Investigating Married Adults' Communal Coping with Genetic Health Risk and Perceived Discrimination

PubMed Central

Smith, Rachel A.; Sillars, Alan; Chesnut, Ryan P.; Zhu, Xun

2017-01-01

Increased genetic testing in personalized medicine presents unique challenges for couples, including managing disease risk and potential discrimination as a couple. This study investigated couples' conflicts and support gaps as they coped with perceived genetic discrimination. We also explored the degree to which communal coping was beneficial in reducing support gaps, and ultimately stress. Dyadic analysis of married adults (N = 266, 133 couples), in which one person had the genetic risk for serious illness, showed that perceived discrimination predicted more frequent conflicts about AATD-related treatment, privacy boundaries, and finances, which, in turn, predicted wider gaps in emotion and esteem support, and greater stress for both spouses. Communal coping predicted lower support gaps for both partners and marginally lower stress. PMID:29731540

Investigating Married Adults' Communal Coping with Genetic Health Risk and Perceived Discrimination.

PubMed

Smith, Rachel A; Sillars, Alan; Chesnut, Ryan P; Zhu, Xun

2018-01-01

Increased genetic testing in personalized medicine presents unique challenges for couples, including managing disease risk and potential discrimination as a couple. This study investigated couples' conflicts and support gaps as they coped with perceived genetic discrimination. We also explored the degree to which communal coping was beneficial in reducing support gaps, and ultimately stress. Dyadic analysis of married adults ( N = 266, 133 couples), in which one person had the genetic risk for serious illness, showed that perceived discrimination predicted more frequent conflicts about AATD-related treatment, privacy boundaries, and finances, which, in turn, predicted wider gaps in emotion and esteem support, and greater stress for both spouses. Communal coping predicted lower support gaps for both partners and marginally lower stress.

Analysis and Classification of Entering Freshmen Mathematic Students Using Multiple Discriminate Function Analysis.

ERIC Educational Resources Information Center

Ahrens, Steve

Predictor variables that could be used effectively to place entering freshmen methematics students into courses of instruction in mathematics were investigated at West Virginia University. Multiple discriminant analysis was used with nearly 6,000 student records collected over a three-year period, and a series of predictive equations were…

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

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

Hooman, A.; Mohammadzadeh, M

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

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.

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

PubMed Central

2010-01-01

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

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.

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

PubMed

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

2014-04-01

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

[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.

Prediction of the space adaptation syndrome

NASA Technical Reports Server (NTRS)

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

1984-01-01

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

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

PubMed Central

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

2018-01-01

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

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

PubMed

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

2017-04-15

HPLC fingerprint analysis combined with chemometrics was developed to discriminate between the red and the white rice bran grown in Indonesia. The major component in rice bran is γ-oryzanol which consisted of 4 main compounds, namely cycloartenol ferulate, cyclobranol ferulate, campesterol ferulate and β-sitosterol ferulate. Separation of these four compounds along with other compounds was performed using C18 and methanol-acetonitrile with gradient elution system. By using these intensity variations, principal component and discriminant analysis were performed to discriminate the two samples. Discriminant analysis was successfully discriminated the red from the white rice bran with predictive ability of the model showed a satisfactory classification for the test samples. The results of this study indicated that the developed method was suitable as quality control method for rice bran in terms of identification and discrimination of the red and the white rice bran. Copyright © 2016 Elsevier Ltd. All rights reserved.

Development and Validation of Discriminant Analysis Models for Student Loan Defaultees and Non-Defaultees.

ERIC Educational Resources Information Center

Myers, Greeley; Siera, Steven

1980-01-01

Default on guaranteed student loans has been increasing. The use of discriminant analysis as a technique to identify "good" v "bad" student loans based on information available from the loan application is discussed. Research to test the ability of models to such predictions is reported. (Author/MLW)

A new method for fingerprinting sediments source contributions using distances from discriminant function analysis

USDA-ARS?s Scientific Manuscript database

Mixing models have been used to predict sediment source contributions. The inherent problem of the mixing models limited the number of sediment sources. The objective of this study is to develop and evaluate a new method using Discriminant Function Analysis (DFA) to fingerprint sediment source contr...

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.

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

Treesearch

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

1985-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

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.

Fast discrimination of hydroxypropyl methyl cellulose using portable Raman spectrometer and multivariate methods

NASA Astrophysics Data System (ADS)

Song, Biao; Lu, Dan; Peng, Ming; Li, Xia; Zou, Ye; Huang, Meizhen; Lu, Feng

2017-02-01

Raman spectroscopy is developed as a fast and non-destructive method for the discrimination and classification of hydroxypropyl methyl cellulose (HPMC) samples. 44 E series and 41 K series of HPMC samples are measured by a self-developed portable Raman spectrometer (Hx-Raman) which is excited by a 785 nm diode laser and the spectrum range is 200-2700 cm-1 with a resolution (FWHM) of 6 cm-1. Multivariate analysis is applied for discrimination of E series from K series. By methods of principal components analysis (PCA) and Fisher discriminant analysis (FDA), a discrimination result with sensitivity of 90.91% and specificity of 95.12% is achieved. The corresponding receiver operating characteristic (ROC) is 0.99, indicting the accuracy of the predictive model. This result demonstrates the prospect of portable Raman spectrometer for rapid, non-destructive classification and discrimination of E series and K series samples of HPMC.

Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy

NASA Astrophysics Data System (ADS)

He, Shixuan; Xie, Wanyi; Zhang, Wei; Zhang, Liqun; Wang, Yunxia; Liu, Xiaoling; Liu, Yulong; Du, Chunlei

2015-02-01

A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety.

[Discriminant Analysis of Lavender Essential Oil by Attenuated Total Reflectance Infrared Spectroscopy].

PubMed

Tang, Jun; Wang, Qing; Tong, Hong; Liao, Xiang; Zhang, Zheng-fang

2016-03-01

This work aimed to use attenuated total reflectance Fourier transform infrared spectroscopy to identify the lavender essential oil by establishing a Lavender variety and quality analysis model. So, 96 samples were tested. For all samples, the raw spectra were pretreated as second derivative, and to determine the 1 750-900 cm(-1) wavelengths for pattern recognition analysis on the basis of the variance calculation. The results showed that principal component analysis (PCA) can basically discriminate lavender oil cultivar and the first three principal components mainly represent the ester, alcohol and terpenoid substances. When the orthogonal partial least-squares discriminant analysis (OPLS-DA) model was established, the 68 samples were used for the calibration set. Determination coefficients of OPLS-DA regression curve were 0.959 2, 0.976 4, and 0.958 8 respectively for three varieties of lavender essential oil. Three varieties of essential oil's the root mean square error of prediction (RMSEP) in validation set were 0.142 9, 0.127 3, and 0.124 9, respectively. The discriminant rate of calibration set and the prediction rate of validation set had reached 100%. The model has the very good recognition capability to detect the variety and quality of lavender essential oil. The result indicated that a model which provides a quick, intuitive and feasible method had been built to discriminate lavender oils.

The prediction of swimming performance in competition from behavioral information.

PubMed

Rushall, B S; Leet, D

1979-06-01

The swimming performances of the Canadian Team at the 1976 Olympic Games were categorized as being improved or worse than previous best times in the events contested. The two groups had been previously assessed on the Psychological Inventories for Competitive Swimmers. A stepwise multiple-discriminant analysis of the inventory responses revealed that 13 test questions produced a perfect discrimination of group membership. The resultant discriminant functions for predicting performance classification were applied to the test responses of 157 swimmers at the 1977 Canadian Winter National Swimming Championships. Using the same performance classification criteria the accuracy of prediction was not better than chance in three of four sex by performance classifications. This yielded a failure to locate a set of behavioral factors which determine swimming performance improvements in elite competitive circumstances. The possibility of sets of factors which do not discriminate between performances in similar environments or between similar groups of swimmers was raised.

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

PubMed

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

2009-10-15

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

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

NASA Astrophysics Data System (ADS)

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

2013-10-01

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

New Discriminant Method for Identifying the Aggressive Disease Phenotype of Non-alcoholic Fatty Liver Disease.

PubMed

Kawamura, Yusuke; Ikeda, Kenji; Arase, Yasuji; Fujiyama, Shunichiro; Hosaka, Tetsuya; Kobayashi, Masahiro; Saitoh, Satoshi; Sezaki, Hitomi; Akuta, Norio; Suzuki, Fumitaka; Suzuki, Yoshiyuki; Kumada, Hiromitsu

2017-01-01

Objective To detect the aggressive phenotype (AP) of non-alcoholic fatty liver disease (NAFLD) based on the initial laboratory data and clinical characteristics. Methods We enrolled 144 patients with histologically proven NAFLD. For the first analysis, 24 NAFLD patients underwent repeat biopsy to establish a discriminant formula for predicting the AP of NAFLD (D-APN). The AP was defined by NAFLD that had been maintained or progressed to a fibrotic stage beyond stage 2. In the second analysis, we analyzed the distribution of the AP in each stage of disease and the incidence of the PNPLA3 rs738409 GG genotype in AP in 120 other patients. Results After the analysis, the following function was found to discriminate the disease phenotype: z=0.150×body mass index (kg/m 2 )+0.085×age (years)+1.112×ln (AST) (IU/L)+0.127×ln (m-AST)-12.96. A positive result indicates the AP of NAFLD. The discriminant functions had a positive predictive value of 94% and a negative predictive value of 71%. The distribution of the AP and the incidence of the PNPLA3 GG genotype in the AP in each stage of the disease among the 120 patients were as follows: non-alcoholic fatty liver, 30%/33%; non-alcoholic steatohepatitis (NASH) stage 1, 53%/26%; stage 2, 71%/70%; stage 3, 92%/57%; and stage 4, 93%/64%; there was a significant increase in the incidence of the AP as the disease progressed (p<0.001). Conclusion The new discriminant formula was useful for predicting disease progression potential in NAFLD patients and the incidence of the PNPLA3 GG genotype was elevated according to the distribution of AP.

Graphical methods for the sensitivity analysis in discriminant analysis

DOE PAGES

Kim, Youngil; Anderson-Cook, Christine M.; Dae-Heung, Jang

2015-09-30

Similar to regression, many measures to detect influential data points in discriminant analysis have been developed. Many follow similar principles as the diagnostic measures used in linear regression in the context of discriminant analysis. Here we focus on the impact on the predicted classification posterior probability when a data point is omitted. The new method is intuitive and easily interpretative compared to existing methods. We also propose a graphical display to show the individual movement of the posterior probability of other data points when a specific data point is omitted. This enables the summaries to capture the overall pattern ofmore » the change.« less

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.

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

USGS Publications Warehouse

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

1984-01-01

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

Effects of Cerebral Blood Flow and Vessel Conditions on Speech Recognition in Patients With Postlingual Adult Cochlear Implant: Predictable Factors for the Efficacy of Cochlear Implant.

PubMed

Ishino, Takashi; Ragaee, Mahmoud Ali; Maruhashi, Tatsuya; Kajikawa, Masato; Higashi, Yukihito; Sonoyama, Toru; Takeno, Sachio; Hirakawa, Katsuhiro

Cochlear implantation (CI) has been the most successful procedure for restoring hearing in a patient with severe and profound hearing loss. However, possibly owing to the variable brain functions of each patient, its performance and the associated patient satisfaction are widely variable. The authors hypothesize that peripheral and cerebral circulation can be assessed by noninvasive and globally available methods, yielding superior presurgical predictive factors of the performance of CI in adult patients with postlingual hearing loss who are scheduled to undergo CI. Twenty-two adult patients with cochlear implants for postlingual hearing loss were evaluated using Doppler sonography measurement of the cervical arteries (reflecting cerebral blood flow), flow-mediated dilation (FMD; reflecting the condition of cerebral arteries), and their pre-/post-CI best score on a monosyllabic discrimination test (pre-/post-CI best monosyllabic discrimination [BMD] score). Correlations between post-CI BMD score and the other factors were examined using univariate analysis and stepwise multiple linear regression analysis. The prediction factors were calculated by examining the receiver-operating characteristic curve between post-CI BMD score and the significantly positively correlated factors. Age and duration of deafness had a moderately negative correlation. The mean velocity of the internal carotid arteries and FMD had a moderate-to-strong positive correlation with the post-CI BMD score in univariate analysis. Stepwise multiple linear regression analysis revealed that only FMD was significantly positively correlated with post-CI BMD score. Analysis of the receiver-operating characteristic curve showed that a FMD cutoff score of 1.8 significantly predicted post-CI BMD score. These data suggest that FMD is a convenient, noninvasive, and widely available tool for predicting the efficacy of cochlear implants. An FMD cutoff score of 1.8 could be a good index for determining whether patients will hear well with cochlear implants. It could also be used to predict whether cochlear implants will provide good speech recognition benefits to candidates, even if their speech discrimination is poor. This FMD index could become a useful predictive tool for candidates with poor speech discrimination to determine the efficacy of CI before surgery.

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.

[Etiological analysis and establishment of a discriminant model for lower respiratory tract infections in hospitalized patients].

PubMed

Chen, Y S; Lin, X H; Li, H R; Hua, Z D; Lin, M Q; Huang, W S; Yu, T; Lyu, H Y; Mao, W P; Liang, Y Q; Peng, X R; Chen, S J; Zheng, H; Lian, S Q; Hu, X L; Yao, X Q

2017-12-12

Objective: To analyze the pathogens of lower respiratory tract infection(LRTI) including bacterial, viral and mixed infection, and to establish a discriminant model based on clinical features in order to predict the pathogens. Methods: A total of 243 hospitalized patients with lower respiratory tract infections were enrolled in Fujian Provincial Hospital from April 2012 to September 2015. The clinical data and airway (sputum and/or bronchoalveolar lavage) samples were collected. Microbes were identified by traditional culture (for bacteria), loop-mediated isothermal amplification(LAMP) and gene sequencing (for bacteria and atypical pathogen), or Real-time quantitative polymerase chain reaction (Real-time PCR)for viruses. Finally, a discriminant model was established by using the discriminant analysis methods to help to predict bacterial, viral and mixed infections. Results: Pathogens were detected in 53.9% (131/243) of the 243 cases.Bacteria accounted for 23.5%(57/243, of which 17 cases with the virus, 1 case with Mycoplasma pneumoniae and virus), mainly Pseudomonas Aeruginosa and Klebsiella Pneumonia. Atypical pathogens for 4.9% (12/243, of which 3 cases with the virus, 1 case of bacteria and viruses), all were mycoplasma pneumonia. Viruses for 34.6% (84/243, of which 17 cases of bacteria, 3 cases with Mycoplasma pneumoniae, 1 case with Mycoplasma pneumoniae and bacteria) of the cases, mainly Influenza A virus and Human Cytomegalovirus, and other virus like adenovirus, human parainfluenza virus, respiratory syncytial virus, human metapneumovirus, human boca virus were also detected fewly. Seven parameters including mental status, using antibiotics prior to admission, complications, abnormal breath sounds, neutrophil alkaline phosphatase (NAP) score, pneumonia severity index (PSI) score and CRUB-65 score were enrolled after univariate analysis, and discriminant analysis was used to establish the discriminant model by applying the identified pathogens as the dependent variable. The total positive predictive value was 64.7%(77/119), with 66.7% for bacterial infection, 78.0% for viral infection and 33.3% for the mixed infection. Conclusions: The mostly detected pathogens were Pseudomonas aeruginosa, atypitcal pathogens, Klebsiella pneumoniae, influenza A virus and human cytomegalovirus in hospitalized patients with LRTI in this hospital. The discriminant diagnostic model established by clinical features may contribute to predict the pathogens of LRTI.

Nomogram Prediction of Overall Survival After Curative Irradiation for Uterine Cervical Cancer

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

Seo, YoungSeok; Yoo, Seong Yul; Kim, Mi-Sook

Purpose: The purpose of this study was to develop a nomogram capable of predicting the probability of 5-year survival after radical radiotherapy (RT) without chemotherapy for uterine cervical cancer. Methods and Materials: We retrospectively analyzed 549 patients that underwent radical RT for uterine cervical cancer between March 1994 and April 2002 at our institution. Multivariate analysis using Cox proportional hazards regression was performed and this Cox model was used as the basis for the devised nomogram. The model was internally validated for discrimination and calibration by bootstrap resampling. Results: By multivariate regression analysis, the model showed that age, hemoglobin levelmore » before RT, Federation Internationale de Gynecologie Obstetrique (FIGO) stage, maximal tumor diameter, lymph node status, and RT dose at Point A significantly predicted overall survival. The survival prediction model demonstrated good calibration and discrimination. The bootstrap-corrected concordance index was 0.67. The predictive ability of the nomogram proved to be superior to FIGO stage (p = 0.01). Conclusions: The devised nomogram offers a significantly better level of discrimination than the FIGO staging system. In particular, it improves predictions of survival probability and could be useful for counseling patients, choosing treatment modalities and schedules, and designing clinical trials. However, before this nomogram is used clinically, it should be externally validated.« less

Cross-national validation of prognostic models predicting sickness absence and the added value of work environment variables.

PubMed

Roelen, Corné A M; Stapelfeldt, Christina M; Heymans, Martijn W; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V; Bültmann, Ute; Jensen, Chris

2015-06-01

To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. 2,562 municipal eldercare workers (95% women) participated in the Working in Eldercare Survey. Predictor variables were measured by questionnaire at baseline in 2005. Prognostic models were validated for predictions of high (≥30) SA days and high (≥3) SA episodes retrieved from employer records during 1-year follow-up. The accuracy of predictions was assessed by calibration graphs and the ability of the models to discriminate between high- and low-risk workers was investigated by ROC-analysis. The added value of work environment variables was measured with Integrated Discrimination Improvement (IDI). 1,930 workers had complete data for analysis. The models underestimated the risk of high SA in eldercare workers and the SA episodes model had to be re-calibrated to the Danish data. Discrimination was practically useful for the re-calibrated SA episodes model, but not the SA days model. Physical workload improved the SA days model (IDI = 0.40; 95% CI 0.19-0.60) and psychosocial work factors, particularly the quality of leadership (IDI = 0.70; 95% CI 053-0.86) improved the SA episodes model. The prognostic model predicting high SA days showed poor performance even after physical workload was added. The prognostic model predicting high SA episodes could be used to identify high-risk workers, especially when psychosocial work factors are added as predictor variables.

Identify Secretory Protein of Malaria Parasite with Modified Quadratic Discriminant Algorithm and Amino Acid Composition.

PubMed

Feng, Yong-E

2016-06-01

Malaria parasite secretes various proteins in infected red blood cell for its growth and survival. Thus identification of these secretory proteins is important for developing vaccine or drug against malaria. In this study, the modified method of quadratic discriminant analysis is presented for predicting the secretory proteins. Firstly, 20 amino acids are divided into five types according to the physical and chemical characteristics of amino acids. Then, we used five types of amino acids compositions as inputs of the modified quadratic discriminant algorithm. Finally, the best prediction performance is obtained by using 20 amino acid compositions, the sensitivity of 96 %, the specificity of 92 % with 0.88 of Mathew's correlation coefficient in fivefold cross-validation test. The results are also compared with those of existing prediction methods. The compared results shown our method are prominent in the prediction of secretory proteins.

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

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

PubMed

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

2014-02-01

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

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.

Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy.

PubMed

He, Shixuan; Xie, Wanyi; Zhang, Wei; Zhang, Liqun; Wang, Yunxia; Liu, Xiaoling; Liu, Yulong; Du, Chunlei

2015-02-25

A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety. Copyright © 2014 Elsevier B.V. All rights reserved.

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

PubMed

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

2016-07-01

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

Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures

NASA Astrophysics Data System (ADS)

Li, Quanbao; Wei, Fajie; Zhou, Shenghan

2017-05-01

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

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

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

PubMed

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

2018-03-01

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

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.

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

PubMed Central

2011-01-01

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

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.

Spatial prediction of landslide hazard using discriminant analysis and GIS

Treesearch

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

2000-01-01

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

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

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.

Calculation and application of activity discriminants in lead optimization.

PubMed

Luo, Xincai; Krumrine, Jennifer R; Shenvi, Ashok B; Pierson, M Edward; Bernstein, Peter R

2010-11-01

We present a technique for computing activity discriminants of in vitro (pharmacological, DMPK, and safety) assays and the application to the prediction of in vitro activities of proposed synthetic targets during the lead optimization phase of drug discovery projects. This technique emulates how medicinal chemists perform SAR analysis and activity prediction. The activity discriminants that are functions of 6 commonly used medicinal chemistry descriptors can be interpreted easily by medicinal chemists. Further, visualization with Spotfire allows medicinal chemists to analyze how the query molecule is related to compounds tested previously, and to evaluate easily the relevance of the activity discriminants to the activities of the query molecule. Validation with all compounds synthesized and tested in AstraZeneca Wilmington since 2006 demonstrates that this approach is useful for prioritizing new synthetic targets for synthesis. Copyright © 2010 Elsevier Inc. All rights reserved.

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

PubMed

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

2016-08-01

At present, the geographical origin of extra virgin olive oils can be ensured by documented traceability, although chemical analysis may add information that is useful for possible confirmation. This preliminary study investigated the effectiveness of flash gas chromatography electronic nose and multivariate data analysis to perform rapid screening of commercial extra virgin olive oils characterized by a different geographical origin declared in the label. A comparison with solid phase micro extraction coupled to gas chromatography mass spectrometry was also performed. The new method is suitable to verify the geographic origin of extra virgin olive oils based on principal components analysis and discriminant analysis applied to the volatile profile of the headspace as a fingerprint. The selected variables were suitable in discriminating between "100% Italian" and "non-100% Italian" oils. Partial least squares discriminant analysis also allowed prediction of the degree of membership of unknown samples to the classes examined. Copyright © 2016. Published by Elsevier Ltd.

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.

Discrimination in lexical decision

PubMed Central

Feldman, Laurie Beth; Ramscar, Michael; Hendrix, Peter; Baayen, R. Harald

2017-01-01

In this study we present a novel set of discrimination-based indicators of language processing derived from Naive Discriminative Learning (ndl) theory. We compare the effectiveness of these new measures with classical lexical-distributional measures—in particular, frequency counts and form similarity measures—to predict lexical decision latencies when a complete morphological segmentation of masked primes is or is not possible. Data derive from a re-analysis of a large subset of decision latencies from the English Lexicon Project, as well as from the results of two new masked priming studies. Results demonstrate the superiority of discrimination-based predictors over lexical-distributional predictors alone, across both the simple and primed lexical decision tasks. Comparable priming after masked corner and cornea type primes, across two experiments, fails to support early obligatory segmentation into morphemes as predicted by the morpho-orthographic account of reading. Results fit well with ndl theory, which, in conformity with Word and Paradigm theory, rejects the morpheme as a relevant unit of analysis. Furthermore, results indicate that readers with greater spelling proficiency and larger vocabularies make better use of orthographic priors and handle lexical competition more efficiently. PMID:28235015

Discrimination in lexical decision.

PubMed

Milin, Petar; Feldman, Laurie Beth; Ramscar, Michael; Hendrix, Peter; Baayen, R Harald

2017-01-01

In this study we present a novel set of discrimination-based indicators of language processing derived from Naive Discriminative Learning (ndl) theory. We compare the effectiveness of these new measures with classical lexical-distributional measures-in particular, frequency counts and form similarity measures-to predict lexical decision latencies when a complete morphological segmentation of masked primes is or is not possible. Data derive from a re-analysis of a large subset of decision latencies from the English Lexicon Project, as well as from the results of two new masked priming studies. Results demonstrate the superiority of discrimination-based predictors over lexical-distributional predictors alone, across both the simple and primed lexical decision tasks. Comparable priming after masked corner and cornea type primes, across two experiments, fails to support early obligatory segmentation into morphemes as predicted by the morpho-orthographic account of reading. Results fit well with ndl theory, which, in conformity with Word and Paradigm theory, rejects the morpheme as a relevant unit of analysis. Furthermore, results indicate that readers with greater spelling proficiency and larger vocabularies make better use of orthographic priors and handle lexical competition more efficiently.

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.

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

PubMed

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

2015-11-03

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

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

PubMed Central

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

2015-01-01

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

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

PubMed

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

2015-01-01

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

Assessment of a remote sensing-based model for predicting malaria transmission risk in villages of Chiapas, Mexico

NASA Technical Reports Server (NTRS)

Beck, L. R.; Rodriguez, M. H.; Dister, S. W.; Rodriguez, A. D.; Washino, R. K.; Roberts, D. R.; Spanner, M. A.

1997-01-01

A blind test of two remote sensing-based models for predicting adult populations of Anopheles albimanus in villages, an indicator of malaria transmission risk, was conducted in southern Chiapas, Mexico. One model was developed using a discriminant analysis approach, while the other was based on regression analysis. The models were developed in 1992 for an area around Tapachula, Chiapas, using Landsat Thematic Mapper (TM) satellite data and geographic information system functions. Using two remotely sensed landscape elements, the discriminant model was able to successfully distinguish between villages with high and low An. albimanus abundance with an overall accuracy of 90%. To test the predictive capability of the models, multitemporal TM data were used to generate a landscape map of the Huixtla area, northwest of Tapachula, where the models were used to predict risk for 40 villages. The resulting predictions were not disclosed until the end of the test. Independently, An. albimanus abundance data were collected in the 40 randomly selected villages for which the predictions had been made. These data were subsequently used to assess the models' accuracies. The discriminant model accurately predicted 79% of the high-abundance villages and 50% of the low-abundance villages, for an overall accuracy of 70%. The regression model correctly identified seven of the 10 villages with the highest mosquito abundance. This test demonstrated that remote sensing-based models generated for one area can be used successfully in another, comparable area.

Kin discrimination within honey bee (Apis mellifera) colonies: An analysis of the evidence.

PubMed

Breed, M D; Welch, C K; Cruz, R

1994-12-01

Compelling evolutionary arguments lead to the prediction that honey bee workers should discriminate between supersisters and half-sisters within colonies. We review the theoretical support for discrimination during swarming, queen rearing, feeding, and grooming. A survey of the data that tests whether such discrimination takes place shows that, despite substantial effort in a number of laboratories, there is no conclusive evidence for intracolony discrimination in any of the postulated contexts. The strongest suggestive data is in the critical context of queen rearing, but flaws in experimental design or analysis make the best available tests inconclusive. We present new data that shows that cues exist on which discriminations can be made among adult workers in nestmate recognition interactions and in feeding interactions, but our data does not differentiate between subfamily recognition and recognition associated with color phenotypes. We conclude that while selection may favor discrimination between supersisters and half-sisters, as a practical matter such discriminations play no role, or only a minor role, in the biology of the honey bee. Copyright © 1994. Published by Elsevier B.V.

Feature extraction with deep neural networks by a generalized discriminant analysis.

PubMed

Stuhlsatz, André; Lippel, Jens; Zielke, Thomas

2012-04-01

We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.

Racial and nonracial discrimination and smoking status among South African adults ten years after apartheid

PubMed Central

Dutra, Lauren M; Williams, David R; Kawachi, Ichiro; Okechukwu, Cassandra A

2014-01-01

Background Despite a long history of discrimination and persisting racial disparities in smoking prevalence, little research exists on the relationship between discrimination and smoking in South Africa. Methods This analysis examined chronic (day to day) and acute (lifetime) experiences of racial and nonracial (e.g., age, gender, or physical appearance) discrimination and smoking status among respondents to the South Africa Stress and Health Study (SASH). Logistic regression models were constructed using SAS-Callable SUDAAN. Results Both chronic racial discrimination (RR=1.45, 95%CI: 1.14–1.85) and chronic nonracial discrimination (RR=1.69, 95%CI: 1.37–2.08) predicted a higher risk of smoking, but neither type of acute discrimination did. Total (sum of racial and nonracial) chronic discrimination (RR=1.46, 95%CI: 1.20–1.78) and total acute discrimination (RR=1.28, 95%CI: 1.01–1.60) predicted a higher risk of current smoking. Conclusions Racial and nonracial discrimination may be related to South African adults’ smoking behavior, but this relationship likely varies by the timing and frequency of these experiences. Future research should use longitudinal data to identify the temporal ordering of the relationships studied, include areas outside of South Africa to increase generalizability, and consider the implications of these findings for smoking cessation approaches in South Africa. PMID:24789604

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

PubMed Central

Blind, P-J; Eriksson, S.

1991-01-01

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

Sex determination of the Acadian Flycatcher using discriminant analysis

USGS Publications Warehouse

Wilson, R.R.

1999-01-01

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

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

ERIC Educational Resources Information Center

Fan, Xitao; Wang, Lin

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

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

PubMed

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

2017-04-01

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

Gene features selection for three-class disease classification via multiple orthogonal partial least square discriminant analysis and S-plot using microarray data.

PubMed

Yang, Mingxing; Li, Xiumin; Li, Zhibin; Ou, Zhimin; Liu, Ming; Liu, Suhuan; Li, Xuejun; Yang, Shuyu

2013-01-01

DNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes. Here we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub's leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.

Right atrial pressure predicts worsening renal function in patients with acute right ventricular myocardial infarction.

PubMed

Ivey-Miranda, Juan Betuel; Posada-Martínez, Edith Liliana; Almeida-Gutiérrez, Eduardo; Borrayo-Sánchez, Gabriela; Flores-Umanzor, Eduardo

2018-08-01

Right ventricular myocardial infarction (RVMI) is associated with serious complications in the short-term. Worsening renal function (WRF) is a frequent and dangerous complication. We investigated if right atrial pressure (RAP) predicts WRF in these patients. We prospectively studied patients with RVMI. RAP was obtained invasively at admission to coronary care unit. Blood samples were extracted from patients at baseline and every 24h for creatinine measurements for seven days. We defined WRF as an increase of 25% or 0.5mg/dl in serum creatinine during the first seven days compared to baseline creatinine. We included forty-five patients (age 68±10years, male 71%). WRF occurred in 51%. The best cut-off value of RAP for WRF prediction was 11mmHg. RAP ≥11mmHg was associated with WRF at univariate analysis (OR 5.5, 95% CI 1.27-24.3, p=0.023) and multivariate analysis (OR 6.1, 95% CI 1.07-35.4, p=0.042). RAP ≥11mmHg improved reclassification and discrimination after usual prediction with the Mehran score (net reclassification improvement 64.8%, p=0.030; integrated discrimination improvement 7.5%, p=0.037). In patients with RVMI, RAP ≥11mmHg predicted WRF and improved discrimination. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

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

ERIC Educational Resources Information Center

Wilkinson, L. David; Frazer, Linda H.

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

Revised Framingham Stroke Risk Score, Nontraditional Risk Markers, and Incident Stroke in a Multiethnic Cohort.

PubMed

Flueckiger, Peter; Longstreth, Will; Herrington, David; Yeboah, Joseph

2018-02-01

Limited data exist on the performance of the revised Framingham Stroke Risk Score (R-FSRS) and the R-FSRS in conjunction with nontraditional risk markers. We compared the R-FSRS, original FSRS, and the Pooled Cohort Equation for stroke prediction and assessed the improvement in discrimination by nontraditional risk markers. Six thousand seven hundred twelve of 6814 participants of the MESA (Multi-Ethnic Study of Atherosclerosis) were included. Cox proportional hazard, area under the curve, net reclassification improvement, and integrated discrimination increment analysis were used to assess and compare each stroke prediction risk score. Stroke was defined as fatal/nonfatal strokes (hemorrhagic or ischemic). After mean follow-up of 10.7 years, 231 of 6712 (3.4%) strokes were adjudicated (2.7% ischemic strokes). Mean stroke risks using the R-FSRS, original FSRS, and Pooled Cohort Equation were 4.7%, 5.9%, and 13.5%. The R-FSRS had the best calibration (Hosmer-Lemeshow goodness-of-fit, χ 2 =6.55; P =0.59). All risk scores were predictive of incident stroke. C statistics of R-FSRS (0.716) was similar to Pooled Cohort Equation (0.716), but significantly higher than the original FSRS (0.653; P =0.01 for comparison with R-FSRS). Adding nontraditional risk markers individually to the R-FSRS did not improve discrimination of the R-FSRS in the area under the curve analysis, but did improve category-less net reclassification improvement and integrated discrimination increment for incident stroke. The addition of coronary artery calcium to R-FSRS produced the highest category-less net reclassification improvement (0.36) and integrated discrimination increment (0.0027). Similar results were obtained when ischemic strokes were used as the outcome. The R-FSRS downgraded stroke risk but had better calibration and discriminative ability for incident stroke compared with the original FSRS. Nontraditional risk markers modestly improved the discriminative ability of the R-FSRS, with coronary artery calcium performing the best. © 2018 American Heart Association, Inc.

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.

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

PubMed

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

2017-01-01

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

External validation and clinical utility of a prediction model for 6-month mortality in patients undergoing hemodialysis for end-stage kidney disease.

PubMed

Forzley, Brian; Er, Lee; Chiu, Helen Hl; Djurdjev, Ognjenka; Martinusen, Dan; Carson, Rachel C; Hargrove, Gaylene; Levin, Adeera; Karim, Mohamud

2018-02-01

End-stage kidney disease is associated with poor prognosis. Health care professionals must be prepared to address end-of-life issues and identify those at high risk for dying. A 6-month mortality prediction model for patients on dialysis derived in the United States is used but has not been externally validated. We aimed to assess the external validity and clinical utility in an independent cohort in Canada. We examined the performance of the published 6-month mortality prediction model, using discrimination, calibration, and decision curve analyses. Data were derived from a cohort of 374 prevalent dialysis patients in two regions of British Columbia, Canada, which included serum albumin, age, peripheral vascular disease, dementia, and answers to the "the surprise question" ("Would I be surprised if this patient died within the next year?"). The observed mortality in the validation cohort was 11.5% at 6 months. The prediction model had reasonable discrimination (c-stat = 0.70) but poor calibration (calibration-in-the-large = -0.53 (95% confidence interval: -0.88, -0.18); calibration slope = 0.57 (95% confidence interval: 0.31, 0.83)) in our data. Decision curve analysis showed the model only has added value in guiding clinical decision in a small range of threshold probabilities: 8%-20%. Despite reasonable discrimination, the prediction model has poor calibration in this external study cohort; thus, it may have limited clinical utility in settings outside of where it was derived. Decision curve analysis clarifies limitations in clinical utility not apparent by receiver operating characteristic curve analysis. This study highlights the importance of external validation of prediction models prior to routine use in clinical practice.

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.

Functional status and mortality prediction in community-acquired pneumonia.

PubMed

Jeon, Kyeongman; Yoo, Hongseok; Jeong, Byeong-Ho; Park, Hye Yun; Koh, Won-Jung; Suh, Gee Young; Guallar, Eliseo

2017-10-01

Poor functional status (FS) has been suggested as a poor prognostic factor in both pneumonia and severe pneumonia in elderly patients. However, it is still unclear whether FS is associated with outcomes and improves survival prediction in community-acquired pneumonia (CAP) in the general population. Data on hospitalized patients with CAP and FS, assessed by the Eastern Cooperative Oncology Group (ECOG) scale were prospectively collected between January 2008 and December 2012. The independent association of FS with 30-day mortality in CAP patients was evaluated using multivariable logistic regression. Improvement in mortality prediction when FS was added to the CRB-65 (confusion, respiratory rate, blood pressure and age 65) score was evaluated for discrimination, reclassification and calibration. The 30-day mortality of study participants (n = 1526) was 10%. Mortality significantly increased with higher ECOG score (P for trend <0.001). In multivariable analysis, ECOG ≥3 was strongly associated with 30-day mortality (adjusted OR: 5.70; 95% CI: 3.82-8.50). Adding ECOG ≥3 significantly improved the discriminatory power of CRB-65. Reclassification indices also confirmed the improvement in discrimination ability when FS was combined with the CRB-65, with a categorized net reclassification index (NRI) of 0.561 (0.437-0.686), a continuous NRI of 0.858 (0.696-1.019) and a relative integrated discrimination improvement in the discrimination slope of 139.8 % (110.8-154.6). FS predicted 30-day mortality and improved discrimination and reclassification in consecutive CAP patients. Assessment of premorbid FS should be considered in mortality prediction in patients with CAP. © 2017 Asian Pacific Society of Respirology.

A simple randomisation procedure for validating discriminant analysis: a methodological note.

PubMed

Wastell, D G

1987-04-01

Because the goal of discriminant analysis (DA) is to optimise classification, it designedly exaggerates between-group differences. This bias complicates validation of DA. Jack-knifing has been used for validation but is inappropriate when stepwise selection (SWDA) is employed. A simple randomisation test is presented which is shown to give correct decisions for SWDA. The general superiority of randomisation tests over orthodox significance tests is discussed. Current work on non-parametric methods of estimating the error rates of prediction rules is briefly reviewed.

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

PubMed

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

2003-01-01

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

School-Based Racial and Gender Discrimination among African American Adolescents: Exploring Gender Variation in Frequency and Implications for Adjustment

PubMed Central

Chavous, Tabbye M.; Griffin, Tiffany M.

2012-01-01

The present study examined school-based racial and gender discrimination experiences among African American adolescents in Grade 8 (n = 204 girls; n = 209 boys). A primary goal was exploring gender variation in frequency of both types of discrimination and associations of discrimination with academic and psychological functioning among girls and boys. Girls and boys did not vary in reported racial discrimination frequency, but boys reported more gender discrimination experiences. Multiple regression analyses within gender groups indicated that among girls and boys, racial discrimination and gender discrimination predicted higher depressive symptoms and school importance and racial discrimination predicted self-esteem. Racial and gender discrimination were also negatively associated with grade point average among boys but were not significantly associated in girls’ analyses. Significant gender discrimination X racial discrimination interactions resulted in the girls’ models predicting psychological outcomes and in boys’ models predicting academic achievement. Taken together, findings suggest the importance of considering gender- and race-related experiences in understanding academic and psychological adjustment among African American adolescents. PMID:22837794

School-Based Racial and Gender Discrimination among African American Adolescents: Exploring Gender Variation in Frequency and Implications for Adjustment.

PubMed

Cogburn, Courtney D; Chavous, Tabbye M; Griffin, Tiffany M

2011-01-03

The present study examined school-based racial and gender discrimination experiences among African American adolescents in Grade 8 (n = 204 girls; n = 209 boys). A primary goal was exploring gender variation in frequency of both types of discrimination and associations of discrimination with academic and psychological functioning among girls and boys. Girls and boys did not vary in reported racial discrimination frequency, but boys reported more gender discrimination experiences. Multiple regression analyses within gender groups indicated that among girls and boys, racial discrimination and gender discrimination predicted higher depressive symptoms and school importance and racial discrimination predicted self-esteem. Racial and gender discrimination were also negatively associated with grade point average among boys but were not significantly associated in girls' analyses. Significant gender discrimination X racial discrimination interactions resulted in the girls' models predicting psychological outcomes and in boys' models predicting academic achievement. Taken together, findings suggest the importance of considering gender- and race-related experiences in understanding academic and psychological adjustment among African American adolescents.

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

PubMed

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

2018-05-01

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

Statistical prediction of space motion sickness

NASA Technical Reports Server (NTRS)

Reschke, Millard F.

1990-01-01

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

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.

Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors.

PubMed

Sun, Meijian; Wang, Xia; Zou, Chuanxin; He, Zenghui; Liu, Wei; Li, Honglin

2016-06-07

RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers. In this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631. The good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind .

A comparative UPLC-Q/TOF-MS-based metabolomics approach for distinguishing Zingiber officinale Roscoe of two geographical origins.

PubMed

Mais, Enos; Alolga, Raphael N; Wang, Shi-Lei; Linus, Loveth O; Yin, Xiaojin; Qi, Lian-Wen

2018-02-01

Ginger, the rhizome of Zingiber officinale Roscoe, is a popular spice used in the food, beverage and confectionary industries. In this study, we report an untargeted UPLC-Q/TOF-MS-based metabolomics approach for comprehensively discriminating between ginger from two geographical locations, Ghana in West Africa and China. Forty batches of fresh ginger from both countries were discriminated using principal component analysis and orthogonal partial least squares discrimination analysis. Sixteen differential metabolites were identified between the gingers from the two geographical locations, six of which were identified as the marker compounds responsible for the discrimination. Our study highlights the essence and predictive power of metabolomics in detecting minute differences in same varieties of plants/plant samples based on the levels and composition of their metabolites. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Workplace discrimination and autism spectrum disorders: The National EEOC Americans with Disabilities Act Research project.

PubMed

Van Wieren, Todd A; Reid, Christine A; McMahon, Brian T

2008-01-01

Using the Integrated Mission System of the Equal Employment Opportunity Commission (EEOC), the employment discrimination experience of Americans with autism spectrum disorders (ASDs) is documented for Title I of the Americans with Disabilities Act. The researchers examine demographic characteristics of the charging parties; the industry designation, location, and size of employers against whom complaints are filed; the nature of discrimination (i.e., type of complaint) alleged to occur; and the legal outcome or resolution of these complaints. Researchers compare and contrast these key dimensions of workplace discrimination involving individuals with ASDs and persons with other physical, sensory, and neurological impairments. Researchers also attempt to discern whether or not the resolutions of the ASD charges can be predicted using the variables available for analysis. The comparative findings of this study indicate that individuals with ASDs were more likely to make charges of discrimination against Retail industry employers. Persons with ASDs were also more likely to make charges of discrimination when they were younger, male, and/or of Native American/Alaskan Native ethnicity. The predictive findings of this study indicate that the odds of ASD charges resulting in meritorious resolution (i.e., discrimination determined by the EEOC to have occurred) increase when the discrimination was encountered in Service industries and by larger employers. Implications for policy, advocacy and further research efforts are addressed.

Predictors of Early Termination in a University Counseling Training Clinic

ERIC Educational Resources Information Center

Lampropoulos, Georgios K.; Schneider, Mercedes K.; Spengler, Paul M.

2009-01-01

Despite the existence of counseling dropout research, there are limited predictive data for counseling in training clinics. Potential predictor variables were investigated in this archival study of 380 client files in a university counseling training clinic. Multinomial logistic regression, predictive discriminant analysis, and classification and…

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

PubMed

Fellous, J M

1997-07-01

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

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

PubMed

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

2012-07-10

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

[Prognostic parameters in liver cirrhosis, varicose bleeding and sclerosing therapy. Prospective comparison of a prognostic system with the Child classification obtained by discriminant analysis].

PubMed

Sauerbruch, T; Ansari, H; Wotzka, R; Soehendra, N; Köpcke, W

1988-01-08

Prospective prognosis systems for predicting half-year death-rate after bleeding from oesophageal varices and sclerotherapy were tested on 129 patients. The receiver-operating-characteristic curves of three discriminant scores were compared with the Child-Pugh classification. It was found that the latter is still the best for prognosticating the course of the disease. A simplified discriminant score which contains as its only factors bilirubin and the Quick value does, however, give nearly as good information.

Socialization of Culture and Coping with Discrimination Among American Indian Families: Examining Cultural Correlates of Youth Outcomes

PubMed Central

Yasui, Miwa; Dishion, Thomas J.; Stormshak, Elizabeth; Ball, Alison

2016-01-01

Objective The current study examines the interrelations between observed parental cultural socialization and socialization of coping with discrimination, and youth outcomes among a sample of 92 American Indian adolescents and their parents in a rural reservation. Method Path analysis is used to examine the relationships among observed parental socialization (cultural socialization and socialization of coping with discrimination), and youth-reported perceived discrimination, ethnic identity and depression. Results Findings reveal that higher levels of observed parental cultural socialization and socialization of coping with discrimination predict lower levels of depression as reported by youth 1 year later. Path analyses also show that observed parental cultural socialization and socialization of coping with discrimination are positively associated with youth ethnic identity. Conclusions These findings point to the importance of integrating familial socialization of culture and coping with discrimination in fostering resilience among American Indian youth. PMID:28503256

Persistence of discrimination: Revisiting Axtell, Epstein and Young

NASA Astrophysics Data System (ADS)

Weisbuch, Gérard

2018-02-01

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

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.

Enhancement of plant metabolite fingerprinting by machine learning.

PubMed

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

2010-08-01

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

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

PubMed Central

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

2013-01-01

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

Prediction of Outcome after Moderate and Severe Traumatic Brain Injury: External Validation of the IMPACT and CRASH Prognostic Models

PubMed Central

Roozenbeek, Bob; Lingsma, Hester F.; Lecky, Fiona E.; Lu, Juan; Weir, James; Butcher, Isabella; McHugh, Gillian S.; Murray, Gordon D.; Perel, Pablo; Maas, Andrew I.R.; Steyerberg, Ewout W.

2012-01-01

Objective The International Mission on Prognosis and Analysis of Clinical Trials (IMPACT) and Corticoid Randomisation After Significant Head injury (CRASH) prognostic models predict outcome after traumatic brain injury (TBI) but have not been compared in large datasets. The objective of this is study is to validate externally and compare the IMPACT and CRASH prognostic models for prediction of outcome after moderate or severe TBI. Design External validation study. Patients We considered 5 new datasets with a total of 9036 patients, comprising three randomized trials and two observational series, containing prospectively collected individual TBI patient data. Measurements Outcomes were mortality and unfavourable outcome, based on the Glasgow Outcome Score (GOS) at six months after injury. To assess performance, we studied the discrimination of the models (by AUCs), and calibration (by comparison of the mean observed to predicted outcomes and calibration slopes). Main Results The highest discrimination was found in the TARN trauma registry (AUCs between 0.83 and 0.87), and the lowest discrimination in the Pharmos trial (AUCs between 0.65 and 0.71). Although differences in predictor effects between development and validation populations were found (calibration slopes varying between 0.58 and 1.53), the differences in discrimination were largely explained by differences in case-mix in the validation studies. Calibration was good, the fraction of observed outcomes generally agreed well with the mean predicted outcome. No meaningful differences were noted in performance between the IMPACT and CRASH models. More complex models discriminated slightly better than simpler variants. Conclusions Since both the IMPACT and the CRASH prognostic models show good generalizability to more recent data, they are valid instruments to quantify prognosis in TBI. PMID:22511138

Racial and non-racial discrimination and smoking status among South African adults 10 years after apartheid.

PubMed

Dutra, Lauren M; Williams, David R; Kawachi, Ichiro; Okechukwu, Cassandra A

2014-11-01

Despite a long history of discrimination and persisting racial disparities in smoking prevalence, little research exists on the relationship between discrimination and smoking in South Africa. This analysis examined chronic (day-to-day) and acute (lifetime) experiences of racial and non-racial (eg, age, gender or physical appearance) discrimination and smoking status among respondents to the South Africa Stress and Health study. Logistic regression models were constructed using SAS-Callable SUDAAN. Both chronic racial discrimination (RR=1.45, 95% CI 1.14 to 1.85) and chronic non-racial discrimination (RR=1.69, 95% CI 1.37 to 2.08) predicted a higher risk of smoking, but neither type of acute discrimination did. Total (sum of racial and non-racial) chronic discrimination (RR=1.46, 95% CI 1.20 to 1.78) and total acute discrimination (RR=1.28, 95% CI 1.01 to 1.60) predicted a higher risk of current smoking. Racial and non-racial discrimination may be related to South African adults' smoking behaviour, but this relationship likely varies by the timing and frequency of these experiences. Future research should use longitudinal data to identify the temporal ordering of the relationships studied, include areas outside of South Africa to increase generalisability and consider the implications of these findings for smoking cessation approaches in South Africa. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

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

PubMed

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

2011-09-26

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

Prediction models for clustered data: comparison of a random intercept and standard regression model

PubMed Central

2013-01-01

Background When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Methods Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. Results The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. Conclusion The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters. PMID:23414436

Prediction models for clustered data: comparison of a random intercept and standard regression model.

PubMed

Bouwmeester, Walter; Twisk, Jos W R; Kappen, Teus H; van Klei, Wilton A; Moons, Karel G M; Vergouwe, Yvonne

2013-02-15

When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters.

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

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

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

PubMed

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

2010-04-01

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

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

PubMed

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

2010-03-10

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

Sex estimation from sternal measurements using multidetector computed tomography.

PubMed

Ekizoglu, Oguzhan; Hocaoglu, Elif; Inci, Ercan; Bilgili, Mustafa Gokhan; Solmaz, Dilek; Erdil, Irem; Can, Ismail Ozgur

2014-12-01

We aimed to show the utility and reliability of sternal morphometric analysis for sex estimation.Sex estimation is a very important step in forensic identification. Skeletal surveys are main methods for sex estimation studies. Morphometric analysis of sternum may provide high accuracy rated data in sex discrimination. In this study, morphometric analysis of sternum was evaluated in 1 mm chest computed tomography scans for sex estimation. Four hundred forty 3 subjects (202 female, 241 male, mean age: 44 ± 8.1 [distribution: 30-60 year old]) were included the study. Manubrium length (ML), mesosternum length (2L), Sternebra 1 (S1W), and Sternebra 3 (S3W) width were measured and also sternal index (SI) was calculated. Differences between genders were evaluated by student t-test. Predictive factors of sex were determined by discrimination analysis and receiver operating characteristic (ROC) analysis. Male sternal measurement values are significantly higher than females (P < 0.001) while SI is significantly low in males (P < 0.001). In discrimination analysis, MSL has high accuracy rate with 80.2% in females and 80.9% in males. MSL also has the best sensitivity (75.9%) and specificity (87.6%) values. Accuracy rates were above 80% in 3 stepwise discrimination analysis for both sexes. Stepwise 1 (ML, MSL, S1W, S3W) has the highest accuracy rate in stepwise discrimination analysis with 86.1% in females and 83.8% in males. Our study showed that morphometric computed tomography analysis of sternum might provide important information for sex estimation.

Reports of insurance-based discrimination in health care and its association with access to care.

PubMed

Han, Xinxin; Call, Kathleen Thiede; Pintor, Jessie Kemmick; Alarcon-Espinoza, Giovann; Simon, Alisha Baines

2015-07-01

We examined reports of insurance-based discrimination and its association with insurance type and access to care in the early years of the Patient Protection and Affordable Care Act. We used data from the 2013 Minnesota Health Access Survey to identify 4123 Minnesota adults aged 18 to 64 years who reported about their experiences of insurance-based discrimination. We modeled the association between discrimination and insurance type and predicted odds of having reduced access to care among those reporting discrimination, controlling for sociodemographic factors. Data were weighted to represent the state's population. Reports of insurance-based discrimination were higher among uninsured (25%) and publicly insured (21%) adults than among privately insured adults (3%), which held in the regression analysis. Those reporting discrimination had higher odds of lacking a usual source of care, lacking confidence in getting care, forgoing care because of cost, and experiencing provider-level barriers than those who did not. Further research and policy interventions are needed to address insurance-based discrimination in health care settings.

Demographic and clinical features related to perceived discrimination in schizophrenia.

PubMed

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

2018-04-01

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

Latino adolescents' academic success: the role of discrimination, academic motivation, and gender.

PubMed

Alfaro, Edna C; Umaña-Taylor, Adriana J; Gonzales-Backen, Melinda A; Bámaca, Mayra Y; Zeiders, Katharine H

2009-08-01

Guided by the academic resilience perspective, the current longitudinal study examined whether academic motivation mediated the relation between Latino adolescents' (N=221) experiences with discrimination and their academic success. The potential moderating role of gender was also examined. Using multiple group analysis in structural equation modeling, findings indicated that perceived discrimination at Wave 2 significantly predicted academic motivation at Waves 2 and 3 for boys but not girls. Additionally, for boys, academic motivation significantly mediated the relation between perceived discrimination and academic success. Findings underscore the importance of considering the long-term implications of discrimination for Latino boys' academic success. Furthermore, findings encourage moving beyond the examination of gender differences in specific academic outcomes (e.g., academic success) and focusing on how the processes leading to academic success vary by gender.

Pitch ranking, electrode discrimination, and physiological spread-of-excitation using Cochlear's dual-electrode mode.

PubMed

Goehring, Jenny L; Neff, Donna L; Baudhuin, Jacquelyn L; Hughes, Michelle L

2014-08-01

This study compared pitch ranking, electrode discrimination, and electrically evoked compound action potential (ECAP) spatial excitation patterns for adjacent physical electrodes (PEs) and the corresponding dual electrodes (DEs) for newer-generation Cochlear devices (Cochlear Ltd., Macquarie, New South Wales, Australia). The first goal was to determine whether pitch ranking and electrode discrimination yield similar outcomes for PEs and DEs. The second goal was to determine if the amount of spatial separation among ECAP excitation patterns (separation index, Σ) between adjacent PEs and the PE-DE pairs can predict performance on the psychophysical tasks. Using non-adaptive procedures, 13 subjects completed pitch ranking and electrode discrimination for adjacent PEs and the corresponding PE-DE pairs (DE versus each flanking PE) from the basal, middle, and apical electrode regions. Analysis of d' scores indicated that pitch-ranking and electrode-discrimination scores were not significantly different, but rather produced similar levels of performance. As expected, accuracy was significantly better for the PE-PE comparison than either PE-DE comparison. Correlations of the psychophysical versus ECAP Σ measures were positive; however, not all test/region correlations were significant across the array. Thus, the ECAP separation index is not sensitive enough to predict performance on behavioral tasks of pitch ranking or electrode discrimination for adjacent PEs or corresponding DEs.

Pitch ranking, electrode discrimination, and physiological spread-of-excitation using Cochlear's dual-electrode mode

PubMed Central

Goehring, Jenny L.; Neff, Donna L.; Baudhuin, Jacquelyn L.; Hughes, Michelle L.

2014-01-01

This study compared pitch ranking, electrode discrimination, and electrically evoked compound action potential (ECAP) spatial excitation patterns for adjacent physical electrodes (PEs) and the corresponding dual electrodes (DEs) for newer-generation Cochlear devices (Cochlear Ltd., Macquarie, New South Wales, Australia). The first goal was to determine whether pitch ranking and electrode discrimination yield similar outcomes for PEs and DEs. The second goal was to determine if the amount of spatial separation among ECAP excitation patterns (separation index, Σ) between adjacent PEs and the PE-DE pairs can predict performance on the psychophysical tasks. Using non-adaptive procedures, 13 subjects completed pitch ranking and electrode discrimination for adjacent PEs and the corresponding PE-DE pairs (DE versus each flanking PE) from the basal, middle, and apical electrode regions. Analysis of d′ scores indicated that pitch-ranking and electrode-discrimination scores were not significantly different, but rather produced similar levels of performance. As expected, accuracy was significantly better for the PE-PE comparison than either PE-DE comparison. Correlations of the psychophysical versus ECAP Σ measures were positive; however, not all test/region correlations were significant across the array. Thus, the ECAP separation index is not sensitive enough to predict performance on behavioral tasks of pitch ranking or electrode discrimination for adjacent PEs or corresponding DEs. PMID:25096106

Performance of International Classification of Diseases-based injury severity measures used to predict in-hospital mortality: A systematic review and meta-analysis.

