positive and others in the negative ion mode. Normal brain parenchyma, comprised of grey matter and white matter, was differentiated from glioma using positive and negative ion mode DESI-MS lipid profiles with the aid of principal component analysis along with linear discriminant analysis. Principal component–linear discriminant analyses of the positive mode lipid profiles was able to distinguish grey matter, white matter, and glioma with an average sensitivity of 93.2% and specificity of 96.6%, while the negative mode lipid profiles had an average sensitivity of 94.1% and specificity of 97.4%. The positive and negative mode lipid profiles provided complementary information. Principal component–linear discriminant analysis of the combined positive and negative mode lipid profiles, via data fusion, resulted in approximately the same average sensitivity (94.7%) and specificity (97.6%) of the positive and negative modes when used individually. However, they complemented each other by improving the sensitivity and specificity of all classes (grey matter, white matter, and glioma) beyond 90% when used in combination. Further principal component analysis using the fused data resulted in the subgrouping of glioma into two groups associated with grey and white matter, respectively, a separation not apparent in the principal component analysis scores plots of the separate positive and negative mode data. The interrelationship of tumor cell percentage and the lipid profiles is discussed, and how such a measure could be used to measure residual tumor at surgical margins. PMID:27658243

Quality Evaluation and Chemical Markers Screening of Salvia miltiorrhiza Bge. (Danshen) Based on HPLC Fingerprints and HPLC-MSn Coupled with Chemometrics.

PubMed

Liang, Wenyi; Chen, Wenjing; Wu, Lingfang; Li, Shi; Qi, Qi; Cui, Yaping; Liang, Linjin; Ye, Ting; Zhang, Lanzhen

2017-03-17

Danshen, the dried root of Salvia miltiorrhiza Bge., is a widely used commercially available herbal drug, and unstable quality of different samples is a current issue. This study focused on a comprehensive and systematic method combining fingerprints and chemical identification with chemometrics for discrimination and quality assessment of Danshen samples. Twenty-five samples were analyzed by HPLC-PAD and HPLC-MS n . Forty-nine components were identified and characteristic fragmentation regularities were summarized for further interpretation of bioactive components. Chemometric analysis was employed to differentiate samples and clarify the quality differences of Danshen including hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis. Consistent results were that the samples were divided into three categories which reflected the difference in quality of Danshen samples. By analyzing the reasons for sample classification, it was revealed that the processing method had a more obvious impact on sample classification than the geographical origin, it induced the different content of bioactive compounds and finally lead to different qualities. Cryptotanshinone, trijuganone B, and 15,16-dihydrotanshinone I were screened out as markers to distinguish samples by different processing methods. The developed strategy could provide a reference for evaluation and discrimination of other traditional herbal medicines.

Modeling the Impact of Social Discrimination and Financial Hardship on the Sexual Risk of HIV Among Latino and Black Men Who Have Sex With Men

PubMed Central

Bingham, Trista; Kim, Junyeop; Wheeler, Darrell P.; Millett, Gregorio A.

2012-01-01

Objectives. We examined the impact of social discrimination and financial hardship on unprotected anal intercourse with a male sex partner of serodiscordant or unknown HIV status in the past 3 months among 1081 Latino and 1154 Black men who have sex with men (MSM; n = 2235) residing in Los Angeles County, California; New York, New York; and Philadelphia, Pennsylvania. Methods. We administered HIV testing and a questionnaire assessing 6 explanatory variables. We combined traditional mediation analysis with the results of a path analysis to simultaneously examine the direct, indirect, and total effects of these variables on the outcome variable. Results. Bivariate analysis showed that homophobia, racism, financial hardship, and lack of social support were associated with unprotected anal intercourse with a serodiscordant or sero-unknown partner. Path analysis determined that these relations were mediated by participation in risky sexual situations and lack of social support. However, paths between the explanatory variable and 2 mediating variables varied by participants’ serostatus. Conclusions. Future prevention research and program designs should specifically address the differential impact of social discrimination and financial hardship on lack of social support and risky sexual situations among Latino and Black MSM. PMID:22401516

Virtual Assessment of Sex: Linear and Angular Traits of the Mandibular Ramus Using Three-Dimensional Computed Tomography.

PubMed

Inci, Ercan; Ekizoglu, Oguzhan; Turkay, Rustu; Aksoy, Sema; Can, Ismail Ozgur; Solmaz, Dilek; Sayin, Ibrahim

2016-10-01

Morphometric analysis of the mandibular ramus (MR) provides highly accurate data to discriminate sex. The objective of this study was to demonstrate the utility and accuracy of MR morphometric analysis for sex identification in a Turkish population.Four hundred fifteen Turkish patients (18-60 y; 201 male and 214 female) who had previously had multidetector computed tomography scans of the cranium were included in the study. Multidetector computed tomography images were obtained using three-dimensional reconstructions and a volume-rendering technique, and 8 linear and 3 angular values were measured. Univariate, bivariate, and multivariate discriminant analyses were performed, and the accuracy rates for determining sex were calculated.Mandibular ramus values produced high accuracy rates of 51% to 95.6%. Upper ramus vertical height had the highest rate at 95.6%, and bivariate analysis showed 89.7% to 98.6% accuracy rates with the highest ratios of mandibular flexure upper border and maximum ramus breadth. Stepwise discrimination analysis gave a 99% accuracy rate for all MR variables.Our study showed that the MR, in particular morphometric measures of the upper part of the ramus, can provide valuable data to determine sex in a Turkish population. The method combines both anthropological and radiologic studies.

Sea-Ice Feature Mapping using JERS-1 Imagery

NASA Technical Reports Server (NTRS)

Maslanik, James; Heinrichs, John

1994-01-01

JERS-1 SAR and OPS imagery are examined in combination with other data sets to investigate the utility of the JERS-1 sensors for mapping fine-scale sea ice conditions. Combining ERS-1 C band and JERS-1 L band SAR aids in discriminating multiyear and first-year ice. Analysis of OPS imagery for a field site in the Canadian Archipelago highlights the advantages of OPS's high spatial and spectral resolution for mapping ice structure, melt pond distribution, and surface albedo.

Talent identification and selection in elite youth football: An Australian context.

PubMed

O'Connor, Donna; Larkin, Paul; Mark Williams, A

2016-10-01

We identified the perceptual-cognitive skills and player history variables that differentiate players selected or not selected into an elite youth football (i.e. soccer) programme in Australia. A sample of elite youth male football players (n = 127) completed an adapted participation history questionnaire and video-based assessments of perceptual-cognitive skills. Following data collection, 22 of these players were offered a full-time scholarship for enrolment at an elite player residential programme. Participants selected for the scholarship programme recorded superior performance on the combined perceptual-cognitive skills tests compared to the non-selected group. There were no significant between group differences on the player history variables. Stepwise discriminant function analysis identified four predictor variables that resulted in the best categorization of selected and non-selected players (i.e. recent match-play performance, region, number of other sports participated, combined perceptual-cognitive performance). The effectiveness of the discriminant function is reflected by 93.7% of players being correctly classified, with the four variables accounting for 57.6% of the variance. Our discriminating model for selection may provide a greater understanding of the factors that influence elite youth talent selection and identification.

E-nose based rapid prediction of early mouldy grain using probabilistic neural networks

PubMed Central

Ying, Xiaoguo; Liu, Wei; Hui, Guohua; Fu, Jun

2015-01-01

In this paper, early mouldy grain rapid prediction method using probabilistic neural network (PNN) and electronic nose (e-nose) was studied. E-nose responses to rice, red bean, and oat samples with different qualities were measured and recorded. E-nose data was analyzed using principal component analysis (PCA), back propagation (BP) network, and PNN, respectively. Results indicated that PCA and BP network could not clearly discriminate grain samples with different mouldy status and showed poor predicting accuracy. PNN showed satisfying discriminating abilities to grain samples with an accuracy of 93.75%. E-nose combined with PNN is effective for early mouldy grain prediction. PMID:25714125

The Oppression of Latina Mothers: Experiences of Exploitation, Violence, Marginalization, Cultural Imperialism, and Powerlessness in Their Everyday Lives.

PubMed

Ayón, Cecilia; Messing, Jill T; Gurrola, Maria; Valencia-Garcia, Dellanira

2018-06-01

Despite Latinos being the largest growing population in the United States, research has not examined the impact of social structures on the well-being of Latina immigrants; negative social discourse and restrictive laws exacerbate inequality and discrimination in this population. Through combined inductive/deductive analysis of in-depth semistructured interviews, we examined immigrant Mexican mothers' ( N = 32) descriptions of oppression in the United States. All five forms of oppression, described in Young's oppression framework are evident: exploitation, violence, marginalization, cultural imperialism, and powerlessness. Discrimination places a high burden on Latinas due to the intersection of forms of oppression and nondominant identities.

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

PubMed

Bonetti, Jennifer; Quarino, Lawrence

2014-05-01

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

Temporal stability of otolith elemental fingerprints discriminates among lagoon nursery habitats

NASA Astrophysics Data System (ADS)

Tournois, Jennifer; Ferraton, Franck; Velez, Laure; McKenzie, David J.; Aliaume, Catherine; Mercier, Lény; Darnaude, Audrey M.

2013-10-01

The chemical composition of fish otoliths reflects that of the water masses that they inhabit. Otolith elemental compositions can, therefore, be used as natural tags to discriminate among habitats. However, for retrospective habitat identification to be valid and reliable for any adult, irrespective of its age, significant differences in environmental conditions, and therefore otolith signatures, must be temporally stable within each habitat, otherwise connectivity studies have to be carried out by matching year-classes to the corresponding annual fingerprints. This study investigated how various different combinations of chemical elements in otoliths could distinguish, over three separate years, between four coastal lagoon habitats used annually as nurseries by gilthead sea bream (Sparus aurata L.) in the Gulf of Lions (NW Mediterranean). A series of nine elements were measured in otoliths of 301 S. aurata juveniles collected in the four lagoons in 2008, 2010 and 2011. Percentages of correct re-assignment of juveniles to their lagoon of origin were calculated with the Random Forest classification method, considering every possible combination of elements. This revealed both spatial and temporal variations in accuracy of habitat identification, with correct re-assignment to each lagoon ranging from 44 to 99% depending on the year and the lagoon. There were also annual differences in the combination of elements that provided the best discrimination among the lagoons. Despite this, when the data from the three years were pooled, a combination of eight elements (B, Ba, Cu, Li, Mg, Rb, Sr and Y) provided greater than 70% correct re-assignment to each single lagoon, with a multi-annual global accuracy of 79%. When considering the years separately, discrimination accuracy with these elemental fingerprints was above 90% for 2008 and 2010. It decreased to 61% in 2011, when unusually heavy rainfall occurred, which presumably reduced chemical differences among several of the lagoons. This study highlights the need for multi-annual sampling, and multi-elemental analysis, when developing otolith microchemical fingerprints to explore nursery habitat use in coastal fishes.

A novel combined approach of diffuse reflectance UV-Vis-NIR spectroscopy and multivariate analysis for non-destructive examination of blue ballpoint pen inks in forensic application

NASA Astrophysics Data System (ADS)

Kumar, Raj; Sharma, Vishal

2017-03-01

The present research is focused on the analysis of writing inks using destructive UV-Vis spectroscopy (dissolution of ink by the solvent) and non-destructive diffuse reflectance UV-Vis-NIR spectroscopy along with Chemometrics. Fifty seven samples of blue ballpoint pen inks were analyzed under optimum conditions to determine the differences in spectral features of inks among same and different manufacturers. Normalization was performed on the spectroscopic data before chemometric analysis. Principal Component Analysis (PCA) and K-mean cluster analysis were used on the data to ascertain whether the blue ballpoint pen inks could be differentiated by their UV-Vis/UV-Vis NIR spectra. The discriminating power is calculated by qualitative analysis by the visual comparison of the spectra (absorbance peaks), produced by the destructive and non-destructive methods. In the latter two methods, the pairwise comparison is made by incorporating the clustering method. It is found that chemometric method provides better discriminating power (98.72% and 99.46%, in destructive and non-destructive, respectively) in comparison to the qualitative analysis (69.67%).

Study of the cerrado vegetation in the Federal District area from orbital data. M.S. Thesis

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Aoki, H.; Dossantos, J. R.

1980-01-01

The physiognomic units of cerrado in the area of Distrito Federal (DF) were studied through the visual and automatic analysis of products provided by Multispectral Scanning System (MSS) of LANDSAT. The visual analysis of the multispectral images in black and white, at the 1:250,000 scale, was made based on the texture and tonal patterns. The automatic analysis of the compatible computer tapes (CCT) was made by means of IMAGE-100 system. The following conclusions were obtained: (1) the delimitation of cerrado vegetation forms can be made by the visual and automatic analysis; (2) in the visual analysis, the principal parameter used to discriminate the cerrado forms was the tonal pattern, independently of the year's seasons, and the channel 5 gave better information; (3) in the automatic analysis, the data of the four channels of MSS can be used in the discrimination of the cerrado forms; and (4) in the automatic analysis, the four channels combination possibilities gave more information in the separation of cerrado units when soil types were considered.

Rapid characterization of chemical markers for discrimination of Moutan Cortex and its processed products by direct injection-based mass spectrometry profiling and metabolomic method.

PubMed

Li, Chao-Ran; Li, Meng-Ning; Yang, Hua; Li, Ping; Gao, Wen

2018-06-01

Processing of herbal medicines is a characteristic pharmaceutical technique in Traditional Chinese Medicine, which can reduce toxicity and side effect, improve the flavor and efficacy, and even change the pharmacological action entirely. It is significant and crucial to perform a method to find chemical markers for differentiating herbal medicines in different processed degrees. The aim of this study was to perform a rapid and reasonable method to discriminate Moutan Cortex and its processed products, and to reveal the characteristics of chemical components depend on chemical markers. Thirty batches of Moutan Cortex and its processed products, including 11 batches of Raw Moutan Cortex (RMC), 9 batches of Moutan Cortex Tostus (MCT) and 10 batches of Moutan Cortex Carbonisatus (MCC), were directly injected in electrospray ionization quadrupole time-of-flight mass spectrometry (ESI-QTOF MS) for rapid analysis in positive and negative mode. Without chromatographic separation, each run was completed within 3 min. The raw MS data were automatically extracted by background deduction and molecular feature (MF) extraction algorithm. In negative mode, a total of 452 MFs were obtained and then pretreated by data filtration and differential analysis. After that, the filtered 85 MFs were treated by principal component analysis (PCA) to reduce the dimensions. Subsequently, a partial least squares discrimination analysis (PLS-DA) model was constructed for differentiation and chemical markers detection of Moutan Cortex in different processed degrees. The positive mode data were treated as same as those in negative mode. RMC, MCT and MCC were successfully classified. Moreover, 14 and 3 chemical markers from negative and positive mode respectively, were screened by the combination of their relative peak areas and the parameter variable importance in the projection (VIP) values in PLS-DA model. The content changes of these chemical markers were employed in order to illustrate chemical changes of Moutan Cortex after processed. These results showed that the proposed method which combined non-targeted metabolomics analysis with multivariate statistics analysis is reasonable and effective. It could not only be applied to discriminate herbal medicines and their processing products, but also to reveal the characteristics of chemical components during processing. Copyright © 2018. Published by Elsevier GmbH.

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

Discriminant function analysis as tool for subsurface geologist

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

Chesser, K.

1987-05-01

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

Olive oil sensory defects classification with data fusion of instrumental techniques and multivariate analysis (PLS-DA).

PubMed

Borràs, Eva; Ferré, Joan; Boqué, Ricard; Mestres, Montserrat; Aceña, Laura; Calvo, Angels; Busto, Olga

2016-07-15

Three instrumental techniques, headspace-mass spectrometry (HS-MS), mid-infrared spectroscopy (MIR) and UV-visible spectrophotometry (UV-vis), have been combined to classify virgin olive oil samples based on the presence or absence of sensory defects. The reference sensory values were provided by an official taste panel. Different data fusion strategies were studied to improve the discrimination capability compared to using each instrumental technique individually. A general model was applied to discriminate high-quality non-defective olive oils (extra-virgin) and the lowest-quality olive oils considered non-edible (lampante). A specific identification of key off-flavours, such as musty, winey, fusty and rancid, was also studied. The data fusion of the three techniques improved the classification results in most of the cases. Low-level data fusion was the best strategy to discriminate musty, winey and fusty defects, using HS-MS, MIR and UV-vis, and the rancid defect using only HS-MS and MIR. The mid-level data fusion approach using partial least squares-discriminant analysis (PLS-DA) scores was found to be the best strategy for defective vs non-defective and edible vs non-edible oil discrimination. However, the data fusion did not sufficiently improve the results obtained by a single technique (HS-MS) to classify non-defective classes. These results indicate that instrumental data fusion can be useful for the identification of sensory defects in virgin olive oils. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

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

PubMed Central

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

2017-01-01

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

Stone loaches of Choman River system, Kurdistan, Iran (Teleostei: Cypriniformes: Nemacheilidae).

PubMed

Kamangar, Barzan Bahrami; Prokofiev, Artem M; Ghaderi, Edris; Nalbant, Theodore T

2014-01-20

For the first time, we present data on species composition and distributions of nemacheilid loaches in the Choman River basin of Kurdistan province, Iran. Two genera and four species are recorded from the area, of which three species are new for science: Oxynoemacheilus kurdistanicus, O. zagrosensis, O. chomanicus spp. nov., and Turcinoemacheilus kosswigi Băn. et Nalb. Detailed and illustrated morphological descriptions and univariate and multivariate analysis of morphometric and meristic features are for each of these species. Forty morphometric and eleven meristic characters were used in multivariate analysis to select characters that could discriminate between the four loach species. Discriminant Function Analysis revealed that sixteen morphometric measures and five meristic characters have the most variability between the loach species. The dendrograms based on cluster analysis of Mahalanobis distances of morphometrics and a combination of both characters confirmed two distinct groups: Oxynoemacheilus spp. and T. kosswigi. Within Oxynoemacheilus, O. zagrosensis and O. chomanicus are more similar to one other rather to either is to O. kurdistanicus.

Provenance Establishment of Stingless Bee Honey Using Multi-element Analysis in Combination with Chemometrics Techniques.

PubMed

Shadan, Aidil Fahmi; Mahat, Naji A; Wan Ibrahim, Wan Aini; Ariffin, Zaiton; Ismail, Dzulkiflee

2018-01-01

As consumption of stingless bee honey has been gaining popularity in many countries including Malaysia, ability to identify accurately its geographical origin proves pertinent for investigating fraudulent activities for consumer protection. Because a chemical signature can be location-specific, multi-element distribution patterns may prove useful for provenancing such product. Using the inductively coupled-plasma optical emission spectrometer as well as principal component analysis (PCA) and linear discriminant analysis (LDA), the distributions of multi-elements in stingless bee honey collected at four different geographical locations (North, West, East, and South) in Johor, Malaysia, were investigated. While cross-validation using PCA demonstrated 87.0% correct classification rate, the same was improved (96.2%) with the use of LDA, indicating that discrimination was possible for the different geographical regions. Therefore, utilization of multi-element analysis coupled with chemometrics techniques for assigning the provenance of stingless bee honeys for forensic applications is supported. © 2017 American Academy of Forensic Sciences.

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

PubMed

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

2015-05-01

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

Feasibility of detecting aflatoxin B1 on inoculated maize kernels surface using Vis/NIR hyperspectral imaging.

PubMed

Wang, Wei; Heitschmidt, Gerald W; Windham, William R; Feldner, Peggy; Ni, Xinzhi; Chu, Xuan

2015-01-01

The feasibility of using a visible/near-infrared hyperspectral imaging system with a wavelength range between 400 and 1000 nm to detect and differentiate different levels of aflatoxin B1 (AFB1 ) artificially titrated on maize kernel surface was examined. To reduce the color effects of maize kernels, image analysis was limited to a subset of original spectra (600 to 1000 nm). Residual staining from the AFB1 on the kernels surface was selected as regions of interest for analysis. Principal components analysis (PCA) was applied to reduce the dimensionality of hyperspectral image data, and then a stepwise factorial discriminant analysis (FDA) was performed on latent PCA variables. The results indicated that discriminant factors F2 can be used to separate control samples from all of the other groups of kernels with AFB1 inoculated, whereas the discriminant factors F1 can be used to identify maize kernels with levels of AFB1 as low as 10 ppb. An overall classification accuracy of 98% was achieved. Finally, the peaks of β coefficients of the discrimination factors F1 and F2 were analyzed and several key wavelengths identified for differentiating maize kernels with and without AFB1 , as well as those with differing levels of AFB1 inoculation. Results indicated that Vis/NIR hyperspectral imaging technology combined with the PCA-FDA was a practical method to detect and differentiate different levels of AFB1 artificially inoculated on the maize kernels surface. However, indicated the potential to detect and differentiate naturally occurring toxins in maize kernel. © 2014 Institute of Food Technologists®

Rapid discrimination between buffalo and cow milk and detection of adulteration of buffalo milk with cow milk using synchronous fluorescence spectroscopy in combination with multivariate methods.

PubMed

Durakli Velioglu, Serap; Ercioglu, Elif; Boyaci, Ismail Hakki

2017-05-01

This research paper describes the potential of synchronous fluorescence (SF) spectroscopy for authentication of buffalo milk, a favourable raw material in the production of some premium dairy products. Buffalo milk is subjected to fraudulent activities like many other high priced foodstuffs. The current methods widely used for the detection of adulteration of buffalo milk have various disadvantages making them unattractive for routine analysis. Thus, the aim of the present study was to assess the potential of SF spectroscopy in combination with multivariate methods for rapid discrimination between buffalo and cow milk and detection of the adulteration of buffalo milk with cow milk. SF spectra of cow and buffalo milk samples were recorded between 400-550 nm excitation range with Δλ of 10-100 nm, in steps of 10 nm. The data obtained for ∆λ = 10 nm were utilised to classify the samples using principal component analysis (PCA), and detect the adulteration level of buffalo milk with cow milk using partial least square (PLS) methods. Successful discrimination of samples and detection of adulteration of buffalo milk with limit of detection value (LOD) of 6% are achieved with the models having root mean square error of calibration (RMSEC) and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) values of 2, 7, and 4%, respectively. The results reveal the potential of SF spectroscopy for rapid authentication of buffalo milk.

Application of SWIR hyperspectral imaging and chemometrics for identification of aflatoxin B1 contaminated maize kernels

NASA Astrophysics Data System (ADS)

Kimuli, Daniel; Wang, Wei; Wang, Wei; Jiang, Hongzhe; Zhao, Xin; Chu, Xuan

2018-03-01

A short-wave infrared (SWIR) hyperspectral imaging system (1000-2500 nm) combined with chemometric data analysis was used to detect aflatoxin B1 (AFB1) on surfaces of 600 kernels of four yellow maize varieties from different States of the USA (Georgia, Illinois, Indiana and Nebraska). For each variety, four AFB1 solutions (10, 20, 100 and 500 ppb) were artificially deposited on kernels and a control group was generated from kernels treated with methanol solution. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA) and factorial discriminant analysis (FDA) were applied to explore and classify maize kernels according to AFB1 contamination. PCA results revealed partial separation of control kernels from AFB1 contaminated kernels for each variety while no pattern of separation was observed among pooled samples. A combination of standard normal variate and first derivative pre-treatments produced the best PLSDA classification model with accuracy of 100% and 96% in calibration and validation, respectively, from Illinois variety. The best AFB1 classification results came from FDA on raw spectra with accuracy of 100% in calibration and validation for Illinois and Nebraska varieties. However, for both PLSDA and FDA models, poor AFB1 classification results were obtained for pooled samples relative to individual varieties. SWIR spectra combined with chemometrics and spectra pre-treatments showed the possibility of detecting maize kernels of different varieties coated with AFB1. The study further suggests that increase of maize kernel constituents like water, protein, starch and lipid in a pooled sample may have influence on detection accuracy of AFB1 contamination.

The receiver operational characteristic for binary classification with multiple indices and its application to the neuroimaging study of Alzheimer's disease.

PubMed

Wu, Xia; Li, Juan; Ayutyanont, Napatkamon; Protas, Hillary; Jagust, William; Fleisher, Adam; Reiman, Eric; Yao, Li; Chen, Kewei

2013-01-01

Given a single index, the receiver operational characteristic (ROC) curve analysis is routinely utilized for characterizing performances in distinguishing two conditions/groups in terms of sensitivity and specificity. Given the availability of multiple data sources (referred to as multi-indices), such as multimodal neuroimaging data sets, cognitive tests, and clinical ratings and genomic data in Alzheimer’s disease (AD) studies, the single-index-based ROC underutilizes all available information. For a long time, a number of algorithmic/analytic approaches combining multiple indices have been widely used to simultaneously incorporate multiple sources. In this study, we propose an alternative for combining multiple indices using logical operations, such as “AND,” “OR,” and “at least n” (where n is an integer), to construct multivariate ROC (multiV-ROC) and characterize the sensitivity and specificity statistically associated with the use of multiple indices. With and without the “leave-one-out” cross-validation, we used two data sets from AD studies to showcase the potentially increased sensitivity/specificity of the multiV-ROC in comparison to the single-index ROC and linear discriminant analysis (an analytic way of combining multi-indices). We conclude that, for the data sets we investigated, the proposed multiV-ROC approach is capable of providing a natural and practical alternative with improved classification accuracy as compared to univariate ROC and linear discriminant analysis.

The Receiver Operational Characteristic for Binary Classification with Multiple Indices and Its Application to the Neuroimaging Study of Alzheimer’s Disease

PubMed Central

Wu, Xia; Li, Juan; Ayutyanont, Napatkamon; Protas, Hillary; Jagust, William; Fleisher, Adam; Reiman, Eric; Yao, Li; Chen, Kewei

2014-01-01

Given a single index, the receiver operational characteristic (ROC) curve analysis is routinely utilized for characterizing performances in distinguishing two conditions/groups in terms of sensitivity and specificity. Given the availability of multiple data sources (referred to as multi-indices), such as multimodal neuroimaging data sets, cognitive tests, and clinical ratings and genomic data in Alzheimer’s disease (AD) studies, the single-index-based ROC underutilizes all available information. For a long time, a number of algorithmic/analytic approaches combining multiple indices have been widely used to simultaneously incorporate multiple sources. In this study, we propose an alternative for combining multiple indices using logical operations, such as “AND,” “OR,” and “at least n” (where n is an integer), to construct multivariate ROC (multiV-ROC) and characterize the sensitivity and specificity statistically associated with the use of multiple indices. With and without the “leave-one-out” cross-validation, we used two data sets from AD studies to showcase the potentially increased sensitivity/specificity of the multiV-ROC in comparison to the single-index ROC and linear discriminant analysis (an analytic way of combining multi-indices). We conclude that, for the data sets we investigated, the proposed multiV-ROC approach is capable of providing a natural and practical alternative with improved classification accuracy as compared to univariate ROC and linear discriminant analysis. PMID:23702553

Protein-protein interaction specificity is captured by contact preferences and interface composition.

PubMed

Nadalin, Francesca; Carbone, Alessandra

2018-02-01

Large-scale computational docking will be increasingly used in future years to discriminate protein-protein interactions at the residue resolution. Complete cross-docking experiments make in silico reconstruction of protein-protein interaction networks a feasible goal. They ask for efficient and accurate screening of the millions structural conformations issued by the calculations. We propose CIPS (Combined Interface Propensity for decoy Scoring), a new pair potential combining interface composition with residue-residue contact preference. CIPS outperforms several other methods on screening docking solutions obtained either with all-atom or with coarse-grain rigid docking. Further testing on 28 CAPRI targets corroborates CIPS predictive power over existing methods. By combining CIPS with atomic potentials, discrimination of correct conformations in all-atom structures reaches optimal accuracy. The drastic reduction of candidate solutions produced by thousands of proteins docked against each other makes large-scale docking accessible to analysis. CIPS source code is freely available at http://www.lcqb.upmc.fr/CIPS. alessandra.carbone@lip6.fr. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

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

NASA Astrophysics Data System (ADS)

Tiira, Timo

1996-10-01

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

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

PubMed

Palazón, L; Navas, A

2017-06-01

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

Evaluation of volatile metabolites as markers in Lycopersicon esculentum L. cultivars discrimination by multivariate analysis of headspace solid phase microextraction and mass spectrometry data.

PubMed

Figueira, José; Câmara, Hugo; Pereira, Jorge; Câmara, José S

2014-02-15

To gain insights on the effects of cultivar on the volatile metabolomic expression of different tomato (Lycopersicon esculentum L.) cultivars--Plum, Campari, Grape, Cherry and Regional, cultivated under similar edafoclimatic conditions, and to identify the most discriminate volatile marker metabolites related to the cultivar, the chromatographic profiles resulting from headspace solid phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-qMS) analysis, combined with multivariate analysis were investigated. The data set composed by the 77 volatile metabolites identified in the target tomato cultivars, 5 of which (2,2,6-trimethylcyclohexanone, 2-methyl-6-methyleneoctan-2-ol, 4-octadecyl-morpholine, (Z)-methyl-3-hexenoate and 3-octanone) are reported for the first time in tomato volatile metabolomic composition, was evaluated by chemometrics. Firstly, principal component analysis was carried out in order to visualise data trends and clusters, and then, linear discriminant analysis in order to detect the set of volatile metabolites able to differentiate groups according to tomato cultivars. The results obtained revealed a perfect discrimination between the different Lycopersicon esculentum L. cultivars considered. The assignment success rate was 100% in classification and 80% in prediction ability by using "leave-one-out" cross-validation procedure. The volatile profile was able to differentiate all five cultivars and revealed complex interactions between them including the participation in the same biosynthetic pathway. The volatile metabolomic platform for tomato samples obtained by HS-SPME/GC-qMS here described, and the interrelationship detected among the volatile metabolites can be used as a roadmap for biotechnological applications, namely to improve tomato aroma and their acceptance in the final consumer, and for traceability studies. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2016-02-01

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

Quantitative Determination of Cannabinoids in Cannabis and Cannabis Products Using Ultra-High-Performance Supercritical Fluid Chromatography and Diode Array/Mass Spectrometric Detection.

PubMed

Wang, Mei; Wang, Yan-Hong; Avula, Bharathi; Radwan, Mohamed M; Wanas, Amira S; Mehmedic, Zlatko; van Antwerp, John; ElSohly, Mahmoud A; Khan, Ikhlas A

2017-05-01

Ultra-high-performance supercritical fluid chromatography (UHPSFC) is an efficient analytical technique and has not been fully employed for the analysis of cannabis. Here, a novel method was developed for the analysis of 30 cannabis plant extracts and preparations using UHPSFC/PDA-MS. Nine of the most abundant cannabinoids, viz. CBD, ∆ 8 -THC, THCV, ∆ 9 -THC, CBN, CBG, THCA-A, CBDA, and CBGA, were quantitatively determined (RSDs < 6.9%). Unlike GC methods, no derivatization or decarboxylation was required prior to UHPSFC analysis. The UHPSFC chromatographic separation of cannabinoids displayed an inverse elution order compared to UHPLC. Combining with PDA-MS, this orthogonality is valuable for discrimination of cannabinoids in complex matrices. The developed method was validated, and the quantification results were compared with a standard UHPLC method. The RSDs of these two methods were within ±13.0%. Finally, chemometric analysis including principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to differentiate between cannabis samples. © 2016 American Academy of Forensic Sciences.

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.

Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging

PubMed Central

Mo, Changyeun; Kim, Giyoung; Lim, Jongguk; Kim, Moon S.; Cho, Hyunjeong; Cho, Byoung-Kwan

2015-01-01

Rapid visible/near-infrared (VNIR) hyperspectral imaging methods, employing both a single waveband algorithm and multi-spectral algorithms, were developed in order to discrimination between sound and discolored lettuce. Reflectance spectra for sound and discolored lettuce surfaces were extracted from hyperspectral reflectance images obtained in the 400–1000 nm wavelength range. The optimal wavebands for discriminating between discolored and sound lettuce surfaces were determined using one-way analysis of variance. Multi-spectral imaging algorithms developed using ratio and subtraction functions resulted in enhanced classification accuracy of above 99.9% for discolored and sound areas on both adaxial and abaxial lettuce surfaces. Ratio imaging (RI) and subtraction imaging (SI) algorithms at wavelengths of 552/701 nm and 557–701 nm, respectively, exhibited better classification performances compared to results obtained for all possible two-waveband combinations. These results suggest that hyperspectral reflectance imaging techniques can potentially be used to discriminate between discolored and sound fresh-cut lettuce. PMID:26610510

Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging.

PubMed

Mo, Changyeun; Kim, Giyoung; Lim, Jongguk; Kim, Moon S; Cho, Hyunjeong; Cho, Byoung-Kwan

2015-11-20

Rapid visible/near-infrared (VNIR) hyperspectral imaging methods, employing both a single waveband algorithm and multi-spectral algorithms, were developed in order to discrimination between sound and discolored lettuce. Reflectance spectra for sound and discolored lettuce surfaces were extracted from hyperspectral reflectance images obtained in the 400-1000 nm wavelength range. The optimal wavebands for discriminating between discolored and sound lettuce surfaces were determined using one-way analysis of variance. Multi-spectral imaging algorithms developed using ratio and subtraction functions resulted in enhanced classification accuracy of above 99.9% for discolored and sound areas on both adaxial and abaxial lettuce surfaces. Ratio imaging (RI) and subtraction imaging (SI) algorithms at wavelengths of 552/701 nm and 557-701 nm, respectively, exhibited better classification performances compared to results obtained for all possible two-waveband combinations. These results suggest that hyperspectral reflectance imaging techniques can potentially be used to discriminate between discolored and sound fresh-cut lettuce.

Persistent topographic quantitative EEG sequelae of chronic marihuana use: a replication study and initial discriminant function analysis.

PubMed

Struve, F A; Straumanis, J J; Patrick, G

1994-04-01

In a previous pilot study using psychiatric patients we reported that daily marihuana users had significant elevations of (1) Absolute Alpha Power, (2) Relative Alpha Power, and (3) Interhemispheric Alpha Coherence over both frontal and frontal-central areas when contrasted with subjects who did not use marihuana. We referred to this phenomenon as Hyperfrontality of Alpha. The study presented here is a successful replication of our previous findings using new samples of subjects and identical methods. Post hoc analyses based on the combined sample from both studies suggest that variables of psychiatric diagnoses and medication did not bias our results. In addition, a discriminant function analysis using quantitative EEG variables as candidate predictors generated a 95% correct THC user versus nonuser classification accuracy which received a successful jackknife replication.

Ion-absorption band analysis for the discrimination of iron-rich zones. [Nevada

NASA Technical Reports Server (NTRS)

Rowan, L. C. (Principal Investigator); Wetlaufer, P. H.