PubMed

Gagné, Mathieu; Moore, Lynne; Beaudoin, Claudia; Batomen Kuimi, Brice Lionel; Sirois, Marie-Josée

2016-03-01

The International Classification of Diseases (ICD) is the main classification system used for population-based injury surveillance activities but does not contain information on injury severity. ICD-based injury severity measures can be empirically derived or mapped, but no single approach has been formally recommended. This study aimed to compare the performance of ICD-based injury severity measures to predict in-hospital mortality among injury-related admissions. A systematic review and a meta-analysis were conducted. MEDLINE, EMBASE, and Global Health databases were searched from their inception through September 2014. Observational studies that assessed the performance of ICD-based injury severity measures to predict in-hospital mortality and reported discriminative ability using the area under a receiver operating characteristic curve (AUC) were included. Metrics of model performance were extracted. Pooled AUC were estimated under random-effects models. Twenty-two eligible studies reported 72 assessments of discrimination on ICD-based injury severity measures. Reported AUC ranged from 0.681 to 0.958. Of the 72 assessments, 46 showed excellent (0.80 ≤ AUC < 0.90) and 6 outstanding (AUC ≥ 0.90) discriminative ability. Pooled AUC for ICD-based Injury Severity Score (ICISS) based on the product of traditional survival proportions was significantly higher than measures based on ICD mapped to Abbreviated Injury Scale (AIS) scores (0.863 vs. 0.825 for ICDMAP-ISS [p = 0.005] and ICDMAP-NISS [p = 0.016]). Similar results were observed when studies were stratified by the type of data used (trauma registry or hospital discharge) or the provenance of survival proportions (internally or externally derived). However, among studies published after 2003 the Trauma Mortality Prediction Model based on ICD-9 codes (TMPM-9) demonstrated superior discriminative ability than ICISS using the product of traditional survival proportions (0.850 vs. 0.802, p = 0.002). Models generally showed poor calibration. ICISS using the product of traditional survival proportions and TMPM-9 predict mortality more accurately than those mapped to AIS codes and should be preferred for describing injury severity when ICD is used to record injury diagnoses. Systematic review and meta-analysis, level III.

Can unaided non-linguistic measures predict cochlear implant candidacy?

PubMed Central

Shim, Hyun Joon; Won, Jong Ho; Moon, Il Joon; Anderson, Elizabeth S.; Drennan, Ward R.; McIntosh, Nancy E.; Weaver, Edward M.; Rubinstein, Jay T.

2014-01-01

Objective To determine if unaided, non-linguistic psychoacoustic measures can be effective in evaluating cochlear implant (CI) candidacy. Study Design Prospective split-cohort study including predictor development subgroup and independent predictor validation subgroup. Setting Tertiary referral center. Subjects Fifteen subjects (28 ears) with hearing loss were recruited from patients visiting the University of Washington Medical Center for CI evaluation. Methods Spectral-ripple discrimination (using a 13-dB modulation depth) and temporal modulation detection using 10- and 100-Hz modulation frequencies were assessed with stimuli presented through insert earphones. Correlations between performance for psychoacoustic tasks and speech perception tasks were assessed. Receiver operating characteristic (ROC) curve analysis was performed to estimate the optimal psychoacoustic score for CI candidacy evaluation in the development subgroup and then tested in an independent sample. Results Strong correlations were observed between spectral-ripple thresholds and both aided sentence recognition and unaided word recognition. Weaker relationships were found between temporal modulation detection and speech tests. ROC curve analysis demonstrated that the unaided spectral ripple discrimination shows a good sensitivity, specificity, positive predictive value, and negative predictive value compared to the current gold standard, aided sentence recognition. Conclusions Results demonstrated that the unaided spectral-ripple discrimination test could be a promising tool for evaluating CI candidacy. PMID:24901669

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.

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.

Prediction of villages at risk for filariasis transmission in the Nile Delta using remote sensing and geographic information system technologies.

PubMed

Hassan, A N; Beck, L R; Dister, S

1998-04-01

Remote sensing and geographic information system (GIS) technologies were used to discriminate between 130 villages, in the Nile Delta, at high and low risk for filariasis, as defined by microfilarial prevalence. Landsat Thematic Mapper (TM) data were digitally processed to generate a map of landcover as well as spectral indices such as NDVI and moisture index. A Tasseled Cap transformation was also carried out on the TM data which produced three more indices: brightness, greenness and wetness. GIS functions were used to extract information on landcover and spectral indices within one km buffers around the study villages. The relationship between satellite data and prevalence was investigated using discriminant analysis. The analysis indicated that the most important landscape elements associated with prevalence were water and marginal vegetation, while wetness and moisture index were the most important indices. Discriminant functions generated for these variables were able to correctly predict 80% and 74% of high and low prevalence villages, respectively, with an overall accuracy of 77%. The present approach provides a promising tool for regional filariasis surveillance and helps direct control efforts.

Sex assessment using measurements of the first lumbar vertebra.

PubMed

Zheng, Wen Xu; Cheng, Fu Bo; Cheng, Kai Liang; Tian, Yong; Lai, Ying; Zhang, Wen Song; Zheng, Ya Juan; Li, You Qiong

2012-06-10

Sex determination is a vital part of the medico-legal system but can be difficult in cases where the integrity of the body has been compromised. The purpose of this study was to develop a technique for sex assessment from measurements of the first lumber vertebrate. Twenty-nine linear measurements and five ratios were collected from 113 Chinese adult males and 97 Chinese adult females using digital three-dimensional anthropometry methods. By using discriminant analysis, we found that 23 linear measurements and two ratios identified sexual dimorphism (P<0.01), with predictive accuracy ranging from 57.1% to 86.6%. Using a stepwise method of discriminant function analysis, we found three dimensions predicted sex with 88.6% accuracy: (a) upper end-plate width (EPWu), (b) left pedicle height (PHl), and (c) middle end-plate depth (EPDm). This study shows that a single first lumber vertebra can be used for this purpose, and that the discriminant equation will help forensic determination of sex in the Chinese population. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Refining Stimulus Parameters in Assessing Infant Speech Perception Using Visual Reinforcement Infant Speech Discrimination: Sensation Level.

PubMed

Uhler, Kristin M; Baca, Rosalinda; Dudas, Emily; Fredrickson, Tammy

2015-01-01

Speech perception measures have long been considered an integral piece of the audiological assessment battery. Currently, a prelinguistic, standardized measure of speech perception is missing in the clinical assessment battery for infants and young toddlers. Such a measure would allow systematic assessment of speech perception abilities of infants as well as the potential to investigate the impact early identification of hearing loss and early fitting of amplification have on the auditory pathways. To investigate the impact of sensation level (SL) on the ability of infants with normal hearing (NH) to discriminate /a-i/ and /ba-da/ and to determine if performance on the two contrasts are significantly different in predicting the discrimination criterion. The design was based on a survival analysis model for event occurrence and a repeated measures logistic model for binary outcomes. The outcome for survival analysis was the minimum SL for criterion and the outcome for the logistic regression model was the presence/absence of achieving the criterion. Criterion achievement was designated when an infant's proportion correct score was >0.75 on the discrimination performance task. Twenty-two infants with NH sensitivity participated in this study. There were 9 males and 13 females, aged 6-14 mo. Testing took place over two to three sessions. The first session consisted of a hearing test, threshold assessment of the two speech sounds (/a/ and /i/), and if time and attention allowed, visual reinforcement infant speech discrimination (VRISD). The second session consisted of VRISD assessment for the two test contrasts (/a-i/ and /ba-da/). The presentation level started at 50 dBA. If the infant was unable to successfully achieve criterion (>0.75) at 50 dBA, the presentation level was increased to 70 dBA followed by 60 dBA. Data examination included an event analysis, which provided the probability of criterion distribution across SL. The second stage of the analysis was a repeated measures logistic regression where SL and contrast were used to predict the likelihood of speech discrimination criterion. Infants were able to reach criterion for the /a-i/ contrast at statistically lower SLs when compared to /ba-da/. There were six infants who never reached criterion for /ba-da/ and one never reached criterion for /a-i/. The conditional probability of not reaching criterion by 70 dB SL was 0% for /a-i/ and 21% for /ba-da/. The predictive logistic regression model showed that children were more likely to discriminate the /a-i/ even when controlling for SL. Nearly all normal-hearing infants can demonstrate discrimination criterion of a vowel contrast at 60 dB SL, while a level of ≥70 dB SL may be needed to allow all infants to demonstrate discrimination criterion of a difficult consonant contrast. American Academy of Audiology.

Discriminative ability of commonly used indices to predict adverse outcomes after poster lumbar fusion: a comparison of demographics, ASA, the modified Charlson Comorbidity Index, and the modified Frailty Index.

PubMed

Ondeck, Nathaniel T; Bohl, Daniel D; Bovonratwet, Patawut; McLynn, Ryan P; Cui, Jonathan J; Shultz, Blake N; Lukasiewicz, Adam M; Grauer, Jonathan N

2018-01-01

As research tools, the American Society of Anesthesiologists (ASA) physical status classification system, the modified Charlson Comorbidity Index (mCCI), and the modified Frailty Index (mFI) have been associated with complications following spine procedures. However, with respect to clinical use for various adverse outcomes, no known study has compared the predictive performance of these indices specifically following posterior lumbar fusion (PLF). This study aimed to compare the discriminative ability of ASA, mCCI, and mFI, as well as demographic factors including age, body mass index, and gender for perioperative adverse outcomes following PLF. A retrospective review of prospectively collected data was performed. Patients undergoing elective PLF with or without interbody fusion were extracted from the 2011-2014 American College of Surgeons National Surgical Quality Improvement Program (NSQIP). Perioperative adverse outcome variables assessed included the occurrence of minor adverse events, severe adverse events, infectious adverse events, any adverse event, extended length of hospital stay, and discharge to higher-level care. Patient comorbidity indices and characteristics were delineated and assessed for discriminative ability in predicting perioperative adverse outcomes using an area under the curve analysis from the receiver operating characteristics curves. In total, 16,495 patients were identified who met the inclusion criteria. The most predictive comorbidity index was ASA and demographic factor was age. Of these two factors, age had the larger discriminative ability for three out of the six adverse outcomes and ASA was the most predictive for one out of six adverse outcomes. A combination of the most predictive demographic factor and comorbidity index resulted in improvements in discriminative ability over the individual components for five of the six outcome variables. For PLF, easily obtained patient ASA and age have overall similar or better discriminative abilities for perioperative adverse outcomes than numerically tabulated indices that have multiple inputs and are harder to implement in clinical practice. Copyright © 2017 Elsevier Inc. All rights reserved.

[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.

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

ERIC Educational Resources Information Center

Montoya, Isaac D.

2008-01-01

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

A Generalized Process Model of Human Action Selection and Error and its Application to Error Prediction

DTIC Science & Technology

2014-07-01

Macmillan & Creelman , 2005). This is a quite high degree of discriminability and it means that when the decision model predicts a probability of...ROC analysis. Pattern Recognition Letters, 27(8), 861-874. Retrieved from Google Scholar. Macmillan, N. A., & Creelman , C. D. (2005). Detection

Predicting Occupational Persistence: A Comparison of Teachers and Five Other Occupational Groups.

ERIC Educational Resources Information Center

Benton, Cynthia J.

Using data from the National Longitudinal Study of the Class of 1972, a study examined individual and environmental characteristics which predicted occupational turnover in the three-year period after college graduation. Discriminant analysis was used to distinguish determinants of persistence in six occupations. Three were considered to be in the…

Space sickness predictors suggest fluid shift involvement and possible countermeasures

NASA Technical Reports Server (NTRS)

Simanonok, K. E.; Moseley, E. C.; Charles, J. B.

1992-01-01

Preflight data from 64 first time Shuttle crew members were examined retrospectively to predict space sickness severity (NONE, MILD, MODERATE, or SEVERE) by discriminant analysis. From 9 input variables relating to fluid, electrolyte, and cardiovascular status, 8 variables were chosen by discriminant analysis that correctly predicted space sickness severity with 59 pct. success by one method of cross validation on the original sample and 67 pct. by another method. The 8 variables in order of their importance for predicting space sickness severity are sitting systolic blood pressure, serum uric acid, calculated blood volume, serum phosphate, urine osmolality, environmental temperature at the launch site, red cell count, and serum chloride. These results suggest the presence of predisposing physiologic factors to space sickness that implicate a fluid shift etiology. Addition of a 10th input variable, hours spent in the Weightless Environment Training Facility (WETF), improved the prediction of space sickness severity to 66 pct. success by the first method of cross validation on the original sample and to 71 pct. by the second method. The data suggest that WETF training may reduce space sickness severity.

Can urgency classification of the Manchester triage system predict serious bacterial infections in febrile children?

PubMed

Nijman, Ruud G; Zwinkels, Rob L J; van Veen, Mirjam; Steyerberg, Ewout W; van der Lei, Johan; Moll, Henriëtte A; Oostenbrink, Rianne

2011-08-01

To evaluate the discriminative ability of the Manchester triage system (MTS) to identify serious bacterial infections (SBIs) in children with fever in the emergency department (ED) and to study the association between predictors of SBI and discriminators of MTS urgency of care. This prospective observational study included 1255 children with fever (1 month-16 years) attending the ED of the Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands in 2008-9. Triage urgency was determined with the MTS (urgency (U) level 1-5). The relationship between triage urgency and SBI was assessed with multivariable logistic regression, including effects of age, sex and temperature. Discriminative ability was assessed by receiver operating characteristic curve analysis. SBI prevalence was 11% (n=131, 95% CI 9% to 12%). The discriminative value of the MTS for predicting SBI was 0.57 (95% CI 0.52 to 0.62), and the MTS did not contribute to a model including age, sex and temperature. The sensitivity of the MTS (U1-2 vs U3-5) to detect SBI was 0.42 (95% CI 0.33 to 0.51) and specificity was 0.69 (95% CI 0.66 to 0.72). MTS high urgency discriminators include several known predictors of SBI, such as fever, work of breathing, meningism and oxygen saturation, but apply to non-SBI children as well. The MTS has poor discriminative ability to predict the presence of SBIs in children presenting with fever to the paediatric ED. Important predictors of SBI are represented within the MTS, but are used in a different way to classify urgency.

Perceived discrimination predicts increased support for political rights and life satisfaction mediated by ethnic identity: A longitudinal analysis.

PubMed

Stronge, Samantha; Sengupta, Nikhil K; Barlow, Fiona Kate; Osborne, Danny; Houkamau, Carla A; Sibley, Chris G

2016-07-01

The aim of the current research is to test predictions derived from the rejection-identification model and research on collective action using cross-sectional (Study 1) and longitudinal (Study 2) methods. Specifically, an integration of these 2 literatures suggests that recognition of discrimination can have simultaneous positive relationships with well-being and engagement in collective action via the formation of a strong ingroup identity. We test these predictions in 2 studies using data from a large national probability sample of Māori (the indigenous peoples of New Zealand), collected as part of the New Zealand Attitudes and Values Study (Ns for Study 1 and 2 were 1,981 and 1,373, respectively). Consistent with the extant research, Study 1 showed that perceived discrimination was directly linked with decreased life satisfaction, but indirectly linked with increased life satisfaction through higher levels of ethnic identification. Perceived discrimination was also directly linked with increased support for Māori rights and indirectly linked with increased support for Māori rights through higher levels of ethnic identification. Study 2 replicated these findings using longitudinal data and identified multiple bidirectional paths between perceived discrimination, ethnic identity, well-being, and support for collective action. These findings replicate and extend the rejection-identification model in a novel cultural context by demonstrating via cross-sectional (Study 1) and longitudinal (Study 2) analyses that the recognition of discrimination can both motivate support for political rights and increase well-being by strengthening ingroup identity. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Discriminant function analysis as tool for subsurface geologist

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

Chesser, K.

1987-05-01

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

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model.

PubMed

Snell, Kym I E; Hua, Harry; Debray, Thomas P A; Ensor, Joie; Look, Maxime P; Moons, Karel G M; Riley, Richard D

2016-01-01

Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance. Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

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.

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.

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

PubMed

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

2016-09-01

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

Enhancement of Plant Metabolite Fingerprinting by Machine Learning1[W

PubMed Central

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

2010-01-01

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

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.

Pretreatment Nomogram to Predict the Risk of Acute Urinary Retention After I-125 Prostate Brachytherapy

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

Roeloffzen, Ellen M., E-mail: e.m.a.roeloffzen@umcutrecht.nl; Vulpen, Marco van; Battermann, Jan J.

Purpose: Acute urinary retention (AUR) after iodine-125 (I-125) prostate brachytherapy negatively influences long-term quality of life and therefore should be prevented. We aimed to develop a nomogram to preoperatively predict the risk of AUR. Methods: Using the preoperative data of 714 consecutive patients who underwent I-125 prostate brachytherapy between 2005 and 2008 at our department, we modeled the probability of AUR. Multivariate logistic regression analysis was used to assess the predictive ability of a set of pretreatment predictors and the additional value of a new risk factor (the extent of prostate protrusion into the bladder). The performance of the finalmore » model was assessed with calibration and discrimination measures. Results: Of the 714 patients, 57 patients (8.0%) developed AUR after implantation. Multivariate analysis showed that the combination of prostate volume, IPSS score, neoadjuvant hormonal treatment and the extent of prostate protrusion contribute to the prediction of AUR. The discriminative value (receiver operator characteristic area, ROC) of the basic model (including prostate volume, International Prostate Symptom Score, and neoadjuvant hormonal treatment) to predict the development of AUR was 0.70. The addition of prostate protrusion significantly increased the discriminative power of the model (ROC 0.82). Calibration of this final model was good. The nomogram showed that among patients with a low sum score (<18 points), the risk of AUR was only 0%-5%. However, in patients with a high sum score (>35 points), the risk of AUR was more than 20%. Conclusion: This nomogram is a useful tool for physicians to predict the risk of AUR after I-125 prostate brachytherapy. The nomogram can aid in individualized treatment decision-making and patient counseling.« less

Predicting stability and change in loneliness in later life

PubMed Central

Newall, Nancy E. G.; Chipperfield, Judith G.; Bailis, Daniel S.

2016-01-01

This study examined potential discriminators of groups of older adults showing different patterns of stability or change in loneliness over 5 years: those who became lonely, overcame loneliness, were persistently lonely, and were persistently not lonely. Discriminant function analysis results showed that the persistently lonely, compared with the persistently not lonely, were more often living alone, widowed, and experiencing poorer health and perceived control. Moreover, changes in living arrangements and perceived control predicted loneliness change. In conclusion, perceiving that one is able to meet social needs is a predictor of loneliness and loneliness change and appears to be more important than people’s friendships. Because the predictors were better able to predict entry into loneliness, results point to the promise of prevention approaches to loneliness interventions. PMID:27867246

Spectral reflectance of surface soils - A statistical analysis

NASA Technical Reports Server (NTRS)

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

1983-01-01

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

Object Detection in Natural Backgrounds Predicted by Discrimination Performance and Models

NASA Technical Reports Server (NTRS)

Ahumada, A. J., Jr.; Watson, A. B.; Rohaly, A. M.; Null, Cynthia H. (Technical Monitor)

1995-01-01

In object detection, an observer looks for an object class member in a set of backgrounds. In discrimination, an observer tries to distinguish two images. Discrimination models predict the probability that an observer detects a difference between two images. We compare object detection and image discrimination with the same stimuli by: (1) making stimulus pairs of the same background with and without the target object and (2) either giving many consecutive trials with the same background (discrimination) or intermixing the stimuli (object detection). Six images of a vehicle in a natural setting were altered to remove the vehicle and mixed with the original image in various proportions. Detection observers rated the images for vehicle presence. Discrimination observers rated the images for any difference from the background image. Estimated detectabilities of the vehicles were found by maximizing the likelihood of a Thurstone category scaling model. The pattern of estimated detectabilities is similar for discrimination and object detection, and is accurately predicted by a Cortex Transform discrimination model. Predictions of a Contrast- Sensitivity- Function filter model and a Root-Mean-Square difference metric based on the digital image values are less accurate. The discrimination detectabilities averaged about twice those of object detection.

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

Epileptic Seizure Prediction Using Diffusion Distance and Bayesian Linear Discriminate Analysis on Intracranial EEG.

PubMed

Yuan, Shasha; Zhou, Weidong; Chen, Liyan

2018-02-01

Epilepsy is a chronic neurological disorder characterized by sudden and apparently unpredictable seizures. A system capable of forecasting the occurrence of seizures is crucial and could open new therapeutic possibilities for human health. This paper addresses an algorithm for seizure prediction using a novel feature - diffusion distance (DD) in intracranial Electroencephalograph (iEEG) recordings. Wavelet decomposition is conducted on segmented electroencephalograph (EEG) epochs and subband signals at scales 3, 4 and 5 are utilized to extract the diffusion distance. The features of all channels composing a feature vector are then fed into a Bayesian Linear Discriminant Analysis (BLDA) classifier. Finally, postprocessing procedure is applied to reduce false prediction alarms. The prediction method is evaluated on the public intracranial EEG dataset, which consists of 577.67[Formula: see text]h of intracranial EEG recordings from 21 patients with 87 seizures. We achieved a sensitivity of 85.11% for a seizure occurrence period of 30[Formula: see text]min and a sensitivity of 93.62% for a seizure occurrence period of 50[Formula: see text]min, both with the seizure prediction horizon of 10[Formula: see text]s. Our false prediction rate was 0.08/h. The proposed method yields a high sensitivity as well as a low false prediction rate, which demonstrates its potential for real-time prediction of seizures.

Sex Estimation From Sternal Measurements Using Multidetector Computed Tomography

PubMed Central

Ekizoglu, Oguzhan; Hocaoglu, Elif; Inci, Ercan; Bilgili, Mustafa Gokhan; Solmaz, Dilek; Erdil, Irem; Can, Ismail Ozgur

2014-01-01

Abstract We aimed to show the utility and reliability of sternal morphometric analysis for sex estimation. Sex estimation is a very important step in forensic identification. Skeletal surveys are main methods for sex estimation studies. Morphometric analysis of sternum may provide high accuracy rated data in sex discrimination. In this study, morphometric analysis of sternum was evaluated in 1 mm chest computed tomography scans for sex estimation. Four hundred forty 3 subjects (202 female, 241 male, mean age: 44 ± 8.1 [distribution: 30–60 year old]) were included the study. Manubrium length (ML), mesosternum length (2L), Sternebra 1 (S1W), and Sternebra 3 (S3W) width were measured and also sternal index (SI) was calculated. Differences between genders were evaluated by student t-test. Predictive factors of sex were determined by discrimination analysis and receiver operating characteristic (ROC) analysis. Male sternal measurement values are significantly higher than females (P < 0.001) while SI is significantly low in males (P < 0.001). In discrimination analysis, MSL has high accuracy rate with 80.2% in females and 80.9% in males. MSL also has the best sensitivity (75.9%) and specificity (87.6%) values. Accuracy rates were above 80% in 3 stepwise discrimination analysis for both sexes. Stepwise 1 (ML, MSL, S1W, S3W) has the highest accuracy rate in stepwise discrimination analysis with 86.1% in females and 83.8% in males. Our study showed that morphometric computed tomography analysis of sternum might provide important information for sex estimation. PMID:25501090

Evaluation of MELD score and Maddrey discriminant function for mortality prediction in patients with alcoholic hepatitis.

PubMed

Monsanto, Pedro; Almeida, Nuno; Lrias, Clotilde; Pina, Jos Eduardo; Sofia, Carlos

2013-01-01

Maddrey discriminant function (DF) is the traditional model for evaluating the severity and prognosis in alcoholic hepatitis (AH). However, MELD has also been used for this purpose. We aimed to determine the predictive parameters and compare the ability of Maddrey DF and MELD to predict short-term mortality in patients with AH. Retrospective study of 45 patients admitted in our department with AH between 2000 and 2010. Demographic, clinical and laboratory parameters were collected. MELD and Maddrey DF were calculated on admission. Short-term mortality was assessed at 30 and 90 days. Student t-test, χ2 test, univariate analysis, logistic regression and receiver operating characteristic curves were performed. Thirty-day and 90-day mortality was 27% and 42%, respectively. In multivariate analysis, Maddrey DF was the only independent predictor of mortality for these two periods. Receiver operating characteristic curves for Maddrey DF revealed an excellent discriminatory ability to predict 30-day and 90-day mortality for a Maddrey DF greater than 65 and 60, respectively. Discriminatory ability to predict 30-day and 90-day mortality for MELD was low. AH remains associated with a high short-term mortality. Maddrey DF is a more valuable model than MELD to predict short-term mortality in patients with AH.

Large-scale optimization-based classification models in medicine and biology.

PubMed

Lee, Eva K

2007-06-01

We present novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule); and (5) successive multi-stage classification capability to handle data points placed in the reserved-judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multi-group prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy and tumor metastasis; prediction of protein localization sites; and pattern recognition of satellite images in classification of soil types. In all these applications, the predictive model yields correct classification rates ranging from 80 to 100%. This provides motivation for pursuing its use as a medical diagnostic, monitoring and decision-making tool.

The Utility of Discriminant Analysis for Predicting Farmers' Intentions to Participate in Farmer-Managed Irrigation Systems in Iran

NASA Astrophysics Data System (ADS)

Zarafshani, Kiumars; Hossien Alibaygi, Amir; Afshar, Nasrin

Participatory irrigation management has been problematic in most parts of the world and Iran has been no exception. The purpose of this study was to assess farmers' intentions to participate in irrigation management based on selected variables using discriminant analysis. A survey questionnaire was used to collect information from a sample of Water Cooperatives in Javanrood Townships using stratified random sampling (n = 106). Results indicated that age, educational level, attitude towards PIM, irrigation performance, landholding size, agricultural and non-agricultural income affected farmers' intentions to participate in irrigation management.

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

PubMed

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

2017-12-02

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

A modified TNM staging system for non-metastatic colorectal cancer based on nomogram analysis of SEER database.

PubMed

Kong, Xiangxing; Li, Jun; Cai, Yibo; Tian, Yu; Chi, Shengqiang; Tong, Danyang; Hu, Yeting; Yang, Qi; Li, Jingsong; Poston, Graeme; Yuan, Ying; Ding, Kefeng

2018-01-08

To revise the American Joint Committee on Cancer TNM staging system for colorectal cancer (CRC) based on a nomogram analysis of Surveillance, Epidemiology, and End Results (SEER) database, and to prove the rationality of enhancing T stage's weighting in our previously proposed T-plus staging system. Total 115,377 non-metastatic CRC patients from SEER were randomly grouped as training and testing set by ratio 1:1. The Nomo-staging system was established via three nomograms based on 1-year, 2-year and 3-year disease specific survival (DSS) Logistic regression analysis of the training set. The predictive value of Nomo-staging system for the testing set was evaluated by concordance index (c-index), likelihood ratio (L.R.) and Akaike information criteria (AIC) for 1-year, 2-year, 3-year overall survival (OS) and DSS. Kaplan-Meier survival curve was used to valuate discrimination and gradient monotonicity. And an external validation was performed on database from the Second Affiliated Hospital of Zhejiang University (SAHZU). Patients with T1-2 N1 and T1N2a were classified into stage II while T4 N0 patients were classified into stage III in Nomo-staging system. Kaplan-Meier survival curves of OS and DSS in testing set showed Nomo-staging system performed better in discrimination and gradient monotonicity, and the external validation in SAHZU database also showed distinctly better discrimination. The Nomo-staging system showed higher value in L.R. and c-index, and lower value in AIC when predicting OS and DSS in testing set. The Nomo-staging system showed better performance in prognosis prediction and the weight of lymph nodes status in prognosis prediction should be cautiously reconsidered.

Discrimination of healthy and osteoarthritic articular cartilage by Fourier transform infrared imaging and Fisher’s discriminant analysis

PubMed Central

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

2016-01-01

Fourier transform infrared spectroscopic imaging (FTIRI) technique can be used to obtain the quantitative information of content and spatial distribution of principal components in cartilage by combining with chemometrics methods. In this study, FTIRI combining with principal component analysis (PCA) and Fisher’s discriminant analysis (FDA) was applied to identify the healthy and osteoarthritic (OA) articular cartilage samples. Ten 10-μm thick sections of canine cartilages were imaged at 6.25μm/pixel in FTIRI. The infrared spectra extracted from the FTIR images were imported into SPSS software for PCA and FDA. Based on the PCA result of 2 principal components, the healthy and OA cartilage samples were effectively discriminated by the FDA with high accuracy of 94% for the initial samples (training set) and cross validation, as well as 86.67% for the prediction group. The study showed that cartilage degeneration became gradually weak with the increase of the depth. FTIRI combined with chemometrics may become an effective method for distinguishing healthy and OA cartilages in future. PMID:26977354

Image Discrimination Predictions of a Single Channel Model with Contrast Gain Control

NASA Technical Reports Server (NTRS)

Ahumada, Albert J., Jr.; Null, Cynthia H.

1995-01-01

Image discrimination models predict the number of just-noticeable-differences between two images. We report the predictions of a single channel model with contrast masking for a range of standard discrimination experiments. Despite its computational simplicity, this model has performed as well as a multiple channel model in an object detection task.

Does Perceived Racial Discrimination Predict Changes in Psychological Distress and Substance Use over Time? An Examination among Black Emerging Adults

ERIC Educational Resources Information Center

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

2014-01-01

We assessed whether perceived discrimination predicted changes in psychological distress and substance use over time and whether psychological distress and substance use predicted change in perceived discrimination over time. We also assessed whether associations between these constructs varied by gender. Our sample included 607 Black emerging…

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

PubMed

Guo, Jing; Yue, Tianli; Yuan, Yahong

2012-10-01

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

Phenotype–genotype correlation in Hirschsprung disease is illuminated by comparative analysis of the RET protein sequence

PubMed Central

Kashuk, Carl S.; Stone, Eric A.; Grice, Elizabeth A.; Portnoy, Matthew E.; Green, Eric D.; Sidow, Arend; Chakravarti, Aravinda; McCallion, Andrew S.

2005-01-01

The ability to discriminate between deleterious and neutral amino acid substitutions in the genes of patients remains a significant challenge in human genetics. The increasing availability of genomic sequence data from multiple vertebrate species allows inclusion of sequence conservation and physicochemical properties of residues to be used for functional prediction. In this study, the RET receptor tyrosine kinase serves as a model disease gene in which a broad spectrum (≥116) of disease-associated mutations has been identified among patients with Hirschsprung disease and multiple endocrine neoplasia type 2. We report the alignment of the human RET protein sequence with the orthologous sequences of 12 non-human vertebrates (eight mammalian, one avian, and three teleost species), their comparative analysis, the evolutionary topology of the RET protein, and predicted tolerance for all published missense mutations. We show that, although evolutionary conservation alone provides significant information to predict the effect of a RET mutation, a model that combines comparative sequence data with analysis of physiochemical properties in a quantitative framework provides far greater accuracy. Although the ability to discern the impact of a mutation is imperfect, our analyses permit substantial discrimination between predicted functional classes of RET mutations and disease severity even for a multigenic disease such as Hirschsprung disease. PMID:15956201

Reports of Insurance-Based Discrimination in Health Care and Its Association With Access to Care

PubMed Central

Call, Kathleen Thiede; Pintor, Jessie Kemmick; Alarcon-Espinoza, Giovann; Simon, Alisha Baines

2015-01-01

Objectives. We examined reports of insurance-based discrimination and its association with insurance type and access to care in the early years of the Patient Protection and Affordable Care Act. Methods. We used data from the 2013 Minnesota Health Access Survey to identify 4123 Minnesota adults aged 18 to 64 years who reported about their experiences of insurance-based discrimination. We modeled the association between discrimination and insurance type and predicted odds of having reduced access to care among those reporting discrimination, controlling for sociodemographic factors. Data were weighted to represent the state’s population. Results. Reports of insurance-based discrimination were higher among uninsured (25%) and publicly insured (21%) adults than among privately insured adults (3%), which held in the regression analysis. Those reporting discrimination had higher odds of lacking a usual source of care, lacking confidence in getting care, forgoing care because of cost, and experiencing provider-level barriers than those who did not. Conclusions. Further research and policy interventions are needed to address insurance-based discrimination in health care settings. PMID:25905821

Racial Discrimination and Low Household Education Predict Higher Body Mass Index in African American Youth.

PubMed

Nelson, Devin S; Gerras, Julia M; McGlumphy, Kellye C; Shaver, Erika R; Gill, Amaanat K; Kanneganti, Kamala; Ajibewa, Tiwaloluwa A; Hasson, Rebecca E

The purpose of this study was to examine the relationships between environmental factors, including household education, community violence exposure, racial discrimination, and cultural identity, and BMI in African American adolescents. A community-based sample of 198 African American youth (120 girls, 78 boys; ages 11-19 years) from Washtenaw County, Michigan, were included in this analysis. Violence exposure was assessed by using the Survey of Children's Exposure to Community Violence; racial discrimination by using the Adolescent Discrimination Distress Index; cultural identity by using the Acculturation, Habits, and Interests Multicultural Scale for Adolescents; and household education by using a seven-category variable. Measured height and body weight were used to calculate BMI. Racial discrimination was positively associated with BMI, whereas household education was inversely associated with BMI in African American adolescents (discrimination: β = 0.11 ± 0.04, p = 0.01; education: β = -1.13 ± 0.47, p = 0.02). These relationships were significant when accounting for the confounding effects of stress, activity, diet, and pubertal development. Significant gender interactions were observed with racial discrimination and low household education associated with BMI in girls only (discrimination: β = 0.16 ± 0.05, p = 0.003; education: β = -1.12 ± 0.55, p = 0.045). There were no significant relationships between culture, community violence exposure, and BMI (all p's > 0.05). Environmental factors, including racial discrimination and low household education, predicted higher BMI in African American adolescents, particularly among girls. Longitudinal studies are needed to better understand the mechanisms by which these environmental factors increase obesity risk in African American youth.

NIR spectroscopy as a tool for discriminating between lichens exposed to air pollution.

PubMed

Casale, Monica; Bagnasco, Lucia; Giordani, Paolo; Mariotti, Mauro Giorgio; Malaspina, Paola

2015-09-01

Lichens are used as biomonitors of air pollution because they are extremely sensitive to the presence of substances that alter atmospheric composition. Fifty-one thalli of two different varieties of Pseudevernia furfuracea (var. furfuracea and var. ceratea) were collected far from local sources of air pollution. Twenty-six of these thalli were then exposed to the air for one month in the industrial port of Genoa, which has high levels of environmental pollution. The possibility of using Near-infrared spectroscopy (NIRS) for generating a 'fingerprint' of lichens was investigated. Chemometric methods were successfully applied to discriminate between samples from polluted and non-polluted areas. In particular, Principal Component Analysis (PCA) was applied as a multivariate display method on the NIR spectra to visualise the data structure. This showed that the difference between samples of different varieties was not significant in comparison to the difference between samples exposed to different levels of environmental pollution. Then Linear Discriminant Analysis (LDA) was carried out to discriminate between lichens based on their exposure to pollutants. The distinction between control samples (not exposed) and samples exposed to the air in the industrial port of Genoa was evaluated. On average, 95.2% of samples were correctly classified, 93.0% of total internal prediction (5 cross-validation groups) and 100.0% of external prediction (on the test set) was achieved. Copyright © 2015 Elsevier 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.

Obesity discrimination: the role of physical appearance, personal ideology, and anti-fat prejudice.

PubMed

O'Brien, K S; Latner, J D; Ebneter, D; Hunter, J A

2013-03-01

Self-report measures of anti-fat prejudice are regularly used by the field, however, there is no research showing a relationship between explicit measures of anti-fat prejudice and the behavioral manifestation of them; obesity discrimination. The present study examined whether a recently developed measure of anti-fat prejudice, the universal measure of bias (UMB), along with other correlates of prejudicial attitudes and beliefs (that is, authoritarianism, social dominance orientation; SDO, physical appearance investment) predict obesity discrimination. Under the guise of a personnel selection task, participants (n=102) gave assessments of obese and non-obese females applying for a managerial position across a number of selection criteria (for example, starting salary, likelihood of selecting). Participants viewed resumes that had attached either a photo of a pre-bariatric surgery obese female (body mass index (BMI)=38-41) or a photo of the same female post-bariatric surgery (BMI=22-24). Participants also completed measures of anti-fat prejudice (UMB) authoritarianism, SDO, physical appearance evaluation and orientation. Obesity discrimination was displayed across all selection criteria. Higher UMB subscale scores (distance and negative judgement), authoritarianism, physical appearance evaluation and orientation were associated with greater obesity discrimination. In regression models, UMB 'distance' was a predictor of obesity discrimination for perceived leadership potential, starting salary, and overall employability. UMB 'negative judgement' predicted discrimination for starting salary; and authoritarianism predicted likelihood of selecting an obese applicant and candidate ranking. Finally, physical appearance evaluation and appearance orientation predicted obesity discrimination for predicted career success and leadership potential, respectively. Self-report measures of prejudice act as surrogates for discrimination, but there has been no empirical support for the validity of explicit measures of anti-fat prejudice. Here, the UMB, authoritarianism, and physical appearance investment predicted obesity discrimination. The present results provide support for the use of these measures by researchers seeking to assess, understand, and reduce anti-fat prejudice and discrimination.

Intergenerational Consequences: Women's Experiences of Discrimination in Pregnancy Predict Infant Social-Emotional Development at 6 Months and 1 Year.

PubMed

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

2018-04-01

Racial/ethnic and socioeconomic disparities in infant development in the United States have lifelong consequences. Discrimination predicts poorer health and academic outcomes. This study explored for the first time intergenerational consequences of women's experiences of discrimination reported during pregnancy for their infants' social-emotional development in the first year of life. Data come from a longitudinal study with predominantly Black and Latina, socioeconomically disadvantaged, urban young women (N = 704, Mage = 18.53) across pregnancy through 1 year postpartum. Women were recruited from community hospitals and health centers in a Northeastern US city. Linear regression analyses examined whether women's experiences of everyday discrimination reported during pregnancy predicted social-emotional development outcomes among their infants at 6 months and 1 year of age, controlling for potentially confounding medical and sociodemographic factors. Path analyses tested if pregnancy distress, anxiety, or depressive symptoms mediated significant associations. Everyday discrimination reported during pregnancy prospectively predicted greater inhibition/separation problems and greater negative emotionality, but did not predict attention skills or positive emotionality, at 6 months and 1 year. Depressive symptoms mediated the association of discrimination with negative emotionality at 6 months, and pregnancy distress, anxiety, and depressive symptoms mediated the association of discrimination with negative emotionality at 1 year. Findings support that there are intergenerational consequences of discrimination, extending past findings to infant social-emotional development outcomes in the first year of life. It may be important to address discrimination before and during pregnancy and enhance support to mothers and infants exposed to discrimination to promote health equity across the life span.

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.

Early postoperative repair status after rotator cuff repair cannot be accurately classified using questionnaires of patient function and isokinetic strength evaluation.

PubMed

Colliver, Jessica; Wang, Allan; Joss, Brendan; Ebert, Jay; Koh, Eamon; Breidahl, William; Ackland, Timothy

2016-04-01

This study investigated if patients with an intact tendon repair or partial-thickness retear early after rotator cuff repair display differences in clinical evaluations and whether early tendon healing can be predicted using these assessments. We prospectively evaluated 60 patients at 16 weeks after arthroscopic supraspinatus repair. Evaluation included the Oxford Shoulder Score, 11-item version of the Disabilities of the Arm, Shoulder and Hand, visual analog scale for pain, 12-item Short Form Health Survey, isokinetic strength, and magnetic resonance imaging (MRI). Independent t tests investigated clinical differences in patients based on the Sugaya MRI rotator cuff classification system (grades 1, 2, or 3). Discriminant analysis determined whether intact repairs (Sugaya grade 1) and partial-thickness retears (Sugaya grades 2 and 3) could be predicted. No differences (P < .05) existed in the clinical or strength measures. Although discriminant analysis revealed the 11-item version of the Disabilities of the Arm, Shoulder and Hand produced a 97% true-positive rate for predicting partial thickness retears, it also produced a 90% false-positive rate whereby it incorrectly predicted a retear in 90% of patients whose repair was intact. The ability to discriminate between groups was enhanced with up to 5 variables entered; however, only 87% of the partial-retear group and 36% of the intact-repair group were correctly classified. No differences in clinical scores existed between patients stratified by the Sugaya MRI classification system at 16 weeks. An intact repair or partial-thickness retear could not be accurately predicted. Our results suggest that correct classification of healing in the early postoperative stages should involve imaging. Copyright © 2016 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

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

PubMed

Andrés, J M; Bona, M T

2006-11-15

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

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

DOE PAGES

Wang, Pin; Wang, Yunshan; Hang, Bo; ...

2016-07-11

Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A networkmore » was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.« less

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

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

Wang, Pin; Wang, Yunshan; Hang, Bo

Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A networkmore » was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.« less

Origin Discrimination of Osmanthus fragrans var. thunbergii Flowers using GC-MS and UPLC-PDA Combined with Multivariable Analysis Methods.

PubMed

Zhou, Fei; Zhao, Yajing; Peng, Jiyu; Jiang, Yirong; Li, Maiquan; Jiang, Yuan; Lu, Baiyi

2017-07-01

Osmanthus fragrans flowers are used as folk medicine and additives for teas, beverages and foods. The metabolites of O. fragrans flowers from different geographical origins were inconsistent in some extent. Chromatography and mass spectrometry combined with multivariable analysis methods provides an approach for discriminating the origin of O. fragrans flowers. To discriminate the Osmanthus fragrans var. thunbergii flowers from different origins with the identified metabolites. GC-MS and UPLC-PDA were conducted to analyse the metabolites in O. fragrans var. thunbergii flowers (in total 150 samples). Principal component analysis (PCA), soft independent modelling of class analogy analysis (SIMCA) and random forest (RF) analysis were applied to group the GC-MS and UPLC-PDA data. GC-MS identified 32 compounds common to all samples while UPLC-PDA/QTOF-MS identified 16 common compounds. PCA of the UPLC-PDA data generated a better clustering than PCA of the GC-MS data. Ten metabolites (six from GC-MS and four from UPLC-PDA) were selected as effective compounds for discrimination by PCA loadings. SIMCA and RF analysis were used to build classification models, and the RF model, based on the four effective compounds (caffeic acid derivative, acteoside, ligustroside and compound 15), yielded better results with the classification rate of 100% in the calibration set and 97.8% in the prediction set. GC-MS and UPLC-PDA combined with multivariable analysis methods can discriminate the origin of Osmanthus fragrans var. thunbergii flowers. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Cognitive Strategies and Physical Activity in Older Adults: A Discriminant Analysis

PubMed Central

Ferrand, Claude; Audiffren, Michel

2018-01-01

Background Although a number of studies have examined sociodemographic, psychosocial, and environmental determinants of the level of physical activity (PA) for older people, little attention has been paid to the predictive power of cognitive strategies for independently living older adults. However, cognitive strategies have recently been considered to be critical in the management of day-to-day living. Methods Data were collected from 243 men and women aged 55 years and older living in France using face-to-face interviews between 2011 and 2013. Results A stepwise discriminant analysis selected five predictor variables (age, perceived health status, barriers' self-efficacy, internal memory, and attentional control strategies) of the level of PA. The function showed that the rate of correct prediction was 73% for the level of PA. The calculated discriminant function based on the five predictor variables is useful for detecting individuals at high risk of lapses once engaged in regular PA. Conclusions This study highlighted the need to consider cognitive functions as a determinant of the level of PA and, more specifically, those cognitive functions related to executive functions (internal memory and attentional control), to facilitate the maintenance of regular PA. These results are discussed in relation to successful aging. PMID:29850247

Cognitive Strategies and Physical Activity in Older Adults: A Discriminant Analysis.

PubMed

André, Nathalie; Ferrand, Claude; Albinet, Cédric; Audiffren, Michel

2018-01-01

Although a number of studies have examined sociodemographic, psychosocial, and environmental determinants of the level of physical activity (PA) for older people, little attention has been paid to the predictive power of cognitive strategies for independently living older adults. However, cognitive strategies have recently been considered to be critical in the management of day-to-day living. Data were collected from 243 men and women aged 55 years and older living in France using face-to-face interviews between 2011 and 2013. A stepwise discriminant analysis selected five predictor variables (age, perceived health status, barriers' self-efficacy, internal memory, and attentional control strategies) of the level of PA. The function showed that the rate of correct prediction was 73% for the level of PA. The calculated discriminant function based on the five predictor variables is useful for detecting individuals at high risk of lapses once engaged in regular PA. This study highlighted the need to consider cognitive functions as a determinant of the level of PA and, more specifically, those cognitive functions related to executive functions (internal memory and attentional control), to facilitate the maintenance of regular PA. These results are discussed in relation to successful aging.

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

PubMed

Garcia, E; Klaas, I; Amigo, J M; Bro, R; Enevoldsen, C

2014-12-01

Lameness causes decreased animal welfare and leads to higher production costs. This study explored data from an automatic milking system (AMS) to model on-farm gait scoring from a commercial farm. A total of 88 cows were gait scored once per week, for 2 5-wk periods. Eighty variables retrieved from AMS were summarized week-wise and used to predict 2 defined classes: nonlame and clinically lame cows. Variables were represented with 2 transformations of the week summarized variables, using 2-wk data blocks before gait scoring, totaling 320 variables (2 × 2 × 80). The reference gait scoring error was estimated in the first week of the study and was, on average, 15%. Two partial least squares discriminant analysis models were fitted to parity 1 and parity 2 groups, respectively, to assign the lameness class according to the predicted probability of being lame (score 3 or 4/4) or not lame (score 1/4). Both models achieved sensitivity and specificity values around 80%, both in calibration and cross-validation. At the optimum values in the receiver operating characteristic curve, the false-positive rate was 28% in the parity 1 model, whereas in the parity 2 model it was about half (16%), which makes it more suitable for practical application; the model error rates were, 23 and 19%, respectively. Based on data registered automatically from one AMS farm, we were able to discriminate nonlame and lame cows, where partial least squares discriminant analysis achieved similar performance to the reference method. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Predictive models reduce talent development costs in female gymnastics.

PubMed

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

2017-04-01

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

Predictive factors of pathologic complete response of HER2-positive breast cancer after preoperative chemotherapy with trastuzumab: development of a specific predictor and study of its utilities using decision curve analysis.

PubMed

Jankowski, Clémentine; Guiu, S; Cortet, M; Charon-Barra, C; Desmoulins, I; Lorgis, V; Arnould, L; Fumoleau, P; Coudert, B; Rouzier, R; Coutant, C; Reyal, F

2017-01-01

The aim of this study was to assess the Institut Gustave Roussy/M.D. Anderson Cancer Center (IGR/MDACC) nomogram in predicting pathologic complete response (pCR) to preoperative chemotherapy in a cohort of human epidermal growth factor receptor 2 (HER2)-positive tumors treated with preoperative chemotherapy with trastuzumab. We then combine clinical and pathological variables associated with pCR into a new nomogram specific to HER2-positive tumors treated by preoperative chemotherapy with trastuzumab. Data from 270 patients with HER2-positive tumors treated with preoperative chemotherapy with trastuzumab at the Institut Curie and at the Georges François Leclerc Cancer Center were used to assess the IGR/MDACC nomogram and to subsequently develop a new nomogram for pCR based on multivariate logistic regression. Model performance was quantified in terms of calibration and discrimination. We studied the utility of the new nomogram using decision curve analysis. The IGR/MDACC nomogram was not accurate for the prediction of pCR in HER2-positive tumors treated by preoperative chemotherapy with trastuzumab, with poor discrimination (AUC = 0.54, 95% CI 0.51-0.58) and poor calibration (p = 0.01). After uni- and multivariate analysis, a new pCR nomogram was built based on T stage (TNM), hormone receptor status, and Ki67 (%). The model had good discrimination with an area under the curve (AUC) at 0.74 (95% CI 0.70-0.79) and adequate calibration (p = 0.93). By decision curve analysis, the model was shown to be relevant between thresholds of 0.3 and 0.7. To the best of our knowledge, ours is the first nomogram to predict pCR in HER2-positive tumors treated by preoperative chemotherapy with trastuzumab. To ensure generalizability, this model needs to be externally validated.

Quantitative and Discriminative Evaluation of Contents of Phenolic and Flavonoid and Antioxidant Competence for Chinese Honeys from Different Botanical Origins.

PubMed

Shen, Shi; Wang, Jingbo; Zhuo, Qin; Chen, Xi; Liu, Tingting; Zhang, Shuang-Qing

2018-05-08

Phenolics and flavonoids in honey are considered as the main phytonutrients which not only act as natural antioxidants, but can also be used as floral markers for honey identification. In this study, the chemical profiles of phenolics and flavonoids, antioxidant competences including total phenolic content, DPPH and ABTS assays and discrimination using chemometric analysis of various Chinese monofloral honeys from six botanical origins (acacia, Vitex , linden, rapeseed, Astragalus and Codonopsis ) were examined. A reproducible and sensitive ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was optimized and validated for the simultaneous determination of 38 phenolics, flavonoids and abscisic acid in honey. Formononetin, ononin, calycosin and calycosin-7- O -β-d-glucoside were identified and quantified in honeys for the first time. Principal component analysis (PCA) showed obvious differences among the honey samples in three-dimensional space accounting for 72.63% of the total variance. Hierarchical cluster analysis (HCA) also revealed that the botanical origins of honey samples correlated with their phenolic and flavonoid contents. Partial least squares-discriminant analysis (PLS-DA) classification was performed to derive a model with high prediction ability. Orthogonal partial least squares-discriminant analysis (OPLS-DA) model was employed to identify markers specific to a particular honey type. The results indicated that Chinese honeys contained various and discriminative phenolics and flavonoids, as well as antioxidant competence from different botanical origins, which was an alternative approach to honey identification and nutritional evaluation.

Longitudinal Discriminant Analysis of Hemoglobin Level for Predicting Preeclampsia

PubMed Central

Nasiri, Malihe; Faghihzadeh, Soghrat; Alavi Majd, Hamid; Zayeri, Farid; Kariman, Noorosadat; Safavi Ardebili, Nastaran

2015-01-01

Background: Preeclampsia is one of the most serious complications during pregnancy with important effects on health of mother and fetus that causes maternal and fetal morbidity and mortality. This study was performed to evaluate whether high levels of hemoglobin may increase the risk of preeclampsia. Objectives: The present study aimed to predict preeclampsia by the hemoglobin profiles through longitudinal discriminant analysis and comparing the error rate of discrimination in longitudinal and cross sectional data. Patients and Methods: In a prospective cohort study from October 2010 to July 2011, 650 pregnant women referred to the prenatal clinic of Milad Hospital in Tehran were evaluated in 3 stages. The hemoglobin level of each woman was measured in the first, second, and third trimester of pregnancy by an expert technician. The subjects were followed up to delivery and preeclampsia was the main outcome under study. The covariance pattern and linear-mixed effects models are common methods that were applied for discriminant analysis of longitudinal data. Also Student t, Mann-Whitney U, and chi-square tests were used for comparing the demographic and clinical characteristics between two groups. Statistical analyses were performed using the SAS software version 9.1. Results: The prevalence rate of preeclampsia was 7.2% (47 women). The women with preeclampsia had a higher mean of hemoglobin values and the difference was 0.46 g/dL (P = 0.003). Also the mean of hemoglobin in the first trimester was higher than that of the second trimester, and was lower than that of the third trimester and the differences were significant (P = 0.015 and P < 0.001, respectively). The sensitivity for longitudinal data and cross-sectional data in three trimesters was 90%, 67%, 72%, and 54% and the specificity was 88%, 55%, 63%, and 50%, respectively. Conclusions: The levels of hemoglobin can be used to predict preeclampsia and monitoring the pregnant women and its regular measure in 3 trimesters help us to identify women at risk for preeclampsia. PMID:26019901

Predicting Community College and University Student Success: A Test of the Triadic Reciprocal Model for Two Populations

ERIC Educational Resources Information Center

Heller, Monica L.; Cassady, Jerrell C.

2017-01-01

The current study explored the differential influences that behavioral learning strategies (i.e., cognitive-metacognitive, resource management), motivational profiles, and academic anxiety appraisals have on college-level learners in two unique learning contexts. Using multivariate analysis of variance and discriminant analysis, the study first…

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.

Evaluation of Several Two-Step Scoring Functions Based on Linear Interaction Energy, Effective Ligand Size, and Empirical Pair Potentials for Prediction of Protein-Ligand Binding Geometry and Free Energy

PubMed Central

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

2011-01-01

The performance of several two-step scoring approaches for molecular docking were assessed for their ability to predict binding geometries and free energies. Two new scoring functions designed for “step 2 discrimination” were proposed and compared to our CHARMM implementation of the linear interaction energy (LIE) approach using the Generalized-Born with Molecular Volume (GBMV) implicit solvation model. A scoring function S1 was proposed by considering only “interacting” ligand atoms as the “effective size” of the ligand, and extended to an empirical regression-based pair potential S2. The S1 and S2 scoring schemes were trained and five-fold cross validated on a diverse set of 259 protein-ligand complexes from the Ligand Protein Database (LPDB). The regression-based parameters for S1 and S2 also demonstrated reasonable transferability in the CSARdock 2010 benchmark using a new dataset (NRC HiQ) of diverse protein-ligand complexes. The ability of the scoring functions to accurately predict ligand geometry was evaluated by calculating the discriminative power (DP) of the scoring functions to identify native poses. The parameters for the LIE scoring function with the optimal discriminative power (DP) for geometry (step 1 discrimination) were found to be very similar to the best-fit parameters for binding free energy over a large number of protein-ligand complexes (step 2 discrimination). Reasonable performance of the scoring functions in enrichment of active compounds in four different protein target classes established that the parameters for S1 and S2 provided reasonable accuracy and transferability. Additional analysis was performed to definitively separate scoring function performance from molecular weight effects. This analysis included the prediction of ligand binding efficiencies for a subset of the CSARdock NRC HiQ dataset where the number of ligand heavy atoms ranged from 17 to 35. This range of ligand heavy atoms is where improved accuracy of predicted ligand efficiencies is most relevant to real-world drug design efforts. PMID:21644546

Perceptual discrimination difficulty and familiarity in the Uncanny Valley: more like a "Happy Valley".

PubMed

Cheetham, Marcus; Suter, Pascal; Jancke, Lutz

2014-01-01

The Uncanny Valley Hypothesis (UVH) predicts that greater difficulty perceptually discriminating between categorically ambiguous human and humanlike characters (e.g., highly realistic robot) evokes negatively valenced (i.e., uncanny) affect. An ABX perceptual discrimination task and signal detection analysis was used to examine the profile of perceptual discrimination (PD) difficulty along the UVH' dimension of human likeness (DHL). This was represented using avatar-to-human morph continua. Rejecting the implicitly assumed profile of PD difficulty underlying the UVH' prediction, Experiment 1 showed that PD difficulty was reduced for categorically ambiguous faces but, notably, enhanced for human faces. Rejecting the UVH' predicted relationship between PD difficulty and negative affect (assessed in terms of the UVH' familiarity dimension), Experiment 2 demonstrated that greater PD difficulty correlates with more positively valenced affect. Critically, this effect was strongest for the ambiguous faces, suggesting a correlative relationship between PD difficulty and feelings of familiarity more consistent with the metaphor happy valley. This relationship is also consistent with a fluency amplification instead of the hitherto proposed hedonic fluency account of affect along the DHL. Experiment 3 found no evidence that the asymmetry in the profile of PD along the DHL is attributable to a differential processing bias (cf. other-race effect), i.e., processing avatars at a category level but human faces at an individual level. In conclusion, the present data for static faces show clear effects that, however, strongly challenge the UVH' implicitly assumed profile of PD difficulty along the DHL and the predicted relationship between this and feelings of familiarity.

Perceptual discrimination difficulty and familiarity in the Uncanny Valley: more like a “Happy Valley”

PubMed Central

Cheetham, Marcus; Suter, Pascal; Jancke, Lutz

2014-01-01

The Uncanny Valley Hypothesis (UVH) predicts that greater difficulty perceptually discriminating between categorically ambiguous human and humanlike characters (e.g., highly realistic robot) evokes negatively valenced (i.e., uncanny) affect. An ABX perceptual discrimination task and signal detection analysis was used to examine the profile of perceptual discrimination (PD) difficulty along the UVH' dimension of human likeness (DHL). This was represented using avatar-to-human morph continua. Rejecting the implicitly assumed profile of PD difficulty underlying the UVH' prediction, Experiment 1 showed that PD difficulty was reduced for categorically ambiguous faces but, notably, enhanced for human faces. Rejecting the UVH' predicted relationship between PD difficulty and negative affect (assessed in terms of the UVH' familiarity dimension), Experiment 2 demonstrated that greater PD difficulty correlates with more positively valenced affect. Critically, this effect was strongest for the ambiguous faces, suggesting a correlative relationship between PD difficulty and feelings of familiarity more consistent with the metaphor happy valley. This relationship is also consistent with a fluency amplification instead of the hitherto proposed hedonic fluency account of affect along the DHL. Experiment 3 found no evidence that the asymmetry in the profile of PD along the DHL is attributable to a differential processing bias (cf. other-race effect), i.e., processing avatars at a category level but human faces at an individual level. In conclusion, the present data for static faces show clear effects that, however, strongly challenge the UVH' implicitly assumed profile of PD difficulty along the DHL and the predicted relationship between this and feelings of familiarity. PMID:25477829

Effects of onboard insecticide use on airline flight attendants.