1974-01-01

The author has identified the following significant results. A technique which combines digital computer processing and color composition was devised for detecting hydrothermally altered areas and for discriminating among many rock types in an area in south-central Nevada. Subtle spectral reflectance differences among the rock types are enhanced by ratioing and contrast-stretching MSS radiance values for form ratio images which subsequently are displayed in color-ratio composites. Landform analysis of Nevada shows that linear features compiled without respect to length results in approximately 25 percent coincidence with mapped faults. About 80 percent of the major lineaments coincides with mapped faults, and substantial extension of locally mapped faults is commonly indicated. Seven major lineament systems appear to be old zones of crustal weakness which have provided preferred conduits for rising magma through periodic reactivation.

Attenuated total reflectance-FT-IR spectroscopy for gunshot residue analysis: potential for ammunition determination.

PubMed

Bueno, Justin; Sikirzhytski, Vitali; Lednev, Igor K

2013-08-06

The ability to link a suspect to a particular shooting incident is a principal task for many forensic investigators. Here, we attempt to achieve this goal by analysis of gunshot residue (GSR) through the use of attenuated total reflectance (ATR) Fourier transform infrared spectroscopy (FT-IR) combined with statistical analysis. The firearm discharge process is analogous to a complex chemical process. Therefore, the products of this process (GSR) will vary based upon numerous factors, including the specific combination of the firearm and ammunition which was discharged. Differentiation of FT-IR data, collected from GSR particles originating from three different firearm-ammunition combinations (0.38 in., 0.40 in., and 9 mm calibers), was achieved using projection to latent structures discriminant analysis (PLS-DA). The technique was cross (leave-one-out), both internally and externally, validated. External validation was achieved via assignment (caliber identification) of unknown FT-IR spectra from unknown GSR particles. The results demonstrate great potential for ATR-FT-IR spectroscopic analysis of GSR for forensic purposes.

Preliminary identification of unicellular algal genus by using combined confocal resonance Raman spectroscopy with PCA and DPLS analysis

NASA Astrophysics Data System (ADS)

He, Shixuan; Xie, Wanyi; Zhang, Ping; Fang, Shaoxi; Li, Zhe; Tang, Peng; Gao, Xia; Guo, Jinsong; Tlili, Chaker; Wang, Deqiang

2018-02-01

The analysis of algae and dominant alga plays important roles in ecological and environmental fields since it can be used to forecast water bloom and control its potential deleterious effects. Herein, we combine in vivo confocal resonance Raman spectroscopy with multivariate analysis methods to preliminary identify the three algal genera in water blooms at unicellular scale. Statistical analysis of characteristic Raman peaks demonstrates that certain shifts and different normalized intensities, resulting from composition of different carotenoids, exist in Raman spectra of three algal cells. Principal component analysis (PCA) scores and corresponding loading weights show some differences from Raman spectral characteristics which are caused by vibrations of carotenoids in unicellular algae. Then, discriminant partial least squares (DPLS) classification method is used to verify the effectiveness of algal identification with confocal resonance Raman spectroscopy. Our results show that confocal resonance Raman spectroscopy combined with PCA and DPLS could handle the preliminary identification of dominant alga for forecasting and controlling of water blooms.

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

ERIC Educational Resources Information Center

Lukes, Robin; Bangs, Joann

2014-01-01

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

Analysis and classification of optical tomographic images of rheumatoid fingers with ANOVA and discriminate analysis

NASA Astrophysics Data System (ADS)

Montejo, Ludguier D.; Kim, Hyun K.; Häme, Yrjö; Jia, Jingfei; Montejo, Julio D.; Netz, Uwe J.; Blaschke, Sabine; Zwaka, Paul; Müeller, Gerhard A.; Beuthan, Jürgen; Hielscher, Andreas H.

2011-03-01

We present a study on the effectiveness of computer-aided diagnosis (CAD) of rheumatoid arthritis (RA) from frequency-domain diffuse optical tomographic (FDOT) images. FDOT is used to obtain the distribution of tissue optical properties. Subsequently, the non-parametric Kruskal-Wallis ANOVA test is employed to verify statistically significant differences between the optical parameters of patients affected by RA and healthy volunteers. Furthermore, quadratic discriminate analysis (QDA) of the absorption (μa) and scattering (μa or μ's) distributions is used to classify subjects as affected or not affected by RA. We evaluate the classification efficiency by determining the sensitivity (Se), specificity (Sp), and the Youden index (Y). We find that combining features extracted from μa and μa or μ's images allows for more accurate classification than when μa or μa or μ's features are considered individually on their own. Combining μa and μa or μ's features yields values of up to Y = 0.75 (Se = 0.84 and Sp = 0.91). The best results when μa or μ's features are considered individually are Y = 0.65 (Se = 0.85 and Sp = 0.80) and Y = 0.70 (Se = 0.80 and Sp = 0.90), respectively.

Application of Hyperspectral Imaging and Chemometric Calibrations for Variety Discrimination of Maize Seeds

PubMed Central

Zhang, Xiaolei; Liu, Fei; He, Yong; Li, Xiaoli

2012-01-01

Hyperspectral imaging in the visible and near infrared (VIS-NIR) region was used to develop a novel method for discriminating different varieties of commodity maize seeds. Firstly, hyperspectral images of 330 samples of six varieties of maize seeds were acquired using a hyperspectral imaging system in the 380–1,030 nm wavelength range. Secondly, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of the spectral data. Thirdly, three optimal wavelengths (523, 579 and 863 nm) were selected by implementing PCA directly on each image. Then four textural variables including contrast, homogeneity, energy and correlation were extracted from gray level co-occurrence matrix (GLCM) of each monochromatic image based on the optimal wavelengths. Finally, several models for maize seeds identification were established by least squares-support vector machine (LS-SVM) and back propagation neural network (BPNN) using four different combinations of principal components (PCs), kernel principal components (KPCs) and textural features as input variables, respectively. The recognition accuracy achieved in the PCA-GLCM-LS-SVM model (98.89%) was the most satisfactory one. We conclude that hyperspectral imaging combined with texture analysis can be implemented for fast classification of different varieties of maize seeds. PMID:23235456

Toward a definition of blueprint of virgin olive oil by comprehensive two-dimensional gas chromatography.

PubMed

Purcaro, Giorgia; Cordero, Chiara; Liberto, Erica; Bicchi, Carlo; Conte, Lanfranco S

2014-03-21

This study investigates the applicability of an iterative approach aimed at defining a chemical blueprint of virgin olive oil volatiles to be correlated to the product sensory quality. The investigation strategy proposed allows to fully exploit the informative content of a comprehensive multidimensional gas chromatography (GC×GC) coupled to a mass spectrometry (MS) data set. Olive oil samples (19), including 5 reference standards, obtained from the International Olive Oil Council, and commercial samples, were submitted to a sensory evaluation by a Panel test, before being analyzed in two laboratories using different instrumentation, column set, and software elaboration packages in view of a cross-validation of the entire methodology. A first classification of samples based on untargeted peak features information, was obtained on raw data from two different column combinations (apolar×polar and polar×apolar) by applying unsupervised multivariate analysis (i.e., principal component analysis-PCA). However, to improve effectiveness and specificity of this classification, peak features were reliably identified (261 compounds), on the basis of the MS spectrum and linear retention index matching, and subjected to successive pair-wise comparisons based on 2D patterns, which revealed peculiar distribution of chemicals correlated with samples sensory classification. The most informative compounds were thus identified and collected in a "blueprint" of specific defects (or combination of defects) successively adopted to discriminate Extra Virgin from defected oils (i.e., lampante oil) with the aid of a supervised approach, i.e., partial least squares-discriminant analysis (PLS-DA). In this last step, the principles of sensomics, which assigns higher information potential to analytes with lower odor threshold proved to be successful, and a much more powerful discrimination of samples was obtained in view of a sensory quality assessment. Copyright © 2014 Elsevier B.V. All rights reserved.

Direct Analysis in Real Time-Mass Spectrometry & Kohonen Artificial Neural Networks for the Rapid Species Identification of Larvae, Pupae and Adult Life Stages of Carrion Insects.

PubMed

Beyramysoltan, Samira; Giffen, Justine E; Rosati, Jennifer Y; Musah, Rabi Ann

2018-06-20

Species determination of the various life stages of flies (order: Diptera) is challenging, particularly for the immature forms, because analogous life stages of different species are difficult to differentiate morphologically. It is demonstrated here that DART high-resolution mass spectrometry (DART-HRMS) combined with supervised Kohonen Self-Organizing Maps (SOM) enables accomplishment of species-level identification of larvae, pupae and adult life stages of carrion flies. DART-HRMS data for each life stage were acquired from analysis of ethanol suspensions representing Calliphoridae, Phoridae and Sarcophagidae families, without additional sample preparation. After preprocessing, the data were subjected to a combination of minimum Redundancy Maximal Relevance (mRMR) and Sparse Discriminant Analysis (SDA) methods to select the most significant variables for creating accurate SOM models. The resulting data were divided into training and validation sets, and then analyzed by the SOM method to define the proper discrimination models. The 5-fold venetian blind cross-validation misclassification error was below 7% for all life stages, and the validation samples were correctly identified in all cases. The multiclass SOM model also revealed which chemical components were the most significant markers for each species, with several of these being amino acids. The results show that processing of DART-HRMS data using artificial neural networks (ANNs) based on the Kohonen SOM approach enables rapid discrimination and identification of fly species even for the immature life stages. The ANNs can be continuously expanded to include a larger number of species, and can be used to screen DART-HRMS data from unknowns to rapidly determine species identity.

Differentiation between malignant and non-malignant pleural effusion using cancer ratio and other new parameters.

PubMed

Korczynski, Piotr; Mierzejewski, Michal; Krenke, Rafal; Safianowska, Aleksandra; Light, Richard W

2018-06-05

Introduction In contrast to tuberculous pleurisy (TP), no accurate and commonly accepted biochemical marker of malignant pleural effusion (MPE) has been established. Objectives We aimed to: 1) evaluate the ability of previously reported cancer ratio (CR) to discriminate MPEs and non-MPEs, 2) test whether age may have additional value in differentiating MPEs and non MPEs, and if so, 3) to combine LDH and age with other TP biomarkers in search of an index useful in the identification of MPE. Patients and methods A retrospective analysis of data from 140 patients with malignant (n=74), tuberculous (n=37) and parapneumonic (n=29) pleural effusions was performed. The diagnostic performance of a test to discriminate between MPEs and non-MPEs was evaluated using Receiver Operating Characteristic. Results Three ratios showed the largest AUC: serum LDH/pleural fluid soluble Fas ligand, age/pleural fluid ADA and serum LDH/pleural fluid IL-18 and were characterized by a high sensitivity (95, 93.2, 92.9% respectively) and fair specificity (64.8, 71.2, 58.5% respectively) in discrimination MPE from non-MPEs. AUC for CR was lower than for aforementioned values and showed 94.6% sensitivity and 68.2% specificity. Conclusions Our study showed a lower specificity of CR in discriminating MPEs and non-MPEs than previously reported. We demonstrated that combinations of serum LDH with other pleural fluid biomarkers of TP have a similar diagnostic performance. We also found that age might be an important factor differentiating between MPEs and non-MPEs and propose a new age/pleural fluid ADA ratio which has a discriminative potential similar to that of CR.

Discriminative-stimulus effects of NS9283, a nicotinic α4β2* positive allosteric modulator, in nicotine-discriminating rats.

PubMed

Mohler, Eric G; Franklin, Stanley R; Rueter, Lynne E

2014-01-01

Neuronal α4β2* nicotinic acetylcholine receptors mediate cognition, pain, and the discriminative and reinforcing effects of nicotine. In addition to traditional orthosteric agonists, α4β2* positive allosteric modulators (PAMs) have recently been identified. With increased subtype selectivity relative to agonists, PAMs administered alone or in combination with low-dose α4β2* agonists may be used as powerful tools for increasing our understanding of α4β2* pharmacology. The present experiments tested the nicotine discriminative-stimulus effects of the α4β2* PAM NS9283 (A-969933) in the presence and absence of low-dose nicotine or nicotinic subtype-selective agonist. Rats were trained to discriminate 0.4 mg/kg nicotine from saline in a two-lever drug discrimination paradigm. In subsequent generalization tests, rats were administered nicotine, the α4β2*-preferring agonist ABT-594, and NS9283, alone or in two-drug combinations. Nicotine and ABT-594 showed dose-dependent nicotine generalization. NS9283 alone resulted in a non-significant increase in nicotine-appropriate lever selection. Combination of non-effective doses of nicotine or ABT-594 with escalating doses of NS9283 resulted in a complete conversion to 100 % nicotine-appropriate choice in the case of nicotine combination and incomplete, though significant, generalization for ABT-594. The α4β2* PAM NS9283 alone did not produce nicotine-like discriminative effects, but did demonstrate dose-related increases in nicotine lever choice when combined with a non-effective dose of nicotine or the α4β2* agonist ABT-594. This finding provides confirmation of the positive allosteric modulating effect of NS9283 in a functional in vivo paradigm. NS9283 is a potentially valuable tool for studying the role of α4β2* receptors in various nicotinic acetylcholine receptor-related functions.

Determining sources of elevated salinity in pre-hydraulic fracturing water quality data using a multivariate discriminant analysis model

NASA Astrophysics Data System (ADS)

Lautz, L. K.; Hoke, G. D.; Lu, Z.; Siegel, D. I.

2013-12-01

Hydraulic fracturing has the potential to introduce saline water into the environment due to migration of deep formation water to shallow aquifers and/or discharge of flowback water to the environment during transport and disposal. It is challenging to definitively identify whether elevated salinity is associated with hydraulic fracturing, in part, due to the real possibility of other anthropogenic sources of salinity in the human-impacted watersheds in which drilling is taking place and some formation water present naturally in shallow groundwater aquifers. We combined new and published chemistry data for private drinking water wells sampled across five southern New York (NY) counties overlying the Marcellus Shale (Broome, Chemung, Chenango, Steuben, and Tioga). Measurements include Cl, Na, Br, I, Ca, Mg, Ba, SO4, and Sr. We compared this baseline groundwater quality data in NY, now under a moratorium on hydraulic fracturing, with published chemistry data for 6 different potential sources of elevated salinity in shallow groundwater, including Appalachian Basin formation water, road salt runoff, septic effluent, landfill leachate, animal waste, and water softeners. A multivariate random number generator was used to create a synthetic, low salinity (< 20 mg/L Cl) groundwater data set (n=1000) based on the statistical properties of the observed low salinity groundwater. The synthetic, low salinity groundwater was then artificially mixed with variable proportions of different potential sources of salinity to explore chemical differences between groundwater impacted by formation water, road salt runoff, septic effluent, landfill leachate, animal waste, and water softeners. We then trained a multivariate, discriminant analysis model on the resulting data set to classify observed high salinity groundwater (> 20 mg/L Cl) as being affected by formation water, road salt, septic effluent, landfill leachate, animal waste, or water softeners. Single elements or pairs of elements (e.g. Cl and Br) were not effective at discriminating between sources of salinity, indicating multivariate methods are needed. The discriminant analysis model classified most accurately samples affected by formation water and landfill leachate, whereas those contaminated by road salt, animal waste, and water softeners were more likely to be discriminated as contaminated by a different source. Using this approach, no shallow groundwater samples from NY appear to be affected by formation water, suggesting the source of salinity pre-hydraulic fracturing is primarily a combination of road salt, septic effluent, landfill leachate, and animal waste.

Combinations of elevated tissue miRNA-17-92 cluster expression and serum prostate-specific antigen as potential diagnostic biomarkers for prostate cancer.

PubMed

Feng, Sujuan; Qian, Xiaosong; Li, Han; Zhang, Xiaodong

2017-12-01

The aim of the present study was to investigate the effectiveness of the miR-17-92 cluster as a disease progression marker in prostate cancer (PCa). Reverse transcription-quantitative polymerase chain reaction analysis was used to detect the microRNA (miR)-17-92 cluster expression levels in tissues from patients with PCa or benign prostatic hyperplasia (BPH), in addition to in PCa and BPH cell lines. Spearman correlation was used for comparison and estimation of correlations between miRNA expression levels and clinicopathological characteristics such as the Gleason score and prostate-specific antigen (PSA). Receiver operating curve (ROC) analysis was performed for evaluation of specificity and sensitivity of miR-17-92 cluster expression levels for discriminating patients with PCa from patients with BPH. Kaplan-Meier analysis was plotted to investigate the predictive potential of miR-17-92 cluster for PCa biochemical recurrence. Expression of the majority of miRNAs in the miR-17-92 cluster was identified to be significantly increased in PCa tissues and cell lines. Bivariate correlation analysis indicated that the high expression of unregulated miRNAs was positively correlated with Gleason grade, but had no significant association with PSA. ROC curves demonstrated that high expression of miR-17-92 cluster predicted a higher diagnostic accuracy compared with PSA. Improved discriminating quotients were observed when combinations of unregulated miRNAs with PSA were used. Survival analysis confirmed a high combined miRNA score of miR-17-92 cluster was associated with shorter biochemical recurrence interval. miR-17-92 cluster could be a potential diagnostic and prognostic biomarker for PCa, and the combination of the miR-17-92 cluster and serum PSA may enhance the accuracy for diagnosis of PCa.

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.

Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis

NASA Astrophysics Data System (ADS)

Feng, Shangyuan; Lin, Juqiang; Huang, Zufang; Chen, Guannan; Chen, Weisheng; Wang, Yue; Chen, Rong; Zeng, Haishan

2013-01-01

The capability of using silver nanoparticle based near-infrared surface enhanced Raman scattering (SERS) spectroscopy combined with principal component analysis (PCA) and linear discriminate analysis (LDA) to differentiate esophageal cancer tissue from normal tissue was presented. Significant differences in Raman intensities of prominent SERS bands were observed between normal and cancer tissues. PCA-LDA multivariate analysis of the measured tissue SERS spectra achieved diagnostic sensitivity of 90.9% and specificity of 97.8%. This exploratory study demonstrated great potential for developing label-free tissue SERS analysis into a clinical tool for esophageal cancer detection.

[Correspondence analysis between traditional commercial specifications and quantitative quality indices of Notopterygii Rhizoma et Radix].

PubMed

Jiang, Shun-Yuan; Sun, Hong-Bing; Sun, Hui; Ma, Yu-Ying; Chen, Hong-Yu; Zhu, Wen-Tao; Zhou, Yi

2016-03-01

This paper aims to explore a comprehensive assessment method combined traditional Chinese medicinal material specifications with quantitative quality indicators. Seventy-six samples of Notopterygii Rhizoma et Radix were collected on market and at producing areas. Traditional commercial specifications were described and assigned, and 10 chemical components and volatile oils were determined for each sample. Cluster analysis, Fisher discriminant analysis and correspondence analysis were used to establish the relationship between the traditional qualitative commercial specifications and quantitative chemical indices for comprehensive evaluating quality of medicinal materials, and quantitative classification of commercial grade and quality grade. A herb quality index (HQI) including traditional commercial specifications and chemical components for quantitative grade classification were established, and corresponding discriminant function were figured out for precise determination of quality grade and sub-grade of Notopterygii Rhizoma et Radix. The result showed that notopterol, isoimperatorin and volatile oil were the major components for determination of chemical quality, and their dividing values were specified for every grade and sub-grade of the commercial materials of Notopterygii Rhizoma et Radix. According to the result, essential relationship between traditional medicinal indicators, qualitative commercial specifications, and quantitative chemical composition indicators can be examined by K-mean cluster, Fisher discriminant analysis and correspondence analysis, which provide a new method for comprehensive quantitative evaluation of traditional Chinese medicine quality integrated traditional commodity specifications and quantitative modern chemical index. Copyright© by the Chinese Pharmaceutical Association.

Identification of Medicinal Mugua Origin by Near Infrared Spectroscopy Combined with Partial Least-squares Discriminant Analysis.

PubMed

Han, Bangxing; Peng, Huasheng; Yan, Hui

2016-01-01

Mugua is a common Chinese herbal medicine. There are three main medicinal origin places in China, Xuancheng City Anhui Province, Qijiang District Chongqing City, Yichang City, Hubei Province, and suitable for food origin places Linyi City Shandong Province. To construct a qualitative analytical method to identify the origin of medicinal Mugua by near infrared spectroscopy (NIRS). Partial least squares discriminant analysis (PLSDA) model was established after the Mugua derived from five different origins were preprocessed by the original spectrum. Moreover, the hierarchical cluster analysis was performed. The result showed that PLSDA model was established. According to the relationship of the origins-related important score and wavenumber, and K-mean cluster analysis, the Muguas derived from different origins were effectively identified. NIRS technology can quickly and accurately identify the origin of Mugua, provide a new method and technology for the identification of Chinese medicinal materials. After preprocessed by D1+autoscale, more peaks were increased in the preprocessed Mugua in the near infrared spectrumFive latent variable scores could reflect the information related to the origin place of MuguaOrigins of Mugua were well-distinguished according to K. mean value clustering analysis. Abbreviations used: TCM: Traditional Chinese Medicine, NIRS: Near infrared spectroscopy, SG: Savitzky-Golay smoothness, D1: First derivative, D2: Second derivative, SNV: Standard normal variable transformation, MSC: Multiplicative scatter correction, PLSDA: Partial least squares discriminant analysis, LV: Latent variable, VIP scores: Important score.

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

NASA Astrophysics Data System (ADS)

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

2009-07-01

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

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.

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.

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

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

Kassouf, Amine, E-mail: amine.kassouf@agroparistech.fr; INRA, UMR1145 Ingénierie Procédés Aliments, 1 Avenue des Olympiades, 91300 Massy; AgroParisTech, UMR1145 Ingénierie Procédés Aliments, 16 rue Claude Bernard, 75005 Paris

2014-11-15

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

THE ROLE OF THE HIPPOCAMPUS IN OBJECT DISCRIMINATION BASED ON VISUAL FEATURES.

PubMed

Levcik, David; Nekovarova, Tereza; Antosova, Eliska; Stuchlik, Ales; Klement, Daniel

2018-06-07

The role of rodent hippocampus has been intensively studied in different cognitive tasks. However, its role in discrimination of objects remains controversial due to conflicting findings. We tested whether the number and type of features available for the identification of objects might affect the strategy (hippocampal-independent vs. hippocampal-dependent) that rats adopt to solve object discrimination tasks. We trained rats to discriminate 2D visual objects presented on a computer screen. The objects were defined either by their shape only or by multiple-features (a combination of filling pattern and brightness in addition to the shape). Our data showed that objects displayed as simple geometric shapes are not discriminated by trained rats after their hippocampi had been bilaterally inactivated by the GABA A -agonist muscimol. On the other hand, objects containing a specific combination of non-geometric features in addition to the shape are discriminated even without the hippocampus. Our results suggest that the involvement of the hippocampus in visual object discrimination depends on the abundance of object's features. Copyright © 2018. Published by Elsevier Inc.

Identifying the Physical Fitness, Anthropometric and Athletic Movement Qualities Discriminant of Developmental Level in Elite Junior Australian Football: Implications for the Development of Talent.

PubMed

Gaudion, Sarah L; Doma, Kenji; Sinclair, Wade; Banyard, Harry G; Woods, Carl T

2017-07-01

Gaudion, SL, Doma, K, Sinclair, W, Banyard, HG, and Woods, CT. Identifying the physical fitness, anthropometric and athletic movement qualities discriminant of developmental level in elite junior Australian football: implications for the development of talent. J Strength Cond Res 31(7): 1830-1839, 2017-This study aimed to identify the physical fitness, anthropometric and athletic movement qualities discriminant of developmental level in elite junior Australian football (AF). From a total of 77 players, 2 groups were defined according to their developmental level; under 16 (U16) (n = 40, 15.6 to 15.9 years), and U18 (n = 37, 17.1 to 17.9 years). Players performed a test battery consisting of 7 physical fitness assessments, 2 anthropometric measurements, and a fundamental athletic movement assessment. A multivariate analysis of variance tested the main effect of developmental level (2 levels: U16 and U18) on the assessment criterions, whilst binary logistic regression models and receiver operating characteristic (ROC) curves were built to identify the qualities most discriminant of developmental level. A significant effect of developmental level was evident on 9 of the assessments (d = 0.27-0.88; p ≤ 0.05). However, it was a combination of body mass, dynamic vertical jump height (nondominant leg), repeat sprint time, and the score on the 20-m multistage fitness test that provided the greatest association with developmental level (Akaike's information criterion = 80.84). The ROC curve was maximized with a combined score of 180.7, successfully discriminating 89 and 60% of the U18 and U16 players, respectively (area under the curve = 79.3%). These results indicate that there are distinctive physical fitness and anthropometric qualities discriminant of developmental level within the junior AF talent pathway. Coaches should consider these differences when designing training interventions at the U16 level to assist with the development of prospective U18 AF players.

Analysis of a multisensor image data set of south San Rafael Swell, Utah

NASA Technical Reports Server (NTRS)

Evans, D. L.

1982-01-01

A Shuttle Imaging Radar (SIR-A) image of the southern portion of the San Rafael Swell in Utah has been digitized and registered to coregistered Landsat, Seasat, and HCMM thermal inertia images. The addition of the SIR-A image to the registered data set improves rock type discrimination in both qualitative and quantitative analyses. Sedimentary units can be separated in a combined SIR-A/Seasat image that cannot be seen in either image alone. Discriminant Analyses show that the classification accuracy is improved with addition of the SIR-A image to Landsat images. Classification accuracy is further improved when texture information from the Seasat and SIR-A images is included.

Olfactory discrimination ability of Asian elephants (Elephas maximus) for structurally related odorants.

PubMed

Rizvanovic, Alisa; Amundin, Mats; Laska, Matthias

2013-02-01

Using a food-rewarded two-choice instrumental conditioning paradigm, we assessed the ability of Asian elephants, Elephas maximus, to discriminate between 2 sets of structurally related odorants. We found that the animals successfully discriminated between all 12 odor pairs involving members of homologous series of aliphatic 1-alcohols, n-aldehydes, 2-ketones, and n-carboxylic acids even when the stimuli differed from each other by only 1 carbon. With all 4 chemical classes, the elephants displayed a positive correlation between discrimination performance and structural similarity of odorants in terms of differences in carbon chain length. The animals also successfully discriminated between all 12 enantiomeric odor pairs tested. An analysis of odor structure-activity relationships suggests that a combination of molecular structural properties rather than a single molecular feature may be responsible for the discriminability of enantiomers. Compared with other species tested previously on the same sets of odor pairs (or on subsets thereof), the Asian elephants performed at least as well as mice and clearly better than human subjects, squirrel monkeys, pigtail macaques, South African fur seals, and honeybees. Further comparisons suggest that neither the relative nor the absolute size of the olfactory bulbs appear to be reliable predictors of between-species differences in olfactory discrimination capabilities. In contrast, we found a positive correlation between the number of functional olfactory receptor genes and the proportion of discriminable enantiomeric odor pairs. Taken together, the results of the present study support the notion that the sense of smell may play an important role in regulating the behavior of Asian elephants.

Combining Speed Information Across Space

NASA Technical Reports Server (NTRS)

Verghese, Preeti; Stone, Leland S.

1995-01-01

We used speed discrimination tasks to measure the ability of observers to combine speed information from multiple stimuli distributed across space. We compared speed discrimination thresholds in a classical discrimination paradigm to those in an uncertainty/search paradigm. Thresholds were measured using a temporal two-interval forced-choice design. In the discrimination paradigm, the n gratings in each interval all moved at the same speed and observers were asked to choose the interval with the faster gratings. Discrimination thresholds for this paradigm decreased as the number of gratings increased. This decrease was not due to increasing the effective stimulus area as a control experiment that increased the area of a single grating did not show a similar improvement in thresholds. Adding independent speed noise to each of the n gratings caused thresholds to decrease at a rate similar to the original no-noise case, consistent with observers combining an independent sample of speed from each grating in both the added- and no-noise cases. In the search paradigm, observers were asked to choose the interval in which one of the n gratings moved faster. Thresholds in this case increased with the number of gratings, behavior traditionally attributed to an input bottleneck. However, results from the discrimination paradigm showed that the increase was not due to observers' inability to process these gratings. We have also shown that the opposite trends of the data in the two paradigms can be predicted by a decision theory model that combines independent samples of speed information across space. This demonstrates that models typically used in classical detection and discrimination paradigms are also applicable to search paradigms. As our model does not distinguish between samples in space and time, it predicts that discrimination performance should be the same regardless of whether the gratings are presented in two spatial intervals or two temporal intervals. Our last experiment largely confirmed this prediction.

A characteristic biosignature for discrimination of gastric cancer from healthy population by high throughput GC-MS analysis

PubMed Central

Guo, Lei; Liu, Lei; Wen, Jingran; Xu, Lu; Yan, Min; Li, Zuofeng; Zhang, Xiaoyan; Nan, Peng; Jiang, Jinling; Ji, Jun; Zhang, Jianian; Cai, Wei; Zhuang, Huisheng; Wang, Yan; Zhu, Zhenggang; Yu, Yingyan

2016-01-01

Early diagnosis of gastric cancer is crucial to improve patient′ outcome. A good biomarker will function in early diagnosis for gastric cancer. In order to find practical and cost-effective biomarkers, we used gas chromatography combined mass spectrometer (GC-MS) to profile urinary metabolites on 293 urine samples. Ninety-four samples are taken as training set, others for validating study. Orthogonal partial least squares discriminant analysis (OPLS-DA), significance analysis of microarray (SAM) and Mann-Whitney U test are used for data analysis. The diagnostic value of urinary metabolites was evaluated by ROC curve. As results, Seventeen metabolites are significantly different between patients and healthy controls in training set. Among them, 14 metabolites show diagnostic value better than classic blood biomarkers by quantitative assay on validation set. Ten of them are amino acids and four are organic metabolites. Importantly, proline, p-cresol and 4-hydroxybenzoic acid disclose outcome-prediction value by means of survival analysis. Therefore, the examination of urinary metabolites is a promising noninvasive strategy for gastric cancer screening. PMID:27589838

[Quality evaluation of American ginseng using UPLC coupled with multivariate analysis].

PubMed

Tang, Yan; Yan, Shu-Mo; Wang, Jing-Jing; Yuan, Yuan; Yang, Bin

2016-05-01

An ultra performance liquid chromatography (UPLC)method combined with multivariate data analysis was developed to evaluate the quality of American ginseng by simultaneously determining the concentrations of six ginsenosides (Rg₁, Re, Rb₁, Rc, Ro and Rd)in the samples. For UPLC, acetonitrile with 0.01% formic acid and water with 0.01% formic acid were used as the mobile phase with gradient elution. Under the established chromatographic conditions, the six ginsenosides could be well separated and the results of linearity, stability, precision, repeatability, and recovery rate all reached the requirement of quantification analysis, respectively. The total contents of Rg₁, Re, and Rb₁ in 57 samples all reached the requirement of the 2015 edition of Chinese Pharmacopoeia. At the same time, the experimental data were analyzed by principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The crude drugs and the decoction pieces can be discriminated by a PCA method and the samples with different age can be distinguished by a PLS-DA method. Copyright© by the Chinese Pharmaceutical Association.

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

Dynamic learning and context-dependence in sequential, attribute-based, stated-preference valuation questions

Treesearch

Thomas P. Holmes; Kevin J. Boyle

2005-01-01

A hybrid stated-preference model is presented that combines the referendum contingent valuation response format with an experimentally designed set of attributes. A sequence of valuation questions is asked to a random sample in a mailout mail-back format. Econometric analysis shows greater discrimination between alternatives in the final choice in the sequence, and the...

Development of duplex SYBR Green I-based real-time quantitative reverse-transcription PCR for detection and discrimination of grapevine viruses

USDA-ARS?s Scientific Manuscript database

A SYBR® Green-based real-time quantitative reverse transcription PCR (qRT-PCR) assay in combination with melt curve analysis (MCA) was developed for the detection of nine grapevine viruses. The detection limits for singleplex qRT-PCR for all nine grapevine viruses were determined to be in the range ...

A rapid ATR-FTIR spectroscopic method for detection of sibutramine adulteration in tea and coffee based on hierarchical cluster and principal component analyses.

PubMed

Cebi, Nur; Yilmaz, Mustafa Tahsin; Sagdic, Osman

2017-08-15

Sibutramine may be illicitly included in herbal slimming foods and supplements marketed as "100% natural" to enhance weight loss. Considering public health and legal regulations, there is an urgent need for effective, rapid and reliable techniques to detect sibutramine in dietetic herbal foods, teas and dietary supplements. This research comprehensively explored, for the first time, detection of sibutramine in green tea, green coffee and mixed herbal tea using ATR-FTIR spectroscopic technique combined with chemometrics. Hierarchical cluster analysis and PCA principle component analysis techniques were employed in spectral range (2746-2656cm -1 ) for classification and discrimination through Euclidian distance and Ward's algorithm. Unadulterated and adulterated samples were classified and discriminated with respect to their sibutramine contents with perfect accuracy without any false prediction. The results suggest that existence of the active substance could be successfully determined at the levels in the range of 0.375-12mg in totally 1.75g of green tea, green coffee and mixed herbal tea by using FTIR-ATR technique combined with chemometrics. Copyright © 2017 Elsevier Ltd. All rights reserved.

Discrimination of Medicine Radix Astragali from Different Geographic Origins Using Multiple Spectroscopies Combined with Data Fusion Methods

NASA Astrophysics Data System (ADS)

Wang, Hai-Yan; Song, Chao; Sha, Min; Liu, Jun; Li, Li-Ping; Zhang, Zheng-Yong

2018-05-01

Raman spectra and ultraviolet-visible absorption spectra of four different geographic origins of Radix Astragali were collected. These data were analyzed using kernel principal component analysis combined with sparse representation classification. The results showed that the recognition rate reached 70.44% using Raman spectra for data input and 90.34% using ultraviolet-visible absorption spectra for data input. A new fusion method based on Raman combined with ultraviolet-visible data was investigated and the recognition rate was increased to 96.43%. The experimental results suggested that the proposed data fusion method effectively improved the utilization rate of the original data.

STR data for 15 autosomal STR markers from Paraná (Southern Brazil).

PubMed

Alves, Hemerson B; Leite, Fábio P N; Sotomaior, Vanessa S; Rueda, Fábio F; Silva, Rosane; Moura-Neto, Rodrigo S

2014-03-01

Allelic frequencies for 15 STR autosomal loci, using AmpFℓSTR® Identifiler™, forensic, and statistical parameters were calculated. All loci reached the Hardy-Weinberg equilibrium. The combined power of discrimination and mean power of exclusion were 0.999999999999999999 and 0.9999993, respectively. The MDS plot and NJ tree analysis, generated by FST matrix, corroborated the notion of the origins of the Paraná population as mainly European-derived. The combination of these 15 STR loci represents a powerful strategy for individual identification and parentage analyses for the Paraná population.