PubMed

Kilburn, Kaye H

2004-06-01

Flight attendants (FAs) exposed to insecticide spray in an aircraft were compared with unexposed subjects for neurobehavioral function, pulmonary function, mood states, and symptoms. The 33 symptomatic FAs were self-selected, and 5 had retired for disability. Testing procedures included balance, reaction time, color discrimination, visual fields, grip strength, verbal recall, problem solving, attention and discrimination functions, and long-term memory functions. Measurements were expressed as a percentage of their predicted values (derived from unexposed controls), and the author compared the means of the percentage predicted values by analysis of variance. Symptom frequencies and Profile of Mood States (POMS) scores were assessed. FAs were significantly more impaired than controls with respect to balance with eyes closed, grip strength, and color discrimination. Nearly half had 3 or more abnormal neurobehavioral functions, after adjustment was made for age, sex, and education level. Neither elevated POMS scores nor frequencies of average symptoms correlated with their numbers of abnormal measurements. Occupational exposure to synthetic pyrethrin insecticides on airliners was associated with neurobehavioral impairment and disability retirement.

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

Factors of Predicted Learning Disorders and their Interaction with Attentional and Perceptual Training Procedures.

ERIC Educational Resources Information Center

Friar, John T.

Two factors of predicted learning disorders were investigated: (1) inability to maintain appropriate classroom behavior (BEH), (2) perceptual discrimination deficit (PERC). Three groups of first-graders (BEH, PERC, normal control) were administered measures of impulse control, distractability, auditory discrimination, and visual discrimination.…

Analysis of Moisture Content in Beetroot using Fourier Transform Infrared Spectroscopy and by Principal Component Analysis.

PubMed

Nesakumar, Noel; Baskar, Chanthini; Kesavan, Srinivasan; Rayappan, John Bosco Balaguru; Alwarappan, Subbiah

2018-05-22

The moisture content of beetroot varies during long-term cold storage. In this work, we propose a strategy to identify the moisture content and age of beetroot using principal component analysis coupled Fourier transform infrared spectroscopy (FTIR). Frequent FTIR measurements were recorded directly from the beetroot sample surface over a period of 34 days for analysing its moisture content employing attenuated total reflectance in the spectral ranges of 2614-4000 and 1465-1853 cm -1 with a spectral resolution of 8 cm -1 . In order to estimate the transmittance peak height (T p ) and area under the transmittance curve [Formula: see text] over the spectral ranges of 2614-4000 and 1465-1853 cm -1 , Gaussian curve fitting algorithm was performed on FTIR data. Principal component and nonlinear regression analyses were utilized for FTIR data analysis. Score plot over the ranges of 2614-4000 and 1465-1853 cm -1 allowed beetroot quality discrimination. Beetroot quality predictive models were developed by employing biphasic dose response function. Validation experiment results confirmed that the accuracy of the beetroot quality predictive model reached 97.5%. This research work proves that FTIR spectroscopy in combination with principal component analysis and beetroot quality predictive models could serve as an effective tool for discriminating moisture content in fresh, half and completely spoiled stages of beetroot samples and for providing status alerts.

Electronic tongue-based discrimination of Korean rice wines (makgeolli) including prediction of sensory evaluation and instrumental measurements.

PubMed

Kang, Bo-Sik; Lee, Jang-Eun; Park, Hyun-Jin

2014-05-15

A commercial electronic tongue was used to discriminate Korean rice wines (makgeolli) brewed from nine cultivars of rice with different amino acid and fatty acid compositions. The E-tongue was applied to establish prediction models with sensory evaluation or LC-MS/MS by partial least squares regression (PLSR). All makgeollis were classified into three groups by principal components analysis, and the separation pattern was affected by rice qualities and yeast fermentation. Makgeolli taste changed from the complicated comprising sweetness, saltiness, and umami to the uncomplicated, such as bitterness and then, sourness, with a decrease of amino acids and fatty acids in the rice. The quantitative correlation between E-tongue and sensory scores or LC-MS/MS by PLSR demonstrated that E-tongue could well predict most of the sensory attributes with relatively acceptable r(2), except for bitterness, but could not predict most of the chemical compounds responsible for taste attributes, except for ribose, lactate, succinate, and tryptophan. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

PubMed

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

2017-03-01

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

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.

[Resilience in Individuals with Gender Dysphoria: Association with Perceived Social Support and Discrimination].

PubMed

Başar, Koray; Öz, Gökhan

2016-01-01

Psychological distress associated with discrimination is proposed to have an indirect effect on the development of mental disorders, through its negative influence on individual's cognitive, affective and social coping strategies. The aim of this study was to investigate the association between resilience, perceived social support, and perceived discrimination in individuals with gender dysphoria. Individuals with gender dysphoria were assessed with Turkish validated forms of Resilience Scale for Adults (RSA), Multidimensional Scale of Perceived Social Support (MSPSS), Perceived Discrimination Scale (PDS), and Beck Depression Inventory (BDI). Diagnoses of mental disorders, history of suicide attempt and non-suicidal self injury were assessed with clinical interviews. Self-report forms were used to obtain demographic information and gender transition related features. Participants' (n=116, 88 trans men) median age was 25. Significantly low RSA scores, indicating poor resilience, were obtained in participants with lifetime (59.5 %) and present (27.6 %) diagnosis of any mental disorder, history of suicide attempt (23.3 %). There was significant direct correlation between RSA and MSPSS scores, inverse correlation with BDI and personal PDS scores, but not with group PDS. Regression analysis revealed that only friends domain score in MSPSS predicted better resilience, whereas personal perceived discrimination score predicted poor resilience. Findings support the association between poor resilience and vulnerability to mental and behavioral problems in individuals with gender dysphoria. The associations reveal the significance of addressing discrimination and assisting individuals with gender dysphoria in developing strategies to obtain peer support in providing mental health services.

A computational model that predicts behavioral sensitivity to intracortical microstimulation

PubMed Central

Kim, Sungshin; Callier, Thierri; Bensmaia, Sliman J.

2016-01-01

Objective Intracortical microstimulation (ICMS) is a powerful tool to investigate the neural mechanisms of perception and can be used to restore sensation for patients who have lost it. While sensitivity to ICMS has previously been characterized, no systematic framework has been developed to summarize the detectability of individual ICMS pulse trains or the discriminability of pairs of pulse trains. Approach We develop a simple simulation that describes the responses of a population of neurons to a train of electrical pulses delivered through a microelectrode. We then perform an ideal observer analysis on the simulated population responses to predict the behavioral performance of non-human primates in ICMS detection and discrimination tasks. Main results Our computational model can predict behavioral performance across a wide range of stimulation conditions with high accuracy (R2 = 0.97) and generalizes to novel ICMS pulse trains that were not used to fit its parameters. Furthermore, the model provides a theoretical basis for the finding that amplitude discrimination based on ICMS violates Weber's law. Significance The model can be used to characterize the sensitivity to ICMS across the range of perceptible and safe stimulation regimes. As such, it will be a useful tool for both neuroscience and neuroprosthetics. PMID:27977419

A computational model that predicts behavioral sensitivity to intracortical microstimulation.

PubMed

Kim, Sungshin; Callier, Thierri; Bensmaia, Sliman J

2017-02-01

Intracortical microstimulation (ICMS) is a powerful tool to investigate the neural mechanisms of perception and can be used to restore sensation for patients who have lost it. While sensitivity to ICMS has previously been characterized, no systematic framework has been developed to summarize the detectability of individual ICMS pulse trains or the discriminability of pairs of pulse trains. We develop a simple simulation that describes the responses of a population of neurons to a train of electrical pulses delivered through a microelectrode. We then perform an ideal observer analysis on the simulated population responses to predict the behavioral performance of non-human primates in ICMS detection and discrimination tasks. Our computational model can predict behavioral performance across a wide range of stimulation conditions with high accuracy (R 2  = 0.97) and generalizes to novel ICMS pulse trains that were not used to fit its parameters. Furthermore, the model provides a theoretical basis for the finding that amplitude discrimination based on ICMS violates Weber's law. The model can be used to characterize the sensitivity to ICMS across the range of perceptible and safe stimulation regimes. As such, it will be a useful tool for both neuroscience and neuroprosthetics.

The role of steroids in the prediction of affective disorders in adult men.

PubMed

Šrámková, Monika; Dušková, Michaela; Hill, Martin; Bičíková, Marie; Řípová, Daniela; Mohr, Pavel; Stárka, Luboslav

2017-05-01

Anxiety and mood disorders (AMD) are the most frequent mental disorders in the human population. They have recently shown increasing prevalence, and commonly disrupt personal and working lives. The aim of our study was to analyze the spectrum of circulating steroids in order to discover differences that could potentially be markers of affective depression or anxiety, and identify which steroids could be a predictive component for these diseases. We studied the steroid metabolome including 47 analytes in 20 men with depression (group D), 20 men with anxiety (group AN) and 30 healthy controls. OPLS and multivariate regression models were used for statistical analysis. Discrimination of group D from controls by the OPLS method was absolute, as was group AN from controls (sensitivity=1.000 (0.839, 1.000), specificity=1.000 (0.887, 1.000)). Relatively good predictivity was also found for discrimination between group D from AN (sensitivity=0.850 (0.640, 0.948), specificity=0.900 (0.699, 0.972)). Selected circulating steroids, including those that are neuroactive and neuroprotective, can be useful tools for discriminating between these affective diseases in adult men. Copyright © 2016. Published by Elsevier Inc.

A computational model that predicts behavioral sensitivity to intracortical microstimulation

NASA Astrophysics Data System (ADS)

Kim, Sungshin; Callier, Thierri; Bensmaia, Sliman J.

2017-02-01

Objective. Intracortical microstimulation (ICMS) is a powerful tool to investigate the neural mechanisms of perception and can be used to restore sensation for patients who have lost it. While sensitivity to ICMS has previously been characterized, no systematic framework has been developed to summarize the detectability of individual ICMS pulse trains or the discriminability of pairs of pulse trains. Approach. We develop a simple simulation that describes the responses of a population of neurons to a train of electrical pulses delivered through a microelectrode. We then perform an ideal observer analysis on the simulated population responses to predict the behavioral performance of non-human primates in ICMS detection and discrimination tasks. Main results. Our computational model can predict behavioral performance across a wide range of stimulation conditions with high accuracy (R 2 = 0.97) and generalizes to novel ICMS pulse trains that were not used to fit its parameters. Furthermore, the model provides a theoretical basis for the finding that amplitude discrimination based on ICMS violates Weber’s law. Significance. The model can be used to characterize the sensitivity to ICMS across the range of perceptible and safe stimulation regimes. As such, it will be a useful tool for both neuroscience and neuroprosthetics.

Exposure to Discrimination and Heart Rate Variability Reactivity to Acute Stress among Women with Diabetes.

PubMed

Wagner, Julie; Lampert, Rachel; Tennen, Howard; Feinn, Richard

2015-08-01

Exposure to racial discrimination has been linked to physiological reactivity. This study investigated self-reported exposure to racial discrimination and parasympathetic [high-frequency heart rate variability (HF-HRV)] and sympathetic (norepinephrine and cortisol) activity at baseline and then again after acute laboratory stress. Lifetime exposure to racial discrimination was measured with the Schedule of Racist Events scale. Thirty-two women (16 Black and 16 White) with type 2 diabetes performed a public speaking stressor. Beat-to-beat intervals were recorded on electrocardiograph recorders, and HF-HRV was calculated using spectral analysis and natural log transformed. Norepinephrine and cortisol were measured in blood. Higher discrimination predicted lower stressor HF-HRV, even after controlling for baseline HF-HRV. When race, age, A1c and baseline systolic blood pressure were also controlled, racial discrimination remained a significant independent predictor of stressor HF-HRV. There was no association between lifetime discrimination and sympathetic markers. In conclusion, preliminary data suggest that among women with type 2 diabetes mellitus (T2DM), exposure to racial discrimination is adversely associated with parasympathetic, but not sympathetic, reactivity. Copyright © 2013 John Wiley & Sons, Ltd.

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.

Can CT imaging features of ground-glass opacity predict invasiveness? A meta-analysis.

PubMed

Dai, Jian; Yu, Guoyou; Yu, Jianqiang

2018-04-01

A meta-analysis was conducted to investigate the diagnostic performance of computed tomography (CT) imaging features of ground-glass opacity (GGO) to predict invasiveness. Two reviewers independently searched PubMed, Medline, Web of Science, Cochrane Embase and CNKI for relevant studies. CT imaging signs of bubble lucency, speculation, lobulated margin, and pleural indentation were used as diagnostic references to discriminate pre-invasive and invasive disease. The sensitivity, specificity, diagnostic odds ratio (DOR), summary receiver operating characteristic (SROC) curves, and the area under the SROC curve (AUC) were calculated to evaluate diagnostic efficiency. Twelve studies were finally included. Diagnostic performance ranged from 0.41 to 0.52 for sensitivity and 0.56 to 0.63 for specificity. The diagnostic positive and negative likelihood ratios ranged from 1.03 to 2.13 and 0.52 to 1.05, respectively. The DORs of the GGO CT features for discriminating invasive disease ranged from 1.02 to 4.00. The area under the ROC curve was also low, with a range of 0.60 to 0.67 for discriminating pre-invasive and invasive disease. The diagnostic value of a single CT imaging sign of GGO, such as bubble lucency, speculation, lobulated margin, or pleural indentation is limited for discriminating pre-invasive and invasive disease because of low sensitivity, specificity, and AUC. © 2018 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.

Classification and disease prediction via mathematical programming

NASA Astrophysics Data System (ADS)

Lee, Eva K.; Wu, Tsung-Lin

2007-11-01

In this chapter, we present classification models based on mathematical programming approaches. We first provide an overview on various mathematical programming approaches, including linear programming, mixed integer programming, nonlinear programming and support vector machines. Next, we present our effort of novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule) and (5) successive multi-stage classification capability to handle data points placed in the reserved judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multigroup prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; multistage discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy and tumor metastasis; prediction of protein localization sites; and pattern recognition of satellite images in classification of soil types. In all these applications, the predictive model yields correct classification rates ranging from 80% to 100%. This provides motivation for pursuing its use as a medical diagnostic, monitoring and decision-making tool.

Novel non-invasive biological predictive index for liver fibrosis in hepatitis C virus genotype 4 patients

PubMed Central

Khattab, Mahmoud; Sakr, Mohamed Amin; Fattah, Mohamed Abdel; Mousa, Youssef; Soliman, Elwy; Breedy, Ashraf; Fathi, Mona; Gaber, Salwa; Altaweil, Ahmed; Osman, Ashraf; Hassouna, Ahmed; Motawea, Ibrahim

2016-01-01

AIM To investigate the diagnostic ability of a non-invasive biological marker to predict liver fibrosis in hepatitis C genotype 4 patients with high accuracy. METHODS A cohort of 332 patients infected with hepatitis C genotype 4 was included in this cross-sectional study. Fasting plasma glucose, insulin, C-peptide, and angiotensin-converting enzyme serum levels were measured. Insulin resistance was mathematically calculated using the homeostasis model of insulin resistance (HOMA-IR). RESULTS Fibrosis stages were distributed based on Metavir score as follows: F0 = 43, F1 = 136, F2 = 64, F3 = 45 and F4 = 44. Statistical analysis relied upon reclassification of fibrosis stages into mild fibrosis (F0-F) = 179, moderate fibrosis (F2) = 64, and advanced fibrosis (F3-F4) = 89. Univariate analysis indicated that age, log aspartate amino transaminase, log HOMA-IR and log platelet count were independent predictors of liver fibrosis stage (P < 0.0001). A stepwise multivariate discriminant functional analysis was used to drive a discriminative model for liver fibrosis. Our index used cut-off values of ≥ 0.86 and ≤ -0.31 to diagnose advanced and mild fibrosis, respectively, with receiving operating characteristics of 0.91 and 0.88, respectively. The sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio were: 73%, 91%, 75%, 90% and 8.0 respectively for advanced fibrosis, and 67%, 88%, 84%, 70% and 4.9, respectively, for mild fibrosis. CONCLUSION Our predictive model is easily available and reproducible, and predicted liver fibrosis with acceptable accuracy. PMID:27917265

Novel non-invasive biological predictive index for liver fibrosis in hepatitis C virus genotype 4 patients.

PubMed

Khattab, Mahmoud; Sakr, Mohamed Amin; Fattah, Mohamed Abdel; Mousa, Youssef; Soliman, Elwy; Breedy, Ashraf; Fathi, Mona; Gaber, Salwa; Altaweil, Ahmed; Osman, Ashraf; Hassouna, Ahmed; Motawea, Ibrahim

2016-11-18

To investigate the diagnostic ability of a non-invasive biological marker to predict liver fibrosis in hepatitis C genotype 4 patients with high accuracy. A cohort of 332 patients infected with hepatitis C genotype 4 was included in this cross-sectional study. Fasting plasma glucose, insulin, C-peptide, and angiotensin-converting enzyme serum levels were measured. Insulin resistance was mathematically calculated using the homeostasis model of insulin resistance (HOMA-IR). Fibrosis stages were distributed based on Metavir score as follows: F0 = 43, F1 = 136, F2 = 64, F3 = 45 and F4 = 44. Statistical analysis relied upon reclassification of fibrosis stages into mild fibrosis (F0-F) = 179, moderate fibrosis (F2) = 64, and advanced fibrosis (F3-F4) = 89. Univariate analysis indicated that age, log aspartate amino transaminase, log HOMA-IR and log platelet count were independent predictors of liver fibrosis stage ( P < 0.0001). A stepwise multivariate discriminant functional analysis was used to drive a discriminative model for liver fibrosis. Our index used cut-off values of ≥ 0.86 and ≤ -0.31 to diagnose advanced and mild fibrosis, respectively, with receiving operating characteristics of 0.91 and 0.88, respectively. The sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio were: 73%, 91%, 75%, 90% and 8.0 respectively for advanced fibrosis, and 67%, 88%, 84%, 70% and 4.9, respectively, for mild fibrosis. Our predictive model is easily available and reproducible, and predicted liver fibrosis with acceptable accuracy.

The Relationship between Perceived Discrimination and Generalized Anxiety Disorder among African Americans, Afro Caribbeans and non-Hispanic Whites

PubMed Central

Soto, José A.; Dawson-Andoh, Nana A.; BeLue, Rhonda

2010-01-01

The present study examined the relationship between frequency of race based and non-race based discrimination experiences and Generalized Anxiety Disorder (GAD) in a sample of 3,570 African Americans, 1,438 Afro Caribbeans, and 891 non-Hispanic Whites from the National Survey of American Life (NSAL). Because GAD and the experience of racial discrimination are both associated with symptoms of worry and tension, we expected race based discrimination to predict GAD prevalence for African Americans, but not other groups. We did not expect non-race based discrimination to predict GAD. Results showed that while more frequent experiences of non-race based discrimination predicted GAD for all groups, experiencing race based discrimination was associated with significantly higher odds of endorsing lifetime GAD for African Americans only. Results are interpreted in light of the different contexts that these three ethnic groups represent relative to their history within the United States as well as their present day circumstances. PMID:21041059

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.

The Efficacy of Two Improvement-over-Chance Effect Sizes for Two-Group Univariate Comparisons under Variance Heterogeneity and Nonnormality.

ERIC Educational Resources Information Center

Hess, Brian; Olejnik, Stephen; Huberty, Carl J.

2001-01-01

Studied the efficacy of two improvement-over-chance or "I" effect sizes derived from predictive discriminant analysis and logistic regression analysis for two-group univariate mean comparisons through simulation. Discusses the ways in which the usefulness of each of the indices depends on the population characteristics. (SLD)

Multiple Score Comparison: a network meta-analysis approach to comparison and external validation of prognostic scores.

PubMed

Haile, Sarah R; Guerra, Beniamino; Soriano, Joan B; Puhan, Milo A

2017-12-21

Prediction models and prognostic scores have been increasingly popular in both clinical practice and clinical research settings, for example to aid in risk-based decision making or control for confounding. In many medical fields, a large number of prognostic scores are available, but practitioners may find it difficult to choose between them due to lack of external validation as well as lack of comparisons between them. Borrowing methodology from network meta-analysis, we describe an approach to Multiple Score Comparison meta-analysis (MSC) which permits concurrent external validation and comparisons of prognostic scores using individual patient data (IPD) arising from a large-scale international collaboration. We describe the challenges in adapting network meta-analysis to the MSC setting, for instance the need to explicitly include correlations between the scores on a cohort level, and how to deal with many multi-score studies. We propose first using IPD to make cohort-level aggregate discrimination or calibration scores, comparing all to a common comparator. Then, standard network meta-analysis techniques can be applied, taking care to consider correlation structures in cohorts with multiple scores. Transitivity, consistency and heterogeneity are also examined. We provide a clinical application, comparing prognostic scores for 3-year mortality in patients with chronic obstructive pulmonary disease using data from a large-scale collaborative initiative. We focus on the discriminative properties of the prognostic scores. Our results show clear differences in performance, with ADO and eBODE showing higher discrimination with respect to mortality than other considered scores. The assumptions of transitivity and local and global consistency were not violated. Heterogeneity was small. We applied a network meta-analytic methodology to externally validate and concurrently compare the prognostic properties of clinical scores. Our large-scale external validation indicates that the scores with the best discriminative properties to predict 3 year mortality in patients with COPD are ADO and eBODE.

Perceived Discrimination, Perceived Stress, and Mental and Physical Health among Mexican-Origin Adults

ERIC Educational Resources Information Center

Flores, Elena; Tschann, Jeanne M.; Dimas, Juanita M.; Bachen, Elizabeth A.; Pasch, Lauri A.; de Groat, Cynthia L.

2008-01-01

This study provided a test of the minority status stress model by examining whether perceived discrimination would directly affect health outcomes even when perceived stress was taken into account among 215 Mexican-origin adults. Perceived discrimination predicted depression and poorer general health, and marginally predicted health symptoms, when…

The Effects of General Social Support and Social Support for Racial Discrimination on African American Women’s Well-Being

PubMed Central

Seawell, Asani H.; Cutrona, Carolyn E.; Russell, Daniel W.

2012-01-01

The present longitudinal study examined the role of general and tailored social support in mitigating the deleterious impact of racial discrimination on depressive symptoms and optimism in a large sample of African American women. Participants were 590 African American women who completed measures assessing racial discrimination, general social support, tailored social support for racial discrimination, depressive symptoms, and optimism at two time points (2001–2002 and 2003–2004). Our results indicated that higher levels of general and tailored social support predicted optimism one year later; changes in both types of support also predicted changes in optimism over time. Although initial levels of neither measure of social support predicted depressive symptoms over time, changes in tailored support predicted changes in depressive symptoms. We also sought to determine whether general and tailored social support “buffer” or diminish the negative effects of racial discrimination on depressive symptoms and optimism. Our results revealed a classic buffering effect of tailored social support, but not general support on depressive symptoms for women experiencing high levels of discrimination. PMID:24443614

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

NASA Astrophysics Data System (ADS)

Wimmer, G.

2008-01-01

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

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

PubMed

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

2008-10-01

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

Impulsivity moderates the association between racial discrimination and alcohol problems.

PubMed

Latzman, Robert D; Chan, Wing Yi; Shishido, Yuri

2013-12-01

Alcohol use among university students is a serious public health concern, particularly among minority students who may use alcohol to cope with experiences of racial discrimination. Although the impact of racial discrimination on alcohol use has been well-established, individual differences in factors that may act to either attenuate or exacerbate the negative effects of racial discrimination are largely unknown. One potentially fruitful individual differences trait that has repeatedly been found to predict alcohol problems is the multidimensional personality trait of impulsivity. Nonetheless, the ways in which various aspects of impulsivity interact with racial discrimination is yet unknown. The current study, therefore, examined the joint and interactive contribution of racial discrimination and impulsivity in the prediction of alcohol consumption among racial minority university students. Participants included 336 Black/African-American and Asian/Asian-American university students. Results revealed both racial discrimination and impulsivity to be significantly associated with alcohol problems. Further, individuals' responses to racial discrimination were not uniform. Specifically, the association between racial discrimination and alcohol problems was moderated by lack of Premeditation; racial discrimination was most strongly predictive of alcohol problems for those who reported low level of premeditation. Findings from the present study highlight the importance of investigating risk factors for alcohol problems across multiple levels of the ecology as individual personality traits appear to relate to how one might respond to the experience of racial discrimination. © 2013.

Assessment of intracranial pressure with ultrasonographic retrobulbar optic nerve sheath diameter measurement.

PubMed

Liu, Dachuan; Li, Zhen; Zhang, Xuxiang; Zhao, Liping; Jia, Jianping; Sun, Fei; Wang, Yaxing; Ma, Daqing; Wei, Wenbin

2017-09-29

Ultrasonograpic retrobulbar optic nerve sheath diameter (ONSD) measurement is considered to be an alternative noninvasive method to estimate intracranial pressure,but the further validation is urgently needed. The aim of the current study was to investigate the association of the ultrasonographic ONSD and intracranial pressure (ICP) in patients. One hundred and ten patients whose intracranial pressure measured via lumbar puncture were enrolled in the study. Their retrobulbar ONSD with B-scan ultrasound was determined just before lumber puncture. The correlation between the ICP and the body mass index (BMI), ONSD or age was established respectively with the Pearson correlation coefficient analysis. The discriminant analysis was used to obtain a discriminant formula for predicting ICP with the ONSD、BMI、gender and age. Another 20 patients were recruited for further validation the efficiency of this discriminant equation. The mean ICP was 215.3 ± 81.2 mmH 2 O. ONSD was 5.70 ± 0.80 mm in the right eye and 5.80 ± 0.77 mm in the left eye. A significant correlation was found between ICP and BMI (r = 0.554, p < 0.001), the mean ONSD (r = 0.61, P < 0.001), but not with age (r = -0.131, p = 0.174) and gender (r = 0.03, p = 0.753). Using receiver operating characteristic (ROC) curve analysis, the critical value for the risk mean-ONSD was 5.6 mm from the ROC curve, with the sensitivity of 86.2% and specificity of 73.1%. With 200 mmH 2 O as the cutoff point for a high or low ICP, stepwise discriminant was applied, the sensitivity and specificity of ONSD predicting ICP was 84.5%-85.7% and 86.5%-92.3%. Ophthalmic ultrasound measurement of ONSD may be a good surrogate of invasive ICP measurement. This non-invasive method may be an alternative approach to predict the ICP value of patients whose ICP measurement via lumbar puncture are in high risk. The discriminant formula, which incorporated the factor of BMI, had similar sensitivity and higher specificity than the ROC curve.

Review-of-systems questionnaire as a predictive tool for psychogenic nonepileptic seizures.

PubMed

Robles, Liliana; Chiang, Sharon; Haneef, Zulfi

2015-04-01

Patients with refractory epilepsy undergo video-electroencephalography for seizure characterization, among whom approximately 10-30% will be discharged with the diagnosis of psychogenic nonepileptic seizures (PNESs). Clinical PNES predictors have been described but in general are not sensitive or specific. We evaluated whether multiple complaints in a routine review-of-system (ROS) questionnaire could serve as a sensitive and specific marker of PNESs. We performed a retrospective analysis of a standardized ROS questionnaire completed by patients with definite PNESs and epileptic seizures (ESs) diagnosed in our adult epilepsy monitoring unit. A multivariate analysis of covariance (MANCOVA) was used to determine whether groups with PNES and ES differed with respect to the percentage of complaints in the ROS questionnaire. Tenfold cross-validation was used to evaluate the predictive error of a logistic regression classifier for PNES status based on the percentage of positive complaints in the ROS questionnaire. A total of 44 patients were included for analysis. Patients with PNESs had a significantly higher number of complaints in the ROS questionnaire compared to patients with epilepsy. A threshold of 17% positive complaints achieved a 78% specificity and 85% sensitivity for discriminating between PNESs and ESs. We conclude that the routine ROS questionnaire may be a sensitive and specific predictive tool for discriminating between PNESs and ESs. Published by Elsevier Inc.

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.

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.

Development and Validation of a Disease Severity Scoring Model for Pediatric Sepsis.

PubMed

Hu, Li; Zhu, Yimin; Chen, Mengshi; Li, Xun; Lu, Xiulan; Liang, Ying; Tan, Hongzhuan

2016-07-01

Multiple severity scoring systems have been devised and evaluated in adult sepsis, but a simplified scoring model for pediatric sepsis has not yet been developed. This study aimed to develop and validate a new scoring model to stratify the severity of pediatric sepsis, thus assisting the treatment of sepsis in children. Data from 634 consecutive patients who presented with sepsis at Children's hospital of Hunan province in China in 2011-2013 were analyzed, with 476 patients placed in training group and 158 patients in validation group. Stepwise discriminant analysis was used to develop the accurate discriminate model. A simplified scoring model was generated using weightings defined by the discriminate coefficients. The discriminant ability of the model was tested by receiver operating characteristic curves (ROC). The discriminant analysis showed that prothrombin time, D-dimer, total bilirubin, serum total protein, uric acid, PaO2/FiO2 ratio, myoglobin were associated with severity of sepsis. These seven variables were assigned with values of 4, 3, 3, 4, 3, 3, 3 respectively based on the standardized discriminant coefficients. Patients with higher scores had higher risk of severe sepsis. The areas under ROC (AROC) were 0.836 for accurate discriminate model, and 0.825 for simplified scoring model in validation group. The proposed disease severity scoring model for pediatric sepsis showed adequate discriminatory capacity and sufficient accuracy, which has important clinical significance in evaluating the severity of pediatric sepsis and predicting its progress.

Experiences of Discrimination among Chinese American Adolescents and the Consequences for Socioemotional and Academic Development

PubMed Central

Benner, Aprile D.; Kim, Su Yeong

2009-01-01

This longitudinal study examined the influences of discrimination on socioemotional adjustment and academic performance for a sample of 444 Chinese American adolescents. Using autoregressive and cross-lagged techniques, results indicate that discrimination in early adolescence predicted depressive symptoms, alienation, school engagement, and grades in middle adolescence, but early socioemotional adjustment and academic performance did not predict later experiences of discrimination. Further, our investigation of whether earlier or contemporaneous experiences of discrimination influenced developmental outcomes in middle adolescence indicated differential effects, with contemporaneous experiences of discrimination affecting socioemotional adjustment, while earlier discrimination was more influential for academic performance. Finally, we found a persistent negative effect of acculturation on the link between discrimination and adolescents’ developmental outcomes, such that those adolescents who were more acculturated (in this case, higher in American orientation) experienced more deleterious effects of discrimination on both socioemotional and academic outcomes. PMID:19899924

Metabolomics based predictive biomarker model of ARDS: A systemic measure of clinical hypoxemia

PubMed Central

Viswan, Akhila; Singh, Chandan; Rai, Ratan Kumar; Azim, Afzal; Baronia, Arvind Kumar

2017-01-01

Despite advancements in ventilator technologies, lung supportive and rescue therapies, the outcome and prognostication in acute respiratory distress syndrome (ARDS) remains incremental and ambiguous. Metabolomics is a potential insightful measure to the diagnostic approaches practiced in critical disease settings. In our study patients diagnosed with mild and moderate/severe ARDS clinically governed by hypoxemic P/F ratio between 100–300 but with indistinct molecular phenotype were discriminated employing nuclear magnetic resonance (NMR) based metabolomics of mini bronchoalveolar lavage fluid (mBALF). Resulting biomarker prototype comprising six metabolites was substantiated highlighting ARDS susceptibility/recovery. Both the groups (mild and moderate/severe ARDS) showed distinct biochemical profile based on 83.3% classification by discriminant function analysis and cross validated accuracy of 91% using partial least squares discriminant analysis as major classifier. The predictive performance of narrowed down six metabolites were found analogous with chemometrics. The proposed biomarker model consisting of six metabolites proline, lysine/arginine, taurine, threonine and glutamate were found characteristic of ARDS sub-stages with aberrant metabolism observed mainly in arginine, proline metabolism, lysine synthesis and so forth correlating to diseased metabotype. Thus NMR based metabolomics has provided new insight into ARDS sub-stages and conclusively a precise biomarker model proposed, reflecting underlying metabolic dysfunction aiding prior clinical decision making. PMID:29095932

Metabolomics based predictive biomarker model of ARDS: A systemic measure of clinical hypoxemia.

PubMed

Viswan, Akhila; Singh, Chandan; Rai, Ratan Kumar; Azim, Afzal; Sinha, Neeraj; Baronia, Arvind Kumar

2017-01-01

Despite advancements in ventilator technologies, lung supportive and rescue therapies, the outcome and prognostication in acute respiratory distress syndrome (ARDS) remains incremental and ambiguous. Metabolomics is a potential insightful measure to the diagnostic approaches practiced in critical disease settings. In our study patients diagnosed with mild and moderate/severe ARDS clinically governed by hypoxemic P/F ratio between 100-300 but with indistinct molecular phenotype were discriminated employing nuclear magnetic resonance (NMR) based metabolomics of mini bronchoalveolar lavage fluid (mBALF). Resulting biomarker prototype comprising six metabolites was substantiated highlighting ARDS susceptibility/recovery. Both the groups (mild and moderate/severe ARDS) showed distinct biochemical profile based on 83.3% classification by discriminant function analysis and cross validated accuracy of 91% using partial least squares discriminant analysis as major classifier. The predictive performance of narrowed down six metabolites were found analogous with chemometrics. The proposed biomarker model consisting of six metabolites proline, lysine/arginine, taurine, threonine and glutamate were found characteristic of ARDS sub-stages with aberrant metabolism observed mainly in arginine, proline metabolism, lysine synthesis and so forth correlating to diseased metabotype. Thus NMR based metabolomics has provided new insight into ARDS sub-stages and conclusively a precise biomarker model proposed, reflecting underlying metabolic dysfunction aiding prior clinical decision making.

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

PubMed Central

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

2015-01-01

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

Diagnostic value of fibronectin discriminant score for predicting liver fibrosis stages in chronic hepatitis C virus patients.

PubMed

Attallah, Abdelfattah M; Abdallah, Sanaa O; Attallah, Ahmed A; Omran, Mohamed M; Farid, Khaled; Nasif, Wesam A; Shiha, Gamal E; Abdel-Aziz, Abdel-Aziz F; Rasafy, Nancy; Shaker, Yehia M

2013-01-01

Several noninvasive predictive models were developed to substitute liver biopsy for fibrosis assessment. To evaluate the diagnostic value of fibronectin which reflect extracellular matrix metabolism and standard liver functions tests which reflect alterations in hepatic functions. Chronic hepatitis C (CHC) patients (n = 145) were evaluated using ROC curves and stepwise multivariate discriminant analysis (MDA) and was validated in 180 additional patients. Liver biochemical profile including transaminases, bilirubin, alkaline phosphatase, albumin, complete blood count were estimated. Fibronectin concentration was determined using monoclonal antibody and ELISA. A novel index named fibronectin discriminant score (FDS) based on fibronectin, APRI and albumin was developed. FDS produced areas under ROC curves (AUC) of 0.91 for significant fibrosis and 0.81 for advanced fibrosis. The FDS correctly classified 79% of the significant liver fibrosis patients (F2-F4) with 87% sensitivity and 75% specificity. The relative risk [odds ratio (OR)] of having significant liver fibrosis using the cut-off values determined by ROC curve analyses were 6.1 for fibronectin, 4.9 for APRI, and 4.2 for albumin. FDS predicted liver fibrosis with an OR of 16.8 for significant fibrosis and 8.6 for advanced fibrosis. The FDS had similar AUC and OR in the validation group to the estimation group without statistically significant difference. FDS predicted liver fibrosis with high degree of accuracy, potentially decreasing the number of liver biopsy required.

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

PubMed

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

2018-03-27

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

Fuzzy cluster analysis of simple physicochemical properties of amino acids for recognizing secondary structure in proteins.

PubMed Central

Mocz, G.

1995-01-01

Fuzzy cluster analysis has been applied to the 20 amino acids by using 65 physicochemical properties as a basis for classification. The clustering products, the fuzzy sets (i.e., classical sets with associated membership functions), have provided a new measure of amino acid similarities for use in protein folding studies. This work demonstrates that fuzzy sets of simple molecular attributes, when assigned to amino acid residues in a protein's sequence, can predict the secondary structure of the sequence with reasonable accuracy. An approach is presented for discriminating standard folding states, using near-optimum information splitting in half-overlapping segments of the sequence of assigned membership functions. The method is applied to a nonredundant set of 252 proteins and yields approximately 73% matching for correctly predicted and correctly rejected residues with approximately 60% overall success rate for the correctly recognized ones in three folding states: alpha-helix, beta-strand, and coil. The most useful attributes for discriminating these states appear to be related to size, polarity, and thermodynamic factors. Van der Waals volume, apparent average thickness of surrounding molecular free volume, and a measure of dimensionless surface electron density can explain approximately 95% of prediction results. hydrogen bonding and hydrophobicity induces do not yet enable clear clustering and prediction. PMID:7549882

Minority stress and sexual problems among African-American gay and bisexual men.

PubMed

Zamboni, Brian D; Crawford, Isiaah

2007-08-01

Minority stress, such as racism and gay bashing, may be associated with sexual problems, but this notion has not been examined in the literature. African-American gay/bisexual men face a unique challenge in managing a double minority status, putting them at high risk for stress and sexual problems. This investigation examined ten predictors of sexual problems among 174 African-American gay/bisexual men. Covarying for age, a forward multiple regression analysis showed that the measures of self-esteem, male gender role stress, HIV prevention self-efficacy, and lifetime experiences with racial discrimination significantly added to the prediction of sexual problems. Gay bashing, psychiatric symptoms, low life satisfaction, and low social support were significantly correlated with sexual problems, but did not add to the prediction of sexual problems in the regression analysis. Mediation analyses showed that stress predicted psychiatric symptoms, which then predicted sexual problems. Sexual problems were not significantly related to HIV status, racial/ethnic identity, or gay identity. The findings from this study showed a relationship between experiences with racial and sexual discrimination and sexual problems while also providing support for mediation to illustrate how stress might cause sexual problems. Addressing minority stress in therapy may help minimize and treat sexual difficulties among minority gay/bisexual men.

Cluster analysis and prediction of treatment outcomes for chronic rhinosinusitis.

PubMed

Soler, Zachary M; Hyer, J Madison; Rudmik, Luke; Ramakrishnan, Viswanathan; Smith, Timothy L; Schlosser, Rodney J

2016-04-01

Current clinical classifications of chronic rhinosinusitis (CRS) have weak prognostic utility regarding treatment outcomes. Simplified discriminant analysis based on unsupervised clustering has identified novel phenotypic subgroups of CRS, but prognostic utility is unknown. We sought to determine whether discriminant analysis allows prognostication in patients choosing surgery versus continued medical management. A multi-institutional prospective study of patients with CRS in whom initial medical therapy failed who then self-selected continued medical management or surgical treatment was used to separate patients into 5 clusters based on a previously described discriminant analysis using total Sino-Nasal Outcome Test-22 (SNOT-22) score, age, and missed productivity. Patients completed the SNOT-22 at baseline and for 18 months of follow-up. Baseline demographic and objective measures included olfactory testing, computed tomography, and endoscopy scoring. SNOT-22 outcomes for surgical versus continued medical treatment were compared across clusters. Data were available on 690 patients. Baseline differences in demographics, comorbidities, objective disease measures, and patient-reported outcomes were similar to previous clustering reports. Three of 5 clusters identified by means of discriminant analysis had improved SNOT-22 outcomes with surgical intervention when compared with continued medical management (surgery was a mean of 21.2 points better across these 3 clusters at 6 months, P < .05). These differences were sustained at 18 months of follow-up. Two of 5 clusters had similar outcomes when comparing surgery with continued medical management. A simplified discriminant analysis based on 3 common clinical variables is able to cluster patients and provide prognostic information regarding surgical treatment versus continued medical management in patients with CRS. Copyright © 2015 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Investigating the Predictive Value of Functional MRI to Appetitive and Aversive Stimuli: A Pattern Classification Approach.

PubMed

McCabe, Ciara; Rocha-Rego, Vanessa

2016-01-01

Dysfunctional neural responses to appetitive and aversive stimuli have been investigated as possible biomarkers for psychiatric disorders. However it is not clear to what degree these are separate processes across the brain or in fact overlapping systems. To help clarify this issue we used Gaussian process classifier (GPC) analysis to examine appetitive and aversive processing in the brain. 25 healthy controls underwent functional MRI whilst seeing pictures and receiving tastes of pleasant and unpleasant food. We applied GPCs to discriminate between the appetitive and aversive sights and tastes using functional activity patterns. The diagnostic accuracy of the GPC for the accuracy to discriminate appetitive taste from neutral condition was 86.5% (specificity = 81%, sensitivity = 92%, p = 0.001). If a participant experienced neutral taste stimuli the probability of correct classification was 92. The accuracy to discriminate aversive from neutral taste stimuli was 82.5% (specificity = 73%, sensitivity = 92%, p = 0.001) and appetitive from aversive taste stimuli was 73% (specificity = 77%, sensitivity = 69%, p = 0.001). In the sight modality, the accuracy to discriminate appetitive from neutral condition was 88.5% (specificity = 85%, sensitivity = 92%, p = 0.001), to discriminate aversive from neutral sight stimuli was 92% (specificity = 92%, sensitivity = 92%, p = 0.001), and to discriminate aversive from appetitive sight stimuli was 63.5% (specificity = 73%, sensitivity = 54%, p = 0.009). Our results demonstrate the predictive value of neurofunctional data in discriminating emotional and neutral networks of activity in the healthy human brain. It would be of interest to use pattern recognition techniques and fMRI to examine network dysfunction in the processing of appetitive, aversive and neutral stimuli in psychiatric disorders. Especially where problems with reward and punishment processing have been implicated in the pathophysiology of the disorder.

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.

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

PubMed

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

2014-07-01

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

The discriminative ability of waist circumference, body mass index and waist-to-hip ratio in identifying metabolic syndrome: Variations by age, sex and race.

PubMed

Cheong, Kee C; Ghazali, Sumarni M; Hock, Lim K; Subenthiran, Soobitha; Huey, Teh C; Kuay, Lim K; Mustapha, Feisul I; Yusoff, Ahmad F; Mustafa, Amal N

2015-01-01

Many studies have suggested that there is variation in the capabilities of BMI, WC and WHR in predicting cardiometabolic risk and that it might be confounded by gender, ethnicity and age group. The objective of this study is to examine the discriminative abilities of body mass index (BMI), waist circumference (WC) and waist-hip ratio (WHR) to predict two or more non-adipose components of the metabolic syndrome (high blood pressure, hypertriglyceridemia, low high density lipoprotein-cholesterol and high fasting plasma glucose) among the adult Malaysian population by gender, age group and ethnicity. Data from 2572 respondents (1044 men and 1528 women) aged 25-64 years who participated in the Non Communicable Disease Surveillance 2005/2006, a population-based cross sectional study, were analysed. Participants' socio-demographic details, anthropometric indices (BMI, WC and WHR), blood pressure, fasting lipid profile and fasting glucose level were assessed. Receiver operating characteristics curves analysis was used to evaluate the ability of each anthropometric index to discriminate MetS cases from non-MetS cases based on the area under the curve. Overall, WC had better discriminative ability than WHR for women but did not perform significantly better than BMI in both sexes, whereas BMI was better than WHR in women only. Waist circumference was a better discriminator of MetS compared to WHR in Malay men and women. Waist circumference and BMI performed better than WHR in Chinese women, men aged 25-34 years and women aged 35-44 years. The discriminative ability of BMI and WC is better than WHR for predicting two or more non-adipose components of MetS. Therefore, either BMI or WC measurements are recommended in screening for metabolic syndrome in routine clinical practice in the effort to combat cardiovascular disease and type II diabetes mellitus. Copyright © 2015 Diabetes India. Published by Elsevier Ltd. All rights reserved.

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

PubMed

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

2018-05-01

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

The SAAS-R: A New Instrument to Assess the School Attitudes of Students with High and Low Academic Achievement in Spain

ERIC Educational Resources Information Center

Miñano Pérez, Pablo; Costa, Juan Luis Castejón; Corbi, Raquel Gilar; Iniesta, Alejandro Veas

2017-01-01

We examined the psychometric properties of the School Attitude Assessment Survey-Revised in a Spanish population (n = 1,398). Confirmatory factor analysis procedures supported the instrument's five-factor structure. The results of discriminant analysis demonstrated the predictive power of the School Attitude Assessment Survey-Revised scales as…

Assessing the Classification Accuracy of Early Numeracy Curriculum-Based Measures Using Receiver Operating Characteristic Curve Analysis

ERIC Educational Resources Information Center

Laracy, Seth D.; Hojnoski, Robin L.; Dever, Bridget V.

2016-01-01

Receiver operating characteristic curve (ROC) analysis was used to investigate the ability of early numeracy curriculum-based measures (EN-CBM) administered in preschool to predict performance below the 25th and 40th percentiles on a quantity discrimination measure in kindergarten. Areas under the curve derived from a sample of 279 students ranged…

Measuring attitudes towards suicide: Preliminary evaluation of an attitude towards suicide scale.

PubMed

Cwik, Jan Christopher; Till, Benedikt; Bieda, Angela; Blackwell, Simon E; Walter, Carolin; Teismann, Tobias

2017-01-01

Our study aimed to validate a previously published scale assessing attitudes towards suicide. Factor structure, convergent and discriminant validity, and predictive validity were investigated. Adult German participants (N=503; mean age=24.74years; age range=18-67years) anonymously completed a set of questionnaires. An exploratory factor analysis was conducted, and incongruous items were deleted. Subsequently, scale properties of the reduced scale and its construct validity were analyzed. A confirmatory factor analysis was then conducted in an independent sample (N=266; mean age=28.77years; age range=18-88years) to further confirm the factor structure of the questionnaire. Parallel analysis indicated a three-factor solution, which was also supported by confirmatory factor analysis: right to commit suicide, interpersonal gesture and resilience. The subscales demonstrated acceptable construct and discriminant validity. Cronbach's α for the subscales ranged from 0.67 to 0.83, explaining 49.70% of the total variance. Positive attitudes towards suicide proved to be predictive of suicide risk status, providing preliminary evidence for the utility of the scale. Future studies aiming to reproduce the factor structure in a more heterogeneous sample are warranted. Copyright © 2016 Elsevier Inc. All rights reserved.

The impact of perceived discrimination on personality among Chinese migrant children: The moderating role of parental support.

PubMed

Xiang, Xiaoping; Wong, Daniel Fu Keung; Hou, Ke

2018-05-01

Previous research has indicated that perceived discrimination has harmful effects on migrant children's physical, mental and behavioral health. However, little is known as to whether these harmful effects cumulate to impact on migrant children's personalities. This study examines the effect of perceived discrimination on personality, as well as the moderating role of parental support in the discrimination-personality linkage. A purposeful convenience sample of 215 migrant children in Beijing, China, completed a standardized questionnaire. Migrant children experienced a moderate level of perceived discrimination, with Form 8 students experiencing greater discrimination than lower grades and those with lower family incomes also experiencing greater discrimination than those with higher family incomes. Perceived discrimination significantly predicted neuroticism; parental support significantly predicted extraversion, openness, agreeableness and conscientiousness, but the moderating effect of parental support was only marginally significant for the relation between discrimination and conscientiousness. This study underlines the need for researchers and policy makers to pay more attention to the impact of perceived discrimination on migrant children's personality development.

Prediction of cognitive outcome based on the progression of auditory discrimination during coma.

PubMed

Juan, Elsa; De Lucia, Marzia; Tzovara, Athina; Beaud, Valérie; Oddo, Mauro; Clarke, Stephanie; Rossetti, Andrea O

2016-09-01

To date, no clinical test is able to predict cognitive and functional outcome of cardiac arrest survivors. Improvement of auditory discrimination in acute coma indicates survival with high specificity. Whether the degree of this improvement is indicative of recovery remains unknown. Here we investigated if progression of auditory discrimination can predict cognitive and functional outcome. We prospectively recorded electroencephalography responses to auditory stimuli of post-anoxic comatose patients on the first and second day after admission. For each recording, auditory discrimination was quantified and its evolution over the two recordings was used to classify survivors as "predicted" when it increased vs. "other" if not. Cognitive functions were tested on awakening and functional outcome was assessed at 3 months using the Cerebral Performance Categories (CPC) scale. Thirty-two patients were included, 14 "predicted survivors" and 18 "other survivors". "Predicted survivors" were more likely to recover basic cognitive functions shortly after awakening (ability to follow a standardized neuropsychological battery: 86% vs. 44%; p=0.03 (Fisher)) and to show a very good functional outcome at 3 months (CPC 1: 86% vs. 33%; p=0.004 (Fisher)). Moreover, progression of auditory discrimination during coma was strongly correlated with cognitive performance on awakening (phonemic verbal fluency: rs=0.48; p=0.009 (Spearman)). Progression of auditory discrimination during coma provides early indication of future recovery of cognitive functions. The degree of improvement is informative of the degree of functional impairment. If confirmed in a larger cohort, this test would be the first to predict detailed outcome at the single-patient level. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Stimulus similarity determines the prevalence of behavioral laterality in a visual discrimination task for mice

PubMed Central

Treviño, Mario

2014-01-01

Animal choices depend on direct sensory information, but also on the dynamic changes in the magnitude of reward. In visual discrimination tasks, the emergence of lateral biases in the choice record from animals is often described as a behavioral artifact, because these are highly correlated with error rates affecting psychophysical measurements. Here, we hypothesized that biased choices could constitute a robust behavioral strategy to solve discrimination tasks of graded difficulty. We trained mice to swim in a two-alterative visual discrimination task with escape from water as the reward. Their prevalence of making lateral choices increased with stimulus similarity and was present in conditions of high discriminability. While lateralization occurred at the individual level, it was absent, on average, at the population level. Biased choice sequences obeyed the generalized matching law and increased task efficiency when stimulus similarity was high. A mathematical analysis revealed that strongly-biased mice used information from past rewards but not past choices to make their current choices. We also found that the amount of lateralized choices made during the first day of training predicted individual differences in the average learning behavior. This framework provides useful analysis tools to study individualized visual-learning trajectories in mice. PMID:25524257

Suppression of the µ Rhythm during Speech and Non-Speech Discrimination Revealed by Independent Component Analysis: Implications for Sensorimotor Integration in Speech Processing

PubMed Central

Bowers, Andrew; Saltuklaroglu, Tim; Harkrider, Ashley; Cuellar, Megan

2013-01-01

Background Constructivist theories propose that articulatory hypotheses about incoming phonetic targets may function to enhance perception by limiting the possibilities for sensory analysis. To provide evidence for this proposal, it is necessary to map ongoing, high-temporal resolution changes in sensorimotor activity (i.e., the sensorimotor μ rhythm) to accurate speech and non-speech discrimination performance (i.e., correct trials.) Methods Sixteen participants (15 female and 1 male) were asked to passively listen to or actively identify speech and tone-sweeps in a two-force choice discrimination task while the electroencephalograph (EEG) was recorded from 32 channels. The stimuli were presented at signal-to-noise ratios (SNRs) in which discrimination accuracy was high (i.e., 80–100%) and low SNRs producing discrimination performance at chance. EEG data were decomposed using independent component analysis and clustered across participants using principle component methods in EEGLAB. Results ICA revealed left and right sensorimotor µ components for 14/16 and 13/16 participants respectively that were identified on the basis of scalp topography, spectral peaks, and localization to the precentral and postcentral gyri. Time-frequency analysis of left and right lateralized µ component clusters revealed significant (pFDR<.05) suppression in the traditional beta frequency range (13–30 Hz) prior to, during, and following syllable discrimination trials. No significant differences from baseline were found for passive tasks. Tone conditions produced right µ beta suppression following stimulus onset only. For the left µ, significant differences in the magnitude of beta suppression were found for correct speech discrimination trials relative to chance trials following stimulus offset. Conclusions Findings are consistent with constructivist, internal model theories proposing that early forward motor models generate predictions about likely phonemic units that are then synthesized with incoming sensory cues during active as opposed to passive processing. Future directions and possible translational value for clinical populations in which sensorimotor integration may play a functional role are discussed. PMID:23991030

Suppression of the µ rhythm during speech and non-speech discrimination revealed by independent component analysis: implications for sensorimotor integration in speech processing.

PubMed

Bowers, Andrew; Saltuklaroglu, Tim; Harkrider, Ashley; Cuellar, Megan

2013-01-01

Constructivist theories propose that articulatory hypotheses about incoming phonetic targets may function to enhance perception by limiting the possibilities for sensory analysis. To provide evidence for this proposal, it is necessary to map ongoing, high-temporal resolution changes in sensorimotor activity (i.e., the sensorimotor μ rhythm) to accurate speech and non-speech discrimination performance (i.e., correct trials.). Sixteen participants (15 female and 1 male) were asked to passively listen to or actively identify speech and tone-sweeps in a two-force choice discrimination task while the electroencephalograph (EEG) was recorded from 32 channels. The stimuli were presented at signal-to-noise ratios (SNRs) in which discrimination accuracy was high (i.e., 80-100%) and low SNRs producing discrimination performance at chance. EEG data were decomposed using independent component analysis and clustered across participants using principle component methods in EEGLAB. ICA revealed left and right sensorimotor µ components for 14/16 and 13/16 participants respectively that were identified on the basis of scalp topography, spectral peaks, and localization to the precentral and postcentral gyri. Time-frequency analysis of left and right lateralized µ component clusters revealed significant (pFDR<.05) suppression in the traditional beta frequency range (13-30 Hz) prior to, during, and following syllable discrimination trials. No significant differences from baseline were found for passive tasks. Tone conditions produced right µ beta suppression following stimulus onset only. For the left µ, significant differences in the magnitude of beta suppression were found for correct speech discrimination trials relative to chance trials following stimulus offset. Findings are consistent with constructivist, internal model theories proposing that early forward motor models generate predictions about likely phonemic units that are then synthesized with incoming sensory cues during active as opposed to passive processing. Future directions and possible translational value for clinical populations in which sensorimotor integration may play a functional role are discussed.

Sex differences in discriminating between cues predicting threat and safety.

PubMed

Day, Harriet L L; Reed, Molly M; Stevenson, Carl W

2016-09-01

Post-traumatic stress disorder (PTSD) is more prevalent in women than men. PTSD is characterized by overgeneralization of fear to innocuous stimuli and involves impaired inhibition of learned fear by cues that predict safety. While evidence indicates that learned fear inhibition through extinction differs in males and females, less is known about sex differences in fear discrimination and safety learning. Here we examined auditory fear discrimination in male and female rats. In Experiment 1A, rats underwent 1-3days of discrimination training consisting of one tone predicting threat (CS+; presented with footshock) and another tone predicting safety (CS-; presented alone). Females, but not males, discriminated between the CS+ and CS- after one day of training. After 2-3days of training, however, males discriminated whereas females generalized between the CS+ and CS-. In Experiment 1B, females showed enhanced anxiety-like behaviour and locomotor activity in the open field, although these results were unlikely to explain the sex differences in fear discrimination. In Experiment 2, we found no differences in shock sensitivity between males and females. In Experiment 3, males and females again discriminated and generalized, respectively, after three days of training. Moreover, fear generalization in females resulted from impaired safety learning, as shown by a retardation test. Whereas subsequent fear conditioning to the previous CS- retarded learning in males, females showed no such retardation. These results suggest that, while females show fear discrimination with limited training, they show fear generalization with extended training due to impaired safety learning. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Metabolic profiling using HPLC allows classification of drugs according to their mechanisms of action in HL-1 cardiomyocytes

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

Strigun, Alexander; Wahrheit, Judith; Beckers, Simone

Along with hepatotoxicity, cardiotoxic side effects remain one of the major reasons for drug withdrawals and boxed warnings. Prediction methods for cardiotoxicity are insufficient. High content screening comprising of not only electrophysiological characterization but also cellular molecular alterations are expected to improve the cardiotoxicity prediction potential. Metabolomic approaches recently have become an important focus of research in pharmacological testing and prediction. In this study, the culture medium supernatants from HL-1 cardiomyocytes after exposure to drugs from different classes (analgesics, antimetabolites, anthracyclines, antihistamines, channel blockers) were analyzed to determine specific metabolic footprints in response to the tested drugs. Since most drugsmore » influence energy metabolism in cardiac cells, the metabolite 'sub-profile' consisting of glucose, lactate, pyruvate and amino acids was considered. These metabolites were quantified using HPLC in samples after exposure of cells to test compounds of the respective drug groups. The studied drug concentrations were selected from concentration response curves for each drug. The metabolite profiles were randomly split into training/validation and test set; and then analysed using multivariate statistics (principal component analysis and discriminant analysis). Discriminant analysis resulted in clustering of drugs according to their modes of action. After cross validation and cross model validation, the underlying training data were able to predict 50%-80% of conditions to the correct classification group. We show that HPLC based characterisation of known cell culture medium components is sufficient to predict a drug's potential classification according to its mode of action.« less

[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.

Application of Machine Learning to Arterial Spin Labeling in Mild Cognitive Impairment and Alzheimer Disease.

PubMed

Collij, Lyduine E; Heeman, Fiona; Kuijer, Joost P A; Ossenkoppele, Rik; Benedictus, Marije R; Möller, Christiane; Verfaillie, Sander C J; Sanz-Arigita, Ernesto J; van Berckel, Bart N M; van der Flier, Wiesje M; Scheltens, Philip; Barkhof, Frederik; Wink, Alle Meije

2016-12-01

Purpose To investigate whether multivariate pattern recognition analysis of arterial spin labeling (ASL) perfusion maps can be used for classification and single-subject prediction of patients with Alzheimer disease (AD) and mild cognitive impairment (MCI) and subjects with subjective cognitive decline (SCD) after using the W score method to remove confounding effects of sex and age. Materials and Methods Pseudocontinuous 3.0-T ASL images were acquired in 100 patients with probable AD; 60 patients with MCI, of whom 12 remained stable, 12 were converted to a diagnosis of AD, and 36 had no follow-up; 100 subjects with SCD; and 26 healthy control subjects. The AD, MCI, and SCD groups were divided into a sex- and age-matched training set (n = 130) and an independent prediction set (n = 130). Standardized perfusion scores adjusted for age and sex (W scores) were computed per voxel for each participant. Training of a support vector machine classifier was performed with diagnostic status and perfusion maps. Discrimination maps were extracted and used for single-subject classification in the prediction set. Prediction performance was assessed with receiver operating characteristic (ROC) analysis to generate an area under the ROC curve (AUC) and sensitivity and specificity distribution. Results Single-subject diagnosis in the prediction set by using the discrimination maps yielded excellent performance for AD versus SCD (AUC, 0.96; P < .01), good performance for AD versus MCI (AUC, 0.89; P < .01), and poor performance for MCI versus SCD (AUC, 0.63; P = .06). Application of the AD versus SCD discrimination map for prediction of MCI subgroups resulted in good performance for patients with MCI diagnosis converted to AD versus subjects with SCD (AUC, 0.84; P < .01) and fair performance for patients with MCI diagnosis converted to AD versus those with stable MCI (AUC, 0.71; P > .05). Conclusion With automated methods, age- and sex-adjusted ASL perfusion maps can be used to classify and predict diagnosis of AD, conversion of MCI to AD, stable MCI, and SCD with good to excellent accuracy and AUC values. © RSNA, 2016.

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

USGS Publications Warehouse

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

2007-01-01

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

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.

Identification of cognitive and non-cognitive predictive variables related to attrition in baccalaureate nursing education programs in Mississippi

NASA Astrophysics Data System (ADS)

Hayes, Catherine

2005-07-01

This study sought to identify a variable or variables predictive of attrition among baccalaureate nursing students. The study was quantitative in design and multivariate correlational statistics and discriminant statistical analysis were used to identify a model for prediction of attrition. The analysis then weighted variables according to their predictive value to determine the most parsimonious model with the greatest predictive value. Three public university nursing education programs in Mississippi offering a Bachelors Degree in Nursing were selected for the study. The population consisted of students accepted and enrolled in these three programs for the years 2001 and 2002 and graduating in the years 2003 and 2004 (N = 195). The categorical dependent variable was attrition (includes academic failure or withdrawal) from the program of nursing education. The ten independent variables selected for the study and considered to have possible predictive value were: Grade Point Average for Pre-requisite Course Work; ACT Composite Score, ACT Reading Subscore, and ACT Mathematics Subscore; Letter Grades in the Courses: Anatomy & Physiology and Lab I, Algebra I, English I (101), Chemistry & Lab I, and Microbiology & Lab I; and Number of Institutions Attended (Universities, Colleges, Junior Colleges or Community Colleges). Descriptive analysis was performed and the means of each of the ten independent variables was compared for students who attrited and those who were retained in the population. The discriminant statistical analysis performed created a matrix using the ten variable model that was able to correctly predicted attrition in the study's population in 77.6% of the cases. Variables were then combined and recombined to produce the most efficient and parsimonious model for prediction. A six variable model resulted which weighted each variable according to predictive value: GPA for Prerequisite Coursework, ACT Composite, English I, Chemistry & Lab I, Microbiology & Lab I, and Number of Institutions Attended. Results of the study indicate that it is possible to predict attrition among students enrolled in baccalaureate nursing education programs and that additional investigation on the subject is warranted.

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

PubMed

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

2008-11-06

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

Early Prediction of Intensive Care Unit-Acquired Weakness: A Multicenter External Validation Study.

PubMed

Witteveen, Esther; Wieske, Luuk; Sommers, Juultje; Spijkstra, Jan-Jaap; de Waard, Monique C; Endeman, Henrik; Rijkenberg, Saskia; de Ruijter, Wouter; Sleeswijk, Mengalvio; Verhamme, Camiel; Schultz, Marcus J; van Schaik, Ivo N; Horn, Janneke

2018-01-01

An early diagnosis of intensive care unit-acquired weakness (ICU-AW) is often not possible due to impaired consciousness. To avoid a diagnostic delay, we previously developed a prediction model, based on single-center data from 212 patients (development cohort), to predict ICU-AW at 2 days after ICU admission. The objective of this study was to investigate the external validity of the original prediction model in a new, multicenter cohort and, if necessary, to update the model. Newly admitted ICU patients who were mechanically ventilated at 48 hours after ICU admission were included. Predictors were prospectively recorded, and the outcome ICU-AW was defined by an average Medical Research Council score <4. In the validation cohort, consisting of 349 patients, we analyzed performance of the original prediction model by assessment of calibration and discrimination. Additionally, we updated the model in this validation cohort. Finally, we evaluated a new prediction model based on all patients of the development and validation cohort. Of 349 analyzed patients in the validation cohort, 190 (54%) developed ICU-AW. Both model calibration and discrimination of the original model were poor in the validation cohort. The area under the receiver operating characteristics curve (AUC-ROC) was 0.60 (95% confidence interval [CI]: 0.54-0.66). Model updating methods improved calibration but not discrimination. The new prediction model, based on all patients of the development and validation cohort (total of 536 patients) had a fair discrimination, AUC-ROC: 0.70 (95% CI: 0.66-0.75). The previously developed prediction model for ICU-AW showed poor performance in a new independent multicenter validation cohort. Model updating methods improved calibration but not discrimination. The newly derived prediction model showed fair discrimination. This indicates that early prediction of ICU-AW is still challenging and needs further attention.

The Discriminant Analysis Flare Forecasting System (DAFFS)

NASA Astrophysics Data System (ADS)

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

2016-05-01

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

B-type natriuretic peptides and mortality after stroke: a systematic review and meta-analysis.

PubMed

García-Berrocoso, Teresa; Giralt, Dolors; Bustamante, Alejandro; Etgen, Thorleif; Jensen, Jesper K; Sharma, Jagdish C; Shibazaki, Kensaku; Saritas, Ayhan; Chen, Xingyong; Whiteley, William N; Montaner, Joan

2013-12-03

To measure the association of B-type natriuretic peptide (BNP) and N-terminal fragment of BNP (NT-proBNP) with all-cause mortality after stroke, and to evaluate the additional predictive value of BNP/NT-proBNP over clinical information. Suitable studies for meta-analysis were found by searching MEDLINE and EMBASE databases until October 26, 2012. Weighted mean differences measured effect size; meta-regression and publication bias were assessed. Individual participant data were used to estimate effects by logistic regression and to evaluate BNP/NT-proBNP additional predictive value by area under the receiver operating characteristic curves, and integrated discrimination improvement and categorical net reclassification improvement indexes. Literature-based meta-analysis included 3,498 stroke patients from 16 studies and revealed that BNP/NT-proBNP levels were 255.78 pg/mL (95% confidence interval [CI] 105.10-406.47, p = 0.001) higher in patients who died; publication bias entailed the loss of this association. Individual participant data analysis comprised 2,258 stroke patients. After normalization of the data, patients in the highest quartile had double the risk of death after adjustment for clinical variables (NIH Stroke Scale score, age, sex) (odds ratio 2.30, 95% CI 1.32-4.01 for BNP; and odds ratio 2.63, 95% CI 1.75-3.94 for NT-proBNP). Only NT-proBNP showed a slight added value to clinical prognostic variables, increasing discrimination by 0.028 points (integrated discrimination improvement index; p < 0.001) and reclassifying 8.1% of patients into correct risk mortality categories (net reclassification improvement index; p = 0.003). Neither etiology nor time from onset to death affected the association of BNP/NT-proBNP with mortality. BNPs are associated with poststroke mortality independent of NIH Stroke Scale score, age, and sex. However, their translation to clinical practice seems difficult because BNP/NT-proBNP add only minor predictive value to clinical information.

Application of Fourier transform infrared spectroscopy and chemometrics for differentiation of Salmonella enterica serovar Enteritidis phage types.

PubMed

Preisner, Ornella; Guiomar, Raquel; Machado, Jorge; Menezes, José Cardoso; Lopes, João Almeida

2010-06-01

Fourier transform infrared (FT-IR) spectroscopy and chemometric techniques were used to discriminate five closely related Salmonella enterica serotype Enteritidis phage types, phage type 1 (PT1), PT1b, PT4b, PT6, and PT6a. Intact cells and outer membrane protein (OMP) extracts from bacterial cell membranes were subjected to FT-IR analysis in transmittance mode. Spectra were collected over a wavenumber range from 4,000 to 600 cm(-1). Partial least-squares discriminant analysis (PLS-DA) was used to develop calibration models based on preprocessed FT-IR spectra. The analysis based on OMP extracts provided greater separation between the Salmonella Enteritidis PT1-PT1b, PT4b, and PT6-PT6a groups than the intact cell analysis. When these three phage type groups were considered, the method based on OMP extract FT-IR spectra was 100% accurate. Moreover, complementary local models that considered only the PT1-PT1b and PT6-PT6a groups were developed, and the level of discrimination increased. PT1 and PT1b isolates were differentiated successfully with the local model using the entire OMP extract spectrum (98.3% correct predictions), whereas the accuracy of discrimination between PT6 and PT6a isolates was 86.0%. Isolates belonging to different phage types (PT19, PT20, and PT21) were used with the model to test its robustness. For the first time it was demonstrated that FT-IR analysis of OMP extracts can be used for construction of robust models that allow fast and accurate discrimination of different Salmonella Enteritidis phage types.

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

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

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

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

Performance of PRISM III and PELOD-2 scores in a pediatric intensive care unit.

PubMed

Gonçalves, Jean-Pierre; Severo, Milton; Rocha, Carla; Jardim, Joana; Mota, Teresa; Ribeiro, Augusto

2015-10-01

The study aims were to compare two models (The Pediatric Risk of Mortality III (PRISM III) and Pediatric Logistic Organ Dysfunction (PELOD-2)) for prediction of mortality in a pediatric intensive care unit (PICU) and recalibrate PELOD-2 in a Portuguese population. To achieve the previous goal, a prospective cohort study to evaluate score performance (standardized mortality ratio, discrimination, and calibration) for both models was performed. A total of 556 patients consecutively admitted to our PICU between January 2011 and December 2012 were included in the analysis. The median age was 65 months, with an interquartile range of 1 month to 17 years. The male-to-female ratio was 1.5. The median length of PICU stay was 3 days. The overall predicted number of deaths using PRISM III score was 30.8 patients whereas that by PELOD-2 was 22.1 patients. The observed mortality was 29 patients. The area under the receiver operating characteristics curve for the two models was 0.92 and 0.94, respectively. The Hosmer and Lemeshow goodness-of-fit test showed a good calibration only for PRISM III (PRISM III: χ (2) = 3.820, p = 0.282; PELOD-2: χ (2) = 9.576, p = 0.022). Both scores had good discrimination. PELOD-2 needs recalibration to be a better reliable prediction tool. • PRISM III (Pediatric Risk of Mortality III) and PELOD (Pediatric Logistic Organ Dysfunction) scores are frequently used to assess the performance of intensive care units and also for mortality prediction in the pediatric population. • Pediatric Logistic Organ Dysfunction 2 is the newer version of PELOD and has recently been validated with good discrimination and calibration. What is New: • In our population, both scores had good discrimination. • PELOD-2 needs recalibration to be a better reliable prediction tool.

Relationships among neighborhood environment, racial discrimination, psychological distress, and preterm birth in African American women.

PubMed

Giurgescu, Carmen; Zenk, Shannon N; Dancy, Barbara L; Park, Chang G; Dieber, William; Block, Richard

2012-01-01

To (a) examine the relationships among objective and perceived indicators of neighborhood environment, racial discrimination, psychological distress, and gestational age at birth; (b) determine if neighborhood environment and racial discrimination predicted psychological distress; (c) determine if neighborhood environment, racial discrimination, and psychological distress predicted preterm birth; and (d) determine if psychological distress mediated the effects of neighborhood environment and racial discrimination on preterm birth. Descriptive correlational comparative. Postpartum unit of a medical center in Chicago. African American women (n(1)  = 33 with preterm birth; n(2)  = 39 with full-term birth). Women completed the instruments 24 to 72 hours after birth. Objective measures of the neighborhood were derived using geographic information systems (GIS). Women who reported higher levels of perceived social and physical disorder and perceived crime also reported higher levels of psychological distress. Women who reported more experiences of racial discrimination also had higher levels of psychological distress. Objective social disorder and perceived crime predicted psychological distress. Objective physical disorder and psychological distress predicted preterm birth. Psychological distress mediated the effect of objective social disorder and perceived crime on preterm birth. Women's neighborhood environments and racial discrimination were related to psychological distress, and these factors may increase the risk for preterm birth. © 2012 AWHONN, the Association of Women's Health, Obstetric and Neonatal Nurses.

The impact of racial discrimination and coping strategies on internalizing symptoms in African American youth.

PubMed

Gaylord-Harden, Noni K; Cunningham, Jamila A

2009-04-01

The current study examined the impact of racial discrimination stress on internalizing symptoms and coping strategies in a sample of 268 African American early adolescents (mean age = 12.90; 56% female) from low-income communities. Information about discrimination stress, coping, and internalizing symptoms was obtained via adolescents' self-report. It was predicted that discrimination stress would be positively associated with depression and anxiety, as well as culturally-specific coping. Finally, culturally-relevant coping and mainstream coping were examined as moderators of the association between discrimination stress and internalizing symptoms. Hierarchical regression analyses indicated that discrimination stress was positively associated with depression and anxiety and predicted culturally-relevant coping while controlling for mainstream coping. Communalistic coping moderated the association between discrimination and anxiety, but demonstrated a vulnerability function by increasing anxiety at high levels of discrimination. The results highlight the salience of racial discrimination for African American adolescents and the importance of considering culturally-specific coping behaviors.

The distinct emotional flavor of Gnostic writings from the early Christian era.

PubMed

Whissell, Cynthia

2008-02-01

More than 500,000 scored words in 83 documents were used to conclude that it is possible to identify the source of documents (proto-orthodox Christian versus early Gnostic) on the basis of the emotions underlying the words. Twenty-seven New Testament works and seven Gnostic documents (including the gospels of Thomas, Judas, and Mary [Magdalene]) were scored with the Dictionary of Affect in Language. Patterns of emotional word use focusing on eight types of extreme emotional words were employed in a discriminant function analysis to predict source. Prediction was highly successful (canonical r = .81, 97% correct identification of source). When the discriminant function was tested with more than 30 additional Gnostic and Christian works including a variety of translations and some wisdom books, it correctly classified all of them. The majority of the predictive power of the function (97% of all correct categorizations, 70% of the canonical r2) was associated with the preferential presence of passive and passive/pleasant words in Gnostic documents.

Attrition from an Adolescent Addiction Treatment Program: A Cross Validation.

ERIC Educational Resources Information Center

Mathisen, Kenneth S.; Meyers, Kathleen

Treatment attrition is a major problem for programs treating adolescent substance abusers. To isolate and cross validate factors which are predictive of addiction treatment attrition among adolescent substance abusers, screening interview and diagnostic variables from 119 adolescent in-patients were submitted to a discriminant equation analysis.…

Enhancing ADHD and LD Diagnostic Accuracy Using Career Instruments

ERIC Educational Resources Information Center

Dipeolu, Abiola; Hargrave, Stephanie; Storlie, Cassandra A.

2015-01-01

Individuals diagnosed with mental health disorders may have work-related difficulties that impact functioning in all life domains. With limited research on the integration of career and mental health counseling, authors used a discriminant function analysis to assess the predictability of accurately identifying diagnostic categories among 258…

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...

Mapping Environmental Suitability for Malaria Transmission, Greece

PubMed Central

Sudre, Bertrand; Rossi, Massimiliano; Van Bortel, Wim; Danis, Kostas; Baka, Agoritsa; Vakalis, Nikos

2013-01-01

During 2009–2012, Greece experienced a resurgence of domestic malaria transmission. To help guide malaria response efforts, we used spatial modeling to characterize environmental signatures of areas suitable for transmission. Nonlinear discriminant analysis indicated that sea-level altitude and land-surface temperature parameters are predictive in this regard. PMID:23697370

Maximizing Enrollment Yield through Financial Aid Packaging Policies

ERIC Educational Resources Information Center

Spaulding, Randy; Olswang, Steven

2005-01-01

Using institutional data, this paper presents a model to enable researchers and enrollment managers to assess the effectiveness of financial aid packaging policies in light of student characteristics and institutional market position. The model uses discriminant analysis and a series of hypothetical financial aid award scenarios to predict the…

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

PubMed

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

2013-07-17

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

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.

Prediction of Cortical Defect Using C-Reactive Protein and Urine Sodium to Potassium Ratio in Infants with Febrile Urinary Tract Infection

PubMed Central

Jung, Su Jin

2016-01-01

Purpose We investigated whether C-reactive protein (CRP) levels, urine protein-creatinine ratio (uProt/Cr), and urine electrolytes can be useful for discriminating acute pyelonephritis (APN) from other febrile illnesses or the presence of a cortical defect on 99mTc dimercaptosuccinic acid (DMSA) scanning (true APN) from its absence in infants with febrile urinary tract infection (UTI). Materials and Methods We examined 150 infants experiencing their first febrile UTI and 100 controls with other febrile illnesses consecutively admitted to our hospital from January 2010 to December 2012. Blood (CRP, electrolytes, Cr) and urine tests [uProt/Cr, electrolytes, and sodium-potassium ratio (uNa/K)] were performed upon admission. All infants with UTI underwent DMSA scans during admission. All data were compared between infants with UTI and controls and between infants with or without a cortical defect on DMSA scans. Using multiple logistic regression analysis, the ability of the parameters to predict true APN was analyzed. Results CRP levels and uProt/Cr were significantly higher in infants with true APN than in controls. uNa levels and uNa/K were significantly lower in infants with true APN than in controls. CRP levels and uNa/K were relevant factors for predicting true APN. The method using CRP levels, u-Prot/Cr, u-Na levels, and uNa/K had a sensitivity of 94%, specificity of 65%, positive predictive value of 60%, and negative predictive value of 95% for predicting true APN. Conclusion We conclude that these parameters are useful for discriminating APN from other febrile illnesses or discriminating true APN in infants with febrile UTI. PMID:26632389

Identifying pollution sources and predicting urban air quality using ensemble learning methods

NASA Astrophysics Data System (ADS)

Singh, Kunwar P.; Gupta, Shikha; Rai, Premanjali

2013-12-01

In this study, principal components analysis (PCA) was performed to identify air pollution sources and tree based ensemble learning models were constructed to predict the urban air quality of Lucknow (India) using the air quality and meteorological databases pertaining to a period of five years. PCA identified vehicular emissions and fuel combustion as major air pollution sources. The air quality indices revealed the air quality unhealthy during the summer and winter. Ensemble models were constructed to discriminate between the seasonal air qualities, factors responsible for discrimination, and to predict the air quality indices. Accordingly, single decision tree (SDT), decision tree forest (DTF), and decision treeboost (DTB) were constructed and their generalization and predictive performance was evaluated in terms of several statistical parameters and compared with conventional machine learning benchmark, support vector machines (SVM). The DT and SVM models discriminated the seasonal air quality rendering misclassification rate (MR) of 8.32% (SDT); 4.12% (DTF); 5.62% (DTB), and 6.18% (SVM), respectively in complete data. The AQI and CAQI regression models yielded a correlation between measured and predicted values and root mean squared error of 0.901, 6.67 and 0.825, 9.45 (SDT); 0.951, 4.85 and 0.922, 6.56 (DTF); 0.959, 4.38 and 0.929, 6.30 (DTB); 0.890, 7.00 and 0.836, 9.16 (SVR) in complete data. The DTF and DTB models outperformed the SVM both in classification and regression which could be attributed to the incorporation of the bagging and boosting algorithms in these models. The proposed ensemble models successfully predicted the urban ambient air quality and can be used as effective tools for its management.

Racial Discrimination during Adolescence Predicts Mental Health Deterioration in Adulthood: Gender Differences among Blacks.

PubMed

Assari, Shervin; Moazen-Zadeh, Ehsan; Caldwell, Cleopatra Howard; Zimmerman, Marc A

2017-01-01

Despite the existing knowledge regarding the negative mental health consequences of perceived racial discrimination, very few researchers have used a longitudinal design with long-term follow-up periods to explore gender differences in this association over time. The current longitudinal study aimed to investigate gender differences in predictive role of an increase in perceived racial discrimination during adolescence for mental health deterioration a decade later when they are transitioning to young adulthood. Current study followed 681 Black youths for 18 years from 1994 (mean age 15) to 2012 (mean age 32). All participants spent their adolescence and transition to young adulthood in an economically disadvantaged urban area in the Midwest of the United States. Independent variable was perceived racial discrimination measured in 1999 and 2002. Outcomes were psychological symptoms (anxiety and depression) measured in 1999 and at end of follow-up (2012). Covariates included sociodemographics (age, family structure, and parental employment) measured in 1994. Gender was used to define groups in a multigroup structural equation model to test moderating effects. Multigroup structural equation modeling showed that among male Black youth, an increase in perceived racial discrimination from age 20 to 23 was predictive for an increase in symptoms of anxiety and depression from age 20 to 32. Among female Black youth, change in perceived racial discrimination did not predict future change in depressive or anxiety symptoms. While racial discrimination is associated with negative mental health consequences for both genders, male and female Black youth differ in regard to long-term effects of an increase in perceived discrimination on deterioration of psychological symptoms. Black males seem to be more susceptible than Black females to the psychological effects of an increase in racial discrimination over time.

Study on fast discrimination of varieties of yogurt using Vis/NIR-spectroscopy

NASA Astrophysics Data System (ADS)

He, Yong; Feng, Shuijuan; Deng, Xunfei; Li, Xiaoli

2006-09-01

A new approach for discrimination of varieties of yogurt by means of VisINTR-spectroscopy was present in this paper. Firstly, through the principal component analysis (PCA) of spectroscopy curves of 5 typical kinds of yogurt, the clustering of yogurt varieties was processed. The analysis results showed that the cumulate reliabilities of PC1 and PC2 (the first two principle components) were more than 98.956%, and the cumulate reliabilities from PC1 to PC7 (the first seven principle components) was 99.97%. Secondly, a discrimination model of Artificial Neural Network (ANN-BP) was set up. The first seven principles components of the samples were applied as ANN-BP inputs, and the value of type of yogurt were applied as outputs, then the three-layer ANN-BP model was build. In this model, every variety yogurt includes 27 samples, the total number of sample is 135, and the rest 25 samples were used as prediction set. The results showed the distinguishing rate of the five yogurt varieties was 100%. It presented that this model was reliable and practicable. So a new approach for the rapid and lossless discrimination of varieties of yogurt was put forward.

The Erosive Effects of Racism: Reduced Self-control Mediates the Relation between Perceived Racial Discrimination and Substance Use in African American Adolescents

PubMed Central

Gibbons, Frederick X.; O'Hara, Ross E.; Stock, Michelle L.; Gerrard, Meg; Weng, Chih-Yuan; Wills, Thomas A.

2012-01-01

Perceived racial discrimination, self-control, anger, and either substance use or use cognitions were assessed in two studies conducted with samples of African American adolescents. The primary goal was to examine the relation between discrimination and self-control over time; a second goal was to determine if that relation mediates the link between discrimination and substance use found in previous research. Study 1, which included a latent growth curve analysis with three waves of data, indicated that experience with discrimination (from age 10 to age 18) was associated with reduced self-control, which then predicted increased substance use. Additional analyses indicated anger was also a mediator of this discrimination to use relation. Study 2, which was experimental, showed that envisioning an experience involving discrimination was associated with an increase in substance-related responses to double entendre words (e.g., “pot,” “roach”) in a word association task, especially for participants who were low in dispositional self-control. The effect was again mediated by reports of anger. Thus, the “double mediation” pattern was: discrimination → more anger and reduced self-control → increased substance use and/or substance cognitions. Results are discussed in terms of the long-term impact of discrimination on self-control and health behavior. Implications for interventions aimed at ameliorating the negative effects of discrimination and low self-control on health are also discussed. PMID:22390225

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

NASA Astrophysics Data System (ADS)

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

2015-05-01

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

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.

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

PubMed

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

2012-01-01

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

Seasonal forecasts in the Sahel region: the use of rainfall-based predictive variables

NASA Astrophysics Data System (ADS)

Lodoun, Tiganadaba; Sanon, Moussa; Giannini, Alessandra; Traoré, Pierre Sibiry; Somé, Léopold; Rasolodimby, Jeanne Millogo

2014-08-01

In the Sahel region, seasonal predictions are crucial to alleviate the impacts of climate variability on populations' livelihoods. Agricultural planning (e.g., decisions about sowing date, fertilizer application date, and choice of crop or cultivar) is based on empirical predictive indices whose accuracy to date has not been scientifically proven. This paper attempts to statistically test whether the pattern of rainfall distribution over the May-July period contributes to predicting the real onset date and the nature (wet or dry) of the rainy season, as farmers believe. To that end, we considered historical records of daily rainfall from 51 stations spanning the period 1920-2008 and the different agro-climatic zones in Burkina Faso. We performed (1) principal component analysis to identify climatic zones, based on the patterns of intra-seasonal rainfall, (2) and linear discriminant analysis to find the best rainfall-based variables to distinguish between real and false onset dates of the rainy season, and between wet and dry seasons in each climatic zone. A total of nine climatic zones were identified in each of which, based on rainfall records from May to July, we derived linear discriminant functions to correctly predict the nature of a potential onset date of the rainy season (real or false) and that of the rainy season (dry or wet) in at least three cases out of five. These functions should contribute to alleviating the negative impacts of climate variability in the different climatic zones of Burkina Faso.

MTB-DR-RIF 9G test: Detection and discrimination of tuberculosis and multi-drug resistant tuberculosis strains.

PubMed

Song, Keum-Soo; Nimse, Satish Balasaheb; Cho, Nam Hoon; Sung, Nackmoon; Kim, Hee-Jin; Yang, Jeongseong; Kim, Taisun

2015-12-01

This report describes the evaluation of the novel MTB-DR-RIF 9G test for the accurate detection and discrimination of Mycobacterium tuberculosis (MTB) and rifampicin-resistant M. tuberculosis (MTB-DR-RIF) in the clinical samples. The procedure included the amplification of a nucleotide fragment of the rpoB gene of the MTB and MTB-DR-RIF strains and their hybridization with the immobilized probes. The MTB-DR-RIF 9G test was evaluated for its ability to detect and discriminate MTB and MTB-DR-RIF strains in 113 known clinical samples. The accuracy of the MTB-DR-RIF 9G test was determined by comparing its results with sequencing analysis and drug susceptibility testing. The sensitivity and specificity of the MTB-DR-RIF 9G test at 95% confidence interval were found to be 95.4% (89.5-98.5) and 100% (69.2-100), respectively. The positive predictive value and negative predictive value of the MTB-DR-RIF 9G test at 95% confidence interval were found to be 100% (85.0-95.9) and 66.7% (38.4-88.18), respectively. Sequencing analysis of all samples indicated that the mutations present in the regions identified with the MTB-DR-RIF 9G assay can be detected accurately. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Study of beta-turns in globular proteins].

PubMed

Amirova, S R; Milchevskiĭ, Iu V; Filatov, I V; Esipova, N G; Tumanian, V G

2005-01-01

The formation of beta-turns in globular proteins has been studied by the method of molecular mechanics. Statistical method of discriminant analysis was applied to calculate energy components and sequences of oligopeptide segments, and after this prediction of I type beta-turns has been drawn. The accuracy of true positive prediction is 65%. Components of conformational energy considerably affecting beta-turn formation were delineated. There are torsional energy, energy of hydrogen bonds, and van der Waals energy.

Automatic personality assessment through social media language.

PubMed

Park, Gregory; Schwartz, H Andrew; Eichstaedt, Johannes C; Kern, Margaret L; Kosinski, Michal; Stillwell, David J; Ungar, Lyle H; Seligman, Martin E P

2015-06-01

Language use is a psychologically rich, stable individual difference with well-established correlations to personality. We describe a method for assessing personality using an open-vocabulary analysis of language from social media. We compiled the written language from 66,732 Facebook users and their questionnaire-based self-reported Big Five personality traits, and then we built a predictive model of personality based on their language. We used this model to predict the 5 personality factors in a separate sample of 4,824 Facebook users, examining (a) convergence with self-reports of personality at the domain- and facet-level; (b) discriminant validity between predictions of distinct traits; (c) agreement with informant reports of personality; (d) patterns of correlations with external criteria (e.g., number of friends, political attitudes, impulsiveness); and (e) test-retest reliability over 6-month intervals. Results indicated that language-based assessments can constitute valid personality measures: they agreed with self-reports and informant reports of personality, added incremental validity over informant reports, adequately discriminated between traits, exhibited patterns of correlations with external criteria similar to those found with self-reported personality, and were stable over 6-month intervals. Analysis of predictive language can provide rich portraits of the mental life associated with traits. This approach can complement and extend traditional methods, providing researchers with an additional measure that can quickly and cheaply assess large groups of participants with minimal burden. (c) 2015 APA, all rights reserved).

Isotopic incorporation rates and discrimination factors in mantis shrimp crustaceans.

PubMed

deVries, Maya S; Del Rio, Carlos Martínez; Tunstall, Tate S; Dawson, Todd E

2015-01-01

Stable isotope analysis has provided insights into the trophic ecology of a wide diversity of animals. Knowledge about isotopic incorporation rates and isotopic discrimination between the consumer and its diet for different tissue types is essential for interpreting stable isotope data, but these parameters remain understudied in many animal taxa and particularly in aquatic invertebrates. We performed a 292-day diet shift experiment on 92 individuals of the predatory mantis shrimp, Neogonodactylus bredini, to quantify carbon and nitrogen incorporation rates and isotope discrimination factors in muscle and hemolymph tissues. Average isotopic discrimination factors between mantis shrimp muscle and the new diet were 3.0 ± 0.6 ‰ and 0.9 ± 0.3 ‰ for carbon and nitrogen, respectively, which is contrary to what is seen in many other animals (e.g. C and N discrimination is generally 0-1 ‰ and 3-4 ‰, respectively). Surprisingly, the average residence time of nitrogen in hemolymph (28.9 ± 8.3 days) was over 8 times longer than that of carbon (3.4 ± 1.4 days). In muscle, the average residence times of carbon and nitrogen were of the same magnitude (89.3 ± 44.4 and 72.8 ± 18.8 days, respectively). We compared the mantis shrimps' incorporation rates, along with rates from four other invertebrate taxa from the literature, to those predicted by an allometric equation relating carbon incorporation rate to body mass that was developed for teleost fishes and sharks. The rate of carbon incorporation into muscle was consistent with rates predicted by this equation. Our findings provide new insight into isotopic discrimination factors and incorporation rates in invertebrates with the former showing a different trend than what is commonly observed in other animals.

Isotopic Incorporation Rates and Discrimination Factors in Mantis Shrimp Crustaceans

PubMed Central

deVries, Maya S.; del Rio, Carlos Martínez; Tunstall, Tate S.; Dawson, Todd E.

2015-01-01

Stable isotope analysis has provided insights into the trophic ecology of a wide diversity of animals. Knowledge about isotopic incorporation rates and isotopic discrimination between the consumer and its diet for different tissue types is essential for interpreting stable isotope data, but these parameters remain understudied in many animal taxa and particularly in aquatic invertebrates. We performed a 292-day diet shift experiment on 92 individuals of the predatory mantis shrimp, Neogonodactylus bredini, to quantify carbon and nitrogen incorporation rates and isotope discrimination factors in muscle and hemolymph tissues. Average isotopic discrimination factors between mantis shrimp muscle and the new diet were 3.0 ± 0.6 ‰ and 0.9 ± 0.3 ‰ for carbon and nitrogen, respectively, which is contrary to what is seen in many other animals (e.g. C and N discrimination is generally 0–1 ‰ and 3–4 ‰, respectively). Surprisingly, the average residence time of nitrogen in hemolymph (28.9 ± 8.3 days) was over 8 times longer than that of carbon (3.4 ± 1.4 days). In muscle, the average residence times of carbon and nitrogen were of the same magnitude (89.3 ± 44.4 and 72.8 ± 18.8 days, respectively). We compared the mantis shrimps’ incorporation rates, along with rates from four other invertebrate taxa from the literature, to those predicted by an allometric equation relating carbon incorporation rate to body mass that was developed for teleost fishes and sharks. The rate of carbon incorporation into muscle was consistent with rates predicted by this equation. Our findings provide new insight into isotopic discrimination factors and incorporation rates in invertebrates with the former showing a different trend than what is commonly observed in other animals. PMID:25835953

From discrimination to internalized mental illness stigma: The mediating roles of anticipated discrimination and anticipated stigma.

PubMed

Quinn, Diane M; Williams, Michelle K; Weisz, Bradley M

2015-06-01

Internalizing mental illness stigma is related to poorer well-being, but less is known about the factors that predict levels of internalized stigma. This study explored how experiences of discrimination relate to greater anticipation of discrimination and devaluation in the future and how anticipation of stigma in turn predicts greater stigma internalization. Participants were 105 adults with mental illness who self-reported their experiences of discrimination based on their mental illness, their anticipation of discrimination and social devaluation from others in the future, and their level of internalized stigma. Participants were approached in several locations and completed surveys on laptop computers. Correlational analyses indicated that more experiences of discrimination due to one's mental illness were related to increased anticipated discrimination in the future, increased anticipated social stigma from others, and greater internalized stigma. Multiple serial mediator analyses showed that the effect of experiences of discrimination on internalized stigma was fully mediated by increased anticipated discrimination and anticipated stigma. Experiences of discrimination over one's lifetime may influence not only how much future discrimination people with mental illness are concerned with but also how much they internalize negative feelings about the self. Mental health professionals may need to address concerns with future discrimination and devaluation in order to decrease internalized stigma. (c) 2015 APA, all rights reserved).

Developing and validating a predictive model for stroke progression.

PubMed

Craig, L E; Wu, O; Gilmour, H; Barber, M; Langhorne, P

2011-01-01

Progression is believed to be a common and important complication in acute stroke, and has been associated with increased mortality and morbidity. Reliable identification of predictors of early neurological deterioration could potentially benefit routine clinical care. The aim of this study was to identify predictors of early stroke progression using two independent patient cohorts. Two patient cohorts were used for this study - the first cohort formed the training data set, which included consecutive patients admitted to an urban teaching hospital between 2000 and 2002, and the second cohort formed the test data set, which included patients admitted to the same hospital between 2003 and 2004. A standard definition of stroke progression was used. The first cohort (n = 863) was used to develop the model. Variables that were statistically significant (p < 0.1) on univariate analysis were included in the multivariate model. Logistic regression was the technique employed using backward stepwise regression to drop the least significant variables (p > 0.1) in turn. The second cohort (n = 216) was used to test the performance of the model. The performance of the predictive model was assessed in terms of both calibration and discrimination. Multiple imputation methods were used for dealing with the missing values. Variables shown to be significant predictors of stroke progression were conscious level, history of coronary heart disease, presence of hyperosmolarity, CT lesion, living alone on admission, Oxfordshire Community Stroke Project classification, presence of pyrexia and smoking status. The model appears to have reasonable discriminative properties [the median receiver-operating characteristic curve value was 0.72 (range 0.72-0.73)] and to fit well with the observed data, which is indicated by the high goodness-of-fit p value [the median p value from the Hosmer-Lemeshow test was 0.90 (range 0.50-0.92)]. The predictive model developed in this study contains variables that can be easily collected in practice therefore increasing its usability in clinical practice. Using this analysis approach, the discrimination and calibration of the predictive model appear sufficiently high to provide accurate predictions. This study also offers some discussion around the validation of predictive models for wider use in clinical practice.

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

DOE PAGES

Thomas, Edward V.; Lewis, John R.; Anderson-Cook, Christine M.; ...

2017-11-21

nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factorsmore » and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one another over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.« less

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

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

Thomas, Edward V.; Lewis, John R.; Anderson-Cook, Christine M.

nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factorsmore » and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one another over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.« less

Predictive Accuracy of the Liverpool Lung Project Risk Model for Stratifying Patients for Computed Tomography Screening for Lung Cancer

PubMed Central

Raji, Olaide Y.; Duffy, Stephen W.; Agbaje, Olorunshola F.; Baker, Stuart G.; Christiani, David C.; Cassidy, Adrian; Field, John K.

2013-01-01

Background External validation of existing lung cancer risk prediction models is limited. Using such models in clinical practice to guide the referral of patients for computed tomography (CT) screening for lung cancer depends on external validation and evidence of predicted clinical benefit. Objective To evaluate the discrimination of the Liverpool Lung Project (LLP) risk model and demonstrate its predicted benefit for stratifying patients for CT screening by using data from 3 independent studies from Europe and North America. Design Case–control and prospective cohort study. Setting Europe and North America. Patients Participants in the European Early Lung Cancer (EUELC) and Harvard case–control studies and the LLP population-based prospective cohort (LLPC) study. Measurements 5-year absolute risks for lung cancer predicted by the LLP model. Results The LLP risk model had good discrimination in both the Harvard (area under the receiver-operating characteristic curve [AUC], 0.76 [95% CI, 0.75 to 0.78]) and the LLPC (AUC, 0.82 [CI, 0.80 to 0.85]) studies and modest discrimination in the EUELC (AUC, 0.67 [CI, 0.64 to 0.69]) study. The decision utility analysis, which incorporates the harms and benefit of using a risk model to make clinical decisions, indicates that the LLP risk model performed better than smoking duration or family history alone in stratifying high-risk patients for lung cancer CT screening. Limitations The model cannot assess whether including other risk factors, such as lung function or genetic markers, would improve accuracy. Lack of information on asbestos exposure in the LLPC limited the ability to validate the complete LLP risk model. Conclusion Validation of the LLP risk model in 3 independent external data sets demonstrated good discrimination and evidence of predicted benefits for stratifying patients for lung cancer CT screening. Further studies are needed to prospectively evaluate model performance and evaluate the optimal population risk thresholds for initiating lung cancer screening. Primary Funding Source Roy Castle Lung Cancer Foundation. PMID:22910935

Rapid Prediction of Hematologic Acute Radiation Syndrome in Radiation Injury Patients Using Peripheral Blood Cell Counts.

PubMed

Port, M; Pieper, B; Knie, T; Dörr, H; Ganser, A; Graessle, D; Meineke, V; Abend, M

2017-08-01

Rapid clinical triage of radiation injury patients is essential for determining appropriate diagnostic and therapeutic interventions. We examined the utility of blood cell counts (BCCs) in the first three days postirradiation to predict clinical outcome, specifically for hematologic acute radiation syndrome (HARS). We analyzed BCC test samples from radiation accident victims (n = 135) along with their clinical outcome HARS severity scores (H1-4) using the System for Evaluation and Archiving of Radiation Accidents based on Case Histories (SEARCH) database. Data from nonirradiated individuals (H0, n = 132) were collected from an outpatient facility. We created binary categories for severity scores, i.e., 1 (H0 vs. H1-4), 2 (H0-1 vs. H2-4) and 3 (H0-2 vs. H3-4), to assess the discrimination ability of BCCs using unconditional logistic regression analysis. The test sample contained 454 BCCs from 267 individuals. We validated the discrimination ability on a second independent group comprised of 275 BCCs from 252 individuals originating from SEARCH (HARS 1-4), an outpatient facility (H0) and hospitals (e.g., leukemia patients, H4). Individuals with a score of H0 were easily separated from exposed individuals based on developing lymphopenia and granulocytosis. The separation of H0 and H1-4 became more prominent with increasing hematologic severity scores and time. On day 1, lymphocyte counts were most predictive for discriminating binary categories, followed by granulocytes and thrombocytes. For days 2 and 3, an almost complete separation was achieved when BCCs from different days were combined, supporting the measurement of sequential BCC. We found an almost complete discrimination of H0 vs. irradiated individuals during model validation (negative predictive value, NPV > 94%) for all three days, while the correct prediction of exposed individuals increased from day 1 (positive predictive value, PPV 78-89%) to day 3 (PPV > 90%). The models were unable to provide predictions for 10.9% of the test samples, because the PPVs or NPVs did not reach a 95% likelihood defined as the lower limit for a prediction. We developed a prediction model spreadsheet to provide early and prompt diagnostic predictions and therapeutic recommendations including identification of the worried well, requirement of hospitalization or development of severe hematopoietic syndrome. These results improve the provisional classification of HARS. For the final diagnosis, further procedures (sequential diagnosis, retrospective dosimetry, clinical follow-up, etc.) must be taken into account. Clinical outcome of radiation injury patients can be rapidly predicted within the first three days postirradiation using peripheral BCC.

Stimulus discriminability in visual search.

PubMed

Verghese, P; Nakayama, K

1994-09-01

We measured the probability of detecting the target in a visual search task, as a function of the following parameters: the discriminability of the target from the distractors, the duration of the display, and the number of elements in the display. We examined the relation between these parameters at criterion performance (80% correct) to determine if the parameters traded off according to the predictions of a limited capacity model. For the three dimensions that we studied, orientation, color, and spatial frequency, the observed relationship between the parameters deviates significantly from a limited capacity model. The data relating discriminability to display duration are better than predicted over the entire range of orientation and color differences that we examined, and are consistent with the prediction for only a limited range of spatial frequency differences--from 12 to 23%. The relation between discriminability and number varies considerably across the three dimensions and is better than the limited capacity prediction for two of the three dimensions that we studied. Orientation discrimination shows a strong number effect, color discrimination shows almost no effect, and spatial frequency discrimination shows an intermediate effect. The different trading relationships in each dimension are more consistent with early filtering in that dimension, than with a common limited capacity stage. Our results indicate that higher-level processes that group elements together also play a strong role. Our experiments provide little support for limited capacity mechanisms over the range of stimulus differences that we examined in three different dimensions.

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

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).

Online Learning for Classification of Alzheimer Disease based on Cortical Thickness and Hippocampal Shape Analysis.

PubMed

Lee, Ga-Young; Kim, Jeonghun; Kim, Ju Han; Kim, Kiwoong; Seong, Joon-Kyung

2014-01-01

Mobile healthcare applications are becoming a growing trend. Also, the prevalence of dementia in modern society is showing a steady growing trend. Among degenerative brain diseases that cause dementia, Alzheimer disease (AD) is the most common. The purpose of this study was to identify AD patients using magnetic resonance imaging in the mobile environment. We propose an incremental classification for mobile healthcare systems. Our classification method is based on incremental learning for AD diagnosis and AD prediction using the cortical thickness data and hippocampus shape. We constructed a classifier based on principal component analysis and linear discriminant analysis. We performed initial learning and mobile subject classification. Initial learning is the group learning part in our server. Our smartphone agent implements the mobile classification and shows various results. With use of cortical thickness data analysis alone, the discrimination accuracy was 87.33% (sensitivity 96.49% and specificity 64.33%). When cortical thickness data and hippocampal shape were analyzed together, the achieved accuracy was 87.52% (sensitivity 96.79% and specificity 63.24%). In this paper, we presented a classification method based on online learning for AD diagnosis by employing both cortical thickness data and hippocampal shape analysis data. Our method was implemented on smartphone devices and discriminated AD patients for normal group.

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

PubMed

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

2016-01-01

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

Anti-A2 and anti-A1 domain antibodies are potential predictors of immune tolerance induction outcome in children with hemophilia A.

PubMed

Lapalud, P; Rothschild, C; Mathieu-Dupas, E; Balicchi, J; Gruel, Y; Laune, D; Molina, F; Schved, J F; Granier, C; Lavigne-Lissalde, G

2015-04-01

Hemophilia A (HA) is a congenital bleeding disorder resulting from factor VIII deficiency. The most serious complication of HA management is the appearance of inhibitory antibodies (Abs) against injected FVIII concentrates. To eradicate inhibitors, immune tolerance induction (ITI) is usually attempted, but it fails in up to 30% of cases. Currently, no undisputed predictive marker of ITI outcome is available to facilitate the clinical decision. To identify predictive markers of ITI efficacy. The isotypic and epitopic repertoires of inhibitory Abs were analyzed in plasma samples collected before ITI initiation from 15 children with severe HA and high-titer inhibitors, and their levels were compared in the two outcome groups (ITI success [n = 7] and ITI failure [n = 8]). The predictive value of these candidate biomarkers and of the currently used indicators (inhibitor titer and age at ITI initiation, highest inhibitor titer before ITI, and interval between inhibitor diagnosis and ITI initiation) was then compared by statistical analysis (Wilcoxon test and receiver receiver operating characteristic [ROC] curve analysis). Whereas current indicators seemed to fail in discriminating patients in the two outcome groups (ITI success or failure), anti-A1 and anti-A2 Ab levels before ITI initiation appeared to be good potential predictive markers of ITI outcome (P < 0.018). ROC analysis showed that anti-A1 and anti-A2 Abs were the best at discriminating between outcome groups (area under the ROC curve of > 0.875). Anti-A1 and anti-A2 Abs could represent new promising tools for the development of ITI outcome prediction tests for children with severe HA. © 2015 International Society on Thrombosis and Haemostasis.

[Discrimination of donkey meat by NIR and chemometrics].

PubMed

Niu, Xiao-Ying; Shao, Li-Min; Dong, Fang; Zhao, Zhi-Lei; Zhu, Yan

2014-10-01

Donkey meat samples (n = 167) from different parts of donkey body (neck, costalia, rump, and tendon), beef (n = 47), pork (n = 51) and mutton (n = 32) samples were used to establish near-infrared reflectance spectroscopy (NIR) classification models in the spectra range of 4,000~12,500 cm(-1). The accuracies of classification models constructed by Mahalanobis distances analysis, soft independent modeling of class analogy (SIMCA) and least squares-support vector machine (LS-SVM), respectively combined with pretreatment of Savitzky-Golay smooth (5, 15 and 25 points) and derivative (first and second), multiplicative scatter correction and standard normal variate, were compared. The optimal models for intact samples were obtained by Mahalanobis distances analysis with the first 11 principal components (PCs) from original spectra as inputs and by LS-SVM with the first 6 PCs as inputs, and correctly classified 100% of calibration set and 98. 96% of prediction set. For minced samples of 7 mm diameter the optimal result was attained by LS-SVM with the first 5 PCs from original spectra as inputs, which gained an accuracy of 100% for calibration and 97.53% for prediction. For minced diameter of 5 mm SIMCA model with the first 8 PCs from original spectra as inputs correctly classified 100% of calibration and prediction. And for minced diameter of 3 mm Mahalanobis distances analysis and SIMCA models both achieved 100% accuracy for calibration and prediction respectively with the first 7 and 9 PCs from original spectra as inputs. And in these models, donkey meat samples were all correctly classified with 100% either in calibration or prediction. The results show that it is feasible that NIR with chemometrics methods is used to discriminate donkey meat from the else meat.

Genome analysis of Legionella pneumophila strains using a mixed-genome microarray.

PubMed

Euser, Sjoerd M; Nagelkerke, Nico J; Schuren, Frank; Jansen, Ruud; Den Boer, Jeroen W

2012-01-01

Legionella, the causative agent for Legionnaires' disease, is ubiquitous in both natural and man-made aquatic environments. The distribution of Legionella genotypes within clinical strains is significantly different from that found in environmental strains. Developing novel genotypic methods that offer the ability to distinguish clinical from environmental strains could help to focus on more relevant (virulent) Legionella species in control efforts. Mixed-genome microarray data can be used to perform a comparative-genome analysis of strain collections, and advanced statistical approaches, such as the Random Forest algorithm are available to process these data. Microarray analysis was performed on a collection of 222 Legionella pneumophila strains, which included patient-derived strains from notified cases in The Netherlands in the period 2002-2006 and the environmental strains that were collected during the source investigation for those patients within the Dutch National Legionella Outbreak Detection Programme. The Random Forest algorithm combined with a logistic regression model was used to select predictive markers and to construct a predictive model that could discriminate between strains from different origin: clinical or environmental. Four genetic markers were selected that correctly predicted 96% of the clinical strains and 66% of the environmental strains collected within the Dutch National Legionella Outbreak Detection Programme. The Random Forest algorithm is well suited for the development of prediction models that use mixed-genome microarray data to discriminate between Legionella strains from different origin. The identification of these predictive genetic markers could offer the possibility to identify virulence factors within the Legionella genome, which in the future may be implemented in the daily practice of controlling Legionella in the public health environment.

Visual speech alters the discrimination and identification of non-intact auditory speech in children with hearing loss.

PubMed

Jerger, Susan; Damian, Markus F; McAlpine, Rachel P; Abdi, Hervé

2017-03-01

Understanding spoken language is an audiovisual event that depends critically on the ability to discriminate and identify phonemes yet we have little evidence about the role of early auditory experience and visual speech on the development of these fundamental perceptual skills. Objectives of this research were to determine 1) how visual speech influences phoneme discrimination and identification; 2) whether visual speech influences these two processes in a like manner, such that discrimination predicts identification; and 3) how the degree of hearing loss affects this relationship. Such evidence is crucial for developing effective intervention strategies to mitigate the effects of hearing loss on language development. Participants were 58 children with early-onset sensorineural hearing loss (CHL, 53% girls, M = 9;4 yrs) and 58 children with normal hearing (CNH, 53% girls, M = 9;4 yrs). Test items were consonant-vowel (CV) syllables and nonwords with intact visual speech coupled to non-intact auditory speech (excised onsets) as, for example, an intact consonant/rhyme in the visual track (Baa or Baz) coupled to non-intact onset/rhyme in the auditory track (/-B/aa or/-B/az). The items started with an easy-to-speechread/B/or difficult-to-speechread/G/onset and were presented in the auditory (static face) vs. audiovisual (dynamic face) modes. We assessed discrimination for intact vs. non-intact different pairs (e.g., Baa:/-B/aa). We predicted that visual speech would cause the non-intact onset to be perceived as intact and would therefore generate more same-as opposed to different-responses in the audiovisual than auditory mode. We assessed identification by repetition of nonwords with non-intact onsets (e.g.,/-B/az). We predicted that visual speech would cause the non-intact onset to be perceived as intact and would therefore generate more Baz-as opposed to az- responses in the audiovisual than auditory mode. Performance in the audiovisual mode showed more same responses for the intact vs. non-intact different pairs (e.g., Baa:/-B/aa) and more intact onset responses for nonword repetition (Baz for/-B/az). Thus visual speech altered both discrimination and identification in the CHL-to a large extent for the/B/onsets but only minimally for the/G/onsets. The CHL identified the stimuli similarly to the CNH but did not discriminate the stimuli similarly. A bias-free measure of the children's discrimination skills (i.e., d' analysis) revealed that the CHL had greater difficulty discriminating intact from non-intact speech in both modes. As the degree of HL worsened, the ability to discriminate the intact vs. non-intact onsets in the auditory mode worsened. Discrimination ability in CHL significantly predicted their identification of the onsets-even after variation due to the other variables was controlled. These results clearly established that visual speech can fill in non-intact auditory speech, and this effect, in turn, made the non-intact onsets more difficult to discriminate from intact speech and more likely to be perceived as intact. Such results 1) demonstrate the value of visual speech at multiple levels of linguistic processing and 2) support intervention programs that view visual speech as a powerful asset for developing spoken language in CHL. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Visual Speech Alters the Discrimination and Identification of Non-Intact Auditory Speech in Children with Hearing Loss

PubMed Central

Jerger, Susan; Damian, Markus F.; McAlpine, Rachel P.; Abdi, Hervé

2017-01-01

Objectives Understanding spoken language is an audiovisual event that depends critically on the ability to discriminate and identify phonemes yet we have little evidence about the role of early auditory experience and visual speech on the development of these fundamental perceptual skills. Objectives of this research were to determine 1) how visual speech influences phoneme discrimination and identification; 2) whether visual speech influences these two processes in a like manner, such that discrimination predicts identification; and 3) how the degree of hearing loss affects this relationship. Such evidence is crucial for developing effective intervention strategies to mitigate the effects of hearing loss on language development. Methods Participants were 58 children with early-onset sensorineural hearing loss (CHL, 53% girls, M = 9;4 yrs) and 58 children with normal hearing (CNH, 53% girls, M = 9;4 yrs). Test items were consonant-vowel (CV) syllables and nonwords with intact visual speech coupled to non-intact auditory speech (excised onsets) as, for example, an intact consonant/rhyme in the visual track (Baa or Baz) coupled to non-intact onset/rhyme in the auditory track (/–B/aa or /–B/az). The items started with an easy-to-speechread /B/ or difficult-to-speechread /G/ onset and were presented in the auditory (static face) vs. audiovisual (dynamic face) modes. We assessed discrimination for intact vs. non-intact different pairs (e.g., Baa:/–B/aa). We predicted that visual speech would cause the non-intact onset to be perceived as intact and would therefore generate more same—as opposed to different—responses in the audiovisual than auditory mode. We assessed identification by repetition of nonwords with non-intact onsets (e.g., /–B/az). We predicted that visual speech would cause the non-intact onset to be perceived as intact and would therefore generate more Baz—as opposed to az— responses in the audiovisual than auditory mode. Results Performance in the audiovisual mode showed more same responses for the intact vs. non-intact different pairs (e.g., Baa:/–B/aa) and more intact onset responses for nonword repetition (Baz for/–B/az). Thus visual speech altered both discrimination and identification in the CHL—to a large extent for the /B/ onsets but only minimally for the /G/ onsets. The CHL identified the stimuli similarly to the CNH but did not discriminate the stimuli similarly. A bias-free measure of the children’s discrimination skills (i.e., d’ analysis) revealed that the CHL had greater difficulty discriminating intact from non-intact speech in both modes. As the degree of HL worsened, the ability to discriminate the intact vs. non-intact onsets in the auditory mode worsened. Discrimination ability in CHL significantly predicted their identification of the onsets—even after variation due to the other variables was controlled. Conclusions These results clearly established that visual speech can fill in non-intact auditory speech, and this effect, in turn, made the non-intact onsets more difficult to discriminate from intact speech and more likely to be perceived as intact. Such results 1) demonstrate the value of visual speech at multiple levels of linguistic processing and 2) support intervention programs that view visual speech as a powerful asset for developing spoken language in CHL. PMID:28167003

Assessment of sexual orientation using the hemodynamic brain response to visual sexual stimuli.