Discrimination of probiotic Lactobacillus strains for poultry by repetitive sequenced-based PCR fingerprinting.

PubMed

Lee, Chin Mei; Sieo, Chin Chin; Cheah, Yoke-Kqueen; Abdullah, Norhani; Ho, Yin Wan

2012-02-01

Four repetitive element sequence-based polymerase chain reaction (rep-PCR) methods, namely repetitive extragenic palindromic PCR (REP-PCR), enterobacterial repetitive intergenic consensus PCR (ERIC-PCR), polytrinucleotide (GTG)₅ -PCR and BOX-PCR, were evaluated for the molecular differentiation of 12 probiotic Lactobacillus strains previously isolated from the gastrointestinal tract of chickens and used as a multistrain probiotic. This study represents the first analysis of the comparative efficacy of these four rep-PCR methods and their combination (composite rep-PCR) in the molecular typing of Lactobacillus strains based on a discriminatory index (D). Species-specific and strain-specific profiles were observed from rep-PCR. From the numerical analysis of composite rep-PCR, BOX-PCR, (GTG)₅ -PCR, REP-PCR and ERIC-PCR, D values of 0.9118, 0.9044, 0.8897, 0.8750 and 0.8529 respectively were obtained. Composite rep-PCR analysis was the most discriminative method, with eight Lactobacillus strains, namely L. brevis ATCC 14869(T) , L. reuteri C 10, L. reuteri ATCC 23272(T) , L. gallinarum ATCC 33199(T) , L. salivarius ATCC 11741(T) , L. salivarius I 24, L. panis JCM 11053(T) and L. panis C 17, being differentiated at the strain level. Composite rep-PCR analysis is potentially a useful fingerprinting method to discriminate probiotic Lactobacillus strains isolated from the gastrointestinal tract of chickens. Copyright © 2011 Society of Chemical Industry.

Gait Kinematics in Individuals with Acute and Chronic Patellofemoral Pain.

PubMed

Fox, Aaron; Ferber, Reed; Saunders, Natalie; Osis, Sean; Bonacci, Jason

2018-03-01

This study aimed to identify the discriminating kinematic gait characteristics between individuals with acute and chronic patellofemoral pain (PFP) and healthy controls. Ninety-eight runners with PFP (39 male, 59 female) and 98 healthy control runners (38 male, 60 female) ran on a treadmill at a self-selected speed while three-dimensional lower limb kinematic data were collected. Runners with PFP were split into acute (n = 25) and chronic (n = 73) subgroups on the basis of whether they had been experiencing pain for less or greater than 3 months, respectively. Principal component analysis and linear discriminant analysis were used to determine the combination of kinematic gait characteristics that optimally separated individuals with acute PFP and chronic PFP and healthy controls. Compared with controls, both the acute and chronic PFP subgroups exhibited greater knee flexion across stance and greater ankle dorsiflexion during early stance. The acute PFP subgroup demonstrated greater transverse plane hip motion across stance compared with healthy controls. In contrast, the chronic PFP subgroup demonstrated greater frontal plane hip motion, greater knee abduction, and reduced ankle eversion/greater ankle inversion across stance when compared with healthy controls. This study identified characteristics that discriminated between individuals with acute and chronic PFP when compared with healthy controls. Certain discriminating characteristics were shared between both the acute and chronic subgroups when compared with healthy controls, whereas others were specific to the duration of PFP.

Potential of FTIR spectroscopy for analysis of tears for diagnosis purposes.

PubMed

Travo, Adrian; Paya, Clément; Déléris, Gérard; Colin, Joseph; Mortemousque, Bruno; Forfar, Isabelle

2014-04-01

It has been widely reported that the tear film, which is crucially important as a protective barrier of the eye, undergoes biochemical changes as a result of a wide range of ocular pathology. This tends to suggest the possibility of early detection of ocular diseases on the basis of biochemical analysis of tears. However, studies of tears by conventional methods of biomolecular and biochemical analysis are often limited by methodological difficulties. Moreover, such analysis could not be applied in the clinic, where structural and morphological analyses by, mainly, slit-lamp biomicroscopy remains the recommended method. In this study, we assessed, for the first time, the potential of FTIR spectroscopy combined with advanced chemometric processing of spectral data for analysis of raw tears for diagnosis purposes. We first optimized sampling and spectral acquisition (tears collection method, tear sample volume, and preservation of the samples) for accurate spectral measurement. On the basis of the results, we focused our study on the possibility of discriminating tears from normal individuals from those of patients with different ocular pathologies, and showed that the most discriminating spectral range is that corresponding to variations of CH2 and CH3 of lipid aliphatic chains. We also report more subtle discrimination of tears from patients with keratoconus and those from patients with non-specific inflammatory ocular diseases, on the basis of variations in spectral ranges attributed notably to lipid and carbohydrate vibrations. Finally, we also succeeded in distinguishing tears from patients with early-stage and late-stage keratoconus on the basis of spectral features attributed to protein structure. Therefore, this study strongly suggests that FTIR spectral analysis of tears could be developed as a valuable and cost-saving tool for biochemical-based detection of ocular diseases, potentially before the appearance of the first morphological signs of diseases. Combined with supervised modelling methods and with use of a spectral data base acquired for representative patients, such a spectral approach could be a useful addition to current methods of clinical analysis for improvement of patient care.

Combined chemometric analysis of (1)H NMR, (13)C NMR and stable isotope data to differentiate organic and conventional milk.

PubMed

Erich, Sarah; Schill, Sandra; Annweiler, Eva; Waiblinger, Hans-Ulrich; Kuballa, Thomas; Lachenmeier, Dirk W; Monakhova, Yulia B

2015-12-01

The increased sales of organically produced food create a strong need for analytical methods, which could authenticate organic and conventional products. Combined chemometric analysis of (1)H NMR-, (13)C NMR-spectroscopy data, stable-isotope data (IRMS) and α-linolenic acid content (gas chromatography) was used to differentiate organic and conventional milk. In total 85 raw, pasteurized and ultra-heat treated (UHT) milk samples (52 organic and 33 conventional) were collected between August 2013 and May 2014. The carbon isotope ratios of milk protein and milk fat as well as the α-linolenic acid content of these samples were determined. Additionally, the milk fat was analyzed by (1)H and (13)C NMR spectroscopy. The chemometric analysis of combined data (IRMS, GC, NMR) resulted in more precise authentication of German raw and retail milk with a considerably increased classification rate of 95% compared to 81% for NMR and 90% for IRMS using linear discriminate analysis. Copyright © 2015 Elsevier Ltd. All rights reserved.

Quality Evaluation of Juniperus rigida Sieb. et Zucc. Based on Phenolic Profiles, Bioactivity, and HPLC Fingerprint Combined with Chemometrics

PubMed Central

Liu, Zehua; Wang, Dongmei; Li, Dengwu; Zhang, Shuai

2017-01-01

Juniperus rigida (J. rigida) which is endemic to East Asia, has traditionally been used as an ethnomedicinal plant in China. This study was undertaken to evaluate the quality of J. rigida samples derived from 11 primary regions in China. Ten phenolic compounds were simultaneously quantified using reversed-phase high-performance liquid chromatography (RP-HPLC), and chlorogenic acid, catechin, podophyllotoxin, and amentoflavone were found to be the main compounds in J. rigida needles, with the highest contents detected for catechin and podophyllotoxin. J. rigida from Jilin (S9, S10) and Liaoning (S11) exhibited the highest contents of phenolic profiles (total phenolics, total flavonoids and 10 phenolic compounds) and the strongest antioxidant and antibacterial activities, followed by Shaanxi (S2, S3). A similarity analysis (SA) demonstrated substantial similarities in fingerprint chromatograms, from which 14 common peaks were selected. The similarity values varied from 0.85 to 0.98. Chemometrics techniques, including hierarchical cluster analysis (HCA), principal component analysis (PCA), and discriminant analysis (DA), were further applied to facilitate accurate classification and quantification of the J. rigida samples derived from the 11 regions. The results supported HPLC data showing that all J. rigida samples exhibit considerable variations in phenolic profiles, and the samples were further clustered into three major groups coincident with their geographical regions of origin. In addition, two discriminant functions with a 100% discrimination ratio were constructed to further distinguish and classify samples with unknown membership on the basis of eigenvalues to allow optimal discrimination among the groups. Our comprehensive findings on matching phenolic profiles and bioactivities along with data from fingerprint chromatograms with chemometrics provide an effective tool for screening and quality evaluation of J. rigida and related medicinal preparations. PMID:28469573

T-wave morphology can distinguish healthy controls from LQTS patients.

PubMed

Immanuel, S A; Sadrieh, A; Baumert, M; Couderc, J P; Zareba, W; Hill, A P; Vandenberg, J I

2016-09-01

Long QT syndrome (LQTS) is an inherited disorder associated with prolongation of the QT/QTc interval on the surface electrocardiogram (ECG) and a markedly increased risk of sudden cardiac death due to cardiac arrhythmias. Up to 25% of genotype-positive LQTS patients have QT/QTc intervals in the normal range. These patients are, however, still at increased risk of life-threatening events compared to their genotype-negative siblings. Previous studies have shown that analysis of T-wave morphology may enhance discrimination between control and LQTS patients. In this study we tested the hypothesis that automated analysis of T-wave morphology from Holter ECG recordings could distinguish between control and LQTS patients with QTc values in the range 400-450 ms. Holter ECGs were obtained from the Telemetric and Holter ECG Warehouse (THEW) database. Frequency binned averaged ECG waveforms were obtained and extracted T-waves were fitted with a combination of 3 sigmoid functions (upslope, downslope and switch) or two 9th order polynomial functions (upslope and downslope). Neural network classifiers, based on parameters obtained from the sigmoid or polynomial fits to the 1 Hz and 1.3 Hz ECG waveforms, were able to achieve up to 92% discrimination between control and LQTS patients and 88% discrimination between LQTS1 and LQTS2 patients. When we analysed a subgroup of subjects with normal QT intervals (400-450 ms, 67 controls and 61 LQTS), T-wave morphology based parameters enabled 90% discrimination between control and LQTS patients, compared to only 71% when the groups were classified based on QTc alone. In summary, our Holter ECG analysis algorithms demonstrate the feasibility of using automated analysis of T-wave morphology to distinguish LQTS patients, even those with normal QTc, from healthy controls.

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.

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

PubMed

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

2015-02-01

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

Molecular Characterization and Phylogenetic Analysis of Pseudomonas aeruginosa Isolates Recovered from Greek Aquatic Habitats Implementing the Double-Locus Sequence Typing Scheme.

PubMed

Pappa, Olga; Beloukas, Apostolos; Vantarakis, Apostolos; Mavridou, Athena; Kefala, Anastasia-Maria; Galanis, Alex

2017-07-01

The recently described double-locus sequence typing (DLST) scheme implemented to deeply characterize the genetic profiles of 52 resistant environmental Pseudomonas aeruginosa isolates deriving from aquatic habitats of Greece. DLST scheme was able not only to assign an already known allelic profile to the majority of the isolates but also to recognize two new ones (ms217-190, ms217-191) with high discriminatory power. A third locus (oprD) was also used for the molecular typing, which has been found to be fundamental for the phylogenetic analysis of environmental isolates given the resulted increased discrimination between the isolates. Additionally, the circulation of acquired resistant mechanisms in the aquatic habitats according to their genetic profiles was proved to be more extent. Hereby, we suggest that the combination of the DLST to oprD typing can discriminate phenotypically and genetically related environmental P. aeruginosa isolates providing reliable phylogenetic analysis at a local level.

Ultrasonic analysis to discriminate bread dough of different types of flour

NASA Astrophysics Data System (ADS)

García-Álvarez, J.; Rosell, C. M.; García-Hernández, M. J.; Chávez, J. A.; Turó, A.; Salazar, J.

2012-12-01

Many varieties of bread are prepared using flour coming from wheat. However, there are other types of flours milled from rice, legumes and some fruits and vegetables that are also suitable for baking purposes, used alone or in combination with wheat flour. The type of flour employed strongly influences the dough consistency, which is a relevant property for determining the dough potential for breadmaking purposes. Traditional methods for dough testing are relatively expensive, time-consuming, off-line and often require skilled operators. In this work, ultrasonic analysis are performed in order to obtain acoustic properties of bread dough samples prepared using two different types of flour, wheat flour and rice flour. The dough acoustic properties can be related to its viscoelastic characteristics, which in turn determine the dough feasibility for baking. The main advantages of the ultrasonic dough testing can be, among others, its low cost, fast, hygienic and on-line performance. The obtained results point out the potential of the ultrasonic analysis to discriminate doughs of different types of flour.

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

NASA Astrophysics Data System (ADS)

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

2013-11-01

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

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

PubMed

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

2015-01-01

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

Age determination of female redhead ducks

USGS Publications Warehouse

Dane, C.W.; Johnson, D.H.

1975-01-01

Eighty-seven fall-collected wings from female redhead ducks (Aythya americana) were assigned to the adult or juvenile group based on 'tertial' and 'tertial covert' shape and wear. To obtain spring age-related characters from these fall-collected groupings, we considered parameters of flight feathers retained until after the first breeding season. Parameters measured included: markings on and width of greater secondary coverts, and length, weight, and diameter of primary feathers. The best age categorization was obtained with discriminant analysis based on a combination of the most accurately measured parameters. This analysis, applied to 81 wings with complete measurements, resulted in only 1 being incorrectly aged and 3 placed in a questionable category. Discriminant functions used with covert markings and the three 5th primary parameters were applied to 30 known-age juvenile, hand-reared redhead females, 28 were correctly aged, none was incorrectly aged, and only 2 were placed in the questionable category.

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.

Permutation entropy analysis of heart rate variability for the assessment of cardiovascular autonomic neuropathy in type 1 diabetes mellitus.

PubMed

Carricarte Naranjo, Claudia; Sanchez-Rodriguez, Lazaro M; Brown Martínez, Marta; Estévez Báez, Mario; Machado García, Andrés

2017-07-01

Heart rate variability (HRV) analysis is a relevant tool for the diagnosis of cardiovascular autonomic neuropathy (CAN). To our knowledge, no previous investigation on CAN has assessed the complexity of HRV from an ordinal perspective. Therefore, the aim of this work is to explore the potential of permutation entropy (PE) analysis of HRV complexity for the assessment of CAN. For this purpose, we performed a short-term PE analysis of HRV in healthy subjects and type 1 diabetes mellitus patients, including patients with CAN. Standard HRV indicators were also calculated in the control group. A discriminant analysis was used to select the variables combination with best discriminative power between control and CAN patients groups, as well as for classifying cases. We found that for some specific temporal scales, PE indicators were significantly lower in CAN patients than those calculated for controls. In such cases, there were ordinal patterns with high probabilities of occurrence, while others were hardly found. We posit this behavior occurs due to a decrease of HRV complexity in the diseased system. Discriminant functions based on PE measures or probabilities of occurrence of ordinal patterns provided an average of 75% and 96% classification accuracy. Correlations of PE and HRV measures showed to depend only on temporal scale, regardless of pattern length. PE analysis at some specific temporal scales, seem to provide additional information to that obtained with traditional HRV methods. We concluded that PE analysis of HRV is a promising method for the assessment of CAN. Copyright © 2017 Elsevier Ltd. All rights reserved.

Wavelet coherence analysis: A new approach to distinguish organic and functional tremor types.

PubMed

Kramer, G; Van der Stouwe, A M M; Maurits, N M; Tijssen, M A J; Elting, J W J

2018-01-01

To distinguish tremor subtypes using wavelet coherence analysis (WCA). WCA enables to detect variations in coherence and phase difference between two signals over time and might be especially useful in distinguishing functional from organic tremor. In this pilot study, polymyography recordings were studied retrospectively of 26 Parkinsonian (PT), 26 functional (FT), 26 essential (ET), and 20 enhanced physiological (EPT) tremor patients. Per patient one segment of 20 s in duration, in which tremor was present continuously in the same posture, was selected. We studied several coherence and phase related parameters, and analysed all possible muscle combinations of the flexor and extensor muscles of the upper and fore arm. The area under the receiver operating characteristic curve (AUC-ROC) was applied to compare WCA and standard coherence analysis to distinguish tremor subtypes. The percentage of time with significant coherence (PTSC) and the number of periods without significant coherence (NOV) proved the most discriminative parameters. FT could be discriminated from organic (PT, ET, EPT) tremor by high NOV (31.88 vs 21.58, 23.12 and 10.20 respectively) with an AUC-ROC of 0.809, while standard coherence analysis resulted in an AUC-ROC of 0.552. EMG-EMG WCA analysis might provide additional variables to distinguish functional from organic tremor. WCA might prove to be of additional value to discriminate between tremor types. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

A novel combined approach of diffuse reflectance UV-Vis-NIR spectroscopy and multivariate analysis for non-destructive examination of blue ballpoint pen inks in forensic application.

PubMed

Kumar, Raj; Sharma, Vishal

2017-03-15

The present research is focused on the analysis of writing inks using destructive UV-Vis spectroscopy (dissolution of ink by the solvent) and non-destructive diffuse reflectance UV-Vis-NIR spectroscopy along with Chemometrics. Fifty seven samples of blue ballpoint pen inks were analyzed under optimum conditions to determine the differences in spectral features of inks among same and different manufacturers. Normalization was performed on the spectroscopic data before chemometric analysis. Principal Component Analysis (PCA) and K-mean cluster analysis were used on the data to ascertain whether the blue ballpoint pen inks could be differentiated by their UV-Vis/UV-Vis NIR spectra. The discriminating power is calculated by qualitative analysis by the visual comparison of the spectra (absorbance peaks), produced by the destructive and non-destructive methods. In the latter two methods, the pairwise comparison is made by incorporating the clustering method. It is found that chemometric method provides better discriminating power (98.72% and 99.46%, in destructive and non-destructive, respectively) in comparison to the qualitative analysis (69.67%). Copyright © 2016 Elsevier B.V. All rights reserved.

Multiplexed analysis combining distinctly-sized CdTe-MPA quantum dots and chemometrics for multiple mutually interfering analyte determination.

PubMed

Bittar, Dayana B; Ribeiro, David S M; Páscoa, Ricardo N M J; Soares, José X; Rodrigues, S Sofia M; Castro, Rafael C; Pezza, Leonardo; Pezza, Helena R; Santos, João L M

2017-11-01

Semiconductor quantum dots (QDs) have demonstrated a great potential as fluorescent probes for heavy metals monitoring. However, their great reactivity, whose tunability could be difficult to attain, could impair selectivity yielding analytical results with poor accuracy. In this work, the combination in the same analysis of multiple QDs, each with a particular ability to interact with the analyte, assured a multi-point detection that was not only exploited for a more precise analyte discrimination but also for the simultaneous discrimination of multiple mutually interfering species, in the same sample. Three different MPA-CdTe QDs (2.5, 3.0 and 3.8nm) with a good size distribution, confirmed by the FWHM values of 48.6, 55.4 and 80.8nm, respectively, were used. Principal component analysis (PCA) and partial least squares regression (PLS) were used for fluorescence data analysis. Mixtures of two MPA-CdTe QDs, emitting at different wavelength namely 549/566, 549/634 and 566/634nm were assayed. The 549/634nm emitting QDs mixture provided the best results for the discrimination of distinct ions on binary and ternary mixtures. The obtained RMSECV and R 2 CV values for the binary mixture were good, namely, from 0.01 to 0.08mgL -1 and from 0.74 to 0.89, respectively. Regarding the ternary mixture the RMSECV and R 2 CV values were good for Hg(II) (0.06 and 0.73mgL -1 , respectively) and Pb(II) (0.08 and 0.87mg L -1 , respectively) and acceptable for Cu(II) (0.02 and 0.51mgL -1 , respectively). In conclusion, the obtained results showed that the developed approach is capable of resolve binary and ternary mixtures of Pb (II), Hg (II) and Cu (II), providing accurate information about lead (II) and mercury (II) concentration and signaling the occurrence of Cu (II). Copyright © 2017 Elsevier B.V. All rights reserved.

Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers.

PubMed

Maniruzzaman, Md; Rahman, Md Jahanur; Al-MehediHasan, Md; Suri, Harman S; Abedin, Md Menhazul; El-Baz, Ayman; Suri, Jasjit S

2018-04-10

Diabetes mellitus is a group of metabolic diseases in which blood sugar levels are too high. About 8.8% of the world was diabetic in 2017. It is projected that this will reach nearly 10% by 2045. The major challenge is that when machine learning-based classifiers are applied to such data sets for risk stratification, leads to lower performance. Thus, our objective is to develop an optimized and robust machine learning (ML) system under the assumption that missing values or outliers if replaced by a median configuration will yield higher risk stratification accuracy. This ML-based risk stratification is designed, optimized and evaluated, where: (i) the features are extracted and optimized from the six feature selection techniques (random forest, logistic regression, mutual information, principal component analysis, analysis of variance, and Fisher discriminant ratio) and combined with ten different types of classifiers (linear discriminant analysis, quadratic discriminant analysis, naïve Bayes, Gaussian process classification, support vector machine, artificial neural network, Adaboost, logistic regression, decision tree, and random forest) under the hypothesis that both missing values and outliers when replaced by computed medians will improve the risk stratification accuracy. Pima Indian diabetic dataset (768 patients: 268 diabetic and 500 controls) was used. Our results demonstrate that on replacing the missing values and outliers by group median and median values, respectively and further using the combination of random forest feature selection and random forest classification technique yields an accuracy, sensitivity, specificity, positive predictive value, negative predictive value and area under the curve as: 92.26%, 95.96%, 79.72%, 91.14%, 91.20%, and 0.93, respectively. This is an improvement of 10% over previously developed techniques published in literature. The system was validated for its stability and reliability. RF-based model showed the best performance when outliers are replaced by median values.

Ultrasonographic Evaluation of Cervical Lymph Nodes in Thyroid Cancer.

PubMed

Machado, Maria Regina Marrocos; Tavares, Marcos Roberto; Buchpiguel, Carlos Alberto; Chammas, Maria Cristina

2017-02-01

Objective To determine what ultrasonographic features can identify metastatic cervical lymph nodes, both preoperatively and in recurrences after complete thyroidectomy. Study Design Prospective. Setting Outpatient clinic, Department of Head and Neck Surgery, School of Medicine, University of São Paulo, Brazil. Subjects and Methods A total of 1976 lymph nodes were evaluated in 118 patients submitted to total thyroidectomy with or without cervical lymph node dissection. All the patients were examined by cervical ultrasonography, preoperatively and/or postoperatively. The following factors were assessed: number, size, shape, margins, presence of fatty hilum, cortex, echotexture, echogenicity, presence of microcalcification, presence of necrosis, and type of vascularity. The specificity, sensitivity, positive predictive value, and negative predictive value of each variable were calculated. Univariate and multivariate logistic regression analyses were conducted. A receiver operator characteristic (ROC) curve was plotted to determine the best cutoff value for the number of variables to discriminate malignant lymph nodes. Results Significant differences were found between metastatic and benign lymph nodes with regard to all of the variables evaluated ( P < .05). Logistic regression analysis revealed that size and echogenicity were the best combination of altered variables (odds ratio, 40.080 and 7.288, respectively) in discriminating malignancy. The ROC curve analysis showed that 4 was the best cutoff value for the number of altered variables to discriminate malignant lymph nodes, with a combined specificity of 85.7%, sensitivity of 96.4%, and efficiency of 91.0%. Conclusion Greater diagnostic accuracy was achieved by associating the ultrasonographic variables assessed rather than by considering them individually.

Acculturation Predicts Negative Affect and Shortened Telomere Length.

PubMed

Ruiz, R Jeanne; Trzeciakowski, Jerome; Moore, Tiffany; Ayers, Kimberly S; Pickler, Rita H

2016-10-12

Chronic stress may accelerate cellular aging. Telomeres, protective "caps" at the end of chromosomes, modulate cellular aging and may be good biomarkers for the effects of chronic stress, including that associated with acculturation. The purpose of this analysis was to examine telomere length (TL) in acculturating Hispanic Mexican American women and to determine the associations among TL, acculturation, and psychological factors. As part of a larger cross-sectional study of 516 pregnant Hispanic Mexican American women, we analyzed DNA in blood samples (N = 56) collected at 22-24 weeks gestation for TL as an exploratory measure using monochrome multiplex quantitative telomere polymerase chain reaction (PCR). We measured acculturation with the Acculturation Rating Scale for Mexican Americans, depression with the Beck Depression Inventory, discrimination with the Experiences of Discrimination Scale, and stress with the Perceived Stress Scale. TL was negatively moderately correlated with two variables of acculturation: Anglo orientation and greater acculturation-level scores. We combined these scores for a latent variable, acculturation, and we combined depression, stress, and discrimination scores in another latent variable, "negative affectivity." Acculturation and negative affectivity were bidirectionally correlated. Acculturation significantly negatively predicted TL. Using structural equation modeling, we found the model had an excellent fit with the root mean square error of approximation estimate = .0001, comparative fit index = 1.0, Tucker-Lewis index = 1.0, and standardized root mean square residual = .05. The negative effects of acculturation on the health of Hispanic women have been previously demonstrated. Findings from this analysis suggest a link between acculturation and TL, which may indicate accelerated cellular aging associated with overall poor health outcomes. © The Author(s) 2016.

Pepper seed variety identification based on visible/near-infrared spectral technology

NASA Astrophysics Data System (ADS)

Li, Cuiling; Wang, Xiu; Meng, Zhijun; Fan, Pengfei; Cai, Jichen

2016-11-01

Pepper is a kind of important fruit vegetable, with the expansion of pepper hybrid planting area, detection of pepper seed purity is especially important. This research used visible/near infrared (VIS/NIR) spectral technology to detect the variety of single pepper seed, and chose hybrid pepper seeds "Zhuo Jiao NO.3", "Zhuo Jiao NO.4" and "Zhuo Jiao NO.5" as research sample. VIS/NIR spectral data of 80 "Zhuo Jiao NO.3", 80 "Zhuo Jiao NO.4" and 80 "Zhuo Jiao NO.5" pepper seeds were collected, and the original spectral data was pretreated with standard normal variable (SNV) transform, first derivative (FD), and Savitzky-Golay (SG) convolution smoothing methods. Principal component analysis (PCA) method was adopted to reduce the dimension of the spectral data and extract principal components, according to the distribution of the first principal component (PC1) along with the second principal component(PC2) in the twodimensional plane, similarly, the distribution of PC1 coupled with the third principal component(PC3), and the distribution of PC2 combined with PC3, distribution areas of three varieties of pepper seeds were divided in each twodimensional plane, and the discriminant accuracy of PCA was tested through observing the distribution area of samples' principal components in validation set. This study combined PCA and linear discriminant analysis (LDA) to identify single pepper seed varieties, results showed that with the FD preprocessing method, the discriminant accuracy of pepper seed varieties was 98% for validation set, it concludes that using VIS/NIR spectral technology is feasible for identification of single pepper seed varieties.

New Biomarkers of Coffee Consumption Identified by the Non-Targeted Metabolomic Profiling of Cohort Study Subjects

PubMed Central

Martin, Jean-François; Lyan, Bernard; Pujos-Guillot, Estelle; Fezeu, Leopold; Hercberg, Serge; Comte, Blandine; Galan, Pilar; Touvier, Mathilde; Manach, Claudine

2014-01-01

Coffee contains various bioactives implicated with human health and disease risk. To accurately assess the effects of overall consumption upon health and disease, individual intake must be measured in large epidemiological studies. Metabolomics has emerged as a powerful approach to discover biomarkers of intake for a large range of foods. Here we report the profiling of the urinary metabolome of cohort study subjects to search for new biomarkers of coffee intake. Using repeated 24-hour dietary records and a food frequency questionnaire, 20 high coffee consumers (183–540 mL/d) and 19 low consumers were selected from the French SU.VI.MAX2 cohort. Morning spot urine samples from each subject were profiled by high-resolution mass spectrometry. Partial least-square discriminant analysis of multidimensional liquid chromatography-mass spectrometry data clearly distinguished high consumers from low via 132 significant (p-value<0.05) discriminating features. Ion clusters whose intensities were most elevated in the high consumers were annotated using online and in-house databases and their identities checked using commercial standards and MS-MS fragmentation. The best discriminants, and thus potential markers of coffee consumption, were the glucuronide of the diterpenoid atractyligenin, the diketopiperazine cyclo(isoleucyl-prolyl), and the alkaloid trigonelline. Some caffeine metabolites, such as 1-methylxanthine, were also among the discriminants, however caffeine may be consumed from other sources and its metabolism is subject to inter-individual variation. Receiver operating characteristics curve analysis showed that the biomarkers identified could be used effectively in combination for increased sensitivity and specificity. Once validated in other cohorts or intervention studies, these specific single or combined biomarkers will become a valuable alternative to assessment of coffee intake by dietary survey and finally lead to a better understanding of the health implications of coffee consumption. PMID:24713823

Evidence of a DHA Signature in the Lipidome and Metabolome of Human Hepatocytes.

PubMed

Ghini, Veronica; Di Nunzio, Mattia; Tenori, Leonardo; Valli, Veronica; Danesi, Francesca; Capozzi, Francesco; Luchinat, Claudio; Bordoni, Alessandra

2017-02-08

Cell supplementation with bioactive molecules often causes a perturbation in the whole intracellular environment. Omics techniques can be applied for the assessment of this perturbation. In this study, the overall effect of docosahexaenoic acid (DHA) supplementation on cultured human hepatocyte lipidome and metabolome has been investigated using nuclear magnetic resonance (NMR) in combination with traditional techniques. The effect of two additional bioactives sharing with DHA the lipid-lowering effect-propionic acid (PRO) and protocatechuic acid (PCA)-has also been evaluated in the context of possible synergism. NMR analysis of the cell lipid extracts showed that DHA supplementation, alone or in combination with PCA or PRO, strongly altered the cell lipid profile. The perfect discrimination between cells receiving DHA (alone or in combination) and the other cells reinforced the idea of a global rearrangement of the lipid environment induced by DHA. Notably, gas chromatography and fluorimetric analyses confirmed the strong discrimination obtained by NMR. The DHA signature was evidenced not only in the cell lipidome, but also in the metabolome. Results reported herein indicate that NMR, combined with other techniques, represents a fundamental approach to studying the effect of bioactive supplementation, particularly in the case of molecules with a broad spectrum of mechanisms of action.

Comparing ungulate dietary proxies using discriminant function analysis.

PubMed

Fraser, Danielle; Theodor, Jessica M

2011-12-01

A variety of tooth-wear and morphological dietary proxies have been proposed for ungulates. In turn, they have been applied to fossil specimens with the purpose of reconstructing the diets of extinct taxa. Although these dietary proxies have been used in isolation and in combination, a consistent set of statistical analyses has never been applied to all of the available datasets. The purpose of this study is to determine how well the most commonly used dietary proxies classify ungulates as browsers, grazers, and mixed feeders individually and in combination. Discriminant function analysis is applied to individual dietary proxies (hypsodonty, mesowear, microwear, and several cranial dietary proxies) and to combinations thereof to compare rates of successful dietary classification. In general, the tooth-wear dietary proxies (mesowear and microwear) perform better than morphological dietary proxies, though none are strong proxies in isolation. The success rates of the cranial dietary proxies are not increased substantially when ruminants and bovids are analyzed separately, and significance among the three dietary guilds is reduced when controlling for phylogenetic relatedness. The combination of hypsodonty, mesowear, and microwear is found to have a high rate of successful dietary classification, but a combination of all commonly used proxies increases the success rate to 100%. In most cases, mixed feeders bear the greatest resemblance to browsers suggesting that a morphology intermediate to browsers and grazers may represent a fitness valley resulting from the inability to exploit both browse and graze efficiently. These results are important for future paleoecological studies and should be used as a guide for determining which dietary proxies are appropriate to the research question. Copyright © 2011 Wiley-Liss, Inc.

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

PubMed

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

2016-01-01

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

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

PubMed Central

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

2016-01-01

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

Data fusion for food authentication. Combining rare earth elements and trace metals to discriminate "Fava Santorinis" from other yellow split peas using chemometric tools.

PubMed

Drivelos, Spiros A; Higgins, Kevin; Kalivas, John H; Haroutounian, Serkos A; Georgiou, Constantinos A

2014-12-15

"Fava Santorinis", is a protected designation of origin (PDO) yellow split pea species growing only in the island of Santorini in Greece. Due to its nutritional quality and taste, it has gained a high monetary value. Thus, it is prone to adulteration with other yellow split peas. In order to discriminate "Fava Santorinis" from other yellow split peas, four classification methods utilising rare earth elements (REEs) measured through inductively coupled plasma-mass spectrometry (ICP-MS) are studied. The four classification processes are orthogonal projection analysis (OPA), Mahalanobis distance (MD), partial least squares discriminant analysis (PLS-DA) and k nearest neighbours (KNN). Since it is known that trace elements are often useful to determine geographical origin of food products, we further quantitated for trace elements using ICP-MS. Presented in this paper are results using the four classification processes based on the fusion of the REEs data with the trace element data. Overall, the OPA method was found to perform best with up to 100% accuracy using the fused data. Copyright © 2014 Elsevier Ltd. All rights reserved.

Wave Mode Discrimination of Coded Ultrasonic Guided Waves Using Two-Dimensional Compressed Pulse Analysis.

PubMed

Malo, Sergio; Fateri, Sina; Livadas, Makis; Mares, Cristinel; Gan, Tat-Hean

2017-07-01

Ultrasonic guided waves testing is a technique successfully used in many industrial scenarios worldwide. For many complex applications, the dispersive nature and multimode behavior of the technique still poses a challenge for correct defect detection capabilities. In order to improve the performance of the guided waves, a 2-D compressed pulse analysis is presented in this paper. This novel technique combines the use of pulse compression and dispersion compensation in order to improve the signal-to-noise ratio (SNR) and temporal-spatial resolution of the signals. The ability of the technique to discriminate different wave modes is also highlighted. In addition, an iterative algorithm is developed to identify the wave modes of interest using adaptive peak detection to enable automatic wave mode discrimination. The employed algorithm is developed in order to pave the way for further in situ applications. The performance of Barker-coded and chirp waveforms is studied in a multimodal scenario where longitudinal and flexural wave packets are superposed. The technique is tested in both synthetic and experimental conditions. The enhancements in SNR and temporal resolution are quantified as well as their ability to accurately calculate the propagation distance for different wave modes.

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

PubMed Central

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

2010-01-01

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

Hyperspectral image analysis for rapid and accurate discrimination of bacterial infections: A benchmark study.