PubMed

Ponseti, Jorge; Granert, Oliver; Jansen, Olav; Wolff, Stephan; Mehdorn, Hubertus; Bosinski, Hartmut; Siebner, Hartwig

2009-06-01

The assessment of sexual orientation is of importance to the diagnosis and treatment of sex offenders and paraphilic disorders. Phallometry is considered gold standard in objectifying sexual orientation, yet this measurement has been criticized because of its intrusiveness and limited reliability. To evaluate whether the spatial response pattern to sexual stimuli as revealed by a change in blood oxygen level-dependent (BOLD) signal can be used for individual classification of sexual orientation. We used a preexisting functional MRI (fMRI) data set that had been acquired in a nonclinical sample of 12 heterosexual men and 14 homosexual men. During fMRI, participants were briefly exposed to pictures of same-sex and opposite-sex genitals. Data analysis involved four steps: (i) differences in the BOLD response to female and male sexual stimuli were calculated for each subject; (ii) these contrast images were entered into a group analysis to calculate whole-brain difference maps between homosexual and heterosexual participants; (iii) a single expression value was computed for each subject expressing its correspondence to the group result; and (iv) based on these expression values, Fisher's linear discriminant analysis and the kappa-nearest neighbor classification method were used to predict the sexual orientation of each subject. Sensitivity and specificity of the two classification methods in predicting individual sexual orientation. Both classification methods performed well in predicting individual sexual orientation with a mean accuracy of >85% (Fisher's linear discriminant analysis: 92% sensitivity, 85% specificity; kappa-nearest neighbor classification: 88% sensitivity, 92% specificity). Despite the small sample size, the functional response patterns of the brain to sexual stimuli contained sufficient information to predict individual sexual orientation with high accuracy. These results suggest that fMRI-based classification methods hold promise for the diagnosis of paraphilic disorders (e.g., pedophilia).

Toward an integrative and predictive sperm quality analysis in Bos taurus.

PubMed

Yániz, J L; Soler, C; Alquézar-Baeta, C; Santolaria, P

2017-06-01

There is a need to develop more integrative sperm quality analysis methods, enabling researchers to evaluate different parameters simultaneously cell by cell. In this work, we present a new multi-parametric fluorescent test able to discriminate different sperm subpopulations based on their labeling pattern and motility characteristics. Cryopreserved semen samples from 20 Holstein bulls were used in the study. Analyses of sperm motility using computer-assisted sperm analysis (CASA-mot), membrane integrity by acridine orange-propidium iodide combination and multi-parametric by the ISAS ® 3Fun kit, were performed. The new method allows a clear discrimination of sperm subpopulations based on membrane and acrosomal integrity, motility and morphology. It was also possible to observe live spermatozoa showing signs of capacitation such as hyperactivated motility and changes in acrosomal structure. Sperm subpopulation with intact plasma membrane and acrosome showed a higher proportion of motile sperm than those with damaged acrosome or increased fluorescence intensity. Spermatozoa with intact plasmalemma and damaged acrosome were static or exhibit weak movement. Significant correlations among the different sperm quality parameters evaluated were also described. We concluded that the ISAS ® 3Fun is an integrated method that represents an advance in sperm quality analysis with the potential to improve fertility predictions. Copyright © 2017 Elsevier B.V. All rights reserved.

Discrimination and sexual risk among young urban pregnant women of color.

PubMed

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

2014-01-01

Discrimination predicts increased risk for many negative health outcomes, helping explain a variety of racial and socioeconomic health disparities. Recent research suggests discrimination may play a role in disparities in HIV and other sexually transmitted infections (STIs); however, this research has focused on risk behaviors and has yet to establish a link between discrimination and STI diagnosis specifically. This investigation tested whether discrimination predicted condom use, risky sexual partners, and self-reported STI diagnosis among a population disproportionately affected by HIV and STIs in the U.S.: young, pregnant, socioeconomically disadvantaged, women of color. During second and third trimesters, 885 mostly Latina and Black pregnant women, 14-21 years old, attending 14 hospitals and health centers in New York City for prenatal care, completed interviews. Greater discrimination during second trimester predicted greater odds of STI diagnosis and having a risky sexual partner during third trimester, but not condom use. Whether discrimination was attributed to race, identifying as Black, or identifying as Latina did not moderate effects. This is the first investigation establishing a link between discrimination and STI diagnosis, not just risk behavior. It does so among a sample of at-risk, young, pregnant, women of color. Findings suggest implications for sexual risk during pregnancy and across the life span, and risks for the pregnancy and fetus. It is vital to reduce discrimination to eliminate disparities in HIV and STIs. Future research should continue examining the role of discrimination in sexual risk among different populations and work to uncover potential mechanisms. 2014 APA, all rights reserved

Discrimination and Sexual Risk Among Young Urban Pregnant Women of Color

PubMed Central

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

2014-01-01

Objective Discrimination predicts increased risk for many negative health outcomes, helping explain a variety of racial and socioeconomic health disparities. Recent research suggests discrimination may play a role in disparities in HIV and other sexually transmitted infections (STIs); however, this research has focused on risk behaviors and has yet to establish a link between discrimination and STI diagnosis specifically. This investigation tested whether discrimination predicted condom use, risky sexual partners, and self-reported STI diagnosis among a population disproportionately affected by HIV and STIs in the U.S.: young, pregnant, socioeconomically disadvantaged, women of color. Method During second and third trimesters, 885 mostly Latina and Black pregnant women, 14–21 years old, attending 14 hospitals and health centers in New York City for prenatal care, completed interviews. Results Greater discrimination during second trimester predicted greater odds of STI diagnosis and having a risky sexual partner during third trimester, but not condom use. Whether discrimination was attributed to race, identifying as Black, or identifying as Latina did not moderate effects. Conclusion This is the first investigation establishing a link between discrimination and STI diagnosis, not just risk behavior. It does so among a sample of at-risk, young, pregnant, women of color. Findings suggest implications for sexual risk during pregnancy and across the life span, and risks for the pregnancy and fetus. It is vital to reduce discrimination to eliminate disparities in HIV and STIs. Future research should continue examining the role of discrimination in sexual risk among different populations and work to uncover potential mechanisms. PMID:24417689

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

PubMed Central

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

2012-01-01

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

An exploratory study of organization design configurations in health care delivery organizations.

PubMed

Sheppeck, Mick; Militello, Jack

2014-01-01

Organizations are configurations of variables that support each other to achieve customer satisfaction. Based on Treacy and Wiersema (1995), we predicted the emergence of two configurations, one supporting a product leadership stance and one predicting the customer intimate approach from a set of 73 for profit health care clinics. In addition, we predicted the emergence of a configuration where the scores on most variables were near the mean for each variable. Using cluster analysis and discriminant function analysis, we identified three configurations: one a "master of two" strategy, one "stuck-in-the-middle," and one showing scores well below the mean on most variables. The implications for organization design and manager actions in the health care industry are discussed.

Male Batterer Profiles: Support for an Empirically Generated Typology

ERIC Educational Resources Information Center

Chiffriller, Sheila H.; Hennessy, James J.

2006-01-01

Domestic violence is an issue that affects different sides of society; numerous studies have shown that it is a problem that is increasing. This study used a discriminant analysis to identify those scales that might be more effective at predicting group membership among batterer types: pathological batterers, sexually violent batterers, generally…

Modeling Career Counselor Decisions with Artificial Neural Networks: Predictions of Fit across a Comprehensive Occupational Map.

ERIC Educational Resources Information Center

Carson, Andrew D.; Bizot, Elizabeth B.; Hendershot, Peggy E.; Barton, Margaret G.; Garvin, Mary K.; Kraemer, Barbara

1999-01-01

Career recommendations were made based on aptitude scores of 335 high school freshmen. Artificial neural networks were used to map recommendations to 12 occupational clusters. Overall accuracy of neural networks (.80) approached that of discriminant function analysis (.84). The two methods had different strengths and weaknesses. (SK)

Genetic polymorphisms to predict gains in maximal O2 uptake and knee peak torque after a high intensity training program in humans.

PubMed

Yoo, Jinho; Kim, Bo-Hyung; Kim, Soo-Hwan; Kim, Yangseok; Yim, Sung-Vin

2016-05-01

The study aimed to identify single nucleotide polymorphisms (SNPs) that significantly influenced the level of improvement of two kinds of training responses, including maximal O2 uptake (V'O2max) and knee peak torque of healthy adults participating in the high intensity training (HIT) program. The study also aimed to use these SNPs to develop prediction models for individual training responses. 79 Healthy volunteers participated in the HIT program. A genome-wide association study, based on 2,391,739 SNPs, was performed to identify SNPs that were significantly associated with gains in V'O2max and knee peak torque, following 9 weeks of the HIT program. To predict two training responses, two independent SNPs sets were determined using linear regression and iterative binary logistic regression analysis. False discovery rate analysis and permutation tests were performed to avoid false-positive findings. To predict gains in V'O2max, 7 SNPs were identified. These SNPs accounted for 26.0 % of the variance in the increment of V'O2max, and discriminated the subjects into three subgroups, non-responders, medium responders, and high responders, with prediction accuracy of 86.1 %. For the knee peak torque, 6 SNPs were identified, and accounted for 27.5 % of the variance in the increment of knee peak torque. The prediction accuracy discriminating the subjects into the three subgroups was estimated as 77.2 %. Novel SNPs found in this study could explain, and predict inter-individual variability in gains of V'O2max, and knee peak torque. Furthermore, with these genetic markers, a methodology suggested in this study provides a sound approach for the personalized training program.

Temporal processing impairment in children with attention-deficit-hyperactivity disorder.

PubMed

Huang, Jia; Yang, Bin-rang; Zou, Xiao-bing; Jing, Jin; Pen, Gang; McAlonan, Gráinne M; Chan, Raymond C K

2012-01-01

The current study aimed to investigate temporal processing in Chinese children with Attention-Deficit-Hyperactivity Disorder(ADHD) using time production, time reproduction paradigm and duration discrimination tasks. A battery of tests specifically designed to measure temporal processing was administered to 94 children with ADHD and 100 demographically matched healthy children. A multivariate analysis of variance (MANOVA) and a repeated measure MANOVA indicated that children with ADHD were impaired in time processing functions. The results of pairwise comparisons showed that the probands with a family history of ADHD performed significantly worse than those without family history in the time production tasks and the time reproduction task. Logistic regression analysis showed duration discrimination had a significant role in predicting whether the children were suffering from ADHD or not, while temporal processing had a significant role in predicting whether the ADHD children had a family history or not. This study provides further support for the existence of a generic temporal processing impairment in ADHD children and suggests that abnormalities in time processing and ADHD share some common genetic factors. Copyright © 2011 Elsevier Ltd. All rights reserved.

Accumulator and random-walk models of psychophysical discrimination: a counter-evaluation.

PubMed

Vickers, D; Smith, P

1985-01-01

In a recent assessment of models of psychophysical discrimination, Heath criticises the accumulator model for its reliance on computer simulation and qualitative evidence, and contrasts it unfavourably with a modified random-walk model, which yields exact predictions, is susceptible to critical test, and is provided with simple parameter-estimation techniques. A counter-evaluation is presented, in which the approximations employed in the modified random-walk analysis are demonstrated to be seriously inaccurate, the resulting parameter estimates to be artefactually determined, and the proposed test not critical. It is pointed out that Heath's specific application of the model is not legitimate, his data treatment inappropriate, and his hypothesis concerning confidence inconsistent with experimental results. Evidence from adaptive performance changes is presented which shows that the necessary assumptions for quantitative analysis in terms of the modified random-walk model are not satisfied, and that the model can be reconciled with data at the qualitative level only by making it virtually indistinguishable from an accumulator process. A procedure for deriving exact predictions for an accumulator process is outlined.

Parent Discrimination Predicts Mexican-American Adolescent Psychological Adjustment One Year Later

PubMed Central

Gonzales, Nancy A.; Fuligni, Andrew J.

2016-01-01

This study examined whether Mexican-American parent’s experiences with discrimination are related to adolescent psychological adjustment over time. The extent to which associations between parent discrimination and adolescent adjustment vary as a function of parent’s ethnic socialization of their children was also examined. Participants included 344 high school students from Mexican or Mexican-American backgrounds (primarily second generation; ages 14 – 16 at Wave 1) and their primary caregivers who completed surveys in a two-year longitudinal study. Results revealed that parent discrimination predicted internalizing symptoms and self-esteem among adolescents, one year later. Additionally, adolescents were more likely to report low self-esteem in relation to parents’ increased experiences of discrimination when parents conveyed ethnic socialization messages to them. PMID:27224903

A diagnostic model for the detection of sensitization to wheat allergens was developed and validated in bakery workers.

PubMed

Suarthana, Eva; Vergouwe, Yvonne; Moons, Karel G; de Monchy, Jan; Grobbee, Diederick; Heederik, Dick; Meijer, Evert

2010-09-01

To develop and validate a prediction model to detect sensitization to wheat allergens in bakery workers. The prediction model was developed in 867 Dutch bakery workers (development set, prevalence of sensitization 13%) and included questionnaire items (candidate predictors). First, principal component analysis was used to reduce the number of candidate predictors. Then, multivariable logistic regression analysis was used to develop the model. Internal validation and extent of optimism was assessed with bootstrapping. External validation was studied in 390 independent Dutch bakery workers (validation set, prevalence of sensitization 20%). The prediction model contained the predictors nasoconjunctival symptoms, asthma symptoms, shortness of breath and wheeze, work-related upper and lower respiratory symptoms, and traditional bakery. The model showed good discrimination with an area under the receiver operating characteristic (ROC) curve area of 0.76 (and 0.75 after internal validation). Application of the model in the validation set gave a reasonable discrimination (ROC area=0.69) and good calibration after a small adjustment of the model intercept. A simple model with questionnaire items only can be used to stratify bakers according to their risk of sensitization to wheat allergens. Its use may increase the cost-effectiveness of (subsequent) medical surveillance.

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Investigating the Predictive Value of Functional MRI to Appetitive and Aversive Stimuli: A Pattern Classification Approach

PubMed Central

McCabe, Ciara; Rocha-Rego, Vanessa

2016-01-01

Background Dysfunctional neural responses to appetitive and aversive stimuli have been investigated as possible biomarkers for psychiatric disorders. However it is not clear to what degree these are separate processes across the brain or in fact overlapping systems. To help clarify this issue we used Gaussian process classifier (GPC) analysis to examine appetitive and aversive processing in the brain. Method 25 healthy controls underwent functional MRI whilst seeing pictures and receiving tastes of pleasant and unpleasant food. We applied GPCs to discriminate between the appetitive and aversive sights and tastes using functional activity patterns. Results The diagnostic accuracy of the GPC for the accuracy to discriminate appetitive taste from neutral condition was 86.5% (specificity = 81%, sensitivity = 92%, p = 0.001). If a participant experienced neutral taste stimuli the probability of correct classification was 92. The accuracy to discriminate aversive from neutral taste stimuli was 82.5% (specificity = 73%, sensitivity = 92%, p = 0.001) and appetitive from aversive taste stimuli was 73% (specificity = 77%, sensitivity = 69%, p = 0.001). In the sight modality, the accuracy to discriminate appetitive from neutral condition was 88.5% (specificity = 85%, sensitivity = 92%, p = 0.001), to discriminate aversive from neutral sight stimuli was 92% (specificity = 92%, sensitivity = 92%, p = 0.001), and to discriminate aversive from appetitive sight stimuli was 63.5% (specificity = 73%, sensitivity = 54%, p = 0.009). Conclusions Our results demonstrate the predictive value of neurofunctional data in discriminating emotional and neutral networks of activity in the healthy human brain. It would be of interest to use pattern recognition techniques and fMRI to examine network dysfunction in the processing of appetitive, aversive and neutral stimuli in psychiatric disorders. Especially where problems with reward and punishment processing have been implicated in the pathophysiology of the disorder. PMID:27870866

Explaining negative kin discrimination in a cooperative mammal society

PubMed Central

Cant, Michael A.; Sanderson, Jennifer L.; Gilchrist, Jason S.; Bell, Matthew B. V.; Hodge, Sarah J.; Johnstone, Rufus A.

2017-01-01

Kin selection theory predicts that, where kin discrimination is possible, animals should typically act more favorably toward closer genetic relatives and direct aggression toward less closely related individuals. Contrary to this prediction, we present data from an 18-y study of wild banded mongooses, Mungos mungo, showing that females that are more closely related to dominant individuals are specifically targeted for forcible eviction from the group, often suffering severe injury, and sometimes death, as a result. This pattern cannot be explained by inbreeding avoidance or as a response to more intense local competition among kin. Instead, we use game theory to show that such negative kin discrimination can be explained by selection for unrelated targets to invest more effort in resisting eviction. Consistent with our model, negative kin discrimination is restricted to eviction attempts of older females capable of resistance; dominants exhibit no kin discrimination when attempting to evict younger females, nor do they discriminate between more closely or less closely related young when carrying out infanticidal attacks on vulnerable infants who cannot defend themselves. We suggest that in contexts where recipients of selfish acts are capable of resistance, the usual prediction of positive kin discrimination can be reversed. Kin selection theory, as an explanation for social behavior, can benefit from much greater exploration of sequential social interactions. PMID:28439031

Testing Math or Testing Language? The Construct Validity of the KeyMath-Revised for Children With Intellectual Disability and Language Difficulties.

PubMed

Rhodes, Katherine T; Branum-Martin, Lee; Morris, Robin D; Romski, MaryAnn; Sevcik, Rose A

2015-11-01

Although it is often assumed that mathematics ability alone predicts mathematics test performance, linguistic demands may also predict achievement. This study examined the role of language in mathematics assessment performance for children with intellectual disability (ID) at less severe levels, on the KeyMath-Revised Inventory (KM-R) with a sample of 264 children, in grades 2-5. Using confirmatory factor analysis, the hypothesis that the KM-R would demonstrate discriminant validity with measures of language abilities in a two-factor model was compared to two plausible alternative models. Results indicated that KM-R did not have discriminant validity with measures of children's language abilities and was a multidimensional test of both mathematics and language abilities for this population of test users. Implications are considered for test development, interpretation, and intervention.

Current target acquisition methodology in force on force simulations

NASA Astrophysics Data System (ADS)

Hixson, Jonathan G.; Miller, Brian; Mazz, John P.

2017-05-01

The U.S. Army RDECOM CERDEC NVESD MSD's target acquisition models have been used for many years by the military community in force on force simulations for training, testing, and analysis. There have been significant improvements to these models over the past few years. The significant improvements are the transition of ACQUIRE TTP-TAS (ACQUIRE Targeting Task Performance Target Angular Size) methodology for all imaging sensors and the development of new discrimination criteria for urban environments and humans. This paper is intended to provide an overview of the current target acquisition modeling approach and provide data for the new discrimination tasks. This paper will discuss advances and changes to the models and methodologies used to: (1) design and compare sensors' performance, (2) predict expected target acquisition performance in the field, (3) predict target acquisition performance for combat simulations, and (4) how to conduct model data validation for combat simulations.

Real-Time Classification of Exercise Exertion Levels Using Discriminant Analysis of HRV Data.

PubMed

Jeong, In Cheol; Finkelstein, Joseph

2015-01-01

Heart rate variability (HRV) was shown to reflect activation of sympathetic nervous system however it is not clear which set of HRV parameters is optimal for real-time classification of exercise exertion levels. There is no studies that compared potential of two types of HRV parameters (time-domain and frequency-domain) in predicting exercise exertion level using discriminant analysis. The main goal of this study was to compare potential of HRV time-domain parameters versus HRV frequency-domain parameters in classifying exercise exertion level. Rest, exercise, and recovery categories were used in classification models. Overall 79.5% classification agreement by the time-domain parameters as compared to overall 52.8% classification agreement by frequency-domain parameters demonstrated that the time-domain parameters had higher potential in classifying exercise exertion levels.

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

PubMed

Kruggel, Frithjof; Masaki, Fumitaro; Solodkin, Ana

2017-02-15

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

B-type natriuretic peptides and mortality after stroke

PubMed Central

García-Berrocoso, Teresa; Giralt, Dolors; Bustamante, Alejandro; Etgen, Thorleif; Jensen, Jesper K.; Sharma, Jagdish C.; Shibazaki, Kensaku; Saritas, Ayhan; Chen, Xingyong; Whiteley, William N.

2013-01-01

Objective: To measure the association of B-type natriuretic peptide (BNP) and N-terminal fragment of BNP (NT-proBNP) with all-cause mortality after stroke, and to evaluate the additional predictive value of BNP/NT-proBNP over clinical information. Methods: Suitable studies for meta-analysis were found by searching MEDLINE and EMBASE databases until October 26, 2012. Weighted mean differences measured effect size; meta-regression and publication bias were assessed. Individual participant data were used to estimate effects by logistic regression and to evaluate BNP/NT-proBNP additional predictive value by area under the receiver operating characteristic curves, and integrated discrimination improvement and categorical net reclassification improvement indexes. Results: Literature-based meta-analysis included 3,498 stroke patients from 16 studies and revealed that BNP/NT-proBNP levels were 255.78 pg/mL (95% confidence interval [CI] 105.10–406.47, p = 0.001) higher in patients who died; publication bias entailed the loss of this association. Individual participant data analysis comprised 2,258 stroke patients. After normalization of the data, patients in the highest quartile had double the risk of death after adjustment for clinical variables (NIH Stroke Scale score, age, sex) (odds ratio 2.30, 95% CI 1.32–4.01 for BNP; and odds ratio 2.63, 95% CI 1.75–3.94 for NT-proBNP). Only NT-proBNP showed a slight added value to clinical prognostic variables, increasing discrimination by 0.028 points (integrated discrimination improvement index; p < 0.001) and reclassifying 8.1% of patients into correct risk mortality categories (net reclassification improvement index; p = 0.003). Neither etiology nor time from onset to death affected the association of BNP/NT-proBNP with mortality. Conclusion: BNPs are associated with poststroke mortality independent of NIH Stroke Scale score, age, and sex. However, their translation to clinical practice seems difficult because BNP/NT-proBNP add only minor predictive value to clinical information. PMID:24186915

C-reactive protein in the detection of post-stroke infections: systematic review and individual participant data analysis.

PubMed

Bustamante, Alejandro; Vilar-Bergua, Andrea; Guettier, Sophie; Sánchez-Poblet, Josep; García-Berrocoso, Teresa; Giralt, Dolors; Fluri, Felix; Topakian, Raffi; Worthmann, Hans; Hug, Andreas; Molnar, Tihamer; Waje-Andreassen, Ulrike; Katan, Mira; Smith, Craig J; Montaner, Joan

2017-04-01

We conducted a systematic review and individual participant data meta-analysis to explore the role of C-reactive protein (CRP) in early detection or prediction of post-stroke infections. CRP, an acute-phase reactant binds to the phosphocholine expressed on the surface of dead or dying cells and some bacteria, thereby activating complement and promoting phagocytosis by macrophages. We searched PubMed up to May-2015 for studies measuring CRP in stroke and evaluating post-stroke infections. Individual participants' data were merged into a single database. CRP levels were standardized and divided into quartiles. Factors independently associated with post-stroke infections were determined by logistic regression analysis and the additional predictive value of CRP was assessed by comparing areas under receiver operating characteristic curves and integrated discrimination improvement index. Data from seven studies including 699 patients were obtained. Standardized CRP levels were higher in patients with post-stroke infections beyond 24 h. Standardized CRP levels in the fourth quartile were independently associated with infection in two different logistic regression models, model 1 [stroke severity and dysphagia, odds ratio = 9.70 (3.10-30.41)] and model 2 [age, sex, and stroke severity, odds ratio = 3.21 (1.93-5.32)]. Addition of CRP improved discrimination in both models [integrated discrimination improvement = 9.83% (0.89-18.77) and 5.31% (2.83-7.79), respectively], but accuracy was only improved for model 1 (area under the curve 0.806-0.874, p = 0.036). In this study, CRP was independently associated with development of post-stroke infections, with the optimal time-window for measurement at 24-48 h. However, its additional predictive value is moderate over clinical information. Combination with other biomarkers in a panel seems a promising strategy for future studies. © 2017 International Society for Neurochemistry.

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.

Geographical identification of saffron (Crocus sativus L.) by linear discriminant analysis applied to the UV-visible spectra of aqueous extracts.

PubMed

D'Archivio, Angelo Antonio; Maggi, Maria Anna

2017-03-15

We attempted geographical classification of saffron using UV-visible spectroscopy, conventionally adopted for quality grading according to the ISO Normative 3632. We investigated 81 saffron samples produced in L'Aquila, Città della Pieve, Cascia, and Sardinia (Italy) and commercial products purchased in various supermarkets. Exploratory principal component analysis applied to the UV-vis spectra of saffron aqueous extracts revealed a clear differentiation of the samples belonging to different quality categories, but a poor separation according to the geographical origin of the spices. On the other hand, linear discriminant analysis based on 8 selected absorbance values, concentrated near 279, 305 and 328nm, allowed a good distinction of the spices coming from different sites. Under severe validation conditions (30% and 50% of saffron samples in the evaluation set), correct predictions were 85 and 83%, respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

PubMed

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

2013-12-01

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

From Discrimination to Internalized Mental Illness Stigma: The Mediating Roles of Anticipated Discrimination and Anticipated Stigma

PubMed Central

Quinn, Diane M.; Williams, Michelle K.; Weisz, Bradley M.

2015-01-01

Objective Internalizing mental illness stigma is related to poorer well-being, but less is known about the factors that predict levels of internalized stigma. This study explored how experiences of discrimination relate to greater anticipation of discrimination and devaluation in the future, and how anticipation of stigma, in turn predicts greater stigma internalization. Method Participants were 105 adults with mental illness who self-reported their experiences of discrimination based on their mental illness, their anticipation of discrimination and social devaluation from others in the future, and their level of internalized stigma. Participants were approached in several locations and completed surveys on laptop computers. Results Correlational analyses indicated that more experiences of discrimination due to one’s mental illness were related to increased anticipated discrimination in the future, increased anticipated social stigma from others, and greater internalized stigma. Multiple serial mediator analyses showed that the effect of experiences of discrimination on internalized stigma was fully mediated by increased anticipated discrimination and anticipated stigma. Conclusion and Implications for Practice Experiences of discrimination over the lifetime may influence not only how much future discrimination people with mental illness are concerned with but also how much they internalize negative feelings about the self. Mental health professionals may need to address concerns with future discrimination and devaluation in order to decrease internalized stigma. PMID:25844910

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

PubMed

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

2017-03-10

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

Diagnostic potential of endotoxin scattering photometry for sepsis and septic shock.

PubMed

Shimizu, Tomoharu; Obata, Toru; Sonoda, Hiromichi; Akabori, Hiroya; Miyake, Tohru; Yamamoto, Hiroshi; Tabata, Takahisa; Eguchi, Yutaka; Tani, Tohru

2013-12-01

Endotoxin scattering photometry (ESP) is a novel Limulus amebocyte lysate (LAL) assay that uses a laser light-scattering particle-counting method. In the present study, we compared ESP, standard turbidimetric LAL assay, and procalcitonin assay for the evaluation of sepsis after emergency gastrointestinal surgery. A total of 174 samples were collected from 40 adult patients undergoing emergency gastrointestinal surgery and 10 patients with colorectal cancer undergoing elective surgery as nonseptic controls. Plasma endotoxin levels were measured with ESP and turbidimetric LAL assay, and plasma procalcitonin levels were assessed with a standard procalcitonin assay. Plasma endotoxin and procalcitonin levels increased corresponding to the degree of sepsis. Endotoxin scattering photometry significantly discriminated between patients with or without septic shock: sensitivity, 81.1%; specificity, 76.6%; positive predictive value, 48.4%; negative predictive value, 93.8%; and accuracy, 77.6%. The area under the receiver operating characteristic curve for septic shock with the ESP assay (endotoxin cutoff value, 23.8 pg/mL) was 0.8532 ± 0.0301 (95% confidence interval, 0.7841-0.9030; P < 0.0001). The predictive power of ESP was superior to that of turbidimetric assay (difference, 0.1965 ± 0.0588; 95% confidence interval, 0.0812-0.3117; P = 0.0008). There was no significant difference in predictive power between ESP and procalcitonin assay. Endotoxin scattering photometry also discriminated between patients with and without sepsis. Area under the receiver operating characteristic curve analysis showed that ESP had the best predictive power for diagnosing sepsis. In conclusion, compared with turbidimetric LAL assay, ESP more sensitively detected plasma endotoxin and significantly discriminated between sepsis and septic shock in patients undergoing gastrointestinal emergency surgery.

Image Discrimination Models Predict Object Detection in Natural Backgrounds

NASA Technical Reports Server (NTRS)

Ahumada, Albert J., Jr.; Rohaly, A. M.; Watson, Andrew B.; Null, Cynthia H. (Technical Monitor)

1994-01-01

Object detection involves looking for one of a large set of object sub-images in a large set of background images. Image discrimination models only predict the probability that an observer will detect a difference between two images. In a recent study based on only six different images, we found that discrimination models can predict the relative detectability of objects in those images, suggesting that these simpler models may be useful in some object detection applications. Here we replicate this result using a new, larger set of images. Fifteen images of a vehicle in an other-wise natural setting were altered to remove the vehicle and mixed with the original image in a proportion chosen to make the target neither perfectly recognizable nor unrecognizable. The target was also rotated about a vertical axis through its center and mixed with the background. Sixteen observers rated these 30 target images and the 15 background-only images for the presence of a vehicle. The likelihoods of the observer responses were computed from a Thurstone scaling model with the assumption that the detectabilities are proportional to the predictions of an image discrimination model. Three image discrimination models were used: a cortex transform model, a single channel model with a contrast sensitivity function filter, and the Root-Mean-Square (RMS) difference of the digital target and background-only images. As in the previous study, the cortex transform model performed best; the RMS difference predictor was second best; and last, but still a reasonable predictor, was the single channel model. Image discrimination models can predict the relative detectabilities of objects in natural backgrounds.

A global earthquake discrimination scheme to optimize ground-motion prediction equation selection

USGS Publications Warehouse

Garcia, Daniel; Wald, David J.; Hearne, Michael

2012-01-01

We present a new automatic earthquake discrimination procedure to determine in near-real time the tectonic regime and seismotectonic domain of an earthquake, its most likely source type, and the corresponding ground-motion prediction equation (GMPE) class to be used in the U.S. Geological Survey (USGS) Global ShakeMap system. This method makes use of the Flinn–Engdahl regionalization scheme, seismotectonic information (plate boundaries, global geology, seismicity catalogs, and regional and local studies), and the source parameters available from the USGS National Earthquake Information Center in the minutes following an earthquake to give the best estimation of the setting and mechanism of the event. Depending on the tectonic setting, additional criteria based on hypocentral depth, style of faulting, and regional seismicity may be applied. For subduction zones, these criteria include the use of focal mechanism information and detailed interface models to discriminate among outer-rise, upper-plate, interface, and intraslab seismicity. The scheme is validated against a large database of recent historical earthquakes. Though developed to assess GMPE selection in Global ShakeMap operations, we anticipate a variety of uses for this strategy, from real-time processing systems to any analysis involving tectonic classification of sources from seismic catalogs.

Discriminant cognitive factors in responder and non-responder patients with schizophrenia.

PubMed

Stip, E; Lussier, I; Ngan, E; Mendrek, A; Liddle, P

1999-12-01

To identify which improvements in cognitive function are associated with symptom resolution in schizophrenic patients treated with atypical antipsychotics. a prospective open trial with atypical neuroleptics (risperidone, clozapine, quetiapine). Inpatient and outpatient units, Institute of Psychiatry. Thirty-nine patients with schizophrenia according to DSM-IV criteria were included. Clinical and cognitive assessment were done at baseline (T0) and again after six months of treatment (T2). Twenty-five patients completed the trial. New-generation antipsychotics during six months. Patients were considered as responders if their PANSS score decreased at least 20% (n = 15) and non-responders if it did not (n = 10). a computerized cognitive assessment comprised tests of short-term-memory (digit span), explicit long-term memory (word pair learning), divided attention, selective attention and verbal fluency (orthographic and semantic). Clinical assessment included PANSS and ESRS. A discriminant function analysis was performed to determine which changes in cognitive performance predicted symptomatic response status. Semantic fluency and orthographic fluency were significant predictors. Together they correctly predicted responder status in 88% of cases. Memory was not a significant predictor of symptomatic response. Verbal fluency discriminated the responder from the non-responder group during a pharmacological treatment.

Validation of the Sepsis Severity Score Compared with Updated Severity Scores in Predicting Hospital Mortality in Sepsis Patients.

PubMed

Khwannimit, Bodin; Bhurayanontachai, Rungsun; Vattanavanit, Veerapong

2017-06-01

Recently, the Sepsis Severity Score (SSS) was constructed to predict mortality in sepsis patients. The aim of this study was to compare performance of the SSS with the Acute Physiology and Chronic Health Evaluation (APACHE) II-IV, Simplified Acute Physiology Score (SAPS) II, and SAPS 3 scores in predicting hospital outcome in sepsis patients. A retroprospective analysis was conducted in the medical intensive care unit of a tertiary university hospital. A total of 913 patients were enrolled; 476 of these patients (52.1%) had septic shock. The median SSS was 80 (range 20-137). The SSS presented good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.892. However, the AUC of the SSS did not differ significantly from that of APACHE II (P = 0.07), SAPS II (P = 0.06), and SAPS 3 (P = 0.11). The APACHE IV score showed the best discrimination with an AUC of 0.948 and the overall performance by a Brier score of 0.096. The AUC of the APACHE IV score was statistically greater than the SSS, APACHE II, SAPS II, and SAPS 3 (P <0.0001 for all) and APACHE III (P = 0.0002). The calibration of all scores was poor with the Hosmer-Lemeshow goodness-of-fit H test <0.05. The SSS provided as good discrimination as the APACHE II, SAPS II, and SAPS 3 scores. However, the APACHE IV score had the best discrimination and overall performance in our sepsis patients. The SSS needs to be adapted and modified with new parameters to improve its performance.

A simple test of choice stepping reaction time for assessing fall risk in people with multiple sclerosis.

PubMed

Tijsma, Mylou; Vister, Eva; Hoang, Phu; Lord, Stephen R

2017-03-01

Purpose To determine (a) the discriminant validity for established fall risk factors and (b) the predictive validity for falls of a simple test of choice stepping reaction time (CSRT) in people with multiple sclerosis (MS). Method People with MS (n = 210, 21-74y) performed the CSRT, sensorimotor, balance and neuropsychological tests in a single session. They were then followed up for falls using monthly fall diaries for 6 months. Results The CSRT test had excellent discriminant validity with respect to established fall risk factors. Frequent fallers (≥3 falls) performed significantly worse in the CSRT test than non-frequent fallers (0-2 falls). With the odds of suffering frequent falls increasing 69% with each SD increase in CSRT (OR = 1.69, 95% CI: 1.27-2.26, p = <0.001). In regression analysis, CSRT was best explained by sway, time to complete the 9-Hole Peg test, knee extension strength of the weaker leg, proprioception and the time to complete the Trails B test (multiple R 2   =   0.449, p < 0.001). Conclusions A simple low tech CSRT test has excellent discriminative and predictive validity in relation to falls in people with MS. This test may prove useful in documenting longitudinal changes in fall risk in relation to MS disease progression and effects of interventions. Implications for rehabilitation Good choice stepping reaction time (CSRT) is required for maintaining balance. A simple low-tech CSRT test has excellent discriminative and predictive validity in relation to falls in people with MS. This test may prove useful documenting longitudinal changes in fall risk in relation to MS disease progression and effects of interventions.

Shortened version of the work ability index to identify workers at risk of long-term sickness absence.

PubMed

Schouten, Lianne S; Bültmann, Ute; Heymans, Martijn W; Joling, Catelijne I; Twisk, Jos W R; Roelen, Corné A M

2016-04-01

The Work Ability Index (WAI) identifies non-sicklisted workers at risk of future long-term sickness absence (LTSA). The WAI is a complicated instrument and inconvenient for use in large-scale surveys. We investigated whether shortened versions of the WAI identify non-sicklisted workers at risk of LTSA. Prospective study including two samples of non-sicklisted workers participating in occupational health checks between 2010 and 2012. A heterogeneous development sample (N= 2899) was used to estimate logistic regression coefficients for the complete WAI, a shortened WAI version without the list of diseases, and single-item Work Ability Score (WAS). These three instruments were calibrated for predictions of different (≥2, ≥4 and ≥6 weeks) LTSA durations in a validation sample of non-sicklisted workers (N= 3049) employed at a steel mill, differentiating between manual (N= 1710) and non-manual (N= 1339) workers. The discriminative ability was investigated by receiver operating characteristic analysis. All three instruments under-predicted the LTSA risks in both manual and non-manual workers. The complete WAI discriminated between individuals at high and low risk of LTSA ≥2, ≥4 and ≥6 weeks in manual and non-manual workers. Risk predictions and discrimination by the shortened WAI without the list of diseases were as good as the complete WAI. The WAS showed poorer discrimination in manual and non-manual workers. The WAI without the list of diseases is a good alternative to the complete WAI to identify non-sicklisted workers at risk of future LTSA durations ≥2, ≥4 and ≥6 weeks. © The Author 2015. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.

Pulse shape discrimination and classification methods for continuous depth of interaction encoding PET detectors.

PubMed

Roncali, Emilie; Phipps, Jennifer E; Marcu, Laura; Cherry, Simon R

2012-10-21

In previous work we demonstrated the potential of positron emission tomography (PET) detectors with depth-of-interaction (DOI) encoding capability based on phosphor-coated crystals. A DOI resolution of 8 mm full-width at half-maximum was obtained for 20 mm long scintillator crystals using a delayed charge integration linear regression method (DCI-LR). Phosphor-coated crystals modify the pulse shape to allow continuous DOI information determination, but the relationship between pulse shape and DOI is complex. We are therefore interested in developing a sensitive and robust method to estimate the DOI. Here, linear discriminant analysis (LDA) was implemented to classify the events based on information extracted from the pulse shape. Pulses were acquired with 2×2×20 mm(3) phosphor-coated crystals at five irradiation depths and characterized by their DCI values or Laguerre coefficients. These coefficients were obtained by expanding the pulses on a Laguerre basis set and constituted a unique signature for each pulse. The DOI of individual events was predicted using LDA based on Laguerre coefficients (Laguerre-LDA) or DCI values (DCI-LDA) as discriminant features. Predicted DOIs were compared to true irradiation depths. Laguerre-LDA showed higher sensitivity and accuracy than DCI-LDA and DCI-LR and was also more robust to predict the DOI of pulses with higher statistical noise due to low light levels (interaction depths further from the photodetector face). This indicates that Laguerre-LDA may be more suitable to DOI estimation in smaller crystals where lower collected light levels are expected. This novel approach is promising for calculating DOI using pulse shape discrimination in single-ended readout depth-encoding PET detectors.

Pulse Shape Discrimination and Classification Methods for Continuous Depth of Interaction Encoding PET Detectors

PubMed Central

Roncali, Emilie; Phipps, Jennifer E.; Marcu, Laura; Cherry, Simon R.

2012-01-01

In previous work we demonstrated the potential of positron emission tomography (PET) detectors with depth-of-interaction (DOI) encoding capability based on phosphor-coated crystals. A DOI resolution of 8 mm full-width at half-maximum was obtained for 20 mm long scintillator crystals using a delayed charge integration linear regression method (DCI-LR). Phosphor-coated crystals modify the pulse shape to allow continuous DOI information determination, but the relationship between pulse shape and DOI is complex. We are therefore interested in developing a sensitive and robust method to estimate the DOI. Here, linear discriminant analysis (LDA) was implemented to classify the events based on information extracted from the pulse shape. Pulses were acquired with 2 × 2 × 20 mm3 phosphor-coated crystals at five irradiation depths and characterized by their DCI values or Laguerre coefficients. These coefficients were obtained by expanding the pulses on a Laguerre basis set and constituted a unique signature for each pulse. The DOI of individual events was predicted using LDA based on Laguerre coefficients (Laguerre-LDA) or DCI values (DCI-LDA) as discriminant features. Predicted DOIs were compared to true irradiation depths. Laguerre-LDA showed higher sensitivity and accuracy than DCI-LDA and DCI-LR and was also more robust to predict the DOI of pulses with higher statistical noise due to low light levels (interaction depths further from the photodetector face). This indicates that Laguerre-LDA may be more suitable to DOI estimation in smaller crystals where lower collected light levels are expected. This novel approach is promising for calculating DOI using pulse shape discrimination in single-ended readout depth-encoding PET detectors. PMID:23010690

Effects of stimulus order on discrimination processes in comparative and equality judgements: data and models.

PubMed

Dyjas, Oliver; Ulrich, Rolf

2014-01-01

In typical discrimination experiments, participants are presented with a constant standard and a variable comparison stimulus and their task is to judge which of these two stimuli is larger (comparative judgement). In these experiments, discrimination sensitivity depends on the temporal order of these stimuli (Type B effect) and is usually higher when the standard precedes rather than follows the comparison. Here, we outline how two models of stimulus discrimination can account for the Type B effect, namely the weighted difference model (or basic Sensation Weighting model) and the Internal Reference Model. For both models, the predicted psychometric functions for comparative judgements as well as for equality judgements, in which participants indicate whether they perceived the two stimuli to be equal or not equal, are derived and it is shown that the models also predict a Type B effect for equality judgements. In the empirical part, the models' predictions are evaluated. To this end, participants performed a duration discrimination task with comparative judgements and with equality judgements. In line with the models' predictions, a Type B effect was observed for both judgement types. In addition, a time-order error, as indicated by shifts of the psychometric functions, and differences in response times were observed only for the equality judgement. Since both models entail distinct additional predictions, it seems worthwhile for future research to unite the two models into one conceptual framework.

Discriminative components of data.

PubMed

Peltonen, Jaakko; Kaski, Samuel

2005-01-01

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

Development of a novel score for the prediction of hospital mortality in patients with severe sepsis: the use of electronic healthcare records with LASSO regression.

PubMed

Zhang, Zhongheng; Hong, Yucai

2017-07-25

There are several disease severity scores being used for the prediction of mortality in critically ill patients. However, none of them was developed and validated specifically for patients with severe sepsis. The present study aimed to develop a novel prediction score for severe sepsis. A total of 3206 patients with severe sepsis were enrolled, including 1054 non-survivors and 2152 survivors. The LASSO score showed the best discrimination (area under curve: 0.772; 95% confidence interval: 0.735-0.810) in the validation cohort as compared with other scores such as simplified acute physiology score II, acute physiological score III, Logistic organ dysfunction system, sequential organ failure assessment score, and Oxford Acute Severity of Illness Score. The calibration slope was 0.889 and Brier value was 0.173. The study employed a single center database called Medical Information Mart for Intensive Care-III) MIMIC-III for analysis. Severe sepsis was defined as infection and acute organ dysfunction. Clinical and laboratory variables used in clinical routines were included for screening. Subjects without missing values were included, and the whole dataset was split into training and validation cohorts. The score was coined LASSO score because variable selection was performed using the least absolute shrinkage and selection operator (LASSO) technique. Finally, the LASSO score was evaluated for its discrimination and calibration in the validation cohort. The study developed the LASSO score for mortality prediction in patients with severe sepsis. Although the score had good discrimination and calibration in a randomly selected subsample, external validations are still required.

Variation of δ2H, δ18O & δ13C in crude palm oil from different regions in Malaysia: Potential of stable isotope signatures as a key traceability parameter.

PubMed

Muhammad, Syahidah Akmal; Seow, Eng-Keng; Mohd Omar, A K; Rodhi, Ainolsyakira Mohd; Mat Hassan, Hasnuri; Lalung, Japareng; Lee, Sze-Chi; Ibrahim, Baharudin

2018-01-01

A total of 33 crude palm oil samples were randomly collected from different regions in Malaysia. Stable carbon isotopic composition (δ 13 C) was determined using Flash 2000 elemental analyzer while hydrogen and oxygen isotopic compositions (δ 2 H and δ 18 O) were analyzed by Thermo Finnigan TC/EA, wherein both instruments were coupled to an isotope ratio mass spectrometer. The bulk δ 2 H, δ 18 O and δ 13 C of the samples were analyzed by Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA). Unsupervised HCA and PCA methods have demonstrated that crude palm oil samples were grouped into clusters according to respective state. A predictive model was constructed by supervised OPLS-DA with good predictive power of 52.60%. Robustness of the predictive model was validated with overall accuracy of 71.43%. Blind test samples were correctly assigned to their respective cluster except for samples from southern region. δ 18 O was proposed as the promising discriminatory marker for discerning crude palm oil samples obtained from different regions. Stable isotopes profile was proven to be useful for origin traceability of crude palm oil samples at a narrower geographical area, i.e. based on regions in Malaysia. Predictive power and accuracy of the predictive model was expected to improve with the increase in sample size. Conclusively, the results in this study has fulfilled the main objective of this work where the simple approach of combining stable isotope analysis with chemometrics can be used to discriminate crude palm oil samples obtained from different regions in Malaysia. Overall, this study shows the feasibility of this approach to be used as a traceability assessment of crude palm oils. Copyright © 2017 The Chartered Society of Forensic Sciences. Published by Elsevier B.V. All rights reserved.

Visual discrimination in the pigeon (Columba livia): effects of selective lesions of the nucleus rotundus

NASA Technical Reports Server (NTRS)

Laverghetta, A. V.; Shimizu, T.

1999-01-01

The nucleus rotundus is a large thalamic nucleus in birds and plays a critical role in many visual discrimination tasks. In order to test the hypothesis that there are functionally distinct subdivisions in the nucleus rotundus, effects of selective lesions of the nucleus were studied in pigeons. The birds were trained to discriminate between different types of stationary objects and between different directions of moving objects. Multiple regression analyses revealed that lesions in the anterior, but not posterior, division caused deficits in discrimination of small stationary stimuli. Lesions in neither the anterior nor posterior divisions predicted effects in discrimination of moving stimuli. These results are consistent with a prediction led from the hypothesis that the nucleus is composed of functional subdivisions.

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

PubMed

Sugden, Nicole A; Marquis, Alexandra R

2017-11-01

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

Identification of a discriminative metabolomic fingerprint of potential clinical relevance in saliva of patients with periodontitis using 1H nuclear magnetic resonance (NMR) spectroscopy.

PubMed

Rzeznik, Matthias; Triba, Mohamed Nawfal; Levy, Pierre; Jungo, Sébastien; Botosoa, Eliot; Duchemann, Boris; Le Moyec, Laurence; Bernaudin, Jean-François; Savarin, Philippe; Guez, Dominique

2017-01-01

Periodontitis is characterized by the loss of the supporting tissues of the teeth in an inflammatory-infectious context. The diagnosis relies on clinical and X-ray examination. Unfortunately, clinical signs of tissue destruction occur late in the disease progression. Therefore, it is mandatory to identify reliable biomarkers to facilitate a better and earlier management of this disease. To this end, saliva represents a promising fluid for identification of biomarkers as metabolomic fingerprints. The present study used high-resolution 1H-nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis to identify the metabolic signature of active periodontitis. The metabolome of stimulated saliva of 26 patients with generalized periodontitis (18 chronic and 8 aggressive) was compared to that of 25 healthy controls. Principal Components Analysis (PCA), performed with clinical variables, indicated that the patient population was homogeneous, demonstrating a strong correlation between the clinical and the radiological variables used to assess the loss of periodontal tissues and criteria of active disease. Orthogonal Projection to Latent Structure (OPLS) analysis showed that patients with periodontitis can be discriminated from controls on the basis of metabolite concentrations in saliva with satisfactory explained variance (R2X = 0.81 and R2Y = 0.61) and predictability (Q2Y = 0.49, CV-AUROC = 0.94). Interestingly, this discrimination was irrespective of the type of generalized periodontitis, i.e. chronic or aggressive. Among the main discriminating metabolites were short chain fatty acids as butyrate, observed in higher concentrations, and lactate, γ-amino-butyrate, methanol, and threonine observed in lower concentrations in periodontitis. The association of lactate, GABA, and butyrate to generate an aggregated variable reached the best positive predictive value for diagnosis of periodontitis. In conclusion, this pilot study showed that 1H-NMR spectroscopy analysis of saliva could differentiate patients with periodontitis from controls. Therefore, this simple, robust, non-invasive method, may offer a significant help for early diagnosis and follow-up of periodontitis.

Identification of a discriminative metabolomic fingerprint of potential clinical relevance in saliva of patients with periodontitis using 1H nuclear magnetic resonance (NMR) spectroscopy

PubMed Central

Levy, Pierre; Jungo, Sébastien; Botosoa, Eliot; Duchemann, Boris; Le Moyec, Laurence; Bernaudin, Jean-François; Guez, Dominique

2017-01-01

Periodontitis is characterized by the loss of the supporting tissues of the teeth in an inflammatory-infectious context. The diagnosis relies on clinical and X-ray examination. Unfortunately, clinical signs of tissue destruction occur late in the disease progression. Therefore, it is mandatory to identify reliable biomarkers to facilitate a better and earlier management of this disease. To this end, saliva represents a promising fluid for identification of biomarkers as metabolomic fingerprints. The present study used high-resolution 1H-nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis to identify the metabolic signature of active periodontitis. The metabolome of stimulated saliva of 26 patients with generalized periodontitis (18 chronic and 8 aggressive) was compared to that of 25 healthy controls. Principal Components Analysis (PCA), performed with clinical variables, indicated that the patient population was homogeneous, demonstrating a strong correlation between the clinical and the radiological variables used to assess the loss of periodontal tissues and criteria of active disease. Orthogonal Projection to Latent Structure (OPLS) analysis showed that patients with periodontitis can be discriminated from controls on the basis of metabolite concentrations in saliva with satisfactory explained variance (R2X = 0.81 and R2Y = 0.61) and predictability (Q2Y = 0.49, CV-AUROC = 0.94). Interestingly, this discrimination was irrespective of the type of generalized periodontitis, i.e. chronic or aggressive. Among the main discriminating metabolites were short chain fatty acids as butyrate, observed in higher concentrations, and lactate, γ-amino-butyrate, methanol, and threonine observed in lower concentrations in periodontitis. The association of lactate, GABA, and butyrate to generate an aggregated variable reached the best positive predictive value for diagnosis of periodontitis. In conclusion, this pilot study showed that 1H-NMR spectroscopy analysis of saliva could differentiate patients with periodontitis from controls. Therefore, this simple, robust, non-invasive method, may offer a significant help for early diagnosis and follow-up of periodontitis. PMID:28837579

Structures of the recurrence plot of heart rate variability signal as a tool for predicting the onset of paroxysmal atrial fibrillation.

PubMed

Mohebbi, Maryam; Ghassemian, Hassan; Asl, Babak Mohammadzadeh

2011-05-01

This paper aims to propose an effective paroxysmal atrial fibrillation (PAF) predictor which is based on the analysis of the heart rate variability (HRV) signal. Predicting the onset of PAF, based on non-invasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic interventions and to minimize the risks for the patients. This method consists of four steps: Preprocessing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the HRV signal is extracted. In the next step, the recurrence plot (RP) of HRV signal is obtained and six features are extracted to characterize the basic patterns of the RP. These features consist of length of longest diagonal segments, average length of the diagonal lines, entropy, trapping time, length of longest vertical line, and recurrence trend. In the third step, these features are reduced to three features by the linear discriminant analysis (LDA) technique. Using LDA not only reduces the number of the input features, but also increases the classification accuracy by selecting the most discriminating features. Finally, a support vector machine-based classifier is used to classify the HRV signals. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database which consists of both 30-minutes ECG recordings end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, and positive predictivity were 96.55%, 100%, and 100%, respectively.

Latino/a depression and smoking: an analysis through the lenses of culture, gender, and ethnicity.

PubMed

Lorenzo-Blanco, Elma I; Cortina, Lilia M

2013-06-01

Rates of major depressive disorder (MDD) and cigarette smoking increase with Latino/a acculturation, but this varies by gender and ethnic subgroup. We investigated how lived experiences (i.e., discrimination, family conflict, family cohesion, familismo) clustered together in the everyday lives of Latina/os. We further examined associations of cluster profile and Latino/a subgroup with MDD and smoking, and tested whether gender moderated these associations. Data came from the National Latino Asian American Study, which included 2,554 Latino/as (48 % female; mean age = 38.02 years). K-means cluster analysis revealed six profiles of experience, which varied by gender and socio-cultural characteristics. Proportionately more women than men were in groups with problematic family lives. Acculturated Latino/as were disproportionately represented in profiles reporting frequent discrimination, family conflict, and a lack of shared family values and cohesion. Profiles characterized by high discrimination and family problems also predicted elevated risk for MDD and smoking. Findings suggest that Latino/a acculturation comes jointly with increased discrimination, increased family conflict, and reduced family cohesion and shared family values, exacerbating risk for MDD and smoking. This research on pathways to depression and smoking can inform the development of targeted assessment, prevention, and intervention strategies, tailored to the needs of Latino/as.

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

PubMed

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

2018-05-29

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

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

PubMed

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

2015-04-01

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

Perceived racial discrimination, but not mistrust of medical researchers, predicts the heat pain tolerance of African Americans with symptomatic knee osteoarthritis.

PubMed

Goodin, Burel R; Pham, Quyen T; Glover, Toni L; Sotolongo, Adriana; King, Christopher D; Sibille, Kimberly T; Herbert, Matthew S; Cruz-Almeida, Yenisel; Sanden, Shelley H; Staud, Roland; Redden, David T; Bradley, Laurence A; Fillingim, Roger B

2013-11-01

Studies have shown that perceived racial discrimination is a significant predictor of clinical pain severity among African Americans. It remains unknown whether perceived racial discrimination also alters the nociceptive processing of painful stimuli, which, in turn, could influence clinical pain severity. This study examined associations between perceived racial discrimination and responses to noxious thermal stimuli among African Americans and non-Hispanic Whites. Mistrust of medical researchers was also assessed given its potential to affect responses to the noxious stimuli. One-hundred and 30 (52% African American, 48% non-Hispanic White) community-dwelling older adults with symptomatic knee osteoarthritis completed two study sessions. In session one, individuals provided demographic, socioeconomic, physical and mental health information. They completed questionnaires related to perceived lifetime frequency of racial discrimination and mistrust of medical researchers. In session two, individuals underwent a series of controlled thermal stimulation procedures to assess heat pain sensitivity, particularly heat pain tolerance. African Americans were more sensitive to heat pain and reported greater perceived racial discrimination as well as greater mistrust of medical researchers compared with non-Hispanic Whites. Greater perceived racial discrimination significantly predicted lower heat pain tolerance for African Americans but not non-Hispanic Whites. Mistrust of medical researchers did not significantly predict heat pain tolerance for either racial group. These results lend support to the idea that perceived racial discrimination may influence the clinical pain severity of African Americans via the nociceptive processing of painful stimuli.

Predicting Sobriety from the Employment Status of Dually Diagnosed Clients Who Are Opiate Dependent

ERIC Educational Resources Information Center

Scorzelli, James F.

2007-01-01

The purpose of this study was to determine the relationship between Minnesota Multiphasic Personality Inventory-2 (E. S. Neukrug & R. C. Fawcett, 2006) profiles and employment status for clients who are opiate dependent. A discriminant function analysis indicated that employment was a predictor in maintaining sobriety after 6 months. (Contains…

Ignorance- versus Evidence-Based Decision Making: A Decision Time Analysis of the Recognition Heuristic

ERIC Educational Resources Information Center

Hilbig, Benjamin E.; Pohl, Rudiger F.

2009-01-01

According to part of the adaptive toolbox notion of decision making known as the recognition heuristic (RH), the decision process in comparative judgments--and its duration--is determined by whether recognition discriminates between objects. By contrast, some recently proposed alternative models predict that choices largely depend on the amount of…

Cigarette Smoking Outcomes at Four Years of Follow-Up, Psychosocial Factors, and Reactions to Group Intervention.

ERIC Educational Resources Information Center

Benfari, Robert C.; Eaker, Elaine

1984-01-01

Studied male smokers (N=182) at high risk of coronary heart disease to determine variables that discriminated between successful and nonsuccessful quitters. Analysis revealed that baseline level of smoking, life events, personal security, and selected group process variables were predictive of success or failure in the intervention program.…

Consumers' Motivations and Dairy Production Beliefs Regarding Participation in an Educational Dairy Farm Event

ERIC Educational Resources Information Center

LaFollette, Lindsay K.; Knobloch, Neil A.; Schutz, Michael M.; Brady, Colleen M.

2015-01-01

Exploratory discriminant analysis was used to determine the extent adult consumers' interest motivation to participate in a free educational dairy farm event and their beliefs of the dairy industry could correctly classify the respondents' predicted participation in a nonformal educational event. The most prominent conclusion of the study was that…

Awareness Programs and Change in Taste-Based Caste Prejudice

PubMed Central

Banerjee, Ritwik; Datta Gupta, Nabanita

2015-01-01

Becker's theory of taste-based discrimination predicts that relative employment of the discriminated social group will improve if there is a decrease in the level of prejudice for the marginally discriminating employer. In this paper we experimentally test this prediction offered by Garry Becker in his seminal work on taste based discrimination, in the context of caste in India, with management students (potential employers in the near future) as subjects. First, we measure caste prejudice and show that awareness through a TV social program reduces implicit prejudice against the lower caste and the reduction is sustained over time. Second, we find that the treatment reduces the prejudice levels of those in the left tail of the prejudice distribution - the group which can potentially affect real outcomes as predicted by the theory. And finally, a larger share of the treatment group subjects exhibit favorable opinion about reservation in jobs for the lower caste. PMID:25902290

Adding to the Education Debt: Depressive Symptoms Mediate the Association between Racial Discrimination and Academic Performance in African Americans

PubMed Central

English, Devin; Lambert, Sharon F.; Ialongo, Nicholas S.

2015-01-01

Although the United States faces a seemingly intractable divide between white and African American academic performance, there remains a dearth of longitudinal research investigating factors that work to maintain this gap. The present study examined whether racial discrimination predicted the academic performance of African American students through its effect on depressive symptoms. Participants were a community sample of African American adolescents (N = 495) attending urban public schools from grade 7 to grade 9 (Mage = 12.5). Structural equation modeling revealed that experienced racial discrimination predicted increases in depressive symptoms 1 year later, which, in turn, predicted decreases in academic performance the following year. These results suggest that racial discrimination continues to play a critical role in the academic performance of African American students and, as such, contributes to the maintenance of the race-based academic achievement gap in the United States. PMID:27425564

Awareness programs and change in taste-based caste prejudice.

PubMed

Banerjee, Ritwik; Datta Gupta, Nabanita

2015-01-01

Becker's theory of taste-based discrimination predicts that relative employment of the discriminated social group will improve if there is a decrease in the level of prejudice for the marginally discriminating employer. In this paper we experimentally test this prediction offered by Garry Becker in his seminal work on taste based discrimination, in the context of caste in India, with management students (potential employers in the near future) as subjects. First, we measure caste prejudice and show that awareness through a TV social program reduces implicit prejudice against the lower caste and the reduction is sustained over time. Second, we find that the treatment reduces the prejudice levels of those in the left tail of the prejudice distribution--the group which can potentially affect real outcomes as predicted by the theory. And finally, a larger share of the treatment group subjects exhibit favorable opinion about reservation in jobs for the lower caste.

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.

Study of the method of water-injected meat identifying based on low-field nuclear magnetic resonance

NASA Astrophysics Data System (ADS)

Xu, Jianmei; Lin, Qing; Yang, Fang; Zheng, Zheng; Ai, Zhujun

2018-01-01

The aim of this study to apply low-field nuclear magnetic resonance technique was to study regular variation of the transverse relaxation spectral parameters of water-injected meat with the proportion of water injection. Based on this, the method of one-way ANOVA and discriminant analysis was used to analyse the differences between these parameters in the capacity of distinguishing water-injected proportion, and established a model for identifying water-injected meat. The results show that, except for T 21b, T 22e and T 23b, the other parameters of the T 2 relaxation spectrum changed regularly with the change of water-injected proportion. The ability of different parameters to distinguish water-injected proportion was different. Based on S, P 22 and T 23m as the prediction variable, the Fisher model and the Bayes model were established by discriminant analysis method, qualitative and quantitative classification of water-injected meat can be realized. The rate of correct discrimination of distinguished validation and cross validation were 88%, the model was stable.

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

PubMed

Qiu, Shanshan; Wang, Jun; Gao, Liping

2014-07-09

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

Classification and prediction of pilot weather encounters: A discriminant function analysis.

PubMed

O'Hare, David; Hunter, David R; Martinussen, Monica; Wiggins, Mark

2011-05-01

Flight into adverse weather continues to be a significant hazard for General Aviation (GA) pilots. Weather-related crashes have a significantly higher fatality rate than other GA crashes. Previous research has identified lack of situational awareness, risk perception, and risk tolerance as possible explanations for why pilots would continue into adverse weather. However, very little is known about the nature of these encounters or the differences between pilots who avoid adverse weather and those who do not. Visitors to a web site described an experience with adverse weather and completed a range of measures of personal characteristics. The resulting data from 364 pilots were carefully screened and subject to a discriminant function analysis. Two significant functions were found. The first, accounting for 69% of the variance, reflected measures of risk awareness and pilot judgment while the second differentiated pilots in terms of their experience levels. The variables measured in this study enabled us to correctly discriminate between the three groups of pilots considerably better (53% correct classifications) than would have been possible by chance (33% correct classifications). The implications of these findings for targeting safety interventions are discussed.

Predicting driver from front passenger using only the postmortem pattern of injury following a motor vehicle collision.

PubMed

Curtin, Eleanor; Langlois, Neil E I

2007-10-01

This study aimed to establish whether post-mortem injury patterns can assist in distinguishing drivers from front seat passengers among victims of motor vehicle collisions without regard to collision type, vehicle type or if safety equipment had been used. Injuries sustained by 206 drivers and 91 front seat passengers were catalogued from post-mortem reports. Injuries were coded for the body region, depth and location of the injury. Statistical analysis was used to detect injuries capable of discriminating between driver and passenger. Drivers were more likely to sustain the following injuries: brain injury; fractures to the right femur, right posterior ribs, base of skull, right humerus and right shoulder; and superficial wounds at the right lateral and posterior thigh, right face, right and left anterior knee, right anterior shoulder, lateral right arm and forearm and left anterior thigh. Front passengers were more vulnerable to splenic injury; fractures to the left posterior and anterior ribs, left shoulder and left femur; and superficial wounds at the left anterior shoulder region and left lateral neck. Linear discriminant analysis generated a model for predicting seating position based on the presence of injury to certain regions of the body; the overall predictive accuracy of the model was 69.3%. It was found that driver and front passenger fatalities receive different injury patterns from motor vehicle collisions, regardless of collision type. A larger study is required to improve the predictive accuracy of this model and to ascertain its value to forensic medicine.

Predicting mortality after congenital heart surgeries: evaluation of the Aristotle and Risk Adjustement in Congenital Heart Surgery-1 risk prediction scoring systems: a retrospective single center analysis of 1150 patients.

PubMed

Joshi, Shreedhar S; Anthony, G; Manasa, D; Ashwini, T; Jagadeesh, A M; Borde, Deepak P; Bhat, Seetharam; Manjunath, C N

2014-01-01

To validate Aristotle basic complexity and Aristotle comprehensive complexity (ABC and ACC) and risk adjustment in congenital heart surgery-1 (RACHS-1) prediction models for in hospital mortality after surgery for congenital heart disease in a single surgical unit. Patients younger than 18 years, who had undergone surgery for congenital heart diseases from July 2007 to July 2013 were enrolled. Scoring for ABC and ACC scoring and assigning to RACHS-1 categories were done retrospectively from retrieved case files. Discriminative power of scoring systems was assessed with area under curve (AUC) of receiver operating curves (ROC). Calibration (test for goodness of fit of the model) was measured with Hosmer-Lemeshow modification of χ2 test. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were applied to assess reclassification. A total of 1150 cases were assessed with an all-cause in-hospital mortality rate of 7.91%. When modeled for multivariate regression analysis, the ABC (χ2 = 8.24, P = 0.08), ACC (χ2 = 4.17 , P = 0.57) and RACHS-1 (χ2 = 2.13 , P = 0.14) scores showed good overall performance. The AUC was 0.677 with 95% confidence interval (CI) of 0.61-0.73 for ABC score, 0.704 (95% CI: 0.64-0.76) for ACC score and for RACHS-1 it was 0.607 (95%CI: 0.55-0.66). ACC had an improved predictability in comparison to RACHS-1 and ABC on analysis with NRI and IDI. ACC predicted mortality better than ABC and RCAHS-1 models. A national database will help in developing predictive models unique to our populations, till then, ACC scoring model can be used to analyze individual performances and compare with other institutes.

Comparison of Two Predictive Models for Short-Term Mortality in Patients after Severe Traumatic Brain Injury.

PubMed

Kesmarky, Klara; Delhumeau, Cecile; Zenobi, Marie; Walder, Bernhard

2017-07-15

The Glasgow Coma Scale (GCS) and the Abbreviated Injury Score of the head region (HAIS) are validated prognostic factors in traumatic brain injury (TBI). The aim of this study was to compare the prognostic performance of an alternative predictive model including motor GCS, pupillary reactivity, age, HAIS, and presence of multi-trauma for short-term mortality with a reference predictive model including motor GCS, pupil reaction, and age (IMPACT core model). A secondary analysis of a prospective epidemiological cohort study in Switzerland including patients after severe TBI (HAIS >3) with the outcome death at 14 days was performed. Performance of prediction, accuracy of discrimination (area under the receiver operating characteristic curve [AUROC]), calibration, and validity of the two predictive models were investigated. The cohort included 808 patients (median age, 56; interquartile range, 33-71), median GCS at hospital admission 3 (3-14), abnormal pupil reaction 29%, with a death rate of 29.7% at 14 days. The alternative predictive model had a higher accuracy of discrimination to predict death at 14 days than the reference predictive model (AUROC 0.852, 95% confidence interval [CI] 0.824-0.880 vs. AUROC 0.826, 95% CI 0.795-0.857; p < 0.0001). The alternative predictive model had an equivalent calibration, compared with the reference predictive model Hosmer-Lemeshow p values (Chi2 8.52, Hosmer-Lemeshow p = 0.345 vs. Chi2 8.66, Hosmer-Lemeshow p = 0.372). The optimism-corrected value of AUROC for the alternative predictive model was 0.845. After severe TBI, a higher performance of prediction for short-term mortality was observed with the alternative predictive model, compared with the reference predictive model.

Object detection in natural backgrounds predicted by discrimination performance and models

NASA Technical Reports Server (NTRS)

Rohaly, A. M.; Ahumada, A. J. Jr; Watson, A. B.

1997-01-01

Many models of visual performance predict image discriminability, the visibility of the difference between a pair of images. We compared the ability of three image discrimination models to predict the detectability of objects embedded in natural backgrounds. The three models were: a multiple channel Cortex transform model with within-channel masking; a single channel contrast sensitivity filter model; and a digital image difference metric. Each model used a Minkowski distance metric (generalized vector magnitude) to summate absolute differences between the background and object plus background images. For each model, this summation was implemented with three different exponents: 2, 4 and infinity. In addition, each combination of model and summation exponent was implemented with and without a simple contrast gain factor. The model outputs were compared to measures of object detectability obtained from 19 observers. Among the models without the contrast gain factor, the multiple channel model with a summation exponent of 4 performed best, predicting the pattern of observer d's with an RMS error of 2.3 dB. The contrast gain factor improved the predictions of all three models for all three exponents. With the factor, the best exponent was 4 for all three models, and their prediction errors were near 1 dB. These results demonstrate that image discrimination models can predict the relative detectability of objects in natural scenes.

Illumination discrimination in the absence of a fixed surface-reflectance layout

PubMed Central

Radonjić, Ana; Ding, Xiaomao; Krieger, Avery; Aston, Stacey; Hurlbert, Anya C.; Brainard, David H.

2018-01-01

Previous studies have shown that humans can discriminate spectral changes in illumination and that this sensitivity depends both on the chromatic direction of the illumination change and on the ensemble of surfaces in the scene. These studies, however, always used stimulus scenes with a fixed surface-reflectance layout. Here we compared illumination discrimination for scenes in which the surface reflectance layout remains fixed (fixed-surfaces condition) to those in which surface reflectances were shuffled randomly across scenes, but with the mean scene reflectance held approximately constant (shuffled-surfaces condition). Illumination discrimination thresholds in the fixed-surfaces condition were commensurate with previous reports. Thresholds in the shuffled-surfaces condition, however, were considerably elevated. Nonetheless, performance in the shuffled-surfaces condition exceeded that attainable through random guessing. Analysis of eye fixations revealed that in the fixed-surfaces condition, low illumination discrimination thresholds (across observers) were predicted by low overall fixation spread and high consistency of fixation location and fixated surface reflectances across trial intervals. Performance in the shuffled-surfaces condition was not systematically related to any of the eye-fixation characteristics we examined for that condition, but was correlated with performance in the fixed-surfaces condition. PMID:29904786

An evaluation of the predictive performance of distributional models for flora and fauna in north-east New South Wales.

PubMed

Pearce, J; Ferrier, S; Scotts, D

2001-06-01

To use models of species distributions effectively in conservation planning, it is important to determine the predictive accuracy of such models. Extensive modelling of the distribution of vascular plant and vertebrate fauna species within north-east New South Wales has been undertaken by linking field survey data to environmental and geographical predictors using logistic regression. These models have been used in the development of a comprehensive and adequate reserve system within the region. We evaluate the predictive accuracy of models for 153 small reptile, arboreal marsupial, diurnal bird and vascular plant species for which independent evaluation data were available. The predictive performance of each model was evaluated using the relative operating characteristic curve to measure discrimination capacity. Good discrimination ability implies that a model's predictions provide an acceptable index of species occurrence. The discrimination capacity of 89% of the models was significantly better than random, with 70% of the models providing high levels of discrimination. Predictions generated by this type of modelling therefore provide a reasonably sound basis for regional conservation planning. The discrimination ability of models was highest for the less mobile biological groups, particularly the vascular plants and small reptiles. In the case of diurnal birds, poor performing models tended to be for species which occur mainly within specific habitats not well sampled by either the model development or evaluation data, highly mobile species, species that are locally nomadic or those that display very broad habitat requirements. Particular care needs to be exercised when employing models for these types of species in conservation planning.

Rapid profiling of Swiss cheese by attenuated total reflectance (ATR) infrared spectroscopy and descriptive sensory analysis.

PubMed

Kocaoglu-Vurma, N A; Eliardi, A; Drake, M A; Rodriguez-Saona, L E; Harper, W J

2009-08-01

The acceptability of cheese depends largely on the flavor formed during ripening. The flavor profiles of cheeses are complex and region- or manufacturer-specific which have made it challenging to understand the chemistry of flavor development and its correlation with sensory properties. Infrared spectroscopy is an attractive technology for the rapid, sensitive, and high-throughput analysis of foods, providing information related to its composition and conformation of food components from the spectra. Our objectives were to establish infrared spectral profiles to discriminate Swiss cheeses produced by different manufacturers in the United States and to develop predictive models for determination of sensory attributes based on infrared spectra. Fifteen samples from 3 Swiss cheese manufacturers were received and analyzed using attenuated total reflectance infrared spectroscopy (ATR-IR). The spectra were analyzed using soft independent modeling of class analogy (SIMCA) to build a classification model. The cheeses were profiled by a trained sensory panel using descriptive sensory analysis. The relationship between the descriptive sensory scores and ATR-IR spectra was assessed using partial least square regression (PLSR) analysis. SIMCA discriminated the Swiss cheeses based on manufacturer and production region. PLSR analysis generated prediction models with correlation coefficients of validation (rVal) between 0.69 and 0.96 with standard error of cross-validation (SECV) ranging from 0.04 to 0.29. Implementation of rapid infrared analysis by the Swiss cheese industry would help to streamline quality assurance.

Comparison of Employer Factors in Disability and Other Employment Discrimination Charges

ERIC Educational Resources Information Center

Nazarov, Zafar E.; von Schrader, Sarah

2014-01-01

Purpose: We explore whether certain employer characteristics predict Americans with Disabilities Act (ADA) charges and whether the same characteristics predict receipt of the Age Discrimination in Employment Act and Title VII of the Civil Rights Act charges. Method: We estimate a set of multivariate regressions using the ordinary least squares…

Odontological approach to sexual dimorphism in southeastern France.

PubMed

Lladeres, Emilie; Saliba-Serre, Bérengère; Sastre, Julien; Foti, Bruno; Tardivo, Delphine; Adalian, Pascal

2013-01-01

The aim of this study was to establish a prediction formula to allow for the determination of sex among the southeastern French population using dental measurements. The sample consisted of 105 individuals (57 males and 48 females, aged between 18 and 25 years). Dental measurements were calculated using Euclidean distances, in three-dimensional space, from point coordinates obtained by a Microscribe. A multiple logistic regression analysis was performed to establish the prediction formula. Among 12 selected dental distances, a stepwise logistic regression analysis highlighted the two most significant discriminate predictors of sex: one located at the mandible and the other at the maxilla. A cutpoint was proposed to prediction of true sex. The prediction formula was then tested on a validation sample (20 males and 34 females, aged between 18 and 62 years and with a history of orthodontics or restorative care) to evaluate the accuracy of the method. © 2012 American Academy of Forensic Sciences.

Discrimination, ethnic identity, and academic outcomes of Mexican immigrant children: the importance of school context.

PubMed

Brown, Christia Spears; Chu, Hui

2012-01-01

This study examined ethnic identity, perceptions of discrimination, and academic attitudes and performance of primarily first- and second-generation Mexican immigrant children living in a predominantly White community (N=204, 19 schools, mean age=9years). The study also examined schools' promotion of multiculturalism and teachers' attitudes about the value of diversity in predicting immigrant youth's attitudes and experiences. Results indicated that Latino immigrant children in this White community held positive and important ethnic identities and perceived low overall rates of discrimination. As expected, however, school and teacher characteristics were important in predicting children's perceptions of discrimination and ethnic identity, and moderated whether perceptions of discrimination and ethnic identity were related to attitudes about school and academic performance. © 2012 The Authors. Child Development © 2012 Society for Research in Child Development, Inc.

Discriminating between adaptive and carcinogenic liver hypertrophy in rat studies using logistic ridge regression analysis of toxicogenomic data: The mode of action and predictive models.

PubMed

Liu, Shujie; Kawamoto, Taisuke; Morita, Osamu; Yoshinari, Kouichi; Honda, Hiroshi

2017-03-01

Chemical exposure often results in liver hypertrophy in animal tests, characterized by increased liver weight, hepatocellular hypertrophy, and/or cell proliferation. While most of these changes are considered adaptive responses, there is concern that they may be associated with carcinogenesis. In this study, we have employed a toxicogenomic approach using a logistic ridge regression model to identify genes responsible for liver hypertrophy and hypertrophic hepatocarcinogenesis and to develop a predictive model for assessing hypertrophy-inducing compounds. Logistic regression models have previously been used in the quantification of epidemiological risk factors. DNA microarray data from the Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System were used to identify hypertrophy-related genes that are expressed differently in hypertrophy induced by carcinogens and non-carcinogens. Data were collected for 134 chemicals (72 non-hypertrophy-inducing chemicals, 27 hypertrophy-inducing non-carcinogenic chemicals, and 15 hypertrophy-inducing carcinogenic compounds). After applying logistic ridge regression analysis, 35 genes for liver hypertrophy (e.g., Acot1 and Abcc3) and 13 genes for hypertrophic hepatocarcinogenesis (e.g., Asns and Gpx2) were selected. The predictive models built using these genes were 94.8% and 82.7% accurate, respectively. Pathway analysis of the genes indicates that, aside from a xenobiotic metabolism-related pathway as an adaptive response for liver hypertrophy, amino acid biosynthesis and oxidative responses appear to be involved in hypertrophic hepatocarcinogenesis. Early detection and toxicogenomic characterization of liver hypertrophy using our models may be useful for predicting carcinogenesis. In addition, the identified genes provide novel insight into discrimination between adverse hypertrophy associated with carcinogenesis and adaptive hypertrophy in risk assessment. Copyright © 2017 Elsevier Inc. 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.

Discriminative ability and predictive validity of the timed up and go test in identifying older people who fall: systematic review and meta-analysis.

PubMed

Schoene, Daniel; Wu, Sandy M-S; Mikolaizak, A Stefanie; Menant, Jasmine C; Smith, Stuart T; Delbaere, Kim; Lord, Stephen R

2013-02-01

To investigate the discriminative ability and diagnostic accuracy of the Timed Up and Go Test (TUG) as a clinical screening instrument for identifying older people at risk of falling. Systematic literature review and meta-analysis. People aged 60 and older living independently or in institutional settings. Studies were identified with searches of the PubMed, EMBASE, CINAHL, and Cochrane CENTRAL data bases. Retrospective and prospective cohort studies comparing times to complete any version of the TUG of fallers and non-fallers were included. Fifty-three studies with 12,832 participants met the inclusion criteria. The pooled mean difference between fallers and non-fallers depended on the functional status of the cohort investigated: 0.63 seconds (95% confidence (CI) = 0.14-1.12 seconds) for high-functioning to 3.59 seconds (95% CI = 2.18-4.99 seconds) for those in institutional settings. The majority of studies did not retain TUG scores in multivariate analysis. Derived cut-points varied greatly between studies, and with the exception of a few small studies, diagnostic accuracy was poor to moderate. The findings suggest that the TUG is not useful for discriminating fallers from non-fallers in healthy, high-functioning older people but is of more value in less-healthy, lower-functioning older people. Overall, the predictive ability and diagnostic accuracy of the TUG are at best moderate. No cut-point can be recommended. Quick, multifactorial fall risk screens should be considered to provide additional information for identifying older people at risk of falls. © 2013, Copyright the Authors Journal compilation © 2013, The American Geriatrics Society.

Clinical and Radiological Discrimination of Solitary Pulmonary Lesions in Colorectal Cancer Patients.

PubMed

Ohtaki, Yoichi; Shimizu, Kimihiro; Nagashima, Toshiteru; Nakazawa, Seshiru; Obayashi, Kai; Azuma, Yoko; Iijima, Misaki; Kosaka, Takayuki; Yajima, Toshiki; Ogawa, Hiroomi; Tsutsumi, Soichi; Arai, Motohiro; Mogi, Akira; Kuwano, Hiroyuki

2018-04-01

The lung is one of the most common organs of metastasis from colorectal cancer (CRC), and we have encountered lung cancer patients with a history of CRC. There have been few studies regarding methods used to discriminate between primary lung cancer (PLC) and pulmonary metastasis from CRC (PM-CRC) based only on preoperative findings. We retrospectively investigated predictive factors discriminating between these lesions in patients with a history of CRC. Between 2006 and 2015, 117 patients with a history of CRC (44 patients with 47 PLC and 73 patients with 102 PM-CRC) underwent subsequent or concurrent resection of pulmonary lesions. We compared the clinical and radiological characteristics of 100 patients with solitary lesions (43 PLC and 57 PM-CRC). Using univariate and multivariate analyses, we examined predictive factors for discrimination of these two lesions. All tumors with findings of ground-glass opacity (GGO) were PLC (n = 19). In a multivariate analysis of 81 radiologically solid tumors, two factors were found to be significant independent predictors of PLC: a history of stage I CRC and presence of pleural indentation. All tumors in 26 patients with either GGO or both a stage I CRC history and pleural indentation were PLC, while most tumors in patients without all three factors were PM-CRC (43/44; 97.7%). The presence or absence of GGO, pathological CRC stage, and pleural indentation could be useful factors to distinguish between PLC and PM-CRC.

Validation of the Web-Based IBTR! 2.0 Nomogram to Predict for Ipsilateral Breast Tumor Recurrence After Breast-Conserving Therapy

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

Kindts, Isabelle, E-mail: Isabelle.kindts@uzleuven.be; Department of Radiation Oncology, University Hospitals Leuven, Leuven; Laenen, Annouschka

Purpose: To evaluate the IBTR! 2.0 nomogram, which predicts 10-year ipsilateral breast tumor recurrence (IBTR) after breast-conserving therapy with and without radiation therapy for breast cancer, by using a large, external, and independent cancer center database. Methods and Materials: We retrospectively identified 1898 breast cancer cases, treated with breast-conserving therapy and radiation therapy at the University Hospital Leuven from 2000 to 2007, with requisite data for the nomogram variables. Clinicopathologic factors were assessed. Two definitions of IBTR were considered where simultaneous regional or distant recurrence were either censored (conform IBTR! 2.0) or included as event. Validity of the prediction algorithm wasmore » tested in terms of discrimination and calibration. Discrimination was assessed by the concordance probability estimate and Harrell's concordance index. The mean predicted and observed 10-year estimates were compared for the entire cohort and for 4 risk groups predefined by nomogram-predicted IBTR risks, and a calibration plot was drawn. Results: Median follow-up was 10.9 years. The 10-year IBTR rates were 1.3% and 2.1%, according to the 2 definitions of IBTR. The validation cohort differed from the development cohort with respect to the administration of hormonal therapy, surgical section margins, lymphovascular invasion, and tumor size. In univariable analysis, younger age (P=.002) and a positive nodal status (P=.048) were significantly associated with IBTR, with a trend for the omission of hormonal therapy (P=.061). The concordance probability estimate and concordance index varied between 0.57 and 0.67 for the 2 definitions of IBTR. In all 4 risk groups the model overestimated the IBTR risk. In particular, between the lowest-risk groups a limited differentiation was suggested by the calibration plot. Conclusions: The IBTR! 2.0 predictive model for IBTR in breast cancer patients shows substandard discriminative ability, with an overestimation of the risk in all subgroups.« less

Validation of the Web-Based IBTR! 2.0 Nomogram to Predict for Ipsilateral Breast Tumor Recurrence After Breast-Conserving Therapy.

PubMed

Kindts, Isabelle; Laenen, Annouschka; Peeters, Stephanie; Janssen, Hilde; Depuydt, Tom; Nevelsteen, Ines; Van Limbergen, Erik; Weltens, Caroline

2016-08-01

To evaluate the IBTR! 2.0 nomogram, which predicts 10-year ipsilateral breast tumor recurrence (IBTR) after breast-conserving therapy with and without radiation therapy for breast cancer, by using a large, external, and independent cancer center database. We retrospectively identified 1898 breast cancer cases, treated with breast-conserving therapy and radiation therapy at the University Hospital Leuven from 2000 to 2007, with requisite data for the nomogram variables. Clinicopathologic factors were assessed. Two definitions of IBTR were considered where simultaneous regional or distant recurrence were either censored (conform IBTR! 2.0) or included as event. Validity of the prediction algorithm was tested in terms of discrimination and calibration. Discrimination was assessed by the concordance probability estimate and Harrell's concordance index. The mean predicted and observed 10-year estimates were compared for the entire cohort and for 4 risk groups predefined by nomogram-predicted IBTR risks, and a calibration plot was drawn. Median follow-up was 10.9 years. The 10-year IBTR rates were 1.3% and 2.1%, according to the 2 definitions of IBTR. The validation cohort differed from the development cohort with respect to the administration of hormonal therapy, surgical section margins, lymphovascular invasion, and tumor size. In univariable analysis, younger age (P=.002) and a positive nodal status (P=.048) were significantly associated with IBTR, with a trend for the omission of hormonal therapy (P=.061). The concordance probability estimate and concordance index varied between 0.57 and 0.67 for the 2 definitions of IBTR. In all 4 risk groups the model overestimated the IBTR risk. In particular, between the lowest-risk groups a limited differentiation was suggested by the calibration plot. The IBTR! 2.0 predictive model for IBTR in breast cancer patients shows substandard discriminative ability, with an overestimation of the risk in all subgroups. Copyright © 2016 Elsevier Inc. All rights reserved.

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

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

Thomas, Edward V.; Lewis, John. R.; Anderson-Cook, Christine Michaela

The inverse prediction is important in a variety of scientific and engineering applications, such as to predict properties/characteristics of an object by using multiple measurements obtained from it. Inverse prediction can be accomplished by inverting parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are computational/science based; but often, forward models are empirically based response surface models, obtained by using the results of controlled experimentation. For empirical models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). And while nature dictatesmore » the causal relationships between factors and responses, experimenters can control the complexity, accuracy, and precision of forward models constructed via selection of factors, factor levels, and the set of trials that are performed. Recognition of the uncertainty in the estimated forward models leads to an errors-in-variables approach for inverse prediction. The forward models (estimated by experiments or science based) can also be used to analyze how well candidate responses complement one another for inverse prediction over the range of the factor space of interest. Furthermore, one may find that some responses are complementary, redundant, or noninformative. Simple analysis and examples illustrate how an informative and discriminating subset of responses could be selected among candidates in cases where the number of responses that can be acquired during inverse prediction is limited by difficulty, expense, and/or availability of material.« less

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

DOE PAGES

Thomas, Edward V.; Lewis, John. R.; Anderson-Cook, Christine Michaela; ...

2017-07-01

The inverse prediction is important in a variety of scientific and engineering applications, such as to predict properties/characteristics of an object by using multiple measurements obtained from it. Inverse prediction can be accomplished by inverting parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are computational/science based; but often, forward models are empirically based response surface models, obtained by using the results of controlled experimentation. For empirical models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). And while nature dictatesmore » the causal relationships between factors and responses, experimenters can control the complexity, accuracy, and precision of forward models constructed via selection of factors, factor levels, and the set of trials that are performed. Recognition of the uncertainty in the estimated forward models leads to an errors-in-variables approach for inverse prediction. The forward models (estimated by experiments or science based) can also be used to analyze how well candidate responses complement one another for inverse prediction over the range of the factor space of interest. Furthermore, one may find that some responses are complementary, redundant, or noninformative. Simple analysis and examples illustrate how an informative and discriminating subset of responses could be selected among candidates in cases where the number of responses that can be acquired during inverse prediction is limited by difficulty, expense, and/or availability of material.« less

PREDICTING ABUSE POTENTIAL OF STIMULANTS AND OTHER DOPAMINERGIC DRUGS: OVERVIEW AND RECOMMENDATIONS

PubMed Central

Huskinson, Sally L.; Naylor, Jennifer E.; Rowlett, James K.; Freeman, Kevin B.

2014-01-01

Examination of a drug’s abuse potential at multiple levels of analysis (molecular/cellular action, whole-organism behavior, epidemiological data) is an essential component to regulating controlled substances under the Controlled Substances Act (CSA). We reviewed studies that examined several central nervous system (CNS) stimulants, focusing on those with primarily dopaminergic actions, in drug self-administration, drug discrimination, and physical dependence. For drug self-administration and drug discrimination, we distinguished between experiments conducted with rats and nonhuman primates (NHP) to highlight the common and unique attributes of each model in the assessment of abuse potential. Our review of drug self-administration studies suggests that this procedure is important in predicting abuse potential of dopaminergic compounds, but there were many false positives. We recommended that tests to determine how reinforcing a drug is relative to a known drug of abuse may be more predictive of abuse potential than tests that yield a binary, yes-or-no classification. Several false positives also occurred with drug discrimination. With this procedure, we recommended that future research follow a standard decision-tree approach that may require examining the drug being tested for abuse potential as the training stimulus. This approach would also allow several known drugs of abuse to be tested for substitution, and this may reduce false positives. Finally, we reviewed evidence of physical dependence with stimulants and discussed the feasibility of modeling these phenomena in nonhuman animals in a rational and practical fashion. PMID:24662599

Discrimination against Rural-to-Urban Migrants: The Role of the Hukou System in China

PubMed Central

Kuang, Lei; Liu, Li

2012-01-01

China's rural-urban dual society system is instituted by its unique hukou system. This system causes inequalities in social status between permanent urban and rural residents, and discrimination against rural-to-urban migrants is thus prevalent. A series of studies, based on system justification theory, sought to address the impact of the hukou system on the discrimination against rural-to-urban migrants. Study 1 showed that the justification of the hukou system could predict discrimination operationalized using a social distance measure. Study 2 found that priming of the proposed abolishment of the current hukou system led to reduced social distance. Study 3, using a recruiting scenario, further demonstrated that priming of the proposed abolishment of the system led to reduced discrimination in salary decision. Consistent with our predictions, discrimination against rural-to-urban migrants could be triggered by justifying the current hukou system, while priming of the abolishment of the system serves to decrease discrimination. The present research thereby sheds light on China's reform of its hukou system to achieve social justice and equality from a psychological perspective. PMID:23144794

Discrimination of tomatoes bred by spaceflight mutagenesis using visible/near infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Shao, Yongni; Xie, Chuanqi; Jiang, Linjun; Shi, Jiahui; Zhu, Jiajin; He, Yong

2015-04-01

Visible/near infrared spectroscopy (Vis/NIR) based on sensitive wavelengths (SWs) and chemometrics was proposed to discriminate different tomatoes bred by spaceflight mutagenesis from their leafs or fruits (green or mature). The tomato breeds were mutant M1, M2 and their parent. Partial least squares (PLS) analysis and least squares-support vector machine (LS-SVM) were implemented for calibration models. PLS analysis was implemented for calibration models with different wavebands including the visible region (400-700 nm) and the near infrared region (700-1000 nm). The best PLS models were achieved in the visible region for the leaf and green fruit samples and in the near infrared region for the mature fruit samples. Furthermore, different latent variables (4-8 LVs for leafs, 5-9 LVs for green fruits, and 4-9 LVs for mature fruits) were used as inputs of LS-SVM to develop the LV-LS-SVM models with the grid search technique and radial basis function (RBF) kernel. The optimal LV-LS-SVM models were achieved with six LVs for the leaf samples, seven LVs for green fruits, and six LVs for mature fruits, respectively, and they outperformed the PLS models. Moreover, independent component analysis (ICA) was executed to select several SWs based on loading weights. The optimal LS-SVM model was achieved with SWs of 550-560 nm, 562-574 nm, 670-680 nm and 705-715 nm for the leaf samples; 548-556 nm, 559-564 nm, 678-685 nm and 962-974 nm for the green fruit samples; and 712-718 nm, 720-729 nm, 968-978 nm and 820-830 nm for the mature fruit samples. All of them had better performance than PLS and LV-LS-SVM, with the parameters of correlation coefficient (rp), root mean square error of prediction (RMSEP) and bias of 0.9792, 0.2632 and 0.0901 based on leaf discrimination, 0.9837, 0.2783 and 0.1758 based on green fruit discrimination, 0.9804, 0.2215 and -0.0035 based on mature fruit discrimination, respectively. The overall results indicated that ICA was an effective way for the selection of SWs, and the Vis/NIR combined with LS-SVM models had the capability to predict the different breeds (mutant M1, mutant M2 and their parent) of tomatoes from leafs and fruits.

Prediction of Response to Preoperative Chemoradiotherapy in Rectal Cancer by Multiplex Kinase Activity Profiling

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

Folkvord, Sigurd; Flatmark, Kjersti; Department of Cancer and Surgery, Norwegian Radium Hospital, Oslo University Hospital

2010-10-01

Purpose: Tumor response of rectal cancer to preoperative chemoradiotherapy (CRT) varies considerably. In experimental tumor models and clinical radiotherapy, activity of particular subsets of kinase signaling pathways seems to predict radiation response. This study aimed to determine whether tumor kinase activity profiles might predict tumor response to preoperative CRT in locally advanced rectal cancer (LARC). Methods and Materials: Sixty-seven LARC patients were treated with a CRT regimen consisting of radiotherapy, fluorouracil, and, where possible, oxaliplatin. Pretreatment tumor biopsy specimens were analyzed using microarrays with kinase substrates, and the resulting substrate phosphorylation patterns were correlated with tumor response to preoperative treatmentmore » as assessed by histomorphologic tumor regression grade (TRG). A predictive model for TRG scores from phosphosubstrate signatures was obtained by partial-least-squares discriminant analysis. Prediction performance was evaluated by leave-one-out cross-validation and use of an independent test set. Results: In the patient population, 73% and 15% were scored as good responders (TRG 1-2) or intermediate responders (TRG 3), whereas 12% were assessed as poor responders (TRG 4-5). In a subset of 7 poor responders and 12 good responders, treatment outcome was correctly predicted for 95%. Application of the prediction model on the remaining patient samples resulted in correct prediction for 85%. Phosphosubstrate signatures generated by poor-responding tumors indicated high kinase activity, which was inhibited by the kinase inhibitor sunitinib, and several discriminating phosphosubstrates represented proteins derived from signaling pathways implicated in radioresistance. Conclusions: Multiplex kinase activity profiling may identify functional biomarkers predictive of tumor response to preoperative CRT in LARC.« less

Perceived racial discrimination, but not mistrust of medical researchers, predicts the heat pain tolerance of African Americans with symptomatic knee osteoarthritis

PubMed Central

Pham, Quyen T.; Glover, Toni L.; Sotolongo, Adriana; King, Christopher D.; Sibille, Kimberly T.; Herbert, Matthew S.; Cruz-Almeida, Yenisel; Sanden, Shelley H.; Staud, Roland; Redden, David T.; Bradley, Laurence A.; Fillingim, Roger B.

2014-01-01

Objective Studies have shown that perceived racial discrimination is a significant predictor of clinical pain severity among African Americans. It remains unknown whether perceived racial discrimination also alters the nociceptive processing of painful stimuli, which, in turn, could influence clinical pain severity. This study examined associations between perceived racial discrimination and responses to noxious thermal stimuli among African Americans and non-Hispanic whites. Mistrust of medical researchers was also assessed given its potential to affect responses to the noxious stimuli. Method One hundred and thirty (52% African American, 48% non-Hispanic white) community-dwelling older adults with symptomatic knee osteoarthritis completed two study sessions. In session one, individuals provided demographic, socioeconomic, physical and mental health information. They completed questionnaires related to perceived lifetime frequency of racial discrimination and mistrust of medical researchers. In session two, individuals underwent a series of controlled thermal stimulation procedures to assess heat pain sensitivity, particularly heat pain tolerance. Results African Americans were more sensitive to heat pain and reported greater perceived racial discrimination as well as greater mistrust of medical researchers compared to non-Hispanic whites. Greater perceived racial discrimination significantly predicted lower heat pain tolerance for African Americans but not non-Hispanic whites. Mistrust of medical researchers did not significantly predict heat pain tolerance for either racial group Conclusion These results lend support to the idea that perceived racial discrimination may influence the clinical pain severity of African Americans via the nociceptive processing of painful stimuli. PMID:24219416

Developing and Validating a Predictive Model for Stroke Progression

PubMed Central

Craig, L.E.; Wu, O.; Gilmour, H.; Barber, M.; Langhorne, P.

2011-01-01

Background Progression is believed to be a common and important complication in acute stroke, and has been associated with increased mortality and morbidity. Reliable identification of predictors of early neurological deterioration could potentially benefit routine clinical care. The aim of this study was to identify predictors of early stroke progression using two independent patient cohorts. Methods Two patient cohorts were used for this study – the first cohort formed the training data set, which included consecutive patients admitted to an urban teaching hospital between 2000 and 2002, and the second cohort formed the test data set, which included patients admitted to the same hospital between 2003 and 2004. A standard definition of stroke progression was used. The first cohort (n = 863) was used to develop the model. Variables that were statistically significant (p < 0.1) on univariate analysis were included in the multivariate model. Logistic regression was the technique employed using backward stepwise regression to drop the least significant variables (p > 0.1) in turn. The second cohort (n = 216) was used to test the performance of the model. The performance of the predictive model was assessed in terms of both calibration and discrimination. Multiple imputation methods were used for dealing with the missing values. Results Variables shown to be significant predictors of stroke progression were conscious level, history of coronary heart disease, presence of hyperosmolarity, CT lesion, living alone on admission, Oxfordshire Community Stroke Project classification, presence of pyrexia and smoking status. The model appears to have reasonable discriminative properties [the median receiver-operating characteristic curve value was 0.72 (range 0.72–0.73)] and to fit well with the observed data, which is indicated by the high goodness-of-fit p value [the median p value from the Hosmer-Lemeshow test was 0.90 (range 0.50–0.92)]. Conclusion The predictive model developed in this study contains variables that can be easily collected in practice therefore increasing its usability in clinical practice. Using this analysis approach, the discrimination and calibration of the predictive model appear sufficiently high to provide accurate predictions. This study also offers some discussion around the validation of predictive models for wider use in clinical practice. PMID:22566988

Crop/weed discrimination using near-infrared reflectance spectroscopy (NIRS)

NASA Astrophysics Data System (ADS)

Zhang, Yun; He, Yong

2006-09-01

The traditional uniform herbicide application often results in an over chemical residues on soil, crop plants and agriculture produce, which have imperiled the environment and food security. Near-infrared reflectance spectroscopy (NIRS) offers a promising means for weed detection and site-specific herbicide application. In laboratory, a total of 90 samples (30 for each species) of the detached leaves of two weeds, i.e., threeseeded mercury (Acalypha australis L.) and fourleafed duckweed (Marsilea quadrfolia L.), and one crop soybean (Glycine max) was investigated for NIRS on 325- 1075 nm using a field spectroradiometer. 20 absorbance samples of each species after pretreatment were exported and the lacked Y variables were assigned independent values for partial least squares (PLS) analysis. During the combined principle component analysis (PCA) on 400-1000 nm, the PC1 and PC2 could together explain over 91% of the total variance and detect the three plant species with 98.3% accuracy. The full-cross validation results of PLS, i.e., standard error of prediction (SEP) 0.247, correlation coefficient (r) 0.954 and root mean square error of prediction (RMSEP) 0.245, indicated an optimum model for weed identification. By predicting the remaining 10 samples of each species in the PLS model, the results with deviation presented a 100% crop/weed detection rate. Thus, it could be concluded that PLS was an available alternative of for qualitative weed discrimination on NTRS.

Noncontact discrimination of animal and human blood with vacuum blood vessel and factors affect the discrimination

NASA Astrophysics Data System (ADS)

Zhang, Linna; Zhang, Shengzhao; Sun, Meixiu; Li, Hongxiao; Li, Yingxin; Fu, Zhigang; Guan, Yang; Li, Gang; Lin, Ling

2017-03-01

Discrimination of human and nonhuman blood is crucial for import-export ports and inspection and quarantine departments. Current methods are usually destructive, complicated and time-consuming. We had previously demonstrated that visible diffuse reflectance spectroscopy combining PLS-DA method can successfully realize human blood discrimination. In that research, the spectra were measured with the fiber probe under the surface of blood samples. However, open sampling may pollute the blood samples. Virulence factors in blood samples can also endanger inspectors. In this paper, we explored the classification effect with the blood samples measured in the original containers-vacuum blood vessel. Furthermore, we studied the impact of different conditions of blood samples, such as coagulation and hemolysis, on the prediction ability of the discrimination model. The calibration model built with blood samples in different conditions displayed a satisfactory prediction result. This research demonstrated that visible and near-infrared diffuse reflectance spectroscopy method was potential for noncontact discrimination of human blood.

Information about the US racial demographic shift triggers concerns about anti-White discrimination among the prospective White “minority”

PubMed Central

Richeson, Jennifer A.

2017-01-01

The United States is undergoing a demographic shift in which White Americans are predicted to comprise less than 50% of the US population by mid-century. The present research examines how exposure to information about this racial shift affects perceptions of the extent to which different racial groups face discrimination. In four experiments, making the growing national racial diversity salient led White Americans to predict that Whites will face increasing discrimination in the future, compared with control information. Conversely, regardless of experimental condition, Whites estimated that discrimination against various racial minority groups will decline. Explorations of several psychological mechanisms potentially underlying the effect of the racial shift information on perceived anti-White discrimination suggested a mediating role of concerns about American culture fundamentally changing. Taken together, these findings suggest that reports about the changing national demographics enhance concerns among Whites that they will be the victims of racial discrimination in the future. PMID:28953971

Information about the US racial demographic shift triggers concerns about anti-White discrimination among the prospective White "minority".

PubMed

Craig, Maureen A; Richeson, Jennifer A

2017-01-01

The United States is undergoing a demographic shift in which White Americans are predicted to comprise less than 50% of the US population by mid-century. The present research examines how exposure to information about this racial shift affects perceptions of the extent to which different racial groups face discrimination. In four experiments, making the growing national racial diversity salient led White Americans to predict that Whites will face increasing discrimination in the future, compared with control information. Conversely, regardless of experimental condition, Whites estimated that discrimination against various racial minority groups will decline. Explorations of several psychological mechanisms potentially underlying the effect of the racial shift information on perceived anti-White discrimination suggested a mediating role of concerns about American culture fundamentally changing. Taken together, these findings suggest that reports about the changing national demographics enhance concerns among Whites that they will be the victims of racial discrimination in the future.

Family support, self-esteem, and perceived racial discrimination among Asian American male college students.

PubMed

Wei, Meifen; Yeh, Christine Jean; Chao, Ruth Chu-Lien; Carrera, Stephanie; Su, Jenny C

2013-07-01

This study was conducted to examine under what situation (i.e., when individuals used more or less family support) and for whom (i.e., those with high or low self-esteem) perceived racial discrimination would or would not have a significant positive association with psychological distress. A total of 95 Asian American male college students completed an online survey. A hierarchical regression analysis indicated a significant 3-way interaction of family support, self-esteem, and perceived racial discrimination in predicting psychological distress after controlling for perceived general stress. A simple effect analysis was used to explore the nature of the interaction. When Asian American male college students used more family support to cope with racial discrimination, the association between perceived racial discrimination and psychological distress was not significant for those with high or low self-esteem. The result from the simple interaction indicated that, when more family support was used, the 2 slopes for high and low self-esteem were not significantly different from each other. Conversely, when they used less family support, the association between perceived racial discrimination and psychological distress was not significant for those with high self-esteem, but was significantly positive for those with low self-esteem. The result from the simple interaction indicated that, when less family support was used, the slopes for high and low self-esteem were significantly different. The result suggested that low use of family support may put these male students with low self-esteem at risk for psychological distress. Limitations, future research directions, and clinical implications were discussed. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Social context matters: Ethnicity, discrimination and stress reactivity.

PubMed

Busse, David; Yim, Ilona S; Campos, Belinda

2017-09-01

Exposure to chronic discrimination is associated with increased morbidity and mortality. The study of biobehavioral pathways linking discrimination with health outcomes has mostly focused on the cardiovascular system, with fewer studies addressing the hypothalamus-pituitary-adrenal (HPA) axis. In this study we tested associations between Latino ethnicity, experiences of discrimination, and cortisol responses to an acute laboratory stressor. One hundred fifty eight individuals (92 female, 66 male) between the ages of 18 and 29 years participated in the study. Salivary cortisol was measured once before and eight times after administration of a laboratory stressor (the Trier Social Stress Test). Past experiences of discrimination were measured with the Experiences of Discrimination Scale. Findings from conditional process modeling suggest that Latino ethnicity predicted a) heightened cortisol reactivity and b) more pronounced cortisol recovery through discrimination experiences (mediator), and that this effect was further moderated by sex with a significant indirect effect only among males. The direct path from Latino ethnicity to cortisol reactivity or cortisol recovery was, however, not significant. In sum, findings suggest that Latino ethnicity and discrimination interact to predict cortisol dysregulation, which implies that an appropriate model for understanding minority health discrepancies must incorporate interactive processes and cannot simply rely on the effects of ethnicity or discrimination alone. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

PubMed Central

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

2015-01-01

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

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

ERIC Educational Resources Information Center

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

2017-01-01

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

Can Placement Test Score Predict Success for a First-Semester Community College Student in Their First Online Course?

ERIC Educational Resources Information Center

MacGregor, Philip C.; O'Reilly, Frances L.; Matt, John

2017-01-01

This study examined the following question: What is the relationship, if any, between COMPASS placement scores and the student success in the first online course during the students first semester? Discriminant function analysis was used to examine the relationship. This study used existing data from new students, who took the COMPASS placement…

Sensitivity and specificity of subacute computerized neurocognitive testing and symptom evaluation in predicting outcomes after sports-related concussion.

PubMed

Lau, Brian C; Collins, Michael W; Lovell, Mark R

2011-06-01

Concussions affect an estimated 136 000 high school athletes yearly. Computerized neurocognitive testing has been shown to be appropriately sensitive and specific in diagnosing concussions, but no studies have assessed its utility to predict length of recovery. Determining prognosis during subacute recovery after sports concussion will help clinicians more confidently address return-to-play and academic decisions. To quantify the prognostic ability of computerized neurocognitive testing in combination with symptoms during the subacute recovery phase from sports-related concussion. Cohort study (prognosis); Level of evidence, 2. In sum, 108 male high school football athletes completed a computer-based neurocognitive test battery within 2.23 days of injury and were followed until returned to play as set by international guidelines. Athletes were grouped into protracted recovery (>14 days; n = 50) or short-recovery (≤14 days; n = 58). Separate discriminant function analyses were performed using total symptom score on Post-Concussion Symptom Scale, symptom clusters (migraine, cognitive, sleep, neuropsychiatric), and Immediate Postconcussion Assessment and Cognitive Testing neurocognitive scores (verbal memory, visual memory, reaction time, processing speed). Multiple discriminant function analyses revealed that the combination of 4 symptom clusters and 4 neurocognitive composite scores had the highest sensitivity (65.22%), specificity (80.36%), positive predictive value (73.17%), and negative predictive value (73.80%) in predicting protracted recovery. Discriminant function analyses of total symptoms on the Post-Concussion Symptom Scale alone had a sensitivity of 40.81%; specificity, 79.31%; positive predictive value, 62.50%; and negative predictive value, 61.33%. The 4 symptom clusters alone discriminant function analyses had a sensitivity of 46.94%; specificity, 77.20%; positive predictive value, 63.90%; and negative predictive value, 62.86%. Discriminant function analyses of the 4 computerized neurocognitive scores alone had a sensitivity of 53.20%; specificity, 75.44%; positive predictive value, 64.10%; and negative predictive value, 66.15%. The use of computerized neurocognitive testing in conjunction with symptom clusters results improves sensitivity, specificity, positive predictive value, and negative predictive value of predicting protracted recovery compared with each used alone. There is also a net increase in sensitivity of 24.41% when using neurocognitive testing and symptom clusters together compared with using total symptoms on Post-Concussion Symptom Scale alone.

An HR-MAS MR Metabolomics Study on Breast Tissues Obtained with Core Needle Biopsy

PubMed Central

Cho, Nariya; Chang, Jung Min; Koo, Hye Ryoung; Yi, Ann; Kim, Hyeonjin; Park, Sunghyouk; Moon, Woo Kyung

2011-01-01

Background Much research has been devoted to the development of new breast cancer diagnostic measures, including those involving high-resolution magic angle spinning (HR-MAS) magnetic resonance (MR) spectroscopic techniques. Previous HR-MAS MR results have been obtained from post-surgery samples, which limits their direct clinical applicability. Methodology/Principal Findings In the present study, we performed HR-MAS MR spectroscopic studies on 31 breast tissue samples (13 cancer and 18 non-cancer) obtained by percutaneous core needle biopsy. We showed that cancer and non-cancer samples can be discriminated very well with Orthogonal Projections to Latent Structure-Discriminant Analysis (OPLS-DA) multivariate model on the MR spectra. A subsequent blind test showed 69% sensitivity and 94% specificity in the prediction of the cancer status. A spectral analysis showed that in cancer cells, taurine- and choline-containing compounds are elevated. Our approach, additionally, could predict the progesterone receptor statuses of the cancer patients. Conclusions/Significance HR-MAS MR metabolomics on intact breast tissues obtained by core needle biopsy may have a potential to be used as a complement to the current diagnostic and prognostic measures for breast cancers. PMID:22028780

A systematic review of validated sinus surgery simulators.

PubMed

Stew, B; Kao, S S-T; Dharmawardana, N; Ooi, E H

2018-06-01

Simulation provides a safe and effective opportunity to develop surgical skills. A variety of endoscopic sinus surgery (ESS) simulators has been described in the literature. Validation of these simulators allows for effective utilisation in training. To conduct a systematic review of the published literature to analyse the evidence for validated ESS simulation. Pubmed, Embase, Cochrane and Cinahl were searched from inception of the databases to 11 January 2017. Twelve thousand five hundred and sixteen articles were retrieved of which 10 112 were screened following the removal of duplicates. Thirty-eight full-text articles were reviewed after meeting search criteria. Evidence of face, content, construct, discriminant and predictive validity was extracted. Twenty articles were included in the analysis describing 12 ESS simulators. Eleven of these simulators had undergone validation: 3 virtual reality, 7 physical bench models and 1 cadaveric simulator. Seven of the simulators were shown to have face validity, 7 had construct validity and 1 had predictive validity. None of the simulators demonstrated discriminate validity. This systematic review demonstrates that a number of ESS simulators have been comprehensively validated. Many of the validation processes, however, lack standardisation in outcome reporting, thus limiting a meta-analysis comparison between simulators. © 2017 John Wiley & Sons Ltd.

ADA Title I allegations and the Mining, Quarrying, and Oil/Gas Extraction industry.

PubMed

Van Wieren, Todd A; Rhoades, Laura; McMahon, Brian T

2017-01-01

The majority of research about employment discrimination in the U.S. Mining, Quarrying, and Oil/Gas (MQOGE) industries has concentrated on gender and race, while little attention has focused on disability. To explore allegations of Americans with Disabilities Act (ADA) Title I discrimination made to the Equal Employment Opportunity Commission (EEOC) by individuals with disabilities against MQOGE employers. Key data available to this study included demographic characteristics of charging parties, size of employers, types of allegations, and case outcomes. Using descriptive analysis, allegation profiles were developed for MQOGE's three main sectors (i.e., Oil/Gas Extraction, Mining except Oil/Gas, and Support Activities). These three profiles where then comparatively analyzed. Lastly, regression analysis explored whether some of the available data could partially predict MQOGE case outcomes. The predominant characteristics of MQOGE allegations were found to be quite similar to the allegation profile of U.S. private-sector industry as a whole, and fairly representative of MQOGE's workforce demographics. Significant differences between MQOGE's three main sector profiles were noted on some important characteristics. Lastly, it was found that MQOGE case outcomes could be partially predicted via some of the available variables. The study's limitations were presented and recommendations were offered for further research.

Early detection of fungal growth in bakery products by use of an electronic nose based on mass spectrometry.