PubMed

Arrigoni, Simone; Turra, Giovanni; Signoroni, Alberto

2017-09-01

With the rapid diffusion of Full Laboratory Automation systems, Clinical Microbiology is currently experiencing a new digital revolution. The ability to capture and process large amounts of visual data from microbiological specimen processing enables the definition of completely new objectives. These include the direct identification of pathogens growing on culturing plates, with expected improvements in rapid definition of the right treatment for patients affected by bacterial infections. In this framework, the synergies between light spectroscopy and image analysis, offered by hyperspectral imaging, are of prominent interest. This leads us to assess the feasibility of a reliable and rapid discrimination of pathogens through the classification of their spectral signatures extracted from hyperspectral image acquisitions of bacteria colonies growing on blood agar plates. We designed and implemented the whole data acquisition and processing pipeline and performed a comprehensive comparison among 40 combinations of different data preprocessing and classification techniques. High discrimination performance has been achieved also thanks to improved colony segmentation and spectral signature extraction. Experimental results reveal the high accuracy and suitability of the proposed approach, driving the selection of most suitable and scalable classification pipelines and stimulating clinical validations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cortical sensorimotor alterations classify clinical phenotype and putative genotype of spasmodic dysphonia.

PubMed

Battistella, G; Fuertinger, S; Fleysher, L; Ozelius, L J; Simonyan, K

2016-10-01

Spasmodic dysphonia (SD), or laryngeal dystonia, is a task-specific isolated focal dystonia of unknown causes and pathophysiology. Although functional and structural abnormalities have been described in this disorder, the influence of its different clinical phenotypes and genotypes remains scant, making it difficult to explain SD pathophysiology and to identify potential biomarkers. We used a combination of independent component analysis and linear discriminant analysis of resting-state functional magnetic resonance imaging data to investigate brain organization in different SD phenotypes (abductor versus adductor type) and putative genotypes (familial versus sporadic cases) and to characterize neural markers for genotype/phenotype categorization. We found abnormal functional connectivity within sensorimotor and frontoparietal networks in patients with SD compared with healthy individuals as well as phenotype- and genotype-distinct alterations of these networks, involving primary somatosensory, premotor and parietal cortices. The linear discriminant analysis achieved 71% accuracy classifying SD and healthy individuals using connectivity measures in the left inferior parietal and sensorimotor cortices. When categorizing between different forms of SD, the combination of measures from the left inferior parietal, premotor and right sensorimotor cortices achieved 81% discriminatory power between familial and sporadic SD cases, whereas the combination of measures from the right superior parietal, primary somatosensory and premotor cortices led to 71% accuracy in the classification of adductor and abductor SD forms. Our findings present the first effort to identify and categorize isolated focal dystonia based on its brain functional connectivity profile, which may have a potential impact on the future development of biomarkers for this rare disorder. © 2016 EAN.

Cortical sensorimotor alterations classify clinical phenotype and putative genotype of spasmodic dysphonia

PubMed Central

Battistella, Giovanni; Fuertinger, Stefan; Fleysher, Lazar; Ozelius, Laurie J.; Simonyan, Kristina

2017-01-01

Background Spasmodic dysphonia (SD), or laryngeal dystonia, is a task-specific isolated focal dystonia of unknown causes and pathophysiology. Although functional and structural abnormalities have been described in this disorder, the influence of its different clinical phenotypes and genotypes remains scant, making it difficult to explain SD pathophysiology and to identify potential biomarkers. Methods We used a combination of independent component analysis and linear discriminant analysis of resting-state functional MRI data to investigate brain organization in different SD phenotypes (abductor vs. adductor type) and putative genotypes (familial vs. sporadic cases) and to characterize neural markers for genotype/phenotype categorization. Results We found abnormal functional connectivity within sensorimotor and frontoparietal networks in SD patients compared to healthy individuals as well as phenotype- and genotype-distinct alterations of these networks, involving primary somatosensory, premotor and parietal cortices. The linear discriminant analysis achieved 71% accuracy classifying SD and healthy individuals using connectivity measures in the left inferior parietal and sensorimotor cortex. When categorizing between different forms of SD, the combination of measures from left inferior parietal, premotor and right sensorimotor cortices achieved 81% discriminatory power between familial and sporadic SD cases, whereas the combination of measures from the right superior parietal, primary somatosensory and premotor cortices led to 71% accuracy in the classification of adductor and abductor SD forms. Conclusions Our findings present the first effort to identify and categorize isolated focal dystonia based on its brain functional connectivity profile, which may have a potential impact on the future development of biomarkers for this rare disorder. PMID:27346568

How DNA barcoding can be more effective in microalgae identification: a case of cryptic diversity revelation in Scenedesmus (Chlorophyceae)

PubMed Central

Zou, Shanmei; Fei, Cong; Wang, Chun; Gao, Zhan; Bao, Yachao; He, Meilin; Wang, Changhai

2016-01-01

Microalgae identification is extremely difficult. The efficiency of DNA barcoding in microalgae identification involves ideal gene markers and approaches employed, which however, is still under the way. Although Scenedesmus has obtained much research in producing lipids its identification is difficult. Here we present a comprehensive coalescent, distance and character-based DNA barcoding for 118 Scenedesmus strains based on rbcL, tufA, ITS and 16S. The four genes, and their combined data rbcL + tufA + ITS + 16S, rbcL + tufA and ITS + 16S were analyzed by all of GMYC, P ID, PTP, ABGD, and character-based barcoding respectively. It was apparent that the three combined gene data showed a higher proportion of resolution success than the single gene. In comparison, the GMYC and PTP analysis produced more taxonomic lineages. The ABGD generated various resolution in discrimination among the single and combined data. The character-based barcoding was proved to be the most effective approach for species discrimination in both single and combined data which produced consistent species identification. All the integrated results recovered 11 species, five out of which were revealed as potential cryptic species. We suggest that the character-based DNA barcoding together with other approaches based on multiple genes and their combined data could be more effective in microalgae diversity revelation. PMID:27827440

How DNA barcoding can be more effective in microalgae identification: a case of cryptic diversity revelation in Scenedesmus (Chlorophyceae).

PubMed

Zou, Shanmei; Fei, Cong; Wang, Chun; Gao, Zhan; Bao, Yachao; He, Meilin; Wang, Changhai

2016-11-09

Microalgae identification is extremely difficult. The efficiency of DNA barcoding in microalgae identification involves ideal gene markers and approaches employed, which however, is still under the way. Although Scenedesmus has obtained much research in producing lipids its identification is difficult. Here we present a comprehensive coalescent, distance and character-based DNA barcoding for 118 Scenedesmus strains based on rbcL, tufA, ITS and 16S. The four genes, and their combined data rbcL + tufA + ITS + 16S, rbcL + tufA and ITS + 16S were analyzed by all of GMYC, P ID, PTP, ABGD, and character-based barcoding respectively. It was apparent that the three combined gene data showed a higher proportion of resolution success than the single gene. In comparison, the GMYC and PTP analysis produced more taxonomic lineages. The ABGD generated various resolution in discrimination among the single and combined data. The character-based barcoding was proved to be the most effective approach for species discrimination in both single and combined data which produced consistent species identification. All the integrated results recovered 11 species, five out of which were revealed as potential cryptic species. We suggest that the character-based DNA barcoding together with other approaches based on multiple genes and their combined data could be more effective in microalgae diversity revelation.

Matching mammographic regions in mediolateral oblique and cranio caudal views: a probabilistic approach

NASA Astrophysics Data System (ADS)

Samulski, Maurice; Karssemeijer, Nico

2008-03-01

Most of the current CAD systems detect suspicious mass regions independently in single views. In this paper we present a method to match corresponding regions in mediolateral oblique (MLO) and craniocaudal (CC) mammographic views of the breast. For every possible combination of mass regions in the MLO view and CC view, a number of features are computed, such as the difference in distance of a region to the nipple, a texture similarity measure, the gray scale correlation and the likelihood of malignancy of both regions computed by single-view analysis. In previous research, Linear Discriminant Analysis was used to discriminate between correct and incorrect links. In this paper we investigate if the performance can be improved by employing a statistical method in which four classes are distinguished. These four classes are defined by the combinations of view (MLO/CC) and pathology (TP/FP) labels. We use distance-weighted k-Nearest Neighbor density estimation to estimate the likelihood of a region combination. Next, a correspondence score is calculated as the likelihood that the region combination is a TP-TP link. The method was tested on 412 cases with a malignant lesion visible in at least one of the views. In 82.4% of the cases a correct link could be established between the TP detections in both views. In future work, we will use the framework presented here to develop a context dependent region matching scheme, which takes the number and likelihood of possible alternatives into account. It is expected that more accurate determination of matching probabilities will lead to improved CAD performance.

Two-marker combinations for preoperative discrimination of benign and malignant ovarian masses.

PubMed

Freydanck, Maj Kristin; Laubender, Ruediger Paul; Rack, Brigitte; Schuhmacher, Lan; Jeschke, Udo; Scholz, Christoph

2012-05-01

When caring for patients with ovarian neoplasms, correct preoperative discrimination of benign and malignant disease is deemed vital. In this study, we tested serum biomarkers' alone and in combination, to achieve this aim. We measured the concentrations of Cancer Antigen (CA)-125, CA15-3, CA27-29, Carcinoembryonic Antigen (CEA), CA19-9, human chorionic gonadotropin (hCG), Placental Protein (PP)1490, CA72-4, galectin-3, galectin-1 and Human epididymis protein (HE)4 in sera of 133 patients with pelvic masses by ELISA and correlated the results to subsequent histology. We used the area under the curve (AUC) of biomarkers and their combinations and calculated the 95% confidence intervals by using casewise resampling. The best single biomarkers were CA-125 (sensitivity and AUC) and HE4 (specificity). Combinations with HE4 and CA19-9 improved the predictive power of CA-125. The best discrimination was achieved by the combination of CA-125 and HE4, with an AUC of 0.961. A combination of CA-125 with HE4 could facilitate the identification of women at risk for ovarian cancer.

[Development of the soft independent modelling of class analogies model to discrimination Vibrio parahemolyticus by Smartongue].

PubMed

Huang, Jianfeng; Zhao, Guangying; Dou, Wenchao

2011-04-01

To explore a new rapid detection method for detecting of Food pathogens. We used the Smartongue, to determine the composition informations of the liquid culture samples and combined with soft independent modelling of class analogies (SIMCA) to analyze their respective species, then set up a Smartongue -SIMCA model to discriminate the V. parahaemolyticus. The Smartongue has 6 working electrodes and three frequency segments, we can built 18 discrimination models in one detection. After comparing all the 18 discrimination models, the optimal working electrodes and frequency segments were selected out, they were: palladium electrode in 1 Hz frequency segment, tungsten electrode in 100 Hz and silver electrode in 100 Hz. Then 10 species of pathogenic Vibrio were discriminated by the 3 models. The V. damsela, V. metschnikovii, V. alginalyticus, V. cincinnatiensis, V. metschnikovii and V. cholerae O serogroup samples could be discriminated by the SIMCA model of V. parahaemolyticus with palladium electrode 1 Hz frequency segment; V. mimicus and V. vulnincus samples could be discriminated by the SIMCA model of V. parahaemolyticus with tungsten electrode 100 Hz frequency segment; V. carcariae and V. cholerae non-O serogroup samples could be discriminated with the SIMCA model of V. parahaemolyticus in silver electrode 100 Hz frequency segment. The accurate discrimination of ten species of Vibrio samples is 100%. The Smartongue combined with SIMCA can discriminate V. parahaemolyticus with other pathogenic Vibrio effectively. It has a promising future as a new rapid detection method for V. parahaemolyticus.

REGIONAL SEISMIC CHEMICAL AND NUCLEAR EXPLOSION DISCRIMINATION: WESTERN U.S. EXAMPLES

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

Walter, W R; Taylor, S R; Matzel, E

2006-07-07

We continue exploring methodologies to improve regional explosion discrimination using the western U.S. as a natural laboratory. The western U.S. has abundant natural seismicity, historic nuclear explosion data, and widespread mine blasts, making it a good testing ground to study the performance of regional explosion discrimination techniques. We have assembled and measured a large set of these events to systematically explore how to best optimize discrimination performance. Nuclear explosions can be discriminated from a background of earthquakes using regional phase (Pn, Pg, Sn, Lg) amplitude measures such as high frequency P/S ratios. The discrimination performance is improved if the amplitudesmore » can be corrected for source size and path length effects. We show good results are achieved using earthquakes alone to calibrate for these effects with the MDAC technique (Walter and Taylor, 2001). We show significant further improvement is then possible by combining multiple MDAC amplitude ratios using an optimized weighting technique such as Linear Discriminant Analysis (LDA). However this requires data or models for both earthquakes and explosions. In many areas of the world regional distance nuclear explosion data is lacking, but mine blast data is available. Mine explosions are often designed to fracture and/or move rock, giving them different frequency and amplitude behavior than contained chemical shots, which seismically look like nuclear tests. Here we explore discrimination performance differences between explosion types, the possible disparity in the optimization parameters that would be chosen if only chemical explosions were available and the corresponding effect of that disparity on nuclear explosion discrimination. Even after correcting for average path and site effects, regional phase ratios contain a large amount of scatter. This scatter appears to be due to variations in source properties such as depth, focal mechanism, stress drop, in the near source material properties (including emplacement conditions in the case of explosions) and in variations from the average path and site correction. Here we look at several kinds of averaging as a means to try and reduce variance in earthquake and explosion populations and better understand the factors going into a minimum variance level as a function of epicenter (see Anderson ee et al. this volume). We focus on the performance of P/S ratios over the frequency range from 1 to 16 Hz finding some improvements in discrimination as frequency increases. We also explore averaging and optimally combining P/S ratios in multiple frequency bands as a means to reduce variance. Similarly we explore the effects of azimuthally averaging both regional amplitudes and amplitude ratios over multiple stations to reduce variance. Finally we look at optimal performance as a function of magnitude and path length, as these put limits the availability of good high frequency discrimination measures.« less

Quantitative analysis of professionally trained versus untrained voices.

PubMed

Siupsinskiene, Nora

2003-01-01

The aim of this study was to compare healthy trained and untrained voices as well as healthy and dysphonic trained voices in adults using combined voice range profile and aerodynamic tests, to define the normal range limiting values of quantitative voice parameters and to select the most informative quantitative voice parameters for separation between healthy and dysphonic trained voices. Three groups of persons were evaluated. One hundred eighty six healthy volunteers were divided into two groups according to voice training: non-professional speakers group consisted of 106 untrained voices persons (36 males and 70 females) and professional speakers group--of 80 trained voices persons (21 males and 59 females). Clinical group consisted of 103 dysphonic professional speakers (23 males and 80 females) with various voice disorders. Eighteen quantitative voice parameters from combined voice range profile (VRP) test were analyzed: 8 of voice range profile, 8 of speaking voice, overall vocal dysfunction degree and coefficient of sound, and aerodynamic maximum phonation time. Analysis showed that healthy professional speakers demonstrated expanded vocal abilities in comparison to healthy non-professional speakers. Quantitative voice range profile parameters- pitch range, high frequency limit, area of high frequencies and coefficient of sound differed significantly between healthy professional and non-professional voices, and were more informative than speaking voice or aerodynamic parameters in showing the voice training. Logistic stepwise regression revealed that VRP area in high frequencies was sufficient to discriminate between healthy and dysphonic professional speakers for male subjects (overall discrimination accuracy--81.8%) and combination of three quantitative parameters (VRP high frequency limit, maximum voice intensity and slope of speaking curve) for female subjects (overall model discrimination accuracy--75.4%). We concluded that quantitative voice assessment with selected parameters might be useful for evaluation of voice education for healthy professional speakers as well as for detection of vocal dysfunction and evaluation of rehabilitation effect in dysphonic professionals.

Evaluation of direct and indirect ethanol biomarkers using a likelihood ratio approach to identify chronic alcohol abusers for forensic purposes.

PubMed

Alladio, Eugenio; Martyna, Agnieszka; Salomone, Alberto; Pirro, Valentina; Vincenti, Marco; Zadora, Grzegorz

2017-02-01

The detection of direct ethanol metabolites, such as ethyl glucuronide (EtG) and fatty acid ethyl esters (FAEEs), in scalp hair is considered the optimal strategy to effectively recognize chronic alcohol misuses by means of specific cut-offs suggested by the Society of Hair Testing. However, several factors (e.g. hair treatments) may alter the correlation between alcohol intake and biomarkers concentrations, possibly introducing bias in the interpretative process and conclusions. 125 subjects with various drinking habits were subjected to blood and hair sampling to determine indirect (e.g. CDT) and direct alcohol biomarkers. The overall data were investigated using several multivariate statistical methods. A likelihood ratio (LR) approach was used for the first time to provide predictive models for the diagnosis of alcohol abuse, based on different combinations of direct and indirect alcohol biomarkers. LR strategies provide a more robust outcome than the plain comparison with cut-off values, where tiny changes in the analytical results can lead to dramatic divergence in the way they are interpreted. An LR model combining EtG and FAEEs hair concentrations proved to discriminate non-chronic from chronic consumers with ideal correct classification rates, whereas the contribution of indirect biomarkers proved to be negligible. Optimal results were observed using a novel approach that associates LR methods with multivariate statistics. In particular, the combination of LR approach with either Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA) proved successful in discriminating chronic from non-chronic alcohol drinkers. These LR models were subsequently tested on an independent dataset of 43 individuals, which confirmed their high efficiency. These models proved to be less prone to bias than EtG and FAEEs independently considered. In conclusion, LR models may represent an efficient strategy to sustain the diagnosis of chronic alcohol consumption and provide a suitable gradation to support the judgment. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Correlated neuronal discharges that increase coding efficiency during perceptual discrimination.

PubMed

Romo, Ranulfo; Hernández, Adrián; Zainos, Antonio; Salinas, Emilio

2003-05-22

During a sensory discrimination task, the responses of multiple sensory neurons must be combined to generate a choice. The optimal combination of responses is determined both by their dependence on the sensory stimulus and by their cofluctuations across trials-that is, the noise correlations. Positively correlated noise is considered deleterious, because it limits the coding accuracy of populations of similarly tuned neurons. However, positively correlated fluctuations between differently tuned neurons actually increase coding accuracy, because they allow the common noise to be subtracted without signal loss. This is demonstrated with data recorded from the secondary somatosensory cortex of monkeys performing a vibrotactile discrimination task. The results indicate that positive correlations are not always harmful and may be exploited by cortical networks to enhance the neural representation of features to be discriminated.

Perceived discrimination and physical health among HIV-positive Black and Latino men who have sex with men.

PubMed

Bogart, Laura M; Landrine, Hope; Galvan, Frank H; Wagner, Glenn J; Klein, David J

2013-05-01

We conducted the first study to examine health correlates of discrimination due to race/ethnicity, HIV-status, and sexual orientation among 348 HIV-positive Black (n = 181) and Latino (n = 167) men who have sex with men. Participants completed audio computer-assisted self-interviews. In multivariate analyses, Black participants who experienced greater racial discrimination were less likely to have a high CD4 cell count [OR = 0.7, 95 % CI = (0.5, 0.9), p = 0.02], and an undetectable viral load [OR = 0.8, 95 % CI = (0.6, 1.0), p = 0.03], and were more likely to visit the emergency department [OR = 1.3, 95 % CI = (1.0, 1.7), p = 0.04]; the combined three types of discrimination predicted greater AIDS symptoms [F (3,176) = 3.8, p < 0.01]. Among Latinos, the combined three types of discrimination predicted greater medication side effect severity [F (3,163) = 4.6, p < 0.01] and AIDS symptoms [F (3,163) = 3.1, p < 0.05]. Findings suggest that the stress of multiple types of discrimination plays a role in health outcomes.

Perceived Discrimination and Physical Health among HIV-Positive Black and Latino Men who have Sex with Men

PubMed Central

Bogart, Laura M.; Landrine, Hope; Galvan, Frank H.; Wagner, Glenn J.; Klein, David J.

2012-01-01

We conducted the first study to examine health correlates of discrimination due to race/ethnicity, HIV-status, and sexual orientation among 348 HIV-positive Black (n=181) and Latino (n=167) men who have sex with men. Participants completed audio computer-assisted self-interviews. In multivariate analyses, Black participants who experienced greater racial discrimination were less likely to have a high CD4 cell count [OR=0.7, 95%CI=(0.5, 0.9), p=.02], and an undetectable viral load [OR=0.8, 95%CI=(0.6, 1.0), p=.03], and were more likely to visit the emergency department [OR=1.3, 95%CI=(1.0, 1.7), p=.04]; the combined three types of discrimination predicted greater AIDS symptoms [F (3,176)=3.8, p<0.01]. Among Latinos, the combined three types of discrimination predicted greater medication side effect severity [F (3,163)=4.6, p<0.01] and AIDS symptoms [F (3,163)=3.1, p<0.05]. Findings suggest that the stress of multiple types of discrimination plays a role in health outcomes. PMID:23297084

Investigating the sex-related geometric variation of the human cranium.

PubMed

Bertsatos, Andreas; Papageorgopoulou, Christina; Valakos, Efstratios; Chovalopoulou, Maria-Eleni

2018-01-29

Accurate sexing methods are of great importance in forensic anthropology since sex assessment is among the principal tasks when examining human skeletal remains. The present study explores a novel approach in assessing the most accurate metric traits of the human cranium for sex estimation based on 80 ectocranial landmarks from 176 modern individuals of known age and sex from the Athens Collection. The purpose of the study is to identify those distance and angle measurements that can be most effectively used in sex assessment. Three-dimensional landmark coordinates were digitized with a Microscribe 3DX and analyzed in GNU Octave. An iterative linear discriminant analysis of all possible combinations of landmarks was performed for each unique set of the 3160 distances and 246,480 angles. Cross-validated correct classification as well as multivariate DFA on top performing variables reported 13 craniometric distances with over 85% classification accuracy, 7 angles over 78%, as well as certain multivariate combinations yielding over 95%. Linear regression of these variables with the centroid size was used to assess their relation to the size of the cranium. In contrast to the use of generalized procrustes analysis (GPA) and principal component analysis (PCA), which constitute the common analytical work flow for such data, our method, although computational intensive, produced easily applicable discriminant functions of high accuracy, while at the same time explored the maximum of cranial variability.

Discrimination, Racial Bias, and Telomere Length in African-American Men

PubMed Central

Chae, David H.; Nuru-Jeter, Amani M.; Adler, Nancy E.; Brody, Gene H.; Lin, Jue; Blackburn, Elizabeth H.; Epel, Elissa S.

2013-01-01

Background Leukocyte telomere length (LTL) is an indicator of general systemic aging, with shorter LTL being associated with several chronic diseases of aging and earlier mortality. Identifying factors related to LTL among African Americans may yield insights into mechanisms underlying racial disparities in health. Purpose To test whether the combination of more frequent reports of racial discrimination and holding a greater implicit anti-black racial bias is associated with shorter LTL among African-American men. Methods Cross-sectional study of a community sample of 92 African-American men aged between 30 and 50 years. Participants were recruited from February to May 2010. Ordinary least squares regressions were used to examine LTL in kilobase pairs in relation to racial discrimination and implicit racial bias. Data analysis was completed in July 2013. Results After controlling for chronologic age, socioeconomic, and health-related characteristics, the interaction between racial discrimination and implicit racial bias was significantly associated with LTL (b= −0.10, SE=0.04, p=0.02). Those demonstrating a stronger implicit anti-black bias and reporting higher levels of racial discrimination had the shortest LTL. Household income-to-poverty threshold ratio was also associated with LTL (b=0.05, SE=0.02, p<0.01). Conclusions Results suggest that multiple levels of racism, including interpersonal experiences of racial discrimination and the internalization of negative racial bias, operate jointly to accelerate biological aging among African-American men. Societal efforts to address racial discrimination in concert with efforts to promote positive in-group racial attitudes may protect against premature biological aging in this population. PMID:24439343

Perceived discrimination, socioeconomic disadvantage and refraining from seeking medical treatment in Sweden.

PubMed

Wamala, Sarah; Merlo, Juan; Boström, Gunnel; Hogstedt, Christer

2007-05-01

To analyse the association between perceived discrimination and refraining from seeking required medical treatment and the contribution of socioeconomic disadvantage. Data from the Swedish National Survey of Public Health 2004 were used for analysis. Respondents were asked whether they had refrained from seeking required medical treatment during the past 3 months. Perceived discrimination was based on whether respondents reported that they had been treated in a way that made them feel humiliated (due to ethnicity/race, religion, gender, sexual orientation, age or disability). The Socioeconomic Disadvantage Index (SDI) was developed to measure economic deprivation (social welfare beneficiary, being unemployed, financial crisis and lack of cash reserves). Swedish population-based survey of 14,736 men and 17,115 women. Both perceived discrimination and socioeconomic disadvantage were independently associated with refraining from seeking medical treatment. Experiences of frequent discrimination even without any socioeconomic disadvantage were associated with three to nine-fold increased odds for refraining from seeking medical treatment. A combination of both frequent discrimination and severe SDI was associated with a multiplicative effect on refraining from seeking medical treatment, but this effect was statistically more conclusive among women (OR = 11.6, 95% CI 8.1 to 16.6; Synergy Index (SI) = 2.0 (95% CI 1.2 to 3.2)) than among men (OR = 12, 95% CI 7.7 to 18.7; SI = 1.6 (95% CI 1.3 to 2.1)). The goal of equitable access to healthcare services cannot be achieved without public health strategies that confront and tackle discrimination in society and specifically in the healthcare setting.

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.

Discrimination and classification of acute lymphoblastic leukemia cells by Raman spectroscopy

NASA Astrophysics Data System (ADS)

Managò, Stefano; Valente, Carmen; Mirabelli, Peppino; De Luca, Anna Chiara

2015-05-01

Currently, a combination of technologies is typically required to identify and classify leukemia cells. These methods often lack the specificity and sensitivity necessary for early and accurate diagnosis. Here, we demonstrate the use of Raman spectroscopy to identify normal B cells, collected from healthy patients, and three ALL cell lines (RS4;11, REH and MN60 at different differentiation level, respectively). Raman markers associated with DNA and protein vibrational modes have been identified that exhibit excellent discriminating power for leukemia cell identification. Principal Component Analysis was finally used to confirm the significance of these markers for identify leukemia cells and classifying the data. The obtained results indicate a sorting accuracy of 96% between the three leukemia cell lines.

HPLC-TOF-MS and HPLC-MS/MS combined with multivariate analysis for the characterization and discrimination of phenolic profiles in nonfumigated and sulfur-fumigated rhubarb.

PubMed

Yan, Yan; Zhang, Qianqian; Feng, Fang

2016-07-01

Sulfur fumigation has recently been used during the postharvest handling of rhubarb to reduce the drying duration and control pests. However, a few reports question the effect of sulfur fumigation on the bioactive components of rhubarb, which is crucial for the quality evaluation of the herbal medicine. The bottleneck limiting the study comes from the complex compounds that exist in herb samples with diverse structural features, wide concentration range and the difficulty to obtain all the reference standards. In this study, an integrated strategy based on the highly effective separation and analysis by liquid chromatography coupled with diode-array detection and time-of-flight/triple-quadruple tandem mass spectrometry combined with multivariate analysis was established. 68 phenolic compounds that exist in nonfumigated and sulfur-fumigated herb samples of rhubarb were tentatively assigned based on their retention behavior, UV spectra, accurate molecular weight, and mass spectral fragments. Qualitative and semiquantitative comparison revealed a serious reduction of the majority of phenolic compounds in sulfur-fumigated rhubarb. Furthermore, multivariate analysis was applied to holistically discriminate nonfumigated from sulfur-fumigated rhubarb and explore the characteristic chemical markers. The established approach was specific and rapid for characterizing and screening sulfur-fumigated rhubarb among commercial samples and could be applied for the quality assessment of other sulfur-fumigated herbs. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

PubMed Central

Talaska, Cara A.; Chaiken, Shelly

2013-01-01

Investigations of racial bias have emphasized stereotypes and other beliefs as central explanatory mechanisms and as legitimating discrimination. In recent theory and research, emotional prejudices have emerged as another, more direct predictor of discrimination. A new comprehensive meta-analysis of 57 racial attitude-discrimination studies finds a moderate relationship between overall attitudes and discrimination. Emotional prejudices are twices as closely related to racial discrimination as stereotypes and beliefs are. Moreover, emotional prejudices are closely related to both observed and self-reported discrimination, whereas stereotypes and beliefs are related only to self-reported discrimination. Implications for justifying discrimination are discussed. PMID:24052687

Racial discrimination and the stress process.

PubMed

Ong, Anthony D; Fuller-Rowell, Thomas; Burrow, Anthony L

2009-06-01

The unique and combined effects of chronic and daily racial discrimination on psychological distress were examined in a sample of 174 African American doctoral students and graduates. Using a daily process design, 5 models of the stress process were tested. Multilevel random coefficient modeling analyses revealed that chronic exposure to racial discrimination predicted greater daily discrimination and psychological distress. Further, results show that differences in daily discrimination and negative events accounted for meaningful variation in daily distress responses. Finally, findings indicate that daily discrimination and negative events mediated the relationship between chronic discrimination and psychological distress. The study provides support for the need to measure chronic strains as distinctive from daily stressors in the lives of African Americans.

Acoustic fine structure may encode biologically relevant information for zebra finches.

PubMed

Prior, Nora H; Smith, Edward; Lawson, Shelby; Ball, Gregory F; Dooling, Robert J

2018-04-18

The ability to discriminate changes in the fine structure of complex sounds is well developed in birds. However, the precise limit of this discrimination ability and how it is used in the context of natural communication remains unclear. Here we describe natural variability in acoustic fine structure of male and female zebra finch calls. Results from psychoacoustic experiments demonstrate that zebra finches are able to discriminate extremely small differences in fine structure, which are on the order of the variation in acoustic fine structure that is present in their vocal signals. Results from signal analysis methods also suggest that acoustic fine structure may carry information that distinguishes between biologically relevant categories including sex, call type and individual identity. Combined, our results are consistent with the hypothesis that zebra finches can encode biologically relevant information within the fine structure of their calls. This study provides a foundation for our understanding of how acoustic fine structure may be involved in animal communication.

Home-care workers: work conditions and occupational exclusion: a comparison between carers on early-retirement and regular pensions.

PubMed

Aronsson, G; Astvik, W; Thulin, A B

1998-01-01

The aim of the study was to identify conditions associated with occupational exclusion from home-caring. In a group of 346 home-care workers who responded to a questionnaire, there were 18 newly-retired carers on early-retirement/disability pensions, and 28 carers who had just taken regular retirement. A discriminant analysis was conducted to identify work conditions that differentiated the two groups. The results show that a combination of variables--functional impairment (pain when doing physical work), psychosomatic complaints, and nature of relationship with/attitude to clients--significantly differentiated the two groups. When the discriminant coefficients were applied to other groups--older full-time and part-time employees (n = 224), carers who had undergone job transfers, and carers on long-term sick leave--the order of groups by discriminant-point score was largely as expected. The results are discussed in relation to dilemmas, psychological demands and organizational circumstances prevailing in home-care work.

Sending-country violence and receiving-country discrimination: effects on the health of Colombian refugees in Ecuador.

PubMed

Shedlin, Michele G; Decena, Carlos U; Noboa, Hugo; Betancourt, Óscar

2014-02-01

This study explored factors affecting the health and well being of recent refugees from Colombia in Ecuador. Data collection focused on how sending-country violence and structural violence in a new environment affect immigrant health vulnerability and risk behaviors. A qualitative approach included ethnographic observation, media content analysis, focus groups, and individual interviews with refugees (N = 137). The focus groups (5) provided perspectives on the research domains by sex workers; drug users; male and female refugees; and service providers. Social and economic marginalization are impacting the health and well being of this growing refugee population. Data illustrate how stigma and discrimination affect food and housing security, employment and health services, and shape vulnerabilities and health risks in a new receiving environment. Widespread discrimination in Ecuador reflects fears, misunderstanding, and stereotypes about Colombian refugees. For this displaced population, the sequelae of violence, combined with survival needs and lack of support and protections, shape new risks to health and well-being.

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

Sending-Country Violence & Receiving-Country Discrimination: Effects on the Health of Colombian Refugees in Ecuador

PubMed Central

Shedlin, Michele G.; Decena, Carlos U.; Noboa, Hugo; Betancourt, Óscar

2013-01-01

BACKGROUND This study explored factors affecting the health and well being of recent refugees from Colombia in Ecuador. Data collection focused on how sending-country violence and structural violence in a new environment affect immigrant health vulnerability and risk behaviors. METHODS A qualitative approach included ethnographic observation, media content analysis, focus groups, and individual interviews with refugees (N=137). The focus groups (5) provided perspectives on the research domains by sex workers; drug users; male and female refugees; and service providers. RESULTS Social and economic marginalization are impacting the health and well being of this growing refugee population. Data illustrate how stigma and discrimination affect food and housing security, employment and health services, and shape vulnerabilities and health risks in a new receiving environment. DISCUSSION Widespread discrimination in Ecuador reflects fears, misunderstanding, and stereotypes about Colombian refugees. For this displaced population, the sequelae of violence, combined with survival needs and lack of support and protections, shape new risks to health and well-being. PMID:23377565

Integrated Low-Rank-Based Discriminative Feature Learning for Recognition.

PubMed

Zhou, Pan; Lin, Zhouchen; Zhang, Chao

2016-05-01

Feature learning plays a central role in pattern recognition. In recent years, many representation-based feature learning methods have been proposed and have achieved great success in many applications. However, these methods perform feature learning and subsequent classification in two separate steps, which may not be optimal for recognition tasks. In this paper, we present a supervised low-rank-based approach for learning discriminative features. By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, our approach combines feature learning with classification, so that the regulated classification error is minimized. In this way, the extracted features are more discriminative for the recognition tasks. Our approach benefits from a recent discovery on the closed-form solutions to noiseless LatLRR. When there is noise, a robust Principal Component Analysis (PCA)-based denoising step can be added as preprocessing. When the scale of a problem is large, we utilize a fast randomized algorithm to speed up the computation of robust PCA. Extensive experimental results demonstrate the effectiveness and robustness of our method.

Differences in Clinical Features of Methamphetamine Users with Persistent Psychosis and Patients with Schizophrenia.