PubMed

Vinaixa, Maria; Marín, Sonia; Brezmes, Jesús; Llobet, Eduard; Vilanova, Xavier; Correig, Xavier; Ramos, Antonio; Sanchis, Vicent

2004-10-06

This paper presents the design, optimization, and evaluation of a mass spectrometry-based electronic nose (MS e-nose) for early detection of unwanted fungal growth in bakery products. Seven fungal species (Aspergillus flavus, Aspergillus niger, Eurotium amstelodami, Eurotium herbariorum, Eurotium rubrum, Eurotium repens, and Penicillium corylophillum) were isolated from bakery products and used for the study. Two sampling headspace techniques were tested: static headspace (SH) and solid-phase microextraction (SPME). Cross-validated models based on principal component analysis (PCA), coupled to discriminant function analysis (DFA) and fuzzy ARTMAP, were used as data treatment. When attempting to discriminate between inoculated and blank control vials or between genera or species of in vitro growing cultures, sampling based on SPME showed better results than those based on static headspace. The SPME-MS-based e-nose was able to predict fungal growth with 88% success after 24 h of inoculation and 98% success after 48 h when changes were monitored in the headspace of fungal cultures growing on bakery product analogues. Prediction of the right fungal genus reached 78% and 88% after 24 and 96 h, respectively.

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

PubMed

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

2016-01-01

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

Clinical prediction model to aid emergency doctors managing febrile children at risk of serious bacterial infections: diagnostic study

PubMed Central

Nijman, Ruud G; Vergouwe, Yvonne; Thompson, Matthew; van Veen, Mirjam; van Meurs, Alfred H J; van der Lei, Johan; Steyerberg, Ewout W; Moll, Henriette A

2013-01-01

Objective To derive, cross validate, and externally validate a clinical prediction model that assesses the risks of different serious bacterial infections in children with fever at the emergency department. Design Prospective observational diagnostic study. Setting Three paediatric emergency care units: two in the Netherlands and one in the United Kingdom. Participants Children with fever, aged 1 month to 15 years, at three paediatric emergency care units: Rotterdam (n=1750) and the Hague (n=967), the Netherlands, and Coventry (n=487), United Kingdom. A prediction model was constructed using multivariable polytomous logistic regression analysis and included the predefined predictor variables age, duration of fever, tachycardia, temperature, tachypnoea, ill appearance, chest wall retractions, prolonged capillary refill time (>3 seconds), oxygen saturation <94%, and C reactive protein. Main outcome measures Pneumonia, other serious bacterial infections (SBIs, including septicaemia/meningitis, urinary tract infections, and others), and no SBIs. Results Oxygen saturation <94% and presence of tachypnoea were important predictors of pneumonia. A raised C reactive protein level predicted the presence of both pneumonia and other SBIs, whereas chest wall retractions and oxygen saturation <94% were useful to rule out the presence of other SBIs. Discriminative ability (C statistic) to predict pneumonia was 0.81 (95% confidence interval 0.73 to 0.88); for other SBIs this was even better: 0.86 (0.79 to 0.92). Risk thresholds of 10% or more were useful to identify children with serious bacterial infections; risk thresholds less than 2.5% were useful to rule out the presence of serious bacterial infections. External validation showed good discrimination for the prediction of pneumonia (0.81, 0.69 to 0.93); discriminative ability for the prediction of other SBIs was lower (0.69, 0.53 to 0.86). Conclusion A validated prediction model, including clinical signs, symptoms, and C reactive protein level, was useful for estimating the likelihood of pneumonia and other SBIs in children with fever, such as septicaemia/meningitis and urinary tract infections. PMID:23550046

Predicting rheological behavior and baking quality of wheat flour using a GlutoPeak test.

PubMed

Rakita, Slađana; Dokić, Ljubica; Dapčević Hadnađev, Tamara; Hadnađev, Miroslav; Torbica, Aleksandra

2018-06-01

The purpose of this research was to gain an insight into the ability of the GlutoPeak instrument to predict flour functionality for bread making, as well as to determine which of the GlutoPeak parameters show the best potential in predicting dough rheological behavior and baking performance. Obtained results showed that GlutoPeak parameters correlated better with the indices of extensional rheological tests which consider constant dough hydration than with those which were performed at constant dough consistency. The GlutoPeak test showed that it is suitable for discriminating wheat varieties of good quality from those of poor quality, while the most discriminating index was maximum torque (MT). Moreover, MT value of 50 BU and aggregation energy value of 1,300 GPU were set as limits of wheat flour quality. The backward stepwise regression analysis revealed that a high-level prediction of indices which are highly affected by protein content (gluten content, flour water absorption, and dough tenacity) was achieved by using the GlutoPeak indices. Concerning bread quality, a moderate prediction of specific loaf volume and an intense level prediction of breadcrumb textural properties were accomplished by using the GlutoPeak parameters. The presented results indicated that the application of this quick test in wheat transformation chain for the assessment of baking quality would be useful. Baking test is considered as the most reliable method for assessing wheat-baking quality. However, baking test requires trained stuff, time, and large sample amount. These disadvantages have led to a growing demand to develop new rapid tests which would enable prediction of baked product quality with a limited flour size. Therefore, we tested the possibility of using a GlutoPeak tester to predict loaf volume and breadcrumb textural properties. Discrimination of wheat varieties according to quality with a restricted flour amount was also examined. Furthermore, we proposed the limit values of GlutoPeak parameters which would be highly beneficial for millers and bakers when determine suitability of flour for end-use. © 2017 Wiley Periodicals, Inc.

Predicting Esophagitis After Chemoradiation Therapy for Non-Small Cell Lung Cancer: An Individual Patient Data Meta-Analysis

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

Palma, David A., E-mail: david.palma@uwo.ca; Senan, Suresh; Oberije, Cary

Purpose: Concurrent chemoradiation therapy (CCRT) improves survival compared with sequential treatment for locally advanced non-small cell lung cancer, but it increases toxicity, particularly radiation esophagitis (RE). Validated predictors of RE for clinical use are lacking. We performed an individual-patient-data meta-analysis to determine factors predictive of clinically significant RE. Methods and Materials: After a systematic review of the literature, data were obtained on 1082 patients who underwent CCRT, including patients from Europe, North America, Asia, and Australia. Patients were randomly divided into training and validation sets (2/3 vs 1/3 of patients). Factors predictive of RE (grade ≥2 and grade ≥3) weremore » assessed using logistic modeling, with the concordance statistic (c statistic) used to evaluate the performance of each model. Results: The median radiation therapy dose delivered was 65 Gy, and the median follow-up time was 2.1 years. Most patients (91%) received platinum-containing CCRT regimens. The development of RE was common, scored as grade 2 in 348 patients (32.2%), grade 3 in 185 (17.1%), and grade 4 in 10 (0.9%). There were no RE-related deaths. On univariable analysis using the training set, several baseline factors were statistically predictive of RE (P<.05), but only dosimetric factors had good discrimination scores (c > .60). On multivariable analysis, the esophageal volume receiving ≥60 Gy (V60) alone emerged as the best predictor of grade ≥2 and grade ≥3 RE, with good calibration and discrimination. Recursive partitioning identified 3 risk groups: low (V60 <0.07%), intermediate (V60 0.07% to 16.99%), and high (V60 ≥17%). With use of the validation set, the predictive model performed inferiorly for the grade ≥2 endpoint (c = .58) but performed well for the grade ≥3 endpoint (c = .66). Conclusions: Clinically significant RE is common, but life-threatening complications occur in <1% of patients. Although several factors are statistically predictive of RE, the V60 alone provides the best predictive ability. Efforts to reduce the V60 should be prioritized, with further research needed to identify and validate new predictive factors.« less

Multifactorial discrimination as a fundamental cause of mental health inequities.

PubMed

Khan, Mariam; Ilcisin, Misja; Saxton, Katherine

2017-03-04

The theory of fundamental causes explains why health disparities persist over time, even as risk factors, mechanisms, and diseases change. Using an intersectional framework, we evaluated multifactorial discrimination as a fundamental cause of mental health disparities. Using baseline data from the Project STRIDE: Stress, Identity, and Mental Health study, we examined the health effects of discrimination among individuals who self-identified as lesbian, gay, or bisexual. We used logistic and linear regression to assess whether multifactorial discrimination met the four criteria designating a fundamental cause, namely that the cause: 1) influences multiple health outcomes, 2) affects multiple risk factors, 3) involves access to resources that can be leveraged to reduce consequences of disease, and 4) reproduces itself in varied contexts through changing mechanisms. Multifactorial discrimination predicted high depression scores, psychological well-being, and substance use disorder diagnosis. Discrimination was positively associated with risk factors for high depression scores: chronic strain and total number of stressful life events. Discrimination was associated with significantly lower levels of mastery and self-esteem, protective factors for depressive symptomatology. Even after controlling for risk factors, discrimination remained a significant predictor for high depression scores. Among subjects with low depression scores, multifactorial discrimination also predicted anxiety and aggregate mental health scores. Multifactorial discrimination should be considered a fundamental cause of mental health inequities and may be an important cause of broad health disparities among populations with intersecting social identities.

Heuristics to Facilitate Understanding of Discriminant Analysis.

ERIC Educational Resources Information Center

Van Epps, Pamela D.

This paper discusses the principles underlying discriminant analysis and constructs a simulated data set to illustrate its methods. Discriminant analysis is a multivariate technique for identifying the best combination of variables to maximally discriminate between groups. Discriminant functions are established on existing groups and used to…

Discrimination of Mirror-Image shapes by Young Children

ERIC Educational Resources Information Center

Thompson, G. Brian

1975-01-01

Conducted two experiments which employed discrimination learning methods to test predictions related to the difficulty of discrimination of lateral reversals and of inversions when shapes are presented: (1) successively, (2) simultaneously in lateral alignment, and (3) simultaneously in vertical alignment. Subjects were 6-year-old children. (SDH)

Predicting Ebola infection: A malaria-sensitive triage score for Ebola virus disease

PubMed Central

Okoni-Williams, Harry Henry; Suma, Mohamed; Mancuso, Brooke; Al-Dikhari, Ahmed; Faouzi, Mohamed

2017-01-01

Background The non-specific symptoms of Ebola Virus Disease (EVD) pose a major problem to triage and isolation efforts at Ebola Treatment Centres (ETCs). Under the current triage protocol, half the patients allocated to high-risk “probable” wards were EVD(-): a misclassification speculated to predispose nosocomial EVD infection. A better understanding of the statistical relevance of individual triage symptoms is essential in resource-poor settings where rapid, laboratory-confirmed diagnostics are often unavailable. Methods/Principal findings This retrospective cohort study analyses the clinical characteristics of 566 patients admitted to the GOAL-Mathaska ETC in Sierra Leone. The diagnostic potential of each characteristic was assessed by multivariate analysis and incorporated into a statistically weighted predictive score, designed to detect EVD as well as discriminate malaria. Of the 566 patients, 28% were EVD(+) and 35% were malaria(+). Malaria was 2-fold more common in EVD(-) patients (p<0.05), and thus an important differential diagnosis. Univariate analyses comparing EVD(+) vs. EVD(-) and EVD(+)/malaria(-) vs. EVD(-)/malaria(+) cohorts revealed 7 characteristics with the highest odds for EVD infection, namely: reported sick-contact, conjunctivitis, diarrhoea, referral-time of 4–9 days, pyrexia, dysphagia and haemorrhage. Oppositely, myalgia was more predictive of EVD(-) or EVD(-)/malaria(+). Including these 8 characteristics in a triage score, we obtained an 89% ability to discriminate EVD(+) from either EVD(-) or EVD(-)/malaria(+). Conclusions/Significance This study proposes a highly predictive and easy-to-use triage tool, which stratifies the risk of EVD infection with 89% discriminative power for both EVD(-) and EVD(-)/malaria(+) differential diagnoses. Improved triage could preserve resources by identifying those in need of more specific differential diagnostics as well as bolster infection prevention/control measures by better compartmentalizing the risk of nosocomial infection. PMID:28231242

Predicting Ebola infection: A malaria-sensitive triage score for Ebola virus disease.

PubMed

Hartley, Mary-Anne; Young, Alyssa; Tran, Anh-Minh; Okoni-Williams, Harry Henry; Suma, Mohamed; Mancuso, Brooke; Al-Dikhari, Ahmed; Faouzi, Mohamed

2017-02-01

The non-specific symptoms of Ebola Virus Disease (EVD) pose a major problem to triage and isolation efforts at Ebola Treatment Centres (ETCs). Under the current triage protocol, half the patients allocated to high-risk "probable" wards were EVD(-): a misclassification speculated to predispose nosocomial EVD infection. A better understanding of the statistical relevance of individual triage symptoms is essential in resource-poor settings where rapid, laboratory-confirmed diagnostics are often unavailable. This retrospective cohort study analyses the clinical characteristics of 566 patients admitted to the GOAL-Mathaska ETC in Sierra Leone. The diagnostic potential of each characteristic was assessed by multivariate analysis and incorporated into a statistically weighted predictive score, designed to detect EVD as well as discriminate malaria. Of the 566 patients, 28% were EVD(+) and 35% were malaria(+). Malaria was 2-fold more common in EVD(-) patients (p<0.05), and thus an important differential diagnosis. Univariate analyses comparing EVD(+) vs. EVD(-) and EVD(+)/malaria(-) vs. EVD(-)/malaria(+) cohorts revealed 7 characteristics with the highest odds for EVD infection, namely: reported sick-contact, conjunctivitis, diarrhoea, referral-time of 4-9 days, pyrexia, dysphagia and haemorrhage. Oppositely, myalgia was more predictive of EVD(-) or EVD(-)/malaria(+). Including these 8 characteristics in a triage score, we obtained an 89% ability to discriminate EVD(+) from either EVD(-) or EVD(-)/malaria(+). This study proposes a highly predictive and easy-to-use triage tool, which stratifies the risk of EVD infection with 89% discriminative power for both EVD(-) and EVD(-)/malaria(+) differential diagnoses. Improved triage could preserve resources by identifying those in need of more specific differential diagnostics as well as bolster infection prevention/control measures by better compartmentalizing the risk of nosocomial infection.

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

PubMed

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

2010-11-01

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

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

Predicting dynamic range and intensity discrimination for electrical pulse-train stimuli using a stochastic auditory nerve model: the effects of stimulus noise.

PubMed

Xu, Yifang; Collins, Leslie M

2005-06-01

This work investigates dynamic range and intensity discrimination for electrical pulse-train stimuli that are modulated by noise using a stochastic auditory nerve model. Based on a hypothesized monotonic relationship between loudness and the number of spikes elicited by a stimulus, theoretical prediction of the uncomfortable level has previously been determined by comparing spike counts to a fixed threshold, Nucl. However, no specific rule for determining Nucl has been suggested. Our work determines the uncomfortable level based on the excitation pattern of the neural response in a normal ear. The number of fibers corresponding to the portion of the basilar membrane driven by a stimulus at an uncomfortable level in a normal ear is related to Nucl at an uncomfortable level of the electrical stimulus. Intensity discrimination limens are predicted using signal detection theory via the probability mass function of the neural response and via experimental simulations. The results show that the uncomfortable level for pulse-train stimuli increases slightly as noise level increases. Combining this with our previous threshold predictions, we hypothesize that the dynamic range for noise-modulated pulse-train stimuli should increase with additive noise. However, since our predictions indicate that intensity discrimination under noise degrades, overall intensity coding performance may not improve significantly.

A quantitative link between face discrimination deficits and neuronal selectivity for faces in autism☆

PubMed Central

Jiang, Xiong; Bollich, Angela; Cox, Patrick; Hyder, Eric; James, Joette; Gowani, Saqib Ali; Hadjikhani, Nouchine; Blanz, Volker; Manoach, Dara S.; Barton, Jason J.S.; Gaillard, William D.; Riesenhuber, Maximilian

2013-01-01

Individuals with Autism Spectrum Disorder (ASD) appear to show a general face discrimination deficit across a range of tasks including social–emotional judgments as well as identification and discrimination. However, functional magnetic resonance imaging (fMRI) studies probing the neural bases of these behavioral differences have produced conflicting results: while some studies have reported reduced or no activity to faces in ASD in the Fusiform Face Area (FFA), a key region in human face processing, others have suggested more typical activation levels, possibly reflecting limitations of conventional fMRI techniques to characterize neuron-level processing. Here, we test the hypotheses that face discrimination abilities are highly heterogeneous in ASD and are mediated by FFA neurons, with differences in face discrimination abilities being quantitatively linked to variations in the estimated selectivity of face neurons in the FFA. Behavioral results revealed a wide distribution of face discrimination performance in ASD, ranging from typical performance to chance level performance. Despite this heterogeneity in perceptual abilities, individual face discrimination performance was well predicted by neural selectivity to faces in the FFA, estimated via both a novel analysis of local voxel-wise correlations, and the more commonly used fMRI rapid adaptation technique. Thus, face processing in ASD appears to rely on the FFA as in typical individuals, differing quantitatively but not qualitatively. These results for the first time mechanistically link variations in the ASD phenotype to specific differences in the typical face processing circuit, identifying promising targets for interventions. PMID:24179786

Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort.

PubMed

Fortner, Renée T; Hüsing, Anika; Kühn, Tilman; Konar, Meric; Overvad, Kim; Tjønneland, Anne; Hansen, Louise; Boutron-Ruault, Marie-Christine; Severi, Gianluca; Fournier, Agnès; Boeing, Heiner; Trichopoulou, Antonia; Benetou, Vasiliki; Orfanos, Philippos; Masala, Giovanna; Agnoli, Claudia; Mattiello, Amalia; Tumino, Rosario; Sacerdote, Carlotta; Bueno-de-Mesquita, H B As; Peeters, Petra H M; Weiderpass, Elisabete; Gram, Inger T; Gavrilyuk, Oxana; Quirós, J Ramón; Maria Huerta, José; Ardanaz, Eva; Larrañaga, Nerea; Lujan-Barroso, Leila; Sánchez-Cantalejo, Emilio; Butt, Salma Tunå; Borgquist, Signe; Idahl, Annika; Lundin, Eva; Khaw, Kay-Tee; Allen, Naomi E; Rinaldi, Sabina; Dossus, Laure; Gunter, Marc; Merritt, Melissa A; Tzoulaki, Ioanna; Riboli, Elio; Kaaks, Rudolf

2017-03-15

Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines were evaluated in a step-wise backward selection process; biomarkers were retained at p < 0.157 indicating improvement in the Akaike information criterion (AIC). Improvement in discrimination was assessed using the C-statistic for all biomarkers alone, and change in C-statistic from addition of biomarkers to preexisting absolute risk estimates. We used internal validation with bootstrapping (1000-fold) to adjust for over-fitting. Adiponectin, estrone, interleukin-1 receptor antagonist, tumor necrosis factor-alpha and triglycerides were selected into the model. After accounting for over-fitting, discrimination was improved by 2.0 percentage points when all evaluated biomarkers were included and 1.7 percentage points in the model including the selected biomarkers. Models including etiologic markers on independent pathways and genetic markers may further improve discrimination. © 2016 UICC.

Anti-inflammatory drugs and prediction of new structures by comparative analysis.

PubMed

Bartzatt, Ronald

2012-01-01

Nonsteroidal anti-inflammatory drugs (NSAIDs) are a group of agents important for their analgesic, anti-inflammatory, and antipyretic properties. This study presents several approaches to predict and elucidate new molecular structures of NSAIDs based on 36 known and proven anti-inflammatory compounds. Based on 36 known NSAIDs the mean value of Log P is found to be 3.338 (standard deviation= 1.237), mean value of polar surface area is 63.176 Angstroms2 (standard deviation = 20.951 A2), and the mean value of molecular weight is 292.665 (standard deviation = 55.627). Nine molecular properties are determined for these 36 NSAID agents, including Log P, number of -OH and -NHn, violations of Rule of 5, number of rotatable bonds, and number of oxygens and nitrogens. Statistical analysis of these nine molecular properties provides numerical parameters to conform to in the design of novel NSAID drug candidates. Multiple regression analysis is accomplished using these properties of 36 agents followed with examples of predicted molecular weight based on minimum and maximum property values. Hierarchical cluster analysis indicated that licofelone, tolfenamic acid, meclofenamic acid, droxicam, and aspirin are substantially distinct from all remaining NSAIDs. Analysis of similarity (ANOSIM) produced R = 0.4947, which indicates low to moderate level of dissimilarity between these 36 NSAIDs. Non-hierarchical K-means cluster analysis separated the 36 NSAIDs into four groups having members of greatest similarity. Likewise, discriminant analysis divided the 36 agents into two groups indicating the greatest level of distinction (discrimination) based on nine properties. These two multivariate methods together provide investigators a means to compare and elucidate novel drug designs to 36 proven compounds and ascertain to which of those are most analogous in pharmacodynamics. In addition, artificial neural network modeling is demonstrated as an approach to predict numerous molecular properties of new drug designs that is based on neural training from 36 proven NSAIDs. Comprehensive and effective approaches are presented in this study for the design of new NSAID type agents which are so very important for inhibition of COX-2 and COX-1 isoenzymes.

Application of effective wavelengths and BP neural network for the discrimination of varieties of instant milk tea powders using visible and near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Liu, Fei; He, Yong; Wang, Li

2007-11-01

In order to implement the fast discrimination of different milk tea powders with different internal qualities, visible and near infrared (Vis/NIR) spectroscopy combined with effective wavelengths (EWs) and BP neural network (BPNN) was investigated as a new approach. Five brands of milk teas were obtained and 225 samples were selected randomly for the calibration set, while 75 samples for the validation set. The EWs were selected according to x-loading weights and regression coefficients by PLS analysis after some preprocessing. A total of 18 EWs (400, 401, 452, 453, 502, 503, 534, 535, 594, 595, 635, 636, 688, 689, 987, 988, 995 and 996 nm) were selected as the inputs of BPNN model. The performance was validated by the calibration and validation sets. The threshold error of prediction was set as +/-0.1 and an excellent precision and recognition ratio of 100% for calibration set and 98.7% for validation set were achieved. The prediction results indicated that the EWs reflected the main characteristics of milk tea of different brands based on Vis/NIR spectroscopy and BPNN model, and the EWs would be useful for the development of portable instrument to discriminate the variety and detect the adulteration of instant milk tea powders.

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

NASA Astrophysics Data System (ADS)

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

2013-05-01

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

Use of handheld X-ray fluorescence as a non-invasive method to distinguish between Asian and African elephant tusks

PubMed Central

Buddhachat, Kittisak; Thitaram, Chatchote; Brown, Janine L.; Klinhom, Sarisa; Bansiddhi, Pakkanut; Penchart, Kitichaya; Ouitavon, Kanita; Sriaksorn, Khanittha; Pa-in, Chalermpol; Kanchanasaka, Budsabong; Somgird, Chaleamchat; Nganvongpanit, Korakot

2016-01-01

We describe the use of handheld X-ray fluorescence, for elephant tusk species identification. Asian (n = 72) and African (n = 85) elephant tusks were scanned and we utilized the species differences in elemental composition to develop a functional model differentiating between species with high precision. Spatially, the majority of measured elements (n = 26) exhibited a homogeneous distribution in cross-section, but a more heterologous pattern in the longitudinal direction. Twenty-one of twenty four elements differed between Asian and African samples. Data were subjected to hierarchical cluster analysis followed by a stepwise discriminant analysis, which identified elements for the functional equation. The best equation consisted of ratios of Si, S, Cl, Ti, Mn, Ag, Sb and W, with Zr as the denominator. Next, Bayesian binary regression model analysis was conducted to predict the probability that a tusk would be of African origin. A cut-off value was established to improve discrimination. This Bayesian hybrid classification model was then validated by scanning an additional 30 Asian and 41 African tusks, which showed high accuracy (94%) and precision (95%) rates. We conclude that handheld XRF is an accurate, non-invasive method to discriminate origin of elephant tusks provides rapid results applicable to use in the field. PMID:27097717

Use of handheld X-ray fluorescence as a non-invasive method to distinguish between Asian and African elephant tusks

NASA Astrophysics Data System (ADS)

Buddhachat, Kittisak; Thitaram, Chatchote; Brown, Janine L.; Klinhom, Sarisa; Bansiddhi, Pakkanut; Penchart, Kitichaya; Ouitavon, Kanita; Sriaksorn, Khanittha; Pa-in, Chalermpol; Kanchanasaka, Budsabong; Somgird, Chaleamchat; Nganvongpanit, Korakot

2016-04-01

We describe the use of handheld X-ray fluorescence, for elephant tusk species identification. Asian (n = 72) and African (n = 85) elephant tusks were scanned and we utilized the species differences in elemental composition to develop a functional model differentiating between species with high precision. Spatially, the majority of measured elements (n = 26) exhibited a homogeneous distribution in cross-section, but a more heterologous pattern in the longitudinal direction. Twenty-one of twenty four elements differed between Asian and African samples. Data were subjected to hierarchical cluster analysis followed by a stepwise discriminant analysis, which identified elements for the functional equation. The best equation consisted of ratios of Si, S, Cl, Ti, Mn, Ag, Sb and W, with Zr as the denominator. Next, Bayesian binary regression model analysis was conducted to predict the probability that a tusk would be of African origin. A cut-off value was established to improve discrimination. This Bayesian hybrid classification model was then validated by scanning an additional 30 Asian and 41 African tusks, which showed high accuracy (94%) and precision (95%) rates. We conclude that handheld XRF is an accurate, non-invasive method to discriminate origin of elephant tusks provides rapid results applicable to use in the field.

Mutational analysis of multiple lung cancers: Discrimination between primary and metastatic lung cancers by genomic profile.

PubMed

Goto, Taichiro; Hirotsu, Yosuke; Mochizuki, Hitoshi; Nakagomi, Takahiro; Shikata, Daichi; Yokoyama, Yujiro; Oyama, Toshio; Amemiya, Kenji; Okimoto, Kenichiro; Omata, Masao

2017-05-09

In cases of multiple lung cancers, individual tumors may represent either a primary lung cancer or both primary and metastatic lung cancers. Treatment selection varies depending on such features, and this discrimination is critically important in predicting prognosis. The present study was undertaken to determine the efficacy and validity of mutation analysis as a means of determining whether multiple lung cancers are primary or metastatic in nature. The study involved 12 patients who underwent surgery in our department for multiple lung cancers between July 2014 and March 2016. Tumor cells were collected from formalin-fixed paraffin-embedded tissues of the primary lesions by using laser capture microdissection, and targeted sequencing of 53 lung cancer-related genes was performed. In surgically treated patients with multiple lung cancers, the driver mutation profile differed among the individual tumors. Meanwhile, in a case of a solitary lung tumor that appeared after surgery for double primary lung cancers, gene mutation analysis using a bronchoscopic biopsy sample revealed a gene mutation profile consistent with the surgically resected specimen, thus demonstrating that the tumor in this case was metastatic. In cases of multiple lung cancers, the comparison of driver mutation profiles clarifies the clonal origin of the tumors and enables discrimination between primary and metastatic tumors.

A discriminant function model as an alternative method to spirometry for COPD screening in primary care settings in China.

PubMed

Cui, Jiangyu; Zhou, Yumin; Tian, Jia; Wang, Xinwang; Zheng, Jingping; Zhong, Nanshan; Ran, Pixin

2012-12-01

COPD is often underdiagnosed in a primary care setting where the spirometry is unavailable. This study was aimed to develop a simple, economical and applicable model for COPD screening in those settings. First we established a discriminant function model based on Bayes' Rule by stepwise discriminant analysis, using the data from 243 COPD patients and 112 non-COPD subjects from our COPD survey in urban and rural communities and local primary care settings in Guangdong Province, China. We then used this model to discriminate COPD in additional 150 subjects (50 non-COPD and 100 COPD ones) who had been recruited by the same methods as used to have established the model. All participants completed pre- and post-bronchodilator spirometry and questionnaires. COPD was diagnosed according to the Global Initiative for Chronic Obstructive Lung Disease criteria. The sensitivity and specificity of the discriminant function model was assessed. THE ESTABLISHED DISCRIMINANT FUNCTION MODEL INCLUDED NINE VARIABLES: age, gender, smoking index, body mass index, occupational exposure, living environment, wheezing, cough and dyspnoea. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, accuracy and error rate of the function model to discriminate COPD were 89.00%, 82.00%, 4.94, 0.13, 86.66% and 13.34%, respectively. The accuracy and Kappa value of the function model to predict COPD stages were 70% and 0.61 (95% CI, 0.50 to 0.71). This discriminant function model may be used for COPD screening in primary care settings in China as an alternative option instead of spirometry.

Can purchasing information be used to predict adherence to cardiovascular medications? An analysis of linked retail pharmacy and insurance claims data

PubMed Central

Krumme, Alexis A; Sanfélix-Gimeno, Gabriel; Franklin, Jessica M; Isaman, Danielle L; Mahesri, Mufaddal; Matlin, Olga S; Shrank, William H; Brennan, Troyen A; Brill, Gregory; Choudhry, Niteesh K

2016-01-01

Objective The use of retail purchasing data may improve adherence prediction over approaches using healthcare insurance claims alone. Design Retrospective. Setting and participants A cohort of patients who received prescription medication benefits through CVS Caremark, used a CVS Pharmacy ExtraCare Health Care (ECHC) loyalty card, and initiated a statin medication in 2011. Outcome We evaluated associations between retail purchasing patterns and optimal adherence to statins in the 12 subsequent months. Results Among 11 010 statin initiators, 43% were optimally adherent at 12 months of follow-up. Greater numbers of store visits per month and dollar amount per visit were positively associated with optimal adherence, as was making a purchase on the same day as filling a prescription (p<0.0001 for all). Models to predict adherence using retail purchase variables had low discriminative ability (C-statistic: 0.563), while models with both clinical and retail purchase variables achieved a C-statistic of 0.617. Conclusions While the use of retail purchases may improve the discriminative ability of claims-based approaches, these data alone appear inadequate for adherence prediction, even with the addition of more complex analytical approaches. Nevertheless, associations between retail purchasing behaviours and adherence could inform the development of quality improvement interventions. PMID:28186924

Structures of the Recurrence Plot of Heart Rate Variability Signal as a Tool for Predicting the Onset of Paroxysmal Atrial Fibrillation

PubMed Central

Mohebbi, Maryam; Ghassemian, Hassan; Asl, Babak Mohammadzadeh

2011-01-01

This paper aims to propose an effective paroxysmal atrial fibrillation (PAF) predictor which is based on the analysis of the heart rate variability (HRV) signal. Predicting the onset of PAF, based on non-invasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic interventions and to minimize the risks for the patients. This method consists of four steps: Preprocessing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the HRV signal is extracted. In the next step, the recurrence plot (RP) of HRV signal is obtained and six features are extracted to characterize the basic patterns of the RP. These features consist of length of longest diagonal segments, average length of the diagonal lines, entropy, trapping time, length of longest vertical line, and recurrence trend. In the third step, these features are reduced to three features by the linear discriminant analysis (LDA) technique. Using LDA not only reduces the number of the input features, but also increases the classification accuracy by selecting the most discriminating features. Finally, a support vector machine-based classifier is used to classify the HRV signals. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database which consists of both 30-minutes ECG recordings end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, and positive predictivity were 96.55%, 100%, and 100%, respectively. PMID:22606666

External Validation of European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) for Risk Prioritization in an Iranian Population

PubMed Central

Atashi, Alireza; Amini, Shahram; Tashnizi, Mohammad Abbasi; Moeinipour, Ali Asghar; Aazami, Mathias Hossain; Tohidnezhad, Fariba; Ghasemi, Erfan; Eslami, Saeid

2018-01-01

Introduction The European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) is a prediction model which maps 18 predictors to a 30-day post-operative risk of death concentrating on accurate stratification of candidate patients for cardiac surgery. Objective The objective of this study was to determine the performance of the EuroSCORE II risk-analysis predictions among patients who underwent heart surgeries in one area of Iran. Methods A retrospective cohort study was conducted to collect the required variables for all consecutive patients who underwent heart surgeries at Emam Reza hospital, Northeast Iran between 2014 and 2015. Univariate and multivariate analysis were performed to identify covariates which significantly contribute to higher EuroSCORE II in our population. External validation was performed by comparing the real and expected mortality using area under the receiver operating characteristic curve (AUC) for discrimination assessment. Also, Brier Score and Hosmer-Lemeshow goodness-of-fit test were used to show the overall performance and calibration level, respectively. Results Two thousand five hundred eight one (59.6% males) were included. The observed mortality rate was 3.3%, but EuroSCORE II had a prediction of 4.7%. Although the overall performance was acceptable (Brier score=0.047), the model showed poor discriminatory power by AUC=0.667 (sensitivity=61.90, and specificity=66.24) and calibration (Hosmer-Lemeshow test, P<0.01). Conclusion Our study showed that the EuroSCORE II discrimination power is less than optimal for outcome prediction and less accurate for resource allocation programs. It highlights the need for recalibration of this risk stratification tool aiming to improve post cardiac surgery outcome predictions in Iran. PMID:29617500

External Validation of the Updated Partin Tables in a Cohort of French and Italian Men

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

Bhojani, Naeem; Department of Urology, University of Montreal, Montreal, PQ; Salomon, Laurent

2009-02-01

Purpose: To test the discrimination and calibration properties of the newly developed 2007 Partin Tables in two European cohorts with localized prostate cancer. Methods: Data on clinical and pathologic characteristics were obtained for 1,064 men treated with radical prostatectomy at the Creteil University Health Center in France (n = 839) and at the Milan University Vita-Salute in Italy (n = 225). Overall discrimination was assessed with receiver operating characteristic curve analysis, which quantified the accuracy of stage predictions for each center. Calibration plots graphically explored the relationship between predicted and observed rates of extracapsular extension (ECE), seminal vesicle invasion (SVI)more » and lymph node invasion (LNI). Results: The rates of ECE, SVI, and LNI were 28%, 14%, and 2% in the Creteil cohort vs. 11%, 5%, and 5% in the Milan cohort. In the Creteil cohort, the accuracy of ECE, SVI, and LNI prediction was 61%, 71%, and 82% vs. 66%, 92% and 75% for the Milan cohort. Important departures were recorded between Partin Tables' predicted and observed rates of ECE, SVI, and LNI within both cohorts. Conclusions: The 2007 Partin Tables demonstrated worse performance in European men than they originally did in North American men. This indicates that predictive models need to be externally validated before their implementation into clinical practice.« less

Adding to the Education Debt: Depressive Symptoms Mediate the Association between Racial Discrimination and Academic Performance in African Americans.

PubMed

English, Devin; Lambert, Sharon F; Ialongo, Nicholas S

2016-08-01

Although the United States faces a seemingly intractable divide between white and African American academic performance, there remains a dearth of longitudinal research investigating factors that work to maintain this gap. The present study examined whether racial discrimination predicted the academic performance of African American students through its effect on depressive symptoms. Participants were a community sample of African American adolescents (N=495) attending urban public schools from grade 7 to grade 9 (Mage=12.5). Structural equation modeling revealed that experienced racial discrimination predicted increases in depressive symptoms 1year later, which, in turn, predicted decreases in academic performance the following year. These results suggest that racial discrimination continues to play a critical role in the academic performance of African American students and, as such, contributes to the maintenance of the race-based academic achievement gap in the United States. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Classification of Antibiotic Resistance Patterns of Indicator Bacteria by Discriminant Analysis: Use in Predicting the Source of Fecal Contamination in Subtropical Waters

PubMed Central

Harwood, Valerie J.; Whitlock, John; Withington, Victoria

2000-01-01

The antibiotic resistance patterns of fecal streptococci and fecal coliforms isolated from domestic wastewater and animal feces were determined using a battery of antibiotics (amoxicillin, ampicillin, cephalothin, chlortetracycline, oxytetracycline, tetracycline, erythromycin, streptomycin, and vancomycin) at four concentrations each. The sources of animal feces included wild birds, cattle, chickens, dogs, pigs, and raccoons. Antibiotic resistance patterns of fecal streptococci and fecal coliforms from known sources were grouped into two separate databases, and discriminant analysis of these patterns was used to establish the relationship between the antibiotic resistance patterns and the bacterial source. The fecal streptococcus and fecal coliform databases classified isolates from known sources with similar accuracies. The average rate of correct classification for the fecal streptococcus database was 62.3%, and that for the fecal coliform database was 63.9%. The sources of fecal streptococci and fecal coliforms isolated from surface waters were identified by discriminant analysis of their antibiotic resistance patterns. Both databases identified the source of indicator bacteria isolated from surface waters directly impacted by septic tank discharges as human. At sample sites selected for relatively low anthropogenic impact, the dominant sources of indicator bacteria were identified as various animals. The antibiotic resistance analysis technique promises to be a useful tool in assessing sources of fecal contamination in subtropical waters, such as those in Florida. PMID:10966379

Balance measured by the sway balance smart-device application does not discriminate between older persons with and without a fall history.

PubMed

Vincenzo, Jennifer L; Glenn, Jordan M; Gray, Stephanie M; Gray, Michelle

2016-08-01

Clinical functional assessments of balance often lack specificity and sensitivity in discriminating and predicting falls among community-dwelling older adults. We determined the feasibility of using a smart-device application measuring balance to discriminate fall status among older adults. We also evaluated differences between smart-device balance measurements when secured with or without a harness. A cross-sectional study design to determine the ability of the Sway Balance smart-device application (SWAY) to discriminate older adults based on fall history. The Berg Balance Scale (BBS) and Activities-Specific Balance Confidence Scale (ABC) were used as comparative, clinically based assessments. Community-dwelling older adults with (n = 25) and without (n = 32) a history of fall(s) participated. Multivariate analysis of variance was used to determine differences among assessments based on fall history. Logistic regression models determined the ability of each assessment to discriminate fall history. Older adults with and without a history of falls were not significantly different on SWAY (P = 0.92) but were different on BBS (P = 0.01), and ABC (P < 0.001). Similarly, SWAY did not discriminate fall history (P = 0.92), while BBS and ABC both discriminated fall history (P < 0.01). Paired t tests between SWAY scores with and without a harness indicated no differences (P ≥ 0.05). Among the older adults studied, the BBS and ABC measures discriminated groups defined by fall history, while the SWAY smart-device balance application did not. Modifications to the application may improve the discriminating ability of the measure in the recognition of fall status in older adults.

Diagnostic and prognostic value of amyloid PET textural and shape features: comparison with classical semi-quantitative rating in 760 patients from the ADNI-2 database.

PubMed

Ben Bouallègue, Fayçal; Vauchot, Fabien; Mariano-Goulart, Denis; Payoux, Pierre

2018-02-09

We evaluated the performance of amyloid PET textural and shape features in discriminating normal and Alzheimer's disease (AD) subjects, and in predicting conversion to AD in subjects with mild cognitive impairment (MCI) or significant memory concern (SMC). Subjects from the Alzheimer's Disease Neuroimaging Initiative with available baseline 18 F-florbetapir and T1-MRI scans were included. The cross-sectional cohort consisted of 181 controls and 148 AD subjects. The longitudinal cohort consisted of 431 SMC/MCI subjects, 85 of whom converted to AD during follow-up. PET images were normalized to MNI space and post-processed using in-house software. Relative retention indices (SUVr) were computed with respect to pontine, cerebellar, and composite reference regions. Several textural and shape features were extracted then combined using a support vector machine (SVM) to build a predictive model of AD conversion. Diagnostic and prognostic performance was evaluated using ROC analysis and survival analysis with the Cox proportional hazard model. The three SUVr and all the tested features effectively discriminated AD subjects in cross-sectional analysis (all p < 0.001). In longitudinal analysis, the variables with the highest prognostic value were composite SUVr (AUC 0.86; accuracy 81%), skewness (0.87; 83%), local minima (0.85; 79%), Geary's index (0.86; 81%), gradient norm maximal argument (0.83; 82%), and the SVM model (0.91; 86%). The adjusted hazard ratio for AD conversion was 5.5 for the SVM model, compared with 4.0, 2.6, and 3.8 for cerebellar, pontine and composite SUVr (all p < 0.001), indicating that appropriate amyloid textural and shape features predict conversion to AD with at least as good accuracy as classical SUVr.

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…

Factors affecting flood insurance purchase in residential properties in Johor, Malaysia

NASA Astrophysics Data System (ADS)

Aliagha, U. G.; Jin, T. E.; Choong, W. W.; Nadzri Jaafar, M.; Ali, H. M.

2014-12-01

High-impact floods have become a virtually annual experience in Malaysia, yet flood insurance has remained a grossly neglected part of comprehensive integrated flood risk management. Using discriminant analysis, this study seeks to identify the demand-side variables that best predict flood insurance purchase and risk aversion between two groups of residential homeowners in three districts of Johor State, Malaysia: those who purchased flood insurance and those who did not. Our results revealed an overall 34% purchase rate, with Kota Tinggi district having the highest (44%) and thus the highest degree of flood risk aversion. The Wilks' lambda F test for equality of group means, standardised discriminant function coefficients, structure correlation, and canonical correlation has clearly shown that there are strong significant attribute differences between the two groups of homeowners, based on the measures of objective flood risk exposure, subjective risk perception, and socio-economic cum demographic variables. However, the measures of subjective risk perception were found to be more predictive of flood insurance purchase and flood risk aversion.

Ideal discrimination of discrete clinical endpoints using multilocus genotypes.

PubMed

Hahn, Lance W; Moore, Jason H

2004-01-01

Multifactor Dimensionality Reduction (MDR) is a method for the classification and prediction of discrete clinical endpoints using attributes constructed from multilocus genotype data. Empirical studies with both real and simulated data suggest that MDR has good power for detecting gene-gene interactions in the absence of independent main effects. The purpose of this study is to develop an objective, theory-driven approach to evaluate the strengths and limitations of MDR. To accomplish this goal, we borrow concepts from ideal observer analysis used in visual perception to evaluate the theoretical limits of classifying and predicting discrete clinical endpoints using multilocus genotype data. We conclude that MDR ideally discriminates between low risk and high risk subjects using attributes constructed from multilocus genotype data. We also how that the classification approach used once a multilocus attribute is constructed is similar to that of a naive Bayes classifier. This study provides a theoretical foundation for the continued development, evaluation, and application of the MDR as a data mining tool in the domain of statistical genetics and genetic epidemiology.

Meat mixture detection in Iberian pork sausages.

PubMed

Ortiz-Somovilla, V; España-España, F; De Pedro-Sanz, E J; Gaitán-Jurado, A J

2005-11-01

Five homogenized meat mixture treatments of Iberian (I) and/or Standard (S) pork were set up. Each treatment was analyzed by NIRS as a fresh product (N=75) and as dry-cured sausage (N=75). Spectra acquisition was carried out using DA 7000 equipment (Perten Instruments), obtaining a total of 750 spectra. Several absorption peaks and bands were selected as the most representative for homogenized dry-cured and fresh sausages. Discriminant analysis and mixture prediction equations were carried out based on the spectral data gathered. The best results using discriminant models were for fresh products, with 98.3% (calibration) and 60% (validation) correct classification. For dry-cured sausages 91.7% (calibration) and 80% (validation) of the samples were correctly classified. Models developed using mixture prediction equations showed SECV=4.7, r(2)=0.98 (calibration) and 73.3% of validation set were correctly classified for the fresh product. These values for dry-cured sausages were SECV=5.9, r(2)=0.99 (calibration) and 93.3% correctly classified for validation.

Experiences of Discrimination among Chinese American Adolescents and the Consequences for Socioemotional and Academic Development

ERIC Educational Resources Information Center

Benner, Aprile D.; Kim, Su Yeong

2009-01-01

This longitudinal study examined the influences of discrimination on socioemotional adjustment and academic performance for a sample of 444 Chinese American adolescents. Using autoregressive and cross-lagged techniques, the authors found that discrimination in early adolescence predicted depressive symptoms, alienation, school engagement, and…

Discrimination-Aware Classifiers for Student Performance Prediction

ERIC Educational Resources Information Center

Luo, Ling; Koprinska, Irena; Liu, Wei

2015-01-01

In this paper we consider discrimination-aware classification of educational data. Mining and using rules that distinguish groups of students based on sensitive attributes such as gender and nationality may lead to discrimination. It is desirable to keep the sensitive attributes during the training of a classifier to avoid information loss but…

The effect of thermal treatment on the enhancement of detection of adulteration in extra virgin olive oils by synchronous fluorescence spectroscopy and chemometric analysis.

PubMed

Mabood, F; Boqué, R; Folcarelli, R; Busto, O; Jabeen, F; Al-Harrasi, Ahmed; Hussain, J

2016-05-15

In this study the effect of thermal treatment on the enhancement of synchronous fluorescence spectroscopic method for discrimination and quantification of pure extra virgin olive oil (EVOO) samples from EVOO samples adulterated with refined oil was investigated. Two groups of samples were used. One group was analyzed at room temperature (25 °C) and the other group was thermally treated in a thermostatic water bath at 75 °C for 8h, in contact with air and with light exposure, to favor oxidation. All the samples were then measured with synchronous fluorescence spectroscopy. Synchronous fluorescence spectra were acquired by varying the wavelength in the region from 250 to 720 nm at 20 nm wavelength differential interval of excitation and emission. Pure and adulterated olive oils were discriminated by using partial least-squares discriminant analysis (PLS-DA). It was found that the best PLS-DA models were those built with the difference spectra (75 °C-25 °C), which were able to discriminate pure from adulterated oils at a 2% level of adulteration of refined olive oils. Furthermore, PLS regression models were also built to quantify the level of adulteration. Again, the best model was the one built with the difference spectra, with a prediction error of 3.18% of adulteration. Copyright © 2016 Elsevier B.V. All rights reserved.

The effect of thermal treatment on the enhancement of detection of adulteration in extra virgin olive oils by synchronous fluorescence spectroscopy and chemometric analysis

NASA Astrophysics Data System (ADS)

Mabood, F.; Boqué, R.; Folcarelli, R.; Busto, O.; Jabeen, F.; Al-Harrasi, Ahmed; Hussain, J.

2016-05-01

In this study the effect of thermal treatment on the enhancement of synchronous fluorescence spectroscopic method for discrimination and quantification of pure extra virgin olive oil (EVOO) samples from EVOO samples adulterated with refined oil was investigated. Two groups of samples were used. One group was analyzed at room temperature (25 °C) and the other group was thermally treated in a thermostatic water bath at 75 °C for 8 h, in contact with air and with light exposure, to favor oxidation. All the samples were then measured with synchronous fluorescence spectroscopy. Synchronous fluorescence spectra were acquired by varying the wavelength in the region from 250 to 720 nm at 20 nm wavelength differential interval of excitation and emission. Pure and adulterated olive oils were discriminated by using partial least-squares discriminant analysis (PLS-DA). It was found that the best PLS-DA models were those built with the difference spectra (75 °C-25 °C), which were able to discriminate pure from adulterated oils at a 2% level of adulteration of refined olive oils. Furthermore, PLS regression models were also built to quantify the level of adulteration. Again, the best model was the one built with the difference spectra, with a prediction error of 3.18% of adulteration.

Rapid Analysis of Deoxynivalenol in Durum Wheat by FT-NIR Spectroscopy

PubMed Central

De Girolamo, Annalisa; Cervellieri, Salvatore; Visconti, Angelo; Pascale, Michelangelo

2014-01-01

Fourier-transform-near infrared (FT-NIR) spectroscopy has been used to develop quantitative and classification models for the prediction of deoxynivalenol (DON) levels in durum wheat samples. Partial least-squares (PLS) regression analysis was used to determine DON in wheat samples in the range of <50–16,000 µg/kg DON. The model displayed a large root mean square error of prediction value (1,977 µg/kg) as compared to the EU maximum limit for DON in unprocessed durum wheat (i.e., 1,750 µg/kg), thus making the PLS approach unsuitable for quantitative prediction of DON in durum wheat. Linear discriminant analysis (LDA) was successfully used to differentiate wheat samples based on their DON content. A first approach used LDA to group wheat samples into three classes: A (DON ≤ 1,000 µg/kg), B (1,000 < DON ≤ 2,500 µg/kg), and C (DON > 2,500 µg/kg) (LDA I). A second approach was used to discriminate highly contaminated wheat samples based on three different cut-off limits, namely 1,000 (LDA II), 1,200 (LDA III) and 1,400 µg/kg DON (LDA IV). The overall classification and false compliant rates for the three models were 75%–90% and 3%–7%, respectively, with model LDA IV using a cut-off of 1,400 µg/kg fulfilling the requirement of the European official guidelines for screening methods. These findings confirmed the suitability of FT-NIR to screen a large number of wheat samples for DON contamination and to verify the compliance with EU regulation. PMID:25384107

Rapid analysis of deoxynivalenol in durum wheat by FT-NIR spectroscopy.

PubMed

De Girolamo, Annalisa; Cervellieri, Salvatore; Visconti, Angelo; Pascale, Michelangelo

2014-11-06

Fourier-transform-near infrared (FT-NIR) spectroscopy has been used to develop quantitative and classification models for the prediction of deoxynivalenol (DON) levels in durum wheat samples. Partial least-squares (PLS) regression analysis was used to determine DON in wheat samples in the range of <50-16,000 µg/kg DON. The model displayed a large root mean square error of prediction value (1,977 µg/kg) as compared to the EU maximum limit for DON in unprocessed durum wheat (i.e., 1,750 µg/kg), thus making the PLS approach unsuitable for quantitative prediction of DON in durum wheat. Linear discriminant analysis (LDA) was successfully used to differentiate wheat samples based on their DON content. A first approach used LDA to group wheat samples into three classes: A (DON ≤ 1,000 µg/kg), B (1,000 < DON ≤ 2,500 µg/kg), and C (DON > 2,500 µg/kg) (LDA I). A second approach was used to discriminate highly contaminated wheat samples based on three different cut-off limits, namely 1,000 (LDA II), 1,200 (LDA III) and 1,400 µg/kg DON (LDA IV). The overall classification and false compliant rates for the three models were 75%-90% and 3%-7%, respectively, with model LDA IV using a cut-off of 1,400 µg/kg fulfilling the requirement of the European official guidelines for screening methods. These findings confirmed the suitability of FT-NIR to screen a large number of wheat samples for DON contamination and to verify the compliance with EU regulation.

Independent external validation of predictive models for urinary dysfunction following external beam radiotherapy of the prostate: Issues in model development and reporting.

PubMed

Yahya, Noorazrul; Ebert, Martin A; Bulsara, Max; Kennedy, Angel; Joseph, David J; Denham, James W

2016-08-01

Most predictive models are not sufficiently validated for prospective use. We performed independent external validation of published predictive models for urinary dysfunctions following radiotherapy of the prostate. Multivariable models developed to predict atomised and generalised urinary symptoms, both acute and late, were considered for validation using a dataset representing 754 participants from the TROG 03.04-RADAR trial. Endpoints and features were harmonised to match the predictive models. The overall performance, calibration and discrimination were assessed. 14 models from four publications were validated. The discrimination of the predictive models in an independent external validation cohort, measured using the area under the receiver operating characteristic (ROC) curve, ranged from 0.473 to 0.695, generally lower than in internal validation. 4 models had ROC >0.6. Shrinkage was required for all predictive models' coefficients ranging from -0.309 (prediction probability was inverse to observed proportion) to 0.823. Predictive models which include baseline symptoms as a feature produced the highest discrimination. Two models produced a predicted probability of 0 and 1 for all patients. Predictive models vary in performance and transferability illustrating the need for improvements in model development and reporting. Several models showed reasonable potential but efforts should be increased to improve performance. Baseline symptoms should always be considered as potential features for predictive models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Detection of feigned mental disorders on the personality assessment inventory: a discriminant analysis.

PubMed

Rogers, R; Sewell, K W; Morey, L C; Ustad, K L

1996-12-01

Psychological assessment with multiscale inventories is largely dependent on the honesty and forthrightness of those persons evaluated. We investigated the effectiveness of the Personality Assessment Inventory (PAI) in detecting participants feigning three specific disorders: schizophrenia, major depression, and generalized anxiety disorder. With a simulation design, we tested the PAI validity scales on 166 naive (undergraduates with minimal preparation) and 80 sophisticated (doctoral psychology students with 1 week preparation) participants. We compared their results to persons with the designated disorders: schizophrenia (n = 45), major depression (n = 136), and generalized anxiety disorder (n = 40). Although moderately effective with naive simulators, the validity scales evidenced only modest positive predictive power with their sophisticated counterparts. Therefore, we performed a two-stage discriminant analysis that yielded a moderately high hit rate (> 80%) that was maintained in the cross-validation sample, irrespective of the feigned disorder or the sophistication of the simulators.

Identifying Mobility Types in Cognitively Heterogeneous Older Adults Based on GPS-Tracking: What Discriminates Best?

PubMed

Wettstein, Markus; Wahl, Hans-Werner; Shoval, Noam; Auslander, Gail; Oswald, Frank; Heinik, Jeremia

2015-12-01

Heterogeneity in older adults' mobility and its correlates have rarely been investigated based on objective mobility data and in samples including cognitively impaired individuals. We analyzed mobility profiles within a cognitively heterogeneous sample of N = 257 older adults from Israel and Germany based on GPS tracking technology. Participants were aged between 59 and 91 years (M = 72.9; SD = 6.4) and were either cognitively healthy (CH, n = 146), mildly cognitively impaired (MCI, n = 76), or diagnosed with an early-stage dementia of the Alzheimer's type (DAT, n = 35). Based on cluster analysis, we identified three mobility types ("Mobility restricted," "Outdoor oriented," "Walkers"), which could be predicted based on socio-demographic indicators, activity, health, and cognitive impairment status using discriminant analysis. Particularly demented individuals and persons with worse health exhibited restrictions in mobility. Our findings contribute to a better understanding of heterogeneity in mobility in old age. © The Author(s) 2013.

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.

Predicting need for orthognathic surgery in early permanent dentition patients with unilateral cleft lip and palate using receiver operating characteristic analysis.

PubMed

Yun-Chia Ku, Michelle; Lo, Lun-Jou; Chen, Min-Chi; Wen-Ching Ko, Ellen

2018-03-01

The purpose of this study was to predict the need for orthognathic surgery in patients with unilateral cleft lip and palate (UCLP) in the early permanent dentition. In this retrospective cohort study, we included 61 patients with complete UCLP (36 male, 25 female; mean age, 18.47 years; range, 16.92-26.17 years). The subjects were grouped into an orthognathic surgery group and a nonsurgery group at the time of growth completion. Lateral cephalograms obtained at the age of 11 years were analyzed to compare the 2 groups. The receiver operating characteristic analysis was applied to predict the probability of the need for orthognathic surgery in early adulthood by using the measurements obtained at the age of 11 years. SNB, ANB, SN, overbite, overjet, maxillary length, mandibular body length, and L1-MP were found to be significantly different between the 2 groups. For a person with a score of 2 in the 3-variable-based criteria, the sensitivity and specificity for determining the need for surgical treatment were 90.0% and 83.9%, respectively (ANB, ≤-0.45°; overjet, ≤-2.00 mm; maxillary length, ≤47.25 mm). Three cephalometric variables, the minimum number of discriminators required to obtain the optimum discriminant effectiveness, predicted the future need for orthognathic surgery with an accuracy of 86.9% in patients with UCLP. Copyright © 2017 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

The scent fingerprint of hepatocarcinoma: in-vitro metastasis prediction with volatile organic compounds (VOCs)

PubMed Central

Amal, Haitham; Ding, Lu; Liu, Bin-bin; Tisch, Ulrike; Xu, Zhen-qin; Shi, Da-you; Zhao, Yan; Chen, Jie; Sun, Rui-xia; Liu, Hu; Ye, Sheng-Long; Tang, Zhao-you; Haick, Hossam

2012-01-01

Background: Hepatocellular carcinoma (HCC) is a common and aggressive form of cancer. Due to a high rate of postoperative recurrence, the prognosis for HCC is poor. Subclinical metastasis is the major cause of tumor recurrence and patient mortality. Currently, there is no reliable prognostic method of invasion. Aim: To investigate the feasibility of fingerprints of volatile organic compounds (VOCs) for the in-vitro prediction of metastasis. Methods: Headspace gases were collected from 36 cell cultures (HCC with high and low metastatic potential and normal cells) and analyzed using nanomaterial-based sensors. Predictive models were built by employing discriminant factor analysis pattern recognition, and the classification success was determined using leave-one-out cross-validation. The chemical composition of each headspace sample was studied using gas chromatography coupled with mass spectrometry (GC-MS). Results: Excellent discrimination was achieved using the nanomaterial-based sensors between (i) all HCC and normal controls; (ii) low metastatic HCC and normal controls; (iii) high metastatic HCC and normal controls; and (iv) high and low HCC. Several HCC-related VOCs that could be associated with biochemical cellular processes were identified through GC-MS analysis. Conclusion: The presented results constitute a proof-of-concept for the in-vitro prediction of the metastatic potential of HCC from VOC fingerprints using nanotechnology. Further studies on a larger number of more diverse cell cultures are needed to evaluate the robustness of the VOC patterns. These findings could benefit the development of a fast and potentially inexpensive laboratory test for subclinical HCC metastasis. PMID:22888249

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.