PubMed

Wang, Liang-Jen; Lin, Shih-Ku; Chen, Yi-Chih; Huang, Ming-Chyi; Chen, Tzu-Ting; Ree, Shao-Chun; Chen, Chih-Ken

Methamphetamine exerts neurotoxic effects and elicits psychotic symptoms. This study attempted to compare clinical differences between methamphetamine users with persistent psychosis (MAP) and patients with schizophrenia. In addition, we examined the discrimination validity by using symptom clusters to differentiate between MAP and schizophrenia. We enrolled 53 MAP patients and 53 patients with schizophrenia. The psychopathology of participants was assessed using the Chinese version of the Diagnostic Interview for Genetic Studies and the 18-item Brief Psychiatric Rating Scale. Logistic regression was used to examine the predicted probability scores of different symptom combinations on discriminating between MAP and schizophrenia. The receiver operating characteristic (ROC) analyses and area under the curve (AUC) were further applied to examine the discrimination validity of the predicted probability scores on differentiating between MAP and schizophrenia. We found that MAP and schizophrenia demonstrated similar patterns of delusions. Compared to patients with schizophrenia, MAP experienced significantly higher proportions of visual hallucinations and of somatic or tactile hallucinations. However, MAP exhibited significantly lower severity in conceptual disorganization, mannerism/posturing, blunted affect, emotional withdrawal, and motor retardation compared to patients with schizophrenia. The ROC analysis showed that a predicted probability score combining the aforementioned 7 items of symptoms could significantly differentiate between MAP and schizophrenia (AUC = 0.77). Findings in the current study suggest that nuanced differences might exist in the clinical presentation of secondary psychosis (MAP) and primary psychosis (schizophrenia). Combining the symptoms as a whole may help with differential diagnosis for MAP and schizophrenia. © 2016 S. Karger AG, Basel.

Selecting risk factors: a comparison of discriminant analysis, logistic regression and Cox's regression model using data from the Tromsø Heart Study.

PubMed

Brenn, T; Arnesen, E

1985-01-01

For comparative evaluation, discriminant analysis, logistic regression and Cox's model were used to select risk factors for total and coronary deaths among 6595 men aged 20-49 followed for 9 years. Groups with mortality between 5 and 93 per 1000 were considered. Discriminant analysis selected variable sets only marginally different from the logistic and Cox methods which always selected the same sets. A time-saving option, offered for both the logistic and Cox selection, showed no advantage compared with discriminant analysis. Analysing more than 3800 subjects, the logistic and Cox methods consumed, respectively, 80 and 10 times more computer time than discriminant analysis. When including the same set of variables in non-stepwise analyses, all methods estimated coefficients that in most cases were almost identical. In conclusion, discriminant analysis is advocated for preliminary or stepwise analysis, otherwise Cox's method should be used.

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.

Towards the identification of plant and animal binders on Australian stone knives.

PubMed

Blee, Alisa J; Walshe, Keryn; Pring, Allan; Quinton, Jamie S; Lenehan, Claire E

2010-07-15

There is limited information regarding the nature of plant and animal residues used as adhesives, fixatives and pigments found on Australian Aboriginal artefacts. This paper reports the use of FTIR in combination with the chemometric tools principal component analysis (PCA) and hierarchical clustering (HC) for the analysis and identification of Australian plant and animal fixatives on Australian stone artefacts. Ten different plant and animal residues were able to be discriminated from each other at a species level by combining FTIR spectroscopy with the chemometric data analysis methods, principal component analysis (PCA) and hierarchical clustering (HC). Application of this method to residues from three broken stone knives from the collections of the South Australian Museum indicated that two of the handles of knives were likely to have contained beeswax as the fixative whilst Spinifex resin was the probable binder on the third. Copyright 2010 Elsevier B.V. All rights reserved.

Analysis of lithology: Vegetation mixes in multispectral images

NASA Technical Reports Server (NTRS)

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

1982-01-01

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

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

PubMed

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

2011-06-15

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

ADA perceived disability claims: a decision-tree analysis.

PubMed

Draper, William R; Hawley, Carolyn E; McMahon, Brian T; Reid, Christine A; Barbir, Lara A

2014-06-01

The purpose of this study is to examine the possible interactions of predictor variables pertaining to perceived disability claims contained in a large governmental database. Specifically, it is a retrospective analysis of US Equal Employment Opportunity Commission (EEOC) data for the entire population of workplace discrimination claims based on the "regarded as disabled" prong of the Americans with Disabilities Act (ADA) definition of disability. The study utilized records extracted from a "master database" of over two million charges of workplace discrimination in the Integrated Mission System of the EEOC. This database includes all ADA-related discrimination allegations filed from July 26, 1992 through December 31, 2008. Chi squared automatic interaction detection (CHAID) was employed to analyze interaction effects of relevant variables, such as issue (grievance) and industry type. The research question addressed by CHAID is: What combination of factors are associated with merit outcomes for people making ADA EEOC allegations who are "regarded as" having disabilities? The CHAID analysis shows how merit outcome is predicted by the interaction of relevant variables. Issue was found to be the most prominent variable in determining merit outcome, followed by industry type, but the picture is made more complex by qualifications regarding age and race data. Although discharge was the most frequent grievance among charging parties in the perceived disability group, its merit outcome was significantly less than that for the leading factor of hiring.

THE VISUAL DISCRIMINATION OF INTENSITY AND THE WEBER-FECHNER LAW

PubMed Central

Hecht, Selig

1924-01-01

1. A study of the historical development of the Weber-Fechner law shows that it fails to describe intensity perception; first, because it is based on observations which do not record intensity discrimination accurately, and second, because it omits the essentially discontinuous nature of the recognition of intensity differences. 2. There is presented a series of data, assembled from various sources, which proves that in the visual discrimination of intensity the threshold difference ΔI bears no constant relation to the intensity I. The evidence shows unequivocally that as the intensity rises, the ratio See PDF for Equation first decreases and then increases. 3. The data are then subjected to analysis in terms of a photochemical system already proposed for the visual activity of the rods and cones. It is found that for the retinal elements to discriminate between one intensity and the next perceptible one, the transition from one to the other must involve the decomposition of a constant amount of photosensitive material. 4. The magnitude of this unitary increment in the quantity of photochemical action is greater for the rods than for the cones. Therefore, below a certain critical illumination—the cone threshold—intensity discrimination is controlled by the rods alone, but above this point it is determined by the cones alone. 5. The unitary increments in retinal photochemical action may be interpreted as being recorded by each rod and cone; or as conditioning the variability of the retinal cells so that each increment involves a constant increase in the number of active elements; or as a combination of the two interpretations. 6. Comparison with critical data of such diverse nature as dark adaptation, absolute thresholds, and visual acuity shows that the analysis is consistent with well established facts of vision. PMID:19872133

Chemical profiling of guarana seeds (Paullinia cupana) from different geographical origins using UPLC-QTOF-MS combined with chemometrics.

PubMed

da Silva, Givaldo Souza; Canuto, Kirley Marques; Ribeiro, Paulo Riceli Vasconcelos; de Brito, Edy Sousa; Nascimento, Madson Moreira; Zocolo, Guilherme Julião; Coutinho, Janclei Pereira; de Jesus, Raildo Mota

2017-12-01

Paullinia cupana, commonly known as guarana, is an Amazonian fruit whose seeds are used to produce the powdered guarana, which is rich in caffeine and consumed for its stimulating activity. The metabolic profile of guarana from the two largest producing regions was investigated using UPLC-MS combined with multivariate statistical analysis. The principal component analysis (PCA) showed significant differences between samples produced in the states of Bahia and Amazonas. The metabolites responsible for the differentiation were identified by orthogonal partial least squares discriminant analysis (OPLS-DA). Fourteen phenolic compounds were characterized in guarana powder samples, and catechin, epicatechin, B-type procyanidin dimer, A-type procyanidin trimer and A-type procyanidin dimer were the main compounds responsible for the geographical variation of the samples. Copyright © 2017. Published by Elsevier Ltd.

Discriminability of Personality Profiles in Isolated and Co-Morbid Marijuana and Nicotine Users

PubMed Central

Ketcherside, Ariel; Jeon-Slaughter, Haekyung; Baine, Jessica L.; Filbey, Francesca M

2016-01-01

Specific personality traits have been linked with substance use disorders (SUDs), genetic mechanisms, and brain systems. Thus, determining the specificity of personality traits to types of SUD can advance the field towards defining SUD endophenotypes as well as understanding the brain systems involved for the development of novel treatments. Disentangling these factors is particularly important in highly co-morbid SUDs, such as marijuana and nicotine use, so treatment can occur effectively for both. This study evaluated personality traits that distinguish isolated and co-morbid use of marijuana and nicotine. To that end, we collected the NEO Five Factor Inventory in participants who used marijuana-only (n=59), nicotine-only (n=27), both marijuana and nicotine (n=28), and in non-using controls (n=28). We used factor analyses to identify personality profiles, which are linear combinations of the five NEO Factors. We then conducted Receiver Operating Characteristics (ROC) curve analysis to test accuracy of the personality factors in discriminating isolated and co-morbid marijuana and nicotine users from each other. ROC curve analysis distinguished the four groups based on their NEO personality patterns. Results showed that NEO Factor 2 (openness, extraversion, agreeableness) discriminated marijuana and marijuana + nicotine users from controls and nicotine-only users with high predictability. Additional ANOVA results showed that the openness dimension discriminated marijuana users from nicotine users. These findings suggest that personality dimensions distinguish marijuana users from nicotine users and should be considered in prevention strategies. PMID:27086256

Separate and combined effects of the cannabinoid agonists nabilone and Δ⁹-THC in humans discriminating Δ⁹-THC.

PubMed

Lile, Joshua A; Kelly, Thomas H; Hays, Lon R

2011-07-01

Agonist replacement treatment is a promising strategy to manage cannabis-use disorders. The aim of this study was to assess the combined effects of the synthetic cannabinoid agonist nabilone and Δ⁹-tetrahydrocannabinol (Δ⁹-THC) using drug-discrimination procedures, which are sensitive to drug interactions. Testing the concurrent administration of nabilone and Δ⁹-THC was also conducted to provide initial safety and tolerability data, which is important because cannabis users will likely lapse during treatment. Six cannabis users learned to discriminate 30 mg oral Δ⁹-THC from placebo and then received nabilone (0, 1 and 3mg) and Δ⁹-THC (0, 5, 15 and 30 mg), alone and in combination. Subjects completed the multiple-choice procedure to assess drug reinforcement, and self-report, task performance and physiological measures were collected. Δ⁹-THC and nabilone alone shared discriminative-stimulus effects with the training dose of Δ⁹-THC, increased crossover point on the multiple-choice procedure, produced overlapping subject ratings and decreased skin temperature. Nabilone alone also elevated heart rate. In combination, nabilone shifted the discriminative-stimulus effects of Δ⁹-THC leftward/upward and enhanced Δ⁹-THC effects on the other outcome measures. These results replicate a previous study demonstrating that nabilone shares agonist effects with the active constituent of cannabis in cannabis users, and contribute further by indicating that nabilone would likely be safe and well tolerated when combined with cannabis. These data support the conduct of future studies to determine if nabilone treatment would produce cross-tolerance to the abuse-related effects of cannabis and reduce cannabis use. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Improved neutron-gamma discrimination for a {sup 6}Li-glass neutron detector using digital signal analysis methods

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

Wang, C. L., E-mail: wangc@ornl.gov; Riedel, R. A.

2016-01-15

A {sup 6}Li-glass scintillator (GS20) based neutron Anger camera was developed for time-of-flight single-crystal diffraction instruments at Spallation Neutron Source. Traditional Pulse-Height Analysis (PHA) for Neutron-Gamma Discrimination (NGD) resulted in the neutron-gamma efficiency ratio (defined as NGD ratio) on the order of 10{sup 4}. The NGD ratios of Anger cameras need to be improved for broader applications including neutron reflectometers. For this purpose, six digital signal analysis methods of individual waveforms acquired from photomultiplier tubes were proposed using (i) charge integration, (ii) pulse-amplitude histograms, (iii) power spectrum analysis combined with the maximum pulse-amplitude, (iv) two event parameters (a{sub 1}, b{submore » 0}) obtained from a Wiener filter, (v) an effective amplitude (m) obtained from an adaptive least-mean-square filter, and (vi) a cross-correlation coefficient between individual and reference waveforms. The NGD ratios are about 70 times those from the traditional PHA method. Our results indicate the NGD capabilities of neutron Anger cameras based on GS20 scintillators can be significantly improved with digital signal analysis methods.« less

Separate and combined effects of the GABAA positive allosteric modulator diazepam and Δ⁹-THC in humans discriminating Δ⁹-THC.

PubMed

Lile, Joshua A; Kelly, Thomas H; Hays, Lon R

2014-10-01

Our previous research suggested the involvement of γ-aminobutyric acid (GABA), in particular the GABAB receptor subtype, in the interoceptive effects of Δ(9)-tetrahydrocannabinol (Δ(9)-THC). The aim of the present study was to determine the potential involvement of the GABAA receptor subtype by assessing the separate and combined effects of the GABAA positive allosteric modulator diazepam and Δ(9)-THC using pharmacologically selective drug-discrimination procedures. Ten cannabis users learned to discriminate 30 mg oral Δ(9)-THC from placebo and then received diazepam (5 and 10mg), Δ(9)-THC (5, 15 and 30 mg) and placebo, alone and in combination. Self-report, task performance and physiological measures were also collected. Δ(9)-THC functioned as a discriminative stimulus, produced subjective effects typically associated with cannabinoids (e.g., High, Stoned, Like Drug) and elevated heart rate. Diazepam alone impaired performance on psychomotor performance tasks and increased ratings on a limited number of self-report questionnaire items (e.g., Any Effect, Sedated), but did not substitute for the Δ(9)-THC discriminative stimulus or alter the Δ(9)-THC discrimination dose-response function. Similarly, diazepam had limited impact on the other behavioral effects of Δ(9)-THC. These results suggest that the GABAA receptor subtype has minimal involvement in the interoceptive effects of Δ(9)-THC, and by extension cannabis, in humans. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Differences in Perception of Musical Stimuli among Acoustic, Electric, and Combined Modality Listeners.

PubMed

Prentiss, Sandra M; Friedland, David R; Nash, John J; Runge, Christina L

2015-05-01

Cochlear implants have shown vast improvements in speech understanding for those with severe to profound hearing loss; however, music perception remains a challenge for electric hearing. It is unclear whether the difficulties arise from limitations of sound processing, the nature of a damaged auditory system, or a combination of both. To examine music perception performance with different acoustic and electric hearing configurations. Chord discrimination and timbre perception were tested in subjects representing four daily-use listening configurations: unilateral cochlear implant (CI), contralateral bimodal (CIHA), bilateral hearing aid (HAHA) and normal-hearing (NH) listeners. A same-different task was used for discrimination of two chords played on piano. Timbre perception was assessed using a 10-instrument forced-choice identification task. Fourteen adults were included in each group, none of whom were professional musicians. The number of correct responses was divided by the total number of presentations to calculate scores in percent correct. Data analyses were performed with Kruskal-Wallis one-way analysis of variance and linear regression. Chord discrimination showed a narrow range of performance across groups, with mean scores ranging between 72.5% (CI) and 88.9% (NH). Significant differences were seen between the NH and all hearing-impaired groups. Both the HAHA and CIHA groups performed significantly better than the CI groups, and no significant differences were observed between the HAHA and CIHA groups. Timbre perception was significantly poorer for the hearing-impaired groups (mean scores ranged from 50.3-73.9%) compared to NH (95.2%). Significantly better performance was observed in the HAHA group as compared to both groups with electric hearing (CI and CIHA). There was no significant difference in performance between the CIHA and CI groups. Timbre perception was a significantly more difficult task than chord discrimination for both the CI and CIHA groups, yet the easier task for the NH group. A significant difference between the two tasks was not seen in the HAHA group. Having impaired hearing decreases performance compared to NH across both chord discrimination and timbre perception tasks. For chord discrimination, having acoustic hearing improved performance compared to electric hearing only. Timbre perception distinguished those with acoustic hearing from those with electric hearing. Those with bilateral acoustic hearing, even if damaged, performed significantly better on this task than those requiring electrical stimulation, which may indicate that CI sound processing fails to capture and deliver the necessary acoustic cues for timbre perception. Further analysis of timbre characteristics in electric hearing may contribute to advancements in programming strategies to obtain optimal hearing outcomes. American Academy of Audiology.

Global metabolomics reveals potential urinary biomarkers of esophageal squamous cell carcinoma for diagnosis and staging

NASA Astrophysics Data System (ADS)

Xu, Jing; Chen, Yanhua; Zhang, Ruiping; He, Jiuming; Song, Yongmei; Wang, Jingbo; Wang, Huiqing; Wang, Luhua; Zhan, Qimin; Abliz, Zeper

2016-10-01

We performed a metabolomics study using liquid chromatography-mass spectrometry (LC-MS) combined with multivariate data analysis (MVDA) to discriminate global urine profiles in urine samples from esophageal squamous cell carcinoma (ESCC) patients and healthy controls (NC). Our work evaluated the feasibility of employing urine metabolomics for the diagnosis and staging of ESCC. The satisfactory classification between the healthy controls and ESCC patients was obtained using the MVDA model, and obvious classification of early-stage and advanced-stage patients was also observed. The results suggest that the combination of LC-MS analysis and MVDA may have potential applications for ESCC diagnosis and staging. We then conducted LC-MS/MS experiments to identify the potential biomarkers with large contributions to the discrimination. A total of 83 potential diagnostic biomarkers for ESCC were screened out, and 19 potential biomarkers were identified; the variations between the differences in staging using these potential biomarkers were further analyzed. These biomarkers may not be unique to ESCCs, but instead result from any malignant disease. To further elucidate the pathophysiology of ESCC, we studied related metabolic pathways and found that ESCC is associated with perturbations of fatty acid β-oxidation and the metabolism of amino acids, purines, and pyrimidines.

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

PubMed

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

2013-12-01

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

Discriminant analysis of multiple cortical changes in mild cognitive impairment

NASA Astrophysics Data System (ADS)

Wu, Congling; Guo, Shengwen; Lai, Chunren; Wu, Yupeng; Zhao, Di; Jiang, Xingjun

2017-02-01

To reveal the differences in brain structures and morphological changes between the mild cognitive impairment (MCI) and the normal control (NC), analyze and predict the risk of MCI conversion. First, the baseline and 2-year longitudinal follow-up magnetic resonance (MR) images of 73 NC, 46 patients with stable MCI (sMCI) and 40 patients with converted MCI (cMCI) were selected. Second, the FreeSurfer was used to extract the cortical features, including the cortical thickness, surface area, gray matter volume and mean curvature. Third, the support vector machine-recursive feature elimination method (SVM-RFE) were adopted to determine salient features for effective discrimination. Finally, the distribution and importance of essential brain regions were described. The experimental results showed that the cortical thickness and gray matter volume exhibited prominent capability in discrimination, and surface area and mean curvature behaved relatively weak. Furthermore, the combination of different morphological features, especially the baseline combined with the longitudinal changes, can be used to evidently improve the performance of classification. In addition, brain regions with high weights predominately located in the temporal lobe and the frontal lobe, which were relative to emotional control and memory functions. It suggests that there were significant different patterns in the brain structure and changes between the compared group, which could not only be effectively applied for classification, but also be used to evaluate and predict the conversion of the patients with MCI.

Forensic and population genetic characteristics of 62 X chromosome SNPs revealed by multiplex PCR and MALDI-TOF mass spectrometry genotyping in 4 North Eurasian populations.

PubMed

Stepanov, Vadim; Vagaitseva, Ksenyia; Kharkov, Vladimir; Cherednichenko, Anastasia; Bocharova, Anna; Berezina, Galina; Svyatova, Gulnara

2016-01-01

X chromosome genetic markers are widely used in basic population genetic research as well as in forensic genetics. In this paper we analyze the genetic diversity of 62 X chromosome SNPs in 4 populations using multiplex genotyping based on multi-locus PCR and MALDI-TOF mass spectrometry, and report forensic and population genetic features of the panel of X-linked SNPs (XSNPid). Studied populations represent Siberian (Buryat and Khakas), North Asian (Khanty) and Central Asian (Kazakh) native people. Khanty, Khakas and Kazakh population demonstrate average gene diversity over 0.45. Only East Siberian Buryat population is characterized by lower average heterozygosity (0.436). AMOVA analysis of genetic structure reveals a relatively low but significant level of genetic differentiation in a group of 4 population studied (FST=0.023, p=0.0000). The XSNPid panel provides a very high discriminating power in each population. The combined probability of discrimination in females (PDf) for XSNPid panel ranged between populations from 0.99999999999999999999999982 in Khakas to 0.9999999999999999999999963 in Buryats. The combined discriminating power in males (PDm) varies from 0.999999999999999792 to 0.9999999999999999819. The developed multiplex set of X chromosome SNPs can be a useful tool for population genetic studies and for forensic identity and kinship testing. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Common spatial pattern combined with kernel linear discriminate and generalized radial basis function for motor imagery-based brain computer interface applications

NASA Astrophysics Data System (ADS)

Hekmatmanesh, Amin; Jamaloo, Fatemeh; Wu, Huapeng; Handroos, Heikki; Kilpeläinen, Asko

2018-04-01

Brain Computer Interface (BCI) can be a challenge for developing of robotic, prosthesis and human-controlled systems. This work focuses on the implementation of a common spatial pattern (CSP) base algorithm to detect event related desynchronization patterns. Utilizing famous previous work in this area, features are extracted by filter bank with common spatial pattern (FBCSP) method, and then weighted by a sensitive learning vector quantization (SLVQ) algorithm. In the current work, application of the radial basis function (RBF) as a mapping kernel of linear discriminant analysis (KLDA) method on the weighted features, allows the transfer of data into a higher dimension for more discriminated data scattering by RBF kernel. Afterwards, support vector machine (SVM) with generalized radial basis function (GRBF) kernel is employed to improve the efficiency and robustness of the classification. Averagely, 89.60% accuracy and 74.19% robustness are achieved. BCI Competition III, Iva data set is used to evaluate the algorithm for detecting right hand and foot imagery movement patterns. Results show that combination of KLDA with SVM-GRBF classifier makes 8.9% and 14.19% improvements in accuracy and robustness, respectively. For all the subjects, it is concluded that mapping the CSP features into a higher dimension by RBF and utilization GRBF as a kernel of SVM, improve the accuracy and reliability of the proposed method.

Phoneme categorization and discrimination in younger and older adults: a comparative analysis of perceptual, lexical, and attentional factors.

PubMed

Mattys, Sven L; Scharenborg, Odette

2014-03-01

This study investigates the extent to which age-related language processing difficulties are due to a decline in sensory processes or to a deterioration of cognitive factors, specifically, attentional control. Two facets of attentional control were examined: inhibition of irrelevant information and divided attention. Younger and older adults were asked to categorize the initial phoneme of spoken syllables ("Was it m or n?"), trying to ignore the lexical status of the syllables. The phonemes were manipulated to range in eight steps from m to n. Participants also did a discrimination task on syllable pairs ("Were the initial sounds the same or different?"). Categorization and discrimination were performed under either divided attention (concurrent visual-search task) or focused attention (no visual task). The results showed that even when the younger and older adults were matched on their discrimination scores: (1) the older adults had more difficulty inhibiting lexical knowledge than did younger adults, (2) divided attention weakened lexical inhibition in both younger and older adults, and (3) divided attention impaired sound discrimination more in older than younger listeners. The results confirm the independent and combined contribution of sensory decline and deficit in attentional control to language processing difficulties associated with aging. The relative weight of these variables and their mechanisms of action are discussed in the context of theories of aging and language. (c) 2014 APA, all rights reserved.

Distinction Between Recurrent Glioma and Radiation Injury Using Magnetic Resonance Spectroscopy in Combination With Diffusion-Weighted Imaging

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

Zeng, Q.-S.; Li, C.-F.; Liu Hong

2007-05-01

Purpose: The aim of this study was to explore the diagnostic effectiveness of magnetic resonance (MR) spectroscopy with diffusion-weighted imaging on the evaluation of the recurrent contrast-enhancing areas at the site of treated gliomas. Methods and Materials: In 55 patients who had new contrast-enhancing lesions in the vicinity of the previously resected and irradiated high-grade gliomas, two-dimensional MR spectroscopy and diffusion-weighted imaging were performed. Spectral data for N-acetylaspartate (NAA), choline (Cho), creatine (Cr), lipid (Lip), and lactate (Lac) were analyzed in conjunction with the apparent diffusion coefficient (ADC) in all patients. Diagnosis of these lesions was assigned by means ofmore » follow-up or histopathology. Results: The Cho/NAA and Cho/Cr ratios were significantly higher in recurrent tumor than in regions of radiation injury (p < 0.01). The ADC value and ADC ratios (ADC of contrast-enhancing lesion to matching structure in the contralateral hemisphere) were significantly higher in radiation injury regions than in recurrent tumor (p < 0.01). With MR spectroscopic data, two variables (Cho/NAA and Cho/Cr ratios) were shown to differentiate recurrent glioma from radiation injury, and 85.5% of total subjects were correctly classified into groups. However, with discriminant analysis of MR spectroscopy imaging plus diffusion-weighted imaging, three variables (Cho/NAA, Cho/Cr, and ADC ratio) were identified and 96.4% of total subjects were correctly classified. There was a significant difference between the diagnostic accuracy of the two discriminant analyses (Chi-square = 3.96, p = 0.046). Conclusion: Using discriminant analysis, this study found that MR spectroscopy in combination with ADC ratio, rather than ADC value, can improve the ability to differentiate recurrent glioma and radiation injury.« less

Partial Least Square Discriminant Analysis Based on Normalized Two-Stage Vegetation Indices for Mapping Damage from Rice Diseases Using PlanetScope Datasets.

PubMed

Shi, Yue; Huang, Wenjiang; Ye, Huichun; Ruan, Chao; Xing, Naichen; Geng, Yun; Dong, Yingying; Peng, Dailiang

2018-06-11

In recent decades, rice disease co-epidemics have caused tremendous damage to crop production in both China and Southeast Asia. A variety of remote sensing based approaches have been developed and applied to map diseases distribution using coarse- to moderate-resolution imagery. However, the detection and discrimination of various disease species infecting rice were seldom assessed using high spatial resolution data. The aims of this study were (1) to develop a set of normalized two-stage vegetation indices (VIs) for characterizing the progressive development of different diseases with rice; (2) to explore the performance of combined normalized two-stage VIs in partial least square discriminant analysis (PLS-DA); and (3) to map and evaluate the damage caused by rice diseases at fine spatial scales, for the first time using bi-temporal, high spatial resolution imagery from PlanetScope datasets at a 3 m spatial resolution. Our findings suggest that the primary biophysical parameters caused by different disease (e.g., changes in leaf area, pigment contents, or canopy morphology) can be captured using combined normalized two-stage VIs. PLS-DA was able to classify rice diseases at a sub-field scale, with an overall accuracy of 75.62% and a Kappa value of 0.47. The approach was successfully applied during a typical co-epidemic outbreak of rice dwarf (Rice dwarf virus, RDV), rice blast ( Magnaporthe oryzae ), and glume blight ( Phyllosticta glumarum ) in Guangxi Province, China. Furthermore, our approach highlighted the feasibility of the method in capturing heterogeneous disease patterns at fine spatial scales over the large spatial extents.

Exploring the potential of needle trap microextraction combined with chromatographic and statistical data to discriminate different types of cancer based on urinary volatomic biosignature.

PubMed

Porto-Figueira, Priscilla; Pereira, Jorge A M; Câmara, José S

2018-09-06

The worldwide high cancer incidence and mortality demands for more effective and specific diagnostic strategies. In this study, we evaluated the efficiency of an innovative methodology, Needle Trap Microextraction (NTME), combined with gas chromatography-mass spectrometry (GC-MS), for the establishment of the urinary volatomic biosignature from breast (BC), and colon (CC) cancer patients as well as healthy individuals (CTL). To achieve this, 40 mL of the headspace of acidified urine (4 mL, 20% NaCl, pH = 2), equilibrated at 50 °C during 40 min, were loaded through the DVB/Car1000/CarX sorbent inside the NTD, and subjected to a GC-MS analysis. This allowed the identification of 130 VOMs from different chemical families that were further processed using discriminant analysis through the partial least squares method (PLS-DA). Several pathways are over activated in cancer patients, being phenylalanine pathway in BC and limonene and pinene degradation pathway in CC the most relevant. Butanoate metabolism is also highly activated in both cancers, as well as tyrosine metabolism in a lesser extension. In BC the xenobiotics metabolism by cytochrome P450 and fatty acid biosynthesis are also differentially activated. Different clusters corresponding to the groups recruited allowed to define sets of volatile organic metabolites (VOMs fingerprints) that exhibit high classification rates, sensitivity and specificity in the discrimination of the selected cancers. As far as we are aware, this is the first time that NTME is used for isolation urinary volatile metabolites, being the obtained results very promising. Copyright © 2018 Elsevier B.V. All rights reserved.

Classification and localization of early-stage Alzheimer's disease in magnetic resonance images using a patch-based classifier ensemble.

PubMed

Simões, Rita; van Cappellen van Walsum, Anne-Marie; Slump, Cornelis H

2014-09-01

Classification methods have been proposed to detect Alzheimer’s disease (AD) using magnetic resonance images. Most rely on features such as the shape/volume of brain structures that need to be defined a priori. In this work, we propose a method that does not require either the segmentation of specific brain regions or the nonlinear alignment to a template. Besides classification, we also analyze which brain regions are discriminative between a group of normal controls and a group of AD patients. We perform 3D texture analysis using Local Binary Patterns computed at local image patches in the whole brain, combined in a classifier ensemble.We evaluate our method in a publicly available database including very mild-to-mild AD subjects and healthy elderly controls. For the subject cohort including only mild AD subjects, the best results are obtained using a combination of large (30×30×30 and 40×40×40 voxels) patches. A spatial analysis on the best performing patches shows that these are located in the medial-temporal lobe and in the periventricular regions. When very mild AD subjects are included in the dataset, the small (10×10×10 voxels) patches perform best, with the most discriminative ones being located near the left hippocampus. We show that our method is able not only to perform accurate classification, but also to localize dis-criminative brain regions, which are in accordance with the medical literature. This is achieved without the need to segment-specific brain structures and without performing nonlinear registration to a template, indicating that the method may be suitable for a clinical implementation that can help to diagnose AD at an earlier stage.

Discrimination of several Indonesian specialty coffees using Fluorescence Spectroscopy combined with SIMCA method

NASA Astrophysics Data System (ADS)

Suhandy, D.; Yulia, M.

2018-03-01

Indonesia is one of the important producers of several specialty coffees, which have a particularly high economic value, including Civet coffee (‘kopi luwak’ in Indonesian language) and Peaberry coffee (‘kopi lanang’ in Indonesian language). The production of Civet and Peaberry coffee is very limited. In order to provide authentication of Civet and Peaberry coffee and protect consumers from adulteration, a robust and easy method for evaluating ground Civet and Peaberry coffee and detection of its adulteration is needed. In this study, we investigate the use of fluorescence spectroscopy combined with SIMCA (soft independent modelling of class analogies) method to discriminate three Indonesian specialty coffee: ground Peaberry, Civet and Pagar Alam coffee. Total 90 samples were used (30 samples for Civet, Peaberry and Pagar Alam coffee, respectively). All coffee samples were ground using a home-coffee-grinder. Since particle size in coffee powder has a significant influence on the spectra obtained, we sieved all coffee samples through a nest of U. S. standard sieves (mesh number of 40) on a Meinzer II sieve shaker for 10 minutes to obtain a particle size of 420 µm. The experiments were performed at room temperature (around 27-29°C). All samples were extracted with distilled water and then filtered. For each samples, 3 mL of extracted sample then was pipetted into 10 mm cuvettes for spectral data acquisition. The EEM (excitation-emission matrix) spectral data of coffee samples were acquired using JASCO FP-8300 Fluorescence Spectrometer. The principal component analysis (PCA) result shows that it is possible to discriminate types of coffee based on information from EEM (excitation-emission matrix) spectral data. Using SIMCA method, the discrimination model of Indonesian specialty coffee was successfully developed and resulted in high performance of discrimination with 100% of sensitivity and specificity for Peaberry, Civet and Pagar Alam coffee. This research has opened the possibility to develop a promising method to detect and evaluate authentication of Indonesian specialty coffees using fluorescence spectroscopy.

Discriminating crop, weeds and soil surface with a terrestrial LIDAR sensor.

PubMed

Andújar, Dionisio; Rueda-Ayala, Victor; Moreno, Hugo; Rosell-Polo, Joan Ramón; Escolá, Alexandre; Valero, Constantino; Gerhards, Roland; Fernández-Quintanilla, César; Dorado, José; Griepentrog, Hans-Werner

2013-10-29

In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12-14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying.

Discriminating Crop, Weeds and Soil Surface with a Terrestrial LIDAR Sensor

PubMed Central

Andújar, Dionisio; Rueda-Ayala, Victor; Moreno, Hugo; Rosell-Polo, Joan Ramón; Escolà, Alexandre; Valero, Constantino; Gerhards, Roland; Fernández-Quintanilla, César; Dorado, José; Griepentrog, Hans-Werner

2013-01-01

In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12–14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying. PMID:24172283

Dry-eye screening by using a functional visual acuity measurement system: the Osaka Study.

PubMed

Kaido, Minako; Uchino, Miki; Yokoi, Norihiko; Uchino, Yuichi; Dogru, Murat; Kawashima, Motoko; Komuro, Aoi; Sonomura, Yukiko; Kato, Hiroaki; Kinoshita, Shigeru; Tsubota, Kazuo

2014-05-06

We determined whether functional visual acuity (VA) parameters and a dry eyes (DEs) symptoms questionnaire could predict DEs in a population of visual terminal display (VDT) users. This prospective study included 491 VDT users from the Osaka Study. Subjects with definite DE, diagnosed with the presence of DE symptoms, tear abnormality (Schirmer test ≤ 5 mm or tear breakup time [TBUT] ≤ 5 seconds), and conjunctivocorneal epithelial damage (total staining score of ≥3 points), or probable DE, diagnosed with the presence of two of them, were assigned to a DE group, and the remainder to a non-DE group. Functional VA was assessed, and DE questionnaires were administered. We assessed whether univariate and discriminant analyses could determine to which group a subject belonged. Sensitivity and specificity were assessed. Of 491 subjects, 320 and 171 were assigned to the DE and non-DE groups, respectively. No significant differences were observed between DE and non-DE groups in Schirmer test value and epithelial damage, but TBUT value (3.1 ± 1.5 vs. 5.9 ± 3.0 seconds). The sensitivity and specificity of single test using functional VA parameters were 59% and 49% in functional VA, 60% and 50% in visual maintenance ratio, and 83% and 30% in frequency of blinking, respectively. According to a discriminant analysis using a combination of functional VA parameters and a DE questionnaire, six variables were selected for the discriminant equation, of which area under the curve (AUC) was 0.735. Sensitivity and specificity of diagnoses predicted by the discriminant equation were 85.9% and 45.6%, respectively. The discriminant equation obtained using functional VA measurement combined with a symptoms questionnaire may suggest the possibility for the first step screening of DE with unstable tear film. Since the questionnaire has an overall poor sensitivity and specificity, further amelioration may be necessary for the actual utilization of this screening tool. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

Perceived Discrimination and Health: A Meta-Analytic Review

PubMed Central

Pascoe, Elizabeth A.; Richman, Laura Smart

2009-01-01

Perceived discrimination has been studied with regard to its impact on several types of health effects. This meta-analysis provides a comprehensive account of the relationships between multiple forms of perceived discrimination and both mental and physical health outcomes. In addition, this meta-analysis examines potential mechanisms by which perceiving discrimination may affect health, including through psychological and physiological stress responses and health behaviors. Analysis of 134 samples suggests that when weighting each study’s contribution by sample size, perceived discrimination has a significant negative effect on both mental and physical health. Perceived discrimination also produces significantly heightened stress responses and is related to participation in unhealthy and nonparticipation in healthy behaviors. These findings suggest potential pathways linking perceived discrimination to negative health outcomes. PMID:19586161

Identification of Dyslipidemic Patients Attending Primary Care Clinics Using Electronic Medical Record (EMR) Data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) Database.