Predictors of Suicidal Ideation in a Statewide Sample of Transgender Individuals.

PubMed

Rood, Brian A; Puckett, Julia A; Pantalone, David W; Bradford, Judith B

2015-09-01

Transgender individuals experience violence and discrimination, which, in addition to gender transitioning, are established correlates of psychological distress. In a statewide sample of 350 transgender adults, we investigated whether a history of violence and discrimination increased the odds of reporting lifetime suicidal ideation (SI) and whether differences in SI were predicted by gender transition status. Violence, discrimination, and transition status significantly predicted SI. Compared with individuals with no plans to transition, individuals with plans or who were living as their identified gender reported greater odds of lifetime SI. We discuss implications for SI disparities using Meyer's minority stress model.

When Do We Confront? Perceptions of Costs and Benefits Predict Confronting Discrimination on Behalf of the Self and Others

ERIC Educational Resources Information Center

Good, Jessica J.; Moss-Racusin, Corinne A.; Sanchez, Diana T.

2012-01-01

Across two studies, we tested whether perceived social costs and benefits of confrontation would similarly predict confronting discrimination both when it was experienced and when it was observed as directed at others. Female undergraduate participants were asked to recall past experiences and observations of sexism, as well as their confronting…

Olfactory discrimination predicts cognitive decline among community-dwelling older adults

PubMed Central

Sohrabi, H R; Bates, K A; Weinborn, M G; Johnston, A N B; Bahramian, A; Taddei, K; Laws, S M; Rodrigues, M; Morici, M; Howard, M; Martins, G; Mackay-Sim, A; Gandy, S E; Martins, R N

2012-01-01

The presence of olfactory dysfunction in individuals at higher risk of Alzheimer's disease has significant diagnostic and screening implications for preventive and ameliorative drug trials. Olfactory threshold, discrimination and identification can be reliably recorded in the early stages of neurodegenerative diseases. The current study has examined the ability of various olfactory functions in predicting cognitive decline in a community-dwelling sample. A group of 308 participants, aged 46–86 years old, were recruited for this study. After 3 years of follow-up, participants were divided into cognitively declined and non-declined groups based on their performance on a neuropsychological battery. Assessment of olfactory functions using the Sniffin' Sticks battery indicated that, contrary to previous findings, olfactory discrimination, but not olfactory identification, significantly predicted subsequent cognitive decline (odds ratio=0.869; P<0.05; 95% confidence interval=0.764−0.988). The current study findings confirm previously reported associations between olfactory and cognitive functions, and indicate that impairment in olfactory discrimination can predict future cognitive decline. These findings further our current understanding of the association between cognition and olfaction, and support olfactory assessment in screening those at higher risk of dementia. PMID:22832962

Olfactory discrimination predicts cognitive decline among community-dwelling older adults.

PubMed

Sohrabi, H R; Bates, K A; Weinborn, M G; Johnston, A N B; Bahramian, A; Taddei, K; Laws, S M; Rodrigues, M; Morici, M; Howard, M; Martins, G; Mackay-Sim, A; Gandy, S E; Martins, R N

2012-05-22

The presence of olfactory dysfunction in individuals at higher risk of Alzheimer's disease has significant diagnostic and screening implications for preventive and ameliorative drug trials. Olfactory threshold, discrimination and identification can be reliably recorded in the early stages of neurodegenerative diseases. The current study has examined the ability of various olfactory functions in predicting cognitive decline in a community-dwelling sample. A group of 308 participants, aged 46-86 years old, were recruited for this study. After 3 years of follow-up, participants were divided into cognitively declined and non-declined groups based on their performance on a neuropsychological battery. Assessment of olfactory functions using the Sniffin' Sticks battery indicated that, contrary to previous findings, olfactory discrimination, but not olfactory identification, significantly predicted subsequent cognitive decline (odds ratio = 0.869; P<0.05; 95% confidence interval = 0.764-0.988). The current study findings confirm previously reported associations between olfactory and cognitive functions, and indicate that impairment in olfactory discrimination can predict future cognitive decline. These findings further our current understanding of the association between cognition and olfaction, and support olfactory assessment in screening those at higher risk of dementia.

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.

Predicting primary progressive aphasias with support vector machine approaches in structural MRI data.

PubMed

Bisenius, Sandrine; Mueller, Karsten; Diehl-Schmid, Janine; Fassbender, Klaus; Grimmer, Timo; Jessen, Frank; Kassubek, Jan; Kornhuber, Johannes; Landwehrmeyer, Bernhard; Ludolph, Albert; Schneider, Anja; Anderl-Straub, Sarah; Stuke, Katharina; Danek, Adrian; Otto, Markus; Schroeter, Matthias L

2017-01-01

Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. To validate the potential of structural magnetic resonance imaging data for early individual diagnosis, we used support vector machine classification on grey matter density maps obtained by voxel-based morphometry analysis to discriminate PPA subtypes (44 patients: 16 nonfluent/agrammatic variant PPA, 17 semantic variant PPA, 11 logopenic variant PPA) from 20 healthy controls (matched for sample size, age, and gender) in the cohort of the multi-center study of the German consortium for frontotemporal lobar degeneration. Here, we compared a whole-brain with a meta-analysis-based disease-specific regions-of-interest approach for support vector machine classification. We also used support vector machine classification to discriminate the three PPA subtypes from each other. Whole brain support vector machine classification enabled a very high accuracy between 91 and 97% for identifying specific PPA subtypes vs. healthy controls, and 78/95% for the discrimination between semantic variant vs. nonfluent/agrammatic or logopenic PPA variants. Only for the discrimination between nonfluent/agrammatic and logopenic PPA variants accuracy was low with 55%. Interestingly, the regions that contributed the most to the support vector machine classification of patients corresponded largely to the regions that were atrophic in these patients as revealed by group comparisons. Although the whole brain approach took also into account regions that were not covered in the regions-of-interest approach, both approaches showed similar accuracies due to the disease-specificity of the selected networks. Conclusion, support vector machine classification of multi-center structural magnetic resonance imaging data enables prediction of PPA subtypes with a very high accuracy paving the road for its application in clinical settings.

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

PubMed

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

2016-09-01

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

Perceived ethnic and language-based discrimination and Latina immigrant women's health.

PubMed

Halim, May Ling; Moy, Keith H; Yoshikawa, Hirokazu

2017-01-01

Perceiving ethnic discrimination can have aversive consequences for health. However, little is known about whether perceiving language-based (how one speaks a second language) discrimination poses the same risks. This study examined whether perceptions of language-based and ethnic discrimination are associated with mental and physical health. Among 132 Mexican and Dominican immigrant women, perceiving ethnic and language-based discrimination each predicted psychological distress and poorer physical health. When examined together, only ethnic discrimination remained a significant predictor. These results emphasize the importance of understanding how perceived ethnic and language-based discrimination play an integral role in the health of Latina immigrant women.

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.

Determination of geographical origin and icariin content of Herba Epimedii using near infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Yang, Yue; Wu, Yongjiang; Li, Weili; Liu, Xuesong; Zheng, Jiyu; Zhang, Wentao; Chen, Yong

2018-02-01

Near infrared (NIR) spectroscopy coupled with chemometrics was used to discriminate the geographical origin of Herba Epimedii in this work. Four different classification models, namely discriminant analysis (DA), back propagation neural network (BPNN), K-nearest neighbor (KNN), and support vector machine (SVM), were constructed, and their performances in terms of recognition accuracy were compared. The results indicated that the SVM model was superior over the other models in the geographical origin identification of Herba Epimedii. The recognition rates of the optimum SVM model were up to 100% for the calibration set and 94.44% for the prediction set, respectively. In addition, the feasibility of NIR spectroscopy with the CARS-PLSR calibration model in prediction of icariin content of Herba Epimedii was also investigated. The determination coefficient (RP2) and root-mean-square error (RMSEP) for prediction set were 0.9269 and 0.0480, respectively. It can be concluded that the NIR spectroscopy technique in combination with chemometrics has great potential in determination of geographical origin and icariin content of Herba Epimedii. This study can provide a valuable reference for rapid quality control of food products.

Discriminating a deep defect from shallow acceptors in supercell calculations: gallium antisite in GaAs

NASA Astrophysics Data System (ADS)

Schultz, Peter

To make reliable first principles predictions of defect energies in semiconductors, it is crucial to discriminate between effective-mass-like defects--for which existing supercell methods fail--and deep defects--for which density functional theory calculations can yield reliable predictions of defect energy levels. The gallium antisite GaAs is often associated with the 78/203 meV shallow double acceptor in Ga-rich gallium arsenide. Within a framework of level occupation patterns, analyses of structure and spin stabilization can be used within a supercell approach to distinguish localized deep defect states from shallow acceptors such as BAs. This systematic analysis determines that the gallium antisite is inconsistent with a shallow state, and cannot be the 78/203 shallow double acceptor. The properties of the Ga antisite in GaAs are described, predicting that the Ga antisite is a deep double acceptor and has two donor states, one of which might be accidentally shallow. -- Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Company, for the U.S. Department of Energy's National Nuclear Security Administration under Contract DE-AC04-94AL85000.

Racial and Ethnic Socialization as Moderators of Racial Discrimination and School Adjustment of Adopted and Non-adopted Korean American Adolescents

PubMed Central

Seol, Kyoung Ok; Yoo, Hyung Chol; Lee, Richard M.; Park, Ji Eun; Kyeong, Yena

2015-01-01

This study investigated roles of racial and ethnic socialization in the link between racial discrimination and school adjustment among a sample of 233 adopted Korean American adolescents from White adoptive families and 155 non-adopted Korean American adolescents from immigrant Korean families. Adopted Korean American adolescents reported lower levels of racial discrimination, racial socialization, and ethnic socialization than non-adopted Korean American adolescents. However, racial discrimination was negatively related to school belonging and school engagement, and ethnic socialization was positively related to school engagement for both groups. Racial socialization also had a curvilinear relationship with school engagement for both groups. Moderate level of racial socialization predicted positive school engagement, whereas low and high levels of racial socialization predicted negative school engagement. Finally, ethnic socialization moderated the link between racial discrimination and school belonging, which differed between groups. In particular, ethnic socialization exacerbated the relations between racial discrimination and school belonging for adopted Korean American adolescents, whereas, ethnic socialization buffered this link for non-adopted Korean American adolescents. Findings illustrate the complex relationship between racial and ethnic socialization, racial discrimination, and school adjustment. PMID:26479418

Racial and ethnic socialization as moderators of racial discrimination and school adjustment of adopted and nonadopted Korean American adolescents.

PubMed

Seol, Kyoung Ok; Yoo, Hyung Chol; Lee, Richard M; Park, Ji Eun; Kyeong, Yena

2016-04-01

This study investigated the roles of racial and ethnic socialization in the link between racial discrimination and school adjustment among a sample of 233 adopted Korean American adolescents from White adoptive families and 155 nonadopted Korean American adolescents from immigrant Korean families. Adopted Korean American adolescents reported lower levels of racial discrimination, racial socialization, and ethnic socialization than nonadopted Korean American adolescents. However, racial discrimination was negatively related to school belonging and school engagement, and ethnic socialization was positively related to school engagement for both groups. Racial socialization also had a curvilinear relationship with school engagement for both groups. A moderate level of racial socialization predicted positive school engagement, whereas low and high levels of racial socialization predicted negative school engagement. Finally, ethnic socialization moderated the link between racial discrimination and school belonging, which differed between groups. In particular, ethnic socialization exacerbated the relations between racial discrimination and school belonging for adopted Korean American adolescents, whereas ethnic socialization buffered this link for nonadopted Korean American adolescents. The findings illustrate the complex relationship between racial and ethnic socialization, racial discrimination, and school adjustment. (c) 2016 APA, all rights reserved).

Customization of Discriminant Function Analysis for Prediction of Solar Flares

DTIC Science & Technology

2005-03-01

lives such as telecommunication, commercial airlines, electrical power , wireless services, and terrestrial weather tracking and forecasting...the 1800’s can wreak havoc on today’s power , fuel, and telecommunication lines and finds its origin in solar activity. Enormous amounts of solar...inducing potential differences across large areas of the surface. Earth-bound power , fuel, and telecommunication lines grounded to the Earth provide an

Principal opium alkaloids as possible biochemical markers for the source identification of Indian opium.

PubMed

Mohana, Mudiam; Reddy, Krishna; Jayshanker, Gurumurthy; Suresh, Velayudhan; Sarin, Rajendra Kumar; Sashidhar, R B

2005-08-01

A total of 124 opium samples originating from different licit opium growing divisions of India were analyzed for their principal alkaloid (thebaine, codeine, morphine, papaverine, and narcotine) content by capillary zone electrophoresis (CZE) without derivatization or purification. Absence of papaverine in Bareilly, Tilhar, and most of the samples originating from Kota is a significant observation in relation to the source of Indian opium. Multiple discriminant analysis was applied to the quantitative principal alkaloid data to determine an optimal classifier in order to evaluate the source of Indian opium. The predictive value based on the discriminant analysis was found to be 85% in relation to the source of opium and the study also revealed that all the principal alkaloids have to be analyzed for source identification of Indian opium. Chemometrics performed with principal alkaloids analytical data was used successfully in discriminating the licit opium growing divisions of India into three major groups, viz., group I, II, and III. The methodology developed may find wide forensic application in identifying the source of licit or illicit opium originating from India, and to differentiate it from opium originating from other opium producing countries.

Ethnic Identity, Stereotype Threat, and Perceived Discrimination Among Native American Adolescents.

PubMed

Jaramillo, Jamie; Mello, Zena R; Worrell, Frank C

2016-12-01

In this study, ethnic identity, stereotype threat, and perceived discrimination were examined in relationship to academic achievement and hopelessness in a sample of 129 Native American adolescents aged 14-19. Regression analyses with self-reported data indicated two major findings. Ethnic identity interacted with stereotype threat to predict academic achievement, where participants with high ethnic identity and low stereotype threat scores reported higher grade point averages. Ethnic identity also interacted with perceived discrimination to predict hopelessness, where participants with low ethnic identity and high perceived discrimination scores were higher in hopelessness. Findings are discussed in light of the joint role that ethnic identity and perceived bias have in relationship to developmental outcomes in Native American adolescents. © 2015 The Authors. Journal of Research on Adolescence © 2015 Society for Research on Adolescence.

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

PubMed

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

2016-06-01

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

Characterization and detection of Anopheles vestitipennis and Anopheles punctimacula (Diptera: Culicidae) larval habitats in Belize with field survey and SPOT satellite imagery

NASA Technical Reports Server (NTRS)

Rejmankova, E.; Pope, K. O.; Roberts, D. R.; Lege, M. G.; Andre, R.; Greico, J.; Alonzo, Y.

1998-01-01

Surveys of larval habitats of Anopheles vestitipennis and Anopheles punctimacula were conducted in Belize, Central America. Habitat analysis and classification resulted in delineation of eight habitat types defined by dominant life forms and hydrology. Percent cover of tall dense macrophytes, shrubs, open water, and pH were significantly different between sites with and without An. vestitipennis. For An. punctimacula, percent cover of tall dense macrophytes, trees, detritus, open water, and water depth were significantly different between larvae positive and negative sites. The discriminant function for An. vestitipennis correctly predicted the presence of larvae in 65% of sites and correctly predicted the absence of larvae in 88% of sites. The discriminant function for An. punctimacula correctly predicted 81% of sites for the presence of larvae and 45% for the absence of larvae. Canonical discriminant analysis of the three groups of habitats (An. vestitipennis positive; An. punctimacula positive; all negative) confirmed that while larval habitats of An. punctimacula are clustered in the tree dominated area, larval habitats of An. vestitipennis were found in both tree dominated and tall dense macrophyte dominated environments. The forest larval habitats of An. vestitipennis and An. punctimacula seem to be randomly distributed among different forest types. Both species tend to occur in denser forests with more detritus, shallower water, and slightly higher pH. Classification of dry season (February) SPOT multispectral satellite imagery produced 10 land cover types with the swamp forest and tall dense marsh classes being of particular interest. The accuracy assessment showed that commission errors for the tall, dense marsh and swamp forest appeared to be minor; but omission errors were significant, especially for the swamp forest (perhaps because no swamp forests are flooded in February). This means that where the classification indicates there are An. vestitipennis breeding sites, they probably do exist; but breeding sites in many locations are not identified and could be more abundant than indicated.

Discriminating gastric cancer and gastric ulcer using human plasma amino acid metabolic profile.

PubMed

Jing, Fangyu; Hu, Xin; Cao, Yunfeng; Xu, Minghao; Wang, Yuanyuan; Jing, Yu; Hu, Xiaodan; Gao, Yu; Zhu, Zhitu

2018-06-01

Patients with gastric ulcer (GU) have a significantly higher risk of developing gastric cancer (GC), especially within 2 years after diagnosis. The main way to improve the prognosis of GC is to predict the tumorigenesis and metastasis in the early stage. The objective of this study was to demonstrate the ability of human plasma amino acid metabolic profile for discriminating GC and GU. In this study, we first used liquid chromatography-tandem mass spectrometry technique to characterize the plasma amino acid metabolism in GC and GU patients. Plasma samples were collected from 84 GC patients and 82 GU patients, and 22 amino acids were detected in each patient. Partial least squares-discriminant analysis model was performed to analyze the data of these amino acids. We observed seven differential amino acids between GC and GU. A regression analysis model was established using these seven amino acids. Finally, a panel of five differential amino acids, including glutamine, ornithine, histidine, arginine and tryptophan, was identified for discriminating GC and GU with good specificity and sensitivity. The receiver operating characteristic curve was used to evaluate diagnostic ability of the regression model and area under the curve was 0.922. In conclusion, this study demonstrated the potential values of plasma amino acid metabolic profile and metabolomic analysis technique in assisting diagnosis of GC. More studies are needed to highlight the theoretical strengths of metabolomics to understand the potential metabolic mechanisms in GC. © 2018 IUBMB Life, 70(6):553-562, 2018. © 2018 International Union of Biochemistry and Molecular Biology.

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

PubMed

Bevilacqua, Marta; Marini, Federico

2014-08-01

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

Cortical activity patterns predict robust speech discrimination ability in noise

PubMed Central

Shetake, Jai A.; Wolf, Jordan T.; Cheung, Ryan J.; Engineer, Crystal T.; Ram, Satyananda K.; Kilgard, Michael P.

2012-01-01

The neural mechanisms that support speech discrimination in noisy conditions are poorly understood. In quiet conditions, spike timing information appears to be used in the discrimination of speech sounds. In this study, we evaluated the hypothesis that spike timing is also used to distinguish between speech sounds in noisy conditions that significantly degrade neural responses to speech sounds. We tested speech sound discrimination in rats and recorded primary auditory cortex (A1) responses to speech sounds in background noise of different intensities and spectral compositions. Our behavioral results indicate that rats, like humans, are able to accurately discriminate consonant sounds even in the presence of background noise that is as loud as the speech signal. Our neural recordings confirm that speech sounds evoke degraded but detectable responses in noise. Finally, we developed a novel neural classifier that mimics behavioral discrimination. The classifier discriminates between speech sounds by comparing the A1 spatiotemporal activity patterns evoked on single trials with the average spatiotemporal patterns evoked by known sounds. Unlike classifiers in most previous studies, this classifier is not provided with the stimulus onset time. Neural activity analyzed with the use of relative spike timing was well correlated with behavioral speech discrimination in quiet and in noise. Spike timing information integrated over longer intervals was required to accurately predict rat behavioral speech discrimination in noisy conditions. The similarity of neural and behavioral discrimination of speech in noise suggests that humans and rats may employ similar brain mechanisms to solve this problem. PMID:22098331

Where are the women in women's sports? Predictors of female athletes' interest in a coaching career.

PubMed

Moran-Miller, Kelli; Flores, Lisa Y

2011-03-01

In this study, we used social cognitive career theory (Lent, Brown, & Hackett, 1994) to examine the development of female athletes' career interest in coaching and, specifically, the impact of contextual factors (female coaching role models, working hours, and perceived discrimination) on coaching self-efficacy and outcome expectations. Participants were 205 predominantly White, heterosexual female student athletes. A path analysis indicated that role models and working hours predicted coaching self-efficacy, which predicted coaching outcome expectations. Additionally, coaching self-efficacy, coaching outcome expectations, and contextual factors predicted coaching interest. Practical implications are discussed as well as suggestions for further research in this relatively unexplored area.

Workplace Discrimination Outcomes and Their Predictive Factors for Adults with Multiple Sclerosis

ERIC Educational Resources Information Center

Roessler, Richard T.; Neath, Jeanne; McMahon, Brian T.; Rumrill, Phillip D.

2007-01-01

Because employment is a significant predictor of the quality of life of people with disabilities (Rumrill, Roessler, & Fitzgerald, 2004; Viermo & Krause, 1998), discrimination in the workplace that interferes with successful job acquisition or retention is a serious matter. Unfortunately, this type of discrimination is all too prevalent.…

Relationship between foot sensation and standing balance in patients with multiple sclerosis.

PubMed

Citaker, Seyit; Gunduz, Arzu Guclu; Guclu, Meral Bosnak; Nazliel, Bijen; Irkec, Ceyla; Kaya, Defne

2011-06-01

The aims of the present study were to investigate the relationship between the foot sensations and standing balance in patients with Multiple Sclerosis (MS) and find out the sensation, which best predicts balance. Twenty-seven patients with MS (Expanded Disability Status Scale 1-3.5) and 10 healthy volunteers were included. Threshold of light touch-pressure, duration of vibration, and distance of two-point discrimination of the foot sole were assessed. Duration of static one-leg standing balance was measured. Light touch-pressure, vibration, two-point discrimination sensations of the foot sole, and duration of one-leg standing balance were decreased in patients with MS compared with controls (p<0.05). Sensation of the foot sole was related with duration of one-leg standing balance in patients with MS. In the multiple regression analysis conducted in the 27 MS patients, 47.6% of the variance in the duration of one-leg standing balance was explained by two-point discrimination sensation of the heel (R(2)=0.359, p=0.001) and vibration sensation of the first metatarsal head (R(2)=0.118, p=0.029). As the cutaneous receptors sensitivity decreases in the foot sole the standing balance impairs in patients with MS. Two-point discrimination sensation of the heel and vibration sensation of the first metatarsal head region are the best predictors of the static standing balance in patients with MS. Other factors which could be possible to predict balance and effects of sensorial training of foot on balance should be investigated. Copyright © 2011 Elsevier B.V. All rights reserved.

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

Predicting the duration of sickness absence for patients with common mental disorders in occupational health care.

PubMed

Nieuwenhuijsen, Karen; Verbeek, Jos H A M; de Boer, Angela G E M; Blonk, Roland W B; van Dijk, Frank J H

2006-02-01

This study attempted to determine the factors that best predict the duration of absence from work among employees with common mental disorders. A cohort of 188 employees, of whom 102 were teachers, on sick leave with common mental disorders was followed for 1 year. Only information potentially available to the occupational physician during a first consultation was included in the predictive model. The predictive power of the variables was tested using Cox's regression analysis with a stepwise backward selection procedure. The hazard ratios (HR) from the final model were used to deduce a simple prediction rule. The resulting prognostic scores were then used to predict the probability of not returning to work after 3, 6, and 12 months. Calculating the area under the curve from the ROC (receiver operating characteristic) curve tested the discriminative ability of the prediction rule. The final Cox's regression model produced the following four predictors of a longer time until return to work: age older than 50 years [HR 0.5, 95% confidence interval (95% CI) 0.3-0.8], expectation of duration absence longer than 3 months (HR 0.5, 95% CI 0.3-0.8), higher educational level (HR 0.5, 95% CI 0.3-0.8), and diagnosis depression or anxiety disorder (HR 0.7, 95% CI 0.4-0.9). The resulting prognostic score yielded areas under the curves ranging from 0.68 to 0.73, which represent acceptable discrimination of the rule. A prediction rule based on four simple variables can be used by occupational physicians to identify unfavorable cases and to predict the duration of sickness absence.

Optimal visuotactile integration for velocity discrimination of self-hand movements

PubMed Central

Chancel, M.; Blanchard, C.; Guerraz, M.; Montagnini, A.

2016-01-01

Illusory hand movements can be elicited by a textured disk or a visual pattern rotating under one's hand, while proprioceptive inputs convey immobility information (Blanchard C, Roll R, Roll JP, Kavounoudias A. PLoS One 8: e62475, 2013). Here, we investigated whether visuotactile integration can optimize velocity discrimination of illusory hand movements in line with Bayesian predictions. We induced illusory movements in 15 volunteers by visual and/or tactile stimulation delivered at six angular velocities. Participants had to compare hand illusion velocities with a 5°/s hand reference movement in an alternative forced choice paradigm. Results showed that the discrimination threshold decreased in the visuotactile condition compared with unimodal (visual or tactile) conditions, reflecting better bimodal discrimination. The perceptual strength (gain) of the illusions also increased: the stimulation required to give rise to a 5°/s illusory movement was slower in the visuotactile condition compared with each of the two unimodal conditions. The maximum likelihood estimation model satisfactorily predicted the improved discrimination threshold but not the increase in gain. When we added a zero-centered prior, reflecting immobility information, the Bayesian model did actually predict the gain increase but systematically overestimated it. Interestingly, the predicted gains better fit the visuotactile performances when a proprioceptive noise was generated by covibrating antagonist wrist muscles. These findings show that kinesthetic information of visual and tactile origins is optimally integrated to improve velocity discrimination of self-hand movements. However, a Bayesian model alone could not fully describe the illusory phenomenon pointing to the crucial importance of the omnipresent muscle proprioceptive cues with respect to other sensory cues for kinesthesia. PMID:27385802

Evaluation of predictive capacities of biomarkers based on research synthesis.

PubMed

Hattori, Satoshi; Zhou, Xiao-Hua

2016-11-10

The objective of diagnostic studies or prognostic studies is to evaluate and compare predictive capacities of biomarkers. Suppose we are interested in evaluation and comparison of predictive capacities of continuous biomarkers for a binary outcome based on research synthesis. In analysis of each study, subjects are often classified into two groups of the high-expression and low-expression groups according to a cut-off value, and statistical analysis is based on a 2 × 2 table defined by the response and the high expression or low expression of the biomarker. Because the cut-off is study specific, it is difficult to interpret a combined summary measure such as an odds ratio based on the standard meta-analysis techniques. The summary receiver operating characteristic curve is a useful method for meta-analysis of diagnostic studies in the presence of heterogeneity of cut-off values to examine discriminative capacities of biomarkers. We develop a method to estimate positive or negative predictive curves, which are alternative to the receiver operating characteristic curve based on information reported in published papers of each study. These predictive curves provide a useful graphical presentation of pairs of positive and negative predictive values and allow us to compare predictive capacities of biomarkers of different scales in the presence of heterogeneity in cut-off values among studies. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Discriminant analysis in wildlife research: Theory and applications

USGS Publications Warehouse

Williams, B.K.; Capen, D.E.

1981-01-01

Discriminant analysis, a method of analyzing grouped multivariate data, is often used in ecological investigations. It has both a predictive and an explanatory function, the former aiming at classification of individuals of unknown group membership. The goal of the latter function is to exhibit group separation by means of linear transforms, and the corresponding method is called canonical analysis. This discussion focuses on the application of canonical analysis in ecology. In order to clarify its meaning, a parametric approach is taken instead of the usual data-based formulation. For certain assumptions the data-based canonical variates are shown to result from maximum likelihood estimation, thus insuring consistency and asymptotic efficiency. The distorting effects of covariance heterogeneity are examined, as are certain difficulties which arise in interpreting the canonical functions. A 'distortion metric' is defined, by means of which distortions resulting from the canonical transformation can be assessed. Several sampling problems which arise in ecological applications are considered. It is concluded that the method may prove valuable for data exploration, but is of limited value as an inferential procedure.

Classification of breast tissue in mammograms using efficient coding.

PubMed

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

2011-06-24

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

Molecular Diagnosis and Biomarker Identification on SELDI proteomics data by ADTBoost method.

PubMed

Wang, Lu-Yong; Chakraborty, Amit; Comaniciu, Dorin

2005-01-01

Clinical proteomics is an emerging field that will have great impact on molecular diagnosis, identification of disease biomarkers, drug discovery and clinical trials in the post-genomic era. Protein profiling in tissues and fluids in disease and pathological control and other proteomics techniques will play an important role in molecular diagnosis with therapeutics and personalized healthcare. We introduced a new robust diagnostic method based on ADTboost algorithm, a novel algorithm in proteomics data analysis to improve classification accuracy. It generates classification rules, which are often smaller and easier to interpret. This method often gives most discriminative features, which can be utilized as biomarkers for diagnostic purpose. Also, it has a nice feature of providing a measure of prediction confidence. We carried out this method in amyotrophic lateral sclerosis (ALS) disease data acquired by surface enhanced laser-desorption/ionization-time-of-flight mass spectrometry (SELDI-TOF MS) experiments. Our method is shown to have outstanding prediction capacity through the cross-validation, ROC analysis results and comparative study. Our molecular diagnosis method provides an efficient way to distinguish ALS disease from neurological controls. The results are expressed in a simple and straightforward alternating decision tree format or conditional format. We identified most discriminative peaks in proteomic data, which can be utilized as biomarkers for diagnosis. It will have broad application in molecular diagnosis through proteomics data analysis and personalized medicine in this post-genomic era.

Discriminating between adaptive and carcinogenic liver hypertrophy in rat studies using logistic ridge regression analysis of toxicogenomic data: The mode of action and predictive models

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

Liu, Shujie; Kawamoto, Taisuke; Morita, Osamu

Chemical exposure often results in liver hypertrophy in animal tests, characterized by increased liver weight, hepatocellular hypertrophy, and/or cell proliferation. While most of these changes are considered adaptive responses, there is concern that they may be associated with carcinogenesis. In this study, we have employed a toxicogenomic approach using a logistic ridge regression model to identify genes responsible for liver hypertrophy and hypertrophic hepatocarcinogenesis and to develop a predictive model for assessing hypertrophy-inducing compounds. Logistic regression models have previously been used in the quantification of epidemiological risk factors. DNA microarray data from the Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System weremore » used to identify hypertrophy-related genes that are expressed differently in hypertrophy induced by carcinogens and non-carcinogens. Data were collected for 134 chemicals (72 non-hypertrophy-inducing chemicals, 27 hypertrophy-inducing non-carcinogenic chemicals, and 15 hypertrophy-inducing carcinogenic compounds). After applying logistic ridge regression analysis, 35 genes for liver hypertrophy (e.g., Acot1 and Abcc3) and 13 genes for hypertrophic hepatocarcinogenesis (e.g., Asns and Gpx2) were selected. The predictive models built using these genes were 94.8% and 82.7% accurate, respectively. Pathway analysis of the genes indicates that, aside from a xenobiotic metabolism-related pathway as an adaptive response for liver hypertrophy, amino acid biosynthesis and oxidative responses appear to be involved in hypertrophic hepatocarcinogenesis. Early detection and toxicogenomic characterization of liver hypertrophy using our models may be useful for predicting carcinogenesis. In addition, the identified genes provide novel insight into discrimination between adverse hypertrophy associated with carcinogenesis and adaptive hypertrophy in risk assessment. - Highlights: • Hypertrophy (H) and hypertrophic carcinogenesis (C) were studied by toxicogenomics. • Important genes for H and C were selected by logistic ridge regression analysis. • Amino acid biosynthesis and oxidative responses may be involved in C. • Predictive models for H and C provided 94.8% and 82.7% accuracy, respectively. • The identified genes could be useful for assessment of liver hypertrophy.« less

The diagnostic value of specific IgE to Ara h 2 to predict peanut allergy in children is comparable to a validated and updated diagnostic prediction model.

PubMed

Klemans, Rob J B; Otte, Dianne; Knol, Mirjam; Knol, Edward F; Meijer, Yolanda; Gmelig-Meyling, Frits H J; Bruijnzeel-Koomen, Carla A F M; Knulst, André C; Pasmans, Suzanne G M A

2013-01-01

A diagnostic prediction model for peanut allergy in children was recently published, using 6 predictors: sex, age, history, skin prick test, peanut specific immunoglobulin E (sIgE), and total IgE minus peanut sIgE. To validate this model and update it by adding allergic rhinitis, atopic dermatitis, and sIgE to peanut components Ara h 1, 2, 3, and 8 as candidate predictors. To develop a new model based only on sIgE to peanut components. Validation was performed by testing discrimination (diagnostic value) with an area under the receiver operating characteristic curve and calibration (agreement between predicted and observed frequencies of peanut allergy) with the Hosmer-Lemeshow test and a calibration plot. The performance of the (updated) models was similarly analyzed. Validation of the model in 100 patients showed good discrimination (88%) but poor calibration (P < .001). In the updating process, age, history, and additional candidate predictors did not significantly increase discrimination, being 94%, and leaving only 4 predictors of the original model: sex, skin prick test, peanut sIgE, and total IgE minus sIgE. When building a model with sIgE to peanut components, Ara h 2 was the only predictor, with a discriminative ability of 90%. Cutoff values with 100% positive and negative predictive values could be calculated for both the updated model and sIgE to Ara h 2. In this way, the outcome of the food challenge could be predicted with 100% accuracy in 59% (updated model) and 50% (Ara h 2) of the patients. Discrimination of the validated model was good; however, calibration was poor. The discriminative ability of Ara h 2 was almost comparable to that of the updated model, containing 4 predictors. With both models, the need for peanut challenges could be reduced by at least 50%. Copyright © 2012 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

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.

Tactile orientation perception: an ideal observer analysis of human psychophysical performance in relation to macaque area 3b receptive fields

PubMed Central

Peters, Ryan M.; Staibano, Phillip

2015-01-01

The ability to resolve the orientation of edges is crucial to daily tactile and sensorimotor function, yet the means by which edge perception occurs is not well understood. Primate cortical area 3b neurons have diverse receptive field (RF) spatial structures that may participate in edge orientation perception. We evaluated five candidate RF models for macaque area 3b neurons, previously recorded while an oriented bar contacted the monkey's fingertip. We used a Bayesian classifier to assign each neuron a best-fit RF structure. We generated predictions for human performance by implementing an ideal observer that optimally decoded stimulus-evoked spike counts in the model neurons. The ideal observer predicted a saturating reduction in bar orientation discrimination threshold with increasing bar length. We tested 24 humans on an automated, precision-controlled bar orientation discrimination task and observed performance consistent with that predicted. We next queried the ideal observer to discover the RF structure and number of cortical neurons that best matched each participant's performance. Human perception was matched with a median of 24 model neurons firing throughout a 1-s period. The 10 lowest-performing participants were fit with RFs lacking inhibitory sidebands, whereas 12 of the 14 higher-performing participants were fit with RFs containing inhibitory sidebands. Participants whose discrimination improved as bar length increased to 10 mm were fit with longer RFs; those who performed well on the 2-mm bar, with narrower RFs. These results suggest plausible RF features and computational strategies underlying tactile spatial perception and may have implications for perceptual learning. PMID:26354318

Authentication of vegetable oils on the basis of their physico-chemical properties with the aid of chemometrics.

PubMed

Zhang, Guowen; Ni, Yongnian; Churchill, Jane; Kokot, Serge

2006-09-15

In food production, reliable analytical methods for confirmation of purity or degree of spoilage are required by growers, food quality assessors, processors, and consumers. Seven parameters of physico-chemical properties, such as acid number, colority, density, refractive index, moisture and volatility, saponification value and peroxide value, were measured for quality and adulterated soybean, as well as quality and rancid rapeseed oils. Chemometrics methods were then applied for qualitative and quantitative discrimination and prediction of the oils by methods such exploratory principal component analysis (PCA), partial least squares (PLS), radial basis function-artificial neural networks (RBF-ANN), and multi-criteria decision making methods (MCDM), PROMETHEE and GAIA. In general, the soybean and rapeseed oils were discriminated by PCA, and the two spoilt oils behaved differently with the rancid rapeseed samples exhibiting more object scatter on the PC-scores plot, than the adulterated soybean oil. For the PLS and RBF-ANN prediction methods, suitable training models were devised, which were able to predict satisfactorily the category of the four different oil samples in the verification set. Rank ordering with the use of MCDM models indicated that the oil types can be discriminated on the PROMETHEE II scale. For the first time, it was demonstrated how ranking of oil objects with the use of PROMETHEE and GAIA could be utilized as a versatile indicator of quality performance of products on the basis of a standard selected by the stakeholder. In principle, this approach provides a very flexible method for assessment of product quality directly from the measured data.

Predicting abuse potential of stimulants and other dopaminergic drugs: overview and recommendations.

PubMed

Huskinson, Sally L; Naylor, Jennifer E; Rowlett, James K; Freeman, Kevin B

2014-12-01

Examination of a drug's abuse potential at multiple levels of analysis (molecular/cellular action, whole-organism behavior, epidemiological data) is an essential component to regulating controlled substances under the Controlled Substances Act (CSA). We reviewed studies that examined several central nervous system (CNS) stimulants, focusing on those with primarily dopaminergic actions, in drug self-administration, drug discrimination, and physical dependence. For drug self-administration and drug discrimination, we distinguished between experiments conducted with rats and nonhuman primates (NHP) to highlight the common and unique attributes of each model in the assessment of abuse potential. Our review of drug self-administration studies suggests that this procedure is important in predicting abuse potential of dopaminergic compounds, but there were many false positives. We recommended that tests to determine how reinforcing a drug is relative to a known drug of abuse may be more predictive of abuse potential than tests that yield a binary, yes-or-no classification. Several false positives also occurred with drug discrimination. With this procedure, we recommended that future research follow a standard decision-tree approach that may require examining the drug being tested for abuse potential as the training stimulus. This approach would also allow several known drugs of abuse to be tested for substitution, and this may reduce false positives. Finally, we reviewed evidence of physical dependence with stimulants and discussed the feasibility of modeling these phenomena in nonhuman animals in a rational and practical fashion. This article is part of the Special Issue entitled 'CNS Stimulants'. Copyright © 2014 Elsevier Ltd. All rights reserved.

Recognition of beer brand based on multivariate analysis of volatile fingerprint.

PubMed

Cajka, Tomas; Riddellova, Katerina; Tomaniova, Monika; Hajslova, Jana

2010-06-18

Automated head-space solid-phase microextraction (HS-SPME)-based sampling procedure, coupled to gas chromatography-time-of-flight mass spectrometry (GC-TOFMS), was developed and employed for obtaining of fingerprints (GC profiles) of beer volatiles. In total, 265 speciality beer samples were collected over a 1-year period with the aim to distinguish, based on analytical (profiling) data, (i) the beers labelled as Rochefort 8; (ii) a group consisting of Rochefort 6, 8, 10 beers; and (iii) Trappist beers. For the chemometric evaluation of the data, partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and artificial neural networks with multilayer perceptrons (ANN-MLP) were tested. The best prediction ability was obtained for the model that distinguished a group of Rochefort 6, 8, 10 beers from the rest of beers. In this case, all chemometric tools employed provided 100% correct classification. Slightly worse prediction abilities were achieved for the models "Trappist vs. non-Trappist beers" with the values of 93.9% (PLS-DA), 91.9% (LDA) and 97.0% (ANN-MLP) and "Rochefort 8 vs. the rest" with the values of 87.9% (PLS-DA) and 84.8% (LDA) and 93.9% (ANN-MLP). In addition to chromatographic profiling, also the potential of direct coupling of SPME (extraction/pre-concentration device) with high-resolution TOFMS employing a direct analysis in real time (DART) ion source has been demonstrated as a challenging profiling approach. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Should predictive scores based on vital signs be used in the same way as those based on laboratory data? A hypothesis generating retrospective evaluation of in-hospital mortality by four different scoring systems.

PubMed

Kellett, John; Murray, Alan

2016-05-01

few studies have compared the discrimination of predictive scores of in-hospital mortality that used vital signs with those using laboratory results in different patient populations. a hypothesis generating retrospective observational cohort study. A score that only used vital signs was compared with three other scores that used laboratory changes in 44,985 medical and 20,432 surgical patients. the discrimination of the score based only on vital signs was highest for the prediction of in-hospital death within 24h. In contrast the, albeit lower, discrimination of scores based only on laboratory data remained constant for the prediction of death up to 30 days after hospital admission. Moreover, the discrimination of scores based only on laboratory data was higher in surgical than in medical patients. in acutely ill medical patients a vital sign based score appears to predict mortality within 24h better than scores using laboratory data. This may be because in acutely ill patients vital sign changes indicate how well a patient is responding to a current insult. In contrast, for patients without acute illness laboratory data may be a more valuable indication of the patient's capacity to respond to insults in the future. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Selection of a seventh spectral band for the LANDSAT-D thematic mapper

NASA Technical Reports Server (NTRS)

Holmes, Q. A. (Principal Investigator); Nuesch, D. R.

1978-01-01

The author has identified the following significant results. Each of the candidate bands were examined in terms of the feasibility of gathering high quality imagery from space while taking into account solar illumination, atmospheric attenuation, and the signal/noise ratio achievable within the TM sensor constraints. For the 2.2 micron region and the thermal IR region, inband signal values were calculated from representative spectral reflectance/emittance curves and a linear discriminant analysis was employed to predict classification accuracies. Based upon the substantial improvement (from 78 t0 92%) in discriminating zones of hydrothermally altered rocks from unaltered zones, over a broad range of observation conditions, a 2.08-2.35 micron spectral band having a ground resolution of 30 meters was recommended.

Predicting plantar fasciitis in runners.

PubMed

Warren, B L; Jones, C J

1987-02-01

Ninety-one runners were studied to determine whether specific variables were indicative of runners who had suffered with plantar fasciitis either presently or formerly vs runners who had never suffered with plantar fasciitis. Each runner was asked to complete a running history, was subjected to several anatomical measurements, and was asked to run on a treadmill in both a barefoot and shoe condition at a speed of 3.35 mps (8 min mile pace). Factor coefficients were used in a discriminant function analysis which revealed that, when group membership was predicted, 63% of the runners could be correctly assigned to their group. Considering that 76% of the control group was correctly predicted, it was concluded that the predictor variables were able to correctly predict membership of the control group, but not able to correctly predict the presently or formerly injured sufferers of plantar fasciitis.

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.

Racial/Ethnic Disparities in Influenza Vaccination of Chronically Ill US Adults: The Mediating Role of Perceived Discrimination in Health Care.

PubMed

Bleser, William K; Miranda, Patricia Y; Jean-Jacques, Muriel

2016-06-01

Despite well-established programs, influenza vaccination rates in US adults are well below federal benchmarks and exhibit well-documented, persistent racial and ethnic disparities. The causes of these disparities are multifactorial and complex, though perceived racial/ethnic discrimination in health care is 1 hypothesized mechanism. To assess the role of perceived discrimination in health care in mediating influenza vaccination RACIAL/ETHNIC disparities in chronically ill US adults (at high risk for influenza-related complications). We utilized 2011-2012 data from the Aligning Forces for Quality Consumer Survey on health and health care (n=8127), nationally representative of chronically ill US adults. Logistic regression marginal effects examined the relationship between race/ethnicity and influenza vaccination, both unadjusted and in multivariate models adjusted for determinants of health service use. We then used binary mediation analysis to calculate and test the significance of the percentage of this relationship mediated by perceived discrimination in health care. Respondents reporting perceived discrimination in health care had half the uptake as those without discrimination (32% vs. 60%, P=0.009). The change in predicted probability of vaccination given perceived discrimination experiences (vs. none) was large but not significant in the fully adjusted model (-0.185; 95% CI, -0.385, 0.014). Perceived discrimination significantly mediated 16% of the unadjusted association between race/ethnicity and influenza vaccination, though this dropped to 6% and lost statistical significance in multivariate models. The causes of persistent racial/ethnic disparities are complex and a single explanation is unlikely to be sufficient. We suggest reevaluation in a larger cohort as well as potential directions for future research.

Risk reclassification analysis investigating the added value of fatigue to sickness absence predictions.

PubMed

Roelen, Corné A M; Bültmann, Ute; Groothoff, Johan W; Twisk, Jos W R; Heymans, Martijn W

2015-11-01

Prognostic models including age, self-rated health and prior sickness absence (SA) have been found to predict high (≥ 30) SA days and high (≥ 3) SA episodes during 1-year follow-up. More predictors of high SA are needed to improve these SA prognostic models. The purpose of this study was to investigate fatigue as new predictor in SA prognostic models by using risk reclassification methods and measures. This was a prospective cohort study with 1-year follow-up of 1,137 office workers. Fatigue was measured at baseline with the 20-item checklist individual strength and added to the existing SA prognostic models. SA days and episodes during 1-year follow-up were retrieved from an occupational health service register. The added value of fatigue was investigated with Net Reclassification Index (NRI) and integrated discrimination improvement (IDI) measures. In total, 579 (51 %) office workers had complete data for analysis. Fatigue was prospectively associated with both high SA days and episodes. The NRI revealed that adding fatigue to the SA days model correctly reclassified workers with high SA days, but incorrectly reclassified workers without high SA days. The IDI indicated no improvement in risk discrimination by the SA days model. Both NRI and IDI showed that the prognostic model predicting high SA episodes did not improve when fatigue was added as predictor variable. In the present study, fatigue increased false-positive rates which may reduce the cost-effectiveness of interventions for preventing SA.

Sensory evaluation and electronic tongue for sensing flavored mineral water taste attributes.

PubMed

Sipos, László; Gere, Attila; Szöllősi, Dániel; Kovács, Zoltán; Kókai, Zoltán; Fekete, András

2013-10-01

In this article a trained sensory panel evaluated 6 flavored mineral water samples. The samples consisted of 3 different brands, each with 2 flavors (pear-lemon grass and josta berry). The applied sensory method was profile analysis. Our aim was to analyze the sensory profiles and to investigate the similarities between the sensitivity of the trained human panel and an electronic tongue device. Another objective was to demonstrate the possibilities for the prediction of sensory attributes from electronic tongue measurements using a multivariate statistical method (Partial Least Squares regression [PLS]). The results showed that the products manufactured under different brand name but with the same aromas had very similar sensory profiles. The panel performance evaluation showed that it is appropriate (discrimination ability, repeatability, and panel consensus) to compare the panel's results with the results of the electronic tongue. The samples can be discriminated by the electronic tongue and an accurate classification model can be built. Principal Component Analysis BiPlot diagrams showed that Brand A and B were similar because the manufacturers use the same aroma brands for their products. It can be concluded that Brand C was quite different compared to the other samples independently of the aroma content. Based on the electronic tongue results good prediction models can be obtained with high correlation coefficient (r(2) > 0.81) and low prediction error (RMSEP < 13.71 on the scale of the sensory evaluation from 0 to 100). © 2013 Institute of Food Technologists®

Development and validation of an APCI-MS/GC–MS approach for the classification and prediction of Cheddar cheese maturity

PubMed Central

Gan, Heng Hui; Yan, Bingnan; Linforth, Robert S.T.; Fisk, Ian D.

2016-01-01

Headspace techniques have been extensively employed in food analysis to measure volatile compounds, which play a central role in the perceived quality of food. In this study atmospheric pressure chemical ionisation-mass spectrometry (APCI-MS), coupled with gas chromatography–mass spectrometry (GC–MS), was used to investigate the complex mix of volatile compounds present in Cheddar cheeses of different maturity, processing and recipes to enable characterisation of the cheeses based on their ripening stages. Partial least squares-linear discriminant analysis (PLS-DA) provided a 70% success rate in correct prediction of the age of the cheeses based on their key headspace volatile profiles. In addition to predicting maturity, the analytical results coupled with chemometrics offered a rapid and detailed profiling of the volatile component of Cheddar cheeses, which could offer a new tool for quality assessment and accelerate product development. PMID:26212994

Discrimination in measures of knowledge monitoring accuracy

PubMed Central

Was, Christopher A.

2014-01-01

Knowledge monitoring predicts academic outcomes in many contexts. However, measures of knowledge monitoring accuracy are often incomplete. In the current study, a measure of students’ ability to discriminate known from unknown information as a component of knowledge monitoring was considered. Undergraduate students’ knowledge monitoring accuracy was assessed and used to predict final exam scores in a specific course. It was found that gamma, a measure commonly used as the measure of knowledge monitoring accuracy, accounted for a small, but significant amount of variance in academic performance whereas the discrimination and bias indexes combined to account for a greater amount of variance in academic performance. PMID:25339979

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

NASA Astrophysics Data System (ADS)

Lalbakhsh, Pooia; Chen, Yi-Ping Phoebe

2017-05-01

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

Acoustic Properties Predict Perception of Unfamiliar Dutch Vowels by Adult Australian English and Peruvian Spanish Listeners

PubMed Central

Alispahic, Samra; Mulak, Karen E.; Escudero, Paola

2017-01-01

Research suggests that the size of the second language (L2) vowel inventory relative to the native (L1) inventory may affect the discrimination and acquisition of L2 vowels. Models of non-native and L2 vowel perception stipulate that naïve listeners' non-native and L2 perceptual patterns may be predicted by the relationship in vowel inventory size between the L1 and the L2. Specifically, having a smaller L1 vowel inventory than the L2 impedes L2 vowel perception, while having a larger one often facilitates it. However, the Second Language Linguistic Perception (L2LP) model specifies that it is the L1–L2 acoustic relationships that predict non-native and L2 vowel perception, regardless of L1 vowel inventory. To test the effects of vowel inventory size vs. acoustic properties on non-native vowel perception, we compared XAB discrimination and categorization of five Dutch vowel contrasts between monolinguals whose L1 contains more (Australian English) or fewer (Peruvian Spanish) vowels than Dutch. No effect of language background was found, suggesting that L1 inventory size alone did not account for performance. Instead, participants in both language groups were more accurate in discriminating contrasts that were predicted to be perceptually easy based on L1–L2 acoustic relationships, and were less accurate for contrasts likewise predicted to be difficult. Further, cross-language discriminant analyses predicted listeners' categorization patterns which in turn predicted listeners' discrimination difficulty. Our results show that listeners with larger vowel inventories appear to activate multiple native categories as reflected in lower accuracy scores for some Dutch vowels, while listeners with a smaller vowel inventory seem to have higher accuracy scores for those same vowels. In line with the L2LP model, these findings demonstrate that L1–L2 acoustic relationships better predict non-native and L2 perceptual performance and that inventory size alone is not a good predictor for cross-language perceptual difficulties. PMID:28191001

Utterance-Final Lengthening Is Predictive of Infants' Discrimination of English Accents

ERIC Educational Resources Information Center

White, Laurence; Floccia, Caroline; Goslin, Jeremy; Butler, Joseph

2014-01-01

Infants in their first year manifest selective patterns of discrimination between languages and between accents of the same language. Prosodic differences are held to be important in whether languages can be discriminated, together with the infant's familiarity with one or both of the accents heard. However, the nature of the prosodic cues that…

Immunological discrimination of Atlantic striped bass stocks

USGS Publications Warehouse

Schill, W.B.; Dorazio, R.M.

1990-01-01

Stocks of Atlantic striped bass Morone saxatilis that were assumed to be geographically isolated during spawning showed strong antigenic differences in blood serum albumin. A discriminant function was estimated from the immunologic responses of northern (Canadian and Hudson River) and southern (Chesapeake Bay and Roanoke River) stocks to two reference antisera. The function correctly classified 92% of the northern and 95% of the southern fish in the training set. Cross-validation revealed similar percentages of correct classification for fish that were of known origin but not used to estimate the discriminant function. Monte Carlo experiments were used to evaluate the ability of the discriminant function to predict the relative contribution of northern fish in samples of various size and stock composition. Averages of predicted proportions of northern fish in the samples agreed well with actual proportions. Coefficients of variation (100 × SD/mean) in the predicted proportions ranged from 1.5 to 36% for samples of 50–400 fish that contained at least 10% northern stock. In samples that contained only 2% northern stock, however, at least 1,600 fish were required to achieve similar levels of precision.

Neural activity in cortical area V4 underlies fine disparity discrimination.

PubMed

Shiozaki, Hiroshi M; Tanabe, Seiji; Doi, Takahiro; Fujita, Ichiro

2012-03-14

Primates are capable of discriminating depth with remarkable precision using binocular disparity. Neurons in area V4 are selective for relative disparity, which is the crucial visual cue for discrimination of fine disparity. Here, we investigated the contribution of V4 neurons to fine disparity discrimination. Monkeys discriminated whether the center disk of a dynamic random-dot stereogram was in front of or behind its surrounding annulus. We first behaviorally tested the reference frame of the disparity representation used for performing this task. After learning the task with a set of surround disparities, the monkey generalized its responses to untrained surround disparities, indicating that the perceptual decisions were generated from a disparity representation in a relative frame of reference. We then recorded single-unit responses from V4 while the monkeys performed the task. On average, neuronal thresholds were higher than the behavioral thresholds. The most sensitive neurons reached thresholds as low as the psychophysical thresholds. For subthreshold disparities, the monkeys made frequent errors. The variable decisions were predictable from the fluctuation in the neuronal responses. The predictions were based on a decision model in which each V4 neuron transmits the evidence for the disparity it prefers. We finally altered the disparity representation artificially by means of microstimulation to V4. The decisions were systematically biased when microstimulation boosted the V4 responses. The bias was toward the direction predicted from the decision model. We suggest that disparity signals carried by V4 neurons underlie precise discrimination of fine stereoscopic depth.

Enhanced NMR Discrimination of Pharmaceutically Relevant Molecular Crystal Forms through Fragment-Based Ab Initio Chemical Shift Predictions.

PubMed

Hartman, Joshua D; Day, Graeme M; Beran, Gregory J O

2016-11-02

Chemical shift prediction plays an important role in the determination or validation of crystal structures with solid-state nuclear magnetic resonance (NMR) spectroscopy. One of the fundamental theoretical challenges lies in discriminating variations in chemical shifts resulting from different crystallographic environments. Fragment-based electronic structure methods provide an alternative to the widely used plane wave gauge-including projector augmented wave (GIPAW) density functional technique for chemical shift prediction. Fragment methods allow hybrid density functionals to be employed routinely in chemical shift prediction, and we have recently demonstrated appreciable improvements in the accuracy of the predicted shifts when using the hybrid PBE0 functional instead of generalized gradient approximation (GGA) functionals like PBE. Here, we investigate the solid-state 13 C and 15 N NMR spectra for multiple crystal forms of acetaminophen, phenobarbital, and testosterone. We demonstrate that the use of the hybrid density functional instead of a GGA provides both higher accuracy in the chemical shifts and increased discrimination among the different crystallographic environments. Finally, these results also provide compelling evidence for the transferability of the linear regression parameters mapping predicted chemical shieldings to chemical shifts that were derived in an earlier study.