PubMed

Aref-Eshghi, Erfan; Oake, Justin; Godwin, Marshall; Aubrey-Bassler, Kris; Duke, Pauline; Mahdavian, Masoud; Asghari, Shabnam

2017-03-01

The objective of this study was to define the optimal algorithm to identify patients with dyslipidemia using electronic medical records (EMRs). EMRs of patients attending primary care clinics in St. John's, Newfoundland and Labrador (NL), Canada during 2009-2010, were studied to determine the best algorithm for identification of dyslipidemia. Six algorithms containing three components, dyslipidemia ICD coding, lipid lowering medication use, and abnormal laboratory lipid levels, were tested against a gold standard, defined as the existence of any of the three criteria. Linear discriminate analysis, and bootstrapping were performed following sensitivity/specificity testing and receiver's operating curve analysis. Two validating datasets, NL records of 2011-2014, and Canada-wide records of 2010-2012, were used to replicate the results. Relative to the gold standard, combining laboratory data together with lipid lowering medication consumption yielded the highest sensitivity (99.6%), NPV (98.1%), Kappa agreement (0.98), and area under the curve (AUC, 0.998). The linear discriminant analysis for this combination resulted in an error rate of 0.15 and an Eigenvalue of 1.99, and the bootstrapping led to AUC: 0.998, 95% confidence interval: 0.997-0.999, Kappa: 0.99. This algorithm in the first validating dataset yielded a sensitivity of 97%, Negative Predictive Value (NPV) = 83%, Kappa = 0.88, and AUC = 0.98. These figures for the second validating data set were 98%, 93%, 0.95, and 0.99, respectively. Combining laboratory data with lipid lowering medication consumption within the EMR is the best algorithm for detecting dyslipidemia. These results can generate standardized information systems for dyslipidemia and other chronic disease investigations using EMRs.

HIV-related stigma and self-disclosure: the mediating and moderating role of anticipated discrimination among people living with HIV/AIDS in Akure Nigeria.

PubMed

Olley, B O; Ogunde, M J; Oso, P O; Ishola, A

2016-01-01

Although links between HIV-related stigma and self-disclosure of HIV status among people living with HIV have been well established, it is unclear whether levels of perceived discrimination are differentially associated with self-disclosure. The present study using a multi-factorial survey design investigated the role of stigma and other self-related factors (e.g., anticipated discrimination, self-esteem, HIV-related factors [e.g., drug use combination; knowledge of duration of HIV diagnosis] and socio-demographic factors [e.g., multiple spouse; age, gender, educational level] and psychological distress [depression]) in self-disclosure among People living with HIV/AIDs has been added (PLWHA) on follow-up management in State Specialist Hospital Akure, Nigeria. One hundred and thirty nine HIV/AIDS patients (49 males and 90 females) participated in the study. Mean age and mean time in months since diagnosis were 39.56 ± 10.26 and 37.78 ± 48.34, respectively. Four variables: multiple spouse, anticipated discrimination, HIV-related stigma and self-esteem were related to self-disclosure at (p < .05). Product-term regression analyses demonstrated that perceived discrimination mediated the relationship between self-esteem (Sobel test: z = 2.09, Aroian = 2.06, p < .001), perceived stigma (Sobel test: z = 2.78, Aroaian = 2.75 p < .01) and self-disclosure. Interaction term analysis between HIV-related stigma t (5, 137) = 1.69, p > .05, self-esteem t (5, 137) = .59, p > .05 and anticipated discrimination were non-significant, suggesting a non-moderation effect of discrimination and disclosure. The results indicate that anticipated discrimination may impact HIV-related stigma to reduce self-disclosure among the PLWHAs in Akure, Nigeria. Interventions should incorporate anticipated discrimination in educational programs of HIV stigma in encouraging self-disclosure among PLWHAs.

Voltammetric fingerprinting of oils and its combination with chemometrics for the detection of extra virgin olive oil adulteration.

PubMed

Tsopelas, Fotios; Konstantopoulos, Dimitris; Kakoulidou, Anna Tsantili

2018-07-26

In the present work, two approaches for the voltammetric fingerprinting of oils and their combination with chemometrics were investigated in order to detect the adulteration of extra virgin olive oil with olive pomace oil as well as the most common seed oils, namely sunflower, soybean and corn oil. In particular, cyclic voltammograms of diluted extra virgin olive oils, regular (pure) olive oils (blends of refined olive oils with virgin olive oils), olive pomace oils and seed oils in presence of dichloromethane and 0.1 M of LiClO 4 in EtOH as electrolyte were recorded at a glassy carbon working electrode. Cyclic voltammetry was also employed in methanolic extracts of olive and seed oils. Datapoints of cyclic voltammograms were exported and submitted to Principal Component Analysis (PCA), Partial Least Square- Discriminant Analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA). In diluted oils, PLS-DA provided a clear discrimination between olive oils (extra virgin and regular) and olive pomace/seed oils, while SIMCA showed a clear discrimination of extra virgin olive oil in regard to all other samples. Using methanolic extracts and considering datapoints recorded between 0.6 and 1.3 V, PLS-DA provided more information, resulting in three clusters-extra virgin olive oils, regular olive oils and seed/olive pomace oils-while SIMCA showed inferior performance. For the quantification of extra virgin olive oil adulteration with olive pomace oil or seed oils, a model based on Partial Least Square (PLS) analysis was developed. Detection limit of adulteration in olive oil was found to be 2% (v/v) and the linearity range up to 33% (v/v). Validation and applicability of all models was proved using a suitable test set. In the case of PLS, synthetic oil mixtures with 4 known adulteration levels in the range of 4-26% were also employed as a blind test set. Copyright © 2018 Elsevier B.V. All rights reserved.

Appearance-based human gesture recognition using multimodal features for human computer interaction

NASA Astrophysics Data System (ADS)

Luo, Dan; Gao, Hua; Ekenel, Hazim Kemal; Ohya, Jun

2011-03-01

The use of gesture as a natural interface plays an utmost important role for achieving intelligent Human Computer Interaction (HCI). Human gestures include different components of visual actions such as motion of hands, facial expression, and torso, to convey meaning. So far, in the field of gesture recognition, most previous works have focused on the manual component of gestures. In this paper, we present an appearance-based multimodal gesture recognition framework, which combines the different groups of features such as facial expression features and hand motion features which are extracted from image frames captured by a single web camera. We refer 12 classes of human gestures with facial expression including neutral, negative and positive meanings from American Sign Languages (ASL). We combine the features in two levels by employing two fusion strategies. At the feature level, an early feature combination can be performed by concatenating and weighting different feature groups, and LDA is used to choose the most discriminative elements by projecting the feature on a discriminative expression space. The second strategy is applied on decision level. Weighted decisions from single modalities are fused in a later stage. A condensation-based algorithm is adopted for classification. We collected a data set with three to seven recording sessions and conducted experiments with the combination techniques. Experimental results showed that facial analysis improve hand gesture recognition, decision level fusion performs better than feature level fusion.

Ethnicity identification from face images

NASA Astrophysics Data System (ADS)

Lu, Xiaoguang; Jain, Anil K.

2004-08-01

Human facial images provide the demographic information, such as ethnicity and gender. Conversely, ethnicity and gender also play an important role in face-related applications. Image-based ethnicity identification problem is addressed in a machine learning framework. The Linear Discriminant Analysis (LDA) based scheme is presented for the two-class (Asian vs. non-Asian) ethnicity classification task. Multiscale analysis is applied to the input facial images. An ensemble framework, which integrates the LDA analysis for the input face images at different scales, is proposed to further improve the classification performance. The product rule is used as the combination strategy in the ensemble. Experimental results based on a face database containing 263 subjects (2,630 face images, with equal balance between the two classes) are promising, indicating that LDA and the proposed ensemble framework have sufficient discriminative power for the ethnicity classification problem. The normalized ethnicity classification scores can be helpful in the facial identity recognition. Useful as a "soft" biometric, face matching scores can be updated based on the output of ethnicity classification module. In other words, ethnicity classifier does not have to be perfect to be useful in practice.

Electrochemical approach for acute myocardial infarction diagnosis based on direct antibodies-free analysis of human blood plasma.

PubMed

Suprun, Elena V; Saveliev, Anatoly A; Evtugyn, Gennady A; Lisitsa, Alexander V; Bulko, Tatiana V; Shumyantseva, Victoria V; Archakov, Alexander I

2012-03-15

A novel direct antibodies-free electrochemical approach for acute myocardial infarction (AMI) diagnosis has been developed. For this purpose, a combination of the electrochemical assay of plasma samples with chemometrics was proposed. Screen printed carbon electrodes modified with didodecyldimethylammonium bromide were used for plasma charactrerization by cyclic (CV) and square wave voltammetry and square wave (SWV) voltammetry. It was shown that the cathodic peak in voltammograms at about -250 mV vs. Ag/AgCl can be associated with AMI. In parallel tests, cardiac myoglobin and troponin I, the AMI biomarkers, were determined in each sample by RAMP immunoassay. The applicability of the electrochemical testing for AMI diagnostics was confirmed by statistical methods: generalized linear model (GLM), linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), artificial neural net (multi-layer perception, MLP), and support vector machine (SVM), all of which were created to obtain the "True-False" distribution prediction where "True" and "False" are, respectively, positive and negative decision about an illness event. Copyright © 2011 Elsevier B.V. All rights reserved.

Comparative study of wine tannin classification using Fourier transform mid-infrared spectrometry and sensory analysis.

PubMed

Fernández, Katherina; Labarca, Ximena; Bordeu, Edmundo; Guesalaga, Andrés; Agosin, Eduardo

2007-11-01

Wine tannins are fundamental to the determination of wine quality. However, the chemical and sensorial analysis of these compounds is not straightforward and a simple and rapid technique is necessary. We analyzed the mid-infrared spectra of white, red, and model wines spiked with known amounts of skin or seed tannins, collected using Fourier transform mid-infrared (FT-MIR) transmission spectroscopy (400-4000 cm(-1)). The spectral data were classified according to their tannin source, skin or seed, and tannin concentration by means of discriminant analysis (DA) and soft independent modeling of class analogy (SIMCA) to obtain a probabilistic classification. Wines were also classified sensorially by a trained panel and compared with FT-MIR. SIMCA models gave the most accurate classification (over 97%) and prediction (over 60%) among the wine samples. The prediction was increased (over 73%) using the leave-one-out cross-validation technique. Sensory classification of the wines was less accurate than that obtained with FT-MIR and SIMCA. Overall, these results show the potential of FT-MIR spectroscopy, in combination with adequate statistical tools, to discriminate wines with different tannin levels.

Dynamic elementary mode modelling of non-steady state flux data.

PubMed

Folch-Fortuny, Abel; Teusink, Bas; Hoefsloot, Huub C J; Smilde, Age K; Ferrer, Alberto

2018-06-18

A novel framework is proposed to analyse metabolic fluxes in non-steady state conditions, based on the new concept of dynamic elementary mode (dynEM): an elementary mode activated partially depending on the time point of the experiment. Two methods are introduced here: dynamic elementary mode analysis (dynEMA) and dynamic elementary mode regression discriminant analysis (dynEMR-DA). The former is an extension of the recently proposed principal elementary mode analysis (PEMA) method from steady state to non-steady state scenarios. The latter is a discriminant model that permits to identify which dynEMs behave strongly different depending on the experimental conditions. Two case studies of Saccharomyces cerevisiae, with fluxes derived from simulated and real concentration data sets, are presented to highlight the benefits of this dynamic modelling. This methodology permits to analyse metabolic fluxes at early stages with the aim of i) creating reduced dynamic models of flux data, ii) combining many experiments in a single biologically meaningful model, and iii) identifying the metabolic pathways that drive the organism from one state to another when changing the environmental conditions.

Discrimination, racial bias, and telomere length in African-American men.

PubMed

Chae, David H; Nuru-Jeter, Amani M; Adler, Nancy E; Brody, Gene H; Lin, Jue; Blackburn, Elizabeth H; Epel, Elissa S

2014-02-01

Leukocyte telomere length (LTL) is an indicator of general systemic aging, with shorter LTL being associated with several chronic diseases of aging and earlier mortality. Identifying factors related to LTL among African Americans may yield insights into mechanisms underlying racial disparities in health. To test whether the combination of more frequent reports of racial discrimination and holding a greater implicit anti-black racial bias is associated with shorter LTL among African-American men. Cross-sectional study of a community sample of 92 African-American men aged between 30 and 50 years. Participants were recruited from February to May 2010. Ordinary least squares regressions were used to examine LTL in kilobase pairs in relation to racial discrimination and implicit racial bias. Data analysis was completed in July 2013. After controlling for chronologic age and socioeconomic and health-related characteristics, the interaction between racial discrimination and implicit racial bias was significantly associated with LTL (b=-0.10, SE=0.04, p=0.02). Those demonstrating a stronger implicit anti-black bias and reporting higher levels of racial discrimination had the shortest LTL. Household income-to-poverty threshold ratio was also associated with LTL (b=0.05, SE=0.02, p<0.01). Results suggest that multiple levels of racism, including interpersonal experiences of racial discrimination and the internalization of negative racial bias, operate jointly to accelerate biological aging among African-American men. Societal efforts to address racial discrimination in concert with efforts to promote positive in-group racial attitudes may protect against premature biological aging in this population. © 2013 American Journal of Preventive Medicine Published by American Journal of Preventive Medicine All rights reserved.

Multidimensional analysis of the abnormal neural oscillations associated with lexical processing in schizophrenia.

PubMed

Xu, Tingting; Stephane, Massoud; Parhi, Keshab K

2013-04-01

The neural mechanisms of language abnormalities, the core symptoms in schizophrenia, remain unclear. In this study, a new experimental paradigm, combining magnetoencephalography (MEG) techniques and machine intelligence methodologies, was designed to gain knowledge about the frequency, brain location, and time of occurrence of the neural oscillations that are associated with lexical processing in schizophrenia. The 248-channel MEG recordings were obtained from 12 patients with schizophrenia and 10 healthy controls, during a lexical processing task, where the patients discriminated correct from incorrect lexical stimuli that were visually presented. Event-related desynchronization/synchronization (ERD/ERS) was computed along the frequency, time, and space dimensions combined, that resulted in a large spectral-spatial-temporal ERD/ERS feature set. Machine intelligence techniques were then applied to select a small subset of oscillation patterns that are abnormal in patients with schizophrenia, according to their discriminating power in patient and control classification. Patients with schizophrenia showed abnormal ERD/ERS patterns during both lexical encoding and post-encoding periods. The top-ranked features were located at the occipital and left frontal-temporal areas, and covered a wide frequency range, including δ (1-4 Hz), α (8-12 Hz), β (12-32 Hz), and γ (32-48 Hz) bands. These top features could discriminate the patient group from the control group with 90.91% high accuracy, which demonstrates significant brain oscillation abnormalities in patients with schizophrenia at the specific frequency, time, and brain location indicated by these top features. As neural oscillation abnormality may be due to the mechanisms of the disease, the spectral, spatial, and temporal content of the discriminating features can offer useful information for helping understand the physiological basis of the language disorder in schizophrenia, as well as the pathology of the disease itself.

Semi-supervised learning for ordinal Kernel Discriminant Analysis.

PubMed

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

2016-12-01

Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Ecology of Early Childhood Risk: A Canonical Correlation Analysis of Children’s Adjustment, Family, and Community Context in a High-Risk Sample

PubMed Central

Aiyer, Sophie M.; Wilson, Melvin N.; Shaw, Daniel S.; Dishion, Thomas J.

2013-01-01

The ecology of the emergence of psycho-pathology in early childhood is often approached by the analysis of a limited number of contextual risk factors. In the present study, we provide a comprehensive analysis of ecological risk by conducting a canonical correlation analysis of 13 risk factors at child age 2 and seven narrow-band scales of internalizing and externalizing problem behaviors at child age 4, using a sample of 364 geographically and ethnically diverse, disadvantaged primary caregivers, alternative caregivers, and preschool-age children. Participants were recruited from Special Supplemental Nutrition Program for Women, Infants, and Children sites and were screened for family risk. Canonical correlation analysis revealed that (1) a first latent combination of family and individual risks of caregivers predicted combinations of child emotional and behavioral problems, and that (2) a second latent combination of contextual and structural risks predicted child somatic complaints. Specifically, (1) the combination of chaotic home, conflict with child, parental depression, and parenting hassles predicted a co-occurrence of internalizing and externalizing behaviors, and (2) the combination of father absence, perceived discrimination, neighborhood danger, and fewer children living in the home predicted child somatic complaints. The research findings are discussed in terms of the development of psychopathology, as well as the potential prevention needs of families in high-risk contexts. PMID:23700232

Study on bayes discriminant analysis of EEG data.

PubMed

Shi, Yuan; He, DanDan; Qin, Fang

2014-01-01

In this paper, we have done Bayes Discriminant analysis to EEG data of experiment objects which are recorded impersonally come up with a relatively accurate method used in feature extraction and classification decisions. In accordance with the strength of α wave, the head electrodes are divided into four species. In use of part of 21 electrodes EEG data of 63 people, we have done Bayes Discriminant analysis to EEG data of six objects. Results In use of part of EEG data of 63 people, we have done Bayes Discriminant analysis, the electrode classification accuracy rates is 64.4%. Bayes Discriminant has higher prediction accuracy, EEG features (mainly αwave) extract more accurate. Bayes Discriminant would be better applied to the feature extraction and classification decisions of EEG data.

Divergent effects of cannabidiol on the discriminative stimulus and place conditioning effects of Δ9-tetrahydrocannabinol

PubMed Central

Vann, Robert E.; Gamage, Thomas F.; Warner, Jonathan A.; Marshall, Ericka M.; Taylor, Nathan L.; Martin, Billy R.; Wiley, Jenny L.

2008-01-01

Cannabis sativa (marijuana plant) contains myriad cannabinoid compounds; yet, investigative attention has focused almost exclusively on Δ9-tetrahydrocannabinol (THC), its primary psychoactive substituent. Interest in modulation of THC’s effects by these other cannabinoids [e.g., cannabidiol (CBD)] has been stimulated anew by recent approval by Canada of Sativex (a 1:1 dose ratio combination of CBD:THC) for the treatment of multiple sclerosis. The goal of this study was to determine the degree to which THC’s abuse-related effects were altered by co-administration of CBD. To this end, CBD and THC were assessed alone and in combination in a two-lever THC discrimination procedure in Long-Evans rats and in a conditioned place preference/aversion (CPP/A) model in ICR mice. CBD did not alter the discriminative stimulus effects of THC at any CBD:THC dose ratio tested. In contrast, CBD, at CBD:THC dose ratios of 1:1 and 1:10, reversed CPA produced by acute injection with 10 mg/kg THC. When administered alone, CBD did not produce effects in either procedure. These results suggest that CBD, when administered with THC at therapeutically relevant ratios, may ameliorate aversive effects (e.g., dysphoria) often associated with initial use of THC alone. While this effect may be beneficial for therapeutic usage of a CBD:THC combination medication, our discrimination results showing that CBD did not alter THC’s discriminative stimulus effects suggest that CBD:THC combination medications may also produce THC-like subjective effects at these dose ratios. PMID:18206320

Social status correlates of reporting gender discrimination and racial discrimination among racially diverse women.

PubMed

Ro, Annie E; Choi, Kyung-Hee

2009-01-01

The growing body of research on discrimination and health indicates a deleterious effect of discrimination on various health outcomes. However, less is known about the sociodemographic correlates of reporting racial discrimination and gender discrimination among racially diverse women. We examined the associations of social status characteristics with lifetime experiences of racial discrimination and gender discrimination using a racially-diverse sample of 754 women attending family planning clinics in North California (11.4% African American, 16.8% Latina, 10.1% Asian and 61.7% Caucasian). A multivariate analysis revealed that race, financial difficulty and marital status were significantly correlated with higher reports of racial discrimination, while race, education, financial difficulty and nativity were significantly correlated with gender discrimination scores. Our findings suggest that the social patterning of perceiving racial discrimination is somewhat different from that of gender discrimination. This has implications in the realm of discrimination research and applied interventions, as different forms of discrimination may have unique covariates that should be accounted for in research analysis or program design.

RNAcode: Robust discrimination of coding and noncoding regions in comparative sequence data

PubMed Central

Washietl, Stefan; Findeiß, Sven; Müller, Stephan A.; Kalkhof, Stefan; von Bergen, Martin; Hofacker, Ivo L.; Stadler, Peter F.; Goldman, Nick

2011-01-01

With the availability of genome-wide transcription data and massive comparative sequencing, the discrimination of coding from noncoding RNAs and the assessment of coding potential in evolutionarily conserved regions arose as a core analysis task. Here we present RNAcode, a program to detect coding regions in multiple sequence alignments that is optimized for emerging applications not covered by current protein gene-finding software. Our algorithm combines information from nucleotide substitution and gap patterns in a unified framework and also deals with real-life issues such as alignment and sequencing errors. It uses an explicit statistical model with no machine learning component and can therefore be applied “out of the box,” without any training, to data from all domains of life. We describe the RNAcode method and apply it in combination with mass spectrometry experiments to predict and confirm seven novel short peptides in Escherichia coli and to analyze the coding potential of RNAs previously annotated as “noncoding.” RNAcode is open source software and available for all major platforms at http://wash.github.com/rnacode. PMID:21357752

RNAcode: robust discrimination of coding and noncoding regions in comparative sequence data.

PubMed

Washietl, Stefan; Findeiss, Sven; Müller, Stephan A; Kalkhof, Stefan; von Bergen, Martin; Hofacker, Ivo L; Stadler, Peter F; Goldman, Nick

2011-04-01

With the availability of genome-wide transcription data and massive comparative sequencing, the discrimination of coding from noncoding RNAs and the assessment of coding potential in evolutionarily conserved regions arose as a core analysis task. Here we present RNAcode, a program to detect coding regions in multiple sequence alignments that is optimized for emerging applications not covered by current protein gene-finding software. Our algorithm combines information from nucleotide substitution and gap patterns in a unified framework and also deals with real-life issues such as alignment and sequencing errors. It uses an explicit statistical model with no machine learning component and can therefore be applied "out of the box," without any training, to data from all domains of life. We describe the RNAcode method and apply it in combination with mass spectrometry experiments to predict and confirm seven novel short peptides in Escherichia coli and to analyze the coding potential of RNAs previously annotated as "noncoding." RNAcode is open source software and available for all major platforms at http://wash.github.com/rnacode.

Quantifying heterogeneity of lesion uptake in dynamic contrast enhanced MRI for breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Karahaliou, A.; Vassiou, K.; Skiadopoulos, S.; Kanavou, T.; Yiakoumelos, A.; Costaridou, L.

2009-07-01

The current study investigates whether texture features extracted from lesion kinetics feature maps can be used for breast cancer diagnosis. Fifty five women with 57 breast lesions (27 benign, 30 malignant) were subjected to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) on 1.5T system. A linear-slope model was fitted pixel-wise to a representative lesion slice time series and fitted parameters were used to create three kinetic maps (wash out, time to peak enhancement and peak enhancement). 28 grey level co-occurrence matrices features were extracted from each lesion kinetic map. The ability of texture features per map in discriminating malignant from benign lesions was investigated using a Probabilistic Neural Network classifier. Additional classification was performed by combining classification outputs of most discriminating feature subsets from the three maps, via majority voting. The combined scheme outperformed classification based on individual maps achieving area under Receiver Operating Characteristics curve 0.960±0.029. Results suggest that heterogeneity of breast lesion kinetics, as quantified by texture analysis, may contribute to computer assisted tissue characterization in DCE-MRI.

EXTRACTING PRINCIPLE COMPONENTS FOR DISCRIMINANT ANALYSIS OF FMRI IMAGES

PubMed Central

Liu, Jingyu; Xu, Lai; Caprihan, Arvind; Calhoun, Vince D.

2009-01-01

This paper presents an approach for selecting optimal components for discriminant analysis. Such an approach is useful when further detailed analyses for discrimination or characterization requires dimensionality reduction. Our approach can accommodate a categorical variable such as diagnosis (e.g. schizophrenic patient or healthy control), or a continuous variable like severity of the disorder. This information is utilized as a reference for measuring a component’s discriminant power after principle component decomposition. After sorting each component according to its discriminant power, we extract the best components for discriminant analysis. An application of our reference selection approach is shown using a functional magnetic resonance imaging data set in which the sample size is much less than the dimensionality. The results show that the reference selection approach provides an improved discriminant component set as compared to other approaches. Our approach is general and provides a solid foundation for further discrimination and classification studies. PMID:20582334

EXTRACTING PRINCIPLE COMPONENTS FOR DISCRIMINANT ANALYSIS OF FMRI IMAGES.

PubMed

Liu, Jingyu; Xu, Lai; Caprihan, Arvind; Calhoun, Vince D

2008-05-12

This paper presents an approach for selecting optimal components for discriminant analysis. Such an approach is useful when further detailed analyses for discrimination or characterization requires dimensionality reduction. Our approach can accommodate a categorical variable such as diagnosis (e.g. schizophrenic patient or healthy control), or a continuous variable like severity of the disorder. This information is utilized as a reference for measuring a component's discriminant power after principle component decomposition. After sorting each component according to its discriminant power, we extract the best components for discriminant analysis. An application of our reference selection approach is shown using a functional magnetic resonance imaging data set in which the sample size is much less than the dimensionality. The results show that the reference selection approach provides an improved discriminant component set as compared to other approaches. Our approach is general and provides a solid foundation for further discrimination and classification studies.

Discrimination of edible oils and fats by combination of multivariate pattern recognition and FT-IR spectroscopy: A comparative study between different modeling methods

NASA Astrophysics Data System (ADS)

Javidnia, Katayoun; Parish, Maryam; Karimi, Sadegh; Hemmateenejad, Bahram

2013-03-01

By using FT-IR spectroscopy, many researchers from different disciplines enrich the experimental complexity of their research for obtaining more precise information. Moreover chemometrics techniques have boosted the use of IR instruments. In the present study we aimed to emphasize on the power of FT-IR spectroscopy for discrimination between different oil samples (especially fat from vegetable oils). Also our data were used to compare the performance of different classification methods. FT-IR transmittance spectra of oil samples (Corn, Colona, Sunflower, Soya, Olive, and Butter) were measured in the wave-number interval of 450-4000 cm-1. Classification analysis was performed utilizing PLS-DA, interval PLS-DA, extended canonical variate analysis (ECVA) and interval ECVA methods. The effect of data preprocessing by extended multiplicative signal correction was investigated. Whilst all employed method could distinguish butter from vegetable oils, iECVA resulted in the best performances for calibration and external test set with 100% sensitivity and specificity.

Determination of the geographical origin of green coffee by principal component analysis of carbon, nitrogen and boron stable isotope ratios.

PubMed

Serra, Francesca; Guillou, Claude G; Reniero, Fabiano; Ballarin, Luciano; Cantagallo, Maria I; Wieser, Michael; Iyer, Sundaram S; Héberger, Károly; Vanhaecke, Frank

2005-01-01

In this study we show that the continental origin of coffee can be inferred on the basis of coupling the isotope ratios of several elements determined in green beans. The combination of the isotopic fingerprints of carbon, nitrogen and boron, used as integrated proxies for environmental conditions and agricultural practices, allows discrimination among the three continental areas producing coffee (Africa, Asia and America). In these continents there are countries producing 'specialty coffees', highly rated on the market that are sometimes mislabeled further on along the export-sale chain or mixed with cheaper coffees produced in other regions. By means of principal component analysis we were successful in identifying the continental origin of 88% of the samples analyzed. An intra-continent discrimination has not been possible at this stage of the study, but is planned in future work. Nonetheless, the approach using stable isotope ratios seems quite promising, and future development of this research is also discussed. (c) 2005 John Wiley & Sons, Ltd.

Trimodal spectra for high discrimination of benign and malignant prostate tissue

NASA Astrophysics Data System (ADS)

Al Salhi, Mohamad; Masilamani, Vadivel; Trinka, Vijmasi; Rabah, Danny; Al Turki, Mohammed R.

2011-02-01

High false positives and over diagnosis is a major problem with management of prostate cancer. A non-invasive or a minimally invasive technique to accurately distinguish malignant prostate cancers from benign tumors will be extremely helpful to overcome this problem. In this paper, we had used three different fluorescence spectroscopy techniques viz., Fluorescence Emission Spectrum (FES), Stokes' Shift Spectrum (SSS) and Reflectance Spectrum (RS) to discriminate benign prostate tumor tissues (N=12) and malignant prostate cancer tissues (N=8). These fluorescence techniques were used to determine the relative concentration of naturally occurring biomolecules such as tryptophan, elastin, NADH and flavin which are found to be out of proportion in cancer tissues. Our studies show that combining all three techniques, benign and malignant prostate tissues could be classified with accuracy greater than 90%. This preliminary report is based on in vitro spectroscopy analysis. However, by employing fluorescence endoscopy techniques, this can be extended to in vivo analysis as well. This technique has the potential to identify malignant prostate tissues without surgery.

DNA barcoding coupled to HRM analysis as a new and simple tool for the authentication of Gadidae fish species.

PubMed

Fernandes, Telmo J R; Costa, Joana; Oliveira, M Beatriz P P; Mafra, Isabel

2017-09-01

This work aimed to exploit the use of DNA mini-barcodes combined with high resolution melting (HRM) for the authentication of gadoid species: Atlantic cod (Gadus morhua), Pacific cod (Gadus macrocephalus), Alaska pollock (Theragra chalcogramma) and saithe (Pollachius virens). Two DNA barcode regions, namely cytochrome c oxidase subunit I (COI) and cytochrome b (cytb), were analysed in silico to identify genetic variability among the four species and used, subsequently, to develop a real-time PCR method coupled with HRM analysis. The cytb mini-barcode enabled best discrimination of the target species with a high level of confidence (99.3%). The approach was applied successfully to identify gadoid species in 30 fish-containing foods, 30% of which were not as declared on the label. Herein, a novel approach for rapid, simple and cost-effective discrimination/clustering, as a tool to authenticate Gadidae fish species, according to their genetic relationship, is proposed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Analysis of 16 autosomal STRs and 17 Y-STRs in an indigenous Maya population from Guatemala.

PubMed

Cardoso, Sergio; Sevillano, Rubén; Illescas, María J; de Pancorbo, Marian Martínez

2016-03-01

The aim of this study was to contribute new data on autosomal STR and Y-STR markers of the Mayas from Guatemala in order to improve available databases of forensic interest. We analyzed 16 autosomal STR markers in a population sample of 155 indigenous Maya and 17 Y-chromosomal STR markers in the 100 males of the sample. Deviations from Hardy-Weinberg equilibrium and linkage disequilibrium between autosomal STR markers were not observed at any loci. The combined power of exclusion was estimated as 99.9991% and the combined power of discrimination was >99.999999999999%. Haplotype diversity of Y-STRs was calculated as 0.9984 ± 0.0018 and analysis of pairwise genetic distances (Rst) supported the Native American background of the population.

[Analysis of different forms Linderae Radix based on HPLC and NIRS fingerprints].

PubMed

Du, Wei-Feng; Yue, Xian-Ke; Wu, Yao; Ge, Wei-Hong; Lu, Tu-Lin; Wang, Zhi-Min

2016-10-01

Three different forms of Linderae Radix were evaluated by HPLC combined with NIRS fingerprint. The Linderae Radix was divided into three forms, including spindle root, straight root and old root. The HPLC fingerprints were developed, and then cluster analysis was performed using the SPSS software. The near-infrared spectra of Linderae Radix was collected, and then established the discriminant analysis model. The similarity values of the spindle root and straight root all were above 0.990, while the similarity value of the old root was less than 0.850. Two forms of Linderae Radix were obviously divided into three parts by the NIRS model and Cluster analysis. The results of HPLC and FT-NIR analysis showed the quality of Linderae Radix old root was different from the spindle root and straight root. The combined use of the two methods could identify different forms of Linderae Radix quickly and accurately. Copyright© by the Chinese Pharmaceutical Association.

Noninvasive detection of nasopharyngeal carcinoma based on saliva proteins using surface-enhanced Raman spectroscopy

NASA Astrophysics Data System (ADS)

Lin, Xueliang; Lin, Duo; Ge, Xiaosong; Qiu, Sufang; Feng, Shangyuan; Chen, Rong

2017-10-01

The present study evaluated the capability of saliva analysis combining membrane protein purification with surface-enhanced Raman spectroscopy (SERS) for noninvasive detection of nasopharyngeal carcinoma (NPC). A rapid and convenient protein purification method based on cellulose acetate membrane was developed. A total of 659 high-quality SERS spectra were acquired from purified proteins extracted from the saliva samples of 170 patients with pathologically confirmed NPC and 71 healthy volunteers. Spectral analysis of those saliva protein SERS spectra revealed specific changes in some biochemical compositions, which were possibly associated with NPC transformation. Furthermore, principal component analysis combined with linear discriminant analysis (PCA-LDA) was utilized to analyze and classify the saliva protein SERS spectra from NPC and healthy subjects. Diagnostic sensitivity of 70.7%, specificity of 70.3%, and diagnostic accuracy of 70.5% could be achieved by PCA-LDA for NPC identification. These results show that this assay based on saliva protein SERS analysis holds promising potential for developing a rapid, noninvasive, and convenient clinical tool for NPC screening.

Coincidence measurements in α/β/γ spectrometry with phoswich detectors using digital pulse shape discrimination analysis

NASA Astrophysics Data System (ADS)

de Celis, B.; de la Fuente, R.; Williart, A.; de Celis Alonso, B.

2007-09-01

A novel system has been developed for the detection of low radioactivity levels using coincidence techniques. The device combines a phoswich detector for α/β/γ ray recognition with a fast digital card for electronic pulse analysis. The detector is able to discriminate different types of radiation in a mixed α/β/γ field and can be used in a coincidence mode by identifying the composite signal produced by the simultaneous detection of β particles in a plastic scintillator and γ rays in an NaI(Tl) scintillator. Use of a coincidence technique with phoswich detectors was proposed recently to verify the Nuclear Test Ban Treaty, which made it necessary to monitor the low levels of xenon radioisotopes produced by underground nuclear explosions. Previous studies have shown that combining CaF 2(Eu) for β ray detection and NaI(Tl) for γ ray detection makes it difficult to identify the coincidence signals because of the similar fluorescence decay times of the two scintillators. With the device proposed here, it is possible to identify the coincidence events owing to the short fluorescence decay time of the plastic scintillator. The sensitivity of the detector may be improved by employing liquid scintillators, which allow low radioactivity levels from actinides to be measured when present in environmental samples. The device developed is simpler to use than conventional coincidence equipment because it uses a single detector and electronic circuit, and it offers fast and precise analysis of the coincidence signals by employing digital pulse shape analysis.

DNA barcoding of Rhododendron (Ericaceae), the largest Chinese plant genus in biodiversity hotspots of the Himalaya-Hengduan Mountains.

PubMed

Yan, Li-Jun; Liu, Jie; Möller, Michael; Zhang, Lin; Zhang, Xue-Mei; Li, De-Zhu; Gao, Lian-Ming

2015-07-01

The Himalaya-Hengduan Mountains encompass two global biodiversity hotspots with high levels of biodiversity and endemism. This area is one of the diversification centres of the genus Rhododendron, which is recognized as one of the most taxonomically challenging plant taxa due to recent adaptive radiations and rampant hybridization. In this study, four DNA barcodes were evaluated on 531 samples representing 173 species of seven sections of four subgenera in Rhododendron, with a high sampling density from the Himalaya-Hengduan Mountains employing three analytical methods. The varied approaches (nj, pwg and blast) had different species identification powers with blast performing best. With the pwg analysis, the discrimination rates for single barcodes varied from 12.21% to 25.19% with ITS < rbcL < matK < psbA-trnH. Combinations of ITS + psbA-trnH + matK and the four barcodes showed the highest discrimination ability (both 41.98%) among all possible combinations. As a single barcode, psbA-trnH performed best with a relatively high performance (25.19%). Overall, the three-marker combination of ITS + psbA-trnH + matK was found to be the best DNA barcode for identifying Rhododendron species. The relatively low discriminative efficiency of DNA barcoding in this genus (~42%) may possibly be attributable to too low sequence divergences as a result of a long generation time of Rhododendron and complex speciation patterns involving recent radiations and hybridizations. Taking the morphology, distribution range and habitat of the species into account, DNA barcoding provided additional information for species identification and delivered a preliminary assessment of biodiversity for the large genus Rhododendron in the biodiversity hotspots of the Himalaya-Hengduan Mountains. © 2014 John Wiley & Sons Ltd.

Surface-enhanced Raman spectroscopy of saliva proteins for the noninvasive differentiation of benign and malignant breast tumors

PubMed Central

Feng, Shangyuan; Huang, Shaohua; Lin, Duo; Chen, Guannan; Xu, Yuanji; Li, Yongzeng; Huang, Zufang; Pan, Jianji; Chen, Rong; Zeng, Haishan

2015-01-01

The capability of saliva protein analysis, based on membrane protein purification and surface-enhanced Raman spectroscopy (SERS), for detecting benign and malignant breast tumors is presented in this paper. A total of 97 SERS spectra from purified saliva proteins were acquired from samples obtained from three groups: 33 healthy subjects; 33 patients with benign breast tumors; and 31 patients with malignant breast tumors. Subtle but discernible changes in the mean SERS spectra of the three groups were observed. Tentative assignments of the saliva protein SERS spectra demonstrated that benign and malignant breast tumors led to several specific biomolecular changes of the saliva proteins. Multiclass partial least squares–discriminant analysis was utilized to analyze and classify the saliva protein SERS spectra from healthy subjects, benign breast tumor patients, and malignant breast tumor patients, yielding diagnostic sensitivities of 75.75%, 72.73%, and 74.19%, as well as specificities of 93.75%, 81.25%, and 86.36%, respectively. The results from this exploratory work demonstrate that saliva protein SERS analysis combined with partial least squares–discriminant analysis diagnostic algorithms has great potential for the noninvasive and label-free detection of breast cancer. PMID:25609959

Authentication of animal fats using direct analysis in real time (DART) ionization-mass spectrometry and chemometric tools.

PubMed

Vaclavik, Lukas; Hrbek, Vojtech; Cajka, Tomas; Rohlik, Bo-Anne; Pipek, Petr; Hajslova, Jana

2011-06-08

A combination of direct analysis in real time (DART) ionization coupled to time-of-flight mass spectrometry (TOFMS) and chemometrics was used for animal fat (lard and beef tallow) authentication. This novel instrumentation was employed for rapid profiling of triacylglycerols (TAGs) and polar compounds present in fat samples and their mixtures. Additionally, fat isolated from pork, beef, and pork/beef admixtures was analyzed. Mass spectral records were processed by principal component analysis (PCA) and stepwise linear discriminant analysis (LDA). DART-TOFMS profiles of TAGs were found to be more suitable for the purpose of discrimination among the examined fat types as compared to profiles of polar compounds. The LDA model developed using TAG data enabled not only reliable classification of samples representing neat fats but also detection of admixed lard and tallow at adulteration levels of 5 and 10% (w/w), respectively. The presented approach was also successfully applied to minced meat prepared from pork and beef with comparable fat content. Using the DART-TOFMS TAG profiles of fat isolated from meat mixtures, detection of 10% pork added to beef and vice versa was possible.

Authentication of the botanical and geographical origin of honey by front-face fluorescence spectroscopy.

PubMed

Ruoff, Kaspar; Luginbühl, Werner; Künzli, Raphael; Bogdanov, Stefan; Bosset, Jacques Olivier; von der Ohe, Katharina; von der Ohe, Werner; Amado, Renato

2006-09-06

Front-face fluorescence spectroscopy, directly applied on honey samples, was used for the authentication of 11 unifloral and polyfloral honey types (n = 371 samples) previously classified using traditional methods such as chemical, pollen, and sensory analysis. Excitation spectra (220-400 nm) were recorded with the emission measured at 420 nm. In addition, emission spectra were recorded between 290 and 500 nm (excitation at 270 nm) as well as between 330 and 550 nm (excitation at 310 nm). A total of four different spectral data sets were considered for data analysis. Chemometric evaluation of the spectra included principal component analysis and linear discriminant analysis; the error rates of the discriminant models were calculated by using Bayes' theorem. They ranged from <0.1% (polyfloral and chestnut honeys) to 9.9% (fir honeydew honey) by using single spectral data sets and from <0.1% (metcalfa honeydew, polyfloral, and chestnut honeys) to 7.5% (lime honey) by combining two data sets. This study indicates that front-face fluorescence spectroscopy is a promising technique for the authentication of the botanical origin of honey and may also be useful for the determination of the geographical origin within the same unifloral honey type.

Metabolic Signature of Remote Ischemic Preconditioning Involving a Cocktail of Amino Acids and Biogenic Amines.

PubMed

Chao de la Barca, Juan Manuel; Bakhta, Oussama; Kalakech, Hussein; Simard, Gilles; Tamareille, Sophie; Catros, Véronique; Callebert, Jacques; Gadras, Cédric; Tessier, Lydie; Reynier, Pascal; Prunier, Fabrice; Mirebeau-Prunier, Delphine

2016-09-24

Remote ischemic preconditioning (RIPC) is an attractive therapeutic procedure for protecting the heart against ischemia/reperfusion injury. Despite evidence of humoral mediators transported through the circulation playing a critical role, their actual identities so far remain unknown. We sought to identify plasmatic RIPC-induced metabolites that may play a role. Rat plasma samples from RIPC and control groups were analyzed using a targeted metabolomic approach aimed at measuring 188 metabolites. Principal component analysis and orthogonal partial least-squares discriminant analysis were used to identify the metabolites that discriminated between groups. Plasma samples from 50 patients subjected to RIPC were secondarily explored to confirm the results obtained in rats. Finally, a combination of the metabolites that were significantly increased in both rat and human plasma was injected prior to myocardial ischemia/reperfusion in rats. In the rat samples, 124 molecules were accurately quantified. Six metabolites (ornithine, glycine, kynurenine, spermine, carnosine, and serotonin) were the most significant variables for marked differentiation between the RIPC and control groups. In human plasma, analysis confirmed ornithine decrease and kynurenine and glycine increase following RIPC. Injection of the glycine and kynurenine alone or in combination replicated the protective effects of RIPC seen in rats. We have hereby reported significant variations in a cocktail of amino acids and biogenic amines after remote ischemic preconditioning in both rat and human plasma. URL: http://www.clinicaltrials.gov. Unique identifier: NCT01390129. © 2016 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Development of algorithms for detecting citrus canker based on hyperspectral reflectance imaging.

PubMed

Li, Jiangbo; Rao, Xiuqin; Ying, Yibin

2012-01-15

Automated discrimination of fruits with canker from other fruit with normal surface and different type of peel defects has become a helpful task to enhance the competitiveness and profitability of the citrus industry. Over the last several years, hyperspectral imaging technology has received increasing attention in the agricultural products inspection field. This paper studied the feasibility of classification of citrus canker from other peel conditions including normal surface and nine peel defects by hyperspectal imaging. A combination algorithm based on principal component analysis and the two-band ratio (Q(687/630)) method was proposed. Since fewer wavelengths were desired in order to develop a rapid multispectral imaging system, the canker classification performance of the two-band ratio (Q(687/630)) method alone was also evaluated. The proposed combination approach and two-band ratio method alone resulted in overall classification accuracy for training set samples and test set samples of 99.5%, 84.5% and 98.2%, 82.9%, respectively. The proposed combination approach was more efficient for classifying canker against various conditions under reflectance hyperspectral imagery. However, the two-band ratio (Q(687/630)) method alone also demonstrated effectiveness in discriminating citrus canker from normal fruit and other peel diseases except for copper burn and anthracnose. Copyright © 2011 Society of Chemical Industry.

A combined cICA-EEMD analysis of EEG recordings from depressed or schizophrenic patients during olfactory stimulation

NASA Astrophysics Data System (ADS)

Götz, Th; Stadler, L.; Fraunhofer, G.; Tomé, A. M.; Hausner, H.; Lang, E. W.

2017-02-01

Objective. We propose a combination of a constrained independent component analysis (cICA) with an ensemble empirical mode decomposition (EEMD) to analyze electroencephalographic recordings from depressed or schizophrenic subjects during olfactory stimulation. Approach. EEMD serves to extract intrinsic modes (IMFs) underlying the recorded EEG time. The latter then serve as reference signals to extract the most similar underlying independent component within a constrained ICA. The extracted modes are further analyzed considering their power spectra. Main results. The analysis of the extracted modes reveals clear differences in the related power spectra between the disease characteristics of depressed and schizophrenic patients. Such differences appear in the high frequency γ-band in the intrinsic modes, but also in much more detail in the low frequency range in the α-, θ- and δ-bands. Significance. The proposed method provides various means to discriminate both disease pictures in a clinical environment.

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

ERIC Educational Resources Information Center

Lee, Debbiesiu L.; Ahn, Soyeon

2012-01-01

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

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

PubMed Central

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

2017-01-01

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

Hybrid generative-discriminative human action recognition by combining spatiotemporal words with supervised topic models

NASA Astrophysics Data System (ADS)

Sun, Hao; Wang, Cheng; Wang, Boliang

2011-02-01

We present a hybrid generative-discriminative learning method for human action recognition from video sequences. Our model combines a bag-of-words component with supervised latent topic models. A video sequence is represented as a collection of spatiotemporal words by extracting space-time interest points and describing these points using both shape and motion cues. The supervised latent Dirichlet allocation (sLDA) topic model, which employs discriminative learning using labeled data under a generative framework, is introduced to discover the latent topic structure that is most relevant to action categorization. The proposed algorithm retains most of the desirable properties of generative learning while increasing the classification performance though a discriminative setting. It has also been extended to exploit both labeled data and unlabeled data to learn human actions under a unified framework. We test our algorithm on three challenging data sets: the KTH human motion data set, the Weizmann human action data set, and a ballet data set. Our results are either comparable to or significantly better than previously published results on these data sets and reflect the promise of hybrid generative-discriminative learning approaches.

Initial empirical analysis of nuclear power plant organization and its effect on safety performance

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

Olson, J.; McLaughlin, S.D.; Osborn, R.N.

This report contains an analysis of the relationship between selected aspects of organizational structure and the safety-related performance of nuclear power plants. The report starts by identifying and operationalizing certain key dimensions of organizational structure that may be expected to be related to plant safety performance. Next, indicators of plant safety performance are created by combining existing performance measures into more reliable indicators. Finally, the indicators of plant safety performance using correlational and discriminant analysis. The overall results show that plants with better developed coordination mechanisms, shorter vertical hierarchies, and a greater number of departments tend to perform more safely.

Determination of ferroelectric contributions to electromechanical response by frequency dependent piezoresponse force microscopy.

PubMed

Seol, Daehee; Park, Seongjae; Varenyk, Olexandr V; Lee, Shinbuhm; Lee, Ho Nyung; Morozovska, Anna N; Kim, Yunseok

2016-07-28

Hysteresis loop analysis via piezoresponse force microscopy (PFM) is typically performed to probe the existence of ferroelectricity at the nanoscale. However, such an approach is rather complex in accurately determining the pure contribution of ferroelectricity to the PFM. Here, we suggest a facile method to discriminate the ferroelectric effect from the electromechanical (EM) response through the use of frequency dependent ac amplitude sweep with combination of hysteresis loops in PFM. Our combined study through experimental and theoretical approaches verifies that this method can be used as a new tool to differentiate the ferroelectric effect from the other factors that contribute to the EM response.

Determination of ferroelectric contributions to electromechanical response by frequency dependent piezoresponse force microscopy

PubMed Central

Seol, Daehee; Park, Seongjae; Varenyk, Olexandr V.; Lee, Shinbuhm; Lee, Ho Nyung; Morozovska, Anna N.; Kim, Yunseok

2016-01-01

Hysteresis loop analysis via piezoresponse force microscopy (PFM) is typically performed to probe the existence of ferroelectricity at the nanoscale. However, such an approach is rather complex in accurately determining the pure contribution of ferroelectricity to the PFM. Here, we suggest a facile method to discriminate the ferroelectric effect from the electromechanical (EM) response through the use of frequency dependent ac amplitude sweep with combination of hysteresis loops in PFM. Our combined study through experimental and theoretical approaches verifies that this method can be used as a new tool to differentiate the ferroelectric effect from the other factors that contribute to the EM response. PMID:27466086

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

PubMed

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

2018-01-09

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

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Gene Ranking of RNA-Seq Data via Discriminant Non-Negative Matrix Factorization.

PubMed

Jia, Zhilong; Zhang, Xiang; Guan, Naiyang; Bo, Xiaochen; Barnes, Michael R; Luo, Zhigang

2015-01-01

RNA-sequencing is rapidly becoming the method of choice for studying the full complexity of transcriptomes, however with increasing dimensionality, accurate gene ranking is becoming increasingly challenging. This paper proposes an accurate and sensitive gene ranking method that implements discriminant non-negative matrix factorization (DNMF) for RNA-seq data. To the best of our knowledge, this is the first work to explore the utility of DNMF for gene ranking. When incorporating Fisher's discriminant criteria and setting the reduced dimension as two, DNMF learns two factors to approximate the original gene expression data, abstracting the up-regulated or down-regulated metagene by using the sample label information. The first factor denotes all the genes' weights of two metagenes as the additive combination of all genes, while the second learned factor represents the expression values of two metagenes. In the gene ranking stage, all the genes are ranked as a descending sequence according to the differential values of the metagene weights. Leveraging the nature of NMF and Fisher's criterion, DNMF can robustly boost the gene ranking performance. The Area Under the Curve analysis of differential expression analysis on two benchmarking tests of four RNA-seq data sets with similar phenotypes showed that our proposed DNMF-based gene ranking method outperforms other widely used methods. Moreover, the Gene Set Enrichment Analysis also showed DNMF outweighs others. DNMF is also computationally efficient, substantially outperforming all other benchmarked methods. Consequently, we suggest DNMF is an effective method for the analysis of differential gene expression and gene ranking for RNA-seq data.

A non-targeted metabolomic approach to identify food markers to support discrimination between organic and conventional tomato crops.

PubMed

Martínez Bueno, María Jesús; Díaz-Galiano, Francisco José; Rajski, Łukasz; Cutillas, Víctor; Fernández-Alba, Amadeo R

2018-04-20

In the last decade, the consumption trend of organic food has increased dramatically worldwide. However, the lack of reliable chemical markers to discriminate between organic and conventional products makes this market susceptible to food fraud in products labeled as "organic". Metabolomic fingerprinting approach has been demonstrated as the best option for a full characterization of metabolome occurring in plants, since their pattern may reflect the impact of both endogenous and exogenous factors. In the present study, advanced technologies based on high performance liquid chromatography-high-resolution accurate mass spectrometry (HPLC-HRAMS) has been used for marker search in organic and conventional tomatoes grown in greenhouse under controlled agronomic conditions. The screening of unknown compounds comprised the retrospective analysis of all tomato samples throughout the studied period and data processing using databases (mzCloud, ChemSpider and PubChem). In addition, stable nitrogen isotope analysis (δ 15 N) was assessed as a possible indicator to support discrimination between both production systems using crop/fertilizer correlations. Pesticide residue analyses were also applied as a well-established way to evaluate the organic production. Finally, the evaluation by combined chemometric analysis of high-resolution accurate mass spectrometry (HRAMS) and δ 15 N data provided a robust classification model in accordance with the agricultural practices. Principal component analysis (PCA) showed a sample clustering according to farming systems and significant differences in the sample profile was observed for six bioactive components (L-tyrosyl-L-isoleucyl-L-threonyl-L-threonine, trilobatin, phloridzin, tomatine, phloretin and echinenone). Copyright © 2018 Elsevier B.V. All rights reserved.

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

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

PubMed

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

2016-08-01

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

Two traditional maize inbred lines of contrasting technological abilities are discriminated by the seed flour proteome.

PubMed

Pinheiro, Carla; Sergeant, Kjell; Machado, Cátia M; Renaut, Jenny; Ricardo, Cândido P

2013-07-05

The seed proteome of two traditional maize inbred lines (pb269 and pb369) contrasting in grain hardness and in preferable use for bread-making was evaluated. The pb269 seeds, of flint type (i.e., hard endosperm), are preferably used by manufacturers, while pb369 (dent, soft endosperm) is rejected. The hypothesis that the content and relative amounts of specific proteins in the maize flour are relevant for such discrimination of the inbred lines was tested. The flour proteins were sequentially extracted following the Osborne fractionation (selective solubilization), and the four Osborne fractions were submitted to two-dimensional electrophoresis (2DE). The total amount of protein extracted from the seeds was not significantly different, but pb369 flour exhibited significantly higher proportions of salt-extracted proteins (globulins) and ethanol-extracted proteins (alcohol-soluble prolamins). The proteome analysis allowed discrimination between the two inbred lines, with pb269 demonstrating higher heterogeneity than pb369. From the 967 spots (358 common to both lines, 208 specific to pb269, and 401 specific to pb369), 588 were submitted to mass spectrometry (MS). Through the combined use of trypsin and chymotrypsin it was possible to identify proteins in 436 spots. The functional categorization in combination with multivariate analysis highlighted the most discriminant biological processes (carbohydrate metabolic process, response to stress, chitin catabolic process, oxidation-reduction process) and molecular function (nutrient reservoir activity). The inbred lines exhibited quantitative and qualitative differences in these categories. Differences were also revealed in the amounts, proportions, and distribution of several groups of storage proteins, which can have an impact on the organization of the protein body and endosperm hardness. For some proteins (granule-bound starch synthase-1, cyclophilin, zeamatin), a change in the protein solubility rather than in the total amount extracted was observed, which reveals distinct in vivo associations and/or changes in binding strength between the inbred lines. Our approach produced information that relates protein content, relative protein content, and specific protein types to endosperm hardness and to the preferable use for "broa" bread-making.

[Traceability of Wine Varieties Using Near Infrared Spectroscopy Combined with Cyclic Voltammetry].

PubMed

Li, Meng-hua; Li, Jing-ming; Li, Jun-hui; Zhang, Lu-da; Zhao, Long-lian

2015-06-01

To achieve the traceability of wine varieties, a method was proposed to fuse Near-infrared (NIR) spectra and cyclic voltammograms (CV) which contain different information using D-S evidence theory. NIR spectra and CV curves of three different varieties of wines (cabernet sauvignon, merlot, cabernet gernischt) which come from seven different geographical origins were collected separately. The discriminant models were built using PLS-DA method. Based on this, D-S evidence theory was then applied to achieve the integration of the two kinds of discrimination results. After integrated by D-S evidence theory, the accuracy rate of cross-validation is 95.69% and validation set is 94.12% for wine variety identification. When only considering the wine that come from Yantai, the accuracy rate of cross-validation is 99.46% and validation set is 100%. All the traceability models after fusion achieved better results on classification than individual method. These results suggest that the proposed method combining electrochemical information with spectral information using the D-S evidence combination formula is benefit to the improvement of model discrimination effect, and is a promising tool for discriminating different kinds of wines.

Data Mining of Satellite-Based Measurements to Distinguish Natural From Man-Made Oil Slicks at the Sea Surface in Campeche Bay (Mexico)

NASA Astrophysics Data System (ADS)

Carvalho, G. D. A.; Minnett, P. J.; de Miranda, F. P.; Landau, L.; Paes, E.

2016-02-01

Campeche Bay, located in the Mexican portion of the Gulf of Mexico, has a well-established activity engaged with numerous oil rigs exploring and producing natural gas and oil. The associated risk of oil slicks in this region - that include oil spills (i.e. oil floating at the sea surface solely attributed to man-made activities) and oil seeps (i.e. surface footprint of the oil that naturally comes out of the seafloor reaching the surface of the ocean) - leads Pemex to be in a continuous state of alert for reducing possible negative influence on marine and coastal ecosystems. Focusing on a monitoring strategy, a multi-year dataset (2008-2012) of synthetic aperture radar (SAR) measurements from the RADARSAT-2 satellite is used to investigate the spatio-temporal distribution of the oil slicks observed at the surface of the ocean in the Campeche Bay region. The present study is an exploratory data analysis that seeks to discriminate between these two possible oil slick types: oil seeps and oil spills. Multivariate data analysis techniques (e.g. Principal Components Analysis, Clustering Analysis, Discriminant Function, etc.) are explored to design a data-learning classification algorithm to distinguish natural from man-made oil slicks. This analysis promotes a novel idea bridging geochemistry and remote sensing research to express geophysical differences between seeped and spilled oil. Here, SAR backscatter coefficients - i.e. sigma-naught (σo), beta-naught (βo), and gamma-naught (γo) - are combined with attributes referring to the geometry, shape, and dimension that describe the oil slicks. Results indicate that the synergy of combining these various characteristics is capable of distinguishing oil seeps from oil spills observed on the sea surface to a useful accuracy.

Moment Tensor Analysis of Shallow Sources

NASA Astrophysics Data System (ADS)

Chiang, A.; Dreger, D. S.; Ford, S. R.; Walter, W. R.; Yoo, S. H.

2015-12-01

A potential issue for moment tensor inversion of shallow seismic sources is that some moment tensor components have vanishing amplitudes at the free surface, which can result in bias in the moment tensor solution. The effects of the free-surface on the stability of the moment tensor method becomes important as we continue to investigate and improve the capabilities of regional full moment tensor inversion for source-type identification and discrimination. It is important to understand these free surface effects on discriminating shallow explosive sources for nuclear monitoring purposes. It may also be important in natural systems that have shallow seismicity such as volcanoes and geothermal systems. In this study, we apply the moment tensor based discrimination method to the HUMMING ALBATROSS quarry blasts. These shallow chemical explosions at approximately 10 m depth and recorded up to several kilometers distance represent rather severe source-station geometry in terms of vanishing traction issues. We show that the method is capable of recovering a predominantly explosive source mechanism, and the combined waveform and first motion method enables the unique discrimination of these events. Recovering the correct yield using seismic moment estimates from moment tensor inversion remains challenging but we can begin to put error bounds on our moment estimates using the NSS technique.

Analysis of discriminants for experimental 3D SAR imagery of human targets

NASA Astrophysics Data System (ADS)

Chan, Brigitte; Sévigny, Pascale; DiFilippo, David D. J.

2014-10-01

Development of a prototype 3-D through-wall synthetic aperture radar (SAR) system is currently underway at Defence Research and Development Canada. The intent is to map out building wall layouts and to detect targets of interest and their location behind walls such as humans, arms caches, and furniture. This situational awareness capability can be invaluable to the military working in an urban environment. Tools and algorithms are being developed to exploit the resulting 3-D imagery. Current work involves analyzing signatures of targets behind a wall and understanding the clutter and multipath signals in a room of interest. In this paper, a comprehensive study of 3-D human target signature metrics in free space is presented. The aim is to identify features for discrimination of the human target from other targets. Targets used in this investigation include a human standing, a human standing with arms stretched out, a chair, a table, and a metallic plate. Several features were investigated as potential discriminants and five which were identified as good candidates are presented in this paper. Based on this study, no single feature could be used to fully discriminate the human targets from all others. A combination of at least two different features is required to achieve this.

Discriminability of personality profiles in isolated and Co-morbid marijuana and nicotine users.

PubMed

Ketcherside, Ariel; Jeon-Slaughter, Haekyung; Baine, Jessica L; Filbey, Francesca M

2016-04-30

Specific personality traits have been linked with substance use disorders (SUDs), genetic mechanisms, and brain systems. Thus, determining the specificity of personality traits to types of SUD can advance the field towards defining SUD endophenotypes as well as understanding the brain systems involved for the development of novel treatments. Disentangling these factors is particularly important in highly co morbid SUDs, such as marijuana and nicotine use, so treatment can occur effectively for both. This study evaluated personality traits that distinguish isolated and co-morbid use of marijuana and nicotine. To that end, we collected the NEO Five Factor Inventory in participants who used marijuana-only (n=59), nicotine-only (n=27), both marijuana and nicotine (n=28), and in non-using controls (n=28). We used factor analyses to identify personality profiles, which are linear combinations of the five NEO Factors. We then conducted Receiver Operating Characteristics (ROC) curve analysis to test accuracy of the personality factors in discriminating isolated and co-morbid marijuana and nicotine users from each other. ROC curve analysis distinguished the four groups based on their NEO personality patterns. Results showed that NEO Factor 2 (openness, extraversion, agreeableness) discriminated marijuana and marijuana+nicotine users from controls and nicotine-only users with high predictability. Additional ANOVA results showed that the openness dimension discriminated marijuana users from nicotine users. These findings suggest that personality dimensions distinguish marijuana users from nicotine users and should be considered in prevention strategies. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

[Application of ICP-MS to Identify the Botanic Source of Characteristic Honey in South Yunnan].

PubMed

Wei, Yue; Chen, Fang; Wang, Yong; Chen, Lan-zhen; Zhang, Xue-wen; Wang, Yan-hui; Wu, Li-ming; Zhou, Qun

2016-01-01

By adopting inductively coupled plasma mass spectrometry (ICP-MS) combined with chemometric analysis technology, 23 kinds of minerals in four kinds of characteristic honey derived from Yunnan province were analyzed. The result showed that 21 kinds of mineral elements, namely Na, Mg, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Sr, Mo, Cd, Sb, Ba, Tl and Pb, have significant differences among different varieties of honey. The results of principal component analysis (PCA) showed that the cumulative variance contribution rate of the first four main components reached 77.74%, seven kinds of elements (Mg, Ca, Mn, Co, Sr, Cd, Ba) from the first main component contained most of the honey information. Through the stepwise discriminant analysis, seven kinds of elements (Mg, K, Ca, Cr, Mn, Sr, Pb) were filtered. out and used to establish the discriminant function model, and the correct classification rates of the proposed model reached 90% and 86.7%, respectively, which showed elements contents could be effectively indicators to discriminate the four kinds characteristic honey in southern Yunnan Province. In view of all the honey samples were harvested from apiaries located at south Yunnan Province where have similar climate, soil and other environment conditions, the differences of the mineral elements contents for the honey samples mainly due to their corresponding nectariferous plant. Therefore, it is feasible to identify honey botanical source through the differences of mineral elements.

Micro-Raman spectroscopy for meat type detection

NASA Astrophysics Data System (ADS)

De Biasio, M.; Stampfer, P.; Leitner, R.; Huck, C. W.; Wiedemair, V.; Balthasar, D.

2015-06-01

The recent horse meat scandal in Europe increased the demand for optical sensors that can identify meat type. Micro-Raman spectroscopy is a promising technique for the discrimination of meat types. Here, we present micro-Raman measurements of chicken, pork, turkey, mutton, beef and horse meat test samples. The data was analyzed with different combinations of data normalization and classification approaches. Our results show that Raman spectroscopy can discriminate between different meat types. Red and white meat are easily discriminated, however a sophisticated chemometric model is required to discriminate species within these groups.

Social Status Correlates of Reporting Racial Discrimination and Gender Discrimination among Racially Diverse Women

PubMed Central

Ro, Annie E.; Choi, Kyung-Hee

2009-01-01

The growing body of research on discrimination and health indicates a deleterious effect of discrimination on various health outcomes. However, less is known about the sociodemographic correlates of reporting racial discrimination and gender discrimination among racially diverse women. We examined the associations of social status characteristics with lifetime experiences of racial discrimination and gender discrimination using a racially-diverse sample of 754 women attending family planning clinics in Northern California (11.4% African American, 16.8% Latina, 10.1% Asian and 61.7% Caucasian). A multivariate analysis revealed that race, financial difficulty and marital status were significantly correlated with higher reports of racial discrimination, while race, education, financial difficulty and nativity were significantly correlated with gender discrimination scores. Our findings suggest that the social patterning of perceiving racial discrimination is somewhat different from that of gender discrimination. This has implications in the realm of discrimination research and applied interventions, as different forms of discrimination may have unique covariates that should be accounted for in research analysis or program design. PMID:19485231

Resolution versus speckle relative to geologic interpretability of spaceborne radar images - A survey of user preference

NASA Technical Reports Server (NTRS)

Ford, J. P.

1982-01-01

A survey conducted to evaluate user preference for resolution versus speckle relative to the geologic interpretability of spaceborne radar images is discussed. Thirteen different resolution/looks combinations are simulated from Seasat synthetic-aperture radar data of each of three test sites. The SAR images were distributed with questionnaires for analysis to 85 earth scientists. The relative discriminability of geologic targets at each test site for each simulation of resolution and speckle on the images is determined on the basis of a survey of the evaluations. A large majority of the analysts respond that for most targets a two-look image at the highest simulated resolution is best. For a constant data rate, a higher resolution is more important for target discrimination than a higher number of looks. It is noted that sand dunes require more looks than other geologic targets. At all resolutions, multiple-look images are preferred over the corresponding single-look image. In general, the number of multiple looks that is optimal for discriminating geologic targets is inversely related to the simulated resolution.

Combustion monitoring of a water tube boiler using a discriminant radial basis network.

PubMed

Sujatha, K; Pappa, N

2011-01-01

This research work includes a combination of Fisher's linear discriminant (FLD) analysis and a radial basis network (RBN) for monitoring the combustion conditions for a coal fired boiler so as to allow control of the air/fuel ratio. For this, two-dimensional flame images are required, which were captured with a CCD camera; the features of the images-average intensity, area, brightness and orientation etc of the flame-are extracted after preprocessing the images. The FLD is applied to reduce the n-dimensional feature size to a two-dimensional feature size for faster learning of the RBN. Also, three classes of images corresponding to different burning conditions of the flames have been extracted from continuous video processing. In this, the corresponding temperatures, and the carbon monoxide (CO) emissions and those of other flue gases have been obtained through measurement. Further, the training and testing of Fisher's linear discriminant radial basis network (FLDRBN), with the data collected, have been carried out and the performance of the algorithms is presented. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Investigating the limitations of tree species classification using the Combined Cluster and Discriminant Analysis method for low density ALS data from a dense forest region in Aggtelek (Hungary)

NASA Astrophysics Data System (ADS)

Koma, Zsófia; Deák, Márton; Kovács, József; Székely, Balázs; Kelemen, Kristóf; Standovár, Tibor

2016-04-01

Airborne Laser Scanning (ALS) is a widely used technology for forestry classification applications. However, single tree detection and species classification from low density ALS point cloud is limited in a dense forest region. In this study we investigate the division of a forest into homogenous groups at stand level. The study area is located in the Aggtelek karst region (Northeast Hungary) with a complex relief topography. The ALS dataset contained only 4 discrete echoes (at 2-4 pt/m2 density) from the study area during leaf-on season. Ground-truth measurements about canopy closure and proportion of tree species cover are available for every 70 meter in 500 square meter circular plots. In the first step, ALS data were processed and geometrical and intensity based features were calculated into a 5×5 meter raster based grid. The derived features contained: basic statistics of relative height, canopy RMS, echo ratio, openness, pulse penetration ratio, basic statistics of radiometric feature. In the second step the data were investigated using Combined Cluster and Discriminant Analysis (CCDA, Kovács et al., 2014). The CCDA method first determines a basic grouping for the multiple circle shaped sampling locations using hierarchical clustering and then for the arising grouping possibilities a core cycle is executed comparing the goodness of the investigated groupings with random ones. Out of these comparisons difference values arise, yielding information about the optimal grouping out of the investigated ones. If sub-groups are then further investigated, one might even find homogeneous groups. We found that low density ALS data classification into homogeneous groups are highly dependent on canopy closure, and the proportion of the dominant tree species. The presented results show high potential using CCDA for determination of homogenous separable groups in LiDAR based tree species classification. Aggtelek Karst/Slovakian Karst Caves" (HUSK/1101/221/0180, Aggtelek NP), data evaluation: 'Multipurpose assessment serving forest biodiversity conservation in the Carpathian region of Hungary', Swiss-Hungarian Cooperation Programme (SH/4/13 Project). BS contributed as an Alexander von Humboldt Research Fellow. J. Kovács, S. Kovács, N. Magyar, P. Tanos, I. G. Hatvani, and A. Anda (2014), Classification into homogeneous groups using combined cluster and discriminant analysis, Environmental Modelling & Software, 57, 52-59.

Discriminative power of Campylobacter phenotypic and genotypic typing methods.

PubMed

Duarte, Alexandra; Seliwiorstow, Tomasz; Miller, William G; De Zutter, Lieven; Uyttendaele, Mieke; Dierick, Katelijne; Botteldoorn, Nadine

2016-06-01

The aim of this study was to compare different typing methods, individually and combined, for use in the monitoring of Campylobacter in food. Campylobacter jejuni (n=94) and Campylobacter coli (n=52) isolated from different broiler meat carcasses were characterized using multilocus sequence typing (MLST), flagellin gene A restriction fragment length polymorphism typing (flaA-RFLP), antimicrobial resistance profiling (AMRp), the presence/absence of 5 putative virulence genes; and, exclusively for C. jejuni, the determination of lipooligosaccharide (LOS) class. Discriminatory power was calculated by the Simpson's index of diversity (SID) and the congruence was measured by the adjusted Rand index and adjusted Wallace coefficient. MLST was individually the most discriminative typing method for both C. jejuni (SID=0.981) and C. coli (SID=0.957). The most discriminative combination with a SID of 0.992 for both C. jejuni and C. coli was obtained by combining MLST with flaA-RFLP. The combination of MLST with flaA-RFLP is an easy and feasible typing method for short-term monitoring of Campylobacter in broiler meat carcass. Copyright © 2016 Elsevier B.V. All rights reserved.

Classification of debtor credit status and determination amount of credit risk by using linier discriminant function

NASA Astrophysics Data System (ADS)

Aidi, Muhammad Nur; Sari, Resty Indah

2012-05-01

A decision of credit that given by bank or another creditur must have a risk and it called credit risk. Credit risk is an investor's risk of loss arising from a borrower who does not make payments as promised. The substantial of credit risk can lead to losses for the banks and the debtor. To minimize this problem need a further study to identify a potential new customer before the decision given. Identification of debtor can using various approaches analysis, one of them is by using discriminant analysis. Discriminant analysis in this study are used to classify whether belonging to the debtor's good credit or bad credit. The result of this study are two discriminant functions that can identify new debtor. Before step built the discriminant function, selection of explanatory variables should be done. Purpose of selection independent variable is to choose the variable that can discriminate the group maximally. Selection variables in this study using different test, for categoric variable selection of variable using proportion chi-square test, and stepwise discriminant for numeric variable. The result of this study are two discriminant functions that can identify new debtor. The selected variables that can discriminating two groups of debtor maximally are status of existing checking account, credit history, credit amount, installment rate in percentage of disposable income, sex, age in year, other installment plans, and number of people being liable to provide maintenance. This classification produce a classification accuracy rate is good enough, that is equal to 74,70%. Debtor classification using discriminant analysis has risk level that is small enough, and it ranged beetwen 14,992% and 17,608%. Based on that credit risk rate, using discriminant analysis on the classification of credit status can be used effectively.

Neuronal pattern separation of motion-relevant input in LIP activity

PubMed Central

Berberian, Nareg; MacPherson, Amanda; Giraud, Eloïse; Richardson, Lydia

2016-01-01

In various regions of the brain, neurons discriminate sensory stimuli by decreasing the similarity between ambiguous input patterns. Here, we examine whether this process of pattern separation may drive the rapid discrimination of visual motion stimuli in the lateral intraparietal area (LIP). Starting with a simple mean-rate population model that captures neuronal activity in LIP, we show that overlapping input patterns can be reformatted dynamically to give rise to separated patterns of neuronal activity. The population model predicts that a key ingredient of pattern separation is the presence of heterogeneity in the response of individual units. Furthermore, the model proposes that pattern separation relies on heterogeneity in the temporal dynamics of neural activity and not merely in the mean firing rates of individual neurons over time. We confirm these predictions in recordings of macaque LIP neurons and show that the accuracy of pattern separation is a strong predictor of behavioral performance. Overall, results propose that LIP relies on neuronal pattern separation to facilitate decision-relevant discrimination of sensory stimuli. NEW & NOTEWORTHY A new hypothesis is proposed on the role of the lateral intraparietal (LIP) region of cortex during rapid decision making. This hypothesis suggests that LIP alters the representation of ambiguous inputs to reduce their overlap, thus improving sensory discrimination. A combination of computational modeling, theoretical analysis, and electrophysiological data shows that the pattern separation hypothesis links neural activity to behavior and offers novel predictions on the role of LIP during sensory discrimination. PMID:27881719

Racial discrimination experiences and African American youth adjustment: The role of parenting profiles based on racial socialization and involved-vigilant parenting.

PubMed

Varner, Fatima A; Hou, Yang; Hodzic, Tajma; Hurd, Noelle M; Butler-Barnes, Sheretta T; Rowley, Stephanie J

2018-04-01

The purpose of this study was to test whether parenting profiles based on racial socialization and involved-vigilant parenting would compensate for or moderate associations between racial discrimination experiences and academic outcomes and psychological well-being among African American adolescents. Participants were 1,363 African American adolescents (M age = 14.19; 52.3% female) from 3 Midwestern suburban school districts. Latent profile analysis was used to examine whether there were distinct combinations of parenting. The relationships among racial discrimination experiences, parenting profiles, and adjustment were examined using structural equation modeling (SEM). Three distinct parenting profiles were found: moderate positive (n = 767; moderately high involved-vigilant parenting and racial barrier, racial pride, behavioral, and egalitarian messages, and low negative messages), unengaged (n = 351; low racial socialization messages and moderately low involved-vigilant parenting), and high negative parenting (n = 242; high negative messages, moderate other racial socialization messages, and moderately low involved-vigilant parenting). Racial discrimination experiences were negatively associated with youth adjustment. Moderate positive parenting was related to the best academic outcomes and unengaged parenting was associated with more positive academic outcomes than high negative parenting. Moderate positive parenting was associated with better psychological well-being than unengaged or high negative parenting although the benefits were greater for adolescents with fewer racial discrimination experiences. Distinct patterns of racial socialization messages and involved-vigilant parenting contribute to differences in African American youth adjustment. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Commentary on "predicting metastasized seminoma using gene expression." Ruf CG, Linbecker M, Port M, Riecke A, Schmelz HU, Wagner W, Meineke V, Abend M, Department of Urology, Federal Armed Forces Hospital, Hamburg, Germany: BJU Int 2012;110:E14.

PubMed

Richie, Jerome

2013-02-01

Treatment options for testis cancer depend on the histological subtype as well as on the clinical stage. An accurate staging is essential for correct treatment. The 'golden standard' for staging purposes is CT, but occult metastasis cannot be detected with this method. Currently, parameters such as primary tumour size, vessel invasion or invasion of the rete testis are used for predicting occult metastasis. Last year the association of these parameters with metastasis could not be validated in a new independent cohort. Gene expression analysis in testis cancer allowed discrimination between the different histological subtypes (seminoma and non-seminoma) as well as testis cancer and normal testis tissue. In a two-stage study design we (i) screened the whole genome (using human whole genome microarrays) for candidate genes associated with the metastatic stage in seminoma and (ii) validated and quantified gene expression of our candidate genes (real-time quantitative polymerase chain reaction) on another independent group. Gene expression measurements of two of our candidate genes (dopamine receptor D1 [DRD1] and family with sequence similarity 71, member F2 [FAM71F2]) examined in primary testis cancers made it possible to discriminate the metastasis status in seminoma. The discriminative ability of the genes exceeded the predictive significance of currently used histological/pathological parameters. Based on gene expression analysis the present study provides suggestions for improved individual decision making either in favour of early adjuvant therapy or increased surveillance. To evaluate the usefulness of gene expression profiling for predicting metastatic status in testicular seminoma at the time of first diagnosis compared with established clinical and pathological parameters. Total RNA was isolated from testicular tumours of metastasized patients (12 patients, clinical stage IIa-III), non-metastasized patients (40, clinical stage I) and adjacent 'normal' tissue (n = 36). The RNA was then converted into cDNA and real-time quantitative polymerase chain reaction was run on 94 candidate genes selected from previous work. Normalised gene expression of these genes and histological variables, e.g. tumour size and rete testis infiltration, were analysed using logistic regression analysis. Expression of two genes (dopamine receptor D1 [DRD1] and family with sequence similarity 71, member F2 [FAM71F2], P = 0.005 and 0.024 in separate analysis and P = 0.004 and 0.016 when combining both genes, respectively) made it possible to significantly discriminate the metastasis status. Concordance increased from 77.9% (DRD1) and 72.3% (FAM71F2) in separate analysis and up to 87.7% when combining both genes in one model. Only primary tumour size in separate analysis (continuous or categorical with tumour size>6cm) was significantly associated with metastasis (P = 0.039/P = 0.02), but concordance was lower (61%). When we combined tumour size with our two genes in one model there was no further statistical improvement or increased concordance. Based on gene expression analysis our study provides suggestions for improved individual decision making either in favour of early adjuvant therapy or increased surveillance. Copyright © 2013 Elsevier Inc. All rights reserved.

Combined elemental and microstructural analysis of genuine and fake copper-alloy coins

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

Bartoli, L; Agresti, J; Mascalchi, M

2011-07-31

Innovative noninvasive material analysis techniques are applied to determine archaeometallurgical characteristics of copper-alloy coins from Florence's National Museum of Archaeology. Three supposedly authentic Roman coins and three hypothetically fraudolent imitations are thoroughly investigated using laser-induced plasma spectroscopy and time of flight neutron diffraction along with 3D videomicroscopy and electron microscopy. Material analyses are aimed at collecting data allowing for objective discrimination between genuine Roman productions and late fakes. The results show the mentioned techniques provide quantitative compositional and textural data, which are strictly related to the manufacturing processes and aging of copper alloys. (laser applications)

Improved neutron-gamma discrimination for a 6Li-glass neutron detector using digital signal analysis methods

DOE PAGES

Wang, Cai -Lin; Riedel, Richard A.

2016-01-14

A 6Li-glass scintillator (GS20) based neutron Anger camera was developed for time-of-flight single-crystal diffraction instruments at SNS. Traditional pulse-height analysis (PHA) for neutron-gamma discrimination (NGD) resulted in the neutron-gamma efficiency ratio (defined as NGD ratio) on the order of 10 4. The NGD ratios of Anger cameras need to be improved for broader applications including neutron reflectometers. For this purpose, five digital signal analysis methods of individual waveforms from PMTs were proposed using: i). pulse-amplitude histogram; ii). power spectrum analysis combined with the maximum pulse amplitude; iii). two event parameters (a 1, b 0) obtained from Wiener filter; iv). anmore » effective amplitude (m) obtained from an adaptive least-mean-square (LMS) filter; and v). a cross-correlation (CC) coefficient between an individual waveform and a reference. The NGD ratios can be 1-102 times those from traditional PHA method. A brighter scintillator GS2 has better NGD ratio than GS20, but lower neutron detection efficiency. The ultimate NGD ratio is related to the ambient, high-energy background events. Moreover, our results indicate the NGD capability of neutron Anger cameras can be improved using digital signal analysis methods and brighter neutron scintillators.« less

Single-trial classification of motor imagery differing in task complexity: a functional near-infrared spectroscopy study

PubMed Central

2011-01-01

Background For brain computer interfaces (BCIs), which may be valuable in neurorehabilitation, brain signals derived from mental activation can be monitored by non-invasive methods, such as functional near-infrared spectroscopy (fNIRS). Single-trial classification is important for this purpose and this was the aim of the presented study. In particular, we aimed to investigate a combined approach: 1) offline single-trial classification of brain signals derived from a novel wireless fNIRS instrument; 2) to use motor imagery (MI) as mental task thereby discriminating between MI signals in response to different tasks complexities, i.e. simple and complex MI tasks. Methods 12 subjects were asked to imagine either a simple finger-tapping task using their right thumb or a complex sequential finger-tapping task using all fingers of their right hand. fNIRS was recorded over secondary motor areas of the contralateral hemisphere. Using Fisher's linear discriminant analysis (FLDA) and cross validation, we selected for each subject a best-performing feature combination consisting of 1) one out of three channel, 2) an analysis time interval ranging from 5-15 s after stimulation onset and 3) up to four Δ[O2Hb] signal features (Δ[O2Hb] mean signal amplitudes, variance, skewness and kurtosis). Results The results of our single-trial classification showed that using the simple combination set of channels, time intervals and up to four Δ[O2Hb] signal features comprising Δ[O2Hb] mean signal amplitudes, variance, skewness and kurtosis, it was possible to discriminate single-trials of MI tasks differing in complexity, i.e. simple versus complex tasks (inter-task paired t-test p ≤ 0.001), over secondary motor areas with an average classification accuracy of 81%. Conclusions Although the classification accuracies look promising they are nevertheless subject of considerable subject-to-subject variability. In the discussion we address each of these aspects, their limitations for future approaches in single-trial classification and their relevance for neurorehabilitation. PMID:21682906

3D surface rendered MR images of the brain and its vasculature.

PubMed

Cline, H E; Lorensen, W E; Souza, S P; Jolesz, F A; Kikinis, R; Gerig, G; Kennedy, T E

1991-01-01

Both time-of-flight and phase contrast magnetic resonance angiography images are combined with stationary tissue images to provide data depicting two contrast relationships yielding intrinsic discrimination of brain matter and flowing blood. A computer analysis is based on nearest neighbor segmentation and the connection between anatomical structures to partition the images into different tissue categories: from which, high resolution brain parenchymal and vascular surfaces are constructed and rendered in juxtaposition, aiding in surgical planning.

Geometric, Statistical, and Topological Modeling of Intrinsic Data Manifolds: Application to 3D Shapes

DTIC Science & Technology

2009-01-01

representation to a simple curve in 3D by using the Whitney embedding theorem. In a very ludic way, we propose to combine phases one and two to...elimination principle which takes advantage of the designed parametrization. To further refine discrimination among objects, we introduce a post...packing numbers and design of principal curves. IEEE transactions on Pattern Analysis and Machine Intel- ligence, 22(3):281-297, 2000. [68] M. H. Yang, Face

Laser-induced breakdown spectroscopy (LIBS) for rapid analysis of ash, potassium and magnesium in gluten free flours.

PubMed

Markiewicz-Keszycka, Maria; Casado-Gavalda, Maria P; Cama-Moncunill, Xavier; Cama-Moncunill, Raquel; Dixit, Yash; Cullen, Patrick J; Sullivan, Carl

2018-04-01

Gluten free (GF) diets are prone to mineral deficiency, thus effective monitoring of the elemental composition of GF products is important to ensure a balanced micronutrient diet. The objective of this study was to test the potential of laser-induced breakdown spectroscopy (LIBS) analysis combined with chemometrics for at-line monitoring of ash, potassium and magnesium content of GF flours: tapioca, potato, maize, buckwheat, brown rice and a GF flour mixture. Concentrations of ash, potassium and magnesium were determined with reference methods and LIBS. PCA analysis was performed and presented the potential for discrimination of the six GF flours. For the quantification analysis PLSR models were developed; R 2 cal were 0.99 for magnesium and potassium and 0.97 for ash. The study revealed that LIBS combined with chemometrics is a convenient method to quantify concentrations of ash, potassium and magnesium and present the potential to classify different types of flours. Copyright © 2017 Elsevier Ltd. All rights reserved.

Efficient Data Mining for Local Binary Pattern in Texture Image Analysis

PubMed Central

Kwak, Jin Tae; Xu, Sheng; Wood, Bradford J.

2015-01-01

Local binary pattern (LBP) is a simple gray scale descriptor to characterize the local distribution of the grey levels in an image. Multi-resolution LBP and/or combinations of the LBPs have shown to be effective in texture image analysis. However, it is unclear what resolutions or combinations to choose for texture analysis. Examining all the possible cases is impractical and intractable due to the exponential growth in a feature space. This limits the accuracy and time- and space-efficiency of LBP. Here, we propose a data mining approach for LBP, which efficiently explores a high-dimensional feature space and finds a relatively smaller number of discriminative features. The features can be any combinations of LBPs. These may not be achievable with conventional approaches. Hence, our approach not only fully utilizes the capability of LBP but also maintains the low computational complexity. We incorporated three different descriptors (LBP, local contrast measure, and local directional derivative measure) with three spatial resolutions and evaluated our approach using two comprehensive texture databases. The results demonstrated the effectiveness and robustness of our approach to different experimental designs and texture images. PMID:25767332

Neural networks using broadband spectral discriminators reduces illumination required for broccoli identification in weedy fields

NASA Astrophysics Data System (ADS)

Hahn, Federico

1996-03-01

Statistical discriminative analysis and neural networks were used to prove that crop/weed/soil discrimination by optical reflectance was feasible. The wavelengths selected as inputs on those neural networks were ten nanometers width, reducing the total collected radiation for the sensor. Spectral data collected from several farms having different weed populations were introduced to discriminant analysis. The best discriminant wavelengths were used to build a wavelength histogram which selected the three best spectral broadbands for broccoli/weed/soil discrimination. The broadbands were analyzed using a new single broadband discriminator index named the discriminative integration index, DII, and the DII values obtained were used to train a neural network. This paper introduces the index concept, its results and its use for minimizing artificial lightning requirements with broadband spectral measurements for broccoli/weed/soil discrimination.

Race, biology, and health care: reassessing a relationship.

PubMed

Byrd, W M

1990-01-01

Recent reports reaffirm huge disparities in the health of blacks compared to other Americans. These disparities persist in part because of the current attempt by health policy makers to frame racially based health differences in non-racial terms. Yet an historical analysis shows that since ancient times, blacks have been the victims of racism in the biomedical sciences; health-system discrimination and deprivation; and later, medical and scientific exploitation. Race- and class-based structuring of the health delivery system has combined with other factors, including physicians' attitudes conditioned by their participation in slavery, and the scientific myth of black biological and intellectual inferiority, to establish a "slave health deficit" that has never been corrected. Until the persistent institutional racism and racial discrimination in health policy, health delivery, and medical educational systems are eradicated, African-Americans will continue to experience poor health outcome.

Comparison of base composition analysis and Sanger sequencing of mitochondrial DNA for four U.S. population groups.

PubMed

Kiesler, Kevin M; Coble, Michael D; Hall, Thomas A; Vallone, Peter M

2014-01-01

A set of 711 samples from four U.S. population groups was analyzed using a novel mass spectrometry based method for mitochondrial DNA (mtDNA) base composition profiling. Comparison of the mass spectrometry results with Sanger sequencing derived data yielded a concordance rate of 99.97%. Length heteroplasmy was identified in 46% of samples and point heteroplasmy was observed in 6.6% of samples in the combined mass spectral and Sanger data set. Using discrimination capacity as a metric, Sanger sequencing of the full control region had the highest discriminatory power, followed by the mass spectrometry base composition method, which was more discriminating than Sanger sequencing of just the hypervariable regions. This trend is in agreement with the number of nucleotides covered by each of the three assays. Published by Elsevier Ireland Ltd.

Complexity-entropy causality plane: A useful approach for distinguishing songs

NASA Astrophysics Data System (ADS)

Ribeiro, Haroldo V.; Zunino, Luciano; Mendes, Renio S.; Lenzi, Ervin K.

2012-04-01

Nowadays we are often faced with huge databases resulting from the rapid growth of data storage technologies. This is particularly true when dealing with music databases. In this context, it is essential to have techniques and tools able to discriminate properties from these massive sets. In this work, we report on a statistical analysis of more than ten thousand songs aiming to obtain a complexity hierarchy. Our approach is based on the estimation of the permutation entropy combined with an intensive complexity measure, building up the complexity-entropy causality plane. The results obtained indicate that this representation space is very promising to discriminate songs as well as to allow a relative quantitative comparison among songs. Additionally, we believe that the here-reported method may be applied in practical situations since it is simple, robust and has a fast numerical implementation.

Separate and combined effects of the GABAB agonist baclofen and Δ9-THC in humans discriminating Δ9-THC

PubMed Central

Lile, Joshua A.; Kelly, Thomas H.; Hays, Lon R.

2012-01-01

Background Our previous research with the GABA reuptake inhibitor tiagabine suggested the involvement GABA in the interoceptive effects of Δ9-THC. The aim of the present study was to determine the potential involvement of the GABAB receptor subtype by assessing the separate and combined effects of the GABAB-selective agonist baclofen and Δ9-THC using pharmacologically specific drug-discrimination procedures. Methods Eight cannabis users learned to discriminate 30 mg oral Δ9-THC from placebo and then received baclofen (25 and 50 mg), Δ9-THC (5, 15 and 30 mg) and placebo, alone and in combination. Self-report, task performance and physiological measures were also collected. Results Δ9-THC functioned as a discriminative stimulus, produced subjective effects typically associated with cannabinoids (e.g., High, Stoned, Like Drug), elevated heart rate and impaired rate and accuracy on a psychomotor performance task. Baclofen alone (50 mg) substituted for the Δ9-THC discriminative stimulus, and both baclofen doses shifted the discriminative-stimulus effects of Δ9-THC leftward/upward. Similar results were observed on other cannabinoid-sensitive outcomes, although baclofen generally did not engender Δ9-THC-like subjective responses when administered alone. Conclusions These results suggest that the GABAB receptor subtype is involved in the abuse-related effects of Δ9-THC, and that GABAB receptors were responsible, at least in part, for the effects of tiagabine-induced elevated GABA on cannabinoid-related behaviors in our previous study. Future research should test GABAergic compounds selective for other GABA receptor subtypes (i.e., GABAA) to determine the contribution of the different GABA receptors in the effects of Δ9-THC, and by extension cannabis, in humans. PMID:22699093

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

PubMed Central

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

2016-01-01

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

A Complex Approach to UXO Discrimination: Combining Advanced EMI Forward Models and Statistical Signal Processing

DTIC Science & Technology

2012-01-01

discrimination at live-UXO sites. Namely, under this project first we developed and implemented advanced, physically complete forward EMI models such as, the...detection and discrimination at live-UXO sites. Namely, under this project first we developed and implemented advanced, physically complete forward EMI...Shubitidze of Sky Research and Dartmouth College, conceived, implemented , and tested most of the approaches presented in this report. He developed

Discrimination and Mental Health–Related Service Use in a National Study of Asian Americans

PubMed Central

Chen, Juan; Gee, Gilbert C.; Fabian, Cathryn G.; Takeuchi, David T.

2010-01-01

Objectives. We examined the association between perceived discrimination and use of mental health services among a national sample of Asian Americans. Methods. Our data came from the National Latino and Asian American Study, the first national survey of Asian Americans. Our sample included 600 Chinese, 508 Filipinos, 520 Vietnamese, and 467 other Asians (n=2095). We used logistic regression to examine the association between discrimination and formal and informal service use and the interactive effect of discrimination and English language proficiency. Results. Perceived discrimination was associated with more use of informal services, but not with less use of formal services. Additionally, higher levels of perceived discrimination combined with lower English proficiency were associated with more use of informal services. Conclusions. The effect of perceived discrimination and language proficiency on service use indicates a need for more bilingual services and more collaborations between formal service systems and community resources. PMID:20299649

S-cone discrimination in the presence of two adapting fields: data and model

PubMed Central

Cao, Dingcai

2014-01-01

This study investigated S-cone discrimination using a test annulus surrounded by an inner and outer adapting field with systematic manipulation of the adapting l = L/(L + M) or s = S/(L + M) chromaticities. The results showed that different adapting l chromaticities altered S-cone discrimination for a high adapting s chromaticity due to parvocellular input to the koniocellular pathway. In addition, S-cone discrimination was determined by the combined spectral signals arising from both adapting fields. The “white” adapting field or an adapting field with a different l chromaticity from the other fields was more likely to have a stronger influence on discrimination thresholds. These results indicated that the two cardinal axes are not independent in S-cone discrimination, and the two adapting fields jointly contribute to S-cone discrimination through a cortical summation mechanism. PMID:24695204

Combination of near infrared spectroscopy and chemometrics for authentication of taro flour from wheat and sago flour

NASA Astrophysics Data System (ADS)

Rachmawati; Rohaeti, E.; Rafi, M.

2017-05-01

Taro flour on the market is usually sold at higher price than wheat and sago flour. This situation could be a cause for adulteration of taro flour from wheat and sago flour. For this reason, we will need an identification and authentication. Combination of near infrared (NIR) spectrum with multivariate analysis was used in this study to identify and authenticate taro flour from wheat and sago flour. The authentication model of taro flour was developed by using a mixture of 5%, 25%, and 50% of adulterated taro flour from wheat and sago flour. Before subjected to multivariate analysis, an initial preprocessing signal was used namely normalization and standard normal variate to the NIR spectrum. We used principal component analysis followed by discriminant analysis to make an identification and authentication model of taro flour. From the result obtained, about 90.48% of the taro flour mixed with wheat flour and 85% of taro flour mixed with sago flour were successfully classified into their groups. So the combination of NIR spectrum with chemometrics could be used for identification and authentication of taro flour from wheat and sago flour.

Geographic identification of Boletus mushrooms by data fusion of FT-IR and UV spectroscopies combined with multivariate statistical analysis

NASA Astrophysics Data System (ADS)

Yao, Sen; Li, Tao; Li, JieQing; Liu, HongGao; Wang, YuanZhong

2018-06-01

Boletus griseus and Boletus edulis are two well-known wild-grown edible mushrooms which have high nutrition, delicious flavor and high economic value distributing in Yunnan Province. In this study, a rapid method using Fourier transform infrared (FT-IR) and ultraviolet (UV) spectroscopies coupled with data fusion was established for the discrimination of Boletus mushrooms from seven different geographical origins with pattern recognition method. Initially, the spectra of 332 mushroom samples obtained from the two spectroscopic techniques were analyzed individually and then the classification performance based on data fusion strategy was investigated. Meanwhile, the latent variables (LVs) of FT-IR and UV spectra were extracted by partial least square discriminant analysis (PLS-DA) and two datasets were concatenated into a new matrix for data fusion. Then, the fusion matrix was further analyzed by support vector machine (SVM). Compared with single spectroscopic technique, data fusion strategy can improve the classification performance effectively. In particular, the accuracy of correct classification of SVM model in training and test sets were 99.10% and 100.00%, respectively. The results demonstrated that data fusion of FT-IR and UV spectra can provide higher synergic effect for the discrimination of different geographical origins of Boletus mushrooms, which may be benefit for further authentication and quality assessment of edible mushrooms.

Near-infrared Raman spectroscopy for estimating biochemical changes associated with different pathological conditions of cervix

NASA Astrophysics Data System (ADS)

Daniel, Amuthachelvi; Prakasarao, Aruna; Ganesan, Singaravelu

2018-02-01

The molecular level changes associated with oncogenesis precede the morphological changes in cells and tissues. Hence molecular level diagnosis would promote early diagnosis of the disease. Raman spectroscopy is capable of providing specific spectral signature of various biomolecules present in the cells and tissues under various pathological conditions. The aim of this work is to develop a non-linear multi-class statistical methodology for discrimination of normal, neoplastic and malignant cells/tissues. The tissues were classified as normal, pre-malignant and malignant by employing Principal Component Analysis followed by Artificial Neural Network (PC-ANN). The overall accuracy achieved was 99%. Further, to get an insight into the quantitative biochemical composition of the normal, neoplastic and malignant tissues, a linear combination of the major biochemicals by non-negative least squares technique was fit to the measured Raman spectra of the tissues. This technique confirms the changes in the major biomolecules such as lipids, nucleic acids, actin, glycogen and collagen associated with the different pathological conditions. To study the efficacy of this technique in comparison with histopathology, we have utilized Principal Component followed by Linear Discriminant Analysis (PC-LDA) to discriminate the well differentiated, moderately differentiated and poorly differentiated squamous cell carcinoma with an accuracy of 94.0%. And the results demonstrated that Raman spectroscopy has the potential to complement the good old technique of histopathology.

Geographic identification of Boletus mushrooms by data fusion of FT-IR and UV spectroscopies combined with multivariate statistical analysis.

PubMed

Yao, Sen; Li, Tao; Li, JieQing; Liu, HongGao; Wang, YuanZhong

2018-06-05

Boletus griseus and Boletus edulis are two well-known wild-grown edible mushrooms which have high nutrition, delicious flavor and high economic value distributing in Yunnan Province. In this study, a rapid method using Fourier transform infrared (FT-IR) and ultraviolet (UV) spectroscopies coupled with data fusion was established for the discrimination of Boletus mushrooms from seven different geographical origins with pattern recognition method. Initially, the spectra of 332 mushroom samples obtained from the two spectroscopic techniques were analyzed individually and then the classification performance based on data fusion strategy was investigated. Meanwhile, the latent variables (LVs) of FT-IR and UV spectra were extracted by partial least square discriminant analysis (PLS-DA) and two datasets were concatenated into a new matrix for data fusion. Then, the fusion matrix was further analyzed by support vector machine (SVM). Compared with single spectroscopic technique, data fusion strategy can improve the classification performance effectively. In particular, the accuracy of correct classification of SVM model in training and test sets were 99.10% and 100.00%, respectively. The results demonstrated that data fusion of FT-IR and UV spectra can provide higher synergic effect for the discrimination of different geographical origins of Boletus mushrooms, which may be benefit for further authentication and quality assessment of edible mushrooms. Copyright © 2018 Elsevier B.V. All rights reserved.

Forensic and population genetic analysis of Xinjiang Uyghur population on 21 short tandem repeat loci of 6-dye GlobalFiler™ PCR Amplification kit.

PubMed

Zhang, Honghua; Xia, Mingying; Qi, Lijie; Dong, Lei; Song, Shuang; Ma, Teng; Yang, Shuping; Jin, Li; Li, Liming; Li, Shilin

2016-05-01

Estimating the allele frequencies and forensic statistical parameters of commonly used short tandem repeat (STR) loci of the Uyghur population, which is the fifth largest group in China, provides a more precise reference database for forensic investigation. The 6-dye GlobalFiler™ Express PCR Amplification kit incorporates 21 autosomal STRs, which have been proven that could provide reliable DNA typing results and enhance the power of discrimination. Here we analyzed the GlobalFiler STR loci on 1962 unrelated individuals from Chinese Uyghur population of Xinjiang, China. No significant deviations from Hardy-Weinberg equilibrium and linkage disequilibrium were detected within and between the GlobalFiler STR loci. SE33 showed the greatest power of discrimination in Uyghur population, whereas TPOX showed the lowest. The combined power of discrimination was 99.999999999999999999999998746%. No significant difference was observed between Uyghur and the other two Uyghur populations at all tested STRs, as well as Dai and Mongolian. Significant differences were only observed between Uyghur and other Chinese populations at TH01, as well as Central-South Asian at D13S317, East Asian at TH01 and VWA. The phylogenetic analysis showed that Uyghur is genetically close to Chinese populations, as well as East Asian and Central-South Asian. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

PubMed

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

2013-09-23

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

Discrimination Enhancement with Transient Feature Analysis of a Graphene Chemical Sensor.

PubMed

Nallon, Eric C; Schnee, Vincent P; Bright, Collin J; Polcha, Michael P; Li, Qiliang

2016-01-19

A graphene chemical sensor is subjected to a set of structurally and chemically similar hydrocarbon compounds consisting of toluene, o-xylene, p-xylene, and mesitylene. The fractional change in resistance of the sensor upon exposure to these compounds exhibits a similar response magnitude among compounds, whereas large variation is observed within repetitions for each compound, causing a response overlap. Therefore, traditional features depending on maximum response change will cause confusion during further discrimination and classification analysis. More robust features that are less sensitive to concentration, sampling, and drift variability would provide higher quality information. In this work, we have explored the advantage of using transient-based exponential fitting coefficients to enhance the discrimination of similar compounds. The advantages of such feature analysis to discriminate each compound is evaluated using principle component analysis (PCA). In addition, machine learning-based classification algorithms were used to compare the prediction accuracies when using fitting coefficients as features. The additional features greatly enhanced the discrimination between compounds while performing PCA and also improved the prediction accuracy by 34% when using linear discrimination analysis.

Differentiation of orbital lymphoma and idiopathic orbital inflammatory pseudotumor: combined diagnostic value of conventional MRI and histogram analysis of ADC maps.

PubMed

Ren, Jiliang; Yuan, Ying; Wu, Yingwei; Tao, Xiaofeng

2018-05-02

The overlap of morphological feature and mean ADC value restricted clinical application of MRI in the differential diagnosis of orbital lymphoma and idiopathic orbital inflammatory pseudotumor (IOIP). In this paper, we aimed to retrospectively evaluate the combined diagnostic value of conventional magnetic resonance imaging (MRI) and whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in the differentiation of the two lesions. In total, 18 patients with orbital lymphoma and 22 patients with IOIP were included, who underwent both conventional MRI and diffusion weighted imaging before treatment. Conventional MRI features and histogram parameters derived from ADC maps, including mean ADC (ADC mean ), median ADC (ADC median ), skewness, kurtosis, 10th, 25th, 75th and 90th percentiles of ADC (ADC 10 , ADC 25 , ADC 75 , ADC 90 ) were evaluated and compared between orbital lymphoma and IOIP. Multivariate logistic regression analysis was used to identify the most valuable variables for discriminating. Differential model was built upon the selected variables and receiver operating characteristic (ROC) analysis was also performed to determine the differential ability of the model. Multivariate logistic regression showed ADC 10 (P = 0.023) and involvement of orbit preseptal space (P = 0.029) were the most promising indexes in the discrimination of orbital lymphoma and IOIP. The logistic model defined by ADC 10 and involvement of orbit preseptal space was built, which achieved an AUC of 0.939, with sensitivity of 77.30% and specificity of 94.40%. Conventional MRI feature of involvement of orbit preseptal space and ADC histogram parameter of ADC 10 are valuable in differential diagnosis of orbital lymphoma and IOIP.

Characteristic fingerprinting based on macamides for discrimination of maca (Lepidium meyenii) by LC/MS/MS and multivariate statistical analysis.

PubMed

Pan, Yu; Zhang, Ji; Li, Hong; Wang, Yuan-Zhong; Li, Wan-Yi

2016-10-01

Macamides with a benzylalkylamide nucleus are characteristic and major bioactive compounds in the functional food maca (Lepidium meyenii Walp). The aim of this study was to explore variations in macamide content among maca from China and Peru. Twenty-seven batches of maca hypocotyls with different phenotypes, sampled from different geographical origins, were extracted and profiled by liquid chromatography with ultraviolet detection/tandem mass spectrometry (LC-UV/MS/MS). Twelve macamides were identified by MS operated in multiple scanning modes. Similarity analysis showed that maca samples differed significantly in their macamide fingerprinting. Partial least squares discriminant analysis (PLS-DA) was used to differentiate samples according to their geographical origin and to identify the most relevant variables in the classification model. The prediction accuracy for raw maca was 91% and five macamides were selected and considered as chemical markers for sample classification. When combined with a PLS-DA model, characteristic fingerprinting based on macamides could be recommended for labelling for the authentication of maca from different geographical origins. The results provided potential evidence for the relationships between environmental or other factors and distribution of macamides. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

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.